model submission

North Atlantic Hurricane Model RMS

® RiskLink 13.0 (Build 1509)

May 2013

Submitted in Compliance with the 2011 Standards of the Florida Commission on Hurricane Loss Projection Methodology

Risk Management Solutions, Inc.

7575 Gateway Boulevard, Newark, CA 94560 USA http://www.rms.com © Risk Management Solutions, Inc. All rights reserved.

http://www.rms.com/

RMS™

North Atlantic Hurricane Model, May 2013. Printed in the U.S.A.

© 2013 Risk Management Solutions, Inc. All rights reserved.

Licenses and Trademarks

RiskLink, RiskBrowser, and ALM are registered trademarks of Risk Management Solutions, Inc. RMS and the RMS logo are trademarks of Risk Management Solutions, Inc. Windows, Excel, MS-DOS, and SQL Server are registered trademarks of Microsoft Corporation. SQL Server is a trademark of Microsoft Corporation. dBASE is a registered trademark of Borland International. IBM and PC are registered trademarks of International Business Machines Corporation. ZIP Code and ZIP+4 are registered trademarks of the U.S. Postal Service. All other trademarks are the property of their respective owners.

Chair, Florida Commission on Hurricane Loss Projection Methodology State Board of Administration 1801 Hermitage Boulevard, Suite 100 Tallahassee, FL 32308 Re: Certification of the RMS North Atlantic Hurricane Model, RiskLink

® 13.0 (Build 1509)

Dear Chair: We are pleased to offer for your review the documentation, data, and exhibits supporting our request

for certification of the above-referenced model.

Professionals having credentials and/or experience in the areas of meteorology, engineering,

actuarial science, statistics, and computer science have reviewed RiskLink 13.0 (Build 1509); with

model settings as specified in the FCHLPM Certified Hurricane Losses DLM profile for compliance

with the Commission’s 2011 standards. As shown in the enclosed Expert Certification Forms (G-1 to

G-7), these persons have, in accordance with their professional standards and code of ethical

conduct, certified that the model meets or exceeds the 2011 Standards adopted by the Florida

Commission on Hurricane Loss Projection Methodology, and the model is ready to be reviewed by

the Professional Team.

Enclosed with this letter please find all the required documentation as outlined in the attached model

submission checklist.

Please do not hesitate to contact me if there are any questions. We thank you for your consideration.

Sincerely,

Michael Young Senior Director Model Product Management Enc

RMS® North Atlantic Hurricane Model, RiskLink 13.0 (Build 1509) May 2013

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Model Submission Checklist


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MODEL SUBMISSION CHECKLIST

1. Please indicate by checking below that the following has been included in your submission to the Florida Commission on Hurricane Loss Projection Methodology.

Yes No Item

1. Letter to the Commission a. Refers to the Certification Forms and states that professionals having

credentials and/or experience in the areas of meteorology, engineering, actuarial science, statistics, and computer science have reviewed the model for compliance with the Standards

b. States model is ready to be reviewed by the Professional Team c. Any caveats to the above statements noted with a complete explanation 2. Summary statement of compliance with each individual Standard and the data and

analyses required in the Disclosures and Forms 3. General description of any trade secret information the modeling organization intends

to present to the Professional Team 4. Model Identification 5. 7 Bound Copies (duplexed) 6. Link containing: a. Submission text in PDF format b. PDF file highlightable and bookmarked by Standard, Form, and section c. Data file names include abbreviated name of modeling organization, Standards

year, and Form name (when applicable)

d. Form S-6 (if required) in PDF format e. Forms M-1, M-3, V-2, A-1, A-2, A-3, A-4, A-5, A-7, and A-8 in Excel format

f. Form S-6 in ASCII format (if required) 7. Table of Contents 8. Materials consecutively numbered from beginning to end starting with the first page

(including cover) using a single numbering system 9. All tables, graphs, and other non-text items consecutively numbered using whole

numbers 10. All tables, graphs, and other non-text items specifically listed in Table of Contents 11. All tables, graphs, and other non-text items clearly labeled with abbreviations defined 12. All column headings shown and repeated at the top of every subsequent page for

Forms and tables 13. Standards, Disclosures, and Forms in italics, modeling organization responses in non-

italics 14. Graphs accompanied by legends and labels for all elements 15. All units of measurement clearly identified with appropriate units used 16. Hard copy of all Forms included in a submission document Appendix except Forms V-

3, A-6, and S-6

2. Explanation of ―No‖ responses indicated above. (Attach additional pages if needed.)

RMS has submitted Form S-6 at the previous submission cycle, in compliance with the 2009

Standards. No aspect of the model that would affect the results in Form S-6 has been changed.

RiskLink 13.0 (Build 1509)

05/14/2013

Model Name Modeler Signature Date

Model Submission Checklist


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Supplemental Information


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SUPPLEMENTAL INFORMATION (CHECKLIST ITEM 3)

Information that has been requested regarding the disclosure of trade secret information to the Commission and Professional Team are described below:

Trade secret information that RMS will make available to the Professional Team for review during their

upcoming visit has been noted at various points throughout the submission document.

Supplemental Information


8

Model Identification


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MODEL IDENTIFICATION

Name of Model and Version: North Atlantic Hurricane Model in RiskLink 13.0 (Build 1509)

Name of Modeling Organization: Risk Management Solutions, Inc.

Street Address: 7575 Gateway Boulevard

City, State, ZIP Code: Newark, CA 94560

Mailing Address, if different from above: Same as above

Contact Person: Kay Cleary

Phone Number: 850-386-5292 Fax Number:

E-mail Address: [email protected]

Date: May 2013

Model Identification


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TABLE OF CONTENTS

Model Submission Checklist 5

Supplemental Information (Checklist Item 3) 7

Model Identification 9

General Standards 25

G-1 Scope of the Computer Model and Its Implementation 25

G-2 Qualifications of Modeling Organization Personnel and Consultants 45

G-3 Risk Location 57

G-4 Independence of Model Components 58

G-5 Editorial Compliance 59

Meteorological Standards 61

M-1 Base Hurricane Storm Set 61

M-2 Hurricane Parameters and Characteristics 62

M-3 Hurricane Probabilities 68

M-4 Hurricane Wind Field Structure 71

M-5 Landfall and Over-Land Weakening Methodologies 80

M-6 Logical Relationships of Hurricane Characteristics 86

Vulnerability Standards 89

V-1 Derivation of Vulnerability Functions 89

V-2 Derivation of Contents and Time Element Vulnerability Functions 103

V-3 Mitigation Measures 106

Actuarial Standards 113

A-1 Modeling Input Data 113

A-2 Event Definition 117

A-3 Modeled Loss Cost and Probable Maximum Loss Considerations 118

A-4 Policy Conditions 121

A-5 Coverages 123

A-6 Loss Output 124

Statistical Standards 129

S-1 Modeled Results and Goodness-of-Fit 129

S-2 Sensitivity Analysis for Model Output 141

S-3 Uncertainty Analysis for Model Output 142

S-4 County Level Aggregation 143

S-5 Replication of Known Hurricane Losses 144

S-6 Comparison of Projected Hurricane Loss Costs 148


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Computer Standards 149

C-1 Documentation 149

C-2 Requirements 150

C-3 Model Architecture and Component Design 152

C-4 Implementation 154

C-5 Verification 157

C-6 Model Maintenance and Revision 161

C-7 Security 167

Appendix A—FCHLPM Forms 169

Form G-1: General Standards Expert Certification 170

Form G-2: Meteorological Standards Expert Certification 171

Form G-3: Vulnerability Standards Expert Certification 172

Form G-4: Actuarial Standards Expert Certification 173

Form G-5: Statistical Standards Expert Certification 174

Form G-6: Computer Standards Expert Certification 175

Form G-7: Editorial Certification 176

Form M-1: Annual Occurrence Rates 177

Form M-2: Maps of Maximum Winds 185

Form M-3: Radius of Maximum Winds and Radii of Standard Wind Thresholds 192

Form V-1: One Hypothetical Event 196

Form V-2: Mitigation Measures – Range of Changes in Damage 199

Form V-3: Mitigation Measures – Mean Damage Ratio (Trade Secret Item) 201

Form A-1: Zero Deductible Personal Residential Loss Costs by ZIP Code 202

Form A-2: Base Hurricane Set Statewide Loss Costs 207

Form A-3: Cumulative Losses from the 2004 Hurricane Season 210

Form A-4: Output Ranges 245

Form A-5: Percentage Change in Output Ranges 260

Form A-6: Logical Relationship to Risk (Trade Secret Item) 271

Form A-7: Percentage Change in Logical Relationship to Risk 272

Form A-8: Probable Maximum Loss for Florida 283

Form S-1: Probability and Frequency of Florida Landfalling Hurricanes per Year 287

Form S-2: Examples of Loss Exceedance Estimates 288

Form S-3: Distributions of Stochastic Hurricane Parameters 289

Form S-4: Validation Comparisons 291

Form S-5: Average Annual Zero Deductible Statewide Loss Costs – Historical versus Modeled298

Form S-6: Hypothetical Events for Sensitivity and Uncertainty Analysis 299

Appendix B—RMS Technical Staff 301


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Appendix C—External Expert Review of Hazard Module 325

Appendix D—External Expert Review of Vulnerability Module 331

Appendix E—RiskLink User Interface Screen Shots 335

Appendix F—RiskLink Reports 339


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LIST OF FIGURES

Figure 1: Comparison of Observations from 58 Years of HURDAT Tracks (1950-2007), to one “58 year” Model Realization of the RMS Statistical Track Model 26

Figure 2: Examples of Stochastic VRG (yellow boundaries) and ZIP Codes (red boundaries) in South Florida 28

Figure 3: Flow Diagram of Major Model Components 32

Figure 4: Percentage Change in Average Annual Loss with Zero Deductible by County due to Geocoding Changes 42

Figure 5: Percentage Change in Average Annual Loss with Zero Deductible by County due to Hazard Module Changes 43

Figure 6: Percentage Change in Average Annual Loss with Zero Deductible by County due to All Changes Combined 44

Figure 7: RMS Model Development, Testing, and Maintenance Business Workflow Diagram 53

Figure 8: RMS Landfall Gates 66

Figure 9: Historical Landfall Counts (1900-2011) by Landfall Gate for Category 1–2 Storms 67

Figure 10: Historical Landfall Counts (1900-2011) by Landfall Gate for Category 3–5 Storms 67

Figure 11: Observed (black) and Modeled (red) Histograms of Storm Heading for Landfalls in each Florida Region and Adjacent Regions. Storm Heading “N” Stands for a Storm Heading North. 69

Figure 12: Radially Averaged Velocity Profile Based on the Parameters Given in the Text 72

Figure 13: Hurricane Charley on August 13th 2004 – 16:30 UTC. a) H*Wind Snapshot (ftp://ftp.aoml.noaa.gov/hrd/pub/hwind/), b) H*Wind Composite, c) Best Fit for the Georgiou/Holland Model, d) Best Fit for the RMS Wind Field Model. All wind speeds are 1-minute mean 10m winds in mph. 73

Figure 14: As Figure 13, but for Hurricane Andrew on August 24th 1992–04:00 UTC 73

Figure 15: Footprint of Hurricane Charley (2004). Shown is the maximum 3-sec peak gust (in mph). The triangles are stations and are colored according to the observed maximum peak gust. Grey triangles indicate stations that failed and did not record the maximum 3-sec gust. The pink markers indicate the stations for which a time series is shown in Figure 16. 76

Figure 16: Two Station Time Series of 3-second Gust Wind Speeds Comparing Model with Observations for Hurricane Charley (2004) 76

Figure 17: As Figure 15 but for Hurricane Jeanne (2004) 77

Figure 18: Two Station Time Series of 3-second Gust Wind Speeds Comparing Model with Observations for Hurricane Jeanne (2004) 77

Figure 19: As Figure 15 but for Hurricane Wilma (2005) 78


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Figure 20: Two Station Time Series of 3-second Gust Wind Speeds Comparing Model with Observations for Hurricane Wilma (2005) 78

Figure 21: Normalized central pressure time series as a function of time from landfall. The dashed black lines give the stochastic model envelope (1

st and 99

th percentiles). Colored time series

correspond to historical central pressure time series. 81

Figure 22: 3-second Gust Wind footprint (in mph) for Hurricane Frances (2004). Triangles locate a subset of stations used for the reconstruction. As mentioned in the text, the central pressure time series is given by the RMS inland filling model and not by the HURDAT time series. 82

Figure 23: Scatter Plot of Modeled versus Observed 3-second Gusts (mph) for Hurricane Frances 83

Figure 24: Roughness Coefficient as a Function of Distance to Coast (in miles). Each Point Corresponds to a Florida ZIP Code. 84

Figure 25: Radius of Hurricane Force Wind Histograms Comparing Observed Radii Extended Best Track dataset (EBT) (in black) with Simulated Radii (in red) for Hurricanes Having a Central Pressure between 930 and 970hPa 87

Figure 26: Process for Deriving and Implementing Vulnerability Functions 91

Figure 27: Observed Damage ratios (dots and triangles) and modeled Mean Damage Ratios (MDR) (solid line) versus Peak Gust Wind Speed for Contents and Time Element Losses 99

Figure 28: Observed Damage ratios (dots and triangles) and modeled Mean Damage Ratios (MDR) (solid line) versus Peak Gust Wind Speed for Appurtenant Structure Losses 99

Figure 29: Observed Damage Ratio (dots and triangles) and Modeled Mean Damage Ratio (MDR) (solid line) versus Peak Gust Wind Speed 101

Figure 30: Relative Structure and Additional Living Expense (ALE) Damage Ratios: Actual Claims Data 104

Figure 31: Modeled versus Observed Wind speeds (3-second Peak Gust) 132

Figure 32: Uncertainty in Loss Costs due to Vmax 133

Figure 33: Uncertainty in Loss Costs Due to Rmax 134

Figure 34: Central Pressure Cumulative Distribution Function (CDF) 135

Figure 35: Vmax Cumulative Distribution Function (CDF) 136

Figure 36: Pressure Time Series Over Land with Observed (black), Predicted (red) and Simulated (dark blue for 50% and light blue for 90% bands) Filling Rate 137

Figure 37: Translational Speed (Forward Speed) Cumulative Distribution Function (CDF) 138

Figure 38: Rmax Cumulative Distribution Function (CDF) 139

Figure 39: Angle to Maximum Winds Histogram 140

Figure 40: Florida Industry Loss Estimates (Residential) for Recent Storms 10F 145


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Figure 41: Company Specific Loss Comparisons for Residential (RES) Structure Types 146

Figure 42: RiskLink Top Level Data Flow 153

Figure 43: High-Level Description of Model-Revision Policy 161

Figure 44: Detailed Description of Model-Revision Policy 163

Figure 45: RMS Product Development Process Diagram 164

Figure 46: Change in Track Parameters for the 2 of 7 Modified Hurricanes. Part (a) Left: Previous Submission, Right: Current Submission 179

Figure 47: Change in Track Parameters for 2 of the 7 Modified Hurricanes. Part (b) Left: Previous Submission, Right: Current Submission 180

Figure 48: Change in Track Parameters for 2 of the 7 Modified Hurricanes. Part (c) Left: Previous Submission, Right: Current Submission 181

Figure 49: Change in Track Parameters for 1 of the 7 Modified Hurricanes. Part (d) Left: Previous Submission, Right: Current Submission. 182

Figure 50: Comparison of Historical and Modeled Multiple Landfall Occurrences by Region 184

Figure 51: Maximum 1-minute Mean Wind Speed (mph) at ZIP Code level. Historical Set (1900-

2011)—Open Terrain 186

Figure 52: Maximum 1-minute Mean Wind Speed (mph) at ZIP Code level. Historical Set (1900-

2011)—Real Terrain 187

Figure 53: 100-year Return Period 1-minute Mean Wind Speed (mph) at ZIP Code level. Stochastic

Set—Open Terrain 188


Set—Real Terrain 189


Set—Open Terrain 190


Set—Real Terrain 191

Figure 57: Box Plot of Rmax (miles) as a Function of Central Pressure (hPa) using a 10hPa Central Pressure Increment 193

Figure 58: Frequency Histogram of the Radius of Maximum Winds (miles) 194

Figure 59: Frequency Histogram Central Pressure (hPa) 194

Figure 60: Ratio of Estimated Damage and Subject Exposure versus One-Minute Wind Speed 198

Figure 61: Percent Change in Damage for Various Mitigation Measures 200

Figure 62: Zero Deductible Loss Costs by 5-Digit ZIP Code for Frame 204


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Figure 63: Zero Deductible Loss Costs by 5-Digit ZIP Code for Masonry 205

Figure 64: Zero Deductible Loss Costs by 5-Digit ZIP Code for Mobile Home 206

Figure 65: Percentage of Residential Losses from Hurricane Charley (2004) by ZIP Code 240

Figure 66: Percentage of Residential Losses from Hurricane Frances (2004) by ZIP Code 241

Figure 67: Percentage of Residential Losses from Hurricane Ivan (2004) by ZIP Code 242

Figure 68: Percentage of Residential Losses from Hurricane Jeanne (2004) by ZIP Code 243

Figure 69: Percentage of Cumulative Residential Losses from 2004 Events by ZIP Code 244

Figure 70: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Frame Owners from the Output Ranges from the Previously Accepted Model263

Figure 71: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Masonry Owners from the Output Ranges from the Previously Accepted Model 264

Figure 72: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Mobile Home from the Output Ranges from the Previously Accepted Model 265

Figure 73: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Frame Renters from the Output Ranges from the Previously Accepted Model266

Figure 74: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified deductibles for Masonry Renters from the Output Ranges from the Previously Accepted Model 267

Figure 75: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Frame Condo Unit Owners from the Output Ranges from the Previously Accepted Model 268

Figure 76: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Masonry Condo Unit Owners from the Output Ranges from the Previously Accepted Model 269

Figure 77: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Commercial Residential from the Output Ranges from the Previously Accepted Model 270

Figure 78: Comparison of Current Submission Return Times to the Prior Year’s Submission Return Times 284

Figure 79: Example A1 Comparison of Modeled and Actual Losses by ZIP Code 292






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Figure 84: Example B1 Comparison of Modeled and Actual Losses by ZIP Code 297

Figure 85: Screen Shot of Model Location Input Form (Part 1) 335






Figure 91: Screen Shot of the About RiskLink Screen, Showing Model Name and Version Number 338

Figure 92: Analysis Summary Report (Page 1 of 3) 339



Figure 95: Post Import Summary (Page 1 of 4) 343





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LIST OF TABLES

Table 1: Percentage Difference by Module 41

Table 2: Individuals Involved in Meteorological Aspects of the Model 47

Table 3: Individuals Involved in Vulnerability Aspects of the Model 47

Table 4: Individuals Involved in Actuarial Aspects of the Model 48

Table 5: Individuals Involved in Statistical Aspects of the Model 48

Table 6: Individuals Involved in Computer Science Aspects of the Model 49

Table 7: Individuals who are not Full-Time Employees 54

Table 8: Gust Factors for Typical Land Use Classes 64

Table 9: Observed and Modeled Maximum Peak Gusts at the Stations with Locations given on Figure 22 83

Table 10: Sample of Residential Datasets Used for Development and Calibration of Vulnerability Functions 93

Table 11: Post-Storm Reconnaissance Missions Conducted by RMS 95

Table 12: RMS Hurricane Primary Building Classification Options 97

Table 13: RMS Secondary Characteristic Options in North Atlantic Hurricane Model 108

Table 14: Model settings corresponding to the DLM Profile called "FCHLPM Certified Hurricane Losses". 115

Table 15: Geocoding Match Levels 119

Table 16: Example of Insurer Loss Calculation 122

Table 17: Portion of Modeled Wind Speeds within 10%, 20%, 30%, and 40% of the Observed Value 131

Table 18: Comparison of Actual and Estimated Industry Loss ($ million) 145

Table 19: Sample Client Loss Data Comparison 146

Table 20: Comparison of Modeled and Historical Annual Occurrence Rates 177

Table 21: Ranges of Rmax used in Model’s Stochastic Storm Set 192

Table 22: Base Hurricane Storm Set Average Annual Zero Deductible 207

Table 23: Hurricane Charley (2004) Percent of Losses 210

Table 24: Hurricane Frances (2004) Percent of Losses 215


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Table 25: Hurricane Ivan (2004) Percent of Losses 228

Table 26: Hurricane Jeanne (2004) Percent of Losses 229

Table 27: Loss Costs per $1000 for 0% Deductible 248

Table 28: Loss Costs per $1000 with Specified Deductibles 254

Table 29: Percentage Change in $0 Deductible Output Ranges 262

Table 30: Percentage Change in Specified Deductible Output Ranges 262

Table 31: Percent Change in Logical Relationship to Risk—Deductible 273

Table 32: Percent Change in Logical Relationship to Risk—Construction 275

Table 33: Percent Change in Logical Relationship to Risk—Policy Form 276

Table 34: Percent Change in Logical Relationship to Risk—Coverage 276

Table 35: Percent Change in Logical Relationship to Risk—Building Code / Enforcement (Year Built) Sensitivity 278

Table 36: Percent Change in Logical Relationship to Risk—Building Strength 280

Table 37: Percent Change in Logical Relationship to Risk—Condo Unit Floor 281

Table 38: Percent Change in Logical Relationship to Risk—Number of Stories 282

Table 39: Distribution of Hurricanes by Size of Loss for the 2007 FHCF Combined Personal and Commercial Residential Aggregate Exposure Data 285

Table 40: Estimated Loss for Each of the Return Periods Given for the 2007 FHCF Combined Personal and Commercial Residential Aggregate Exposure Data 286

Table 41: Model Results—Probability and Frequency of Hurricanes per Year 287

Table 42: Examples of Loss Exceedance Estimates 288

Table 43: Average Annual Loss for Loss Exceedance Distribution 288

Table 44: Distributions of Hurricane Parameters 289

Table 45: Example A1 Portfolio Comparison of Modeled and Actual Loss 292





Table 50: Example B1 Portfolio Comparison of Modeled and Actual Loss 297


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Table 51: Average Annual Zero Deductible Statewide Personal and Commercial Residential Loss Costs 298

Table 52: Example of Client Output Table Showing Application of Annual Deductible Factors 347


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General Standards G-1 Scope of the Computer Model and Its Implementation


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GENERAL STANDARDS

G-1 Scope of the Computer Model and Its Implementation

A. The computer model shall project loss costs and probable maximum loss levels for residential property insured damage from hurricane events.

The RMS® North Atlantic Hurricane Model projects loss costs and probable maximum loss levels from

hurricanes for residential property for the following coverages, as appropriate to the type and

composition of the policy form in question: primary structures, appurtenant structures, con tents, and

additional living expenses. Output from the model can explicitly and separately define expected losses

for each of these coverages.

B. The modeling organization shall maintain a documented process to assure continual agreement and correct correspondence of databases, data files, and computer source code to slides, technical papers, and/or modeling organization documents.

RMS uses a variety of systems to track and maintain documentation, data and computer source code.

These systems include the use of source control software, bug tracking systems, and internal

documentation standards and protocols.

G-1.1 Specify the model and program version number.

The model being submitted for rate filing in Florida is the North Atlantic Hurricane Model in RiskLink

13.0 (Build 1509).

G-1.2 Provide a comprehensive summary of the model. This summary shall include a technical description of the model including each major component of the model used to produce residential loss costs and probable maximum loss levels in the State of Florida. Describe the theoretical basis of the model and include a description of the methodology, particularly the wind components, the damage components, and the insured loss components used in the model. The description shall be complete and shall not reference unpublished work.

The RMS North Atlantic Hurricane Model consists of four major model components, or modules:

Stochastic Module

Wind Field (or Wind Hazard) Module

Vulnerability or Damage Assessment Module

Financial Loss Module

Descriptions of each of the modules follow.

Stochastic Module

The stochastic module is made of a set of thousands of stochastic events that represents more than

100,000 years of hurricane activity. RMS scientists have used state-of-the-art modeling technologies to

develop a stochastic event set made of events that are physically realistic and span the range of all

possible storms that could occur in the coming years.

At the heart of the stochastic module is a statistical track model that relies on advanced statistical

techniques (Hall & Jewson 2007) to extrapolate the HURDAT catalogue (Jarvinen et al. 1984) and

generate a set of stochastic tracks having similar statistical characteristics to the HURDAT historical



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tracks (see example in Figure 1). Stochastic tracks are simulated from genesis (starting point) to lysis

(last point) using a semi-parametric statistical track model that is based upon historical data. Simulated

hurricane tracks provide the key drivers of risk, including landfall intensity, landfall frequency and

landfall correlation.

(a) Observed (b) Modeled

Figure 1: Comparison of Observations from 58 Years of HURDAT Tracks (1950-2007), to one ―58 year‖ Model Realization of the RMS Statistical Track Model

Track genesis location is sampled from a spatial Poisson process. The intensity field is derived from

historical genesis locations, weighed according to their distance from site. The length scale involved in

the smoothing process is optimized through cross validation to avoid both over fitting and unrealistic

genesis points. Once the location of the first track point has been simulated, the central pressure (used

as a measure of storm intensity) is sampled from the observed distribution of genesis central pressure.

Then the track is simulated forward in time with a 6 hour increment, , using the following equations

(Hall & Jewson 2007):

where and are the zonal and meridional components of the translational speed derived by running

a weighted average of the historical records. The variable is the 6-hourly change in central

pressure. When the storm center is located over water, the model for is a local linear regression

with predictors that include the previous change in central pressure and the zonal and meridional

components of the translational speed. When the storm center is located over land, is computed

using the filling rate associated with the landfall of interest (Colette et al . 2010). At each time step,

central pressure is constrained to fall within the local Maximal Potential Intensity (Emanuel 1986)

(when over water) and the local far field pressure.

RMS scientists have also used the best elements of numerical modeling in an effort to complement the

historical records in areas where historical data is sparse. Because historical landfall details are

generally poorly known, RMS has used a bogusing technique (Kurihara et al. 1993) to generate

thousands of synthetic storms which inform the inland filling model (Colette et al. 2010), even though

the model has been thoroughly tested and validated against the limited historical records.



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Eventually, tracks are killed by sampling a logistic regression model at each time step. The model has

various predictors including the difference between far field pressure and central pressure, making

storms more likely to vanish when this difference is small.

Although central pressure is the main intensity variable in the model, RMS also derives a maximum

wind time series (Vmax) that is similar to the HURDAT Vmax time series when the storm is over water but

different when the storm is over land as our modeled time series provides equivalent over water Vmax.

The Vmax model is a log-linear regression with pressure difference, and latitude as predictors. Note that

only over water HURDAT points are used to fit the regression.

The last step is a calibration process ensuring that simulated landfall frequencies are in agreement with

the historical record. Target landfall rates are computed on a set of 69 linear coastal segments by

smoothing the historical landfall rates. This smoothing technique is widely used in the scientific

community to reduce the local under-sampling or over-sampling issues associated with the limited

historical records (112 years). The stochastic set is then adjusted toward these targets using methods

such as selecting the optimum intensity time series among several candidates.

Importance sampling of the simulated tracks is performed to create the computationally efficient event

set used for loss cost determinations. The hurricane model contains 20,247 stochastic events affecting

Florida.

Wind Field (or Wind Hazard) Module

Once tracks and intensities have been simulated by the stochastic module, the wind field module

simulates 10 meter 3-second gusts on a variable resolution grid (VRG) to be saved in the stochastic

hazard database.

There are four parts of the Wind Field Module:

UVariable resolution grid—Geographic framework used to store high resolution hazard information.

UAssign wind field parameters—Parameters, associated with the size and shape of the wind field,

are generated for each track point along each stochastic event. For each track (and every five

minutes) 10 meter, 1-minute mean winds equivalent over water are computed on the variable

resolution grid.

UDownscale and convert wind speeds—Downscale and apply directional roughness and gust

coefficients to generate 10 meter, 3-second gust wind speeds over local terrain.

Maximum peak gust—Determine final hazard footprint from maximum gusts simulated at each site

over the entire lifecycle of the storm.

UVariable resolution grid: U Terrain, coastline, and hurricane hazard can often vary dramatically across an

individual ZIP Code. To capture this detail, RMS stores hazard data in a patented standard high-

resolution grid, called a variable resolution grid (Carttar, 2012). VRG grid cell sizes are established

such that the smallest cells occur where the hazard gradient is highest and/or high densities of

exposure exist. Like U.S. ZIP Codes and counties, the VRG constitutes a set of geographic boundaries

that can be used to store hazard information. Figure 2 compares the VRG for stochastic data in the

North Atlantic Hurricane Model (shown in yellow) with ZIP Codes (in red). While relative size o f both is

similar—with ZIP Codes also varying in size with population density—the VRG resolution is always

finer than the ZIP Code.



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Figure 2: Examples of Stochastic VRG (yellow boundaries) and ZIP Codes (red boundaries) in South Florida

UWind field parameters U: Size and shape of the time stepping wind fields are generated using an

analytical wind profile derived from Willoughby et al. (2006), with parameters fitted from the extended

best track dataset (Demuth et al. 2006) and the H*Wind product (Powell et al. 2010).

At any given point in time and space, the 1-minute mean wind (equivalent over water) is entirely

prescribed by the position from the storm center and the following set of parameters: maximum wind

(Vmax), radius of maximum wind (Rmax), two shape parameters giving the radial profile inside and

outside the eyewall, the angle between the location of the maximum winds and the track, and four

additional parameters (empirical orthogonal functions, or EOFs) that reduce the variance between

observed and modeled wind fields.

Rmax time series are given by a regression model with central pressure and latitude as predictors. The

Rmax model is fitted on observations available in the extended best track dataset. RMS has filtered out

years with missing Rmax values set to climatology.

All other wind field parameters have been fitted to the H*Wind dataset, and additional validation has

been performed using the extended best track dataset, especially for the radius of hurricane force

winds. The H*Wind database has been filtered to keep only snapshots with damaging winds. For each

of the remaining 629 snapshots, the best values of the wind field parameters have been fitted, applying



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a high weight around the location of the maximum wind. These best fit values are then used as a

training dataset to build linear regression models.

UDownscale and convert wind speed: UThe simulated 1-minute mean wind (equivalent over water) at a

site is then downscaled to account for local and upstream roughness conditions. This captures the

transition from sea to land or any change in upstream roughness. The model formulation is based on a

peer reviewed wind engineering model (Cook 1985, 1997) and roughness lengths are derived from the

15-30m resolution ASTER satellite imagery (Advanced Spaceborne Thermal Emission and Reflection

Radiometer, Uhttp://asterweb.jpl.nasa.gov U) with a 2001-2007 vintage. An additional component of the

roughness model converts mean winds over local terrain to 3-second gusts over local terrain (Deaves

and Harris 1978; Harris and Deaves 1980; Deaves 1981; Cook 1985; Cook 1997; Wieringa 1993 and

2001; Vickery and Skerlj 2005).

The wind field model has been validated through the reconstruction of all damaging storms in the

HURDAT database. The model is able to reproduce accurately hourly gust observations for a large

range of wind stations (including coastal and inland stations). When considering Hurricane Andrew

1992 and all the major post 2004 U.S. landfalling hurricanes, the root mean squared error between

observed and modeled hourly gusts is approximately 10mph, which is acceptable given the uncertainty

associated with hurricane force wind observations.

UMaximum peak gust footprints: U The output from the wind field module is the hazard database that is

made of the stochastic footprints. Each footprint contains the maximum damaging 3 -second gust wind

speed to affect each of the variable resolution grid cells. This information is pre-compiled for efficient

access at run-time for loss calculation in the subsequent modules.

Vulnerability or Damage Assessment Module

Given an event, the model estimates the wind and surge (optional) hazards present at a user -specified

site. Local wind and surge hazards are measured in terms of peak gust wind speed and flood depth,

respectively. These parameters are then used to derive the estimate of damage to a specific location.

Estimated damage is measured in terms of a mean damage ratio (MDR) and a deviation around the

mean represented by the coefficient of variation (CV). The MDR is defined as the ratio of the repair

cost divided by replacement cost of the asset. The curve that relates the MDR to the peak gust wind

speed is called a vulnerability function. RMS has developed vulnerability functions for hundreds of

building classifications per vulnerability region. Each classification has a vulnerability function for

damage to buildings due to wind and a vulnerability function for damage to building contents due to

wind, as well as similar vulnerability functions for surge damage. Additional living expenses (ALE) /

business interruption (BI) vulnerability functions are based upon the building damage function and the

occupancy of the structure.

The vulnerability classes depend on a combination of:

Construction Class

Building Height (number of stories)

Building Occupancy

Year Built

Floor Area (single family residential only)

Region of State (vulnerability region)

http://asterweb.jpl.nasa.gov/



30

The possible classifications for each of the six primary characteristics are described in

Disclosure V-1.6.

The vulnerability functions consist of a matrix of wind speed levels (measured as peak gust in mph)

and corresponding MDRs. To calculate a MDR for a given location, RiskLink first determines an

expected wind speed, and then looks up the corresponding MDRs for building and contents based on

the building classification. RMS has also developed CVs associated with each MDR. The CV is used to

develop a probability distribution for the damage at each wind speed and for each classification. A beta

distribution is used for this purpose.

The vulnerability relationships are developed using structural and wind engineering principles

underlying the RMS Component Vulnerability Model (CVM) (Khanduri, 2003) coupled with analysis of

historical storm loss data, building codes, published studies, and RMS internal engineering

developments in consultation with wind engineering experts including the late Dr. Dale Perry and Dr.

Norris Stubbs of Texas A&M University. The CVM allows objective modeling of the vulnerability

functions, especially at higher wind speed ranges where little historical loss data is available. The CVM

is also used to obtain the vulnerability relativities by building class and gain insight into the effects of

hurricane mitigation. These approaches also build on the earlier input received from Dr. Peter Sparks

of Clemson University, and the late Dr. Alan Davenport of the University of Western Ontario.

The engineering model based on the CVM is calibrated using historical claims data at ZIP Code

resolution for building, contents, and additional living expenses (ALE) coverages. The calibration

process involves a comparison of modeled MDR with that obtained from observed losses. Since the

vulnerability model is a function of the wind speed, the calibration involves varying both wind speed

and vulnerability within the bounds established by i) the science and historical observations governing

the hazard at a given location and ii) the engineering and historical observations governing the

damageability of property at that location. Thus, one primary goal of calibration is to ensure that the

vulnerability function is confined within the high and low vulnerability bounds as established by the

CVM.

RMS also uses published documents, expert opinion, and conventional structural engineering analysis.

RMS has reviewed research and data contained in numerous technical reports, special publications,

and books related to wind engineering and damage to structures due to wind. References are provided

in Disclosure G-1.4.

The RMS engineering staff includes several engineers with PhD qualifications in Civil and Structural

Engineering. These engineers have significant experience and expertise in the understanding of

building performance and structural vulnerability, and are dedicated to the development of vulnerability

relationships for risk models worldwide. RMS engineers have participated in several reconnaissance

missions as described in Disclosure V-1.4.

The knowledge and data gathered during these site visits has been used in the calibration and

validation of vulnerability functions. The final calibration of the vulnerability functions has been made

using over $11 billion of loss data, with corresponding exposure information.

The vulnerability of buildings modeled by each of the building classes represents the ―average‖

vulnerability of a portfolio of buildings in that class. The vulnerability will vary depending upon specific



31

characteristics of buildings in that portfolio. This variation can be addressed in the model through the

use of secondary modifiers that can consider secondary building characteristics or mitigation measures

to improve a building’s wind resistance. The secondary modifiers could be building-characteristic

specific (e.g., improved roof sheathing or anchors) or external (e.g., storm shutters). These secondary

modifiers modify the base, ―average‖ vulnerability functions according to specific building

characteristics or mitigation measures. The secondary modifiers are discussed in Standard V-3.

Financial Loss Module

To calculate losses, the damage ratio for each stochastic event derived in the Vulnerability Module is

translated into dollar loss by multiplying the damage ratio (including loss amplification as appropriate)

by the value of the property. This is done for each coverage at each location. Using the mean and

coefficient of variation, a beta distribution is fit to represent the loss distribution. From the loss

distribution one can find the expected loss and the loss corresponding to a selected quantile.

RiskLink uses the loss distribution to estimate the portion of loss carried by each participant within a

financial structure (insured, insurer, re-insurer). This distribution is used to calculate the loss net of any

deductibles and limits.

Demand surge impacts on estimated losses are incorporated in the Post-event Loss Amplification

(PLA) component of the North Atlantic Hurricane Model. This component estimates the degree to which

losses are escalated by a combination of economic, social and operational conditions that follow after a

given event. The PLA component accounts for three separate mechanisms of escalation arising from:

Economic Demand Surge (EDS)—increase in the costs of building materials and labor costs as

demand exceeds supply

Claims Inflation (CI)—cost inflation due to the difficulties in fully adjusting claims following a

catastrophic event

Super Catastrophe Scenarios—coverage and loss expansion due to a complex collection of factors

such as containment failures, evacuation effects, and systemic economic downturns in selected

urban areas

These loss amplification factors are developed for each stochastic event in the model by coverage and

applied to the damage ratio on a ground up basis.

G-1.3 Provide a flow diagram that illustrates interactions among major model components.

The high-level flow chart is shown in Figure 3.



32

Figure 3: Flow Diagram of Major Model Components

G-1.4 Provide a comprehensive list of complete references pertinent to the submission by Standard grouping, (according to professional citation standards).

Meteorological Standards References

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Carttar; David; Sharma; Mohan; Gunturi; Surya; Muir-Wood; Robert; Stojanovski; Pane, Inventors. (2012) "System and method for producing a flexible geographical grid." US Patent Number US 8,229,766. Filed Aug 5, 2004. Issued July 24, 2012.

Colette, A, N. Leith, V. Daniel, E. Bellone, D.S. Nolan (2010), ―Using Mesoscale Simulations to Train Statistical Models of Tropical Cyclone Intensity over Land.” Mon. Wea. Rev., 138, 2058–2073.

Cook, N. J. (1997) “The Deaves and Harris ABL model applied to a heterogeneous terrain.” J. Wind Eng. Ind. Aerodyn, 66, 197-214, March 1997.

Roughness & Gust Coeff. Database

Stochastic Module

- Statistical Track Model generates set of stochastic storms

Windfield Module

- Windfield Parameter

Model generates Rmax, shape parameters for each stochastic event- Wind Footprint generated for each event at 5 min intervals at Variable Resolution Grid locations- Determine Peak wind footprint per event (output as 3-sec gust at 10 m height over land)

Track Path, Central Pressure, V_max

Stochastic Event Generation

Vulnerability Module

- Damage ratios calculated for Building, Contents, Time Element

Hazard Database

Vulnerability Curve

Database

Financial Module

- Quantify Loss Amplification per event- Loss is product of Damage Ratio and Replacement Value--Loss represented by mean damage ratio and standard deviation

Financial Loss to Policy Participants

Loss Calculation

User Input:Building InformationPrimary:- Construction Class- Occupancy- Number of Stories- Year Built

- LocationSecondary:- Roof Shape- Roof Cover- Opening Protection

- etc.

User Input:Insurance Policy Information-Building, Contents & Time Element Values- Policy Limits- Deductible- Coinsurance- etc.

Event Database



33

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34

Wieringa, J., (2001), ―New Revision of Davenport Roughness Classification,” in Proc. 3EACWE, Eindhoven, Netherlands, July 2-6, 2001, 285-292.

Wieringa, J., (1992), ―Updating the Davenport roughness classification,‖ J. Wind Eng. Ind. Aerodyn, Volume 41, 357-368.

Willoughby, H.E., R.W.R. Darling, M.E. Rahn (2006), ―Parametric representation of the primary hurricane vortex. Part II: A family of sectionally continuous profiles.‖ Mon. Wea. Rev. 134, 1102-1120.

Zhu, Ping, Jun A. Zhang, Forrest J. Masters, 2010: ―Wavelet Analyses of Turbulence in the Hurricane Surface Layer during Landfalls.‖ J. Atmos. Sci., 67, 3793–3805.

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36

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IBHS (2010), ―Surviving Nature’s Fury: Performance of Asphalt Shingle Roofs in the Real World,‖ Disaster Safety Review. Volume 9 Summer 2010, The Institute for Business & Home Safety, http://DisasterSafety.org

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Conference on Wind Engineering, Texas Tech University, June 2-5, 2003.

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th International Conference on Wind Engineering, Copenhagen, Denmark.

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http://www.partnerre.com/



39

Lee, Y.S. (1988), “The Mathematics of Excess of Loss Coverages and Retrospective Rating – A Graphical Approach,” Proceedings of the Casualty Actuarial Society Vol. LXXV.

Miccolis, R.S. (1977), “On the Theory of Increased Limits and Excess of Loss Pricing,” Proceedings of the

Casualty Actuarial Society, Vol. LXIV.

Robertson, J.P. (1992), ―The Computation of Aggregate Loss Distributions,‖ Proceedings of the Casualty Actuarial Society, Vol. LXXIX.

Statistical Standards References

Colette, A, N. Leith, V. Daniel, E. Bellone, D.S. Nolan (2010), ―Using Mesoscale Simulations to Train Statistical Models of Tropical Cyclone Intensity over Land.” Mon. Wea. Rev., 138, 2058–2073.

Demuth, J., M. DeMaria, and J.A. Knaff, (2006): ―Improvement of advanced microwave sounder unit tropical cyclone intensity and size estimation algorithms.‖ J. Appl. Meteor., 45, 1573-1581.

Emanuel, K.A. (1986), An air-sea interaction theory for tropical cyclones. Part I: Steady state maintenance. J. Atmos. Sci., 43, 585-604

Hall, T. and S. Jewson (2007a), ―Statistical modeling of North Atlantic tropical cyclone tracks.‖ Tellus 59A:486–498.

Ho, F.P., J.C. Su., K.L. Hanevich, R.J. Smith., and F.P. Richards (1987), "Hurricane Climatology for the Atlantic and Gulf Coasts of the United States," study completed under agreement EMW-84-E-1589 for Federal Emergency Management Agency, U.S. Department of Commerce, NOAA Technical Report NWS 38.

Iman, R.L., M.E. Johnson and T.A. Schroeder (2002a), ―Assessing Hurricane Effects. Part 1. Sensitivity Analysis,‖ Reliability Engineering and System Safety 78.

Iman, R.L., M.E. Johnson and T.A. Schroeder (2002b), ―Assessing Hurricane Effects. Part 2. Uncertainty Analysis,‖ Reliability Engineering and System Safety 78.

Jarvinen, B. R., C. J. Neumann, and M. A. S. Davis (1984), ―A Tropical Cyclone Data Tape for the North Atlantic Basin, 1886-1983: Contents, limitations, and uses.‖ NOAA Technical Memorandum NWS NHC 22, Coral Gables, Florida.

Knaff, J.A. and R.M. Zehr, (2007). Reexamination of tropical cyclone wind pressure relationships. Weather. forecasting, 22, 71–88

Kurihara, Y., M.A. Bender, and R.J. Ross, (1993): An Initialisation Scheme of Hurricane Models by Vortex Specification. Mon. Wea. Rev., 121, 2030–2045

Landsea, C. W., C. Anderson, N. Charles, G. Clark, J. Dunion, J. Fernandez-Partagas, P. Hungerford, C. Neumann, and M. Zimmer, (2004) : "The Atlantic Hurricane Database Re-analysis Project: Documentation for the 1851-1910 Alterations and Additions to the HURDAT Database." Hurricanes and Typhoons: Past, Present and Future, R. J. Murname and K.-B. Liu, Eds., Columbia University Press, 177-221.

McCullagh, Peter; Nelder, John (1989). ―Generalized Linear Models.‖ Chapman and Hall. London.

NOAA (1979), ―Meteorological Criteria for Standard Project Hurricane and Probable Maximum Hurricane Windfields, Gulf and East Coasts of the United States,” NOAA Technical Report, NWS 23, Washington, D.C., September, 1979.

Vickery, P.J. (2005). ―Simple Empirical Models for Estimating the Increase in the Central Pressure of Tropical Cyclones after Landfall along the Coastline of the United States.” J. Appl. Meteor., 44, 1807-1826.

Weisberg, S. (1985). ―Applied Linear Regression,” 2nd Edition. New York: Wiley



40

G-1.5 Provide the following information related to changes in the model from the previously accepted submission to the initial submission this year.

G-1.5.A Model changes:

1. A summary description of changes that affect the personal or commercial residential loss costs or probable maximum loss levels,

2. A list of all other changes, and 3. The rationale for each change.

The following significant changes have been revised in the model relative to the previously

submitted version:

Hazard module changes: There are two changes related to the hazard component -

hazard module changes are the main driver of the change in statewide loss costs.

Stochastic Module—For RiskLink 13.0 (Build 1509), the rates associated with the

stochastic event set have been revised based on updated data from the 2011 version

of the HURDAT dataset.

Historical Footprint Recreations—The version of the HURDAT database published

as of November 2011 includes re-analysis of years 1926-1930. RMS has revised the

historical footprint recreations of seven events in the model accordingly.

Geocoding: Updates to the geocoding module have been incorporated. There are two

components to the update:

2012 postal code vintage data has been incorporated as per our policy to update

geocoding data at least every 24 months.

Revision of 'reverse geocoding' methodology (assignment of ZIP Code to user-input

latitude and longitude coordinates) to correct occasional problems with assigning

correct postal code to user defined coordinates. Note this issue does not affect

normal 'address-driven' geocoding, nor zip-aggregate loss costs.

Financial Model: Refinements related to the application of annual aggregate deductibles

to probable maximum loss.

G-1.5.B Percentage difference in average annual zero deductible statewide loss costs for:

1. All changes combined, and

RMS has compiled the percentage difference in Average Annual Zero Deductible

Statewide Loss Costs relative to RiskLink 11.0.SP2c using the 2007 FHCF data.

Overall, RiskLink 13.0 (Build 1509) is 1.2% lower than the previous submission.

2. Each individual model component change.

The contribution of significant model components is shown in Table 1. The changes

are calculated progressively so that the changes to the hazard module are calculated

after incorporating the updated geocoding. The percentage differences are

calculated in an additive format, such that the total change is equal to the sum of the

changes for each significant component change.



41

Table 1: Percentage Difference by Module

Statewide

Percentage

Difference

Component Module

Geocoding Hazard Financial

-1.2% 0.0% -1.2% 0.0%

G-1.5.C Color-coded maps by county reflecting the percentage difference in average annual zero deductible statewide loss costs for each model component change.

Maps of the changes by significant component at a county resolution are shown in

Figure 4 to Figure 6. Note that the scale in each map has been held constant to facilitate

comparisons between components



42

Figure 4: Percentage Change in Average Annual Loss with Zero Deductible by County due to Geocoding Changes



43

Figure 5: Percentage Change in Average Annual Loss with Zero Deductible by County due to Hazard Module Changes



44

Figure 6: Percentage Change in Average Annual Loss with Zero Deductible by County due to All Changes Combined

General Standards G-2 Qualifications of Modeling Organization Personnel and Consultants


45

G-2 Qualifications of Modeling Organization Personnel and Consultants

A. Model construction, testing, and evaluation shall be performed by modeling organization personnel or consultants who possess the necessary skills, formal education, and experience to develop the relevant components for hurricane loss projection methodologies.

Overall, RMS employs 190 engineers and scientists who participate in various areas of model

development (not all on the North Atlantic Hurricane Model). The team possesses a wide range of

multi-disciplinary skills in engineering, the physical sciences, actuarial science, statistics data

development, data analysis and numerical modeling, computer science/engineering, and quality

assurance engineering. Of this staff, about 75% hold advanced degrees and over 50 possess PhD

level qualifications in their fields of expertise. These individuals possess the necessary skill s, formal

education, and experience, in all required disciplines, to develop hurricane loss projection

methodologies.

B. The model or any modifications to an accepted model shall be reviewed by either modeling organization personnel or consultants in the following professional disciplines: structural/wind engineering (licensed Professional Engineer), statistics (advanced degree), actuarial science (Associate or Fellow of Casualty Actuarial Society), meteorology (advanced degree), and computer/information science (advanced degree). These individuals shall certify Forms G-1 through G-6 as applicable and shall abide by the standards of their profession.

The education and experience of RMS staff and consultants reflect all of the professional disciplines

listed above and are outlined in Disclosure G-2.2A. Qualified modeling personnel and/or independent

experts review all model modifications. These individuals abide by the standards of professional

conduct adopted by their profession.

G-2.1 Organization Background

G-2.1.A Describe the ownership structure of the modeling organization. Describe affiliations with other companies and the nature of the relationship, if any. Indicate if your organization has changed its name and explain the circumstances.

Risk Management Solutions, Inc. (RMS) is a wholly owned subsidiary of DMG Information,

Inc., part of the Daily Mail and General Trust plc, a U.K. Corporation.

G-2.1.B If the model is developed by an entity other than a modeling company, describe its organizational structure and indicate how proprietary rights and control over the model and its critical components is exercised. If more than one entity is involved in the development of the model, describe all involved.

The RMS North Atlantic Hurricane Model was developed only by employees of RMS and

its consultants.

G-2.1.C If the model is developed by an entity other than a modeling company, describe the funding source for the model.

The RMS North Atlantic Hurricane Model was developed only by employees of RMS and

its consultants.



46

G-2.1.D Describe the modeling organization’s services.

RMS provides products and services for the quantification and management of

catastrophe risks. The company’s natural hazard risk modeling solutions are used by over

400 insurers, reinsurers, trading companies, and other financial institutions worldwide.

RMS receives revenues from software licenses, analytical reports, consulting services,

and miscellaneous other services.

G-2.1.E Indicate if the modeling organization has ever been involved directly in litigation or challenged by a statutory authority where the credibility of one of its U.S. hurricane model versions for projection of loss costs or probable maximum loss levels was disputed. Describe the nature of each case and its conclusion.

RMS has interacted with several departments of insurance (DOIs) (such as FL, HI, and

LA) in the context of hurricane rate making. None of these relationships have been

adversarial.

In 2005 and 2007, the Massachusetts Department of Insurance initiated reviews of rate

filings for the Massachusetts Property Insurance Underwriting Association (MPIUA).

Hearings on the MPIUA's proposed rates covered a variety of issues related to rate

setting, including the catastrophe models used to estimate potential insured losses from

hurricanes impacting Massachusetts. The MPIUA was asked to demonstrate that the RMS

general U.S. Hurricane Model (version 6.0) was appropriate for developing rates in

Massachusetts. The decision on the 2005 filing concluded that it was reasonable for the

MPIUA to use the RMS model. The decision on the 2007 filing concluded that the MPIUA

did not demonstrate that the RMS model was appropriately calibrated to Massachusetts.

G-2.2 Professional Credentials

G-2.2.A Provide in a chart format (a) the highest degree obtained (discipline and University), (b) employment or consultant status and tenure in years, and (c) relevant experience and responsibilities of individuals currently involved in the acceptability process or in any of the following aspects of the model:

1. Meteorology

2. Vulnerability

3. Actuarial Science

4. Statistics

5. Computer Science

The highest degree obtained, employment or consultant status, and tenure is provided in

the following tables. The relevant experience of these individuals is contained in the brief

biographies provided in Appendix B.



47

Table 2: Individuals Involved in Meteorological Aspects of the Model

Name Credentials Staff (S)/

Consultant (C)

Tenure (Years)

Dr. Enrica Bellone PhD, Statistics University of Washington

S 7

Dr. Auguste Boissonnade PhD, Civil Engineering Stanford University

S 17

Dr. Mark Dixon PhD, Physics,

University of Warwick S 2

Dr. Michael Drayton PhD, Applied Mathematics Cambridge University

S/C 8/9

Dr. Shree Khare PhD, Program in Atmospheric and Oceanic Sciences

Princeton University

S 6

Dr. Timothy Hall PhD, Physics Cornell University

C N.A.1

Dr. Joss Matthewman PhD, Applied Mathematics University College London (UCL), 2009

S .5

Dr. Thomas Loridan PhD, Physical Geography

King’s College London S 1

Dr. Robert Muir-Wood PhD, Earth Sciences Cambridge University

S 16.5

Dr. Emilie Scherer PhD, Atmospheric Science Paris VI University, France

S 3

Dr. Michael Smith PhD, Civil Engineering

Dundee University S 2

Dr. Christine Ziehmann PhD, Meteorology Frie University of Berlin

S 12

Table 3: Individuals Involved in Vulnerability Aspects of the Model


Consultant (C)

Tenure (Years)


S 17

Mr. Manabu Masuda MS, Civil Engineering, Stanford University

S 8.5

Mr. Rohit Mehta MS, Statistics, California State University, Hayward

S 12

Dr. Charles Menun PhD, Structural Engineering University of California, Berkeley

S/C 4/3.5

Dr. Mohsen Rahnama PhD, Structural Engineering, Stanford University

S 13.5

Mr. Agustin Rodriguez MS, Structural Engineering University of California-Berkeley

S 11

Dr. Pooya Sarabandi PhD, Structural Engineering Stanford university

S 5.5

Dr. Nilesh Shome PhD, Structural Engineering

Stanford University S 3

Mr. Michael Young MS, Engineering Science University of Western Ontario, Canada

S 9

1 Non-RMS Staff



48

Table 4: Individuals Involved in Actuarial Aspects of the Model


Consultant (C)

Tenure (Years)


S 17

Ms. Kay Cleary BA, Psychology Northwestern University FCAS, MAAA

S 6

Dr. Weimin Dong PhD, Civil Engineering Stanford University

S 23

Ms. Nathalie Grima MS, Mathematics San Jose State University

S 8

Mr. Matthew Nielsen MS, Atmospheric Science Colorado State University

S 7

Dr. Ambica Rajagopal PhD, Mathematics Purdue University

S 5

Ms. Neha Shah BS, Applied Mathematics University of California, Los Angeles

S 5.5

Dr. Bronislava Sigal PhD, Statistics Stanford University

S 3.5

Dr. Ajay Singhal PhD, Civil Engineering Stanford University

S 10.5

Ms. Beth Stamann Certificate of General Ins Insurance Institute of America

S 16.5

Mr. Joel Taylor BS Mathematics Bradley University

S 5.5


S 9

Table 5: Individuals Involved in Statistical Aspects of the Model


Consultant (C) Tenure (Years)

Dr. Enrica Bellone PhD, Statistics University of Washington

S 7.5


S 17

Dr. Timothy Hall PhD, Physics Cornell University

C N.A.2

Dr. Joss Matthewman PhD, Applied Mathematics University College London (UCL), 2009

S .5

Dr. Charles Menun PhD, Structural Engineering University of California, Berkeley

S/C 4/3.5

Dr. Robert Muir-Wood PhD, Earth Sciences Cambridge University

S 16

Mr. Edida Rajesh MS, Technology (Geophysics) Andhra University

S 15.5

Dr. Emilie Scherer PhD, Atmospheric Science Paris VI University, France

S 3

Dr. Nilesh Shome PhD, Structural Engineering

Stanford University S 3

Ms. Taronne Tabucchi MS, Civil & Environmental Engineering, Cornell University

S 5

2 Non-RMS Staff.



49



Mr. Joel Taylor BS, Mathematics Bradley University

S 5.5


S 7.5

Dr. Christine Ziehmann PhD, Meteorology Frie University of Berlin

S 12

Table 6: Individuals Involved in Computer Science Aspects of the Model



Ms. Shobana Azariah M.Phil., Public Administration University of Madras, India

S 10.5

Mrs. Victoria Babina BS, Computer Science BS, Psychology Moscow State University

S 4.5

Ms. Ramani Balijepalli MS, Computer Applications Andhra University, India

S 6

Mr. Aman Bhardwaj MS, Computer Applications Institute of Management Technology India

S 12

Mr. Suman Bhattacharya Diploma in Electrical Engineering RK Mission Shilpamandira, Kolkata, India

S 5

Mr. Jim Bull MS, Computer Science, Washington University - Sever Institute of Technology, St. Louis

S 17

Dr. Stan Buyanov PhD, Technical Science, Academy of Science, Moscow

S 4

Mr. Jordan Byk MBA, Marketing and Finance Rutgers – The State University of New Jersey

S 6

Mr. Chris Campbell BA, Geography University of Texas San Antonio, San Antonio, TX

S 5

Mr. David Carttar MS, City Planning University of California, Berkeley

S 18

Mr. Steven Chau BS. MIS, BS Finance University of Iowa

S 12

Ms. Monisha Chahal MS, Computer Programming IBM Education, New Delhi

S 12

Mr. Deval Chauhan MS, Computer Science Illinois Institute of Technology, Chicago

S 5

Dr. Han Chen PhD, Geophysics Institute of Geophysics at SSB, China

S 17.5

Mr. Tommy Chou BA, Developmental Studies of Industrial Societies University of California, Berkeley

S 7.5

Mrs. Karishma Dambe BS, Computer Engineering Pune University, India

S 5

Mr. Ravisher Dhillon MS, Software Engineering, San Jose State University

S 4

Ms. Anjali Garg MS, Computer Applications (MCA) Institute of Management Studies, Uttar Pradesh, India

S 11.5



50



Mr. Garrett Girod BS, Computer Science Louisiana Tech University

S 9.5

Mr. David Glaubman BS, Mathematics Northeastern University, Boston

S 8

Ms. Olga Goldin BS, Power Engineering, Azerbaijan University of Oil and Chemistry

S 17

Mr. Atin Jain MS, Physics (Spl. Electronics) Rewa University, India

S 3

Ms. Pratiksha Kadam MS, Modeling and Simulation University of Central Florida, Orlando, FL

S 5.5

Mrs. Vidya Karthigeyan MS, Computer Information Systems California State University, East Bay

S 5

Mr. Joseph Kim MS, Computer Science University of Southern California

S 4

Mr. Swaminathan Krishnamoorthy MS, Computer Applications University of Madras, India

S 6.5

Ms. Veena Krishnamoorthy MS, Physics Madurai Kamaraj University

S 5

Mr. Punit Kumar BS, Computer Science & Engineering Karnataka University, India

S 3.8

Mr. Tanmay Kumar MS, Computer Applications, MNREC Allahabad University, India

S 4

Ms. Nereida Lark MS, Computer Information Systems, University of Phoenix

S 3

Ms. Siyuan (Terry) Liu MS, Computer Science University of Tennessee, U.S.

S 6.5

Ms. Sonja Liu MS, Computer Engineering Santa Clara University

S 7

Mr. James Lord MS, Civil Engineering Carnegie Mellon university

S 5

Ms. Reenal Mahajan MS, Computer Science Virginia Polytechnic Institute and State University, Blacksburg, VA

S 5

Mr. Rohit Mehta MS, Statistics, California State University, Hayward

S 12

Mr. Bruce Miller BS, Engineering Physics University of Colorado

S 17

Ms. Nayna Mistry MS, Computer Science, California State University Hayward

S 3

Mr. Venkat Morampudi MS, Computer Science University of Alabama

S 6

Ms. Roopa Nair MS, Statistics Delhi University, India

S 4

Mr. Hans Nelsen BA, Philosophy Creighton University

S 3

Mr. Geoffrey Overton BS, Geography University of Nebraska at Omaha

S 6

Mr. Narvdeshwar Pandey MS, Future Studies and Planning, Dev Ahilya University, Indore, India MS, Mathematics, Gorakhpur University, India

S 8.5

Mr. Ghanshyam Parasram BA, Mechanical Engineering Jawahar Lal Nehru Technological University, India

S 13



51



Mr. Rupesh Parikh BS, Engineering Sciences-Chemical University of California, San Diego

S 7

Mr. Rahul Patasariya BS, Civil Engineering, Indian Institute of Technology, India

S 4.5

Ms. Chris Perianayagam MS, Information Technology & Management Illinois Institute of Technology, Chicago

S 4

Ms. Lekshmi Prakash MS, Computer Engineering San Jose State University

S 5.5

Ms. Sudha Raghavan Masters in Computer Applications, Mother Teresa University

S 4

Mr. Pranav Raval MS, Computer Science Illinois Institute of Technology, Chicago, IL

S 7

Mr. Venkata (Subba) Ravilisetty MS, Computer Information Sciences, University of South Alabama

S 10

Mr. Rhoderick Rivera BS, Computer Engineering University of Illinois, Urbana-Champaign

S 7

Mr. Ricardo Ruiz BS, Computer Science De La Salle University, Manila

S 5

Ms. Shraddha Sahay BS, Electrical Engineering Visvesvaraya Technological University, Karnataka, India

S 5

Mr. Majid Sameni MSc, Mechanical Engineering University of Waterloo, Canada

S 3

Mr. Chris Sams BA Geography University of Kansas

S 9.5

Ms. Pooja Sayal BS, Civil Engineering, Delhi College of Engineering, New Delhi, India

S 10

Ms. Debjani Sen MS, Liberal Arts Southern Methodist University

S 5

Ms. Neha Shah BS, Applied Mathematics University of California, Los Angeles

S 5.5

Mr. Maulik Shukla MS, Computer Science Illinois Institute of Technology, Chicago

S 5.5

Dr. Rajesh Singh PhD, Civil Engineering Stanford University Registered Professional Engineer, State of California

S 19

Dr. Ajay Singhal PhD, Civil Engineering Stanford University

S 10.5

Mr. Puja Sinha BS, Electrical Engineering Nagpur University, India

S 5.5

Mr. Jayant Srivastava MS, Computer Science, Institute of Management and Technology, India

S 11.5

Mr. Philippe Stephan MS, Computer Science, Ingenieur civil des Mines French Ecole Nationale Superieure des Mines de St Etienne

S 3.5

Mr. Cody Stumpo MS, Engineering Purdue University

S 5

Dr. Shengjun (John) Su PhD, Computational Analysis and Modeling Louisiana Tech University

S 6.5



52



Mr. William Suchland BA, Geography, Computer Assisted Cartography, University of Washington

S 16

Mr. Avinash Takale MS, Computer Application Shivaji University, Maharashtra, India

S 4

Ms. Monika Tomar MS, Computer Applications (MCA) Bundelkhand University, Jhasi, India

S 9.5

Ms. Yen-Tin Yang MS, Management Science & Engineering Stanford University MS, Structural Engineering National Taiwan University

S 7.5

Mr. Yogesh Vani MS, Computing Technologies, Telecommunication Systems, California State University, Hayward

S 7

Ms. Mimi von Kugelgen BS, Genetics University of California, Berkeley

S 9.5

Ms. Ji Zhang MS, Computer Science California State University, East Bay

S 6.5

G-2.2.B Identify any new employees or consultants (since the previous submission) working on the model or the acceptability process.

This submission includes six new individuals: Mark Dixon, Shree Khare, Punit Kumar,

Thomas Loridan, Joss Matthewman and Michael Smith. Their education, employment

status, tenure, and relevant experience are included in Disclosure G-2.2A and

Appendix B.

G-2.2.C Provide visual business workflow documentation connecting all personnel related to model design, testing, execution, maintenance, and decision-making.

Figure 7 illustrates a typical workflow used at RMS.



53

Figure 7: RMS Model Development, Testing, and Maintenance Business Workflow Diagram

In Figure 7, Model Development includes all individuals listed in Table 2 to Table 5. Software

Development and QA includes the individuals listed in Table 6. Users are RMS clients (internal and

external).

Model Development

Model Dev and

Software Dev

Solution Identified

Review

Fails

QA & Model Dev Software Dev

QA

Software Dev, Model

Dev, QA

Software Dev &

Model Dev

QA & Model Dev

User

Model Dev

Model Dev

Problem Verified

Problem Reported Software and Data are Tested Using Plan

QA Executes Final Certification Plan

Software / Files Released

Technical review (internal/external) of model methodologies and results

QA and Model Dev Write Test Plan and Test Cases

Model Dev Creates Data Files

Model & Software Specs

Develops Model Requirements

Test

Fails

Maintenance Testing

Software Development Writes Code

Test

Fails



54

G-2.2.D Indicate specifically whether individuals listed in A. and B. are associated with the insurance industry, a consumer advocacy group, or a government entity, as well as their involvement in consulting activities.

Table 7: Individuals who are not Full-Time Employees

Name Position/Credentials Development Role Association

Dr. Michael Drayton Director, Three Letters Ltd. Meteorology Private consulting firm; consults full time

Dr. Charles Menun Consulting Engineer Vulnerability Private consulting firm, consults full time

Dr. Timothy Hall Senior Scientist, NASA Goddard Institute for Space Studies in New York

Meteorology Consults part time

G-2.3 Independent Peer Review

G-2.3.A Provide dates of external independent peer reviews that have been performed on the following components as currently functioning in the model:

1. Meteorology

2. Vulnerability

3. Actuarial Science

4. Statistics

5. Computer Science

The methodology used in the current hurricane model has evolved over time. In addition

to the extensive testing that RMS has itself performed on its North Atlantic Hurricane

Model, contributions and model reviews performed by external experts whose names and

reputations rest upon the quality of their work, have contributed to model improvements.

When significant changes to a model component are made, RMS may retain the services

of an external expert to review the methodology, techniques, and other relevant changes

to the model. This submission involves significant changes to the hazard and vulnerability

modules and therefore RMS has engaged with experts for two external reviews.

Dr. Robert Hart is an Associate Profession of Meteorology at the Florida State University.

Dr. Hart received his PhD in Meteorology in 2001 from Pennsylvania State University. Dr.

Hart’s career has focused on hurricane modeling and track forecasting, and has been

doing periodic consulting with RMS since 2007. RMS has retained Dr. Hart’s services to

conduct a peer review of changes to the meteorological aspects of the North Atlantic

Hurricane model in RiskLink 11.0. His review was completed on Oct 29, 2010.

Mr. Thomas Smith is president of TLSmith Consulting, Inc. and is an internationally

recognized expert on wind performance of buildings. Mr. Smith has performed building

investigations after several tornados and 15 hurricanes – for eight of the hurricane

investigations he was a member of the FEMA research teams. Mr. Smith contributed to

several FEMA guides and documents including, FEMA’s residential Coastal Construction

Manual (FEMA 55), Home Builder’s Guide to Coastal Construction (FEMA 499), and

Design Guide for Improving Critical Facility Safety from Flooding and High Winds (FEMA

543). He is also a contributing author of AIA’s Buildings at Risk: Wind Design Basics for



55

Practicing Architects (1997), and he authored Low Slope Roofing II (NCARB, 2003). Tom

Smith was retained by RMS to conduct an external review of the vulnerability model

changes being made in RiskLink 11.0 in September 2010.

G-2.3.B Provide documentation of independent peer reviews directly relevant to the modeling organization’s responses to the current Standards, Disclosures, or Forms. Identify any unresolved or outstanding issues as a result of these reviews.

RMS engages with external consultants, researchers, or experts using one of two

methods; publication in a peer reviewed journal, or external expert reviews conducted

under the condition of non-disclosure agreements. The following peer reviews relevant to

this version of the model in each of these two categories are:

External Expert Reviews

Copies of Dr. Robert Hart’s and Mr. Tom Smith’s assessment reports as described under

Disclosure G-2.3.A are attached in Appendix C and Appendix D. There are no unresolved

or outstanding issues related to these reviews.

Peer Reviewed Journals

RMS has published details about the development of its statistical track module and wind

field module in the following papers listed below. Upon publication, no unresolved or

outstanding issues were identified.

Hall, T.M. and S. Jewson (2007a) ―Statistical modeling of North Atlantic tropical

cyclone tracks.‖ Tellus 59A:486–498.

Colette, A, Leith N., Daniel V., Bellone E., Nolan D.S. (2010): ―Using Mesoscale

Simulations to Train Statistical Models of Tropical Cyclone Intensity over Land.‖ Mon.

Weather. Review, 138, 2058–2073.

Hall, T. and S. Jewson (2007 b): ―Comparison of Local and Basin-Wide Methods for

Risk Assessment of Tropical Cyclone Landfall.‖ Journal of Applied Meteorology and

Climatology, 47, 361–367.

G-2.3.C Describe the nature of any on-going or functional relationship the organization has with any of the persons performing the independent peer reviews.

There currently is no on-going or functional relationship with the reviewers.

G-2.4 Provide a completed Form G-1, General Standards Expert Certification. Provide a link to the location of the form here.

Form G-1: General Standards Expert Certification

G-2.5 Provide a completed Form G-2, Meteorological Standards Expert Certification. Provide a link to the location of the form here.

Form G-2: Meteorological Standards Expert Certification

G-2.6 Provide a completed Form G-3, Vulnerability Standards Expert Certification. Provide a link to the location of the form here.

Form G-3: Vulnerability Standards Expert Certification



56

G-2.7 Provide a completed Form G-4, Actuarial Standards Expert Certification. Provide a link to the location of the form here.

Form G-4: Actuarial Standards Expert Certification

G-2.8 Provide a completed Form G-5, Statistical Standards Expert Certification. Provide a link to the location of the form here.

Form G-5: Statistical Standards Expert Certification

G-2.9 Provide a completed Form G-6, Computer Standards Expert Certification. Provide a link to the location of the form here.

Form G-6: Computer Standards Expert Certification

General Standards G-3 Risk Location


57

G-3 Risk Location

A. ZIP Codes used in the model shall not differ from the United States Postal Service publication date by more than 24 months at the date of submission of the model. ZIP Code information shall originate from the United States Postal Service.

RMS acquires its ZIP Code data primarily from a third-party developer, which bases its information on

the ZIP Code definitions issued by the United States Postal Service. It is RMS policy to update these

ZIP Codes at least every 24 months.

B. ZIP Code centroids, when used in the model, shall be based on population data.

The RMS model does not use ZIP Code centroids as proxies for exposure. If a building location is

entered as a ZIP Code, then the model uses wind speeds that are exposure weighted averages of wind

speeds across the ZIP Code extent. These exposure weighted averages are derived from residential

population data.

C. ZIP Code information purchased by the modeling organization shall be verified by the modeling organization for accuracy and appropriateness

ZIP Code information is examined by RMS for consistency and is subject to standardized quality

control testing and checking by experts employed by RMS for that purpose.

G-3.1 List the current ZIP Code databases used by the model and the components of the model to which they relate. Provide the effective (official United States Postal Service) date corresponding to the ZIP Code databases.

A set of three internal databases is used: one for assigning a geographical coordinate to user-input ZIP

Codes; and another two for assigning exposure-weighted wind-speed averages to individual events

(stochastic and historical) in the model. The USPS vintage of the ZIP Code data used in the submitted

model is September 2011.

G-3.2 Describe in detail how invalid ZIP Codes are handled.

There are two reasons for a ZIP Code to be considered invalid by RiskLink. First, the ZIP Code in

question may not exist, either because of a typographical error or because of an expired ZIP Code.

Second, the ZIP Code may be more current than the ZIP Codes in the reference database in the

product.

In cases when a building cannot be geocoded, its vulnerability and financial characteristics are

excluded from consideration in the analysis. Locations that are not included in the analysis are easily

identified.

General Standards G-4 Independence of Model Components


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G-4 Independence of Model Components

The meteorological, vulnerability, and actuarial components of the model shall each be theoretically sound without compensation for potential bias from the other two components.

In the RMS North Atlantic Hurricane Model, vulnerability, meteorological, and actuarial functions are

theoretically sound and are developed independently without compensation for potential bias from the

other two components. For example, vulnerability functions relating damage ratios to wind speeds are

fixed within the model and are not dependent on other aspects of the loss model. Relationships within

the model among the meteorological, vulnerability, and actuarial components are reasonable.

General Standards G-5 Editorial Compliance


59

G-5 Editorial Compliance

The submission and any revisions provided to the Commission throughout the review process shall be reviewed and edited by a person or persons with experience in reviewing technical documents who shall certify on Form G-7 that the submission has been personally reviewed and is editorially correct.

The preparation of the submission follows a development and editorial review process that involves

multiple personnel who review and edit appropriate sections depending on areas of expertise. For

RMS’ 2012 Submission to the FCHLPM, Beth Stamann has coordinated the editorial process as

described in the disclosure below. Beth has reviewed and edited where necessary all documents for

accuracy and completeness.

Beth joined RMS in August of 1995. She worked within the client development organization until

October 2007 when she moved to the public policy group as senior documentation specialist. Her

responsibilities have included formatting, review of grammar, and contributing to verification of

accuracy, completeness and compliance of a wide-range of documents including but not limited to:

change impact reports, client requests for proposals, meeting documentation, contracts, affidavits,

analytical service reports, presentations, exhibits, client communications, marketing collateral,

correspondence, and client invoicing. She has been involved with the RMS submissions for the last five

years and development of other regulatory support documents.

Through her career at RMS, Beth has demonstrated proficiency in the use of Microsoft Word, Excel,

and PowerPoint applications.

G-5.1 Describe the process used for document control of the submission. Describe the process used to ensure that the paper and electronic versions of specific files are identical in content.

RMS uses source control software to control the document creation and editing process for the

submission document, form development, and related information. For the main submission document,

RMS maintains and tracks edits to the document using edit tracking features in Microsoft Word and the

source control system. Subject matter experts make edits on ―sub-documents‖ that are submitted to the

submission editor for inclusion into the main document, in accordance to a set of standard operating

procedures maintained by the regulatory practice. Incremental changes to the document are checked-

in by the submission editor, Beth Stamann. Form development is also tracked and edited within our

source control system.

RMS follows a review process with multiple reviewers to ensure that final subject matter content

reflects edits suggested by each subject matter expert. The submission editor maintains a list of review

responsibilities and review tasks. This review process also includes specific checks to ensure that the

paper and electronic version of specific files are identical in content.

G-5.2 Describe the process used by the signatories on Forms G-1 through G-6 to ensure that the information contained under each set of standards is accurate and complete.

Each signatory is responsible for the content of their respective standards. Signatories, subject matter

experts, and forms analysts submit information to be included in the submission to the submission

General Standards G-5 Editorial Compliance


60

editor. Once incorporated, signatories must verify that all changes have been incorporated and approve

the final version of the document.

RMS also uses a two-person review process whereby the content of each section/form is reviewed by

someone other than the content provider. When appropriate, signatories may also review other

standard sections to ensure consistency between results and submission language.

G-5.3 Provide a completed Form G-7, Editorial Certification. Provide a link to the location of the form here.

Form G-7: Editorial Certification

Meteorological Standards M-1 Base Hurricane Storm Set


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METEOROLOGICAL STANDARDS

M-1 Base Hurricane Storm Set

A. Annual frequencies used in both model calibration and model validation shall be based upon the National Hurricane Center HURDAT starting at 1900 as of August 15, 2011 (or later). Complete additional season increments based on updates to HURDAT approved by the Tropical Prediction Center/National Hurricane Center are acceptable modifications to these storm sets. Peer reviewed atmospheric science literature can be used to justify modifications to the Base Hurricane Storm Set.

The RMS hurricane model has been developed and validated using the official NHC HURDAT

database (as available in November 2011 with the addition of the 2011 season as available May 2012)

spanning the time frame from 1900 to 2011 inclusive. There has not been any modification to the

official HURDAT track set.

B. Any trends, weighting, or partitioning shall be justified and consistent with currently accepted scientific literature and statistical techniques. Calibration and validation shall encompass the complete Base Hurricane Storm Set as well as any partitions.

No trends, weighting or partitioning of the Base Hurricane Set are used in this model.

M-1.1 Identify the Base Hurricane Storm Set, the release date, and the time period included to develop and implement landfall and by-passing storm frequencies into the model.

The Base Hurricane Storm Set is made of all hurricanes contained in the official HURDAT database (as

available in November 2011 with the addition of the 2011 season as available May 2012) spanning the

time frame from 1900 to 2011 inclusive. The HURDAT database is referenced in Jarvinen et al. (1984)

and Landsea et al. (2004).

M-1.2 If the modeling organization has made any modifications to the Base Hurricane Storm Set related to landfall frequency and characteristics, provide justification for such modifications.

There has not been any modification to the official HURDAT track set.

M-1.3 Where the model incorporates short-term or long-term modification of the historical data leading to differences between modeled climatology and that in the entire Base Hurricane Storm Set, describe how this is incorporated.

There has not been any modification to the official HURDAT track set.

M-1.4 Provide a completed Form M-1, Annual Occurrence Rates. Provide a link to the location of the form here.

Form M-1: Annual Occurrence Rates

Meteorological Standards M-2 Hurricane Parameters and Characteristics


62

M-2 Hurricane Parameters and Characteristics

Methods for depicting all modeled hurricane parameters and characteristics, including but not limited to windspeed, radial distributions of wind and pressure, minimum central pressure, radius of maximum winds, landfall frequency, tracks, spatial and time variant windfields, and conversion factors, shall be based on information documented in currently accepted scientific literature.

Each component of the hazard model is based on information documented in currently accepted

scientific literature:

The track path model is based on Hall and Jewson (2007)

The over water intensity model is similar in concept to the track path model

The inland filling model (modeling the central pressure time series when the storm moves over

land) is described in Colette et al. (2010)

The Vmax and Rmax models are regression models (e.g., Weisberg 1985) with autocorrelated errors

The analytical wind profile is a modified version of the profile proposed in Willoughby et al. (2006)

The wind profile parameters are modeled as generalized linear models (e.g. , McCullagh and

Nelder, 1989)

The roughness and gust models are based on the methodologies proposed by Cook (1985) and

Cook (1997)

M-2.1 Identify the hurricane parameters (e.g., central pressure or radius of maximum winds) that are used in the model.

The hurricane parameters used in the hazard model are:

Translation speed and storm heading (also known as bearing)

Central pressure

Inland filling rate

"Equivalent over water" maximum wind

Radius of maximum winds

Wind profile parameters

Far field pressure

M-2.2 Describe the dependencies among variables in the wind field component and how they are represented in the model, including the mathematical dependence of modeled windfield as a function of distance and direction from the center position.

The variables defining the wind speed at a site are:

Radial distance from the storm center to the site (dependent on site location)

Angle between the translational speed and the site radial vector (dependent on site location)

Translational speed of the storm (dependent on the storm center location)

Equivalent over water 1-minute mean wind (dependent on central pressure and far field pressure)

Radius of maximum winds (dependent on central pressure and latitude)

Wind profile parameters (dependent on the radius of maximum wind and central pressure)

Roughness and gust coefficients (dependent on site location)



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M-2.3 Identify whether hurricane parameters are modeled as random variables, as functions, or as fixed values for the stochastic storm set. Provide rationale for the choice of parameter representations.

The hurricane parameters are modeled as described below:

Translational speed and heading

The track translational speed and heading are derived from the zonal and meridional track speeds. The

mean of the zonal and meridional components vary in space and are distance weighted functions of

zonal and meridional steps from HURDAT. Deviations from the zonal and meridional means are

modeled as Gaussian random variables that are both autocorrelated and cross-correlated. Variance

and correlation coefficients also vary in space and are estimated from HURDAT tracks using weights

that depend on the distance between site and HURDAT track points.

Central pressure

Central Pressure is the main intensity variable in the model. Central pressure time series are obtained

through the change in central pressure ( ). The model for is a linear regression with predictors

that include the previous change in pressure, the total pressure drop from genesis and the zonal and

meridional track steps. The coefficients of the model are estimated locally using HURDAT data

weighted according to the distance from site to HURDAT track point.

Inland filling rate

The inland filling rate is drawn from a normal distribution with a mean that depends on pressure

difference (FFP- ), translational speed and Rmax at the time of landfall, as well as two predictors that

describe the proportion of the storm over different terrain at and just after the time of landfall: the

proportion of the storm to the right of the track that is over water, and the proportion of the storm that is

over terrain classified as urban or forest.

―Equivalent over water‖ maximum wind

Vmax is modeled as a lognormal random variable, with a mean that depends on latitude and pressure

difference. Deviation from the mean exhibits 1st

order autocorrelation. Central pressure, Vmax and

latitude data from HURDAT are used to estimate the coefficients of the model.

Radius of maximum winds

Rmax is modeled as a lognormal random variable, with a mean that depends on latitude and central

pressure. Deviation from the mean exhibits 1st

order autocorrelation. Simulated Rmax values are

truncated on the right according to their category by pressure. The coefficients for the model are

estimated using the extended best track dataset as discussed in Demuth et al. (2006).

Wind profile parameters

The shape parameters X1 and N are modeled as Gamma random variables that depend on Rmax and

translational speed, as well as a lagged version of X1 and N respectively (lag 1). The position of Vmax

with respect to the track is described by the wind field parameter Amax, which is assumed to follow a

truncated Gaussian distribution. The mean depends on translational speed, Rmax and previous values

of Amax. EOF coefficients are modeled as Gaussian random variables.



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Far field pressure

Far field pressure is not modeled as a random variable, but it varies according to spatial position and

time of the year. The monthly climatology of sea level pressure over a grid covering the model domain

is used as a proxy for far field pressure.

M-2.4 Describe how any hurricane parameters are treated differently in the historical and stochastic storm sets (e.g., has a fixed value in one set and not the other).

For historical storms, hurricane parameters are treated as in the stochastic set except that the

longitude, latitude, central pressure (when available) and over water Vmax are fixed and set to the

corresponding HURDAT values.

M-2.5 State whether the model simulates surface winds directly or requires conversion between some other reference level or layer and the surface. Describe the source(s) of conversion factors and the rationale for their use. Describe the process for converting the modeled vortex winds to surface winds including the treatment of the inherent uncertainties in the conversion factor with respect to location of the site compared to the radius of maximum winds over time. Justify the variation in the surface winds conversion factor as a function of hurricane intensity and distance from the hurricane center.

The wind field model directly simulates 1-minute mean winds equivalent over water.

M-2.6 Describe how the windspeeds generated in the windfield model are converted from sustained to gust and identify the averaging time.

The wind field model first simulates 1-minute mean winds equivalent over water. These are converted

to local 3-second gust wind speeds in two stages: first, the 1-minute mean winds equivalent over water

are converted to 1-minute mean winds over local terrain by applying the local roughness coefficient.

Then, these 1-minute mean winds over local terrain are converted to 3-second gusts over local terrain

by applying the local gust coefficient. The RMS gust coefficients are a function of roughness lengths

and follow the ones published in the scientific literature: Deaves and Harris (1978), Harris and Deaves

(1980), Deaves (1981).

The table below lists the gust factor values for four different land use classes.

Table 8: Gust Factors for Typical Land Use Classes

Typical land use 1-minute to 3-second gust factor

Water 1.15

Open terrain 1.22

Suburban 1.39

City center 1.52

M-2.7 Describe the historical data used as the basis for the model’s hurricane tracks. Discuss the appropriateness of the model stochastic hurricane tracks with reference to the historical storm database.

Genesis and translational speeds are derived by smoothing the historical HURDAT records. Only post

1950 HURDAT tracks are used as historical data was less reliable before airplane reconnaissance.



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The 6 hourly changes in central pressure are derived by smoothing the historical HURDAT records.

Only post 1979 pressure increments are considered as the central pressure HURDAT records are

complete only since the second half of the 1970’s when visible and infrared satellite imagery started to

be used.

Modeled Vmax are calibrated and validated using HURDAT and the landfall summaries

(http://www.aoml.noaa.gov/hrd/hurdat/All_U.S._Hurricanes.html) for years within the 1900-2011 time

frame.

Modeled central pressures are derived from HURDAT records from the years 1900-2008.

Stochastic tracks are simulated using the model described in Disclosure M-2.3, based on analysis of

historical storm tracks in the Atlantic basin taken from the HURDAT database. Tracks are simulated

from genesis to decay, and the central pressure is superimposed on the tracks by taking into account

interaction with land along the track. More details on stochastic hurricane tracks are given in

Disclosure G-1.2.

M-2.8 If the historical data are partitioned or modified, describe how the hurricane parameters are affected.

The historical data has not been partitioned or modified.

M-2.9 Describe how the coastline is segmented (or partitioned) in determining the parameters for hurricane frequency used in the model. Provide the hurricane frequency distribution by intensity for each segment.

RMS makes use of the RMS landfall gates to validate landfall frequencies. These landfall gates are 50

mile long coastal segments as shown on Figure 8. Hurricane frequency distributions along the RMS

landfall gates are given on Figure 9 and Figure 10 for Category 1–2 and Category 3–5 hurricanes.

Saffir-Simpson category is based on one-minute wind speed at time of landfall.

http://www.aoml.noaa.gov/hrd/hurdat/All_U.S._Hurricanes.html



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Figure 8: RMS Landfall Gates



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Figure 9: Historical Landfall Counts (1900-2011) by Landfall Gate for Category 1–2 Storms

Figure 10: Historical Landfall Counts (1900-2011) by Landfall Gate for Category 3–5 Storms

M-2.10 Describe any evolution of the functional representation of hurricane parameters during an individual storm life cycle.

Hurricane parameters in the RMS model evolve with the changes that each storm experiences. As a

storm travels over water, the central pressure is simulated using the RMS over water intensity model

and as it moves over land it is modeled using the RMS inland filling model. For hurricanes that are

transitioning to extra-tropical storms, the calculations for the Vmax and Rmax time series gradually evolve

to represent the extra-tropical nature of the storm. The methodology used to calculate the roughness

factors, however, remain the same everywhere, even as the storm moves over water.

Meteorological Standards M-3 Hurricane Probabilities


68

M-3 Hurricane Probabilities

A. Modeled probability distributions of hurricane parameters and characteristics shall be consistent with historical hurricanes in the Atlantic basin

Modeled distributions of hurricane parameters and characteristics are consistent with historical

hurricanes in the Atlantic basin:

Forward speed—Modeled and historical distributions are compared in Disclosure S-1.6 for Florida

and adjacent states.

Storm heading—Figure 11 shows the comparison between observed and modeled storm heading

distribution for the each of the four Florida regions and adjacent regions. There is generally a good

agreement between both distributions.

Central pressure—modeled and historical distributions are compared in Disclosure S-1.6.

Inland filling rate—The range of modeled filling rates is compared against historical central

pressure time series in Disclosure M-5.2.

―Equivalent over water‖ maximum wind (Vmax)—Modeled and historical landfall frequencies (by

intensity and by region) are compared in Form M-1. Modeled and historical Vmax distributions at

landfall are compared in Disclosure S-1.6.

Radius of maximum winds—Modeled and historical distributions are compared in

Disclosure S-1.6.

Wind profile parameters—As described in Disclosure M-4.1, the wind parameters have been

fitted using H*Wind snapshots. The range of modeled radii (>110mph, >74mph and >40mph) is

presented in Form M-3 and it compares well with historical observations available in the extended

best track dataset (Demuth et al. 2006) (Disclosure M-6.3).



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Figure 11: Observed (black) and Modeled (red) Histograms of Storm Heading for Landfalls in each Florida Region and Adjacent Regions. Storm Heading ―N‖ Stands for a Storm Heading North.

B. Modeled hurricane landfall frequency distributions shall reflect the Base Hurricane Storm Set used for category 1 to 5 hurricanes and shall be consistent with those observed for each coastal segment of Florida and neighboring states (Alabama, Georgia, and Mississippi).

Modeled landfall frequencies are consistent with what has been observed historically for each

geographical area of Florida and neighboring states, as demonstrated in Form M-1. The model is

consistent both in terms of the total rate of hurricanes making landfall by region, and the rate of

hurricanes of various intensities by region.



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C. Models shall use maximum one-minute sustained 10-meter windspeed when defining hurricane landfall intensity. This applies both to the Base Hurricane Storm Set used to develop landfall frequency distributions as a function of coastal location and to the modeled winds in each hurricane which causes damage. The associated maximum one-minute sustained 10-meter windspeed shall be within the range of windspeeds (in statute miles per hour) categorized by the Saffir-Simpson Scale.

Saffir-Simpson Hurricane Scale:

Category Winds (mph) Damage

1 74 – 95 Minimal

2 96 – 110 Moderate

3 111 – 130 Extensive

4 131 – 155 Extreme

5 Over 155 Catastrophic

Hurricane intensities are defined using the maximum one-minute sustained 10-meter wind speed. This

applies both to modeled hurricanes from the RMS stochastic set and historical hurricanes from the

base hurricane storm set.

M-3.1 List assumptions used in creating the hurricane characteristic databases.

No additional assumptions were made in creating any of these databases.

M-3.2 Provide a brief rationale for the probability distributions used for all hurricane parameters and characteristics.

A description of the probability distributions used for all hurricane parameters is given in Form S-3.

Meteorological Standards M-4 Hurricane Wind Field Structure


71

M-4 Hurricane Wind Field Structure

A. Windfields generated by the model shall be consistent with observed historical storms affecting Florida.

Wind fields generated by the RMS model are consistent with observed historical hurricanes in the

Atlantic basin. The basis for developing the wind field structure is the record of historical hurricanes.

The functions used to model the wind fields have been tested thoroughly against various historical

storms.

B. The translation of land use and land cover or other source information into a surface roughness distribution shall be consistent with current state-of-the-science and shall be implemented with appropriate geographic information system data.

The RMS database that describes the land use is derived from the 15-30m resolution ASTER satellite

imagery (Advanced Spaceborne Thermal Emission and Reflection Radiometer) with a 2001-2007

vintage. It has been validated against Google Earth and NLCD 2001 (released in 2006/2007 by USGS).

It was shown that the ASTER data is more accurate than NLCD data, especially in urban areas where

ASTER imagery can lead to more than one urban land use class. The raw LU classes are merged into

10 typical LU classes grouping classes of similar roughness together. Each class is assigned a

representative roughness length which is within the range of published mapping schemes from

scientific literature (e.g., Cook 1985; Wieringa, 1992, 1993; ASCE 7-98).

C. With respect to multi-story structures, the model windfield shall account for the effects of the vertical variation of winds if not accounted for in the vulnerability functions.

The effects of the vertical variation of winds are accounted for in the vulnerability curves.

M-4.1 Provide a rotational windspeed (y-axis) versus radius (x-axis) plot of the average or default symmetric wind profile used in the model and justify the choice of this wind profile.

The RMS model is based on an optimized version of the Willoughby profile (Willoughby et al. 2006).

Figure 12 shows the radially averaged profile for typical Florida values:

Translational velocity 5m/s (11.2 mph)

Latitude 27.5 N

Pressure difference 58.5 hPa

Given these parameters, the stochastic model yields the following mean values for the remaining wind

parameters used to generate the average wind profile:

Rmax 36 km (22 miles)

Vmax 49 m/s (110 mph)

X1 (decay length parameter) 107 km (66.5 miles)

N (power law parameter) 1.85



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The wind profile models the 10m winds directly and has been derived using more than 600 over-water

H*Wind snapshots (e.g., Powell et al. 2010).

Figure 12: Radially Averaged Velocity Profile Based on the Parameters Given in the Text

Past modeling approaches at RMS have relied on the Holland profile (Holland 1980). Figure 13 and

Figure 14 show the comparison between H*Wind wind fields and modeled wind fields for Hurricane

Charley (August 13, 2004 – 16:30 UTC) and Hurricane Andrew (August 24, 1992 – 04:00 UTC) based

on the Holland and Willoughby models. In order to better assess the model skills across different

snapshots, we present ―composite wind fields‖ where both the size and the orientation have been

normalized. From the plots it is clearly seen that the optimized Willoughby model out performs the

Georgiou/Holland model.



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a) b)

c) d)

Figure 13: Hurricane Charley on August 13th 2004 – 16:30 UTC. a) H*Wind Snapshot (ftp://ftp.aoml.noaa.gov/hrd/pub/hwind/), b) H*Wind Composite, c) Best Fit for the Georgiou/Holland Model, d) Best Fit for the RMS Wind Field Model. All wind speeds are 1-minute mean 10m winds in mph.

a) b)

c) d)

Figure 14: As Figure 13, but for Hurricane Andrew on August 24th 1992–04:00 UTC

ftp://ftp.aoml.noaa.gov/hrd/pub/hwind/



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M-4.2 If the model windfield has been modified in any way from the previous submission, provide a rotational windspeed (y-axis) versus radius (x-axis) plot of the average or default symmetric wind profile for both the new and old functions. The choice of average or default shall be consistent for the new and old functions.

The wind profile has not changed since the previous submission.

M-4.3 If the model windfield has been modified in any way from the previous submission, describe variations between the new and old windfield functions with reference to historical storms.

The model wind field has not changed in any way since the previous submission.

M-4.4 Describe how the vertical variation of winds is accounted for in the model where applicable. Document and justify any difference in the methodology for treating historical and stochastic storm sets.

The vertical variation of winds is accounted for in the vulnerability curves, where the curves depend on

the height of the building. Historical and stochastic storms are treated in the same way.

M-4.5 Describe the relevance of the formulation of gust factor(s) used in the model.

The model calculates the over land gust wind speeds by location via modeling the local surface

roughness as well as the change in the local roughness conditions upstream of a particular location.

The RMS gust model incorporates these roughness conditions into the computation of the peak gust

wind speed at the 10 m elevation. The gust factor methodology follows peer-reviewed wind engineering

literature (Deaves and Harris 1978; Harris and Deaves 1980; Deaves 1981; Cook 1985; Cook 1997;

Wieringa 1993 and 2001; Vickery and Skerj 2005).

M-4.6 Identify all non-meteorological variables that affect windspeed estimation (e.g., surface roughness, topography, etc.).

Variables that affect the modeled wind speed are the surface roughness conditions, both at the site and

upstream to the site by direction. The effect of topography on wind speeds in Florida is negligible.

M-4.7 Provide the collection and publication dates of the land use and land cover data used in the model and justify their timeliness for Florida.

The land use land cover data for Florida was developed from ASTER (Advanced Spaceborne Thermal

Emission and Reflection Radiometer, http://asterweb.jpl.nasa.gov/) satellite imagery collected between

2001 and 2007. For validation purposes, it was compared to the NLCD 2001 (National Land Cover

Data, released in 2006/2007, http://www.epa.gov/mrlc/nlcd-2001.html) data set, which is based on

satellite data collected around 2001 and released in 2006.

When comparing both data sets to Google Earth imagery, the ASTER LU data was shown to be

superior, especially in urban areas, showing more detail and better differentiation of rough and smooth

urban land cover.

M-4.8 Describe the methodology used to convert land use and land cover information into a spatial distribution of roughness coefficients in Florida and adjacent states.

The land use is available at 15-30 m resolution. The raw land use classes are merged into 10 typical

land use classes grouping classes of similar roughness together. Each class is assigned a

representative roughness length which is within the range of published mapping schemes from



75

scientific literature (e.g., Cook 1985; Wieringa1992, 1993; ASCE 7-98). Aggregate roughness maps are

generated on a 200 m resolution grid, and are used by the roughness model to calculate roughness

coefficients (roughness and gust factors) on the same grid. The roughness and gust model is based on

Cook (1985, 1997) modified by a local correction factor. The correction factor was derived from station

data of actual hurricanes. The 200 m roughness factors on the 200 m grid are aggregated to the RMS

Variable Resolution Grid (1-10 km) using weights that depend on insured exposure.

M-4.9 Demonstrate the consistency of the spatial distribution of model-generated winds with observed windfields for hurricanes affecting Florida.

For the generation of historical footprints, the HURDAT and extended best track dataset are

insufficient, since the wind model requires additional parameters. For this reason the stochastic model

is used to generate various versions of the historic storms all having a different time series of the wind

model parameters. The realization (time series of track parameters) is chosen, that yields the best

agreement with the station observations. Therefore the historic reconstructions agree well with the

observed spatial patterns as can be seen when comparing to wind station data. Figure 15 to Figure 20

show footprints and time series at two example stations for hurricanes Charley (2004), Jeanne (2004)

and Wilma (2005).



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Figure 15: Footprint of Hurricane Charley (2004). Shown is the maximum 3-sec peak gust (in mph). The triangles are stations and are colored according to the observed maximum peak gust. Grey triangles indicate stations that failed and did not record the maximum 3-sec gust. The pink markers indicate the stations for which a time series is shown in Figure 16.

Figure 16: Two Station Time Series of 3-second Gust Wind Speeds Comparing Model with Observations for Hurricane Charley (2004)



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Figure 17: As Figure 15 but for Hurricane Jeanne (2004)

Figure 18: Two Station Time Series of 3-second Gust Wind Speeds Comparing Model with Observations for Hurricane Jeanne (2004)



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Figure 19: As Figure 15 but for Hurricane Wilma (2005)

Figure 20: Two Station Time Series of 3-second Gust Wind Speeds Comparing Model with Observations for Hurricane Wilma (2005)



79

M-4.10 Describe how the model’s windfield is consistent with the inherent differences in windfields for such diverse hurricanes as Hurricane Charley (2004), Hurricane Jeanne (2004), and Hurricane Wilma (2005).

Charley (2004), Jeanne (2004) and Wilma (2005) vary substantially from each other. Charley was an

intense storm (Category 4) with a small Rmax and a fast filling rate, while Wilma was weaker (Category

3) but affecting a larger area (large Rmax) with a slow filling rate. Jeanne was a category 3 at landfall,

but differed from both Wilma and Charley in that it made landfall on the southeastern coast of Florida,

the first major hurricane to hit in that area since 1899. This variability is taken into consideration by

assigning a set of realistic track parameters (Vmax, Rmax, etc.) to each of these storms. As demonstrated

in Disclosure M-4.9, modeled wind fields are in agreement with observations for these three hurricanes.

M-4.11 Describe any variations in the treatment of the model windfield for stochastic versus historical storms and justify this variation.

Stochastic and historic storms are modeled with the same wind field model.

M-4.12 Provide a completed Form M-2, Maps of Maximum Winds. Explain the differences between the spatial distributions of maximum winds for open terrain and actual terrain for historical storms. Provide a link to the location of the form here.

Form M-2: Maps of Maximum Winds

The open terrain land use type is the smoothest land surface in the model. This is why the wind fields

assuming open terrain have generally higher wind speeds than the wind fields assuming real terrain (as

the roughness length over land are generally larger).

Meteorological Standards M-5 Landfall and Over-Land Weakening Methodologies


80

M-5 Landfall and Over-Land Weakening Methodologies

A. The hurricane overland weakening rate methodology used by the model shall be consistent with historical records and with current state-of-the-science.

The RMS inland filling model simulates central pressure decay rates that are consistent with historical

records as demonstrated in Disclosures M-5.1 and S-1.6. The model follows a methodology similar to

the one proposed in Vickery (2005) and is described in detail in Colette et al. (2010).

B. The transition of winds from over-water to over-land within the model shall be consistent with current state-of-the-science.

The transition of winds from over water to over land is modeled using well accepted wind engineering

methods following Cook (1985, 1997), Land use land cover data is sampled upstream of each site

along eight different directional sectors. The methodology has been validated against the most recent

measurements (e.g., Zhu et al. 2010, Masters 2004) from Hurricanes Rita and Ike.

M-5.1 Describe and justify the functional form of hurricane decay rates used by the model.

Hurricane decay rates are modeled through the RMS over land intensity model, also called ―inland

filling model.‖ This filling process happens shortly after landfall as storms are removed from their

primary energy source, namely the heat fluxes from the warm oceanic tropical waters. The formulation

of the model follows the one proposed by Vickery (2005):

where:

Pc is the storm central pressure,

t0 is the time of landfall

FFP is the far field pressure

is the inland filling rate

In Florida, the inland filling rate is drawn from a normal distribution with a mean that depends on

pressure difference (FFP-Pc(t0)), translational speed and Rmax at the time of landfall, as well as two

predictors that describe the proportion of the storm over different terrain at and just after the time of

landfall: the proportion of the storm to the right of the track that is over water, and the proportion of the

storm that is over terrain classified as urban or forest.

On average, small storms fill faster than large storms, intense storms fill faster than weak storms and

fast moving storms fill faster than slow moving storms. Also, storms hitting the South tip of Florida and

keeping a large area of their circulation over water will fill more slowly than more generic landfalling

storms.

M-5.2 Provide a graphical representation of the modeled decay rates for Florida hurricanes over time compared to wind observations.

Figure 21 illustrates a comparison of the normalized central pressure time series for key historical

Florida land-falling storms compared with the RMS stochastic set’s fastest (1st

percentile) and slowest



81

(99th

percentile) filling rates. This figure demonstrates that the RMS inland filling model is able to

capture the full population of observed decay rates ranging from fast decay (Andrew 1992, Charley

2004) to slow decay (Erin 1995, Jeanne 2004) or even weak intensification for low intensity storms

hitting the South tip of Florida (Irene 1999).

Figure 21: Normalized central pressure time series as a function of time from landfall. The dashed black lines give the stochastic model envelope (1

st and 99

th percentiles). Colored time series

correspond to historical central pressure time series.

To perform a comparison between observed and modeled wind speeds, RMS scientists have

reconstructed historical events starting from the modeled central pressure time series and not the

historical HURDAT central pressure time series. The modeled pressure time series has been

calculated using the RMS inland filling model (given the storm characteristics). This pressure time

series is used to develop a wind footprint which is compared to station observations. Figure 22

presents one of these comparisons for Hurricane Frances 2004. This figure shows the model -derived

peak gust footprint (in mph) for Hurricane Frances 2004 with wind stations used for comparison. Figure

23 is a scatter plot of modeled 3-second gusts compared against observed hourly maximum 3-second

gusts from the stations. Table 9 presents the maximum gusts recorded and modeled at both inland and

coastal stations. There is no systematic bias between modeled and observed gusts (both for coastal

and inland stations).



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Figure 22: 3-second Gust Wind footprint (in mph) for Hurricane Frances (2004). Triangles locate a subset of stations used for the reconstruction. As mentioned in the text, the central pressure time series is given by the RMS inland filling model and not by the HURDAT time series.



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Figure 23: Scatter Plot of Modeled versus Observed 3-second Gusts (mph) for Hurricane Frances

Table 9: Observed and Modeled Maximum Peak Gusts at the Stations with Locations given on Figure 22

Code Station_name Observed Peak

Gust (mph)

Modeled Peak

Gust(mph)

FWY FWYF1 65.8 64.1

SET SETTLEMENT POINT 91.4 106.8

SPG SPGF1 111 106.0

TEX TexasTech Frances 04 SBCCOM_Cl 95.3 91.6

MIA MIAMI/OPA LOCKA 55.8 54.3

LAK LAKE WORTH 75.1 90.1

TEX TexasTech Frances 04 WEMITE 1 85.7 104.7

FOW FOWEY ROCKS 65.7 64.1

ORL ORLANDO INTL ARPT 71.2 73.8

FOR FORT LAUDERDALE HOLLYWOOD INT 56.9 56.7

MAY MAYPORT NS 60.4 59.9

LEE LEESBURG MUNI ARPT 61.8 70.3

GAI GAINESVILLE REGIONAL AP 66.4 55.4



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M-5.3 Describe the transition from over-water to over-land boundary layer simulated in the model.

RMS models the transition from over-water to over-land boundary layer using the roughness and gust

factors described in Standard M-4. Over-water roughness lengths have been derived from GPS sonde

observations (Powell et al. 2003).

The water to land transition occurs over a finite fetch and the model accounts for the surface

roughness upwind of each site of interest and along eight directional sectors. Figure 24 illustrates the

dependency of the ZIP Code roughness factors with distance to coast. Roughness coefficients of

coastal ZIP Codes are close to 1 and the drop in the roughness coefficient is localized within the first

couple of miles from the coast. The spread around the mean is an outcome of the different roughness

environments of each ZIP Code, with more built-up ZIP Codes having lower roughness factors.

Figure 24: Roughness Coefficient as a Function of Distance to Coast (in miles). Each Point Corresponds to a Florida ZIP Code.

M-5.4 Describe any changes in hurricane parameters, other than intensity, resulting from the transition from over-water to over-land.

Except for central pressure, all other hurricane parameters have a single model that is applied both

over-water and over-land. As a reminder, because the Rmax model is dependent on central pressure,

storms have a tendency to increase in size after landfall.

M-5.5 Describe the representation in the model of passage over non-continental U.S. land masses on hurricanes affecting Florida.

Hurricanes affecting Florida are part of the RMS North Atlantic hurricane track set. If a storm hits Cuba

or Hispaniola, the inland filling model is triggered causing a weakening in storm intensity before it goes

back over water. In the vicinity of Puerto Rico, storms have also a tendency to decay rather than

intensify (as can be demonstrated from the HURDAT records).



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M-5.6 Document any differences in the treatment of decay rates in the model for stochastic hurricanes compared to historical hurricanes affecting Florida.

When modeling historical events, RMS uses observed central pressure time series as available in

HURDAT. Nevertheless, Disclosure M-5.2 has demonstrated that historical reconstructions using

modeled central pressure time series can accurately simulate station wind observations.

Meteorological Standards M-6 Logical Relationships of Hurricane Characteristics


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M-6 Logical Relationships of Hurricane Characteristics

A. The magnitude of asymmetry shall increase as the translation speed increases, all other factors held constant.

The magnitude of the asymmetry increases with increasing translational speeds, all other factors being

held constant.

B. The mean wind speed shall decrease with increasing surface roughness (friction), all other factors held constant.

The mean wind speeds decrease with increasing surface roughness, all other factors being held

constant.

M-6.1 Describe how the asymmetric structure of hurricanes is represented in the model.

At each time step, the wind field is first computed relative to the moving frame. At this stage, the wind

field is symmetric and is given by the vector field: . The absolute wind field is obtained by

performing the following vector sum:

where: is the translational speed of the storm and is a scalar lower than 1.

M-6.2 Provide a completed Form M-3, Radius of Maximum Winds and Radii of Standard Wind Thresholds. Provide a link to the location of the form here.

Form M-3: Radius of Maximum Winds and Radii of Standard Wind Thresholds

M-6.3 Discuss the radii values for each wind threshold in Form M-3 with reference to available hurricane observations.

Modeled radii of maximum winds (Rmax), hurricane force winds and gale force winds have been

compared to historical values available in the extended best track dataset (Demuth et al. 2006). The

comparison has been made using all tropical hurricanes in the basin, and not only storms hitting

Florida. The comparison is generally very good, with the stochastic model spanning the range of

observed values.

As an example, Figure 25 shows the comparison between historical and modeled radii of hurricane

force winds for a hurricane having a central pressure between 930 and 970hPa. We can see that the

two distributions are close and that the model is capturing small storms like Charley 2004 and large

storms like Isabel 2003 or Ike 2008.

For the radius of gale force winds, it should be noted that the model has a tendency to simulate slightly

smaller radii than the values available in the extended best track dataset, especially on the low side of

the distribution.



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Figure 25: Radius of Hurricane Force Wind Histograms Comparing Observed Radii Extended Best Track dataset (EBT) (in black) with Simulated Radii (in red) for Hurricanes Having a Central Pressure between 930 and 970hPa



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Vulnerability Standards V-1 Derivation of Vulnerability Functions


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VULNERABILITY STANDARDS

V-1 Derivation of Vulnerability Functions

A. Development of the vulnerability functions shall be based on any or a combination of the following: (1) historical data, (2) tests, (3) structural calculations, (4) expert opinion, or (5) site inspections. However, any development of the vulnerability functions based on structural calculations or expert opinion shall be supported by tests, site inspections, and historical data.

The development of vulnerability functions for residential classes of construction in Florida (including

mobile homes) for structure, contents, and Additional Living Expenses (ALE) coverages, is primarily

based upon well-supported structural and wind engineering principles and detailed analyses of

historical claims data. This has been supplemented by expert input, post-storm site inspections and an

extensive review of published literature on building damage assessment.

As outlined in Disclosure G-2.2 the individuals within RMS involved in the development of vulnerability

functions have extensive experience in the field of structural and wind engineering and data analysis.

B. The method of derivation of the vulnerability functions and their associated uncertainties shall be theoretically sound and consistent with fundamental engineering principles.

The methods used by RMS to derive the vulnerability functions and associated uncertainties are

theoretically sound and consistent with fundamental engineering principles. Details of the methodology

are provided in Disclosure V-1.3.

C. Residential building stock classification shall be representative of Florida construction for personal and commercial residential properties.

The schema used to classify buildings and assign appropriate vulnerability curves to each risk is able

to representative all typical types of Florida construction for personal and commercial residential

properties.

D. Building height/number of stories, primary construction material, year of construction, location, and other construction characteristics, as applicable, shall be used in the derivation and application of vulnerability functions.

Unique vulnerability functions are defined based on a combination of the construction material, building

occupancy, building height, year built and location as explained in Disclosure V-1.6. Floor area can

also have an impact for single-family construction.

E. Vulnerability functions shall be separately derived for commercial residential building structures, personal residential structures, mobile homes, appurtenant structures, contents, and additional living expense time element coverages.

Damage curves for all classes of construction, including mobile homes, are developed separately.

Separate vulnerability functions have been derived for damage to building contents for each of the

hurricane building classes. RMS has derived separate functions to explicitly deal with residential and

commercial appurtenant structures, such as fences, carports, and screen enclosures.



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Time element vulnerability functions were derived separately and exist for each occupancy class

supported by the model. Time element vulnerability is related to the building damage state. Time

element losses consider only direct losses (i.e., expense paid to a policy holder while the house is

being repaired). RMS has used historical loss data to calibrate time element vulnerability functions.

F. The minimum wind speed that generates damage shall be consistent with fundamental engineering principles.

Damage associated with a declared hurricane includes damage incurred for wind speeds above and

below the hurricane threshold of 74 mph (one minute sustained). The minimum peak gust wind speed

that generates damage is consistent with fundamental engineering principles.

G. Vulnerability functions shall include damage as attributable to windspeed and wind pressure, water infiltration, and missile impact associated with hurricanes. Vulnerability functions shall not include explicit damage to the structure due to flood, storm surge, or wave action.

The wind vulnerability functions include damage caused by wind speed and pressure, water infiltration

(from rain water entering through breaches in the building envelope) and missile impact. The wind

vulnerability functions exclude damages due to flooding, storm surge and wave action. Damage caused

by storm surge and wave action can be modeled, if desired; however, to do so, the model uses a

separate set of storm surge and wave vulnerability functions that are not applied for wind -only

analyses.

V-1.1 Provide a flow chart documenting the process by which the vulnerability functions are derived and implemented.

The general procedure used to process such data is diagrammed in Figure 26.



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Figure 26: Process for Deriving and Implementing Vulnerability Functions

The implementation of vulnerability functions is described in the response to Disclosure V-1.6.

V-1.2 Describe the nature and extent of actual insurance claims data used to develop the model’s vulnerability functions. Describe in detail what is included, such as, number of policies, number of insurers, date of loss, and number of units of dollar exposure, separated into personal residential, commercial residential, and mobile home.

RMS has collected loss data from its clients for the purpose of developing and calibrating the model’s

vulnerability functions. Construction characteristics and insured value information of the associated

exposure is supplied directly to us by our clients. This information is assumed to be correct, but is also

INPUT - DATA COLLECTION

Claims data from historical hurricanes

Associated exposure data at the time of the hurricane

Best estimate of the wind field from each historical hurricane

DATA PROCESS

Develop Loss Ratios

by company

by ZIP Code/ Peak Gust

by construction class

by coverage type

Regression analysis of loss ratios and wind field estimates to calibrate the basic vulnerability functions

VULNERABILITY DEVELOPMENT

Use basic vulnerability curves to calibrate the Component Vulnerability Model (CVM)

Use CVM to develop vulnerability curves for classes and mitigation techniques not well represented in the claims data

VALIDATION

Validate against loss experience from various insurance portfolios

Validate against industry loss across a large set of events

IMPLEMENTATION



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subjected to checks by RMS. Summaries of exposure and loss data sets and their use in the

development of vulnerability functions will be available for on-site review by the Professional Team.

The data sets vary in resolution and are used for different validation purposes. Data containing detailed

information on damage, loss by construction class and exposure by ZIP Code or street address is used

for calibration of vulnerability functions. Aggregated data is used primarily for sensitivity analysis. To

adequately use loss data for development of vulnerability functions, the data must contain several

types of information including: loss per coverage (A, B, C, and D), line of business, exposure value per

coverage, description of structures (construction type, etc.), and actual location of struc tures.

Overall, RMS has used over $11 billion of hurricane loss data from the U.S. and over $1.4 trillion in

corresponding exposure data in the development and calibration of damage functions. This includes

the following amounts of loss data by line of business: $10.4 billion for residential, $430 million for

mobile home, $20 million for condo unit owners, $161 million for homeowners association, and $61

million for multi-family dwelling. A sample of the datasets is shown in the following table.



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Table 10: Sample of Residential Datasets Used for Development and Calibration of Vulnerability Functions

LOB* Storm Company Data Resolution

RES Andrew A ZIP/Coverage/Construction Class

RES Andrew B ZIP/Coverage

RES Andrew C ZIP/Construction Class

RES Bob A ZIP/Coverage/Construction Class

RES Erin A ZIP/Coverage/Construction Class

RES Fran A ZIP/Coverage/Construction Class

RES Fran B ZIP/Coverage

RES Hugo A ZIP/Coverage/Construction Class

RES Hugo B ZIP/Coverage

RES Opal A ZIP/Coverage/Construction Class

RES Georges D ZIP/Coverage/Construction Class

MH Fran E ZIP/Coverage/Construction Class

MH Hugo F ZIP/Coverage/Construction Class

MH Charley/ Frances/ Jeanne 2004 H Location/Construction Class

MH Charley/ Frances/ Jeanne 2004 I Location/Construction Class

RES Charley/Frances/Jeanne/Ivan 2004 J Location/Coverage/Construction Class

MH Charley/Frances/Jeanne/Ivan 2004 J Location/Coverage/Construction Class

MFD Charley/Frances/Jeanne/Ivan 2004 J Location/Coverage/Construction Class

RES Charley/Frances/Jeanne/Ivan 2004 K Location/Construction Class

RES Charley/Frances/Jeanne/Ivan 2004 L Location/Coverage/Construction Class

RES Charley/Frances/Jeanne/Ivan 2004 M Location/Coverage/Construction Class

MH Charley/Frances/Jeanne/Ivan 2004 M Location/Coverage/Construction Class

RES Charley/Frances/Jeanne/Ivan 2004 N Location/Coverage/Construction Class

RES Charley/Frances/Jeanne/Ivan 2004 O Location/Coverage/Construction Class

RES Charley/Frances/Jeanne/Ivan 2004 P Location/Coverage/Construction Class

RES Charley/Frances/Jeanne/Ivan 2004 Q Location/Coverage/Construction Class

RES Wilma 2005 J Location/Coverage/Construction Class

RES Wilma 2005 K Location/Coverage/Construction Class

RES Wilma 2005 R Location/Coverage/Construction Class

HOA Wilma 2005 J Location/Coverage/Construction Class

CO Wilma 2005 J Location/Coverage/Construction Class

MH Wilma 2005 K Location/Coverage/Construction Class

RES Ike 2008 B Location/Coverage/Construction Class

RES Ike 2008 L Location/Coverage/Construction Class

RES Ike 2008 S Location/Coverage/Construction Class

RES Ike 2008 T Location/Coverage/Construction Class

RES Ike 2008 U Location/Coverage/Construction Class

MH Ike 2008 B Location/Coverage/Construction Class

*RES – Residential; MH – Mobile Homes; MFD–Multi-Family; CO – Condo Owners; HOA – Condo Association



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V-1.3 Provide support for the development of the vulnerability functions.

The vulnerability functions are based on analyses of historical building loss data (see Disclosure V-1.5)

and engineering principles using the component vulnerability model (CVM). The CVM enables an

engineering-based approach to assess the reasonableness of the vulnerability functions derived from

the analysis of historical building loss data, especially for higher wind speed ranges or building classes

for which historical loss data is sparse or incomplete. Additionally, the CVM is used to gain insights into

the potential reduction of losses associated with building features and hurricane mitigation measures.

The vulnerability of each component in the CVM is defined by its failure load, which is informed by

building code provisions, results of wind tunnel experiments, field observations, and technical reports.

These include building codes and standards such as ASCE-7 (1994, 1998, 2010), FBC (2001),

SBC (1997) and SFBC (1994). The reports and studies include those by FEMA (FEMA 1992, FEMA

2005), the U.S. Department of Housing and Urban Development (HUD 1993), Natural Hazards

Research and Applications Information Center, University of Colorado (Ayscue 1996), and Florida

International University (Mitrani et al. 1995 and Peacock et al. 1998). Component vulnerabilities are

combined to obtain the overall vulnerability function of buildings with unique building characteristics

simulating a range of mitigated, un-mitigated, and average buildings.

The uncertainties associated with the vulnerability functions are derived from statistical analyses of

historical building loss data for different building classes and coverage types. These statist ical analyses

indicate that the uncertainty, as measured by the coefficient of variation (CV), is a function of the MDR.

The MDR-CV relationship derived for a vulnerability function is used to compute the uncertainty to

associate with the computed MDR.

V-1.4 Summarize site inspections, including the source, and a brief description of the resulting use of these data in development, validation, or verification of vulnerability functions.

RMS has conducted post-event reconnaissance missions for the hurricanes listed in Table 11.

Typically, immediately after a hurricane makes landfall, teams of two or three wind and/or structural

engineers are deployed to areas affected by the storm to collect information necessary to assess the

extent and nature of the damage and to provide qualitative insights into the overall performance of the

building stock.

The primary objective of the reconnaissance teams deployed immediately after a storm makes landfall

is to provide a real-time, first-hand assessment of the severity of the damage in different areas and to

different building types, and to identify the primary causes of the damage. It is critical that the

reconnaissance teams conduct these initial field inspections quickly and thoroughly immediately after

the storm makes landfall in order to document the damage before it is cleaned up or concealed. For

example, building owners typically start cleaning up their properties within days of an event and once

the debris is removed, any evidence of poor construction practices is lost. Similarly, once tarps are

placed on leaking roofs, it is difficult to assess the true extent of the roof damage caused by a storm.

The data collected during these reconnaissance missions is used by RMS in two ways. First, it is

provided to RMS’ catastrophe response team, who uses this information, in conjunction with modeled

loss estimates based on reported wind speeds for the storm, to develop an estimate of the overall

industry loss and its geographic extent. This estimate is then provided to RMS’ clients to help them

better manage their response to the event (e.g., to help them set appropriate reserve amounts and



95

manage the deployment of their claims adjusters). The second use of the data collected during these

reconnaissance missions is to suggest and guide any modifications to the vulnerability functions for the

affected region. For example, the severity of the roof damage caused by Hurricane Ike noted by RMS’

reconnaissance teams was greater than expected for the wind speeds recorded during the storm. In

response to this observation, RMS convened a workshop of roof engineers to better understand the

reasons for the poor performance of the roofs in Texas and whether or not similar problems exist

elsewhere in the U.S. The conclusions drawn from this workshop were incorporated into the

2011 U.S. Hurricane Model.

Table 11: Post-Storm Reconnaissance Missions Conducted by RMS

Hurricane/Typhoon Year Region

Opal 1995 Florida

Erin 1995 Florida

Marilyn 1995 Virgin Islands, Puerto Rico

Fran 1996 North Carolina

Bonnie 1998 North Carolina

Georges 1998 U.S. Gulf Coast, Puerto Rico

Floyd 1999 North Carolina

T. Paka 1997 Guam

Fabian 2003 Bermuda

Isabel 2003 North Carolina, Virginia

Charley 2004 Florida

Frances 2004 Florida

Ivan 2004 Mississippi, Alabama, Florida, Louisiana

Jeanne 2004 Florida

Dennis 2005 Florida

Katrina 2005 Mississippi, Alabama, Florida, Louisiana

Rita 2005 Texas

Wilma 2005 Florida

Gustav 2008 Louisiana

Ike 2008 Texas

V-1.5 Describe the research used in the development of the model’s vulnerability functions, including any unknown construction classification utilized.

The vulnerability functions are developed on the basis of structural and wind engineering principles

coupled with analyses of historical storm loss data, building codes and published studies.

The RMS Component Vulnerability Model is based on the methodology outlined by Professors Dale

Perry and Norris Stubbs of Texas A&M University (Stubbs et al. 1995). This methodology has been

augmented by internal research by RMS staff, and has been published by RMS staff (Khanduri 2003).

References used by RMS for developing the vulnerability functions include references listed in

Disclosure G-1.4 including: Davenport et al. (1989), Hart (1976), Liu et al. (1989), McDonald (1986,



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1990), Mehta (1983, 1992), Minor (1979), Cook (1985), Sparks (1988, 1990, 1993), Stubbs (1993),

Zollo (1993), Skerlj (2004), FEMA (2005a, 2005b, 2006a, 2006b, 2009), IBHS (2007, 2009, 2010), and

Gurley (2006).

RMS has used historical storm loss data and research from the 2004/2005 storm seasons as well as

the work from Sparks and Bhinderwal (1993) from Clemson University, in calibration of the vulnerability

functions, as well as other loss data obtained from RMS clients.

When construction class is unknown, as described in Disclosure V-1.10, the model creates a composite

curve based on a building inventory distribution. The Florida inventory distributions are based on an

extensive building specific attribute database compiled by RMS from third party data sources and RMS

in-house research.

V-1.6 Describe the categories of the different vulnerability functions. Specifically, include descriptions of the structure types and characteristics, building height, year of construction, and coverages in which a unique vulnerability function is used. Provide the total number of vulnerability functions available for use in the model for personal and commercial residential classifications.

There are a total of 605 building vulnerability classes per vulnerability region. Each class has both

building and contents damage functions. The vulnerability classes depend on a combination of:

Construction Material

Building Height (number of stories)

Building Occupancy

Year Built

Floor Area (single-family residential only)

Region of State (vulnerability region)

Many of these functions are applicable to commercial building classes. Of the 605 per region, there are

256 that are applicable to the residential lines (including multi-family and manufactured homes). The

state of Florida is divided into four vulnerability regions that have their own unique set of functions. The

possible classifications for each of the six primary characteristics are listed in the following table.



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Table 12: RMS Hurricane Primary Building Classification Options

Construction Class # of Stories Occupancy Year Band (FL)

Unknown Unknown Unknown Unknown

Wood Frame 1 Single Family Pre 1995

Masonry 2-3 Multiple Family 1995-2001

Reinforced Concrete 4-7 Condo Unit Owner 2002 +later

Steel Frame 8-14 Condo Association (For MH only)

Light Metal Frame 15+ Non-Residential1 Pre 1976

Mobile Home w/o Tie-Downs 1976-1994

Mobile Home with Tie-Downs 1995 + later

Floor Area Region

≤ 1,500 sq.ft. (≤139 m2) South FL inland (≥0.5 miles from coast)

1,500-2,500 sq.ft. (140 – 232 m2) South FL coastal (<0.5 miles from coast)

2,500-5,000 sq.ft. (233-464 m2) Central FL coastal (<0.5 miles from coast)

5,000 – 10,000 sq.ft. (465-929 m2) Rest of state

≥ 10,000 sq.ft (≥930 m2)

1There are multiple sub-categories of Non-Residential occupancy in the model that are not listed in detail here.

The various vulnerability classes were defined to allow for the grouping together of structures with

similar performance under wind loads.

V-1.7 Describe the process by which local construction and building code criteria are considered in the model.

The model represents local construction and building code criteria via two mechanisms—vulnerability

regions and vulnerability functions for specific year bands. The changes in building codes and building

construction practices are modeled through vulnerability functions that vary by year of construction of

the building, and also region of the state (vulnerability regions). The vulnerability functions for different

year bands and different regions are reasonable and theoretically sound based on RMS research on

the changes of building code provisions and construction practices in Florida and across the U.S., as

well as the region’s experience with natural catastrophes.

V-1.8 Describe the development of the vulnerability functions for appurtenant structures, contents, and time element.

Appurtenant structures are modeled separately using the same vulnerability functions as buildings.

The damage to contents is a function of the amount of damage to the building structure and in

particular damage to the roof, openings (i.e., windows and doors) and envelope (i.e., cladding). This

function depends on the building class and establishes the rate at which damage to contents

accumulates as a function of damage to the building structure. The hurricane model has separate

vulnerability functions for damage to contents associated with each of the hurricane building classes.

RMS has used actual loss data to calibrate the contents vulnerability functions.



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The hurricane model has separate time element vulnerability functions. There is a time e lement

function for each occupancy class supported by the model. Time element vulnerability is related to the

building damage state. Time element losses consider only direct losses (i.e., expense paid to a policy

holder while the house is being repaired). RMS has used actual loss data to calibrate time element

vulnerability functions. Indirect losses are not separated from the actual loss data and therefore the

modeled functions include both direct and indirect loss to the building.

V-1.9 Describe the relationship between building structure and appurtenant structure vulnerability functions.

Appurtenant structures are modeled separately using the same vulnerability functions as buildings.

V-1.10 Identify the assumptions used to develop vulnerability functions for unknown residential construction types.

For a specified occupancy type (e.g., single-family dwelling or commercial residential), the loss cost for

an unknown residential construction type is computed using a composite vulnerability function that is a

weighted average of the vulnerability functions corresponding to unique combinations of height, year

built, and construction class for the specified occupancy type. The weight applied to each vulnerability

function included in the composite curve is specified by the inventory distribution for the location of the

building and is dependent on the occupancy type. The Florida inventory distributions implemented in

RiskLink are specified by ZIP Code and are based on an extensive industry database compiled by RMS

within the last few years by reviewing insurance exposure data and studying aerial and satellite

imagery.

V-1.11 Identify the assumptions used to develop vulnerability functions for commercial residential construction types.

1. It is assumed that two unrelated entities have insurable interests in commercial residential

properties. The nature of the insured interests held by these entities depends on the type of

commercial residential property as follows:

a. For condominium or townhouse complexes in which the units are owned by different

individuals, the home owner’s association holds title to the building envelope and common

areas and contents while the unit owners hold title to the interior space of their units and any

personal belongings.

b. For apartment buildings in which the units are rented from the building owner, the building

owner holds title to the structure and any contents in the common areas of the building, while

the renters hold title to their personal belongings.

2. The building and contents vulnerability functions for the insured interests of the entities identified

above are derived under the assumption that the building envelope must be breached before the

interior spaces or contents contained within are damaged.

3. It is assumed that the contents vulnerability functions for all entities identified above are the same,

reflecting the fact that the contents held by these different entities are similar in nature.



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V-1.12 Describe any assumptions included in vulnerability function development and validation concerning insurance company claim payment practices including the effects of contractual obligations on the claim payment process.

The underlying assumption is that future claims practices will be the same as the claims practices that

were in effect at the time that the historical losses used in the model development and validation were

paid.

V-1.13 Demonstrate that vulnerability function relationships by type of coverage (structures, appurtenant structures, contents, time element) are consistent with actual insurance data.

RMS has used actual insurance data to validate the vulnerability functions used to represent structure,

contents, time element and appurtenant structure losses.

Comparisons of structure vulnerability functions with actual insurance data are shown in

Disclosure V-1.14. Figure 27 shows representative comparisons of contents and time element

vulnerability functions with samples of contents and time element insurance claims, respectively.

Figure 28 shows a representative comparison for appurtenant structure insurance claims.

Contents

Time Element

Figure 27: Observed Damage ratios (dots and triangles) and modeled Mean Damage Ratios (MDR) (solid line) versus Peak Gust Wind Speed for Contents and Time Element Losses

Figure 28: Observed Damage ratios (dots and triangles) and modeled Mean Damage Ratios (MDR) (solid line) versus Peak Gust Wind Speed for Appurtenant Structure Losses

0.0001

0.001

0.01

0.1

1

10

100

MD

R (%

)

Peak Gust (mph)

Company 1

Company 2

0.0001

0.001

0.01

0.1

1

10

100M

DR

(%

)

Peak Gust (mph)

Company 1

Company 2

0.01

0.1

1

10

100

MD

R (%

)

Peak Gust (mph)

Company 2

Company 1



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V-1.14 Demonstrate that vulnerability function relationships by construction type are consistent with actual insurance data.

Frame, masonry, and mobile home vulnerability curves reflect the actual hurricane loss data upon

which the curves are largely based. The following figure illustrates the relationship between observed

losses and the model’s damage functions.



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(a) Wood Frame

(b) Masonry

(c) Mobile Homes

Figure 29: Observed Damage Ratio (dots and triangles) and Modeled Mean Damage Ratio (MDR) (solid line) versus Peak Gust Wind Speed



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V-1.15 Identify the one-minute average sustained windspeed at which the model begins to estimate damage.

The model begins to estimate losses at one-minute sustained wind speeds above 42 mph.

V-1.16 Describe how the duration of wind speeds at a particular location over the life of a hurricane is considered.

The model does not explicitly consider the duration of wind speed at a particular location over the life of

a hurricane. There is a general consensus among experts that for extreme wind conditions generated

by hurricanes, damage should be correlated to peak gust. However, RMS vulnerability functions are

based on observed losses during hurricanes. These observed losses include a variety of factors,

including duration of wind speeds above a certain threshold at which damage occurs due to fatigue

under repeated loading, and thus implicitly include wind duration effects.

V-1.17 Provide a completed Form V-1, One Hypothetical Event. Provide a link to the location of the form here.

Form V-1: One Hypothetical Event

Vulnerability Standards V-2 Derivation of Contents and Time Element Vulnerability Functions


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V-2 Derivation of Contents and Time Element Vulnerability Functions

A. The relationship between the modeled building and contents vulnerability functions and historical structure and contents losses shall be reasonable.

RMS develops and calibrates relationships between modeled building and content vulnerability

functions from actual loss data and as such the relationship between functions and historical losses is

reasonable.

B. Time element vulnerability function derivations shall consider the estimated time required to repair or replace the property.

The time required to repair or replace a property used in the derivation of the time element vulnerability

functions is inferred from the ratio of the time element claims and exposure values reported by

insurance companies.

C. The relationship between the modeled building and time element vulnerability functions and historical structure and time element losses shall be reasonable.

In a manner similar to contents, losses to time element coverages are dependent on the damage to the

structure. Time element loss ratios will be relatively small compared to structure loss ratios up to the

point where the structure is severely damaged resulting in the building being uninhabitable. In the RMS

Hurricane model, the time element vulnerability functions have been validated against actual coverage

specific loss data ensuring that the relationships between these vulnerability functions is consistent

with loss data.

D. Time element vulnerability functions used by the model shall include time element coverage claims associated with wind, flood, and storm surge damage to the infrastructure caused by a hurricane.

Since the time element model is calibrated with actual historic loss data, it implicitly includes claims

arising from damage to the infrastructure, to the degree to which they are included in the historic loss

data.

Direct flood damage to infrastructure is not calculated in the model; however, the impact on time

element losses due to storm surge damage to infrastructure was not excluded in calibrating time

element loss functions.

V-2.1 Describe the methods used in the model to develop vulnerability functions for contents coverage associated with personal and commercial residential structures.




accumulates as a function of damage to the building structure. Content curves are derived from

structure curves for each of the hurricane building classes and are stored as separate vulnerability

functions. The development of stand-alone contents damage curves as described above provides a

separate mathematical representation of damage to contents in order to provide reasonable

representations of contents only policies and policies without contents coverage.



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V-2.2 Describe the methods used to develop vulnerability functions for time element coverage associated with personal and commercial residential structures. State whether the model considers both direct and indirect loss to the insured property. For example, direct loss could be for expenses paid to house policyholders in an apartment while their home is being repaired. Indirect loss could be for expenses incurred for loss of power (e.g., food spoilage).

The hurricane model has separate time element vulnerability functions. There is a time element

function for each occupancy class supported by the model. Time element vulnerabil ity is related to the





V-2.3 State the minimum threshold at which time element loss is calculated (e.g., loss is estimated for structure damage greater than 20% or only for category 3, 4, 5 events). Provide documentation of validation test results to verify the approach used.

Calculated time element losses are dependent on the structure damage, starting at the same threshold

as building damage. The minimum threshold of time element loss is based upon an analysis of

coverage specific claims data as explained in response to Disclosure V-2.2. Validation tests on claims

data has been used to verify the approach of starting time element loss at the threshold of building

damage. From claims data reviewed from Hurricane Andrew less than 0.1% of ALE claims were

associated with no structure coverage claim.

Figure 30: Relative Structure and Additional Living Expense (ALE) Damage Ratios: Actual Claims Data

Structure Damage Ratio

ALE

Da

mag

e R

atio



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V-2.4 Describe how modeled time element loss costs take into consideration the damage (including damage due to storm surge, flood, and wind) to local and regional infrastructure.

Modeled time element loss costs include direct losses only—expense paid to a policyholder while the

house is being repaired. However, the impact of storm surge damage to infrastructure on time element

losses was not excluded in calibrating wind-based time element loss functions. Local and regional

infrastructure damage is also considered in RMS’ PLA methodology (see Standard A-3).

V-2.5 Describe the relationship between building structure and contents vulnerability functions.

Losses to contents are dependent on the damage to the structure. From an engineering standpoint,

losses to contents will be relatively small in comparison to structure losses until the envelope of the

structure is breached. At that point, both structure and contents damage will quickl y escalate with

increasing wind speeds with the contents damage curve approaching that of the structure as wind

speeds increase.

V-2.6 Describe the relationship between building structure and time element vulnerability functions.

Time element functions are proportional to the effective down time (EDT) of a structure, which is

computed as a function the physical damage state of the structure. The ratio of time element mean

damage ratios to structure mean damage ratios is small at low building damage ratios and increases

with increasing building damage ratio.

Vulnerability Standards V-3 Mitigation Measures


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V-3 Mitigation Measures

A. Modeling of mitigation measures to improve a structure’s wind resistance and the corresponding effects on vulnerability shall be theoretically sound and consistent with fundamental engineering principles. These measures shall include fixtures or construction techniques that enhance the performance of the structure and its contents and shall consider:

Roof strength

Roof covering performance

Roof-to-wall strength

Wall-to-floor-to-foundation strength

Opening protection

Window, door, and skylight strength

The RMS North Atlantic Hurricane Model supports modification of the base vulnerability functions

through the application of secondary modifiers developed using the component vulnerability model. The

modifiers can be building-characteristic specific (e.g., improved roof sheathing or anchors) or external

(e.g., storm shutters). These characteristics must be specifically selected by the user. The default case

is to not include any modifiers. If modifiers are selected they are clearly identified in the input files and

output reports. The secondary modifiers available in the model include the fixtures or construction

techniques required in the standard and are listed in Table 13.

B. Application of mitigation measures that enhance the performance of the structure and its contents shall be justified as to the impact on reducing damage whether done individually or in combination.

Mitigation measures impact both the mean damage ratio (MDR) and the coefficient of variation (CV) of

the damage ratio. The application of mitigation measures is reasonable when applied both individually

and in combination.

V-3.1 Provide a completed Form V-2, Mitigation Measures – Range of Changes in Damage. Provide a link to the location of the form here.

Form V-2: Mitigation Measures – Range of Changes in Damage

Form V-2 has been calculated using zero deductible structural losses only on $100,000 base structure

wood frame and masonry buildings as described in Form V-1.

V-3.2 Provide a description of the mitigation measures used by the model that are not listed in Form V-2.

RMS has added two additional mitigation options to Form V-2.

Using 10d nails at a high wind schedule (HWS) to tie the wood deck down at spacing of 6‖ edge

and 6‖ field nailing on a typical piece of plywood.

Using 8d nails at a high wind schedule (HWS), which is using a spacing of 6‖ edge and 6‖ field

nailing on a typical piece of plywood. (Note: we have assumed that the given 8d nails option

corresponds to 8d nails at the standard 6‖/12‖ pattern.)



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In addition to the mitigation measures listed in Form V-2, the RMS model also provides other mitigation

measures which have not been included in Form V-2:

Options for the Fortified for Safer Living Program, and the Fortified for Existing Homes program as

defined by the Institute for Business and Home Safety (IBHS)

Options for retrofitting flashing and coping

The following table lists all the secondary modifier options available in the model. Only modifiers

relevant to wind hazard for residential occupancy are presented here; option numbers within a modifier

are not necessarily consecutive as a result.



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Table 13: RMS Secondary Characteristic Options in North Atlantic Hurricane Model

Characteristic Options Notes

2–Construction Quality 0-Unknown Buildings with poor

construction quality will suffer

more losses than buildings

with certified design and

construction.

New options are introduced to

account for the Institute for

Business and Home Safety

(IBHS) hurricane risk

mitigation programs.

1-Obvious signs of duress or distress

2-Fortified for Existing Homes, Bronze, Option 1

(US Only)

3-Fortified for Existing Homes, Bronze, Option 2

(US Only)

4-Fortified for Existing Homes, Silver, Option 1

(US Only)

5-Fortified for Existing Homes, Silver, Option 2

(US Only)

6-Fortified for Existing Homes, Gold , Option 1

(US Only)

7-Fortified for Existing Homes, Gold , Option 2

(US Only)

8-Fortified for Safer Buildings – [Post 2001] (US

Only)

9-Certified design & construction

4–Roof Covering 0-Unknown Damage is directly correlated

to the type of roof covering

material used on the building. 1-Metal sheathing with exposed fasteners

2-Metal sheathing with concealed fasteners

3-Built-up roof or single-ply membrane roof with

the presence of gutters

4-Built-up roof or single-ply membrane roof

without the presence of gutters

5-Concrete/clay tiles

6-Wood shakes

7-Normal shingle (55 mph)

8-Normal shingle (55 mph) with Secondary Water

Resistance (SWR)

9-Shingle rated for high wind speeds (110 mph)

10-Shingle rated for high wind speeds (110 mph)

with SWR

6–Roof Age / Condition 0- Unknown Older roofs will suffer more

loss than newer roofs. 1- 0-5 years

2- 6-10 years

3- 11 years or more

4- Obvious signs of duress and distress

7–Roof Geometry 0-Unknown Roof geometry directly impacts

the type of wind forces a roof

is likely to experience. Flat

roofs are more likely to

experience more loading than

1-Flat roof with parapets

2-Flat roof without parapets

3-Hip roof with slope less than or equal to 6:12

(26.5 degrees)



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4-Hip roof with slope greater than 6:12 (26.5

degrees)

hipped or high-pitched roofs.

5-Gable roof with slope less than or equal to 6:12

(26.5 degrees)

6-Gable roof with slope greater than 6:12 (26.5

degrees)

7-Braced gable roof with slope less than or equal

to 6:12 (26.5 degrees)

8-Braced gable roof with slope greater than 6:12

(26.5 degrees)

9–Roof Anchor 0-Unknown The strength of roof anchorage

has a direct impact on the

damageability of the building

envelope. Stronger roof

anchors provide more

resistance against wind forces.

1-Toe nailing / No anchorage

2-Clips

3-Single wraps

4-Double wraps

5-Structural

10–Roof Equipment

Hurricane Bracing

0-Unknown If the equipment anchorage is

not adequate it can

compromise the roof’s

integrity.

1-Properly installed with adequate anchorage

2-Obvious signs of deficiencies in the installation

12–Commercial

Appurtenant Structures

0-Unknown Commercial appurtenant

structures include large signs

and extensive ornamentation

that can shake loose from

either the roof or structural

elements of the building.

1-Large signs

2-Extensive ornamentation

13–Cladding Type 0-Unknown Various types of claddings

provide various degrees of

resistance against wind loads.

If there is a combination of two

or more cladding types used

on the structure, select the one

of dominant use.

1-Brick veneer

2-Metal sheathing

3-Wood

4-EIFS / Stucco

5-Designed for impact

6-Not designed for impact with gravel rooftop on

building or adjacent buildings within 1000 ft

7-Not designed for impact without gravel rooftop

on building or adjacent buildings within 1000 ft

8-Vinyl siding / Hardboard

14–Roof Sheathing

Attachment

0-Unknown Roof sheathing is one of the

main components of a

building. It helps keep the

integrity of the building and is a

major line of defense against

losses to building and contents

due to both wind and rain. The

strength of sheathing depends

on the way it is attached to the

1-Batten decking / Skipped sheathing

2-6d nails – Any nail schedule

3-8d Nails – Minimum nail schedule

4-8d Nails – High wind nail schedule

5-10d Nails – High wind nail schedule

6-Dimensional lumber / Tongue & groove decking



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with a minimum of 2 nails per board roof rafters. This option

accounts for nail size and

spacing.

15–Frame-Foundation

Connection

0-Unknown A building properly connected

to its foundation can resist

wind loads more effectively,

especially at high wind speeds.

1-Bolted

2-Unbolted

16–Residential Appurtenant

Structures

0-Unknown The presence of appurtenant

structures, such as carports

and screen enclosures may

cause increased wind damage

to the structure.

1-None

2-Fences / Carport

3-Screen enclosure / Lanai (more than 15% of

Bldg. value)

4-Screen enclosure / Lanai (less than 15% of

Bldg. value)

19–Opening Protection 0-Unknown Openings with poor wind

resistance can expose interior

building components and

contents to more wind and

water hazards than those with

good wind resistance. In

general, ―glazed openings‖

refers to windows, and ―all

openings‖ refers to both doors

and windows.

1-All openings designed for large missiles

2-All openings designed for medium missiles

3-All openings designed for small missiles

4-All glazed openings designed for large missiles

5-All glazed openings designed for medium

missiles

6-All glazed openings designed for small missiles

7-All glazed openings covered with

plywood/oriented strand board (OSB)

8-At least one glazed exterior opening does not

have wind-borne debris protection

9-No glazed exterior openings have wind-borne

debris protection

26–Flashing and Coping

Quality

0-Unknown Roof covering failures are

often attributed to initial failure

of roof flashing and coping. 1-Compliant with ES1

2-Not compliant with ES1

Note that modifiers related to surge are not shown in this list.

V-3.3 Describe how mitigation is implemented in the model. Identify any assumptions.

A series of modifiers and options for each modifier are available for the user to select. The base

(unmodified) vulnerability curves are adjusted based on modifier selections chosen by the user. The

modifier values vary by base vulnerability curve, modifier option, and wind speed. The modifier values

can decrease or increase the base vulnerability curves, depending on the modifier. The default setting

for each of the modifiers is ―unknown.‖ Therefore, if no modifier options are chosen the base (average)

vulnerability curve is utilized.



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V-3.4 Describe the process used to ensure that multiple mitigation factors are correctly combined in the model.

Users may change one or more mitigation factors from the unknown state based on specific attributes

for the modeled structure. The individual impact of single mitigation factors are combined together with

a multiplicative methodology to reflect the combined effect of different attributes which may increase or

decrease the loss. In addition, caps are placed on the maximum change evoked through the application

of many modifiers to prevent unrealistic values from being returned from the model. RMS has validated

the impact of multiple features through a variety of tests and comparisons to external publications ( i.e.,

FEMA 2009b, ARA 2008). RMS also considers how additional information changes the CV of the loss

estimates, and uses a probabilistic methodology to quantify the contribution of each of the mitigation

measures to the CV.



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Actuarial Standards A-1 Modeling Input Data


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ACTUARIAL STANDARDS

A-1 Modeling Input Data

A. When used in the modeling process or for verification purposes, adjustments, edits, inclusions, or deletions to insurance company input data used by the modeling organization shall be based upon accepted actuarial, underwriting, and statistical procedures.

Any adjustments, edits, inclusions, or deletions to insurance company input used for model verification

are based upon accepted actuarial, underwriting, and statistical procedures and are documented in

writing.

Insurance company input data used in the modeling process contains information provided by the

company and RMS does not make any adjustments, edits, inclusions or deletions to the input data.

B. All modifications, adjustments, assumptions, inputs and/or input file identification, and defaults necessary to use the model shall be actuarially sound and shall be included with the model output report. Treatment of missing values for user inputs required to run the model shall be actuarially sound and described with the model output report.

Input data to the RMS Hurricane model is explicitly provided by the user for each particular analysis.

The model assumes that inputs provided by the user reflect actual exposures. Specifically:

Insurance to Value

The model does not make any assumptions regarding insurance to value. The location value and

insurance limits are provided as separate input. No adjustments are made to these values within the

model.

Primary Characteristics

The model itself does not make adjustments for exposure characteristics unless the user is unable to

specify any of the primary characteristics or the specific location of the policy. If any of the primary

characteristics are unknown, the model defaults to a county level average mix of the unknown

characteristic(s).

Appurtenant Structures

Values of appurtenant structures for each location are a user input. The model does not make

assumptions regarding the value of appurtenant structures.

Contents

Contents limits and values are part of the user input. No assumptions are made within the model.

Additional Living Expenses (ALE)

ALE limits and values are part of the user input. The model assumes that the value represents one

year of ALE.



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Insurer Exposures by ZIP Code

As part of the analysis process, each location analyzed is ―geocoded‖ (i.e., geographically positioned).

If the location does not geocode (for example, if a ZIP Code is invalid), the location is excluded from

the analysis. All locations that are not included in the analysis are easily identified. If the analysis is run

at ZIP Code level, the exposure is assumed to be distributed across the ZIP Code. Given that all

exposure information is provided as part of the user analysis input, this information can be summarized

and clearly identified as part of any rate filing submission. See the Analysis Summary Report in

Appendix F.

A-1.1 Identify depreciation assumptions and describe the methods and assumptions used to reduce insured losses on account of depreciation. Provide a sample calculation for determining the amount of depreciation and the actual cash value (ACV) losses.

RiskLink contains no assumptions regarding depreciation. To model actual cash value provisions, the

user must input the actual cash values instead of the replacement cost values into RiskLink.

Depreciation assumptions are made by the user prior to running RiskLink.

A-1.2 Identify insurance-to-value assumptions and describe the methods and assumptions used to determine the true property value and associated losses. Provide a sample calculation for determining the property value and guaranteed replacement cost losses.

RiskLink assumes that the value input into it is the true property value. Any assumptions regarding

insurance to value must be made by the user prior to running RiskLink.

RiskLink has separate inputs for values and limits. This provides the flexib ility to estimate policies with

or without guaranteed replacement cost coverage. For example, assume an insurer has a policy on its

books for a building with an insured value of $100,000. If the insurer assumes that this building is 10%

underinsured, the value input is $100,000 / (1-0.1) = $111,111. If the policy has guaranteed

replacement cost coverage, the limit input will also be $111,111. If the policy does not have guaranteed

replacement cost coverage, the limit input will be $100,000.

A-1.3 Describe the methods used to distinguish among policy form types (e.g., homeowners, dwelling property, mobile home, tenants, condo unit owners).

Policy forms vary in their terms and conditions, and RiskLink can model these variable terms. The

modeling capabilities include variability in construction (several types of construction classes including

mobile homes), occupancy, coverages for building, contents and time element, or A, B, C, and D.

Given these variables as input, any combination or policy form can be modeled for either commercial or

personal lines.

A-1.4 Disclose, in a model output report, the specific type of input that is required to use the model or model output in a residential property insurance rate filing. Such input includes, but is not limited to, optional features of the model, type of data to be supplied by the model user and needed to derive loss projections from the model, and any variables that a model user is authorized to set in using the model. Include the model name and version number on the model output report. All items included in the output form submitted to the Commission shall be clearly labeled and defined.

All required input needed to derive loss projections from the model are clearly labeled and defined in

the Post Import Summary. All variables that a model user is authorized to set are clearly labeled and

defined in the Analysis Summary Report. See Appendix F for an example of both output reports.



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The RiskLink model contains several optional features that are not part of the FCHLPM approval, such

as alternative rate sets reflecting ‖medium term‖ versus ‖historical‖ perspectives, and settings related to

storm surge modeling. All user modifiable model options are described in sets of model option profiles

called "DLM profiles"—to ensure that results from the certified version of the model are clearly labeled,

RMS provides a specified DLM profile, with approved settings, labeled "FCHLPM Certified Hurricane

Losses." Regulators may audit that FCHLPM approved model settings were used by verifying that the

field "DLM Profile Name" is set to "FCHLPM Certified Hurricane Losses" in the Analysis Summary

Report (Appendix F). The model settings corresponding to this certified profile are shown in Table 14

below:

Table 14: Model Settings Corresponding to the DLM Profile called "FCHLPM Certified Hurricane Losses"

Analysis Option Approved Setting Notes

Peril Windstorm

Region North Atlantic (including Hawaii) Florida is included in this region.

Analysis Mode / Type Distributed / Exceedance Probability

Event Rate Set RMS 2013 Historical Event Rates

Vulnerability Curves Vulnerability - Default Alternate vulnerability curves should

be used only for sensitivity analyses

only.

Assume 2% Deductible when

UNKNOWN

Selected This option will cause any residential

locations within the state of Florida,

with an unknown deductible, to

default to 2% of the structure value.

Calculate Losses from: Wind The storm surge option should NOT

be selected for ratemaking purposes

in the state of Florida.

Calculated loss amplification factors

for:

Building and Appurtenant Structures,

Contents, and Business Interruption /

Time Element

Scale Exposure Factors: Equal to 1.0 No exposure modification

Post analysis, users must apply Annual Deductible Factors to model output in order to convert average

annual loss and return period loss using occurrence-deductibles to average annual and return period

loss using annual-deductibles, as required by Florida Statute 627.701. The table of relevant Annual

Deductible Factors is provided with each software version. In order to demonstrate these factors have

been applied post analysis, users should complete a copy of the form included in Table 52 of Appendix

F.

A-1.5 Provide a copy of the input form used by a model user to provide input criteria to be used in the model. Describe the process followed by the user to generate the model output produced from the input form. Include the model name and version number on the input form. All items included in the input form submitted to the Commission shall be clearly labeled and defined.

Appendix E includes screen shots of the RiskLink user interface showing the location level user inputs.

All valid data input in this form is directly used in generating model output. The model name and



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version number are accessible in the RiskLink user interface via the About RiskLink option under the

Help menu. The screen shot of this is also available in the Appendix E. All items in the RiskLink model

input forms are clearly labeled and defined.

A-1.6 Describe actions performed to ensure the validity of insurer data used for model inputs or validation/verification.

The following validations are done during the import or while entering the data:

Any location that does not geocode to a county level or more geographically detailed resolution will

not have any expected loss associated with it. If the user is unable to specify the ZIP Code, but is

able to specify the county, the model allocates the exposure to ZIP Codes within the county in

proportion to the appropriate exposure for the line of business under consideration. RMS does not

perform loss costs analyses for ratemaking purposes at the county level, and strongly advises its

clients not to do so. Limits and deductibles must be greater than or equal to 0. The limits are

defaulted to the total value and deductibles default to 2% of Coverage A, respectively, if they are

not specified.

The construction and occupancy classes default to unknown if the data is not present or is invalid

or if the scheme is not present or are invalid

A location must have a building, appurtenant structure, contents, or ALE coverage specified or the

location will be excluded from the analysis.

The percentage completion for all the locations must be between 0 and 100. The default value for

percentage completion is 100%.

A location can have only one combined coverage (building plus contents).

The value of the insured asset defaults to zero if not specified. If the currency type is not specified,

all monetary units are defaulted to the RiskLink system currency.

All primary characteristics must be coded in order for secondary modifiers to be invoked.

All hurricane secondary modifiers are defaulted to unknown if not specified.

All policies must have a valid peril specified.

All percentage entries in the user interface must be between 0 and 100.

The number of buildings at a location defaults to 1.

The square-footage of a building is defaulted to a weighted average of the four square-foot bands

when specified as unknown, based on an average square footage of 1,950 sq. ft. for single -family

residential structures.

The following additional validations are done to user-input addresses during geocoding:

Street-level addresses are compared to a complete USPS database, weighing combinations of all

address elements (street name and number, city, ZIP Code, and state) to minimize incorrect

matches.

ZIP Code level addresses are validated against a database that is organized by county and state,

to insure that matches are constrained to the proper geographic region.

Actuarial Standards A-2 Event Definition


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A-2 Event Definition

A. Modeled loss costs and probable maximum loss levels shall reflect all insured wind related damages from storms that reach hurricane strength and produce minimum damaging wind speeds or greater on land in Florida.

The track and pressure of each tropical cyclone are modeled throughout its lifetime in the Atlantic basin

from genesis to decay. For the purposes of calculating losses, a storm is first considered when

maximum winds reach hurricane strength and damage is caused in Florida. From that point on, wind

speeds and losses are calculated regardless of whether maximum winds are greater than or less than

hurricane strength.

B. Time element loss costs shall reflect losses due to infrastructure damage caused by a hurricane.

Time element loss costs reflect losses due to infrastructure damage caused by a hurricane.

A-2.1 Describe how damage from model generated storms (landfalling and by-passing) is excluded or included in the calculation of loss costs and probable maximum loss levels for the state of Florida.

The stochastic database contains events making landfall in the U.S. and by-passing storms. Losses

from by-passing storms are considered only once the storm reaches hurricane strength wind speeds

and causes loss in Florida. The wind speeds causing damage for that hurricane could be greater than

or less than hurricane strength, but the hurricane’s maximum winds must correspond to at least

hurricane strength for the storm to be considered.

A-2.2 Describe how damage resulting from concurrent or preceding flood or hurricane storm surge is treated in the calculation of loss costs and probable maximum loss levels for the state of Florida.

Loss due to coastal flood or storm surge is not included in the calculations of loss costs or probable

maximum loss levels for structure or contents coverages.

Actuarial Standards A-3 Modeled Loss Cost and Probable Maximum Loss Considerations


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A-3 Modeled Loss Cost and Probable Maximum Loss Considerations

A. Loss cost projections and probable maximum loss levels shall not include expenses, risk load, investment income, premium reserves, taxes, assessments, or profit margin.

Neither loss cost projections nor probable maximum loss levels include expenses, risk load, investment

income, premium reserves, taxes, assessments or profit margin.

B. Loss cost projections and probable maximum loss levels shall not make a prospective provision for economic inflation.

Neither loss cost projections nor probable maximum loss levels include any prospective provision for

economic inflation. Vulnerability functions project losses as a percentage of coverage values. Coverage

values are input by the user and no modifications are made within the program to account for economic

inflation.

C. Loss Cost Projections and probable maximum loss levels shall not include any provision for direct hurricane storm surge losses.

Loss Cost Projections and probable maximum loss levels do not include any provision for direct

hurricane storm surge losses.

D. Loss cost projections and probable maximum loss levels shall be capable of being calculated from exposures at a geocode (latitude-longitude) level of resolution.

RiskLink is capable of calculating loss cost projections and probable maximum loss levels at a geocode

(latitude-longitude) level of resolution.

E. Demand surge shall be included in the model’s calculation of loss costs and probable maximum loss levels using relevant data.

Following a major catastrophic event, claims costs can exceed the normal cost of settlement due to a

unique set of economic, social, and operational factors. Commonly called demand surge, these factors

are quantified using a methodology that RMS calls Post-event Loss Amplification (PLA). These factors

are included in the software, its loss costs, and its probable maximum loss levels.

F. The methods, data, and assumptions used in the estimation of demand surge shall be actuarially sound.

The RMS North Atlantic Hurricane Model’s treatment of demand surge is based on data, methods and

assumptions that are actuarially sound.

A-3.1 Describe the method or methods used to estimate annual loss costs and probable maximum loss levels. Identify any source documents used and research performed.

Expected losses associated with each stochastic storm are multiplied by the annual rate of occurrence

for the corresponding storm. These are summed over all storms to determine the average annual loss.

Probable maximum loss levels are associated with exceedance probability (EP) curves. Occurrence

exceedance probability (OEP) curves provide information on the largest loss from a single occurrence

in a year and are generated from the event frequency distribution and the event severity distribution.

Aggregate exceedance probability (AEP) curves provide information on losses from the accumulation



119

of all events in a year and are generated using the Fast Fourier Transform methodology described in

Robertson (Proceedings of the Casualty Actuarial Society, Vol. LXXIX, 1992).

As explained in the response to Disclosure G-1.2, beta distributions are fitted to each stochastic event

which are used to obtain the severity distribution that describes the distribution of the size of losses,

given that an event has occurred. A Poisson distribution is used for event frequency with the mean

frequency obtained as the sum of all the event rates. The OEP curve is calculated on an occurrence

basis and is obtained from the severity distribution along with the overall mean frequency. The AEP

curve is calculated on an aggregate basis, showing the probability that aggregate losses in a year (the

sum of losses from all occurrences in a year) will be greater than a given loss threshold. Thus, multiple

occurrences in a year are considered for which the severity distribution is convolved as many times as

occurrences may happen in a year. Model output statistics are provided for various financial

perspectives. Gross losses are net of primary company deductibles, as demonstrated in the response

to Disclosure A-4.1. In addition to these perspectives, the model includes the capability for the model

user to include reinsurance terms that form the basis of information such as pure premium and

variability for treaty layers, which can be seen from either the ceding or assuming company

perspective.

A-3.2 Identify the highest level of resolution for which loss costs and probable maximum loss levels can be provided. Identify all possible resolutions available for the reported output ranges.

The table below shows RiskLink’s available resolutions.

Table 15: Geocoding Match Levels

Name Description

Coordinate Geocodes to the exact position of the property or structure. Requires prior

knowledge of the latitude/longitude coordinate pair.

Building Geocodes to the exact center of the building footprint.

Parcel Geocodes to the exact center of the Parcel boundaries for street-address

match.

Street The geocoder achieves a fine level of positional accuracy by interpolating the

location of the property along a street segment.

Postcode The geocoder places the location on the centroid of the postal code (e.g.,

U.S. ZIP Code) in which it falls. In the U.S., postal code centroids are

population weighted to provide a better representation of exposure.

Populated-weighted centroids and geographic centroids are not usually the

same place.

County

The geocoder validates the name of the county/state and sets both latitude

and longitude to zero.



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A-3.3 Describe how the model incorporates demand surge in the calculation of loss costs and probable maximum loss levels.

The RMS North Atlantic Hurricane Model component that quantifies demand surge is called post-event

loss amplification (PLA). The PLA model has three major components that escalate loss following

major catastrophic events:

―Economic‖ demand surge (EDS)—Increase in the costs of building materials and labor costs as

demand exceeds supply. This factor has the biggest overall impact.

Claims Inflation (CI)—Cost inflation due to the difficulties in fully adjusting claims following a

catastrophic event. For example, shortcuts such as setting a threshold loss amount under which

claims are simply paid with little to no investigation is a practice historically taken by insurers that

are overloaded with claims following a catastrophic event. Intuitively, the impact of this factor

varies with the estimated number of claims occurring for an event. Overall CI has a minor impact

compared to the other two PLA components.

Super Catastrophe Scenarios—Coverage and loss expansion due to a complex collection of

factors such as containment failures, evacuation effects, and systemic economic downturns in

selected urban areas. This factor has an impact for high return period events striking earthquake

and hurricane exposed metropolitan areas. Primary escalation for super catastrophe events occurs

with respect to BI losses.

Each of these PLA components has a different type of trigger and a unique loss escalation function that

quantifies actual aspects of loss amplification noted in historical catastrophe events. PLA factors are

quantified uniquely by coverage (building, contents, and BI/ALE) and are applied uniformly to all

ground up loss estimates on a per-storm basis before the application of any financial structures such

as deductibles, or limits.

A-3.4 Provide citations to published papers, if any, that were used to develop how the model estimates demand surge.

There are references that address in very general terms economic theories of demand and supply with

applications to demand surge (for example, Dacy and Kunreuther, 1969). However, because of the lack

of research specific to this area, RMS is not aware of publicly published papers that specifically

address the topic of quantification of demand surge following natural disasters and therefore none have

been referenced.

Actuarial Standards A-4 Policy Conditions


121

A-4 Policy Conditions

A. The methods used in the development of mathematical distributions to reflect the effects of deductibles and policy limits shall be actuarially sound.

The methods used in the development of mathematical distributions to reflect the effects of

deductibles, policy limits, and coinsurance are actuarially sound.

B. The relationship among the modeled deductible loss costs shall be reasonable.

The relationship among the modeled deductible loss costs is reasonable.

C. Deductible loss costs shall be calculated in accordance with s. 627.701(5)(a), F.S.

Deductible loss costs are calculated in accordance with s. 627.701(5)(a), F.S.

A-4.1 Describe the methods used in the model to treat deductibles (both flat and percentage), policy limits, replacement costs, and insurance-to-value when estimating projecting loss costs.

RiskLink uses a distributed approach for estimating losses net of deductibles and limits for each event.

When projecting losses, RiskLink considers not only the mean damage ratio, but also the loss

distribution around the mean. It does this by fitting a beta distribution by way of matching the first two

moments of the distribution. The loss net of deductible and limit is calculated considering the pdf of the

loss distribution between these two quantities as indicated in the example below.

Loss net of deductible and limit =

LD

D

LDFLdxxfDx )(1)(

where x = ground-up loss

D = deductible

L = limit

f(x) = pdf of the ground-up loss

F(x) = cdf of the ground-up loss

RiskLink computes the loss as a percentage of the property values, which are input parameters. The

insured value is assumed to be the same as the property value unless a different insured value is input.

If the insured value is lower than the property value, the insured value is treated as a limit to the

insurer’s liability.

RiskLink assumes that the property value input into it is the true property value. Any assumptions

regarding insurance to value must be made by the user prior to running RiskLink.

RiskLink has separate inputs for values and limits. This gives it the flexibility to estimate policies with or

without guaranteed replacement cost coverage. For example, assume an insurer has a policy on its

books with an insured value of $100,000. If the insurer assumes that this policy is 10% underinsured,

Actuarial Standards A-4 Policy Conditions


122

the value input is $100,000 / (1 – 0.1) = $111,111. If the policy has guaranteed replacement cost

coverage, the limit input will also be $111,111. If the policy does not have guaranteed replacement cost

coverage, the limit input will be $100,000.

A-4.2 Provide an example of how insurer loss (loss net of deductibles) is calculated. Discuss data or documentation used to confirm or validate the method used by the model.

Table 16: Example of Insurer Loss Calculation

(A) (B) (C) (D) (E) (F) (G) (H)=(A)*(D) (I)

Building

Value

Policy

Limit Deductible

Mean Damage

Ratio

Coefficient of Variation

α β Ground Up

Loss

Loss Net of

Deductible and Limit

100,000 90,000 2% 1.5% 4.184 0.041 2.716 $1,497.57 $1,224.68

In Table 16 and are the parameters of a beta distribution with a mean of 1.5% and a coefficient of

variation of 4.184.

The calculation of the loss net of deductibles as shown in the formula in Disclosure A-4.1 is based on

actuarial theory of deductibles and limits. See Hogg and Klugman, 1984. The distributions of the losses

given that an event has occurred are validated using engineering studies and claims data.

Additional refinements to insurer gross loss due to deductibles and/or limits may be effective when

more than one limit and/or deductible is applicable, such as when there are limits on individual

locations as well as a policy limit in a multi-location policy.

A-4.3 Describe how the model calculates annual deductibles.

The approach is to estimate the loss net of the deductible for each event in the year times the

probability that there are that many occurrences.

Let Nk = loss net of the deductible for the kth

event in the year.

And let p(k) = probability that there are exactly k events in the year.

Then the projected loss cost net of the deductible is

1k Nk p(k).

The values of the Nk’s depend on k. For example, if k = 1, then Nk is calculated using the full deductible

amount. If k = 2, then Nk is calculated using the amount of the deductible left over after the first

occurrence.

Actuarial Standards A-5 Coverages


123

A-5 Coverages

A. The methods used in the development of contents loss costs shall be actuarially sound.

The RMS North Atlantic Hurricane Model’s treatment of contents damage is derived from and reflects

the relationships apparent in the data and is actuarially sound.

B. The methods used in the development of time element coverage loss costs shall be actuarially sound.

In the RMS Hurricane model, time element losses include only factors that are hurricane related, are

theoretically sound, and consider the time to repair the structure. Time element losses are determined

based upon the estimated damage to the structure. Additionally, time element loss functions have been

calibrated/validated with actual hurricane event ALE/BI coverage losses.

A-5.1 Describe the methods used in the model to calculate loss costs for contents coverage associated with personal and commercial residential structures.




accumulates as a function of damage to the building structure.

The hurricane model has separate vulnerability functions for damage to contents associated with each

of the hurricane building classes.

A-5.2 Describe the methods used to develop loss costs for time element coverage associated with personal and commercial residential structures. State whether the model considers both direct and indirect loss to the insured property. For example, direct loss could be for expenses paid to house policyholders in an apartment while their home is being repaired. Indirect loss could be for expenses incurred for loss of power (e.g., food spoilage).

The hurricane model has separate time element vulnerability functions. There is a time element

function for each occupancy class supported by the model. Time element vulnerability is related to the





Actuarial Standards A-6 Loss Output


124

A-6 Loss Output

A. The methods, data, and assumptions used in the estimation of probable maximum loss levels shall be actuarially sound.

The methods, data, and assumptions used in the estimation of probable maximum loss levels are

actuarially sound.

B. Loss costs shall not exhibit an illogical relation to risk, nor shall loss costs exhibit a significant change when the underlying risk does not change significantly.

Loss costs generated by RMS do not show an illogical relation to risk nor do they exhibit a significant

change when the underlying risk does not change significantly.

C. Loss costs produced by the model shall be positive and non-zero for all valid Florida ZIP Codes.

Loss costs produced by the model are positive and non-zero for all ZIP Codes.

D. Loss costs cannot increase as the quality of construction type, materials and workmanship increases, all other factors held constant.

Loss costs do not increase as the quality of construction type, materials and workmanship increases,

all other factors held constant.

E. Loss costs cannot increase as the presence of fixtures or construction techniques designed for hazard mitigation increases, all other factors held constant.

Loss costs do not increase as the presence of fixtures or construction techniques designed for hazard

mitigation increases, all other factors held constant. The model incorporates information related to

fixtures and construction techniques designed for hazard mitigation as secondary modifiers as

explained in Standard V-3. Details regarding these fixtures and construction techniques are input by

the user.

F. Loss costs cannot increase as the quality of building codes and enforcement increases, all other factors held constant.

The model addresses building code quality and enforcement implicitly through vulnerability functions

that vary with different year bands and vulnerability regions of the state. Loss costs do not increase as

quality increases.

G. Loss costs shall decrease as deductibles increase, all other factors held constant.

Loss costs decrease as deductibles increase, all other factors held constant.

H. The relationship of loss costs for individual coverages, (e.g., structures and appurtenant structures, contents, and time element) shall be consistent with the coverages provided.

The relationship of loss costs for individual coverages is consistent with the coverages provided.

I. Output ranges shall be logical for the type of risk being modeled and deviations supported.

Output ranges provided by RMS are logical.



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J. All other factors held constant, output ranges produced by the model shall in general reflect lower loss costs for:

a. masonry construction versus frame construction,

Output ranges derived from RMS’ model reflect lower loss costs for masonry construction as compared

to frame construction, all other factors held constant.

b. personal residential risk exposure versus mobile home risk exposure,

Output ranges derived from RMS’ model reflect lower loss costs for the residential risk exposure as

compared to mobile home risk exposure, all other factors held constant.

c. inland counties versus coastal counties, and

Output ranges derived from RMS’ model reflect lower loss costs for inland counties as compared to

coastal counties in general, all other factors held constant.

d. northern counties versus southern counties.

Output ranges derived from RMS’ model reflect lower loss costs for northern counties as compared to

southern counties in general, all other factors held constant.

K. For loss cost and probable maximum loss level estimates derived from or validated with historical insured hurricane losses, the assumptions in the derivations concerning (1) construction characteristics, (2) policy provisions, (3) coinsurance, (4) contractual provisions, and (5) relevant underwriting practices underlying those losses, as well as any actuarial modifications, shall be appropriate based on the type of risk being modeled.

As noted in Disclosure V-1.2, historical loss information is used in the development of the RMS

vulnerability functions. This information, including construction type, line of business, policy structure,

insured value, coinsurance and certain contractual provisions, is supplied directly to us by our clients

as part of the exposure information provided with claim information. The information is reviewed by

RMS and any peculiarities are clarified directly with the client. Underwr iting practices, and contractual

provisions not explicitly described in the exposure data are assumed to be representative of residential

insurance underwriting in general; that is, the vulnerability of property observed in historical events is

assumed to be indicative of vulnerability of such property types in future events where the property is

subjected to similar wind loads.

A-6.1 Provide a completed Form A-1, Zero Deductible Personal Residential Loss Costs by ZIP Code. Provide a link to the location of the form here.

Form A-1: Zero Deductible Personal Residential Loss Costs by ZIP Code

A-6.2 Provide a completed Form A-2, Base Hurricane Storm Set Statewide Loss Costs. Provide a link to the location of the form here.

Form A-2: Base Hurricane Set Statewide Loss Costs

A-6.3 Provide a completed Form A-3, Cumulative Losses from the 2004 Hurricane Season. Provide a link to the location of the form here.

Form A-3: Cumulative Losses from the 2004 Hurricane Season



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A-6.4 Provide a completed Form A-4, Output Ranges. Provide a link to the location of the form here.

Form A-4: Output Ranges

A-6.5 Provide a completed Form A-5, Percentage Change in Output Ranges. Provide a link to the location of the form here.

Form A-5: Percentage Change in Output Ranges

A-6.6 A completed Form A-6, Logical Relationship to Risk (Trade Secret item) shall be provided during the closed meeting portion of the commission meeting to review the model for acceptability.

The required form will be provided to the professional team during the on-site audit as well as the

closed portion of the commission meeting.

A-6.7 Provide a completed Form A-7, Percentage Change in Logical Relationship to Risk. Provide a link to the location of the form here.

Form A-7: Percentage Change in Logical Relationship to Risk

A-6.8 Provide a completed Form A-8, Probable Maximum Loss for Florida. Provide a link to the location of the form here.

Form A-8: Probable Maximum Loss for Florida

A-6.9 Describe how the model produces probable maximum loss levels.

See the response to Disclosure A-3.1.

A-6.10 Provide citations to published papers, if any that were used to estimate probable maximum loss levels.

See the response to Disclosure A-3.1.

A-6.11 Describe how the probable maximum loss levels produced by the model include the effects of personal and commercial residential insurance coverage.

Probable maximum loss levels produced by the model are based on exposure and coverage

information that is input by the user. This input includes identification of personal or commercial

residential coverage.

A-6.12 Explain any differences between the values provided on Form A-8 and those provided on Form S-2.

Form A-8 shows ground-up expected losses while Form S-2 shows gross expected losses.

A-6.13 Demonstrate that loss cost relationships among coverages, territories, and regions are consistent and reasonable.

Loss costs relationships between coverages, territories, and regions genera ted by the hurricane model

are consistent and reasonable. The figures in Form A-1 show the variation in loss costs by ZIP Code.

See the response to Disclosure V-1.13 for a discussion of the relationship among coverages.



127

A-6.14 Provide an explanation for all anomalies in the loss costs that are not consistent with the requirements of this Standard.

Loss costs are consistent with the requirements of this Standard with no anomalies.

A-6.15 Provide an explanation of the differences in output ranges between the previously accepted submission and the current submission.

The differences are due to the following factors:

The rates associated with the stochastic event set have been revised.

Updates to the postal codes have been incorporated.

A-6.16 Identify the assumptions used to account for the effects of coinsurance on commercial residential construction loss costs.

The underlying assumption is that the exposure information received with claims data accurately

represents the coinsurance provisions. The RiskLink financial model has specific logic to calculate

coinsurance provisions.

A-6.17 Describe how loss adjustment expenses are considered within the loss cost and probable maximum loss level estimates.

RiskLink loss results do not include any loss adjustment expenses.



128

Statistical Standards S-1 Modeled Results and Goodness-of-Fit


129

STATISTICAL STANDARDS

S-1 Modeled Results and Goodness-of-Fit

A. The use of historical data in developing the model shall be supported by rigorous methods published in currently accepted scientific literature.

RMS uses empirical methods in model development and implementation to match stochastic storm

generation to historical data. These methods are supported by those described in currently accepted

scientific literature and are outlined in Standards M-2 and M-3.

B. Modeled and historical results shall reflect statistical agreement using currently accepted scientific and statistical methods for the academic disciplines appropriate for the various model components or characteristics.

The results of the RMS model are checked at all stages of development to ensure that the stochastic

storm set includes physically realistic hurricanes and preserves the statistical characteristics of

historical data. In addition, vulnerability curves have been developed based largely on actual event

insured loss data.

Extensive comparisons using accepted scientific and statistical methods reflect good agreement

between modeled and historical data. The checks performed by RMS include goodness of fit tests for

the following:

Central Pressure (CP)

Maximum 1-minute sustained wind (Vmax)

Translational Speed (also known as Forward Speed)

Radius-to-maximum Winds (Rmax)

Landfall Frequency

Track crossing frequencies over a grid covering Florida

S-1.1 Identify the form of the probability distributions used for each function or variable, if applicable. Identify statistical techniques used for the estimates and the specific goodness-of-fit tests applied. Describe whether the p-values associated with the fitted distributions provide a reasonable agreement with the historical data. Provide a completed Form S-3, Distributions of Stochastic Hurricane Parameters. Provide a link to the location of the form here.

Form S-3: Distributions of Stochastic Hurricane Parameters

A list of variables and the distributions RMS uses for each follows.

Central Pressure

Central pressure is modeled through the change in pressure along the tracks. The mean pressure

change varies in space and depends on several predictors. Deviations from the mean are assumed to

be Gaussian. Central pressure is re-calibrated at landfall to match the distribution of historical values

from HURDAT (Jarvinen et al. 1984.) RMS performed Kolmogorov-Smirnov and chi-square goodness-

of-fit tests for the cumulative distribution function of pressure at landfall. The corresponding p -values

show a reasonable agreement with the historical data.



130

Inland Filling Rate

The filling rate as hurricanes hit land follows a Gaussian distribution. The mean depends on several

predictors which describe the intensity, size and speed of the storm at landfall and other characteristics

such as the area of the storm over different types of terrain. The coefficients of the relationship are

estimated using least squares from synthetic storms created using the bogusing technique (Kurihara et

al. 1993). The model fitting, selection and validation are described in Colette et al. (2010). The

predictive power of the filling model is assessed using pressure time series over land from the

HURDAT dataset. Histograms of observed and simulated filling rates in Florida, as well as plots of

predicted pressure series over land for several historical hurricanes, show good agreement between

model and data.

Maximum 1-Minute Sustained Winds (equivalent over water)

RMS uses a lognormal distribution for Vmax. The mean of Vmax depends on pressure difference (FFP-

CP) and latitude, with the coefficients of the relationship estimated using HURDAT over-water data. A

further calibration step ensures that the Vmax distributions by landfall region reproduce well the

historical distributions from HURDAT (1900-2011). RMS performed Kolmogorov-Smirnov and chi-

square goodness-of-fit tests for the cumulative distribution function of Vmax at landfall. The

corresponding p-values show a reasonable agreement with the historical data.

Track Translational Speed and Heading

Translational speed and storm heading are derived from the zonal and meridional track steps. The

mean steps in both directions are location dependent and estimated from historical tracks in HURDAT.

Deviations from the mean are assumed to be Gaussian with variances, autocorrelations and cross -

correlations that are also location dependent and estimated by smoothing historical data. All length-

scales involved in the smoothing weights are estimated using leave-one-out cross-validation (Hall and

Jewson, 2007). RMS performed Kolmogorov-Smirnov and chi-square goodness-of-fit tests for the

cumulative distribution function of translational speed at landfall. The corresponding p-values show a

reasonable agreement with the historical data. The storm heading is validated using histograms by

landfall segment.

Radius to Maximum Winds

RMS uses a lognormal distribution, truncated on the right side according to the storm category by

pressure. The mean is a function of central pressure and latitude, the coefficients of the relationship

being estimated using the extended best track dataset (Demuth et al. 2006). RMS performed

Kolmogorov-Smirnov and chi-square goodness-of-fit tests for the cumulative distribution function of

Rmax at landfall. The p-values for these tests showed a reasonable agreement with the historical data.

Wind Profile Parameters

The parameters X1 and N, which govern the shape of the wind field inside and outside the eye, are

modeled as Gamma random variables. The means depend on previous values of X1 and N

respectively, and on Rmax and translational speed. The coefficients of the model are estimated via

iteratively reweighted least squares using H*Wind data (Powell et al. 2010). The modeled distributions

for these parameters are validated by comparing histograms of various wind radii to the observed

equivalents.



131

The position of Vmax with respect to the track is described by the wind profile parameter Amax, which is

assumed to follow a truncated Gaussian distribution. The mean depends on translational speed, R max

and previous values of Amax. The standard deviation depends on translational speed. A histogram of

observed and stochastic Amax values is shown in Figure 39.

The coefficients corresponding to the 4 EOFs described in Disclosure G-2.1 are assumed to follow an

autoregressive model of order 1.

Storm Frequency

RMS uses a Poisson frequency distribution with storm specific mean. The means of these distributions

are calibrated towards the smoothed numbers of landfalls by coastal segment in the full historical set.

RMS has performed the conditional chi-square and Neyman-Scott tests. RMS also performed a chi-

square goodness-of-fit test to compare the modeled and historical landfall distributions over different

sub-regions and intensities. The p-values show overall reasonable agreement.

Completed Form S-3 is provided at Form S-3: Distributions of Stochastic Hurricane Parameters.

S-1.2 Describe the nature and results of the tests performed to validate the wind speeds generated.

Wind speeds have been extensively validated against station data (from NOAA, Florida Coastal

Monitoring Program, and Texas Tech University) over land, and H*Wind and buoy data (from the

National Data Buoy Center) over water. The modeled wind speeds are compared to observed data both

at specific time steps (snapshot comparisons) and in terms of the maximum achieved at each location

(footprint comparisons).

The example validations presented in this Section compare model and station data for several storms,

including Andrew (1992) and all the historical events from 2004 onward, at locations in Florida and a

buffer surrounding it. Observed and modeled wind speeds are 3-second peak gusts.

We tested for overall bias in the estimates by comparing the average modeled wind speed to the

average observed wind speed. This comparison shows a neglig ible over-estimate of less than 1 mph

over all footprints. The comparison gives similar results over all snapshots, indicating a very slight

over-estimate of less than 2 mph. The proportion of data points for which the modeled values are within

10%, 20%, 30%, and 40% of the observed wind speeds are shown in Table 17, for all footprints and all

snapshots.

Table 17: Portion of Modeled Wind Speeds within 10%, 20%, 30%, and 40% of the Observed Value

Difference Range

Proportion of Data Points

Footprints Snapshots

± 10% 55% 58%

± 20% 81% 85%

± 30% 93% 95%

± 40% 99% 98%



132

Several graphical validations of observed versus modeled wind speeds are shown in Standard M-4,

including footprints with overlaid observations, as well as example modeled and observed time series

at several stations.

Additional graphical comparisons are shown in Figure 31. Figure 31(a) is a scatterplot of modeled

versus observed wind speeds for the footprints of Charley (2004), Katrina (2005) and Wilma (2005).

Observed and modeled snapshot data are compared in Figure 31(b) for Hurricane Wilma (2005). Both

scatterplots demonstrate a reasonable agreement between modeled and observed wind speeds.

(a) (b)

Figure 31: Modeled versus Observed Wind speeds (3-second Peak Gust)

S-1.3 Provide the date of loss of the insurance company data available for validation and verification of the model.

The year of the loss is given below:

Hugo–1989

Bob–1991

Andrew–1992

Erin–1995

Opal–1995

Fran–1996

Georges–1998

Charley–2004

Frances–2004

Ivan–2004

Jeanne–2004

Wilma–2005

Ike–2008



133

S-1.4 Provide an assessment of uncertainty in loss costs for output ranges using confidence intervals or other accepted scientific characterizations of uncertainty.

The uncertainty analysis presented in the previous submission in Form S-6 identified far field pressure

(FFP) and central pressure (CP) as the main contributors to the uncertainty in loss costs, followed by

the radius to maximum winds (Rmax). Further analysis of the uncertainty in loss cost for output ranges

focuses on intensity and size, as previous submissions have shown the other parameters to contribute

considerably less to the total uncertainty. Since the maximum sustained wind (Vmax) depends on both

FFP and CP, this one parameter is used in this disclosure to assess the uncertainty in loss cost due to

uncertainty in intensity.

Figure 32 shows the uncertainty in loss costs for output ranges due to the uncertainty in Vmax. In the

figure, each point represents the average annual loss per $1,000 of exposure for a ZIP Code. Vmax is

set to ―Low‖ and ―High‖ values to obtain alternate loss costs, which are compared to the original loss es.

The 5% and 95% confidence bounds on the Vmax CDF are used to set the ―Low‖ and ―High‖ limits (the

99% confidence bounds are shown in Figure 35). The blue (purple) points show the ratio of alternate to

original loss costs when Vmax is set to ―Low‖ (―High‖) versus the loss cost resulting from the original

modeled Vmax.

Figure 32: Uncertainty in Loss Costs due to Vmax

The uncertainty in the loss costs for output ranges due to the uncertainty in the Rmax cumulative

distribution function is shown in Figure 33. Each point represents the average annual loss per $1,000

of exposure for a ZIP Code. Rmax is set to ―Low‖ and ―High‖ values to obtain alternate loss costs for

Altern

ate

Loss C

ost/

Mo

dele

d L

oss C

ost



134

comparison with the original losses. The 5% and 95% confidence bounds on the R max CDF are used to

set the ―Low‖ and ―High‖ limits (the 99% confidence bounds are shown in Figure 38). The blue (purple)

points show the ratio of alternate to original loss costs when Rmax is set to ―Low‖ (―High‖) versus the

loss costs corresponding to the original modeled Rmax.

Figure 33: Uncertainty in Loss Costs Due to Rmax

S-1.5 Justify any differences between the historical and modeled results using current accepted scientific and statistical methods in the appropriate disciplines.

Historical and modeled results are in agreement according to currently accepted statistical methods.



135

S-1.6 Provide graphical comparisons of modeled and historical data and goodness-of-fit tests. Examples include hurricane frequencies, tracks, intensities, and physical damage.

Intensity—Central Pressure, Vmax and Inland Filling Rate

Figure 34 shows a comparison of stochastic and observed central pressure distributions in Florida and

neighboring states. The observed and modeled cumulative distribution functions (CDFs) are shown in

black and red respectively. The gray area represents a pointwise 99% band around the modeled CDF.

Figure 34: Central Pressure Cumulative Distribution Function (CDF)

The Kolmogorov-Smirnov test corresponding to the CDFs in the figure above produces a p-value of

94%. A chi-square test with eight cells produces a p-value of 83%. The historical data used for

comparison is from the HURDAT database landfall summary, as of November, 2011.



136

Figure 35 shows a similar comparison for Vmax.

Figure 35: Vmax Cumulative Distribution Function (CDF)

The Kolmogorov-Smirnov test corresponding to the CDFs in the figure above produces a p-value of

70%. A chi-square test with eight cells produces a p-value of 57%. The historical data used for

comparison is from the HURDAT 6-hourly database as of November, 2011.



137

A graphical comparison for the inland filling rate is shown in Standard M-5, where the central pressure

series over land, normalized by the corresponding landfall pressures, are plotted for fast and slow

modeled filling in Florida, together with several historical cases. In addition, Figure 36 shows the

historical pressure series over Florida for Charley (2004; black line), together with the pressures

corresponding to the predicted filling rate (red), and the middle 50% (dark blue) and 90% (light blue)

simulated filling rates for this storm.

Figure 36: Pressure Time Series Over Land with Observed (black), Predicted (red) and Simulated (dark blue for 50% and light blue for 90% bands) Filling Rate



138

Translational Speed and Heading

Figure 37 compares historical and modeled distributions of translational speed.

Figure 37: Translational Speed (Forward Speed) Cumulative Distribution Function (CDF)

The Kolmogorov-Smirnov test on the translational speed produces a p-value of 23%, and a chi-square

test with eight equal cells produces a p-value of 48%. The historical data used for comparison is from

the HURDAT 6-hourly database as of November, 2011.

Histograms of historical and modeled storm heading for different coastal segments in Florida and

neighboring states are shown in Standard M-3.



139

Radius to Maximum Winds

Figure 38 shows a comparison of the observed and modeled Rmax distributions in Florida and

neighboring states.

Figure 38: Rmax Cumulative Distribution Function (CDF)

The reasonable agreement suggested by the figure above is confirmed by the goodness of fit test

results. The Kolmogorov-Smirnov test on the radius of maximum winds produces a p-value of 87%, and

a chi-square test with eight equal cells produces a p-value of 83%. The historical data used for the

comparison covers the 1900 to 2011 period.



140


The optimized Willoughby profile (see Disclosure M-4.1) prescribes the shape of the windfield outside

and inside the eye of the hurricane through parameters X1 (decay length parameter) and N (power law

parameter), respectively. X1 and N determine the modeled wind radii, which are validated against the

historical values from the extended best track dataset. Standard M-6 contains a graphical comparison

of observed and modeled histograms for radii of hurricane force winds. Figure 39 compares observed

(black) and modeled (red) distributions of Amax, the wind profile parameter that describes the angle

between track and location of Vmax. The comparison is shown for the whole basin, rather than Florida

only, and uses H*Wind snapshots to derive the historical values.

Figure 39: Angle to Maximum Winds Histogram

The figure above shows the histograms of observed (black) and modeled (red) Amax. A value of

0 radians corresponds to the location of Vmax being 90 degrees clockwise from the direction of storm

movement.

S-1.7 Provide a completed Form S-1, Probability and Frequency of Florida Landfalling Hurricanes per Year. Provide a link to the location of the form here.

Form S-1: Probability and Frequency of Florida Landfalling Hurricanes per Year

S-1.8 Provide a completed Form S-2, Examples of Loss Exceedance Estimates. Provide a link to the location of the form here.

Form S-2: Examples of Loss Exceedance Estimates

Statistical Standards S-2 Sensitivity Analysis for Model Output


141

S-2 Sensitivity Analysis for Model Output

The modeling organization shall have assessed the sensitivity of temporal and spatial outputs with respect to the simultaneous variation of input variables using currently accepted scientific and statistical methods in the appropriate disciplines and have taken appropriate action.

RMS has assessed the sensitivity of temporal and spatial outputs with respect to the simultaneous

variation of input variables using currently accepted scientific and statistical methods and has taken

appropriate action.

S-2.1 Identify the most sensitive aspect of the model and the basis for making this determination. Provide a full discussion of the degree to which these sensitivities affect output results and illustrate with an example.

The most sensitive aspects of the model are the intensity and size of the hurricane at landfall,

specifically the far field pressure (FFP), central pressure (CP) and radius to maximum winds (R max) at

landfall. This determination was based on the results of the sensitivity analyses described in the

previous submission, in Form S-6. The standardized regression coefficients showed that for category 1

and 3 storms, losses are most sensitive to variations in FFP and CP. For category 5 hurricanes, losses

are mostly affected by changes in Rmax.

S-2.2 Describe how other aspects of the model may have a significant impact on the sensitivities in output results and the basis for making this determination.

The filling rate (Alpha) has a significant impact on the sensitivities in output results, especially for more

intense storms. Translational speed and wind profile parameters have a lesser impact on the output.

This determination is based on the standardized regression coefficients presented in the previous

submission.

S-2.3 Describe and justify action or inaction as a result of the sensitivity analyses performed.

The results of the sensitivity analyses confirmed previous RMS research. Additionally, the sensitivity

study highlighted the importance of the filling rate, which is a model component that has been

extensively researched and revised prior to the last submission. No action was necessary after review

of the sensitivity results.

S-2.4 Provide a completed Form S-6, Hypothetical Events for Sensitivity and Uncertainty Analysis. (Requirement for models submitted by modeling organizations which have not previously provided the Commission with this analysis. For models previously found acceptable, the Commission will determine, at the meeting to review modeling organization submissions, if an existing modeling organization will be required to provide Form S-6 prior to the Professional Team on-site review). If applicable, provide a link to the location of the form here.


Standards. No aspect of the model that would affect the results in Form S-6 has been changed.

Statistical Standards S-3 Uncertainty Analysis for Model Output


142

S-3 Uncertainty Analysis for Model Output

The modeling organization shall have performed an uncertainty analysis on the temporal and spatial outputs of the model using currently accepted scientific and statistical methods in the appropriate disciplines and have taken appropriate action. The analysis shall identify and quantify the extent that input variables impact the uncertainty in model output as the input variables are simultaneously varied.

RMS has performed an uncertainty analysis on the temporal and spatial outputs of the model using

currently accepted scientific and statistical methods and has taken appropriate action.

S-3.1 Identify the major contributors to the uncertainty in model outputs and the basis for making this determination. Provide a full discussion of the degree to which these uncertainties affect output results and illustrate with an example.

The major contributors to the uncertainty in model outputs are the intensity and size of the hurricane at

landfall, specifically far field pressure (FFP), central pressure (CP) and radius to maximum winds

(Rmax). FFP and CP are the main contributors to the uncertainty in loss costs for category 1 and

category 3 storms. For category 5 hurricanes, the input with the most impact on the uncertainty in the

model outputs is Rmax. This determination is based on the analyses described in the previous

submission, in Form S-6.

The large contributions of intensity and size of the storms at landfall on the uncertainty in the loss costs

is confirmed by Figure 32 and Figure 33. These figures summarize the changes in the loss costs by ZIP

that result from setting the maximum 1-minute sustained winds (Vmax) and Rmax to the 5% and 95%

limits on the respective cumulative distribution functions.

S-3.2 Describe how other aspects of the model that may have a significant impact on the uncertainties in output results and the basis for making this determination.

The filling rate (Alpha) has a significant impact on the uncertainties in the output results, especially for

intense hurricanes, The basis for this determination are the expected percentage reductions (EPRs)

presented in the previous submission. Translational speed and wind profile parameters have a much

smaller impact on the uncertainties.

S-3.3 Describe and justify action or inaction as a result of the uncertainty analyses performed.

No action was necessary after reviewing the results of the uncertainty analyses. The results of these

analyses confirmed results described in previous submissions. Additionally, the uncertainty analyses

highlighted the importance of the filling rate, which is a model component that has been extensively

researched and revised prior to the last submission.

S-3.4 Form S-6, if disclosed under Standard S-2, will be used in the verification of Standard S-3.


Standards. No aspect of the model that would affect the results in Form S-6 has been changed. .

Statistical Standards S-4 County Level Aggregation


143

S-4 County Level Aggregation

At the county level of aggregation, the contribution to the error in loss costs estimates induced by the sampling process shall be negligible.

The RMS North Atlantic Hurricane stochastic track set is based on approximately 100,000 years of

simulated time. The track paths and associated parameters are simulated and calibrated according to

the methods described in Standards M-2 and S-1. The number of storms is then reduced to a

representative sub-sample through a selection process described below. Loss convergence testing

verified that the county level error in loss cost estimates induced by the sampling process is negligible.

S-4.1 Describe the sampling plan used to obtain the average annual loss costs and output ranges. For a direct Monte Carlo simulation, indicate steps taken to determine sample size. For an importance sampling design, describe the underpinnings of the design.

The target for the storm selection process is the county level average annual loss, with additional

constraints to ensure that other loss criteria are met and the landfall distributions of the main physical

parameters are preserved. The procedure is iterative and can be described by the following steps:

1. Group storms according to their loss in different regions and their landfall parameters

2. Eliminate each storm in turn

a. Redistribute rate within appropriate bin

b. Calculate maximum percentage change in average annual loss over all counties

3. Choose storm that minimizes the cost function in 2b for deletion

4. Repeat steps 2 and 3 as long as the target remains in a satisfactory range

Statistical Standards S-5 Replication of Known Hurricane Losses


144

S-5 Replication of Known Hurricane Losses

The model shall estimate incurred losses in an unbiased manner on a sufficient body of past hurricane events from more than one company, including the most current data available to the modeling organization. This Standard applies separately to personal residential and, to the extent data are available, to commercial residential. Personal residential experience may be used to replicate structure-only and contents-only losses. The replications shall be produced on an objective body of loss data by county or an appropriate level of geographic detail and shall include loss data from both 2004 and 2005.

The RMS model is able to reliably and without significant bias reproduce incurred losses on a large

body of past hurricanes, both for personal residential and commercial residential. Validations of known

storm losses have been performed in several ways, including:

For recent events, on an industry basis. The RMS model is able to reasonably reproduce aggregate

incurred industry losses in recent events.

For recent events, on a company-specific basis. The RMS model is able to reasonably reproduce

aggregate incurred losses for a diverse set of insurers.

For recent events, on a geographic and demographic basis. The RMS model is able to reasonably

reproduce the geographic spread of company specific losses, and the spread of losses between

various lines of business and between various types of coverages.

For less recent events, on an industry basis. The RMS model is able to reasonably reproduce

industry losses for less recent hurricanes, both in aggregate and on a broad geographic basis, for

which some level of industry loss data is available 9F

3.

S-5.1 Describe the nature and results of the analyses performed to validate the loss projections generated by the model for personal and commercial residential separately. Include analyses for the 2004 and 2005 hurricane seasons.

RMS has compiled reported loss information from industry sources at the time of key historical events.

The reported losses are normalized to the year 2011 with a methodology that accounts for increases in

cost of construction, growth of the building population, the change in building quality over time, and the

change in average living area per house from the time of the event until 2011. Comparisons are made

to modeled losses based on the RMS Industry Exposure Model.

In addition, insurance companies have supplied RMS with datasets containing the locations and

building types associated with coverage and loss amounts. These datasets have been run against

historical storms and the computed losses have been compared to the actual losses.

Figure 40 and Figure 41 show the results of representative samples of the comparative analyses that

have been performed.

3 From 1950 onwards, Property Claims Services (PCS) has tracked the aggregate industry losses from hurricanes. While these

estimates, particularly the older ones, are potentially unreliable and must be adjusted to reflect current demographic and economic conditions, these older events do provide a means for checking potential bias in the model.



145

Figure 40: Florida Industry Loss Estimates (Residential) for Recent Storms 10F

4

Table 18: Comparison of Actual and Estimated Industry Loss ($ million)

Storm Year PCS Estimate (2) FL-OIR Estimate (1) RMS Estimate (3)

Andrew 1992 38,883 - 38,175

Erin 1995 815 - 603

Opal 1995 3,168 - 1,343

Georges 1998 570 - 180

Charley 2004 7,646 10,238 8,602

Ivan 2004 5,039 2,659 1,355

Jeanne+Frances 2004 8,688 13,780 11,583

Wilma 2005 10,908 7,703 11,548

Katrina 2005 594 564 783

Dennis 2005 794 241 554

*See notes for Figure 40

4 Notes on Figure and Table: (1) Estimates from Florida Office of Insurance Regulation report, ―Hurricane Summary Data: CY 2004 and CY 2005‖ from August 2006. Losses are normalized to 2011 values, represent residential lines, include demand surge and underreporting estimates, and exclude loss adjustment expense. (2) Property Claims Services estimate of losses. Losses for Florida are normalized to 2011 values, represent residential lines and includes demand surge and excludes loss adjustment expense. (3) RMS estimates for residential lines and are based on RMS Industry Exposure for 2011. Losses include demand surge and exclude loss adjustment expenses. Industry feedback indicates that Hurricanes Frances and Jeanne have been treated as one event from a claims and adjusting standpoint due to the inability of claims and adjusters to differentiate loss between the two events

$-

$5

$10

$15

$20

$25

$30

$35

$40

$45

$50

Gro

ss In

du

str

y L

oss (

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in 2

011

Bil

lio

ns

FL-OIR Estimate (1)

PCS Estimate (2)

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146

*Loss includes demand surge but does not include loss adjustment expense.

Figure 41: Company Specific Loss Comparisons for Residential (RES) Structure Types

The following table shows a sampling of aggregated loss comparisons by company.

Table 19: Sample Client Loss Data Comparison

(Losses normalized such that maximum actual loss = $1,000,000)

Comparison Storm TIV* Actual Loss** Predicted Loss ** Ratio

A Andrew 17,496,000 1,000,000 908,796 0.91

B Charley 7,962,000 131,670 121,349 0.92

B Frances+Jeanne 68,468,000 180,632 142,107 0.79

C Charley 347,000 5,948 5,537 0.93

C Frances+Jeanne 2,352,000 5,896 5,080 0.86

D Charley 928,000 24,050 20,683 0.86

D Frances+Jeanne 7,451,000 27,321 20,646 0.76

E Charley 1,693,000 54,949 51,147 0.93

E Frances+Jeanne 48,269,000 140,340 101,361 0.72

F1 Charley 1,749,000 17,114 18,316 1.07

F1 Frances+Jeanne 16,097,000 64,280 54,230 0.84

F2 Charley 3,108,000 23,769 28,953 1.22

F2 Frances+Jeanne 21,448,000 31,030 42,432 1.37

G Wilma 9,491,000 97,056 162,659 1.68

H Wilma 22,652,000 219,822 213,071 0.97

*Abbreviation: Total Insured Value (TIV) **Includes demand surge

$1,000

$10,000

$100,000

$1,000,000

Lo

ss (

No

rmali

zed

to

$1M

)

Company/Storm

Observed Loss*

RMS Estimate*

Losses indexed such that $1M = maximum

company loss



147

S-5.2 Provide a completed Form S-4, Validation Comparisons. Provide a link to the location of the form here.

Form S-4: Validation Comparisons

Statistical Standards S-6 Comparison of Projected Hurricane Loss Costs


148

S-6 Comparison of Projected Hurricane Loss Costs

The difference, due to uncertainty, between historical and modeled annual average statewide loss costs shall be reasonable, given the body of data, by established statistical expectations and norms.

The difference between historical and modeled annual average statewide loss costs provided in this

year’s submission is statistically reasonable, given the body of data, by established statistical

expectations and norms.

S-6.1 Describe the nature and results of the tests performed to validate the expected loss projections generated. If a set of simulated hurricanes or simulation trials was used to determine these loss projections, specify the convergence tests that were used and the results. Specify the number of hurricanes or trials that were used.

The losses produced by the set of stochastic storms have been compared to losses produced by

historical storms impacting Florida.

RMS has validated estimates by first comparing the modeled frequency of various storm characteristics

with the historic record. The number of modeled storms of various intensities making landfall in each of

the segments was compared to the historical record. For most region/category combinations, we found

a reasonable agreement.

The losses produced by the set of stochastic storms have been compared to losses produced by

historical storms impacting Florida. For example, historical and stochastic storm sets were compared in

terms of exceedance probability curves for industry level losses. In addition, the geographic

progression of loss costs by ZIP Code was reviewed for smoothness, consistency, and logical relation

to risk.

The RMS model contains 20,247 hurricanes that cause damaging winds in Florida. In order to ensure

that the set of stochastic storms is sufficient and converges, the county level standard errors for the

average annual loss have been checked to verify that the error in loss costs estimates induced by the

sampling process is negligible.

S-6.2 Identify and justify differences, if any, in how the model produces loss costs for specific historical events versus loss costs for events in the stochastic hurricane set.

Available observed track paths, central pressure series, and over-water Vmax values are used to model

historical events. Other storm parameters are realizations of the same model used for the stochastic

set, with additional constraints derived from added wind field data, if available. Vulnerability and

financial modeling functions are identical for both stochastic and historic storms.

S-6.3 Provide a completed Form S-5, Average Annual Zero Deductible Statewide Loss Costs – Historical versus Modeled. Provide a link to the location of the form here.

Form S-5: Average Annual Zero Deductible Statewide Loss Costs – Historical versus Modeled

Computer Standards C-1 Documentation


149

COMPUTER STANDARDS

C-1 Documentation

A. Model functionality and technical descriptions shall be documented formally in an archival format separate from the use of letters, slides, and unformatted text files.

Model functionality and technical descriptions are documented for our users through a series of user

guides, reference manuals, and white papers available from a limited-access portion of a website

maintained by RMS.

B. The modeling organization modeler shall maintain a primary document binder, containing or referencing a complete set of documentation specifying the model structure, detailed software description, and functionality. Development of the documentation shall be indicative of accepted software engineering practices.

A Computer Standards primary document binder in electronic form has been prepared by RMS and is

available for on-site review by the Professional Team. The primary document binder contains an index

that links each subsequent Computer Standard to one or more sections within the binder and, where

appropriate, to other more detailed documents such as the RiskLink System Adminis tration Guide. All

documentation is easily accessible from a central location. This collection of material specifies the

model structure, detailed software description, and functionality. This material is indicative of the

accepted software engineering practices that are followed by the RiskLink development team.

C. All computer software (i.e., user interface, scientific, engineering, actuarial, data preparation and validation) relevant to the submission shall be consistently documented and dated.

Through the use of various techniques such as documentation templates and development standards,

the RiskLink software and model development tools are documented and dated in a consistent manner.

Appropriate personnel for software, data preparation and validation, as well as internal users of the

software, will be available to the Professional Team when the Computer Standards are being audited.

D. The modeling organization shall maintain (1) a table of all changes in the model from the previously accepted submission to the initial submission this year and (2) a table of all substantive changes since this year’s initial submission.

A table containing items listed in Standard G-1, Disclosure 5 has been prepared. The table contains an

item number in the first column, and the remaining columns contain specific document or file

references for affected components or data relating to Computer Standards C-2, C-3, C-4, C-5, and

C-6.

E. Documentation shall be created separately from the source code.

Modeling and software documentation has been created separately from and is maintained consistently

with the source code. This external documentation is augmented by detailed technical documentation

that is integrated with the source code.

Computer Standards C-2 Requirements


150

C-2 Requirements

The modeling organization shall maintain a complete set of requirements for each software component as well as for each database or data file accessed by a component. Requirements shall be updated whenever changes are made to the model.

RMS maintains a complete set of requirements for each component, database, and data file accessed

by a component that is relevant to this submission. These requirements are updated whenever

changes are to be made to the model. This documentation, which is described in the response to

Standard C-2, is available for on-site review by the Professional Team.

C-2.1 Provide a description of the documentation for interface, human factors, functionality, documentation, data, human and material resources, security, and quality assurance.

RMS maintains documentation of user interface/human factors requirements, functional specifications,

documentation requirements, data specifications, human resource requirements, security measures,

and quality assurance requirements.

Requirements documentation available for on-site review by the Professional Team includes:

RiskLink System Administration Guide—detailed user-level documentation of product configuration

and platform considerations, setup and installation, database maintenance, and advanced

configuration settings

RiskLink System Recommendations—computer system recommendations, certified platforms, and

possible deployment configurations for RiskLink

Database Schema Guide—database schema changes summary, and documentation of database

schema tables

RiskLink DLM User Guide—product reference guide that describes detailed steps on getting

started with RiskLink DLM, importing data, managing exposure data, running analyses, viewing

results, administering databases, and understanding the financial model

RiskLink DLM Reference Guide—reference material necessary to use RiskLink effectively,

including import file structures, construction classes and occupancy types, country -specific

information, and a glossary

Coding Standards—a collection of documents listing standards for software coding, database

development, development environment setup, component design, file versioning, and source

control system usage

Market Requirements Documents—a collection of documents, typically generated by the RMS

Model Management or Product Management groups, describing the business need for major

feature or product changes, along with a summary of what the feature/change is intended to do

(versus how it is to be implemented)

Functional Specifications—a collection of documents, typically generated by RMS Product

Management or senior modeling personnel, describing how a feature or product change is to be

implemented, covering all aspects that have impact on the product end user (for example, user

interface, loss calculations, database schema, data validity checking, documentation, and testing

recommendations)

Project Management Documents—a collection of Microsoft Project files, Microsoft Excel

spreadsheets, and Microsoft Word documents that track the human resource requirements of

project tasks

Microsoft Team Foundation Server (TFS) and Visual SourceSafe 6.0—documentation of the

version control management systems used by RMS to provide secure access, auditing, and

backup facilities for source code

Computer Standards C-2 Requirements


151

Information Technology Security Documents—a collection of documents explaining RMS

requirements related to password protection, data backup, and other security policies and

procedures

Quality Assurance Test Plans—documents that outline testing requirements for product

components, and are used to guide test case development

Computer Standards C-3 Model Architecture and Component Design


152

C-3 Model Architecture and Component Design

The modeling organization shall maintain and document (1) detailed control and data flow diagrams, and interface specifications for each software component, and (2) schema definitions for each database and data file. Documentation shall be to the level of components that make significant contributions to the model output.

RMS maintains documentation of detailed control and data flow, interface specifications, and the

schema definitions for all data files and database tables. Data flow diagrams are used to illustrate the

relationship between software components and data using a network representation consisting of

labeled component processes connected by data arcs, with components expanded into more detailed

sub-component diagrams where appropriate. The top-level data flow diagram for the RMS RiskLink

software is shown in the following figure.

The architecture for the hurricane model involves breaking the basic components into smaller modules

and sub-modules, such as the wind hazard module and the vulnerability module. This structure is

carried over into the software architecture. This internal model architecture and component design

documentation, as well as the developers or modelers responsible for each component, are available

for on-site review by the Professional Team.

Computer Standards C-3 Model Architecture and Component Design


153

1. Input

Exposure

Data

Import Data

Sources

Data

Entry

User

Exposure Data Entry

Exposure

Database

2. Prepare

Exposure

Data

Partial Address Data

Complete Address Data

Geotechnical Hazard Results

Analysis Exposure Data

3. Analyze

Exposure

Data

Results

Database

Event Loss Results

Geocoding

Database Geocoding Data

Geotechnical

Hazard

Database

Geotech Hazard Data

Hazard

DatabaseHazard Data

Vulnerability

Database

Vulnerability Data

5. Process

Post-Analysis

Data

Post-processing Results Input

Post-processing Results Output

Report Output

Results Reports

4. Display

Results

Viewable Results Data

Computer

Display

Viewable Results Display

DLM Profile

Database

Analysis UserAnalysis Setup User Input

DLM Profile Edits

DLM Profiles For Analysis

Exposure Import Data

Exposure From Import

Exposure From User

RiskLink Top Level Data Flow

Event

Information

Database

Event

Information

System

DatabaseSystem Data (1)

Figure 42: RiskLink Top Level Data Flow

Computer Standards C-4 Implementation


154

C-4 Implementation

A. The modeling organization shall maintain a complete procedure of coding guidelines consistent with accepted software engineering practices.

RMS has developed and maintained a set of coding guideline documents, consistent with accepted

software engineering practices. These documents contain standards for software coding, database

development, development environment setup, component design, file versioning, and source control

system usage. Compliance with these standards are monitored through peer and management review.

B. The modeling organization shall maintain a complete procedure used in creating, deriving, or procuring and verifying databases or data files accessed by components.

RMS maintains a complete procedure used in creating, deriving, or procuring and verifying databases

or data files accessed by components. This procedure includes extensive validation procedures

designed to guarantee that data integrity is maintained throughout the product development process.

C. All components shall be traceable, through explicit component identification in the flow diagrams, down to the code level.

The software is fully traceable from the flow diagrams to the code level. Detailed data flow diagrams of

the model components will be available for review by the Professional Team. The data flow diagrams

are organized hierarchically, with highest design level components incrementally translated into a

larger number of subcomponents. A data dictionary provides a textual description of each data flow

component in addition to documenting the linkage of those components to the source code.

D. The modeling organization shall maintain a table of all software components affecting loss costs, with the following table columns: (1) Component name, (2) Number of lines of code, minus blank and comment lines; and (3) Number of explanatory comment lines.

RMS maintains a table of all software components affecting loss costs, with the table columns

providing the information required by this standard.

E. Each component shall be sufficiently and consistently commented so that a software engineer unfamiliar with the code shall be able to comprehend the component logic at a reasonable level of abstraction.

As outlined in the RMS coding guidelines, software components are commented with a statement of

purpose (requirements summary), input and output description (interface specification), summary of

important changes, and ―tactical comments‖ explaining any potentially confusing software code. These

comments allow a software engineer unfamiliar with the code to comprehend the component logic at a

reasonable level of abstraction.



155

F. The modeling organization shall maintain the following documentation for all components or data modified by items identified in Standard G-1, Disclosure 5:

1. A list of all equations and formulas used in documentation of the model with definitions of all terms and variables.

2. A cross-referenced list of implementation source code terms and variable names corresponding to items within F.1.

For all components and data modified by items identified in Standard G-1, Disclosure 5, RMS maintains

a list of all equations and formulas used in documentation of the model modifications, with definitions of

all terms and variables, along with a cross-referenced list of implementation source code terms and

variable names corresponding to those equations and formulas.

C-4.1 Specify the hardware, operating system, other software, and all computer languages required to use the model.

The following are required to use the RMS model:

Operating system options:

Microsoft Windows XP Professional, Windows Vista Enterprise, Windows Server 2003, or Windows Server 2003 R2 32 bit operating system for desktop installations and client (remote database) installations

Microsoft Windows 7 Enterprise, Windows Server 2008 Standard or Enterprise, or Windows Server 2008 R2 Enterprise or Standard 64 bit operating system for desktop installations and client installations

Microsoft Windows Server 2003 or Windows Server 2003 R2 32 bit operating system for analysis and job server

Microsoft Windows Server 2008 or Windows Server 2008 R2 64 bit operating system for analysis and job server

Microsoft Windows Server 2003 or Windows Server 2003 R2 32 bit operating system for database server installations

Microsoft Windows Server 2003, Windows Server 2008, or Windows Server 2008 R2 64 bit operating system for database server installations

Microsoft Windows HPC Server 2008 or Windows HPC Server 2008 R2 for Enterprise Grid Computing (EGC) 64 bit compute nodes (analysis servers)

Microsoft Windows HPC Server 2008 R2 for EGC 64 bit head nodes (job servers)

Microsoft Windows Server 2008 or Windows Server 2008 R2 for EGC database installations

Any hardware capable of running one of the Microsoft operating systems listed above, with a

recommended minimum of 8 processor cores, 16 GB RAM, 1024 x 768 display, one available USB

connector, and at least 50 GB disk space

Database options:

Microsoft SQL Server 2005 Standard/Enterprise, or SQL Server 2008 Standard/Enterprise for desktop installations

Microsoft SQL Server 2005 Standard/Enterprise, SQL Server 2008 Standard/Enterprise, or SQL Server 2008 R2 Standard/Enterprise for database server installations

Microsoft SQL Server 2008 Standard/Enterprise or SQL Server 2008 R2 Standard/Enterprise for EGC database server installations

Microsoft .NET 3.5 or greater

SQL Native Client 9.0

SQL Server 2005 or 2008 Server Management Objects (SMO)

Visual C++ 2008 Redistributable

Microsoft XML Core Services (MSXML)

Microsoft Enterprise Library

Microsoft HPC Class Library (for EGC installations)



156

Crystal Reports report display software

Group 1 (Sagent) geocoding software

ESRI ArcGIS software

Objective Grid display software

Objective Toolkit display software

Olectra Chart display software

Rogue Wave C++ class libraries

The primary language for the development of RiskLink is C#. C++ code is being incrementally replaced

by code written in the C# language.

Computer Standards C-5 Verification


157

C-5 Verification

A. General

For each component, the modeling organization shall maintain procedures for verification, such as code inspections, reviews, calculation crosschecks, and walkthroughs, sufficient to demonstrate code correctness. Verification procedures shall include tests performed by modeling organization personnel other than the original component developers.

Modifications or additions to the model are typically designed and prototyped by engineers. Prototypes

are coded, for example, in spreadsheets or in programs written in C, C++, C#, or FORTRAN. Once the

concept has been proven in the prototype, a written specification is prepared to describe the purpose of

the change and to provide a detailed description of the algorithm to be introduced to the production

software. This description typically takes the form of narrative, ―pseudo-code‖ (similar to computer code

but stripped of computer language details for the sake of readability), control flowcharts, or data flow

diagrams. This description is sometimes augmented by actual computer code from the prototype. The

specification is peer-reviewed by other engineers and by senior software developers. Once the

specification is approved, the changes are then made to the production software.

RMS model development and quality assurance (QA) departments rigorously check output generated

from the model. Calculations are performed outside the model and compared to the software-generated

results to ensure that they are correct. A series of test cases are run to ensure that the computer

program generates consistent and reasonable results on a wide variety of client data. Da ta sets include

end-condition test cases using very large and very small values, large-data-volume test datasets of

many locations spread across multiple ZIP Codes, and data sets focused on testing specific areas of

the model.

Code inspections, reviews, and walkthroughs are performed on a regular basis to verify code

correctness. Both software management and model development engineers participate in this process.

Reviewers check code both during and after initial development. Code changes are often isolated and

inspected using the features of our source code management system. Reviewers also use source -code

debugging tools to verify run time behavior.

The software source code contains numerous logical assertions, exception-handling mechanisms, and

flag-triggered output statements that are used to test the values of key variables for correctness.

Verification procedures for each component include tests performed by modeler personnel other than

the original component developers. The RMS QA department has primary responsibility for

independent verification. In addition, peer review of model changes typically includes testing by

development staff other than the original component developers.



158

B. Component Testing

1. The modeling organization shall use testing software to assist in documenting and analyzing all components.

The IBM/Rational Enterprise Suite is the primary software development toolkit used at RMS for

analyzing and testing all components. This suite contains several tools that assist in component

testing. Both software developers and quality assurance personnel use Rational Robot , Rational

Functional Tester, and Rational Test Manager for test plan development, test case generation, and test

case execution.

The Compuware DevPartner toolkit is also used at RMS to supplement the capabilities of the

IBM/Rational Enterprise Suite. DevPartner Studio is primarily used to test software components for

memory corruption, memory leaks, or performance bottlenecks.

BMC’s AppSight tool is used to capture and replay application execution at multiple synchronized

levels. This tool is utilized primarily to help analyze unexpected program behavior.

Microsoft Visual Studio is the primary software development toolkit used at RMS. It contains an

extensive collection of debugging tools that allows developers to ―walk through‖ software components

on a line-by-line basis, and at any point, view the control stack, the value of all variables, debug trace

statement output, etc.

For key ―lower-level‖ components, a custom test driver is developed to execute the methods of the

components using a range of input values, and to test the resulting outputs of the methods. For

―higher-level‖ components that depend upon a large collection of other components or significant

amount of state information (for example, those that implement the RiskLink user interface) custom test

drivers are not practical. Instead, we develop automated test suites using IBM/Rational tools to check,

for example, for specific property values of user interface objects.

2. Unit tests shall be performed and documented for each component.

All software components are unit tested as they are developed or modified. The results of the unit tests

are summarized in technical specification documents that are written by sof tware developers while

implementing and testing software components, or in the Elsinore Visual Intercept problem report

database.

3. Regression tests shall be performed and documented on incremental builds.

A large suite of regression tests are performed and documented on incremental builds of the RiskLink

software. The majority of the regression tests are implemented using automated tools, including

Rational Robot and Rational Functional Tester test scripts, though some additional manual testing is

always performed. The automated regression tests are split into two sets. The first set is a broad but

shallow set of tests that are executed by the software development team before passing the build to the

quality assurance department. The QA department then executes an extensive, broad and deep set to

check for stability of results in all areas of the software.



159

4. Aggregation tests shall be performed and documented to ensure the correctness of all model components. Sufficient testing shall be performed to ensure that all components have been executed at least once.

Aggregation tests are performed and documented to ensure correctness of all components and data

defining the model. Most of the aggregation testing is done by executing the product as a complete

package, using a comprehensive suite of test scripts supplemented with additional manual tests, to

ensure that component interactions that would escape unit testing are checked. These tests cover the

complete start-to-finish workflow of the user of the software, and contain a wide range of possible

inputs, thus ensuring that all components relevant to this submission are executed at least once.

C. Data Testing

1. The modeling organization shall use testing software to assist in documenting and analyzing all databases and data files accessed by components.

RMS uses a range of testing software to assist in documenting and analyzing all databases and data

files accessed by components. In many cases, this involves the use of Excel, Access, or other generic

data manipulation packages. Commercial mapping software (e.g., MapInfo or ArcInfo) is used to check

the spatial distribution of data. In some cases, special-purpose test programs are written to automate

data validation. In addition, database and data file values are validated indirectly via the regression test

scripts described above.

2. The modeling organization shall perform and document integrity, consistency, and correctness checks on all databases and data files accessed by the components.

RMS performs and documents data integrity, consistency, and correctness checks on all databases

and data files accessed by components. Tools such as Excel and Access are used to perform cross

checks, run statistical tests, or generate data visualization output (e.g. , graphs and charts) from

datasets. Visual inspection of geographic data displayed as maps is another key testing methodology

used to check the spatial distribution of data. All data that is packaged as binary files are checked via

software that converts data from text to binary, binary to text, then performs a comparison of the input

and output text files.

C-5.1 State whether two executions of the model with no changes in input data, parameters, code, and seeds of random number generators produce the same loss costs and probable maximum loss levels.

The model produces the same loss costs and probable maximum loss levels if run with the same

information more than once. A random number generator is not used during model execution.

Repeatability of results is tested as part of our standard testing suite.

C-5.2 Provide an overview of the component testing procedures.

The component testing procedures can be grouped in the following categories:



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Unit Tests

Manual unit tests are run when components are created or changed. Actual results are compared

against expected results documented within specification documents or test cases

Automated unit tests are written to test key components that are added or modified. These tests

are run periodically throughout the product development cycle

Aggregation Tests

Manual aggregation tests are developed and run for features added with the current product

release cycle

Automated aggregation tests are developed and run for each new feature once it has been

integrated into the product and manually tested. Each automated test script is added to the overall

product test suite

Performance Tests

A suite of performance regression tests are run at specific time intervals within the product

development cycle

Memory checking tools and code performance profilers are run periodically during the product

release cycle, either as a regression test or to diagnose known or suspected performance

problems

These testing procedures are described in more detail in the responses to Sections A, B, and C of this

standard.

Computer Standards C-6 Model Maintenance and Revision


161

C-6 Model Maintenance and Revision

A. The modeling organization shall maintain a clearly written policy for model revision including verification and validation of revised components, databases, and data files.

The general policy of RMS has been to upgrade its North Atlantic Hurricane Model whenever new data

or research becomes available that results in a non-trivial improvement in the loss modeling

methodology. In the past, updates to accommodate new ZIP Codes were made at least every 24

months where possible. When ZIP Code files were updated, associated ZIP Code-related databases,

such as those containing distance-to-coast and surface roughness, were also updated.

The following figure illustrates, at a high level, the process for deciding on the content of model

revisions.

Figure 43: High-Level Description of Model-Revision Policy

The process of model revision and release is rigorous and well -documented. The figures in

Disclosure C-6.1 illustrate the model-revision process in more detail.

Model

Management

Internal & External

Research

Product

Feedback

RMS Release

Planning Team

Approved Model

Revisions

Business

Case

Other Projects

& Constraints



162

B. A revision to any portion of the model that results in a change in any Florida residential hurricane loss cost shall result in a new model version number.

The RMS North Atlantic Hurricane Model is periodically enhanced to reflect advances in our knowledge

of hurricanes and the consequences of hurricanes. Whenever RMS releases a new model with a

revision to any portion of the model that results in a change in any Florida residential hurricane loss

cost, a new model version number is used to designate that release.

C. The modeling organization shall use tracking software to identify all errors, as well as modifications to code, data, and documentation.

Microsoft Team Foundation Server is used to track modifications to all source code. In past releases,

Microsoft Visual SourceSafe has been used for this purpose. These tools provide, for each file, the

date of each change, the author of the change, file version, and a detailed comparison of the f ile before

and after the change. In addition, documentation in our Elsinore Visual Intercept problem report

database summarizes changes made to the source code and data, and provides a list of the files

affected by the change.

D. The modeling organization shall maintain a list of all model versions since the initial submission for this year. Each model description shall have a unique version identification, and a list of additions, deletions, and changes that define that version.

RMS will maintain a list of all model versions since the initial submission for this year, with unique

version identification and a list of additions, deletions, and changes that define that version.

C-6.1 Identify procedures used to maintain code, data, and documentation.

The following two figures depict the process and procedures used to maintain code, data, and

documentation.



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Product Release

Product Delivery Overview

RequirementsFunctional

Specifications

Technical

SpecificationsFeature

ImplementationBug Fixes

Requirement

ChangesDesign Changes

Certified?

Product Release

Documents

Y

N

Shipment

Requests

Shipment

Approval

Order

Management Package/Ship Client

Client

Response

System

Incident

Database

Problems /

Questions

Incident Review Incident Assignment /

Ordering

Client, Product

Marketing,

Technical Input

Installation GuideSystem Admin

Guide

User Guide

Release Notes)

Product Documentation

Model

Methodology

Reference GuideMapping User

Guide

Project Tracking

Product

Knowledge

Base

Test Plans Test Cases/ ProceduresIncremental Build

(Input from Specs, Incidents, Prod. Mgmt, Prod Dev)

Incremental

Build

Source Data

Reports User

Guide

Figure 44: Detailed Description of Model-Revision Policy



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Planning

2.2Release Candidate Sizing

2.5 Integrated Project Plan

3.4 PreliminaryInternal Communication

Integrating Packaging Supporting

4.5 -4.5.4Documentation

4.2 Model Validation and Optimization

5.3Develop and Integrate Ancillary Data

4.7 Customer Change Management

5.4Client Dev Training

5.5Acceptance Test

5.1Final Certification

5.2Media Fulfillment

6.1Hot Fix Release

6.2Service Pack Release

6.5Deployment

6.6Support

Pro

d M

gm

tD

eve

lop

me

nt

Cli

en

t D

ev

Release

Release Team Formed

RMS Product Development Process

2.3 Release Definition

2.4 Public Roadmap Finalization

3.3 Component Planning and Development

Developing

Release

3.2Manage IPP

3.1Change Mgt Plan 4.1 Test Execution

Component: Feature, Model or Data

3.3.33SW Dev Plan

3.3.34 Sync Plans and Specs with other components

3.3.42Development Validation Plan

3.3.43Develop and Validate

3.3.32 High Level Design

3.3.41Test Plan

3.3.31 Functional Specs / Reqts

Planning and Developing

3.3.11Component MRD

3.3.12Model Dev Plan

3.3.13Vendor Mgmt SW

3.3.21Model Methodology

3.3.23Model Build / Test

3.3.22 Model SW Functional Spec

TM

2.1Create Release Candidate List

4.3Mid Point Review

6.3QA Post Release Activities

3.3.14Vendor Mgmt Data & Model

Prioritizing

1.1Set Product

Strategy

1.2Roadmap Workshop

1.3Forced Ranking

5.6Go No Go Decision

6.4Quality Post Mortem

33.3.44 Create and Validate Build

3.3.41.1 Create Test Cases

6.6 ASupport

4.4Bug Management

4.6RFC

Figure 45: RMS Product Development Process Diagram

Input from clients, technical resources, product marketing, product management, and other internal and

external sources drives the creation of marketing requirements documents, which describe the key

goals and constraints of planned upgrades. Those requirements are translated into functional

specifications, which map out how those requirements are to be met within the model implementation.

Software design specifications (technical specifications) are created to detail the planned

implementation.

As implementation proceeds, the need for design changes and, sometimes, requirement changes

becomes apparent. Once approved, these changes are reflected as updates to the documents

described in the previous paragraph.

The development process is carefully monitored by numerous individuals within RMS, using several

project tracking tools and procedures. For example, a problem report (―incident‖) record is created

using the Visual Intercept incident tracking system for each requested model change. This is done

whether the change is viewed as a new feature or a bug fix. Each incident record is maintained

throughout the life cycle of that incident, including resolution and re-testing.



165

Documentation is developed for proposed engineering enhancements to the model, and this

documentation, including the software specification, is used in the development of new or updated test

plans and test cases for that release.

When a release is certified by quality assurance management, a product release document, extensive

user-level product documentation, and the software and data that comprise the released product are

packaged and shipped to clients. Various cross-checks and tests in this final fulfillment step assure that

clients are provided a complete and correct package.

Standard test cases are shipped with the release, to allow post-installation verification. All post-

installation questions or problems are tracked within the RMS Client Response System. A product

Knowledge Base is being maintained and enhanced to assist RMS client development teams in

supporting RMS client needs.

C-6.2 Describe the rules underlying the model and code revision numbering systems.

RMS products that implement specific models are comprised of two parts:

Model infrastructure, i.e. the software code and data that implements the generic processes that

underlie a model implementation. This includes not only the computational aspects of a model, but

also the features that are a part of model workflow, such as exposure data import, and results

viewing.

Model data which, when used by the model infrastructure, generates modeled loss results.

RMS uses a four-part revision numbering system to precisely identify a model version in the format

[MajorRevision].[MinorRevision].[BuildNumber].[PatchRevision] where:

MajorRevision signifies a significant revision to the infrastructure.

MinorRevision indicates a smaller update, but one that still includes a change in product

functionality.

BuildNumber identifies a particular snapshot or iteration of the model infrastructure and data during

a release development cycle.

PatchRevision is an optional portion of the numbering scheme that signifies a revision that fixes

model infrastructure functionality or model data that has been previously released to RMS clients.

RMS typically bundles revisions to model data with revisions to model infrastructure; in other words,

infrastructure and data updates are released in one package with one revision number, reflected in the

MajorRevision number. For the sake of simplicity, revisions are typically communicated externally in a

simplified manner. For example, 11.0.1411.0 may simply be referred to externally with clients as

version 11.0, when the PatchRevision is zero. If, however, the model is updated and released outside

of the primary product release cycle, RMS will increment either the MinorRevision or the PatchRevision

depending on the type and magnitude of the change. The criteria regarding which part of the revision

numbering is incremented depends on whether the update can be distributed to clients by an

incremental software download (PatchRevision) or requires a new installation package (MinorRevision).

When the PatchRevision method is used, the notation used in the model identifier displayed to clients

in the ―Help | About‖ splash screen (Appendix E) follows the format

[ MajorRevision ]. [MinorRevision ].SP[PatchRevision]. Note the splash screen also lists the internal

BuildNumber in brackets after the model designation. Software component (DLL files or binaries files)



166

and analysis results are tagged with an identifier in the format

[MajorRevision].[MinorRevision]. [BuildNumber].[PatchRevision].

For this submission, the model designation is 13.0 (Build 1509), which has software components

identified as ―13.0.1509.0.‖ All analysis results generated by the software will contain a field called

EngineVersion which contains the identified ―13.0.1509.0.‖

Computer Standards C-7 Security


167

C-7 Security

The modeling organization shall have implemented and fully documented security procedures for: (1) secure access to individual computers where the software components or data can be created or modified, (2) secure operation of the model by clients, if relevant, to ensure that the correct software operation cannot be compromised, (3) anti-virus software installation on all machines where components and data are being accessed, and (4) secure access to documentation, software, and data in the event of a catastrophe.

RMS has implemented security procedures for access to code, data, and documentation in accordance

with standard industry practices. These procedures are described in the disclosure for this standard.

C-7.1 Describe methods used to ensure the security and integrity of the code, data, and documentation.

The following is a summary of key aspects of RMS security procedures:

Security requirements are documented and enforced by the RMS Legal and Information

Technology Departments

All company personnel are trained in security requirements and procedures

All company personnel are required to sign a non-disclosure agreement as a condition of their

employment

Physical security is maintained using locked doors, key-card access, video cameras, and security

patrols

The RMS network is protected via hardware firewalls

All servers and desktops are protected with Norton Antivirus software

All servers and desktops are remotely audited for security compliance

Microsoft Visual Source Safe and Microsoft Team Foundation Server are used to track

modifications to all source code. These source control systems maintain source code in an

encrypted form. A login is required to access source code. The nature and author of all changes

are recorded

All servers are backed up nightly. Off-site backups are maintained at a secure commercial facility.

Password and authorized personnel access provisions also apply for client data held on site at RMS for

processing and analysis.

Security for RMS software licensed for use at the customer premises is primarily controlled by the use

of compiled binary files, which are not readily modifiable without access to the original source code

(which is not available). An additional measure of protection is provided by our software licensing

provisions, which provide legal obstacles to manipulation or unauthorized use of RMS software.

Computer Standards C-7 Security


168

Appendix A—FCHLPM Forms


169

APPENDIX A—FCHLPM FORMS

Appendix A—FCHLPM Forms Form G-1: General Standards Expert Certification


170

Form G-1: General Standards Expert Certification

I hereby certify that I have reviewed the current submission of RiskLink Version 13.0 (Build 1509) for compliance with the 2011 Standards adopted by the Florida Commission on Hurricane Loss Projection Methodology and hereby certify that:

1) The model meets the General Standards (G1 – G5); 2) The Disclosures and Forms related to the General Standards section are editorially and

technically accurate, reliable, unbiased, and complete; 3) My review was completed in accordance with the professional standards and code of ethical

conduct for my profession; 4) My review involved ensuring the consistency of the content in all sections of the submission; and 5) In expressing my opinion I have not been influenced by any other party in order to bias or

prejudice my opinion.

Michael Young MSc, Engineering Science Name Professional Credentials (Area of Expertise)

10/30/2012 Signature (original submission) Date

12/21/2012 Signature (response to deficiencies, if any) Date

3/11/2013 Signature (revisions to submission, if any) Date

5/14/2013 Signature (final submission) Date

An updated signature and form is required following any modification of the model and any revision of the original submission. If a signatory differs from the original signatory, provide the printed name and professional credentials for any new signatories. Additional signature lines shall be added as necessary with the following format: Signature (revisions to submission) Date

NOTE: A facsimile or any properly reproduced signature will be acceptable to meet this requirement.

Appendix A—FCHLPM Forms Form G-2: Meteorological Standards Expert Certification


171

Form G-2: Meteorological Standards Expert Certification

I hereby certify that I have reviewed the current submission of RiskLink Version 13.0 (Build 1509) for compliance with the 2011Standards adopted by the Florida Commission on Hurricane Loss Projection Methodology and hereby certify that:

1) The model meets the Meteorological Standards (M1 – M6); 2) The Disclosures and Forms related to the Meteorological Standards section are editorially and


conduct for my profession; and 4) In expressing my opinion I have not been influenced by any other party in order to bias or

prejudice my opinion.

Shree Khare PhD, Atmospheric Science Name Professional Credentials (Area of Expertise)







Appendix A—FCHLPM Forms Form G-3: Vulnerability Standards Expert Certification


172

Form G-3: Vulnerability Standards Expert Certification


1) The model meets the Vulnerability Standards (V1 – V3); 2) The Disclosures and Forms related to the Vulnerability Standards section are editorially and



prejudice my opinion. Michael Young MSc, Engineering Science Name Professional Credentials (Area of Expertise)





An updated signature and form is required following any modification of the model and any revision of the original submission. If a signatory differs from the original signatory, provide the printed name and professional credentials for any new signatories. . Additional signature lines shall be added as necessary with the following format: Signature (revisions to submission) Date


Appendix A—FCHLPM Forms Form G-4: Actuarial Standards Expert Certification


173

Form G-4: Actuarial Standards Expert Certification


1) The model meets the Actuarial Standards (A1 – A6); 2) The Disclosures and Forms related to the Actuarial Standards section are editorially and



prejudice my opinion. Kay Cleary FCAS, MAAA Name Professional Credentials (Area of Expertise)


Signature (response to deficiencies, if any) Date





Appendix A—FCHLPM Forms Form G-5: Statistical Standards Expert Certification


174

Form G-5: Statistical Standards Expert Certification


1) The model meets the Statistical Standards (S1 – S6); 2) The Disclosures and Forms related to the Statistical Standards section are editorially and



prejudice my opinion. Enrica Bellone PhD, Statistics Name Professional Credentials (Area of Expertise)







Appendix A—FCHLPM Forms Form G-6: Computer Standards Expert Certification


175

Form G-6: Computer Standards Expert Certification


1) The model meets the Computer Standards (C1 – C7), 2) The Disclosures and Forms related to the Computer Standards section are editorially and

technically accurate, reliable, unbiased, and complete, 3) My review was completed in accordance with the professional standards and code of ethical

conduct for my profession, and 4) In expressing my opinion I have not been influenced by any other party in order to bias or

prejudice my opinion. Swaminathan Krishnamoorthy MS Computer Applications Name Professional Credentials (Area of Expertise)


Signature (response to Deficiencies, if any) Date





Appendix A—FCHLPM Forms Form G-7: Editorial Certification


176

Form G-7: Editorial Certification

I hereby certify that I have reviewed the current submission of RiskLink Version 13.0 (Build 1509) for compliance with the “Process for Determining the Acceptability of a Computer Simulation Model” adopted by the Florida Commission on Hurricane Loss Projection Methodology in its Report of Activities as of December 31, 2011, and hereby certify that:

1) The model submission is in compliance with the Commission’s Notification Requirements and General Standard G-5;

2) The Disclosures and Forms related to each Standards section are editorially accurate and contain complete information and any changes that have been made to the submission during the review process have been reviewed for completeness, grammatical correctness, and typographical errors;

3) There are no incomplete responses, inaccurate citations, charts or graphs, or extraneous text or references;

4) The current version of the model submission has been reviewed for grammatical correctness, typographical errors, completeness, the exclusion of extraneous data/ information and is otherwise acceptable for publication; and

5) In expressing my/our opinion I/we have not been influenced by any other party in order to bias or prejudice my/our opinion.

Beth Stamann Senior Documentation Specialist Name Professional Credentials (Area of Expertise)


12/21/2012 Signature (response to Deficiencies, if any) Date





Appendix A—FCHLPM Forms Form M-1: Annual Occurrence Rates


177

Form M-1: Annual Occurrence Rates

A. Provide annual occurrence rates for landfall from the data set defined by marine exposure that the model generates by hurricane category (defined by maximum windspeed at landfall in the Saffir-Simpson scale) for the entire state of Florida and selected regions as defined in Figure 3. List the annual occurrence rate per hurricane category. Annual occurrence rates shall be rounded to two decimal places. The historical frequencies below have been derived from the Base Hurricane Storm Set as defined in Standard M-1.

The historical rates have been changed to reflect an additional year (2011) with no landfal ls or

by-passes.

Table 20: Comparison of Modeled and Historical Annual Occurrence Rates

Entire State Region A – NW Florida

Historical Modeled Historical Modeled

Category Number Rate Number Rate Number Rate Number Rate

1 25 0.22 25 0.22 13 0.12 16 0.14

2 12 0.11 9 0.08 4 0.04 3 0.02

3 17 0.15 16 0.14 6 0.05 5 0.05

4 8 0.07 8 0.07 0 0.00 1 0.00

5 2 0.02 2 0.02 0 0.00 0 0.00

Region B – SW Florida Region C – SE Florida



1 7 0.06 7 0.06 6 0.05 7 0.06

2 1 0.01 2 0.01 5 0.04 6 0.05

3 4 0.04 8 0.07 7 0.06 5 0.04

4 3 0.03 1 0.01 5 0.04 7 0.06

5 1 0.01 0 0.00 1 0.01 1 0.01

Region D – NE Florida Florida By-Passing Hurricanes



1 1 0.01 1 0.01 4 0.04 9 0.08

2 3 0.03 1 0.01 5 0.04 2 0.02

3 0 0.00 1 0.01 3 0.03 2 0.02

4 0 0.00 0 0.00 0 0.00 0 0.00

5 0 0.00 0 0.00 0 0.00 0 0.00

Region E – Georgia Region F – Alabama/Mississippi



1 4 0.04 2 0.01 7 0.06 6 0.05

2 0 0.00 1 0.01 4 0.04 2 0.02

3 0 0.00 1 0.01 5 0.04 3 0.03

4 0 0.00 0 0.00 1 0.01 1 0.01

5 0 0.00 0 0.00 1 0.01 0 0.00

*All values rounded to 2 decimal places



178

Note: Except where specified, Number of Hurricanes does not include By-Passing Hurricanes. Each

time a hurricane goes from water to land (once per region) it is counted as a landfall in that region.

However, each hurricane is counted only once in the Entire State totals. Hurricanes recorded for

adjacent states need not have reported damaging winds in Florida.

B. Describe model variations from the historical frequencies.

The agreement between modeled and observed frequencies—both by intensity and by region—is

reasonable given the limited historical record.

C. Provide vertical bar graphs depicting distributions of hurricane frequencies by category by region of Florida (Figure 3) and for the neighboring states of Alabama/Mississippi and Georgia. For the neighboring states, statistics based on the closest milepost to the state boundaries used in the model are adequate.

Histograms comparing modeled and observed landfall frequencies by region are given on Figure 50.

D. If the data are partitioned or modified, provide the historical annual occurrence rates for the applicable partition (and its complement) or modification as well as the modeled annual occurrence rates in additional Form M-1.

The data has not been partitioned or modified.

E. List all hurricanes added, removed, or modified from the previously accepted submission version of the Base Hurricane Storm Set.

In agreement with the HURDAT reanalysis as of November 2011, one storm has been removed and track

parameters of six hurricanes have been modified in the 1925-1930 timeframe (Figure 46 to Figure 49).



179

Figure 46: Change in Track Parameters for the 2 of 7 Modified Hurricanes. Part (a) Left: Previous Submission, Right: Current Submission



180

Figure 47: Change in Track Parameters for 2 of the 7 Modified Hurricanes. Part (b) Left: Previous Submission, Right: Current Submission



181

Figure 48: Change in Track Parameters for 2 of the 7 Modified Hurricanes. Part (c) Left: Previous Submission, Right: Current Submission



182

Figure 49: Change in Track Parameters for 1 of the 7 Modified Hurricanes. Part (d) Left: Previous Submission, Right: Current Submission.

F. Provide this form in Excel format. The file name shall include the abbreviated name of the modeling organization, the standards year, and the form name. A hard copy of Form M-1 shall be included in a submission appendix.

This information is provided in Excel format in the file RMS11FormM1.xlsx at the link provided.



183

Figure 3

State of Florida and Neighboring States By Region

87.55 W 30.27 N

81.45 W 30.71 N

E

(Georgia)

)

F

(Alabama/

Mississippi)



184

The region of landfall is determined by RMS gates as follows:

Figure 50: Comparison of Historical and Modeled Multiple Landfall Occurrences by Region

Appendix A—FCHLPM Forms Form M-2: Maps of Maximum Winds


185

Form M-2: Maps of Maximum Winds

A. Provide color maps of the maximum winds for the modeled version of the Base Hurricane Storm Set for land use as set for open terrain and land use set for actual terrain as defined by the modeling organization.

B. Provide color maps of the maximum winds for a 100-year and a 250-year return period from the stochastic storm set for both open terrain and actual terrain.

C. Provide the maximum winds plotted on each contour map and plot their location.

Open terrain Real terrain

Max Historical 154mph 145mph

Max 100y Return Period 130mph 123mph

Max 250y Return Period 141mph 135mph

“Actual terrain” is the roughness distribution used in the standard version of the model. “Open terrain” uses the same roughness value of 0.03 meters at all land points.

All maps shall be color coded at the ZIP Code level.

Maximum winds in these maps are defined as the maximum one-minute sustained winds over the terrain as modeled and recorded at each location.

The same color scheme and increments shall be used for all maps.

Use the following seven isotach values and interval color coding:

(1) 50 mph Blue (2) 65 mph Medium Blue (3) 80 mph Light Blue (4) 95 mph White (5) 110 mph Light Red (6) 125 mph Medium Red (7) 140 mph Red



186

Figure 51: Maximum 1-minute Mean Wind Speed (mph) at ZIP Code level. Historical Set (1900-2011)—Open Terrain



187

Figure 52: Maximum 1-minute Mean Wind Speed (mph) at ZIP Code level. Historical Set (1900-2011)—Real Terrain



188


Set—Open Terrain



189


Set—Real Terrain



190


Set—Open Terrain



191


Set—Real Terrain

Appendix A—FCHLPM Forms Form M-3: Radius of Maximum Winds and Radii of Standard Wind Thresholds


192

Form M-3: Radius of Maximum Winds and Radii of Standard Wind Thresholds

A. For the central pressures in the table below, provide the minimum and maximum values for 1) the radius of maximum winds (Rmax) used by the model to create the stochastic storm set, and the minimum and maximum values for the outer radii (R) of 2) Category 3 winds (>110 mph), 3) Category 1 winds (>73 mph), and 4) gale force winds (>40 mph). This information should be readily calculated from the windfield formula input to the model and does not require running the stochastic storm set. Describe the procedure used to complete this form.

Table 21: Ranges of Rmax used in Model’s Stochastic Storm Set

Central Pressure

(mb)

Rmax (mi)

Outer Radii (>110 mph) (mi)



Min Max Min Max Min Max Min Max

990 16 78 NA NA 18 92 36 201

980 13 70 14 57 17 91 44 246

970 12 65 15 81 19 118 55 311

960 10 54 13 74 20 124 57 315

950 10 48 13 70 22 132 56 318

940 8 43 12 66 24 129 57 307

930 7 40 11 72 22 135 53 303

920 7 33 11 69 22 127 49 276

910 5 28 10 65 21 125 46 259

900 5 25 11 65 21 120 42 247

The radii provided on Table 21 are computed by running the stochastic model on 10 sets of 100 tracks.

Within each set, all tracks have the same length, the same central pressure (as given by the first

column), the same translational speed (15mph Westward) and the same latitude (28N). All other

parameters have their mean values (e.g., Penv=1,013hPa). The thresholds were applied to 1-minute

mean wind speeds at the coast. Radii are defined as the maximum radius over the full azimuthal range.

The reported values correspond to the 5th and 95th percentiles of the simulated radii.

B. Identify the other variables that influence Rmax.

Rmax is a function of central pressure and latitude.



193

C. Provide a box plot and histogram of Central Pressure (x-axis) versus Rmax (y-axis) to demonstrate relative populations and continuity of sampled hurricanes in the stochastic storm set.

Figure 57: Box Plot of Rmax (miles) as a Function of Central Pressure (hPa) using a 10hPa Central Pressure Increment



194

Figure 58: Frequency Histogram of the Radius of Maximum Winds (miles)

Figure 59: Frequency Histogram Central Pressure (hPa)



195

D. Provide this form in Excel format. The file name shall include the abbreviated name of the modeling organization, the standards year, and the form name. A hard copy of Form M-3 shall be included in a submission appendix.

This information is provided in Excel format in the file RMS11FormM3.xlsx at the link provided.

Appendix A—FCHLPM Forms Form V-1: One Hypothetical Event


196

Form V-1: One Hypothetical Event

A. Windspeeds for 335 ZIP Codes and sample personal and commercial residential exposure data are provided in the file named “FormV1Input11.xlsx.” The windspeeds and ZIP Codes represent a hypothetical hurricane track. Model the sample personal and commercial residential exposure data provided in the file against these windspeeds at the specified ZIP Codes and provide the damage ratios summarized by windspeed (mph) and construction type.

The windspeeds provided are one-minute sustained 10-meter windspeeds. The sample personal and commercial residential exposure data provided consists of four structures (one of each construction type – wood frame, masonry, mobile home, and concrete) individually placed at the population centroid of each of the ZIP Codes provided. Each ZIP Code is subjected to a specific windspeed. For completing Part A, Estimated Damage for each individual windspeed range is the sum of Ground Up Loss to all structures in the ZIP Codes subjected to that individual windspeed range, excluding demand surge and storm surge. Subject Exposure is all exposures in the ZIP Codes subjected to that individual windspeed range. For completing Part B, Estimated Damage is the sum of the ground up loss to all structures of a specific type (wood frame, masonry, mobile home, or concrete) in all of the windspeed ranges, excluding demand surge and storm surge. Subject Exposure is all exposures of that specific type in all of the ZIP Codes.

One reference structure for each of the construction types shall be placed at the population centroid of the ZIP Codes. Do not include contents, appurtenant structures, or time element coverages.

Reference Frame Structure: One story Unbraced gable end roof Normal shingles (55mph) ½” plywood deck 6d nails, deck to roof members Toe nail truss to wall anchor Wood framed exterior walls 5/8” diameter anchors at 48” centers for wall/floor/foundation connections No shutters Standard glass windows No door covers No skylight covers Constructed in 1980

Reference Masonry Structure: One story Unbraced gable end roof Normal shingles (55mph) ½” plywood deck 6d nails, deck to roof members Toe nail truss to wall anchor Masonry exterior walls No vertical wall reinforcing No shutters Standard glass windows No door covers No skylight covers Constructed in 1980

Reference Mobile Home Structure: Tie downs Single unit Manufactured in 1980

Reference Concrete Structure: Twenty story Eight apartment units per story No shutters Standard glass windows Constructed in 1980

B. Confirm that the structures used in completing the form are identical to those in the above table for the reference structures. If additional assumptions are necessary to complete this form (for example, regarding structural characteristics, duration, or surface roughness), provide the reasons why the assumptions were necessary as well as a detailed description of how they were included.

C. Provide a plot of the Form V-1, Part A data.



197

Part A

Wind Speed (mph) Estimated Damage/

Subject Exposure

41 – 50

0.2%

51 – 60

0.4%

61 – 70

1.8%

71 – 80

4.8%

81 – 90

10.6%

91 – 100

19.2%

101 – 110

28.6%

111 – 120

47.0%

121 – 130

59.3%

131 – 140

77.6%

141 – 150

86.1%

151 – 160

90.7%

161 – 170

94.5%

Part B

Construction Type Estimated Damage/

Subject Exposure

Wood Frame 11.1%

Masonry 10.9%

Mobile Home 10.1%

Concrete 5.9%



198

Figure 60: Ratio of Estimated Damage and Subject Exposure versus One-Minute Wind Speed

The structure used to complete this form is identical to the structure listed in the table presented on

page 111 in the 2011 Report of Activities.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

41

–5

0

51

–6

0

61

–7

0

71

–8

0

81

–9

0

91

–1

00

10

1 –

11

0

11

1 -

12

0

12

1 –

13

0

13

1 –

14

0

14

1 –

15

0

15

1 –

16

0

16

1 –

17

0

Esti

mate

d D

am

ag

e/S

ub

ject

Exp

osu

re

Wind Speed Range (mph)

Appendix A—FCHLPM Forms Form V-2: Mitigation Measures – Range of Changes in Damage


199

Form V-2: Mitigation Measures – Range of Changes in Damage

A. Provide the change in the zero deductible personal residential reference structure damage rate (not loss cost) for each individual mitigation measure listed in Form V-2 as well as for the combination of the four mitigation measures provided for the Mitigated Frame Structure and the Mitigated Masonry Structure below.

B. If additional assumptions are necessary to complete this Form (for example, regarding duration or surface roughness), provide the rationale for the assumptions as well as a detailed description of how they are included.

C. Provide this Form in Excel format without truncation. The file name shall include the abbreviated name of the modeling organization, the Standards year, and the Form name. A hard copy of Form V-2 shall be included in a submission appendix.

Reference Frame Structure: One story Unbraced gable end roof Normal shingles (55mph) ½” plywood deck 6d nails, deck to roof members Toe nail truss to wall anchor Wood framed exterior walls 5/8” diameter anchors at 48” centers for wall/floor/foundation connections No shutters Standard glass windows No door covers No skylight covers Constructed in 1980

Mitigated Frame Structure: Rated shingles (110mph) 8d nails, deck to roof members Truss straps at roof Plywood Shutters

Reference Masonry Structure: One story Unbraced gable end roof Normal shingles (55mph) ½” plywood deck 6d nails, deck to roof members Toe nail truss to wall anchor Masonry exterior walls No vertical wall reinforcing No shutters Standard glass windows No door covers No skylight covers Constructed in 1980

Mitigated Masonry Structure: Rated shingles (110mph) 8d nails, deck to roof members Truss straps at roof Plywood Shutters

Reference and mitigated structures are fully insured building structures with a zero deductible building only policy.

Place the reference structure at the population centroid for ZIP Code 33921 located in Lee County.

Windspeeds used in the Form are one-minute sustained 10-meter windspeeds.

The required information is provided in the file RMS11FormV2.xlsx at the link provided and appears

below.

Appendix A—FCHLPM Forms Form V-2: Mitigation Measures – Range of Changes in Damage


200

Figure 61: Percent Change in Damage for Various Mitigation Measures

Appendix A—FCHLPM Forms Form V-3: Mitigation Measures – Mean Damage Ratio (Trade Secret Item)


201

Form V-3: Mitigation Measures – Mean Damage Ratio (Trade Secret Item)

This form will be provided during the professional team on-site review as well as the closed meeting

portion of the commission meeting.

Appendix A—FCHLPM Forms Form A-1: Zero Deductible Personal Residential Loss Costs by ZIP Code


202

Form A-1: Zero Deductible Personal Residential Loss Costs by ZIP Code

A. Provide three maps color-coded by ZIP Code (with a minimum of 6 value ranges), displaying zero deductible personal residential loss costs per $1,000 of exposure for frame, masonry, and mobile home.

B. Create exposure sets for these exhibits by modeling all of the structures from Notional Set 3 described in the file “NotionalInput11.xlsx” geocoded to each ZIP Code centroid in the state, as provided in the model. Refer to the Notional Policy Specification below for additional modeling information. Explain any assumptions, deviations, and differences from the prescribed exposure information.

C. Provide the underlying loss cost data rounded to 3 decimal places used for A. above in Excel and PDF format. The file name shall include the abbreviated name of the modeling organization, the standards year, and the form name.

This information is provided in Excel format in the file RMS11FormA1_20130514.xlsx and in PDF

format in the file RMS11FormA1_20130514.pdf at the link provided. The three maps color-coded by

ZIP Code appear below.

Notional Policy Specifications Policy Type Assumptions Owners Coverage A = Structure

Replacement Cost included subject to Coverage A limit

Ordinance or Law not included

Coverage B = Appurtenant Structures

Replacement Cost included subject to Coverage B limit


Coverage C = Contents

Replacement Cost included subject to Coverage C limit

Coverage D = Time Element

Time Limit = 12 months

Per Diem = $150.00/day per policy, if used

Loss costs per $1,000 shall be related to the Coverage A limit. Loss costs for the various specified deductibles shall be determined based

on annual deductibles. All-other perils deductible shall be $500.

Mobile Home Coverage A = Structure










203

Per Diem = $150.00/day per policy, if used

Loss costs per $1,000 shall be related to the Coverage A limit. Loss costs for the various specified deductibles shall be determined based

on annual deductibles. All-other perils deductible shall be $500.



204

Figure 62: Zero Deductible Loss Costs by 5-Digit ZIP Code for Frame



205

Figure 63: Zero Deductible Loss Costs by 5-Digit ZIP Code for Masonry



206

Figure 64: Zero Deductible Loss Costs by 5-Digit ZIP Code for Mobile Home

Appendix A—FCHLPM Forms Form A-2: Base Hurricane Set Statewide Loss Costs


207

Form A-2: Base Hurricane Set Statewide Loss Costs

A. Provide the total insured loss and the dollar contribution to the average annual loss assuming zero deductible policies from each specific hurricane in the Base Hurricane Storm Set, as defined in Standard M-1, for the 2007 Florida Hurricane Catastrophe Fund’s aggregate personal and commercial residential exposure data found in the file named “hlpm2007c.exe”.

The table below contains the minimum number of hurricanes from HURDAT to be included in the Base Hurricane Storm Set. Each hurricane has been assigned an ID number. Additional hurricanes included in the model’s Base Hurricane Storm Set shall be added to the table below and assigned an ID number as the hurricane falls within the given ID numbers.

B. Provide this Form in Excel format. The file name shall include the abbreviated name of the modeling organization, the Standards year, and the Form name. A hard copy of Form A-2 shall be included in a submission appendix.

The total insured loss and dollar contribution to the average annual loss for each storm in the Bas e

Hurricane Storm Set is provided for personal residential and commercial residential policies from the

2007 FHCF aggregate exposure data in the file RMS11FormA2.xlsx at the link provided and appears

below.

Table 22: Base Hurricane Storm Set Average Annual Zero Deductible

Statewide Loss Costs

ID Landfall/Closest Approach Date

Year Name

Personal and Commercial Residential

Insured Losses ($)

Dollar Contribution

001 09/06/1900 1900 NOTNAMED-1900 67,238,918 600,347

002 08/15/1901 1901 NOTNAMED-1901 28,348,049 253,108

005 09/11/1903 1903 NoName3-1903 1,286,626,112 11,487,733

010 10/17/1904 1904 NoName3-1904 646,795,004 5,774,955

015 06/17/1906 1906 NoName2-1906 262,241,325 2,341,440

016 09/28/1906 1906 NOTNAMED-1906 2,109,623,221 18,835,922

020 10/17/1906 1906 NoName8-1906 6,904,676,977 61,648,902

025 10/11/1909 1909 NoName10-1909 136,450,114 1,218,305

030 10/18/1910 1910 NoName5-1910 13,350,760,637 119,203,220

031 08/13/1911 1911 NOTNAMED-1911 88,938,133 794,090

032 08/29/1911 1911 NOTNAMED-1911 24,449,419 218,298

033 09/15/1912 1912 NOTNAMED-1912 66,527,502 593,996

035 08/01/1915 1915 NoName1-1915 350,993,385 3,133,870

036 08/16/1915 1915 NOTNAMED-1915 24,034,845 214,597

040 09/04/1915 1915 NoName4-1915 81,608,899 728,651

041 07/07/1916 1916 NOTNAMED-1916 347,865,422 3,105,941

045 10/18/1916 1916 NoName14-1916 912,161,876 8,144,302

050 09/29/1917 1917 NoName4-1917 1,596,206,198 14,251,841

055 09/10/1919 1919 NoName2-1919 2,024,342 18,074

060 10/25/1921 1921 NoName6-1921 8,007,775,405 71,497,995



208


Year Name


Insured Losses ($)

Dollar Contribution

061 10/17/1923 1923 NOTNAMED-1923 23,528,924 210,080

065 09/15/1924 1924 NoName5-1924 61,128,192 545,787

070 10/21/1924 1924 NoName10-1924 806,649,720 7,202,230

075 12/01/1925 1925 NoName4-1925 0 0

080 07/28/1926 1926 NoName1-1926 2,377,461,895 21,227,338

085 09/18/1926 1926 NoName7-1926 62,142,371,043 554,842,599

086 10/22/1926 1926 NOTNAMED-1926 540,586,813 4,826,668

090 08/08/1928 1928 NoName1-1928 1,918,293,125 17,127,617

095 09/17/1928 1928 NoName4-1928 42,616,895,775 380,507,998

100 09/28/1929 1929 NoName2-1929 1,672,993,639 14,937,443

101 09/03/1932 1932 NOTNAMED-1932 210,923,151 1,883,242

105 07/30/1933 1933 NoName5-1933 585,852,639 5,230,827

106 09/02/1933 1933 NOTNAMED-1933 46,668,195 416,680

110 09/04/1933 1933 NoName12-1933 5,655,088,095 50,491,858

111 10/06/1933 1933 NOTNAMED-1933 165,449,399 1,477,227

115 09/02/1935 1935 NoName2-1935 7,273,610,796 64,942,954

116 09/29/1935 1935 NOTNAMED-1935 275,208,724 2,457,221

120 11/04/1935 1935 NoName6-1935 379,122,874 3,385,026

125 07/31/1936 1936 NoName5-1936 332,866,074 2,972,019

130 08/11/1939 1939 NoName2-1939 1,026,179,444 9,162,316

135 10/06/1941 1941 NoName5-1941 10,752,923,085 96,008,242

140 10/19/1944 1944 NoName11-1944 18,388,168,878 164,180,079

145 06/24/1945 1945 NoName1-1945 138,610,140 1,237,591

150 09/16/1945 1945 NoName9-1945 11,502,655,142 102,702,278

155 10/08/1946 1946 NoName5-1946 995,132,379 8,885,111

160 09/17/1947 1947 NoName4-1947 24,543,116,435 219,134,968

165 10/12/1947 1947 NoName8-1947 521,212,191 4,653,680

170 09/22/1948 1948 NoName7-1948 2,758,395,018 24,628,527

175 10/05/1948 1948 NoName8-1948 1,492,435,700 13,325,319

180 08/27/1949 1949 NoName2-1949 11,303,940,093 100,928,037

181 08/31/1950 1950 BAKER-1950 71,381,209 637,332

185 09/05/1950 1950 Easy-1950 6,487,094,744 57,920,489

190 10/18/1950 1950 King-1950 4,168,172,072 37,215,822

191 10/27/1952 1952 FOX-1952 3,577,522 31,942

195 09/26/1953 1953 Florence-1953 361,096,500 3,224,076

200 09/25/1956 1956 Flossy-1956 300,988,889 2,687,401

205 09/10/1960 1960 Donna-1960 7,133,085,954 63,688,267

210 08/27/1964 1964 Cleo-1964 3,595,902,644 32,106,274

215 09/10/1964 1964 Dora-1964 949,712,659 8,479,577

216 10/03/1964 1964 HILDA-1964 733,284 6,547

220 10/14/1964 1964 Isbell-1964 1,225,208,006 10,939,357

225 09/08/1965 1965 Betsy-1965 4,914,641,177 43,880,725

230 06/09/1966 1966 Alma-1966 754,178,784 6,733,739

235 10/04/1966 1966 Inez-1966 129,559,502 1,156,781

236 06/06/1968 1968 ABBY-1968 69,454,595 620,130



209


Year Name


Insured Losses ($)

Dollar Contribution

240 10/19/1968 1968 Gladys-1968 270,829,842 2,418,124

245 06/19/1972 1972 Agnes-1972 3,713,907 33,160

250 09/23/1975 1975 Eloise-1975 1,253,039,053 11,187,849

255 09/04/1979 1979 David-1979 509,484,082 4,548,965

256 09/13/1979 1979 FREDERIC-1979 278,885,847 2,490,052

257 09/02/1985 1985 ELENA-1985 460,121,142 4,108,224

260 11/21/1985 1985 Kate-1985 162,322,449 1,449,308

265 10/12/1987 1987 Floyd-1987 20,928,512 186,862

270 08/24/1992 1992 Andrew-1992 29,461,426,326 263,048,449

275 08/02/1995 1995 Erin-1995 604,499,365 5,397,316

280 10/04/1995 1995 Opal-1995 1,231,399,579 10,994,639

281 07/21/1997 1997 DANNY-1997 5,403,723 48,248

285 09/03/1998 1998 Earl-1998 148,862,822 1,329,132

286 09/25/1998 1998 GEORGES-1998 230,113,395 2,054,584

287 08/29/1999 1999 DENNIS-1999 1,302 12

288 09/17/1999 1999 FLOYD-1999 28,624,595 255,577

290 10/15/1999 1999 Irene-1999 582,867,030 5,204,170

291 09/19/2000 2000 GORDON-2000 6,019,274 53,744

292 11/05/2001 2001 MICHELLE-2001 3,557,173 31,760

295 08/13/2004 2004 Charley-2004 8,127,708,834 72,568,829

300 09/05/2004 2004 Frances-2004 3,718,224,032 33,198,429

305 09/16/2004 2004 Ivan-2004 1,251,962,714 11,178,239

310 09/26/2004 2004 Jeanne-2004 6,937,669,300 61,943,476

315 07/10/2005 2005 Dennis-2005 499,646,626 4,461,131

320 08/25/2005 2005 Katrina-2005 817,203,129 7,296,457

325 09/21/2005 2005 Rita-2005 21,031,453 187,781

330 10/24/2005 2005 Wilma-2005 11,488,602,692 102,576,810

331 09/11/2008 2008 IKE-2008 2,796,729 24,971

Total 345,094,545,829 3,081,201,302

Note: Total dollar contribution should agree with the total average annual zero deductible statewide

loss costs provided in Form S-5 for current year.

Appendix A—FCHLPM Forms Form A-3: Cumulative Losses from the 2004 Hurricane Season


210

Form A-3: Cumulative Losses from the 2004 Hurricane Season

A. Provide the percentage of total residential zero deductible cumulative losses, rounded to four decimal places, from Hurricane Charley (2004), Hurricane Frances (2004), Hurricane Ivan (2004), and Hurricane Jeanne (2004) for each affected ZIP Code. Include all ZIP Codes where losses are equal to or greater than $500,000.

Use the 2007 Florida Hurricane Catastrophe Fund’s aggregate personal and commercial residential exposure data found in the file named “hlpm2007c.exe.”

Rather than using directly a specified published windfield, the winds underlying the loss cost calculations must be produced by the model being evaluated and should be the same hurricane parameters as used in completing Form A-2.

B. Provide maps color-coded by ZIP Code depicting the percentage of total residential losses from each hurricane, Hurricane Charley (2004), Hurricane Frances (2004), Hurricane Ivan (2004), and Hurricane Jeanne (2004) and for the cumulative losses using the following interval coding:

Red Over 5% Light Red 2% to 5% Pink 1% to 2% Light Pink 0.5% to 1% Light Blue 0.2% to 0.5% Medium Blue 0.1% to 0.2% Blue Below 0.1%

C. Provide this Form in Excel format. The file name shall include the abbreviated name of the

modeling organization, the Standards year, and the Form name. A hard copy of Form A-3 shall be included in a submission appendix.

The contribution and percentage of losses from the 2004 Hurricane Season storms for each ZIP Code

where losses are equal to or greater than $500,000 for personal residential and commercial residential

policies from the 2007 FHCF aggregate exposure data are in RMS11FormA3.xlsx at the link provided

and appear below.

Table 23: Hurricane Charley (2004) Percent of Losses

ZIP Code Personal and Commercial

Residential Monetary Contribution ($)

Percent of Losses (%)

33950 1,054,149,311 12.9822%

33952 620,572,657 7.6425%

33983 433,172,941 5.3347%

33980 283,704,470 3.4939%

33924 225,664,637 2.7791%

33957 210,526,548 2.5927%

33948 191,040,022 2.3527%

33982 165,701,878 2.0407%

34266 162,949,801 2.0068%

34744 149,727,014 1.8439%

33955 141,325,284 1.7405%

33884 133,517,709 1.6443%

33922 119,600,294 1.4729%

33904 114,518,454 1.4103%

33844 102,991,854 1.2684%



211




34743 99,145,928 1.2210%

33914 96,631,009 1.1900%

34746 92,708,730 1.1417%

33853 92,520,735 1.1394%

32765 90,468,163 1.1141%

33954 89,277,001 1.0995%

33908 86,987,426 1.0713%

32825 85,827,428 1.0570%

34269 84,241,599 1.0375%

33921 75,369,753 0.9282%

33919 74,581,384 0.9185%

34769 73,852,880 0.9095%

34759 71,073,749 0.8753%

32837 68,995,515 0.8497%

32812 67,287,025 0.8287%

32822 65,143,139 0.8023%

32828 64,498,625 0.7943%

33931 61,415,179 0.7563%

34741 55,576,042 0.6844%

32792 53,639,587 0.6606%

32817 52,678,413 0.6488%

34758 52,162,406 0.6424%

33873 51,746,687 0.6373%

32708 51,359,934 0.6325%

33990 51,181,537 0.6303%

33898 50,886,593 0.6267%

32824 50,763,373 0.6252%

33903 49,293,993 0.6071%

32806 49,234,131 0.6063%

32789 47,872,041 0.5896%

33912 47,610,911 0.5863%

33859 46,549,094 0.5733%

33956 43,102,038 0.5308%

33917 42,202,891 0.5197%

33981 41,965,915 0.5168%

33993 40,670,405 0.5009%

33881 37,382,113 0.4604%

32819 36,582,012 0.4505%

32807 36,499,413 0.4495%

32127 33,409,161 0.4114%

32826 33,012,939 0.4066%

32803 32,819,766 0.4042%

33880 32,754,844 0.4034%

32809 31,690,816 0.3903%

34772 31,327,700 0.3858%

34134 30,176,786 0.3716%

32707 29,204,891 0.3597%

33991 28,474,891 0.3507%

34108 27,276,610 0.3359%

33946 27,167,454 0.3346%

33843 26,241,125 0.3232%

33841 25,973,925 0.3199%

32751 24,703,810 0.3042%

33901 24,351,670 0.2999%

34135 24,182,743 0.2978%

34286 23,602,374 0.2907%

32832 23,501,777 0.2894%

32829 23,402,357 0.2882%



212




33907 23,334,758 0.2874%

33837 23,250,143 0.2863%

32766 22,889,584 0.2819%

34288 22,720,269 0.2798%

34771 22,716,029 0.2798%

32738 21,717,030 0.2675%

33905 21,484,913 0.2646%

32804 21,178,504 0.2608%

32839 20,629,272 0.2541%

32168 19,464,830 0.2397%

33909 19,348,958 0.2383%

32118 18,664,520 0.2299%

34103 18,427,019 0.2269%

33827 18,412,182 0.2268%

34102 18,370,663 0.2262%

32725 18,182,768 0.2239%

34110 18,012,553 0.2218%

32821 17,299,342 0.2130%

32771 16,965,249 0.2089%

33830 16,556,883 0.2039%

32827 16,308,712 0.2008%

32836 16,284,368 0.2005%

33928 16,217,921 0.1997%

32169 16,197,207 0.1995%

32750 15,480,036 0.1906%

32746 14,546,623 0.1791%

32119 14,542,491 0.1791%

33825 13,898,544 0.1712%

32835 13,399,582 0.1650%

32808 12,654,876 0.1558%

32176 12,625,807 0.1555%

34145 12,604,706 0.1552%

32773 12,198,037 0.1502%

33872 12,141,378 0.1495%

32732 12,096,799 0.1490%

33890 12,072,043 0.1487%

32779 11,916,768 0.1468%

34109 11,403,794 0.1404%

33838 11,012,330 0.1356%

32805 10,882,538 0.1340%

32174 10,705,760 0.1318%

34112 10,520,733 0.1296%

34119 10,253,559 0.1263%

34747 10,072,241 0.1240%

32128 9,831,918 0.1211%

32701 9,666,593 0.1190%

32129 9,232,931 0.1137%

32714 9,148,081 0.1127%

32811 8,892,852 0.1095%

32801 8,862,413 0.1091%

34105 8,790,461 0.1083%

34786 8,675,890 0.1068%

33967 8,630,551 0.1063%

33916 8,363,523 0.1030%

32810 8,113,218 0.0999%

34104 7,905,580 0.0974%

34224 7,797,830 0.0960%

33913 7,767,560 0.0957%



213




33936 7,698,637 0.0948%

34223 7,581,574 0.0934%

32117 7,244,625 0.0892%

32141 7,035,182 0.0866%

33953 6,997,773 0.0862%

34287 6,901,225 0.0850%

32818 6,885,506 0.0848%

32114 6,850,974 0.0844%

33971 6,699,544 0.0825%

32820 5,962,482 0.0734%

34116 5,434,271 0.0669%

32833 5,218,327 0.0643%

34113 5,155,879 0.0635%

32713 4,900,162 0.0603%

33947 4,593,277 0.0566%

33870 4,559,333 0.0561%

33852 4,527,868 0.0558%

33966 4,385,249 0.0540%

34293 4,285,627 0.0528%

33972 4,155,258 0.0512%

33851 4,099,796 0.0505%

32132 3,655,237 0.0450%

32730 3,509,968 0.0432%

34120 3,501,718 0.0431%

33850 3,348,638 0.0412%

33896 3,178,321 0.0391%

32764 3,124,789 0.0385%

32780 2,954,645 0.0364%

34114 2,940,283 0.0362%

33875 2,722,946 0.0335%

33945 2,683,919 0.0331%

32703 2,672,752 0.0329%

32763 2,604,988 0.0321%

32724 2,511,384 0.0309%

33823 2,331,421 0.0287%

32754 2,286,610 0.0282%

32796 2,218,180 0.0273%

33839 2,215,338 0.0273%

34117 2,144,304 0.0264%

32814 2,059,420 0.0254%

33834 1,951,727 0.0240%

34761 1,871,289 0.0230%

33920 1,840,683 0.0227%

33935 1,739,953 0.0214%

34285 1,629,498 0.0201%

33897 1,600,363 0.0197%

34275 1,593,382 0.0196%

33877 1,532,102 0.0189%

34292 1,531,519 0.0189%

32927 1,526,117 0.0188%

33820 1,408,779 0.0173%

32926 1,396,763 0.0172%

32955 1,396,539 0.0172%

32952 1,371,207 0.0169%

34268 1,283,162 0.0158%

32931 1,254,675 0.0155%

33949 1,242,477 0.0153%

33951 1,209,738 0.0149%



214




32940 1,184,902 0.0146%

34267 1,164,938 0.0143%

32709 1,135,451 0.0140%

34265 1,125,075 0.0139%

32953 1,095,005 0.0135%

34289 1,041,799 0.0128%

33876 924,497 0.0114%

33938 906,689 0.0112%

32744 902,765 0.0111%

33848 675,378 0.0083%

32124 664,359 0.0082%

33855 660,716 0.0081%

32759 659,271 0.0081%

34734 635,573 0.0078%

32920 563,351 0.0069%

34142 546,518 0.0067%

34773 532,945 0.0066%

33847 527,696 0.0065%

32136 524,649 0.0065%

33440 517,725 0.0064%



215

Table 24: Hurricane Frances (2004) Percent of Losses




32963 146,780,440 3.9846%

32958 86,975,794 2.3611%

33480 63,755,448 1.7308%

34997 60,415,248 1.6401%

32976 54,658,739 1.4838%

34952 54,270,160 1.4733%

33455 51,334,474 1.3936%

34990 50,057,788 1.3589%

33418 48,661,385 1.3210%

34996 47,612,265 1.2925%

33469 39,904,208 1.0833%

33458 37,716,079 1.0239%

32951 37,189,405 1.0096%

34957 36,756,074 0.9978%

34949 36,546,853 0.9921%

34974 34,628,013 0.9400%

34983 34,149,653 0.9271%

34953 33,450,051 0.9081%

33410 33,123,969 0.8992%

34982 33,044,014 0.8970%

33408 32,860,925 0.8921%

33477 31,948,988 0.8673%

33411 31,154,619 0.8457%

32967 30,734,931 0.8344%

32907 30,695,517 0.8333%

32960 29,255,434 0.7942%

33414 28,578,746 0.7758%

32935 27,579,951 0.7487%

32937 27,381,333 0.7433%

32962 25,745,150 0.6989%

32966 24,327,213 0.6604%

32905 23,190,886 0.6296%

32909 23,189,514 0.6295%

33404 22,944,188 0.6229%

33467 22,727,032 0.6170%

34951 21,816,970 0.5923%

34994 21,076,638 0.5722%

33437 19,830,675 0.5383%

32903 19,312,568 0.5243%

32940 18,300,598 0.4968%

33417 16,624,464 0.4513%

32931 16,609,109 0.4509%

32952 16,405,074 0.4453%

32904 16,241,166 0.4409%

32901 16,102,738 0.4371%

34986 15,939,475 0.4327%

33436 15,517,707 0.4213%

33401 15,407,185 0.4183%

32765 14,809,453 0.4020%

34972 14,639,450 0.3974%

33407 14,594,117 0.3962%

32955 14,281,420 0.3877%

32708 14,094,628 0.3826%

32779 13,957,897 0.3789%

34984 13,666,529 0.3710%



216




33462 13,648,720 0.3705%

33463 13,634,346 0.3701%

32725 13,611,695 0.3695%

32174 13,586,666 0.3688%

33496 13,573,635 0.3685%

32968 13,147,025 0.3569%

34947 12,460,518 0.3383%

33470 12,361,892 0.3356%

32169 12,311,759 0.3342%

32789 12,271,435 0.3331%

33415 12,264,256 0.3329%

32934 12,256,582 0.3327%

34950 12,190,446 0.3309%

32771 11,969,195 0.3249%

34744 11,947,113 0.3243%

33412 11,909,270 0.3233%

32746 11,895,803 0.3229%

32118 11,705,283 0.3178%

33409 11,262,862 0.3058%

33478 11,231,238 0.3049%

32127 11,183,170 0.3036%

33405 10,900,026 0.2959%

33433 10,678,342 0.2899%

32780 10,664,539 0.2895%

32792 10,662,231 0.2894%

32176 10,562,041 0.2867%

32738 10,546,602 0.2863%

32819 10,371,261 0.2815%

32953 10,258,802 0.2785%

33852 10,125,886 0.2749%

33446 9,761,901 0.2650%

32825 9,708,647 0.2636%

33445 9,621,397 0.2612%

34786 9,615,783 0.2610%

32159 9,607,632 0.2608%

32950 9,458,748 0.2568%

33406 9,407,689 0.2554%

33461 9,219,539 0.2503%

34748 9,209,685 0.2500%

33435 9,197,627 0.2497%

32712 9,003,729 0.2444%

32837 8,903,147 0.2417%

33460 8,783,870 0.2385%

32707 8,726,197 0.2369%

33434 8,722,290 0.2368%

33428 8,554,508 0.2322%

34769 8,547,220 0.2320%

34746 8,518,440 0.2312%

33884 8,412,151 0.2284%

32806 8,287,869 0.2250%

34743 8,184,656 0.2222%

32751 8,128,590 0.2207%

33844 7,990,420 0.2169%

32137 7,919,426 0.2150%

32750 7,910,330 0.2147%

32812 7,880,352 0.2139%

33487 7,805,270 0.2119%



217




32720 7,711,818 0.2094%

33484 7,696,258 0.2089%

34711 7,645,958 0.2076%

32724 7,586,282 0.2059%

32828 7,487,496 0.2033%

33483 7,437,287 0.2019%

33881 7,429,565 0.2017%

32119 7,382,222 0.2004%

32926 7,368,291 0.2000%

32817 7,338,258 0.1992%

32703 7,320,156 0.1987%

32822 7,198,820 0.1954%

32927 7,184,338 0.1950%

33403 7,163,156 0.1945%

32713 6,972,099 0.1893%

33872 6,946,136 0.1886%

34946 6,913,152 0.1877%

32714 6,829,570 0.1854%

32835 6,794,637 0.1845%

33064 6,679,528 0.1813%

34471 6,613,851 0.1795%

33813 6,601,353 0.1792%

32803 6,590,577 0.1789%

32804 6,584,858 0.1788%

34788 6,529,661 0.1773%

32796 6,482,497 0.1760%

33498 6,471,751 0.1757%

34787 6,437,134 0.1747%

33825 6,410,641 0.1740%

32949 6,370,274 0.1729%

32168 6,361,820 0.1727%

32141 6,340,137 0.1721%

33065 6,323,578 0.1717%

33071 6,306,940 0.1712%

32082 6,277,572 0.1704%

33880 6,156,156 0.1671%

32778 6,111,983 0.1659%

34491 6,066,591 0.1647%

32908 6,062,142 0.1646%

33870 6,020,623 0.1634%

32757 6,019,083 0.1634%

32818 5,989,977 0.1626%

33062 5,987,223 0.1625%

33063 5,920,780 0.1607%

32808 5,890,583 0.1599%

33426 5,869,701 0.1593%

33432 5,801,800 0.1575%

34772 5,749,020 0.1561%

33308 5,742,221 0.1559%

32726 5,679,926 0.1542%

34771 5,643,729 0.1532%

33823 5,630,291 0.1528%

32773 5,601,995 0.1521%

33898 5,578,413 0.1514%

33321 5,557,916 0.1509%

32920 5,540,601 0.1504%

34761 5,490,083 0.1490%



218




33067 5,476,960 0.1487%

33431 5,354,605 0.1454%

32948 5,348,838 0.1452%

32824 5,255,928 0.1427%

32807 5,254,307 0.1426%

32080 5,249,693 0.1425%

33486 5,168,614 0.1403%

33442 5,136,205 0.1394%

32810 5,075,462 0.1378%

34741 5,034,677 0.1367%

32836 4,976,587 0.1351%

32809 4,930,100 0.1338%

33810 4,907,766 0.1332%

34472 4,828,482 0.1311%

32701 4,797,118 0.1302%

33076 4,585,961 0.1245%

32117 4,541,811 0.1233%

34945 4,523,032 0.1228%

32763 4,488,447 0.1218%

33594 4,484,905 0.1218%

33326 4,426,922 0.1202%

33809 4,426,791 0.1202%

34956 4,411,907 0.1198%

33322 4,411,470 0.1198%

33801 4,371,217 0.1187%

33440 4,295,419 0.1166%

33803 4,262,171 0.1157%

33413 4,160,095 0.1129%

33029 4,159,654 0.1129%

33319 4,145,274 0.1125%

33324 4,132,900 0.1122%

34209 4,089,584 0.1110%

32114 4,046,293 0.1098%

32129 4,040,904 0.1097%

32162 3,993,450 0.1084%

33312 3,989,592 0.1083%

34476 3,986,632 0.1082%

33830 3,968,608 0.1077%

33837 3,932,025 0.1067%

32826 3,905,824 0.1060%

34470 3,895,269 0.1057%

33068 3,847,578 0.1044%

32128 3,843,122 0.1043%

34758 3,828,185 0.1039%

33317 3,826,166 0.1039%

33444 3,759,658 0.1021%

33325 3,686,943 0.1001%

33950 3,682,751 0.1000%

32839 3,668,172 0.0996%

34981 3,641,925 0.0989%

34482 3,639,019 0.0988%

33140 3,618,822 0.0982%

34747 3,600,078 0.0977%

32922 3,576,716 0.0971%

34228 3,564,354 0.0968%

33024 3,546,835 0.0963%

33843 3,537,452 0.0960%



219




34759 3,520,210 0.0956%

33853 3,503,756 0.0951%

32766 3,491,538 0.0948%

33904 3,462,926 0.0940%

33021 3,374,666 0.0916%

32136 3,371,058 0.0915%

34474 3,356,584 0.0911%

34420 3,322,925 0.0902%

34293 3,302,406 0.0896%

33875 3,283,454 0.0891%

33160 3,240,392 0.0880%

32754 3,234,588 0.0878%

33908 3,232,392 0.0877%

33897 3,209,365 0.0871%

34481 3,192,537 0.0867%

33569 3,183,720 0.0864%

34480 3,172,704 0.0861%

32821 3,141,837 0.0853%

33026 3,134,644 0.0851%

32084 3,123,400 0.0848%

34731 3,070,188 0.0833%

33541 3,058,689 0.0830%

33430 3,051,484 0.0828%

33073 3,042,113 0.0826%

33921 3,028,298 0.0822%

33441 3,013,597 0.0818%

33023 3,010,779 0.0817%

34683 2,990,405 0.0812%

34465 2,986,728 0.0811%

33309 2,980,550 0.0809%

32225 2,977,892 0.0808%

33914 2,977,751 0.0808%

32086 2,973,747 0.0807%

33476 2,972,221 0.0807%

33139 2,968,997 0.0806%

33328 2,949,641 0.0801%

33511 2,943,128 0.0799%

33313 2,932,305 0.0796%

34223 2,930,021 0.0795%

32164 2,929,306 0.0795%

33917 2,928,248 0.0795%

32784 2,905,116 0.0789%

33066 2,883,360 0.0783%

34667 2,873,403 0.0780%

32034 2,861,798 0.0777%

33351 2,857,124 0.0776%

33311 2,855,707 0.0775%

34668 2,851,255 0.0774%

34987 2,812,833 0.0764%

33060 2,799,395 0.0760%

33009 2,787,355 0.0757%

34108 2,785,670 0.0756%

34145 2,782,452 0.0755%

34231 2,760,825 0.0749%

32250 2,718,911 0.0738%

34698 2,711,561 0.0736%

33957 2,705,065 0.0734%



220




33331 2,703,350 0.0734%

33903 2,690,380 0.0730%

33629 2,685,136 0.0729%

34609 2,669,039 0.0725%

34479 2,664,163 0.0723%

34446 2,656,032 0.0721%

32811 2,615,656 0.0710%

33952 2,594,316 0.0704%

33323 2,589,632 0.0703%

33919 2,588,687 0.0703%

33027 2,579,655 0.0700%

34608 2,577,202 0.0700%

34606 2,575,592 0.0699%

34442 2,570,467 0.0698%

32210 2,529,910 0.0687%

32832 2,511,054 0.0682%

32805 2,502,878 0.0679%

34221 2,501,766 0.0679%

33156 2,493,563 0.0677%

34135 2,481,300 0.0674%

34432 2,473,012 0.0671%

33334 2,463,316 0.0669%

34613 2,459,410 0.0668%

33647 2,455,610 0.0667%

33912 2,452,396 0.0666%

33542 2,446,043 0.0664%

34785 2,404,225 0.0653%

33180 2,402,353 0.0652%

33149 2,387,121 0.0648%

33316 2,369,940 0.0643%

34266 2,364,067 0.0642%

33069 2,351,104 0.0638%

32829 2,333,448 0.0633%

33706 2,324,211 0.0631%

33154 2,321,619 0.0630%

33141 2,293,922 0.0623%

33860 2,293,738 0.0623%

33624 2,285,702 0.0620%

33859 2,280,244 0.0619%

33186 2,275,237 0.0618%

32132 2,259,782 0.0613%

32736 2,255,302 0.0612%

34655 2,251,922 0.0611%

32073 2,230,726 0.0606%

32608 2,228,334 0.0605%

33015 2,217,765 0.0602%

33025 2,216,804 0.0602%

34134 2,207,574 0.0599%

32605 2,194,050 0.0596%

32776 2,160,156 0.0586%

33931 2,135,844 0.0580%

32732 2,131,108 0.0579%

34242 2,104,123 0.0571%

34224 2,088,491 0.0567%

32833 2,087,075 0.0567%

32223 2,066,248 0.0561%

33176 2,065,547 0.0561%



221




33327 2,063,445 0.0560%

33611 2,059,172 0.0559%

34429 2,055,717 0.0558%

32179 2,045,790 0.0555%

33301 2,045,324 0.0555%

33330 2,045,126 0.0555%

34102 2,044,747 0.0555%

33019 2,041,118 0.0554%

32233 2,040,289 0.0554%

33157 2,037,274 0.0553%

34287 2,032,622 0.0552%

33756 2,019,909 0.0548%

34689 1,999,756 0.0543%

33304 1,995,846 0.0542%

33811 1,994,239 0.0541%

33525 1,993,356 0.0541%

33905 1,991,733 0.0541%

34488 1,977,922 0.0537%

34473 1,973,822 0.0536%

33868 1,971,572 0.0535%

33935 1,933,504 0.0525%

32177 1,931,064 0.0524%

33990 1,929,049 0.0524%

33710 1,914,186 0.0520%

33615 1,913,021 0.0519%

34450 1,909,192 0.0518%

34275 1,904,398 0.0517%

34103 1,900,264 0.0516%

33876 1,880,274 0.0510%

33175 1,874,407 0.0509%

34110 1,847,897 0.0502%

34601 1,833,626 0.0498%

34653 1,812,758 0.0492%

33703 1,804,639 0.0490%

33715 1,801,842 0.0489%

33617 1,798,398 0.0488%

34652 1,793,403 0.0487%

33805 1,783,805 0.0484%

34452 1,777,794 0.0483%

32207 1,770,824 0.0481%

34285 1,760,765 0.0478%

34431 1,756,948 0.0477%

33165 1,756,638 0.0477%

34684 1,756,330 0.0477%

33543 1,740,153 0.0472%

32244 1,732,591 0.0470%

34232 1,731,956 0.0470%

33133 1,725,774 0.0468%

34119 1,724,812 0.0468%

33020 1,718,231 0.0466%

34428 1,714,261 0.0465%

33936 1,708,806 0.0464%

34243 1,705,601 0.0463%

32607 1,704,099 0.0463%

34448 1,696,574 0.0461%

33179 1,694,062 0.0460%

32068 1,688,020 0.0458%



222




33850 1,687,861 0.0458%

33707 1,681,073 0.0456%

33471 1,676,599 0.0455%

33767 1,668,574 0.0453%

32735 1,665,895 0.0452%

33510 1,654,912 0.0449%

33702 1,654,769 0.0449%

33556 1,654,185 0.0449%

34736 1,653,326 0.0449%

34461 1,643,099 0.0446%

33618 1,633,672 0.0443%

33573 1,627,454 0.0442%

33143 1,622,820 0.0441%

32801 1,620,115 0.0440%

33565 1,616,287 0.0439%

34217 1,614,154 0.0438%

33134 1,612,775 0.0438%

33305 1,609,648 0.0437%

34203 1,609,491 0.0437%

33055 1,606,436 0.0436%

32257 1,601,604 0.0435%

32606 1,598,412 0.0434%

33012 1,568,451 0.0426%

33566 1,567,413 0.0426%

33018 1,555,771 0.0422%

33708 1,553,620 0.0422%

32224 1,549,148 0.0421%

33770 1,529,807 0.0415%

33014 1,524,152 0.0414%

32205 1,521,206 0.0413%

33572 1,518,197 0.0412%

33584 1,509,948 0.0410%

32043 1,497,073 0.0406%

33155 1,491,012 0.0405%

33772 1,487,936 0.0404%

32798 1,487,350 0.0404%

34205 1,477,610 0.0401%

32696 1,475,532 0.0401%

32827 1,459,512 0.0396%

34238 1,437,760 0.0390%

33948 1,437,452 0.0390%

32259 1,422,708 0.0386%

33928 1,411,098 0.0383%

33028 1,407,857 0.0382%

33764 1,406,037 0.0382%

33332 1,400,958 0.0380%

34109 1,399,962 0.0380%

34207 1,396,019 0.0379%

33037 1,391,236 0.0378%

34112 1,387,395 0.0377%

34695 1,381,476 0.0375%

33774 1,366,906 0.0371%

34639 1,363,850 0.0370%

33161 1,362,667 0.0370%

33178 1,352,032 0.0367%

33138 1,349,275 0.0366%

32277 1,348,664 0.0366%



223




34453 1,342,129 0.0364%

33983 1,338,359 0.0363%

33704 1,338,172 0.0363%

34236 1,328,454 0.0361%

32246 1,327,110 0.0360%

34241 1,321,336 0.0359%

33606 1,319,504 0.0358%

34239 1,316,801 0.0357%

33755 1,313,265 0.0357%

32820 1,309,298 0.0355%

32134 1,307,648 0.0355%

33547 1,291,055 0.0350%

32003 1,288,239 0.0350%

33162 1,287,493 0.0350%

33857 1,286,635 0.0349%

32656 1,279,834 0.0347%

33604 1,274,307 0.0346%

34715 1,273,378 0.0346%

33540 1,272,535 0.0345%

34654 1,269,654 0.0345%

33982 1,258,895 0.0342%

34677 1,257,963 0.0341%

33609 1,250,366 0.0339%

34208 1,245,006 0.0338%

33612 1,239,418 0.0336%

33314 1,238,233 0.0336%

34292 1,233,706 0.0335%

33771 1,227,409 0.0333%

33614 1,224,699 0.0332%

34714 1,219,330 0.0331%

33513 1,217,467 0.0331%

33549 1,210,855 0.0329%

32730 1,207,136 0.0328%

32065 1,197,296 0.0325%

33016 1,195,246 0.0324%

33056 1,190,497 0.0323%

33955 1,190,281 0.0323%

33196 1,185,175 0.0322%

34607 1,182,515 0.0321%

33761 1,182,049 0.0321%

33924 1,177,546 0.0320%

33169 1,164,151 0.0316%

33827 1,159,351 0.0315%

33873 1,157,884 0.0314%

34475 1,157,866 0.0314%

33173 1,156,849 0.0314%

33901 1,149,911 0.0312%

33177 1,149,767 0.0312%

33776 1,146,337 0.0311%

33613 1,143,366 0.0310%

32640 1,141,075 0.0310%

32211 1,137,278 0.0309%

34685 1,136,992 0.0309%

33713 1,135,364 0.0308%

33841 1,134,507 0.0308%

34202 1,134,300 0.0308%

34105 1,131,630 0.0307%



224




34434 1,123,894 0.0305%

32216 1,121,670 0.0304%

33971 1,117,428 0.0303%

32256 1,117,176 0.0303%

34691 1,116,712 0.0303%

33523 1,116,331 0.0303%

33315 1,114,671 0.0303%

33610 1,114,175 0.0302%

33705 1,105,959 0.0300%

33570 1,101,975 0.0299%

34436 1,099,669 0.0299%

34210 1,090,799 0.0296%

33619 1,088,898 0.0296%

34104 1,082,829 0.0294%

32092 1,075,574 0.0292%

32112 1,075,014 0.0292%

33193 1,067,606 0.0290%

33980 1,065,499 0.0289%

32653 1,064,851 0.0289%

32130 1,053,906 0.0286%

34219 1,053,854 0.0286%

32217 1,042,485 0.0283%

34433 1,037,927 0.0282%

33563 1,030,191 0.0280%

33972 1,023,364 0.0278%

34120 1,015,523 0.0276%

33558 1,000,986 0.0272%

33544 999,090 0.0271%

34610 995,428 0.0270%

34240 988,397 0.0268%

33782 984,169 0.0267%

34229 981,932 0.0267%

33777 969,462 0.0263%

33004 969,067 0.0263%

32615 964,925 0.0262%

33183 962,009 0.0261%

32669 960,917 0.0261%

32148 956,015 0.0260%

33909 954,118 0.0259%

33991 949,437 0.0258%

33913 948,129 0.0257%

34233 947,995 0.0257%

33946 947,487 0.0257%

32266 941,686 0.0256%

33907 933,087 0.0253%

33981 931,778 0.0253%

33709 930,732 0.0253%

33896 930,696 0.0253%

33815 929,647 0.0252%

32668 927,406 0.0252%

33625 923,492 0.0251%

34116 910,225 0.0247%

33993 903,716 0.0245%

33626 899,449 0.0244%

32744 892,224 0.0242%

33181 891,772 0.0242%

34222 891,732 0.0242%



225




34235 889,328 0.0241%

32195 888,587 0.0241%

34234 887,959 0.0241%

32764 885,070 0.0240%

34113 868,533 0.0236%

32113 865,871 0.0235%

33146 859,883 0.0233%

32024 859,875 0.0233%

33129 855,393 0.0232%

32189 843,898 0.0229%

33147 840,574 0.0228%

33145 837,402 0.0227%

33956 837,189 0.0227%

34773 833,038 0.0226%

34756 831,472 0.0226%

34669 830,399 0.0225%

33947 830,234 0.0225%

33773 828,094 0.0225%

33712 822,483 0.0223%

32102 819,979 0.0223%

34690 819,685 0.0223%

32601 818,730 0.0222%

33781 817,837 0.0222%

32759 817,161 0.0222%

33634 816,823 0.0222%

33013 816,040 0.0222%

32609 814,856 0.0221%

33759 813,785 0.0221%

32957 813,588 0.0221%

33538 806,814 0.0219%

33711 805,796 0.0219%

32686 800,062 0.0217%

32618 793,407 0.0215%

33125 792,483 0.0215%

34734 784,571 0.0213%

33603 777,624 0.0211%

33778 773,540 0.0210%

33567 767,521 0.0208%

32226 767,516 0.0208%

34739 767,446 0.0208%

33166 767,149 0.0208%

34602 764,652 0.0208%

32666 757,072 0.0206%

32693 756,336 0.0205%

34737 750,361 0.0204%

33785 749,754 0.0204%

33142 734,667 0.0199%

33527 731,532 0.0199%

33185 731,414 0.0199%

32258 728,486 0.0198%

33174 717,912 0.0195%

32702 712,595 0.0193%

34212 708,234 0.0192%

33010 706,702 0.0192%

33168 704,082 0.0191%

34286 700,087 0.0190%

33812 698,980 0.0190%



226




33126 698,185 0.0190%

33184 691,857 0.0188%

32626 690,953 0.0188%

33607 687,287 0.0187%

32643 687,086 0.0187%

34114 686,744 0.0186%

33855 681,504 0.0185%

32110 679,183 0.0184%

33597 667,806 0.0181%

32091 663,704 0.0180%

32709 660,039 0.0179%

32617 658,638 0.0179%

33838 650,699 0.0177%

32767 649,432 0.0176%

33763 648,252 0.0176%

33144 647,771 0.0176%

33054 640,236 0.0174%

33922 635,214 0.0172%

33172 632,666 0.0172%

32221 627,994 0.0170%

33635 611,459 0.0166%

32025 608,723 0.0165%

34688 608,214 0.0165%

33920 603,755 0.0164%

32095 603,079 0.0164%

33187 601,564 0.0163%

34705 601,088 0.0163%

33954 598,796 0.0163%

33306 592,991 0.0161%

33890 587,372 0.0159%

33109 582,707 0.0158%

33033 579,359 0.0157%

33762 576,512 0.0157%

33559 574,075 0.0156%

33137 573,707 0.0156%

33475 571,067 0.0155%

33616 560,576 0.0152%

33760 556,491 0.0151%

34237 553,022 0.0150%

33967 547,039 0.0149%

32038 544,899 0.0148%

34604 543,997 0.0148%

32209 538,520 0.0146%

33032 536,856 0.0146%

33765 533,558 0.0145%

32131 530,215 0.0144%

32621 528,316 0.0143%

32124 526,794 0.0143%

32641 524,409 0.0142%

34797 522,837 0.0142%

33701 520,133 0.0141%

33189 518,830 0.0141%

34117 517,778 0.0141%

32680 516,892 0.0140%

33714 513,633 0.0139%

34484 511,843 0.0139%

32667 511,575 0.0139%



227




33158 510,477 0.0139%

33182 509,399 0.0138%

33953 507,187 0.0138%

33548 506,534 0.0138%

32060 505,564 0.0137%

33637 504,039 0.0137%

33070 500,442 0.0136%



228

Table 25: Hurricane Ivan (2004) Percent of Losses




32507 208,654,887 16.7784%

32561 123,535,177 9.9338%

32506 105,987,957 8.5227%

32503 78,605,626 6.3209%

32526 75,813,312 6.0963%

32514 69,690,007 5.6039%

32563 63,672,372 5.1200%

32504 63,638,663 5.1173%

32533 45,781,573 3.6814%

32505 36,404,386 2.9274%

32566 34,215,422 2.7513%

32571 33,196,834 2.6694%

32541 32,657,067 2.6260%

32501 26,420,579 2.1245%

32548 22,267,645 1.7906%

32578 20,570,462 1.6541%

32534 19,718,108 1.5856%

32547 18,811,851 1.5127%

32570 18,146,688 1.4592%

32550 16,954,023 1.3633%

32459 15,204,730 1.2226%

32583 15,087,796 1.2132%

32579 12,403,383 0.9974%

32569 12,008,226 0.9656%

32577 7,283,559 0.5857%

32413 6,907,282 0.5554%

32408 5,901,260 0.4745%

32404 4,329,033 0.3481%

32565 4,231,551 0.3403%

32539 3,956,837 0.3182%

32405 3,659,767 0.2943%

32535 3,441,097 0.2767%

32536 3,365,643 0.2706%

32401 3,251,393 0.2615%

32407 3,214,157 0.2585%

32568 3,122,088 0.2511%

32444 2,743,714 0.2206%

32580 2,253,349 0.1812%

32433 2,250,811 0.1810%

32502 2,172,823 0.1747%

32456 2,095,227 0.1685%

32439 1,611,116 0.1296%

32328 1,487,900 0.1196%

32428 1,344,629 0.1081%

32531 1,193,817 0.0960%

32409 980,672 0.0789%

32435 856,932 0.0689%

32425 681,875 0.0548%

32446 637,979 0.0513%

32564 630,672 0.0507%

32466 537,665 0.0432%



229

Table 26: Hurricane Jeanne (2004) Percent of Losses




32963 304,376,517 4.4098%

32958 160,784,565 2.3295%

32907 151,442,499 2.1941%

32976 126,658,081 1.8350%

34997 111,194,866 1.6110%

32951 108,483,587 1.5717%

32935 105,876,301 1.5339%

34952 105,323,748 1.5259%

34990 95,987,212 1.3907%

33480 92,832,306 1.3450%

34949 91,890,474 1.3313%

33455 91,347,566 1.3234%

32909 90,912,772 1.3171%

32905 90,002,260 1.3040%

32960 88,903,963 1.2880%

34996 86,495,995 1.2532%

33418 83,677,331 1.2123%

32962 83,306,023 1.2069%

32937 78,587,606 1.1386%

32904 76,410,823 1.1070%

33469 74,327,049 1.0769%

34983 73,217,633 1.0608%

34953 70,468,203 1.0209%

32940 69,030,441 1.0001%

34957 68,335,198 0.9900%

33458 67,957,724 0.9846%

34982 67,701,195 0.9809%

32967 66,474,457 0.9631%

32966 63,678,197 0.9226%

34974 63,656,412 0.9223%

32901 61,763,758 0.8948%

34951 61,439,732 0.8901%

32903 60,073,750 0.8704%

33477 55,131,699 0.7988%

33410 54,799,589 0.7939%

33408 54,175,690 0.7849%

33852 52,749,356 0.7642%

32934 52,688,609 0.7634%

33884 50,372,951 0.7298%

33411 48,547,034 0.7034%

32955 45,989,611 0.6663%

32952 45,347,135 0.6570%

32931 45,120,128 0.6537%

33872 41,550,904 0.6020%

32968 41,165,209 0.5964%

33844 39,926,187 0.5785%

33414 39,708,985 0.5753%

34994 39,526,453 0.5727%

34986 38,638,384 0.5598%

33825 38,154,726 0.5528%

33870 36,218,954 0.5247%

33404 34,815,409 0.5044%

32950 34,641,581 0.5019%

33881 33,231,501 0.4815%

33898 32,908,430 0.4768%



230




34769 32,091,027 0.4649%

34744 30,958,336 0.4485%

34947 30,757,895 0.4456%

33880 29,802,271 0.4318%

34950 28,670,971 0.4154%

34772 28,059,872 0.4065%

34984 27,443,683 0.3976%

34972 27,285,110 0.3953%

34748 26,904,969 0.3898%

34711 26,544,239 0.3846%

33417 25,950,574 0.3760%

32908 25,762,454 0.3732%

34746 25,580,126 0.3706%

34786 25,471,305 0.3690%

32953 24,464,337 0.3544%

33467 24,411,651 0.3537%

33853 23,812,708 0.3450%

33401 23,435,893 0.3395%

33407 23,277,039 0.3372%

32819 23,153,904 0.3355%

33813 22,636,172 0.3280%

33470 22,332,632 0.3236%

32949 21,616,371 0.3132%

33412 20,883,177 0.3026%

34759 20,789,840 0.3012%

33478 20,775,369 0.3010%

32159 20,311,081 0.2943%

33843 20,168,230 0.2922%

33830 19,386,028 0.2809%

33875 19,210,448 0.2783%

34771 19,073,004 0.2763%

34787 18,953,408 0.2746%

33823 18,867,585 0.2734%

34743 18,218,494 0.2640%

32837 17,889,937 0.2592%

32926 17,610,043 0.2551%

34946 17,427,500 0.2525%

33437 17,334,162 0.2511%

33409 16,807,360 0.2435%

32789 16,773,030 0.2430%

32779 16,418,137 0.2379%

33415 16,326,686 0.2365%

32835 15,225,209 0.2206%

32712 14,830,824 0.2149%

33463 14,663,266 0.2124%

34758 14,492,888 0.2100%

32780 14,448,072 0.2093%

33436 14,167,400 0.2053%

33859 14,138,003 0.2048%

33462 14,043,668 0.2035%

33405 14,020,034 0.2031%

32806 13,744,360 0.1991%

34761 13,570,718 0.1966%

32778 13,442,614 0.1948%

32703 13,414,411 0.1943%

32927 13,152,428 0.1906%

34788 13,091,505 0.1897%



231




32920 12,599,998 0.1825%

32818 12,516,573 0.1813%

32708 12,440,306 0.1802%

34741 12,416,935 0.1799%

33406 12,393,882 0.1796%

33801 12,314,064 0.1784%

33803 12,156,196 0.1761%

32836 12,102,160 0.1753%

34266 12,053,275 0.1746%

32812 12,002,413 0.1739%

33837 11,793,132 0.1709%

32765 11,756,493 0.1703%

34945 11,494,293 0.1665%

32792 11,302,805 0.1638%

32757 11,212,595 0.1624%

33403 11,088,196 0.1606%

32808 11,073,625 0.1604%

34491 10,976,510 0.1590%

32746 10,720,964 0.1553%

32804 10,680,548 0.1547%

32751 10,421,631 0.1510%

33461 10,337,374 0.1498%

32825 10,318,165 0.1495%

33876 10,314,566 0.1494%

32726 10,271,662 0.1488%

32822 10,091,185 0.1462%

33460 10,025,198 0.1452%

33810 9,990,977 0.1447%

32922 9,800,515 0.1420%

33594 9,651,864 0.1398%

32948 9,645,598 0.1397%

33809 9,478,187 0.1373%

32824 9,459,952 0.1371%

33496 9,344,585 0.1354%

32803 9,248,341 0.1340%

33440 9,247,637 0.1340%

32714 8,944,848 0.1296%

32809 8,885,957 0.1287%

34956 8,777,777 0.1272%

32707 8,633,761 0.1251%

32771 8,605,620 0.1247%

34747 8,546,457 0.1238%

32725 8,517,341 0.1234%

33435 8,205,521 0.1189%

34981 8,201,439 0.1188%

32810 8,055,162 0.1167%

32162 8,033,190 0.1164%

32828 7,959,184 0.1153%

34471 7,809,358 0.1131%

33706 7,788,371 0.1128%

32750 7,743,095 0.1122%

33873 7,548,288 0.1094%

34731 7,538,761 0.1092%

33827 7,280,532 0.1055%

33433 7,150,053 0.1036%

33860 7,111,707 0.1030%

32174 7,066,369 0.1024%



232




33897 7,050,668 0.1022%

32821 7,039,226 0.1020%

34698 6,947,628 0.1007%

33445 6,918,115 0.1002%

34472 6,913,038 0.1002%

32817 6,900,324 0.1000%

33446 6,831,933 0.0990%

34987 6,753,838 0.0978%

32839 6,691,933 0.0970%

33841 6,525,636 0.0945%

33569 6,503,952 0.0942%

33430 6,421,284 0.0930%

33511 6,376,257 0.0924%

32796 6,372,962 0.0923%

32807 6,354,293 0.0921%

33850 6,287,559 0.0911%

33767 6,224,078 0.0902%

32738 6,187,961 0.0897%

33756 6,112,960 0.0886%

34785 6,067,949 0.0879%

33434 6,021,505 0.0872%

33541 5,854,979 0.0848%

34667 5,814,882 0.0842%

33413 5,810,855 0.0842%

33710 5,808,365 0.0842%

33428 5,764,809 0.0835%

34683 5,655,558 0.0819%

32176 5,592,234 0.0810%

33487 5,557,016 0.0805%

34476 5,555,740 0.0805%

34668 5,550,314 0.0804%

32118 5,541,810 0.0803%

34420 5,514,426 0.0799%

34609 5,470,417 0.0793%

32701 5,469,358 0.0792%

33483 5,404,338 0.0783%

33484 5,354,677 0.0776%

33629 5,334,476 0.0773%

33426 5,317,561 0.0770%

34209 5,315,495 0.0770%

32811 5,300,761 0.0768%

34608 5,274,642 0.0764%

33708 5,273,198 0.0764%

33707 5,202,528 0.0754%

33811 5,188,800 0.0752%

34606 5,164,394 0.0748%

32169 5,010,938 0.0726%

33476 4,997,433 0.0724%

33471 4,974,567 0.0721%

32127 4,949,940 0.0717%

33647 4,943,000 0.0716%

32720 4,906,961 0.0711%

34446 4,904,456 0.0711%

32713 4,890,169 0.0708%

33542 4,859,898 0.0704%

34736 4,858,480 0.0704%

34228 4,807,920 0.0697%



233




32832 4,728,207 0.0685%

33703 4,666,144 0.0676%

34465 4,658,071 0.0675%

34715 4,600,198 0.0666%

33611 4,598,136 0.0666%

32724 4,595,422 0.0666%

33857 4,574,321 0.0663%

34470 4,559,677 0.0661%

32137 4,551,702 0.0659%

33770 4,546,985 0.0659%

34613 4,541,220 0.0658%

33715 4,492,297 0.0651%

32805 4,454,418 0.0645%

34773 4,428,848 0.0642%

34480 4,406,438 0.0638%

34442 4,388,826 0.0636%

33868 4,352,396 0.0631%

33525 4,283,109 0.0621%

33498 4,281,925 0.0620%

32082 4,262,758 0.0618%

33772 4,223,099 0.0612%

33573 4,220,418 0.0611%

33624 4,205,000 0.0609%

32773 4,185,029 0.0606%

34450 4,158,699 0.0603%

34482 4,080,008 0.0591%

33855 4,069,052 0.0590%

34221 4,065,112 0.0589%

33838 4,001,406 0.0580%

33432 3,988,236 0.0578%

33805 3,973,269 0.0576%

34474 3,961,439 0.0574%

34481 3,896,859 0.0565%

33950 3,864,097 0.0560%

33702 3,851,007 0.0558%

33431 3,839,314 0.0556%

33755 3,837,840 0.0556%

33572 3,835,713 0.0556%

32784 3,788,993 0.0549%

34601 3,750,135 0.0543%

34655 3,708,039 0.0537%

33566 3,703,173 0.0537%

34452 3,690,129 0.0535%

34739 3,647,223 0.0528%

33510 3,638,129 0.0527%

33543 3,636,741 0.0527%

33064 3,624,984 0.0525%

33774 3,617,180 0.0524%

34689 3,603,030 0.0522%

33935 3,597,043 0.0521%

33890 3,522,817 0.0510%

33704 3,517,280 0.0510%

32119 3,503,982 0.0508%

33615 3,474,672 0.0503%

33486 3,428,442 0.0497%

33565 3,418,259 0.0495%

32826 3,390,959 0.0491%



234




33584 3,379,523 0.0490%

33617 3,367,445 0.0488%

34473 3,366,253 0.0488%

32210 3,352,130 0.0486%

34652 3,324,015 0.0482%

34231 3,299,836 0.0478%

32829 3,289,102 0.0477%

33065 3,272,688 0.0474%

33547 3,265,866 0.0473%

33771 3,258,148 0.0472%

34429 3,178,428 0.0460%

33067 3,159,056 0.0458%

33442 3,154,667 0.0457%

34432 3,141,335 0.0455%

33776 3,137,410 0.0455%

34653 3,132,211 0.0454%

34714 3,056,457 0.0443%

34479 3,053,233 0.0442%

33071 3,052,174 0.0442%

34684 3,031,410 0.0439%

33713 3,019,403 0.0437%

33618 3,019,243 0.0437%

34448 3,014,235 0.0437%

33764 3,012,093 0.0436%

33062 2,970,331 0.0430%

32763 2,967,018 0.0430%

33952 2,904,154 0.0421%

33076 2,891,811 0.0419%

34639 2,886,540 0.0418%

32776 2,868,287 0.0416%

33556 2,857,647 0.0414%

33063 2,856,346 0.0414%

34461 2,852,272 0.0413%

32034 2,851,466 0.0413%

32080 2,844,492 0.0412%

33709 2,833,516 0.0411%

32179 2,803,903 0.0406%

32608 2,800,216 0.0406%

33917 2,775,992 0.0402%

32798 2,775,467 0.0402%

33308 2,767,495 0.0401%

33777 2,755,962 0.0399%

33444 2,754,443 0.0399%

32073 2,748,344 0.0398%

33705 2,737,947 0.0397%

34756 2,729,470 0.0395%

34242 2,720,429 0.0394%

32168 2,710,229 0.0393%

32801 2,703,686 0.0392%

32735 2,682,392 0.0389%

33523 2,675,903 0.0388%

34453 2,675,705 0.0388%

33570 2,673,180 0.0387%

32605 2,662,694 0.0386%

32141 2,632,615 0.0381%

32225 2,617,648 0.0379%

34217 2,606,937 0.0378%



235




33321 2,605,625 0.0378%

32833 2,604,863 0.0377%

33513 2,568,940 0.0372%

32068 2,549,434 0.0369%

33540 2,517,679 0.0365%

32736 2,515,115 0.0364%

33606 2,508,945 0.0363%

33563 2,484,015 0.0360%

34695 2,465,148 0.0357%

34737 2,461,752 0.0357%

34243 2,458,148 0.0356%

32827 2,448,977 0.0355%

32223 2,446,380 0.0354%

32766 2,402,935 0.0348%

33549 2,389,227 0.0346%

33782 2,388,599 0.0346%

33812 2,381,376 0.0345%

33982 2,371,418 0.0344%

33711 2,345,522 0.0340%

33785 2,334,987 0.0338%

34293 2,334,346 0.0338%

33604 2,323,968 0.0337%

33609 2,322,363 0.0336%

34203 2,319,306 0.0336%

34232 2,315,184 0.0335%

33619 2,312,290 0.0335%

32117 2,275,317 0.0330%

32244 2,262,714 0.0328%

33839 2,258,159 0.0327%

33612 2,256,023 0.0327%

34607 2,247,300 0.0326%

33781 2,243,812 0.0325%

34677 2,221,698 0.0322%

32754 2,221,385 0.0322%

33614 2,217,650 0.0321%

33610 2,214,779 0.0321%

34654 2,213,356 0.0321%

33815 2,204,635 0.0319%

33983 2,198,051 0.0318%

33896 2,183,896 0.0316%

33712 2,174,559 0.0315%

34436 2,166,116 0.0314%

33761 2,148,464 0.0311%

32207 2,140,346 0.0310%

33613 2,140,315 0.0310%

33773 2,098,341 0.0304%

33544 2,093,175 0.0303%

34691 2,078,103 0.0301%

34488 2,077,835 0.0301%

34428 2,069,518 0.0300%

32205 2,059,614 0.0298%

33778 2,044,967 0.0296%

33322 2,043,506 0.0296%

32607 2,030,974 0.0294%

33903 2,030,734 0.0294%

32250 2,022,028 0.0293%

34207 2,016,480 0.0292%



236




33905 2,005,708 0.0291%

32656 1,967,367 0.0285%

33558 1,963,081 0.0284%

34734 1,949,014 0.0282%

33319 1,947,842 0.0282%

34205 1,947,571 0.0282%

32114 1,907,603 0.0276%

32136 1,903,669 0.0276%

34685 1,902,328 0.0276%

34431 1,886,075 0.0273%

34610 1,883,849 0.0273%

32084 1,874,196 0.0272%

32086 1,865,342 0.0270%

32606 1,856,087 0.0269%

33538 1,849,989 0.0268%

33073 1,844,684 0.0267%

33567 1,843,647 0.0267%

33312 1,838,243 0.0266%

32957 1,828,722 0.0265%

33324 1,820,076 0.0264%

32129 1,819,876 0.0264%

33068 1,814,316 0.0263%

34236 1,813,660 0.0263%

33326 1,797,644 0.0260%

34287 1,791,036 0.0259%

34275 1,790,830 0.0259%

33441 1,789,004 0.0259%

32257 1,779,893 0.0258%

32128 1,766,413 0.0256%

34223 1,764,983 0.0256%

32164 1,761,415 0.0255%

33317 1,753,386 0.0254%

34219 1,747,989 0.0253%

34797 1,736,805 0.0252%

34208 1,719,396 0.0249%

34238 1,690,259 0.0245%

33625 1,678,501 0.0243%

34241 1,670,848 0.0242%

33527 1,645,657 0.0238%

34239 1,640,551 0.0238%

33936 1,630,479 0.0236%

33029 1,624,169 0.0235%

32195 1,620,437 0.0235%

34705 1,613,126 0.0234%

33325 1,609,255 0.0233%

33024 1,603,017 0.0232%

34434 1,602,524 0.0232%

32233 1,586,941 0.0230%

34602 1,585,803 0.0230%

32177 1,578,567 0.0229%

34202 1,549,339 0.0224%

34210 1,541,934 0.0223%

33759 1,537,211 0.0223%

33626 1,533,510 0.0222%

34669 1,532,370 0.0222%

32043 1,529,456 0.0222%

32640 1,524,466 0.0221%



237




33021 1,512,240 0.0219%

33634 1,506,498 0.0218%

33851 1,505,525 0.0218%

32065 1,494,346 0.0217%

32696 1,462,215 0.0212%

32259 1,458,712 0.0211%

33834 1,436,860 0.0208%

33714 1,435,725 0.0208%

33616 1,432,224 0.0208%

33309 1,426,314 0.0207%

32277 1,411,198 0.0204%

32003 1,407,369 0.0204%

33603 1,406,255 0.0204%

33066 1,400,562 0.0203%

33948 1,396,336 0.0202%

33763 1,389,015 0.0201%

34222 1,388,882 0.0201%

33313 1,387,687 0.0201%

33311 1,381,368 0.0200%

33060 1,379,324 0.0200%

33786 1,377,024 0.0200%

32218 1,374,630 0.0199%

33597 1,368,833 0.0198%

34285 1,368,718 0.0198%

34690 1,368,222 0.0198%

33351 1,339,734 0.0194%

34433 1,336,699 0.0194%

34475 1,326,552 0.0192%

32134 1,321,649 0.0191%

34240 1,303,768 0.0189%

33026 1,300,056 0.0188%

32653 1,300,047 0.0188%

33023 1,288,841 0.0187%

33980 1,287,981 0.0187%

33328 1,279,752 0.0185%

33607 1,268,776 0.0184%

34234 1,265,369 0.0183%

34235 1,251,890 0.0181%

32730 1,245,471 0.0180%

32217 1,243,937 0.0180%

32060 1,243,711 0.0180%

32224 1,238,742 0.0179%

32732 1,237,826 0.0179%

33701 1,237,704 0.0179%

33960 1,236,875 0.0179%

33160 1,228,230 0.0178%

34233 1,221,545 0.0177%

33334 1,214,457 0.0176%

32216 1,214,326 0.0176%

32211 1,210,160 0.0175%

32615 1,183,081 0.0171%

32208 1,181,823 0.0171%

32601 1,179,248 0.0171%

32246 1,176,636 0.0170%

33069 1,176,512 0.0170%

33765 1,176,081 0.0170%

33559 1,173,421 0.0170%



238




33762 1,163,556 0.0169%

33323 1,161,411 0.0168%

33140 1,161,310 0.0168%

33972 1,156,051 0.0167%

32091 1,136,703 0.0165%

32024 1,134,705 0.0164%

33331 1,130,243 0.0164%

34224 1,124,368 0.0163%

33957 1,123,303 0.0163%

32666 1,116,587 0.0162%

33760 1,115,478 0.0162%

32609 1,111,265 0.0161%

32820 1,107,286 0.0160%

33009 1,104,130 0.0160%

33971 1,096,980 0.0159%

34484 1,096,921 0.0159%

33990 1,096,312 0.0159%

32148 1,093,085 0.0158%

33635 1,088,721 0.0158%

33316 1,068,105 0.0155%

33027 1,067,928 0.0155%

33598 1,064,850 0.0154%

34604 1,062,223 0.0154%

34292 1,056,859 0.0153%

33548 1,053,283 0.0153%

34229 1,040,806 0.0151%

32221 1,036,519 0.0150%

33592 1,033,933 0.0150%

32025 1,029,384 0.0149%

33637 1,015,734 0.0147%

32113 1,011,035 0.0146%

32256 1,010,318 0.0146%

34212 1,006,986 0.0146%

34688 1,001,717 0.0145%

33921 1,000,819 0.0145%

33475 979,565 0.0142%

33301 977,228 0.0142%

33955 965,159 0.0140%

33180 956,729 0.0139%

33025 947,351 0.0137%

33493 944,471 0.0137%

32669 941,072 0.0136%

33605 938,226 0.0136%

33304 936,116 0.0136%

32668 926,550 0.0134%

33327 918,388 0.0133%

32686 913,320 0.0132%

32132 910,887 0.0132%

34638 908,753 0.0132%

33920 904,258 0.0131%

32209 892,713 0.0129%

33139 892,515 0.0129%

33901 878,967 0.0127%

32055 876,131 0.0127%

33019 876,127 0.0127%

33330 870,577 0.0126%

34286 864,496 0.0125%



239




32092 849,034 0.0123%

33602 847,580 0.0123%

34614 842,957 0.0122%

32709 836,377 0.0121%

33534 830,429 0.0120%

34760 830,076 0.0120%

34991 827,354 0.0120%

32618 817,767 0.0118%

33154 816,359 0.0118%

34753 810,717 0.0117%

32641 788,058 0.0114%

33020 783,146 0.0113%

33305 779,264 0.0113%

33141 778,961 0.0113%

33438 772,897 0.0112%

34269 767,157 0.0111%

32970 765,152 0.0111%

32964 756,581 0.0110%

34237 756,072 0.0110%

32226 755,159 0.0109%

32617 751,332 0.0109%

34251 742,307 0.0108%

32112 728,620 0.0106%

32643 727,995 0.0105%

33954 723,739 0.0105%

34216 721,983 0.0105%

32266 713,210 0.0103%

32702 695,460 0.0101%

33909 694,975 0.0101%

32063 682,638 0.0099%

33149 675,482 0.0098%

32258 673,182 0.0098%

33913 672,466 0.0097%

32693 664,067 0.0096%

32961 661,470 0.0096%

32130 660,457 0.0096%

32667 642,998 0.0093%

32189 636,509 0.0092%

33028 604,315 0.0088%

33576 601,212 0.0087%

32038 592,838 0.0086%

33981 589,820 0.0085%

32102 576,780 0.0084%

33332 568,515 0.0082%

32626 561,698 0.0081%

33993 560,362 0.0081%

33314 546,099 0.0079%

32206 538,186 0.0078%

32767 537,869 0.0078%

33877 532,315 0.0077%

32764 527,454 0.0076%

32814 522,494 0.0076%

32744 521,603 0.0076%

33716 517,761 0.0075%

32220 509,859 0.0074%

33315 509,513 0.0074%

32054 501,168 0.0073%



240

Figure 65: Percentage of Residential Losses from Hurricane Charley (2004) by ZIP Code



241

Figure 66: Percentage of Residential Losses from Hurricane Frances (2004) by ZIP Code



242

Figure 67: Percentage of Residential Losses from Hurricane Ivan (2004) by ZIP Code



243

Figure 68: Percentage of Residential Losses from Hurricane Jeanne (2004) by ZIP Code



244

Figure 69: Percentage of Cumulative Residential Losses from 2004 Events by ZIP Code

Appendix A—FCHLPM Forms Form A-4: Output Ranges


245

Form A-4: Output Ranges

A. Provide personal and commercial residential output ranges in the format shown in the file named “2011FormA4.xlsx” by using an automated program or script. A hard copy of Form A-4 shall be included in a submission appendix. Provide this form in Excel format. The file name shall include the abbreviated name of the modeler, the Standards year, and the form name.

B. Provide loss costs rounded to 3 decimal places by county. Within each county, loss costs shall be shown separately per $1,000 of exposure for frame owners, masonry owners, frame renters, masonry renters, frame condo unit owners, masonry condo unit owners, mobile home, and commercial residential. For each of these categories using ZIP Code centroids, the output range shall show the highest loss cost, the lowest loss cost, and the weighted average loss cost. The aggregate residential exposure data for this form shall be developed from the information in the file named “hlpm2007c.exe,” except for insured value and deductibles information. Insured values shall be based on the output range specifications below. Deductible amounts of 0% and as specified in the output range specifications will be assumed to be uniformly applied to all risks. When calculating the weighted average loss costs, weight the loss costs by the total insured value calculated above. Include the statewide range of loss costs (i.e., low, high, and weighted average).

C. If a modeling organization has loss costs for a ZIP Code for which there is no exposure, give the loss costs zero weight (i.e., assume the exposure in that ZIP Code is zero). Provide a list in the submission document of those ZIP Codes where this occurs.

D. If a modeling organization does not have loss costs for a ZIP Code for which there is some exposure, do not assume such loss costs are zero, but use only the exposures for which there are loss costs in calculating the weighted average loss costs. Provide a list in the submission document of the ZIP Codes where this occurs.

E. All anomalies in loss costs that are not consistent with the requirements of Standard A-6 and have been explained in Disclosure A-6.14 shall be shaded.

Indicate if per diem is used in producing loss costs for Coverage D (ALE) in the personal residential output ranges. If a per diem rate is used in the submission, a rate of $150.00 per day per policy shall be used.

The required file is provided in Excel format in the file RMS11FormA4_20130311.xlsx at the link

provided and appears below. There are no instances of loss costs for a ZIP Code for which there is no

exposure in the submitted Output Ranges. The gross (non-zero deductible) loss costs have been

calculated with the assumption that an insurer will not elect to apply an all other perils deductible to

subsequent hurricane losses. There are no instances where we have a zero loss cost for a ZIP Code

for which there is some exposure in the submitted Output Ranges.

Output Range Specifications

Policy Type Assumptions

Owners Coverage A = Structure

Coverage A limit = $100,000




Coverage B limit = 10% of Coverage A limit





246


Coverage C limit = 50% of Coverage A limit



Coverage D limit = 20% of Coverage A limit


Per Diem = $150.00/day per policy, if used Dominant Coverage = A. Loss costs per $1,000 shall be related to the Coverage A limit. Loss costs for the various specified deductibles shall be determined based

on annual deductibles. 2% Deductible of Coverage A. All-other perils deductible shall be $500.

Renters Coverage C = Contents

Coverage C limit = $25,000



Coverage D limit = 40% of Coverage C limit


Per Diem = $150.00/day per policy, if used Dominate Coverage = C. Loss costs per $1,000 shall be related to the Coverage C limit. Loss costs for the various specified deductibles shall be determined based

on annual deductibles. 2% Deductible of Coverage C. All-other perils deductible shall be $500.

Condo Unit Owners Coverage A = Structure

Coverage A limit = 10% of Coverage C limit



Coverage C limit = $50,000



Coverage D limit = 40% of Coverage C limit


Per Diem = $150.00/day per policy, if used Dominant Coverage = C. Loss costs per $1,000 shall be related to the Coverage C limit. Loss costs for the various specified deductibles shall be determined based

on annual deductibles. 2% Deductible of Coverage C. All-other perils deductible shall be $500.

Mobile Home Coverage A = Structure




Coverage B limit = 10% of Coverage A limit






247







Commercial Residential Coverage A = Structure



Coverage C= Contents



Coverage D= Time Element







248

Table 27: Loss Costs per $1000 for 0% Deductible

County Loss Costs

Frame Owners

Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit

Commercial Residential

Alachua LOW 0.143 0.132 2.667 0.081 0.069 0.097 0.084 0.178

AVERAGE 1.021 1.182 3.707 0.284 0.265 0.365 0.315 0.644

HIGH 2.195 2.231 5.704 0.647 0.542 0.692 0.647 1.318

Baker LOW 0.093 0.088 1.749 0.043 0.048 0.066 0.250 0.290

AVERAGE 0.596 0.640 2.117 0.187 0.182 0.066 0.250 0.336

HIGH 1.013 0.950 2.714 0.238 0.209 0.066 0.250 0.474

Bay LOW 0.224 0.207 3.351 0.167 0.156 0.243 0.128 0.304

AVERAGE 1.969 1.958 5.398 0.686 0.605 1.450 0.748 1.667

HIGH 4.232 3.980 10.050 1.661 1.288 1.836 1.430 2.631

Bradford LOW 0.131 0.124 2.413 0.071 0.055 0.211 0.319 0.222

AVERAGE 0.987 1.017 2.856 0.269 0.225 0.211 0.319 0.593

HIGH 1.342 1.331 3.571 0.343 0.295 0.211 0.319 0.695

Brevard LOW 0.496 0.458 9.090 0.379 0.272 0.454 0.274 0.465

AVERAGE 4.610 3.680 13.075 1.668 1.333 2.473 2.388 3.726

HIGH 10.996 10.467 23.393 4.645 3.314 5.453 3.904 7.602

Broward LOW 0.745 0.679 19.104 0.742 0.362 0.872 0.435 0.855

AVERAGE 5.890 5.289 24.476 3.313 2.437 4.151 3.356 5.730

HIGH 12.893 13.596 30.573 8.429 5.932 9.889 7.019 13.464

Calhoun LOW 0.159 0.148 2.714 0.134 0.070 0.289

AVERAGE 1.136 1.149 3.145 0.337 0.283 0.289

HIGH 1.573 1.464 4.192 0.453 0.376 0.289

Charlotte LOW 0.480 0.447 10.532 0.340 0.288 0.483 0.346 0.540

AVERAGE 5.165 3.910 14.131 1.650 1.265 2.985 1.626 3.113

HIGH 11.570 10.969 23.058 4.157 3.184 4.904 3.776 7.340

Citrus LOW 0.295 0.262 5.787 0.185 0.158 0.257 0.203 0.393

AVERAGE 2.699 2.016 7.094 0.765 0.591 1.074 0.924 1.769

HIGH 3.955 3.761 9.902 1.123 0.925 1.333 1.103 2.244

Clay LOW 0.105 0.098 2.285 0.055 0.049 0.088 0.076 0.170

AVERAGE 0.750 0.916 2.943 0.242 0.229 0.264 0.207 0.593

HIGH 1.914 1.796 5.137 0.483 0.402 0.576 0.530 0.816

Collier LOW 0.700 0.647 14.041 0.614 0.367 0.696 0.364 0.619

AVERAGE 6.030 4.477 16.957 2.380 1.643 3.204 2.499 3.921

HIGH 15.511 14.612 28.886 6.845 4.849 8.039 5.692 10.428

Columbia LOW 0.125 0.103 2.408 0.062 0.053 0.111 0.182 0.387

AVERAGE 0.833 0.848 2.895 0.234 0.204 0.309 0.278 0.567

HIGH 1.557 1.445 4.087 0.426 0.366 0.445 0.299 0.631

DeSoto LOW 0.482 0.433 9.231 0.317 0.231 0.452 0.278 0.743

AVERAGE 4.079 3.609 10.579 1.290 1.082 1.233 1.046 2.001

HIGH 6.092 5.806 13.563 1.600 1.361 1.913 1.631 2.993



249

County Loss Costs

Frame Owners

Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit


Dixie LOW 0.267 0.252 4.110 0.243 0.187 0.455 0.353 0.694

AVERAGE 1.932 1.561 4.550 0.606 0.389 0.519 0.461 0.838

HIGH 3.969 3.718 9.163 1.678 0.486 0.572 1.252 0.865

Duval LOW 0.106 0.092 2.183 0.053 0.046 0.063 0.062 0.123

AVERAGE 1.060 1.138 3.139 0.320 0.285 0.325 0.312 0.707

HIGH 3.341 3.078 8.046 1.195 0.938 0.993 1.042 1.610

Escambia LOW 0.221 0.205 3.402 0.127 0.103 0.303 0.210 0.435

AVERAGE 2.556 2.710 6.531 1.045 0.885 1.819 1.434 1.914

HIGH 6.490 6.096 12.982 2.680 2.026 3.106 2.336 4.161

Flagler LOW 0.209 0.208 3.902 0.129 0.108 0.307 0.085 0.249

AVERAGE 1.937 1.160 5.795 0.589 0.409 1.206 0.515 1.199

HIGH 5.261 4.946 12.377 1.444 1.583 2.378 1.325 2.591

Franklin LOW 0.623 0.725 7.604 0.556 0.756 0.590 0.432 0.858

AVERAGE 3.527 3.858 8.987 1.521 1.404 1.138 1.881 1.993

HIGH 6.209 5.604 12.814 2.363 1.799 2.745 2.103 3.837

Gadsden LOW 0.088 0.091 1.676 0.050 0.042 0.466

AVERAGE 0.682 0.711 1.984 0.229 0.196 0.489

HIGH 1.057 0.982 2.923 0.280 0.230 0.532

Gilchrist LOW 0.242 0.209 3.711 0.147 0.184 0.526 0.631

AVERAGE 1.344 1.366 4.059 0.498 0.442 0.526 0.631

HIGH 2.162 1.844 5.069 0.569 0.489 0.526 0.631

Glades LOW 1.001 0.814 13.201 1.527 0.495

AVERAGE 6.017 5.225 14.542 2.372 1.787

HIGH 7.497 7.138 18.196 2.466 2.017

Gulf LOW 0.235 0.244 3.320 0.302 0.241 0.563 0.412 0.666

AVERAGE 2.302 2.803 5.024 1.207 1.004 1.126 1.010 1.863

HIGH 5.073 4.764 11.770 1.568 1.192 1.835 1.865 2.626

Hamilton LOW 0.089 0.084 1.614 0.053 0.154 0.138 0.200

AVERAGE 0.686 0.698 2.026 0.202 0.198 0.138 0.433

HIGH 0.992 0.928 2.749 0.251 0.219 0.138 0.559

Hardee LOW 0.489 0.363 8.329 0.250 0.215 2.114

AVERAGE 3.582 3.205 9.002 1.134 0.905 2.251

HIGH 4.945 4.696 12.491 1.445 1.225 3.120

Hendry LOW 0.783 0.575 12.078 0.448 0.378 2.256 0.846 1.600

AVERAGE 6.298 5.778 15.143 2.083 1.907 3.567 2.722 5.455

HIGH 10.777 10.234 22.194 3.465 2.712 4.130 3.248 6.621

Hernando LOW 0.342 0.291 6.836 0.224 0.170 0.269 0.249 0.403

AVERAGE 2.892 2.364 7.835 0.813 0.608 0.922 0.996 1.600

HIGH 4.713 4.477 10.742 1.247 1.058 1.489 1.267 2.588



250

County Loss Costs

Frame Owners

Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit


Highlands LOW 0.508 0.454 10.227 0.360 0.292 0.474 0.351 0.687

AVERAGE 4.259 3.876 11.252 1.445 1.121 1.766 1.480 2.605

HIGH 7.123 6.783 16.278 2.095 1.813 2.437 2.010 3.911

Hillsborough LOW 0.322 0.300 7.189 0.198 0.164 0.238 0.160 0.385

AVERAGE 3.166 2.765 9.048 0.857 0.714 1.033 0.947 1.823

HIGH 6.770 6.981 15.881 2.504 2.029 2.540 2.402 4.310

Holmes LOW 0.133 0.140 2.570 0.078 0.102 0.433 0.354

AVERAGE 1.002 1.012 2.737 0.300 0.245 0.433 0.481

HIGH 1.463 1.376 3.607 0.380 0.278 0.433 0.608

Indian River LOW 0.691 0.610 11.251 0.594 0.465 0.735 0.403 1.064

AVERAGE 5.465 3.219 13.354 2.422 1.604 3.265 2.565 4.497

HIGH 9.626 9.054 23.502 4.462 3.217 5.250 3.812 7.452

Jackson LOW 0.139 0.143 2.624 0.079 0.086 0.385 0.331 0.174

AVERAGE 1.038 1.086 2.873 0.317 0.276 0.385 0.354 0.543

HIGH 1.571 1.411 3.945 0.415 0.343 0.385 0.387 0.717

Jefferson LOW 0.103 0.096 1.560 0.056 0.069 0.187

AVERAGE 0.554 0.554 1.717 0.197 0.164 0.370

HIGH 0.924 0.863 2.466 0.269 0.217 0.440

Lafayette LOW 0.164 0.160 2.411 0.150 0.118

AVERAGE 0.992 1.039 3.035 0.324 0.250

HIGH 1.340 1.253 3.564 0.378 0.316

Lake LOW 0.265 0.241 4.547 0.179 0.141 0.293 0.197 0.496

AVERAGE 2.459 1.917 8.148 0.742 0.611 1.331 1.034 1.698

HIGH 5.045 5.018 11.992 1.381 1.162 1.565 1.331 2.792

Lee LOW 0.626 0.579 12.965 0.485 0.385 0.570 0.269 0.496

AVERAGE 5.893 3.616 15.387 2.245 1.274 3.424 1.891 3.483

HIGH 14.689 13.901 28.877 9.683 6.758 11.330 7.956 14.885

Leon LOW 0.087 0.086 1.759 0.054 0.045 0.065 0.039 0.072

AVERAGE 0.643 0.652 2.339 0.182 0.155 0.165 0.159 0.370

HIGH 1.336 1.247 3.455 0.367 0.350 0.355 0.271 0.596

Levy LOW 0.252 0.254 3.511 0.164 0.138 0.196 0.490 0.725

AVERAGE 2.046 1.747 5.089 0.715 0.534 1.468 1.101 1.874

HIGH 5.226 4.923 10.902 1.897 0.973 2.223 1.712 3.239

Liberty LOW 0.154 0.140 2.514 0.089 0.300

AVERAGE 1.034 1.051 2.873 0.320 0.305

HIGH 1.369 1.232 3.593 0.365 0.308

Madison LOW 0.088 0.083 1.451 0.046 0.058 0.431

AVERAGE 0.672 0.662 1.859 0.204 0.183 0.435

HIGH 0.984 0.920 2.722 0.258 0.221 0.490



251

County Loss Costs

Frame Owners

Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit


Manatee LOW 0.493 0.457 10.182 0.352 0.292 0.422 0.230 0.470

AVERAGE 4.702 3.366 11.956 1.679 1.165 2.703 1.892 3.442

HIGH 11.026 11.954 27.118 5.039 3.735 6.518 4.753 9.349

Marion LOW 0.210 0.190 3.751 0.137 0.101 0.170 0.148 0.283

AVERAGE 2.013 1.416 5.733 0.496 0.398 0.729 0.532 0.962

HIGH 4.171 3.956 10.643 1.224 0.721 0.992 0.862 1.810

Martin LOW 0.893 0.813 15.923 0.996 0.594 0.917 0.554 1.317

AVERAGE 7.980 5.458 19.039 3.341 2.116 4.822 3.544 5.703

HIGH 13.766 12.977 23.967 5.513 4.048 6.474 4.797 9.117

Miami-Dade LOW 0.732 0.692 19.212 0.795 0.408 0.929 0.441 0.754

AVERAGE 6.101 5.547 22.416 4.858 3.305 5.809 4.799 6.919

HIGH 14.110 14.692 57.460 18.923 12.763 21.865 14.941 26.542

Monroe LOW 1.596 1.572 27.847 3.792 1.808 3.003 1.856 2.972

AVERAGE 5.927 6.079 36.775 8.109 5.977 9.975 8.436 11.288

HIGH 11.827 11.043 46.864 12.908 9.866 16.291 11.435 19.053

Nassau LOW 0.091 0.086 1.704 0.042 0.038 0.051 0.102 0.100

AVERAGE 1.250 1.121 2.853 0.504 0.450 0.870 0.627 1.166

HIGH 2.908 2.742 6.551 0.900 0.795 1.171 0.857 1.840

Okaloosa LOW 0.173 0.159 2.776 0.098 0.082 0.151 0.221 0.261

AVERAGE 2.277 2.428 4.669 0.895 0.739 1.844 1.150 2.360

HIGH 5.675 5.347 11.630 2.185 1.682 2.546 1.968 3.557

Okeechobee LOW 0.786 0.721 14.106 0.636 0.511 1.044 0.616 1.737

AVERAGE 6.886 5.601 16.555 2.312 1.904 3.559 2.977 5.153

HIGH 10.310 9.791 21.149 3.345 2.620 3.978 3.133 5.374

Orange LOW 0.324 0.286 6.823 0.176 0.123 0.230 0.141 0.259

AVERAGE 2.548 2.325 7.965 0.645 0.553 0.833 0.707 1.395

HIGH 6.611 6.280 14.009 2.151 1.773 1.492 2.121 2.620

Osceola LOW 0.327 0.286 6.105 0.185 0.161 0.222 0.133 0.256

AVERAGE 2.285 2.128 9.093 0.670 0.583 0.755 0.529 1.422

HIGH 5.477 5.193 12.938 1.684 1.261 1.826 1.513 3.163

Palm Beach LOW 0.596 0.571 15.086 0.637 0.398 0.766 0.319 0.836

AVERAGE 5.675 4.234 21.146 3.353 2.234 4.290 3.195 5.528

HIGH 13.674 12.886 36.930 7.990 5.670 9.363 6.701 12.812

Pasco LOW 0.387 0.360 8.289 0.250 0.147 0.299 0.230 0.505

AVERAGE 2.727 2.682 9.716 0.870 0.713 1.294 1.128 2.137

HIGH 5.609 5.355 13.160 1.471 1.252 1.759 1.501 2.968

Pinellas LOW 0.376 0.349 8.182 0.250 0.208 0.300 0.193 0.323

AVERAGE 4.646 4.280 11.021 1.438 1.197 2.111 1.877 3.119

HIGH 9.518 9.023 21.397 6.112 4.403 7.183 5.843 10.165



252

County Loss Costs

Frame Owners

Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit


Polk LOW 0.364 0.305 7.425 0.198 0.173 0.238 0.209 0.432

AVERAGE 3.239 2.594 9.304 0.947 0.768 1.105 1.009 1.897

HIGH 6.052 5.754 14.953 1.903 1.582 2.085 1.707 2.996

Putnam LOW 0.168 0.155 3.024 0.104 0.090 0.250 0.130 0.504

AVERAGE 1.576 1.522 4.347 0.441 0.391 0.672 0.477 1.052

HIGH 2.673 2.577 7.013 0.715 0.631 0.855 0.756 1.510

St. Johns LOW 0.153 0.143 2.962 0.098 0.082 0.118 0.098 0.220

AVERAGE 1.227 1.370 4.696 0.482 0.399 0.775 0.702 1.295

HIGH 3.640 3.423 8.105 1.164 0.918 1.370 1.088 2.139

St. Lucie LOW 0.678 0.620 12.528 0.554 0.443 0.739 0.504 0.999

AVERAGE 5.866 3.148 14.581 2.192 1.302 3.917 3.176 4.602

HIGH 9.968 9.392 31.280 7.467 5.046 8.689 5.954 11.231

Santa Rosa LOW 0.230 0.212 3.517 0.132 0.143 0.452 0.209 0.420

AVERAGE 2.369 2.531 6.269 1.169 1.012 3.523 1.510 3.222

HIGH 9.096 8.554 17.549 3.959 2.946 4.573 3.433 5.933

Sarasota LOW 0.470 0.429 9.677 0.329 0.249 0.394 0.219 0.455

AVERAGE 4.635 3.359 12.388 1.532 1.130 2.439 1.948 3.242

HIGH 9.437 8.965 29.615 5.836 4.221 6.848 4.997 9.770

Seminole LOW 0.310 0.242 6.667 0.158 0.109 0.150 0.129 0.243

AVERAGE 2.628 2.257 7.205 0.612 0.532 0.814 0.691 1.442

HIGH 5.701 5.407 12.094 1.131 1.541 1.351 1.149 2.394

Sumter LOW 0.306 0.286 6.604 0.190 0.165 0.238 0.207 0.388

AVERAGE 0.922 1.174 7.741 0.541 0.534 1.030 0.553 0.942

HIGH 4.014 3.806 10.342 1.103 0.959 1.134 0.984 2.118

Suwannee LOW 0.135 0.111 2.352 0.067 0.088 0.338 0.362

AVERAGE 0.908 0.911 2.728 0.267 0.249 0.338 0.705

HIGH 1.566 1.614 4.153 0.441 0.378 0.338 0.906

Taylor LOW 0.102 0.136 1.580 0.085 0.094 0.136 0.180 0.372

AVERAGE 1.112 1.105 3.169 0.333 0.301 0.365 0.284 0.743

HIGH 2.029 1.900 5.051 0.709 0.372 0.829 0.657 0.908

Union LOW 0.127 0.117 2.212 0.115 0.060 0.084 0.073 0.586

AVERAGE 0.830 0.813 2.612 0.265 0.227 0.084 0.073 0.615

HIGH 1.353 1.267 3.655 0.298 0.258 0.084 0.073 0.659

Volusia LOW 0.269 0.251 4.780 0.165 0.136 0.220 0.168 0.326

AVERAGE 3.060 2.410 7.322 0.946 0.719 1.742 1.470 2.278

HIGH 9.030 8.518 17.410 2.957 2.256 4.644 2.662 4.988

Wakulla LOW 0.180 0.161 2.612 0.128 0.098 0.547 0.437 0.250

AVERAGE 1.052 1.222 3.248 0.392 0.424 0.966 1.248 0.843

HIGH 4.047 3.809 8.624 1.466 1.120 1.708 1.318 2.457



253

County Loss Costs

Frame Owners

Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit


Walton LOW 0.206 0.190 3.179 0.117 0.148 0.764 0.116 0.313

AVERAGE 2.238 1.963 5.216 1.102 0.877 2.221 1.196 2.471

HIGH 6.409 6.031 12.899 2.622 1.966 3.034 2.301 4.080

Washington LOW 0.153 0.143 2.593 0.131 0.203 0.181 0.271

AVERAGE 1.296 1.376 3.667 0.458 0.414 0.473 0.739

HIGH 1.980 1.860 5.054 0.649 0.521 0.766 0.877

Statewide LOW 0.087 0.083 1.451 0.042 0.038 0.051 0.039 0.072

AVERAGE 2.728 3.458 9.678 0.941 1.353 2.042 2.590 4.017

HIGH 15.511 14.692 57.460 18.923 12.763 21.865 14.941 26.542



254

Table 28: Loss Costs per $1000 with Specified Deductibles

County Loss Costs

Frame Owners

Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit


Alachua LOW 0.087 0.078 2.313 0.051 0.042 0.063 0.053 0.115

AVERAGE 0.841 0.980 3.261 0.229 0.211 0.300 0.255 0.517

HIGH 1.876 1.905 5.051 0.556 0.458 0.596 0.552 1.110

Baker LOW 0.055 0.052 1.510 0.024 0.028 0.041 0.200 0.219

AVERAGE 0.477 0.516 1.837 0.146 0.142 0.041 0.200 0.257

HIGH 0.837 0.781 2.372 0.190 0.164 0.041 0.200 0.374

Bay LOW 0.153 0.141 2.924 0.127 0.113 0.194 0.092 0.223

AVERAGE 1.682 1.668 4.805 0.598 0.518 1.309 0.650 1.427

HIGH 3.696 3.464 9.079 1.498 1.139 1.665 1.274 2.293

Bradford LOW 0.080 0.075 2.094 0.044 0.033 0.161 0.256 0.151

AVERAGE 0.808 0.834 2.488 0.215 0.176 0.161 0.256 0.471

HIGH 1.117 1.107 3.135 0.281 0.238 0.161 0.256 0.560

Brevard LOW 0.358 0.328 8.194 0.303 0.211 0.369 0.213 0.349

AVERAGE 4.028 3.192 11.950 1.493 1.175 2.251 2.160 3.272

HIGH 9.974 9.453 21.742 4.310 3.025 5.088 3.586 6.879

Broward LOW 0.565 0.510 17.604 0.623 0.285 0.740 0.346 0.677

AVERAGE 5.178 4.617 22.745 3.032 2.191 3.825 3.053 5.088

HIGH 11.717 12.367 28.608 7.928 5.497 9.341 6.540 12.369

Calhoun LOW 0.100 0.092 2.353 0.095 0.043 0.206

AVERAGE 0.933 0.946 2.737 0.275 0.226 0.206

HIGH 1.308 1.217 3.681 0.379 0.307 0.206

Charlotte LOW 0.343 0.317 9.498 0.265 0.220 0.391 0.266 0.402

AVERAGE 4.491 3.358 12.866 1.460 1.099 2.713 1.430 2.678

HIGH 10.455 9.880 21.397 3.816 2.876 4.529 3.437 6.582

Citrus LOW 0.204 0.175 5.148 0.136 0.114 0.197 0.147 0.284

AVERAGE 2.299 1.698 6.343 0.656 0.497 0.937 0.797 1.494

HIGH 3.421 3.245 8.972 0.986 0.800 1.181 0.963 1.926

Clay LOW 0.060 0.054 1.982 0.032 0.028 0.058 0.048 0.112

AVERAGE 0.609 0.753 2.568 0.192 0.180 0.212 0.162 0.477

HIGH 1.602 1.498 4.534 0.407 0.332 0.491 0.440 0.667

Collier LOW 0.520 0.478 12.839 0.507 0.290 0.579 0.286 0.472

AVERAGE 5.322 3.913 15.595 2.155 1.455 2.934 2.256 3.435

HIGH 14.240 13.369 27.049 6.418 4.470 7.574 5.277 9.516

Columbia LOW 0.078 0.059 2.097 0.039 0.032 0.078 0.141 0.300

AVERAGE 0.679 0.693 2.531 0.188 0.161 0.253 0.225 0.454

HIGH 1.295 1.206 3.614 0.358 0.302 0.374 0.243 0.510

DeSoto LOW 0.346 0.303 8.269 0.242 0.168 0.360 0.207 0.574

AVERAGE 3.498 3.080 9.521 1.122 0.930 1.071 0.897 1.682

HIGH 5.318 5.053 12.331 1.405 1.182 1.695 1.430 2.559



255

County Loss Costs

Frame Owners

Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit


Dixie LOW 0.196 0.181 3.648 0.193 0.144 0.382 0.288 0.560

AVERAGE 1.653 1.320 4.057 0.522 0.324 0.446 0.389 0.693

HIGH 3.511 3.277 8.357 1.531 0.411 0.497 1.106 0.718

Duval LOW 0.062 0.051 1.895 0.031 0.027 0.039 0.038 0.076

AVERAGE 0.877 0.945 2.762 0.262 0.230 0.267 0.254 0.573

HIGH 2.900 2.662 7.261 1.058 0.814 0.871 0.908 1.369

Escambia LOW 0.154 0.141 2.983 0.093 0.071 0.249 0.164 0.335

AVERAGE 2.201 2.343 5.876 0.933 0.777 1.657 1.286 1.651

HIGH 5.824 5.449 11.915 2.467 1.831 2.873 2.126 3.707

Flagler LOW 0.135 0.133 3.422 0.090 0.072 0.244 0.054 0.171

AVERAGE 1.649 0.964 5.158 0.506 0.338 1.074 0.433 1.006

HIGH 4.623 4.331 11.248 1.295 1.406 2.164 1.174 2.257

Franklin LOW 0.501 0.588 6.844 0.477 0.656 0.511 0.361 0.706

AVERAGE 3.087 3.374 8.119 1.375 1.252 1.023 1.696 1.719

HIGH 5.522 4.943 11.682 2.162 1.618 2.523 1.901 3.392

Gadsden LOW 0.049 0.050 1.452 0.030 0.025 0.365

AVERAGE 0.553 0.578 1.719 0.181 0.152 0.383

HIGH 0.869 0.804 2.550 0.223 0.179 0.423

Gilchrist LOW 0.173 0.142 3.277 0.111 0.140 0.446 0.509

AVERAGE 1.125 1.143 3.596 0.422 0.370 0.446 0.509

HIGH 1.849 1.560 4.526 0.486 0.412 0.446 0.509

Glades LOW 0.783 0.626 11.977 1.345 0.396

AVERAGE 5.256 4.541 13.226 2.125 1.574

HIGH 6.589 6.257 16.685 2.211 1.784

Gulf LOW 0.164 0.173 2.888 0.246 0.188 0.482 0.338 0.526

AVERAGE 1.979 2.414 4.446 1.083 0.883 1.008 0.889 1.602

HIGH 4.449 4.163 10.658 1.418 1.054 1.669 1.677 2.289

Hamilton LOW 0.052 0.048 1.388 0.032 0.118 0.103 0.139

AVERAGE 0.555 0.564 1.756 0.159 0.156 0.103 0.339

HIGH 0.816 0.761 2.406 0.201 0.172 0.103 0.447

Hardee LOW 0.358 0.246 7.434 0.184 0.154 1.786

AVERAGE 3.064 2.728 8.056 0.982 0.771 1.910

HIGH 4.274 4.049 11.313 1.266 1.060 2.693

Hendry LOW 0.596 0.417 10.909 0.355 0.293 2.011 0.708 1.319

AVERAGE 5.512 5.056 13.796 1.859 1.689 3.255 2.446 4.806

HIGH 9.662 9.146 20.486 3.156 2.432 3.788 2.935 5.903

Hernando LOW 0.235 0.195 6.064 0.166 0.120 0.204 0.186 0.292

AVERAGE 2.457 1.994 6.996 0.696 0.511 0.796 0.860 1.341

HIGH 4.090 3.874 9.687 1.093 0.917 1.317 1.109 2.214



256

County Loss Costs

Frame Owners

Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit


Highlands LOW 0.368 0.320 9.208 0.281 0.222 0.381 0.271 0.526

AVERAGE 3.682 3.339 10.169 1.270 0.969 1.568 1.297 2.229

HIGH 6.284 5.966 14.904 1.867 1.597 2.189 1.782 3.414

Hillsborough LOW 0.218 0.202 6.401 0.143 0.116 0.176 0.112 0.282

AVERAGE 2.705 2.350 8.131 0.738 0.606 0.898 0.817 1.537

HIGH 5.967 6.175 14.589 2.253 1.800 2.289 2.147 3.784

Holmes LOW 0.080 0.087 2.231 0.051 0.069 0.358 0.267

AVERAGE 0.818 0.828 2.383 0.244 0.194 0.358 0.375

HIGH 1.225 1.147 3.177 0.317 0.223 0.358 0.482

Indian River LOW 0.522 0.454 10.235 0.498 0.379 0.624 0.325 0.862

AVERAGE 4.815 2.783 12.225 2.202 1.425 2.999 2.323 3.982

HIGH 8.698 8.154 21.887 4.136 2.931 4.892 3.496 6.743

Jackson LOW 0.082 0.087 2.269 0.050 0.056 0.316 0.267 0.113

AVERAGE 0.847 0.889 2.497 0.256 0.219 0.316 0.286 0.424

HIGH 1.312 1.170 3.461 0.344 0.278 0.316 0.314 0.573

Jefferson LOW 0.065 0.060 1.356 0.038 0.047 0.135

AVERAGE 0.453 0.453 1.499 0.160 0.129 0.292

HIGH 0.774 0.719 2.177 0.225 0.176 0.354

Lafayette LOW 0.107 0.106 2.103 0.113 0.084

AVERAGE 0.821 0.861 2.661 0.268 0.200

HIGH 1.123 1.046 3.143 0.316 0.259

Lake LOW 0.181 0.159 3.978 0.125 0.094 0.219 0.138 0.362

AVERAGE 2.068 1.594 7.253 0.626 0.507 1.162 0.888 1.418

HIGH 4.360 4.342 10.829 1.210 1.004 1.377 1.157 2.394

Lee LOW 0.462 0.424 11.757 0.390 0.303 0.467 0.201 0.368

AVERAGE 5.151 3.106 14.053 2.022 1.110 3.134 1.679 3.021

HIGH 13.379 12.619 26.948 9.097 6.251 10.692 7.401 13.656

Leon LOW 0.049 0.047 1.532 0.036 0.028 0.043 0.024 0.043

AVERAGE 0.526 0.535 2.053 0.145 0.121 0.131 0.124 0.290

HIGH 1.133 1.052 3.064 0.310 0.291 0.298 0.219 0.485

Levy LOW 0.171 0.172 3.087 0.121 0.098 0.147 0.410 0.584

AVERAGE 1.745 1.475 4.524 0.621 0.450 1.326 0.976 1.618

HIGH 4.652 4.366 9.963 1.725 0.847 2.034 1.541 2.863

Liberty LOW 0.097 0.085 2.182 0.058 0.240

AVERAGE 0.849 0.864 2.501 0.260 0.245

HIGH 1.148 1.018 3.146 0.299 0.247

Madison LOW 0.053 0.049 1.257 0.029 0.038 0.339

AVERAGE 0.547 0.539 1.613 0.163 0.143 0.343

HIGH 0.811 0.754 2.383 0.208 0.175 0.391



257

County Loss Costs

Frame Owners

Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit


Manatee LOW 0.358 0.329 9.171 0.278 0.224 0.339 0.171 0.351

AVERAGE 4.082 2.893 10.861 1.496 1.016 2.459 1.686 2.996

HIGH 9.945 10.826 25.339 4.651 3.395 6.072 4.360 8.466

Marion LOW 0.137 0.116 3.264 0.093 0.063 0.119 0.101 0.193

AVERAGE 1.684 1.167 5.060 0.411 0.323 0.620 0.443 0.781

HIGH 3.571 3.379 9.576 1.065 0.608 0.855 0.735 1.521

Martin LOW 0.695 0.628 14.602 0.863 0.491 0.788 0.456 1.080

AVERAGE 7.107 4.802 17.595 3.061 1.896 4.470 3.234 5.068

HIGH 12.572 11.810 22.322 5.129 3.706 6.053 4.419 8.276

Miami-Dade LOW 0.557 0.523 17.726 0.672 0.323 0.795 0.352 0.589

AVERAGE 5.366 4.844 20.779 4.506 3.011 5.419 4.429 6.196

HIGH 12.705 13.196 54.710 18.041 12.009 20.916 14.121 24.772

Monroe LOW 1.339 1.298 26.031 3.489 1.628 2.773 1.651 2.582

AVERAGE 5.175 5.279 34.599 7.639 5.551 9.452 7.899 10.275

HIGH 10.607 9.858 44.352 12.265 9.275 15.554 10.794 17.664

Nassau LOW 0.054 0.048 1.470 0.024 0.021 0.031 0.071 0.062

AVERAGE 1.049 0.937 2.511 0.430 0.378 0.762 0.536 0.977

HIGH 2.524 2.372 5.891 0.789 0.684 1.037 0.745 1.572

Okaloosa LOW 0.113 0.103 2.430 0.069 0.055 0.114 0.175 0.193

AVERAGE 1.963 2.099 4.170 0.794 0.644 1.679 1.022 2.054

HIGH 5.057 4.748 10.629 1.996 1.513 2.338 1.781 3.142

Okeechobee LOW 0.595 0.542 12.853 0.528 0.413 0.899 0.505 1.439

AVERAGE 6.079 4.909 15.162 2.079 1.687 3.251 2.684 4.532

HIGH 9.245 8.751 19.532 3.043 2.346 3.643 2.828 4.732

Orange LOW 0.216 0.185 6.038 0.121 0.079 0.166 0.093 0.171

AVERAGE 2.150 1.955 7.099 0.539 0.456 0.708 0.593 1.154

HIGH 5.768 5.465 12.700 1.914 1.557 1.314 1.879 2.239

Osceola LOW 0.220 0.185 5.397 0.130 0.110 0.160 0.086 0.169

AVERAGE 1.930 1.789 8.155 0.561 0.482 0.639 0.433 1.179

HIGH 4.789 4.526 11.769 1.490 1.093 1.622 1.325 2.738

Palm Beach LOW 0.439 0.420 13.848 0.529 0.317 0.644 0.245 0.658

AVERAGE 4.973 3.670 19.588 3.066 2.000 3.953 2.897 4.901

HIGH 12.507 11.746 34.766 7.495 5.244 8.823 6.231 11.751

Pasco LOW 0.274 0.252 7.388 0.188 0.103 0.230 0.169 0.378

AVERAGE 2.315 2.276 8.735 0.748 0.605 1.136 0.982 1.815

HIGH 4.890 4.655 11.961 1.292 1.087 1.558 1.316 2.553

Pinellas LOW 0.267 0.246 7.331 0.191 0.156 0.234 0.140 0.232

AVERAGE 4.035 3.704 10.001 1.275 1.047 1.905 1.678 2.708

HIGH 8.439 7.971 19.852 5.683 4.029 6.713 5.401 9.239



258

County Loss Costs

Frame Owners

Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit


Polk LOW 0.247 0.200 6.599 0.139 0.119 0.173 0.147 0.311

AVERAGE 2.764 2.197 8.345 0.813 0.648 0.957 0.866 1.597

HIGH 5.281 5.008 13.636 1.686 1.383 1.863 1.504 2.579

Putnam LOW 0.106 0.095 2.626 0.067 0.056 0.186 0.087 0.378

AVERAGE 1.313 1.265 3.810 0.364 0.318 0.570 0.393 0.866

HIGH 2.261 2.189 6.232 0.611 0.529 0.738 0.642 1.271

St. Johns LOW 0.094 0.086 2.594 0.068 0.054 0.083 0.066 0.152

AVERAGE 1.029 1.151 4.157 0.410 0.331 0.677 0.604 1.091

HIGH 3.174 2.974 7.290 1.033 0.798 1.225 0.955 1.849

St. Lucie LOW 0.513 0.466 11.431 0.460 0.358 0.628 0.411 0.806

AVERAGE 5.167 2.721 13.382 1.983 1.147 3.625 2.905 4.083

HIGH 8.875 8.327 29.373 7.028 4.664 8.211 5.536 10.303

Santa Rosa LOW 0.161 0.147 3.092 0.098 0.107 0.384 0.163 0.323

AVERAGE 2.047 2.200 5.637 1.050 0.896 3.270 1.358 2.845

HIGH 8.238 7.719 16.211 3.676 2.691 4.265 3.154 5.335

Sarasota LOW 0.335 0.307 8.707 0.256 0.189 0.313 0.161 0.338

AVERAGE 4.016 2.879 11.261 1.362 0.985 2.210 1.741 2.813

HIGH 8.343 7.896 27.723 5.418 3.854 6.389 4.592 8.856

Seminole LOW 0.210 0.159 5.904 0.108 0.068 0.099 0.083 0.159

AVERAGE 2.220 1.895 6.404 0.512 0.439 0.693 0.580 1.196

HIGH 4.964 4.696 10.938 0.983 1.347 1.185 0.995 2.043

Sumter LOW 0.202 0.187 5.844 0.135 0.113 0.175 0.148 0.276

AVERAGE 0.737 0.955 6.887 0.448 0.441 0.889 0.458 0.761

HIGH 3.434 3.247 9.299 0.952 0.820 0.984 0.844 1.793

Suwannee LOW 0.085 0.064 2.048 0.043 0.059 0.278 0.278

AVERAGE 0.745 0.748 2.386 0.215 0.199 0.278 0.573

HIGH 1.316 1.359 3.679 0.370 0.313 0.278 0.748

Taylor LOW 0.065 0.088 1.369 0.061 0.065 0.101 0.141 0.288

AVERAGE 0.930 0.923 2.797 0.277 0.246 0.310 0.233 0.612

HIGH 1.743 1.625 4.524 0.622 0.309 0.733 0.569 0.755

Union LOW 0.080 0.068 1.914 0.081 0.037 0.055 0.046 0.469

AVERAGE 0.676 0.662 2.272 0.213 0.179 0.055 0.046 0.495

HIGH 1.126 1.050 3.213 0.243 0.207 0.055 0.046 0.533

Volusia LOW 0.177 0.163 4.199 0.117 0.092 0.163 0.117 0.229

AVERAGE 2.631 2.055 6.551 0.830 0.616 1.574 1.310 1.965

HIGH 8.108 7.623 16.017 2.713 2.035 4.312 2.417 4.447

Wakulla LOW 0.124 0.106 2.292 0.097 0.071 0.475 0.371 0.185

AVERAGE 0.888 1.035 2.873 0.336 0.360 0.862 1.111 0.706

HIGH 3.567 3.346 7.810 1.324 0.991 1.551 1.174 2.145



259

County Loss Costs

Frame Owners

Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit


Walton LOW 0.143 0.130 2.786 0.085 0.111 0.672 0.082 0.232

AVERAGE 1.942 1.687 4.668 0.989 0.771 2.039 1.065 2.157

HIGH 5.737 5.378 11.816 2.410 1.777 2.803 2.093 3.626

Washington LOW 0.099 0.092 2.260 0.095 0.156 0.140 0.195

AVERAGE 1.078 1.149 3.226 0.387 0.344 0.405 0.596

HIGH 1.688 1.580 4.504 0.563 0.442 0.670 0.715

Statewide LOW 0.049 0.047 1.257 0.024 0.021 0.031 0.024 0.043

AVERAGE 2.348 2.980 8.761 0.828 1.196 1.854 2.345 3.535

HIGH 14.240 13.369 54.710 18.041 12.009 20.916 14.121 24.772

Appendix A—FCHLPM Forms Form A-5: Percentage Change in Output Ranges


260

Form A-5: Percentage Change in Output Ranges

A. Provide summaries of the percentage change in average loss cost output range data compiled in Form A-4 relative to the equivalent data compiled from the previously accepted model in the format shown in the file named “2011FormA5.xlsx.”

For the change in output range exhibit, provide the summary by:

Statewide (overall percentage change),

By region, as defined in Figure 4 – North, Central and South,

by county, as defined in Figure 5 – Coastal and Inland.

B. Provide this Form in Excel format. The file name shall include the abbreviated name of the modeling organization, the Standards year, and the Form name. A hard copy of all tables in Form A-5 shall be included in a submission appendix.

C. Provide color-coded maps by county reflecting the percentage changes in the average loss costs with specified deductibles for frame owners, masonry owners, frame renters, masonry renters, frame condo unit owners, masonry condo unit owners, mobile home, and commercial residential from the output ranges from the previously accepted model.

Counties with a negative percentage change (reduction in loss costs) shall be indicated with

shades of blue; counties with a positive percentage change (increase in loss costs) shall be indicated with shades of red; and counties with no percentage change shall be white. The larger the percentage change in the county, the more intense the color-shade.

Figure 4 State of Florida by North/Central/South Regions

North

Central

South



261

Figure 5

State of Florida by Coastal/Inland Counties

The percentage change in the weighted average loss costs from the Output Ranges supplied last year

are shown in the file RMS11FormA5_20130311.xlsx at the link provided and appears below.

Inland

Coastal



262

Table 29: Percentage Change in $0 Deductible Output Ranges

Region Frame

Owners Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit


Coastal -1.06% -1.36% -1.78% -1.18% -1.28% -1.45% -1.32% -1.14%

Inland -1.14% -1.05% -1.83% -0.78% -0.89% -0.98% -1.09% -1.03%

North 0.94% 1.06% 0.83% 0.18% 0.71% 0.41% 0.56% 0.98%

Central -1.82% -2.05% -2.22% -1.97% -2.08% -2.02% -2.37% -2.14%

South -1.42% -0.86% -1.41% -1.19% -0.88% -1.50% -1.08% -0.92%

Statewide -1.09% -1.31% -1.80% -1.16% -1.24% -1.40% -1.30% -1.16%

Table 30: Percentage Change in Specified Deductible Output Ranges

Region Frame

Owners Masonry Owners

Mobile Homes

Frame Renters

Masonry Renters

Frame Condo Unit

Masonry Condo Unit


Coastal -1.12% -1.33% -1.77% -1.13% -1.24% -1.45% -1.25% -1.11%

Inland -1.22% -1.09% -1.84% -0.71% -0.86% -0.82% -0.97% -0.98%

North 0.87% 1.01% 0.81% 0.00% 0.56% 0.37% 0.51% 0.98%

Central -1.82% -2.07% -2.24% -1.87% -2.15% -1.96% -2.33% -2.21%

South -1.43% -0.84% -1.37% -1.16% -0.89% -1.49% -1.05% -0.89%

Statewide -1.14% -1.29% -1.79% -1.19% -1.16% -1.44% -1.26% -1.12%



263

Figure 70: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Frame Owners from the Output Ranges from the Previously Accepted Model



264

Figure 71: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Masonry Owners from the Output Ranges from the Previously Accepted Model



265

Figure 72: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Mobile Home from the Output Ranges from the Previously Accepted Model



266

Figure 73: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Frame Renters from the Output Ranges from the Previously Accepted Model



267

Figure 74: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified deductibles for Masonry Renters from the Output Ranges from the Previously Accepted Model



268

Figure 75: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Frame Condo Unit Owners from the Output Ranges from the Previously Accepted Model



269

Figure 76: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Masonry Condo Unit Owners from the Output Ranges from the Previously Accepted Model



270

Figure 77: Map by County Reflecting the Percentage Changes in the Average Loss Costs with Specified Deductibles for Commercial Residential from the Output Ranges from the Previously Accepted Model

Appendix A—FCHLPM Forms Form A-6: Logical Relationship to Risk (Trade Secret Item)


271

Form A-6: Logical Relationship to Risk (Trade Secret Item)

This form will be provided during the professional team on-site review as well as the closed meeting

portion of the commission meeting.

Appendix A—FCHLPM Forms Form A-7: Percentage Change in Logical Relationship to Risk


272

Form A-7: Percentage Change in Logical Relationship to Risk

A. Provide summaries of the percentage change in logical relationship to risk exhibits from the previously accepted model in the format shown in the file named “2011FormA7.xlsx.”

B. Create exposure sets for each exhibit by modeling all of the structures from the appropriate Notional Set listed below at each of the locations in “Location Grid B” as described in the file “NotionalInput11.xlsx.” Refer to the Notional Policy Specifications provided in Form A-6 for additional modeling information. Explain any assumptions, deviations, and differences from the prescribed exposure information.

Exhibit Notional Set Deductible Sensitivity Set 1 Construction Sensitivity Set 2 Policy Form Sensitivity Set 3 Coverage Sensitivity Set 4 Building Code/Enforcement (Year Built) Sensitivity Set 5 Building Strength Sensitivity Set 6 Condo Unit Floor Sensitivity Set 7 Number of Stories Sensitivity Set 8

Models shall treat points in Location Grid B as coordinates that would result from a geocoding process. Models shall treat points by simulating loss at exact location or by using the nearest modeled parcel/street/cell in the model.

Provide the results statewide (overall percentage change) and by the regions defined in Form A-5.

C. Provide this form in Excel format. The file name shall include the abbreviated name of the modeling

organization, the standards year, and the form name. A hard copy of all tables in Form A-7 shall be included in a submission appendix.

The results are provided in Excel format in the file RMS11FormA7_20130514.xlsx at the link provided

and appears below. The gross (non-zero deductible) loss costs have been calculated with the

assumption that an insurer will not elect to apply an all other perils deductible to subsequent hurricane

losses.



273

Table 31: Percent Change in Logical Relationship to Risk—Deductible

Construction / Policy

Region

Percent Change in Loss Cost

$0 $500 1% 2% 5% 10%

Frame Owners

Coastal -1.9% -2.0% -2.0% -2.0% -2.0% -2.1%

Inland -1.4% -1.4% -1.4% -1.3% -1.4% -1.4%

North 0.6% 0.6% 0.7% 0.6% 0.6% 0.3%

Central -2.1% -2.1% -2.1% -2.1% -2.1% -2.2%

South -2.0% -2.0% -2.0% -2.1% -2.1% -2.2%

Statewide -1.8% -1.9% -1.8% -1.9% -1.9% -2.0%

Masonry Owners

Coastal -2.0% -2.0% -2.0% -2.0% -2.1% -2.1%

Inland -1.3% -1.3% -1.3% -1.4% -1.3% -1.4%

North 0.7% 0.7% 0.7% 0.6% 0.6% 0.5%

Central -2.1% -2.2% -2.1% -2.1% -2.1% -2.1%

South -2.0% -2.0% -2.0% -2.1% -2.1% -2.2%

Statewide -1.9% -1.8% -1.9% -1.9% -1.9% -2.0%

Mobile Homes

Coastal -1.4% -1.4% -1.4% -1.4% -1.4% -1.4%

Inland -1.3% -1.3% -1.3% -1.3% -1.3% -1.4%

North 1.0% 1.0% 1.0% 0.9% 0.9% 0.9%

Central -2.1% -2.1% -2.1% -2.1% -2.1% -2.1%

South -1.2% -1.2% -1.2% -1.2% -1.2% -1.1%

Statewide -1.4% -1.4% -1.4% -1.4% -1.4% -1.4%

Frame Renters

Coastal -1.0% -1.0% -1.0% -1.0% -1.0% -1.1%

Inland -1.5% -1.3% -1.5% -1.3% -1.3% -1.3%

North 0.0% 0.0% 0.0% 0.0% 0.0% -0.4%

Central -2.2% -2.1% -2.2% -2.1% -2.2% -2.1%

South -0.7% -0.6% -0.7% -0.6% -0.7% -0.7%

Statewide -1.1% -1.1% -1.1% -1.1% -1.1% -1.1%

Masonry Renters

Coastal -1.1% -1.1% -1.1% -1.1% -1.1% -1.1%

Inland -1.3% -1.1% -1.3% -1.1% -1.3% -1.6%

North 0.3% 0.0% 0.3% 0.0% 0.4% 0.0%

Central -2.1% -2.2% -2.0% -2.2% -2.1% -2.3%

South -0.9% -0.8% -0.8% -0.8% -0.8% -0.8%

Statewide -1.2% -1.2% -1.2% -1.2% -1.1% -1.1%

Frame Condo Unit

Coastal -1.1% -1.1% -1.1% -1.0% -1.0% -1.1%

Inland -1.4% -1.4% -1.4% -1.5% -1.5% -1.5%

North 0.2% 0.0% 0.0% 0.0% 0.0% -0.3%

Central -2.2% -2.2% -2.2% -2.2% -2.1% -2.2%

South -0.7% -0.7% -0.7% -0.7% -0.7% -0.7%

Statewide -1.1% -1.1% -1.1% -1.1% -1.1% -1.0%



274


Region


$0 $500 1% 2% 5% 10%

Masonry Condo Unit

Coastal -1.2% -1.1% -1.1% -1.1% -1.0% -1.1%

Inland -1.3% -1.3% -1.3% -1.3% -1.3% -1.3%

North 0.3% 0.3% 0.3% 0.3% 0.0% 0.4%

Central -2.1% -2.1% -2.1% -2.0% -2.2% -2.0%

South -0.8% -0.8% -0.8% -0.8% -0.8% -0.8%

Statewide -1.2% -1.1% -1.1% -1.1% -1.1% -1.2%


Region


$0 2% 3% 5% 10%


Coastal -1.2% -1.2% -1.1% -1.1% -1.1%

Inland -1.3% -1.3% -1.4% -1.6% -1.5%

North 0.0% 0.5% 0.5% 0.0% 0.0%

Central -2.2% -2.2% -1.9% -2.1% -2.3%

South -0.9% -0.8% -0.8% -0.8% -0.8%

Statewide -1.2% -1.2% -1.2% -1.2% -1.1%



275

Table 32: Percent Change in Logical Relationship to Risk—Construction

Policy Region Percent Change in Loss Cost

Masonry Frame

Owners

Coastal -2.0% -1.9%

Inland -1.3% -1.4%

North 0.7% 0.6%

Central -2.1% -2.1%

South -2.0% -2.0%

Statewide -1.9% -1.8%

Renters

Coastal -1.1% -1.0%

Inland -1.3% -1.5%

North 0.3% 0.0%

Central -2.1% -2.2%

South -0.9% -0.7%


Condo Unit

Coastal -1.2% -1.1%

Inland -1.3% -1.4%

North 0.3% 0.2%

Central -2.1% -2.2%

South -0.8% -0.7%


Policy Region Percent Change in Loss Cost

Concrete


Coastal -1.1%

Inland -1.5%

North 0.0%

Central -2.3%

South -0.8%

Statewide -1.1%



276

Table 33: Percent Change in Logical Relationship to Risk—Policy Form

Region Percent Change in Loss Cost

Frame Owners Masonry Owners Mobile Homes

Coastal -1.9% -2.0% -1.4%

Inland -1.4% -1.3% -1.3%

North 0.6% 0.7% 1.0%

Central -2.1% -2.1% -2.1%

South -2.0% -2.0% -1.2%

Statewide -1.8% -1.9% -1.4%

Table 34: Percent Change in Logical Relationship to Risk—Coverage


Region


Coverage A

Coverage B

Coverage C

Coverage D

Frame Owners

Coastal -1.8% -2.0% -2.7% -2.4%

Inland -1.4% -1.3% -1.2% 0.0%

North 0.8% 0.0% 0.0% 0.0%

Central -2.1% -2.3% -2.3% -2.0%

South -1.8% -1.7% -3.2% -2.4%

Statewide -1.7% -2.0% -2.7% -2.9%

Masonry Owners

Coastal -1.8% -1.8% -2.9% -1.3%

Inland -1.4% -1.4% -1.3% 0.0%

North 0.8% 0.0% 0.0% 0.0%

Central -2.1% -2.0% -2.5% -2.2%

South -1.8% -1.8% -3.4% -1.8%

Statewide -1.8% -1.7% -2.9% -3.2%

Mobile Homes

Coastal -1.4% -1.4% -1.4% -1.3%

Inland -1.3% -1.4% -1.5% -0.7%

North 1.0% 0.9% 0.7% 1.0%

Central -2.1% -2.0% -2.2% -1.7%

South -1.3% -1.3% -1.1% -1.3%

Statewide -1.4% -1.4% -1.4% -1.6%

Frame Renters

Coastal -1.0% -1.5%

Inland -1.3% -1.4%

North 0.0% 0.0%

Central -2.1% -1.7%

South -0.6% -1.2%




277


Region


Coverage A

Coverage B

Coverage C

Coverage D

Masonry Renters

Coastal -1.1% -1.7%

Inland -1.5% -1.8%

North 0.3% 0.0%

Central -2.1% -2.2%

South -0.8% -1.4%


Frame Condo Unit

Coastal -1.0% -1.0% -1.8%

Inland -1.0% -1.3% -1.4%

North 0.0% 0.0% 0.0%

Central -1.8% -2.1% -1.6%

South -0.8% -0.6% -1.3%

Statewide -1.2% -1.0% -1.4%

Masonry Condo Unit

Coastal -0.8% -1.1% -1.6%

Inland -1.1% -1.5% -1.8%

North 0.0% 0.3% 0.0%

Central -2.1% -2.1% -2.2%

South -0.5% -0.8% -1.3%

Statewide -1.0% -1.2% -1.4%


Coastal -1.2% 0.0% -2.7%

Inland -1.4% -12.5% 0.0%

North 0.4% 0.0% 0.0%

Central -2.1% 0.0% 0.0%

South -0.9% -2.7% 0.0%

Statewide -1.3% -5.3% 0.0%



278

Table 35: Percent Change in Logical Relationship to Risk—Building Code / Enforcement (Year Built) Sensitivity


Region


Year Built 1980

Year Built 1998

Year Built 2004

Frame Owners

Coastal -1.5% -1.6% -1.6%

Inland -1.3% -1.5% -1.4%

North 0.9% 0.4% 0.2%

Central -2.0% -2.2% -2.2%

South -1.3% -1.4% -1.5%

Statewide -1.4% -1.6% -1.6%

Masonry Owners

Coastal -1.5% -1.5% -1.6%

Inland -1.3% -1.4% -1.3%

North 1.0% 0.3% 0.0%

Central -2.0% -2.1% -2.2%

South -1.3% -1.3% -1.4%

Statewide -1.4% -1.5% -1.6%


Region


Year Built 1974

Year Built 1992

Year Built 2004

Mobile Homes

Coastal -1.1% -1.1% -1.1%

Inland -1.3% -1.3% -1.3%

North 1.1% 1.1% 0.9%

Central -2.0% -2.0% -2.1%

South -0.8% -0.7% -0.7%

Statewide -1.1% -1.1% -1.1%


Region


Year Built 1980

Year Built 1998

Year Built 2004

Frame Renters

Coastal -0.9% -1.2% -1.2%

Inland -1.4% -1.5% -1.5%

North 0.5% -0.3% -0.5%

Central -2.2% -2.1% -2.1%

South -0.6% -0.8% -0.8%

Statewide -1.0% -1.2% -1.4%

Masonry Renters

Coastal -1.1% -1.3% -1.4%

Inland -1.4% -1.6% -1.2%

North 0.6% 0.0% 0.0%

Central -2.1% -2.2% -2.3%

South -0.7% -1.0% -1.1%

Statewide -1.1% -1.3% -1.5%



279


Region


Year Built 1980

Year Built 1998

Year Built 2004

Frame Condo Unit

Coastal -1.0% -1.2% -1.2%

Inland -1.5% -1.5% -1.7%

North 0.4% -0.3% -0.4%

Central -2.2% -2.2% -2.0%

South -0.6% -0.8% -0.8%

Statewide -1.0% -1.3% -1.3%

Masonry Condo Unit

Coastal -1.1% -1.3% -1.4%

Inland -1.4% -1.6% -1.5%

North 0.6% 0.0% 0.0%

Central -2.1% -2.3% -2.2%

South -0.7% -0.9% -1.0%

Statewide -1.1% -1.4% -1.5%


Coastal -1.1% -1.3% -1.3%

Inland -1.3% -1.3% -1.3%

North 0.5% 0.0% -0.8%

Central -2.1% -2.2% -2.5%

South -0.8% -1.0% -1.1%

Statewide -1.2% -1.5% -1.4%



280

Table 36: Percent Change in Logical Relationship to Risk—Building Strength



Weak Medium Strong

Frame Owners

Coastal -1.1% -1.4% -1.7%

Inland -1.2% -1.4% -1.3%

North 1.3% 0.4% 0.0%

Central -1.9% -2.2% -2.3%

South -0.8% -1.2% -1.5%

Statewide -1.1% -1.4% -1.8%

Masonry Owners

Coastal -1.1% -1.4% -1.6%

Inland -1.2% -1.5% -1.5%

North 1.4% 0.3% 0.0%

Central -1.9% -2.1% -2.0%

South -0.8% -1.1% -1.6%

Statewide -1.1% -1.4% -1.8%

Mobile Homes

Coastal -1.1% -1.1% -1.1%

Inland -1.1% -1.3% -1.3%

North 1.3% 1.1% 0.9%

Central -1.9% -2.0% -2.1%

South -0.8% -0.7% -0.7%

Statewide -1.1% -1.1% -1.1%

Frame Renters

Coastal -1.0% -1.5% -1.5%

Inland -1.5% -1.3% -1.9%

North 0.3% -0.6% -1.4%

Central -2.2% -2.3% -2.1%

South -0.7% -1.3% -1.6%

Statewide -1.1% -1.4% -1.9%

Masonry Renters

Coastal -0.9% -1.3% -1.4%

Inland -1.4% -1.7% -2.3%

North 0.7% 0.0% 0.0%

Central -2.1% -2.1% -2.7%

South -0.5% -1.2% -1.4%

Statewide -1.0% -1.3% -1.8%

Frame Condo Unit

Coastal -0.9% -1.3% -1.7%

Inland -1.3% -1.7% -2.0%

North 0.8% -0.3% -0.6%

Central -2.1% -2.3% -2.1%

South -0.5% -1.1% -1.4%

Statewide -1.0% -1.4% -1.6%



281



Weak Medium Strong

Masonry Condo Unit

Coastal -0.9% -1.4% -1.4%

Inland -1.3% -1.5% -1.6%

North 1.0% 0.0% 0.0%

Central -2.1% -2.2% -2.0%

South -0.5% -1.0% -1.3%

Statewide -0.9% -1.2% -1.4%


Coastal -1.2% -1.3% -1.4%

Inland -1.4% -1.3% -1.0%

North 0.5% 0.0% 0.0%

Central -2.2% -2.2% -2.6%

South -0.8% -1.0% -1.1%

Statewide -1.2% -1.5% -1.3%

Table 37: Percent Change in Logical Relationship to Risk—Condo Unit Floor



3rd Floor 9th Floor 15th Floor 18th Floor

Condo Unit A

Coastal -1.3% -1.3% -1.3% -1.3%

Inland -1.4% -1.4% -1.4% -1.4%

North 0.0% 0.0% 0.0% 0.0%

Central -1.9% -1.9% -1.9% -1.9%

South -1.1% -1.1% -1.1% -1.1%

Statewide -1.3% -1.3% -1.3% -1.3%

Condo Unit B

Coastal -1.2% -1.2% -1.2% -1.2%

Inland -1.2% -1.2% -1.2% -1.2%

North 0.4% 0.4% 0.4% 0.4%

Central -2.2% -2.2% -2.2% -2.2%

South -0.9% -0.9% -0.9% -0.9%

Statewide -1.2% -1.2% -1.2% -1.2%



282

Table 38: Percent Change in Logical Relationship to Risk—Number of Stories



1 Story 2 Story

Frame Owners

Coastal -1.8% -1.8%

Inland -1.3% -1.4%

North 0.7% 0.6%

Central -2.1% -2.2%

South -1.8% -1.8%


Masonry Owners

Coastal -1.8% -1.8%

Inland -1.4% -1.4%

North 0.7% 0.6%

Central -2.1% -2.2%

South -1.8% -1.8%


Frame Renters

Coastal -1.3% -1.3%

Inland -1.5% -1.4%

North -0.4% 0.2%

Central -2.4% -2.2%

South -1.1% -1.0%


Masonry Renters

Coastal -1.3% -1.2%

Inland -1.4% -1.5%

North 0.0% 0.6%

Central -1.9% -2.1%

South -0.9% -0.9%




5 Story 10 Story 20 Story


Coastal -1.1% -1.2% -1.2%

Inland -1.4% -1.5% -1.3%

North 0.7% 0.6% 0.0%

Central -2.1% -2.1% -2.2%

South -0.8% -0.8% -0.9%

Statewide -1.2% -1.2% -1.2%

Appendix A—FCHLPM Forms Form A-8: Probable Maximum Loss for Florida


283

Form A-8: Probable Maximum Loss for Florida

A. Provide a detailed explanation of how the Expected Annual Hurricane Losses and Return Periods are calculated.

B. Complete Part A showing the personal and commercial residential probable maximum loss for Florida. For the Expected Annual Hurricane Losses column, provide personal and commercial residential, zero deductible statewide loss costs based on the 2007 Florida Hurricane Catastrophe Fund’s aggregate personal and commercial residential exposure data found in the file named “hlpm2007c.exe.”

In the column, Return Period (Years), provide the return period associated with the average loss within the ranges indicated on a cumulative basis.

For example, if the average loss is $4,705 million for the range $4,501 million to $5,000 million, provide the return period associated with a loss that is $4,705 million or greater.

For each loss range in millions ($1,001-$1,500, $1,501-$2,000, $2,001-$2,500) the average loss within that range should be identified and then the return period associated with that loss calculated. The return period is then the reciprocal of the probability of the loss equaling or exceeding this average loss size.

The probability of equaling or exceeding the average of each range should be smaller as the ranges increase (and the average losses within the ranges increase). Therefore, the return period associated with each range and average loss within that range should be larger as the ranges increase. Return periods shall be based on cumulative probabilities.

A return period for an average loss of $4,705 million within the $4,501-$5,000 million range should be lower than the return period for an average loss of $5,455 million associated with a $5,001- $6,000 million range.

C. Provide a graphical comparison of the current submission loss curve Residential Return Periods to the previously accepted submission Residential Return Periods loss curve. Residential Return Period (Years) shall be shown on the y-axis on a log 10 scale with Losses in Billions shown on the x-axis. The legend shall indicate the corresponding submission with a solid line representing the current year and a dotted line representing the previously accepted submission.

D. Provide the estimated loss and uncertainty interval for each of the Personal and Commercial Residential Return Periods given in Part B. Describe how the uncertainty intervals are derived.

E. Provide this Form in Excel format. The file name shall include the abbreviated name of the modeling organization, the Standards year, and the Form name. A hard copy of Form A-8 shall be included in a submission appendix.

To calculate the expected annual hurricane losses, the loss for each event in the range is multiplied by

its annual rate of occurrence, and the products are summed across the range. The return time is

calculated as the reciprocal of the exceedance probability of the average loss in each range. The return

time is rounded to the nearest year.

The 5%/95% uncertainty interval for each return period was derived from the beta distribution of the

event with the mean loss closest to the ―estimated loss level‖ for that return period.

The results of the calculations are shown in the file RMS11FormA8.xlsx at the link provided and appear

in the following tables.



284

Figure 78: Comparison of Current Submission Return Times to the Prior Year’s Submission Return Times

1

10

100

1,000

10,000

$0 $20 $40 $60 $80 $100 $120

Ret

urn

Tim

e (Y

ears

)

Average Loss (Billions)

2012 Submission

2010 Submission



285

Part A – Personal and Commercial Residential Probable Maximum Loss for Florida

Table 39: Distribution of Hurricanes by Size of Loss for the 2007 FHCF Combined Personal and Commercial Residential Aggregate Exposure Data

LOSS RANGE (MILLIONS)

TOTAL LOSS

AVERAGE LOSS

(MILLIONS)

NUMBER OF HURRICANES

EXPECTED ANNUAL

HURRICANE LOSSES*

RETURN PERIOD (YEARS)

$ - to $ 500 $ 745,589 $ 109 6,861 $ 56,712,183 2

$ 501 to $ 1,000 $ 907,941 $ 728 1,247 $ 66,037,331 3

$ 1,001 to $ 1,500 $ 993,665 $ 1,230 808 $ 64,484,337 3

$ 1,501 to $ 2,000 $ 1,079,295 $ 1,744 619 $ 67,912,853 4

$ 2,001 to $ 2,500 $ 1,337,326 $ 2,248 595 $ 65,421,117 4

$ 2,501 to $ 3,000 $ 1,499,409 $ 2,741 547 $ 50,980,422 4

$ 3,001 to $ 3,500 $ 1,670,586 $ 3,244 515 $ 71,964,386 5

$ 3,501 to $ 4,000 $ 1,598,327 $ 3,743 427 $ 43,137,899 5

$ 4,001 to $ 4,500 $ 1,829,796 $ 4,255 430 $ 56,412,754 5

$ 4,501 to $ 5,000 $ 1,617,647 $ 4,744 341 $ 38,038,417 6

$ 5,001 to $ 6,000 $ 3,653,742 $ 5,470 668 $ 102,398,486 6

$ 6,001 to $ 7,000 $ 3,605,476 $ 6,485 556 $ 123,239,462 7

$ 7,001 to $ 8,000 $ 3,648,318 $ 7,491 487 $ 142,437,689 7

$ 8,001 to $ 9,000 $ 4,058,737 $ 8,491 478 $ 170,718,535 8

$ 9,001 to $ 10,000 $ 3,670,572 $ 9,485 387 $ 99,404,831 8

$ 10,001 to $ 11,000 $ 3,625,798 $ 10,479 346 $ 113,354,219 9

$ 11,001 to $ 12,000 $ 3,323,109 $ 11,459 290 $ 107,962,230 10

$ 12,001 to $ 13,000 $ 3,037,733 $ 12,501 243 $ 93,387,838 11

$ 13,001 to $ 14,000 $ 2,710,131 $ 13,483 201 $ 63,885,396 11

$ 14,001 to $ 15,000 $ 2,694,777 $ 14,488 186 $ 48,566,785 12

$ 15,001 to $ 16,000 $ 3,412,351 $ 15,511 220 $ 115,792,213 13

$ 16,001 to $ 17,000 $ 2,786,102 $ 16,486 169 $ 52,859,563 14

$ 17,001 to $ 18,000 $ 3,147,399 $ 17,486 180 $ 88,658,730 15

$ 18,001 to $ 19,000 $ 2,610,223 $ 18,512 141 $ 93,873,982 16

$ 19,001 to $ 20,000 $ 2,593,570 $ 19,501 133 $ 71,017,498 17

$ 20,001 to $ 21,000 $ 2,829,484 $ 20,504 138 $ 86,339,656 18

$ 21,001 to $ 22,000 $ 2,791,568 $ 21,474 130 $ 59,324,341 19

$ 22,001 to $ 23,000 $ 3,217,766 $ 22,502 143 $ 86,112,628 20

$ 23,001 to $ 24,000 $ 3,051,522 $ 23,473 130 $ 55,514,393 21

$ 24,001 to $ 25,000 $ 2,649,012 $ 24,528 108 $ 45,270,121 23

$ 25,001 to $ 26,000 $ 2,675,020 $ 25,476 105 $ 60,703,176 24

$ 26,001 to $ 27,000 $ 2,601,881 $ 26,550 98 $ 47,485,828 25

$ 27,001 to $ 28,000 $ 2,942,828 $ 27,503 107 $ 52,438,077 27

$ 28,001 to $ 29,000 $ 2,622,679 $ 28,507 92 $ 54,719,103 28

$ 29,001 to $ 30,000 $ 2,210,710 $ 29,476 75 $ 36,710,890 30

$ 30,001 to $ 35,000 $12,863,157 $ 32,483 396 $ 243,949,774 34

$ 35,001 to $ 40,000 $10,230,650 $ 37,475 273 $ 131,413,307 44

$ 40,001 to $ 45,000 $ 9,206,804 $ 42,428 217 $ 109,873,144 55

$ 45,001 to $ 50,000 $ 8,785,633 $ 47,490 185 $ 109,665,580 68

$ 50,001 to $ 55,000 $ 7,222,702 $ 52,720 137 $ 107,101,843 83

$ 55,001 to $ 60,000 $ 6,484,990 $ 57,389 113 $ 92,103,620 99

$ 60,001 to $ 65,000 $ 6,645,708 $ 62,695 106 $ 77,660,519 120

$ 65,001 to $ 70,000 $ 5,657,552 $ 67,352 84 $ 66,551,059 141

$ 70,001 to $ 75,000 $ 4,273,840 $ 72,438 59 $ 28,626,698 166

$ 75,001 to $ 80,000 $ 5,815,638 $ 77,542 75 $ 65,785,277 194

$ 80,001 to $ 90,000 $ 9,488,994 $ 84,723 112 $ 70,416,992 240

$ 90,001 to $ 100,000 $ 7,123,821 $ 94,984 75 $ 36,172,820 315

$ 100,001 to $ Maximum $59,323,687 $ 160,769 369 $ 329,792,351 1,214

Total $242,573,268 $ 11,890 20,402 $ 4,122,390,352 10

*Personal and commercial residential zero deductible statewide loss using 2007 FHCF personal and commercial residential exposure data—file name: hlpm2007c.exe.



286

Part B – Personal and Commercial Residential Probable Maximum Loss for Florida

Table 40: Estimated Loss for Each of the Return Periods Given for the 2007 FHCF Combined Personal and Commercial Residential Aggregate Exposure Data

Return Period (Years)

Estimated Loss Level Uncertainty Interval

Top Event 586,164,614,524 397,106,345,205 to 795,996,255,060

1,000 149,779,834,619 81,006,417,968 to 237,591,512,414

500 114,607,065,504 72,856,948,194 to 163,756,148,883

250 86,254,508,038 64,976,811,262 to 109,974,272,604

100 57,608,659,157 31,972,882,993 to 89,272,699,209

50 40,435,968,292 17,685,699,891 to 70,605,812,982

20 22,369,368,662 6,650,175,525 to 45,558,145,334

10 11,649,897,270 2,720,343,464 to 25,615,733,312

5 3,814,857,216 1,463,504,724 to 7,058,424,124

Appendix A—FCHLPM Forms Form S-1: Probability and Frequency of Florida Landfalling Hurricanes per Year


287

Form S-1: Probability and Frequency of Florida Landfalling Hurricanes per Year

Complete the table below showing the probability and modeled frequency of landfalling Florida hurricanes per year. Modeled probability shall be rounded to four decimal places. The historical probabilities and frequencies below have been derived from the Base Hurricane Storm Set as defined in Standard M-1.

If the data are partitioned or modified, provide the historical probabilities and frequencies for the applicable partition (and its complement) or modification as well as the modeled probabilities and frequencies in additional copies of Form S-1.

The table below provides the probability and frequency of landfalling Florida hurricanes per year, for

the period 1900 to 2011. One year with no landfalling Florida hurricanes has been added to the

historical frequencies to account for the 2011 season,

Table 41: Model Results—Probability and Frequency of Hurricanes per Year

Number Of Hurricanes

Per Year

Historical Probabilities

Modeled Probabilities

Historical Frequencies

Modeled Frequencies

0 0.5982 0.5824 67 65

1 0.2589 0.3148 29 35

2 0.1161 0.0851 13 10

3 0.0268 0.0153 3 2

4 0.0000 0.0021 0 0

5 0.0000 0.0002 0 0

6 0.0000 0.0000 0 0

7 0.0000 0.0000 0 0

8 0.0000 0.0000 0 0

9 0.0000 0.0000 0 0

10 or more 0.0000 0.0000 0 0

Appendix A—FCHLPM Forms Form S-2: Examples of Loss Exceedance Estimates


288

Form S-2: Examples of Loss Exceedance Estimates

Provide projections of the aggregate personal and commercial insured losses for various probability levels using the notional risk data set specified in Form A-1 and using the 2007 Florida Hurricane Catastrophe Fund aggregate personal and commercial residential exposure data set provided in the file named “hlpm2007c.exe.” Provide the total average annual loss for the loss exceedance distribution. If the modeling methodology does not allow the model to produce a viable answer, please state so and why.

Part A

Table 42: Examples of Loss Exceedance Estimates

Return Period (years)

Probability of Exceedance

Estimated Loss Notional Risk

Data Set

Estimated Personal and Commercial Residential

Loss FHCF Data Set

Top Event N/A 229,021,381 571,736,230,743

10,000 0.01% 116,953,300 291,995,929,643

5,000 0.02% 98,794,262 242,864,265,958

2,000 0.05% 76,430,085 182,104,407,186

1,000 0.10% 61,164,605 141,400,695,415

500 0.20% 48,303,481 107,146,231,760

250 0.40% 37,689,273 79,774,278,903

100 1.00% 26,327,830 52,425,463,017

50 2.00% 19,108,804 36,265,635,705

20 5.00% 11,098,118 19,463,257,751

10 10.00% 6,147,631 9,721,640,237

5 20.00% 2,240,850 2,933,427,138

Part B

Table 43: Average Annual Loss for Loss Exceedance Distribution

Mean (Total Average Annual Loss) 2,049,911 3,566,865,165

Median 26,259 15,345,280

Standard Deviation 5,727,878 12,013,705,467

Interquartile Range 1,322,694 1,509,210,870

Sample Size

100,000 Years of Simulated Events

100,000 Years of Simulated Events

Appendix A—FCHLPM Forms Form S-3: Distributions of Stochastic Hurricane Parameters


289

Form S-3: Distributions of Stochastic Hurricane Parameters

Provide the probability distribution functional form used for each stochastic hurricane parameter in the model. Provide a summary of the rationale for each functional form selected for each general classification.

Table 44: Distributions of Hurricane Parameters

Stochastic Hurricane Parameter (Function

or Variable)

Functional Form of Distribution

Data Source Year

Range Used

Justification for Functional Form

Storm Frequency Poisson HURDAT 1900-2011 The Poisson assumption is supported by historical data.

Central Pressure at Landfall

Smoothed empirical distribution by landfall region

HURDAT 1900-2008 The distribution of central pressure at landfall is calibrated to match historical data.

Inland Filling Rate Gaussian, with mean that depends on intensity, size, and proportion of the storm over different types of terrain.

HURDAT

NHC Reports

Numerical simulations

1988-2008 The distribution of the filling rate is in good agreement with historical data. The methods used to estimate, select and validate the model are described in Colette et al. (2010)

Vmax Log-normal with mean that depends on central pressure, far field pressure, and latitude.

HURDAT 1900-2011 The dependence of Vmax on central pressure, far field pressure and latitude is documented in scientific literature (e.g., Knaff and Zher 2007).

The form of the relationship has been chosen to match historical data.

Vmax is further calibrated at landfall to ensure the historical distribution is reproduced well.

Translational Speed and Heading

Translational speed and heading follow empirical distributions that derive from the modeling of zonal and meridional track steps.

HURDAT 1950-2007 The model for the zonal and meridional track steps is based on Hall and Jewson (2007). The resulting distribution of translational speed and heading agree with the historical data.

Appendix A—FCHLPM Forms Form S-3: Distributions of Stochastic Hurricane Parameters


290

Stochastic Hurricane Parameter (Function

or Variable)

Functional Form of Distribution

Data Source Year

Range Used

Justification for Functional Form

Rmax Truncated log-normal.

The mean depends on pressure and latitude.

Extended Best Track

1988-2008 The distribution is fitted to historical data.

Truncation is necessary to avoid unrealistic values of Rmax in simulations, especially when extrapolating beyond the range of observed data.


Shape parameters X1 and N: gamma distribution

Angle to maximum winds (Amax): truncated Gaussian

H*Wind 1998-2008 The distributions are chosen to match historical data.

Truncation of Amax ensures that the simulated

values are between 0 and 2.

Appendix A—FCHLPM Forms Form S-4: Validation Comparisons


291

Form S-4: Validation Comparisons

A. Provide five validation comparisons of actual personal residential exposures and loss to modeled exposures and loss. These comparisons must be provided by line of insurance, construction type, policy coverage, county or other level of similar detail in addition to total losses. Include loss as a percent of total exposure. Total exposure represents the total amount of insured values (all coverages combined) in the area affected by the hurricane. This would include exposures for policies that did not have a loss. If this is not available, use exposures for only those policies that had a loss. Specify which was used. Also, specify the name of the hurricane event compared.

B. Provide a validation comparison of actual commercial residential exposures and loss to modeled exposures and loss. Use and provide a definition of the model’s relevant commercial residential classifications.

C. Provide scatter plot(s) of modeled vs. historical losses for each of the required validation comparisons. (Plot the historical losses on the x-axis and the modeled losses on the y-axis.)

Rather than using directly a specific published hurricane windfield, the winds underlying the modeled loss cost calculations must be produced by the model being evaluated and should be the same hurricane parameters as used in completing Form A-2.



292

Example A1: Comparison of a Company’s Personal Residential Modeled and Actual Loss as a Percent of Total Exposure

Hurricane = Charley (2004)

Exposure = Manufactured Homes—Total exposure (modeled and actual losses include demand

surge)

Table 45: Example A1 Portfolio Comparison of Modeled and Actual Loss

Company Actual Modeled

Construction Loss / Exposure Loss / Exposure Difference

Manufactured Home 6.25% 5.89% 0.36%

Figure 79: Example A1 Comparison of Modeled and Actual Losses by ZIP Code

100

1,000

10,000

100,000

1,000,000

10,000,000

100,000,000

100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000

Mo

de

led

Lo

ss

Actual Loss



293



Exposure = Single Family Residential—Total exposure (modeled and actual losses include demand

surge)




Wood Frame 1.73% 1.41% 0.33%

Masonry 2.77% 2.40% 0.37%

Total 2.59% 2.23% 0.36%


100

1,000

10,000

100,000

1,000,000

10,000,000

100 1,000 10,000 100,000 1,000,000 10,000,000

Mo

de

led

Lo

ss

Actual Loss

Wood

Masonry



294




surge)




Wood Frame 1.64% 1.20% 0.44%

Masonry 1.66% 1.57% 0.08%

Total 1.65% 1.52% 0.13%


100

1,000

10,000

100,000

1,000,000

10,000,000

100,000,000

100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000

Mo

de

led

Lo

ss

Actual Loss

Wood

Masonry



295


Hurricane = Andrew (1992)

Exposure = Total exposure (modeled and actual losses include demand surge)



Coverage Loss / Exposure Loss / Exposure Difference

A & B 6.91% 7.07% -0.16%

C 4.46% 3.08% 1.38%

D 3.40% 1.96% 1.45%

Total 5.72% 5.19% 0.52%


100

1,000

10,000

100,000

1,000,000

10,000,000

100,000,000

1,000,000,000

100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000 1,000,000,000

Mo

de

led

Lo

ss

Actual Loss

A&B

C

D



296


Hurricane = Wilma (2005)


surge)




Wood 0.70% 0.79% -0.08%

Masonry 0.98% 0.95% 0.04%

Total 0.97% 0.94% 0.03%


100

1,000

10,000

100,000

1,000,000

10,000,000

100,000,000

100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000

Mo

de

led

Lo

ss

Actual Loss

Wood

Masonry



297

Example B1: Comparison of a Company’s Commercial Residential Modeled and Actual Loss as a Percent of Total Exposure

Hurricane = Wilma (2005)

Exposure = Condominium—Total exposure (modeled and actual losses include demand surge)

Table 50: Example B1 Portfolio Comparison of Modeled and Actual Loss


Line of Business Loss / Exposure Loss / Exposure Difference

Condo Unit Owner 0.57% 0.45% 0.12%

Condo Association 1.13% 2.02% -0.89%

Total 1.02% 1.71% -0.69%

Figure 84: Example B1 Comparison of Modeled and Actual Losses by ZIP Code

100

1,000

10,000

100,000

1,000,000

10,000,000

100,000,000

100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000

Mo

de

led

Lo

ss

Actual Loss

Condo Owners

Condo Association

Appendix A—FCHLPM Forms Form S-5: Average Annual Zero Deductible Statewide Loss Costs – Historical versus Modeled


298

Form S-5: Average Annual Zero Deductible Statewide Loss Costs – Historical versus Modeled

A. Provide the average annual zero deductible statewide personal and commercial residential loss costs produced using the list of hurricanes in the Base Hurricane Storm Set as defined in Standard M-1 based on the 2007 Florida Hurricane Catastrophe Fund’s aggregate personal and commercial residential exposure data found in the file named “hlpm2007c.exe.”

Average Annual Zero Deductible Statewide Personal and Commercial Residential Loss Costs

Table 51: Average Annual Zero Deductible Statewide Personal and Commercial Residential Loss Costs

Time Period Historical Hurricanes Produced by Model

Current Submission $3.08 billion $4.12 billion

Previously Accepted Submission

$3.20 billion $4.17 billion

Percentage Change Current Submission/Previously Accepted Submission

-3.80% -1.21%

Second Previously Accepted Submission

N/A N/A

Percentage Change Current Submission/Second Previously Accepted Submission

N/A N/A

B. Provide a comparison with the statewide personal and commercial residential loss costs produced by the model on an average industry basis.

C. Provide the 95% confidence interval on the differences between the mean of the historical and modeled personal and commercial residential loss.

The RMS Hurricane model calculated historical annual average zero deductible loss for the 2007

Florida Hurricane Catastrophe Funds’ (FHCF) personal and commercial residential aggregate exposure

database is $3.08 billion per year. The RMS Hurricane model simulated annual average zero

deductible loss for the same exposure database is $4.12 billion per year. The 95% confidence interval

on the difference between the mean of the historical and the modeled loss is -$2.8 billion to +$720

million.

D. If the data are partitioned or modified, provide the average annual zero deductible statewide personal and commercial residential loss costs for the applicable partition (and its complement) or modification as well as the modeled average annual zero deductible statewide personal and commercial residential loss costs in additional copies of Form S-5.

The data has not been partitioned or modified.

Appendix A—FCHLPM Forms Form S-6: Hypothetical Events for Sensitivity and Uncertainty Analysis


299

Form S-6: Hypothetical Events for Sensitivity and Uncertainty Analysis

This form was provided and found acceptable in the previous model submission. The form will not be

provided in the current submission unless requested as outlined in Disclosure S-2.4.

.

Appendix A—FCHLPM Forms


300

Appendix B—RMS Technical Staff


301

APPENDIX B—RMS TECHNICAL STAFF

Shobana Azariah, Manager, Director, QA Engineering

Ms. Azariah joined RMS in March 2002, taking a position in the Quality Assurance department. She is currently

the manager of the RiskLink software quality assurance group. She graduated from University Of Madras, India

with MA in Public Administration and spent an additional two years doing research work at the University of

Madras in Tamil Nadu, India.

Hurricane Project Responsibilities: Manages the quality assurance group that tests the RiskLink user

interface.

Ramani Balijepalli, Senior QA Engineer

Ramani received a MCA (Masters in Computer Application) from Andhra University, India. Before joining RMS

she worked for a consulting firm in India where she was involved in testing software related projects. For R MS,

Ramani is involved in regression testing of DLM Analysis for various country peril models on supported

environments.

Hurricane Project Responsibilities: Regression testing of DLM analysis for various country peril models.

Victoria Babina, Principal Software Engineer

Mrs. Babina has a BS in Computer Science and BS in Psychology from Moscow State University. She has over

10 years of extensive experience in software release management and installer development for Windows-

based applications. Mrs. Babina joined RMS in June 2008. She is responsible for the software release cycle,

build/release automation and installer development.

Hurricane Project Responsibilities: Release management, design and implementation of software and data

installer changes related to the hurricane model.

Enrica Bellone, PhD, Principal Modeler

Dr. Bellone is responsible for researching and implementing advanced modeling techniques. Prior to joining

RMS, she conducted postdoctoral research in statistics as applied to the atmospheric sc iences, first at the

National Center for Atmospheric Research in Boulder, Colorado, and then at University College London. Dr.

Bellone received a PhD in Statistics from the University of Washington.

Hurricane Project Responsibilities: (1) Development of the stochastic tracks set; (2) Review of the model

from a statistical point of view.

Aman Bhardwaj, Director, SW Engineering

Mr. Bhardwaj has a BS in General Science from CCS University - Meerut, India and an MS degree in Computer

Applications from the Institute of Management & Technology, India. Mr. Bhardwaj joined RMS in 2000 and has

been involved with designing and developing software for RiskLink, RiskBrowser, and RiskSearch products. For



302

RiskLink, he is responsible for implementation of geotechnical hazard lookup components and libraries, as well

as general technical and project leadership.

Hurricane Project Responsibilities: Maintenance and upgrades to the core hazard libraries and components,

as well as general technical and project leadership.

Suman Kumar Bhattacharya, Manager, Software Engineering

Suman has a Diploma in Electrical Engineering from RK Mission Shilpamandira, Kolkata, India and has worked

for many well-known software technology companies for more than 12 years. For RMS, Suman works on

RiskLink performance, unit tests for improving code quality, TFS administration, and various tools and

technology for the application development team. Suman’s experience includes user interface, business

components, and database programming.

Hurricane Project Responsibilities: RiskLink performance, unit tests for improving code quality, TFS

administration, and various tools and technology for the application development team.

Auguste Boissonnade, PhD, Vice President, Probabilistic Modeling

Dr. Boissonnade was the original architect of the RMS hurricane catastrophe models and has over 20 years of

professional experience in structural analysis and design, natural hazard modeling, and risk assessment of

natural hazards in the U.S., Europe, Africa, and Asia. His expertise includes developing risk assessment models

for natural hazards (earthquakes, extreme winds, floods and other weather phenomena) for applications in risk

assessment of critical facilities and insurance exposures. Dr. Boissonnade has a BS degree from Ecole

Superieure des Travaux Publics (France) and a PhD from Stanford University where he has been a Consulting

Professor. While at Stanford, Dr. Boissonnade performed research on damage estimation with application to the

insurance industry. Prior to joining RMS, Auguste was a project leader at Lawrence Livermore National

Laboratory with responsibilities for developing probabilistic seismic hazard guidelines for the U.S. Nuclear

Regulatory Commission and guidelines on natural phenomena hazards for the Department of Energy. He is a

member of several organizations including the American Meteorological Society and the American Society of

Civil Engineers and a reviewer for the National Science Foundation. Dr. Boissonnade has authored more than

50 publications, including one book.

Hurricane Project Responsibilities: Loss amplification modeling, and advisor on science and technical issues.

Jim Bull, Senior QA Engineer

Mr. Bull has a BS in Mechanical Engineering and MS in Computer Science from Washington Univers ity in St.

Louis. He has 18 years experience in software quality assurance, and 13 years software development before

that. The last 15 have been with RMS, currently focusing in the geospatial area.

Hurricane Project Responsibilities: Responsible for software testing and quality assurance for geocoding and

site hazard lookup functionality.



303

Stan Buyanov, PhD, Principal Software Engineer

Stan has over 25 years of experience in advanced Software Engineering including web, multi -tier, and client-

server technology. Currently Stan is developing tools for RiskLink and RiskBrowser to assist with test

environment and test results analysis.

Hurricane Project Responsibilities: Internal tools to assist with test environment and test results analysis.

Jordan Byk, Director, Product Management

Jordan is responsible for the planning, acquisition, documentation, marketing and high level support for RMS

geocoding software and data. He joined RMS in 2006 to manage the RMS Weather Risk business, taking the

role managing Geocoding in 2008. Before joining RMS, Jordan worked with several large telecom and computer

firms and several start-up companies managing infrastructure and leading edge technology product lines. He is

a graduate of Carnegie Mellon with a BS in Computer Science and Administrative Management Science, and of

Rutgers University with an MBA in Marketing and Finance.

Hurricane Project Responsibilities: Functional specification, data acquisition, documentation, and got to

market responsibilities for geocoding software and data.

Chris Campbell, Lead Geospatial Modeler

Chris has a Diploma in Geography from the University of Texas San Antonio, San Antonio, TX and has worked

in the Geospatial Industry for over 16 years. For RMS, Chris works on the Geospatial Data Applications team

responsible for Geocoding and Mapping for RMS products.

Hurricane Project Responsibilities: Updating geocoding capabilities for all hurricane states.

David Carttar, Senior Director, Geospatial Modeling

Mr. Carttar has BA degrees in Geography and Architectural Studies from the University of Kansas, and a

Master of City Planning degree from the University of California at Berkeley. For RMS, Mr. Carttar coordinates

geocoding and mapping applications for the company's core technology. Mr. Carttar's exper ience revolves

around the application of geographic modeling at a variety of technical levels.


Monisha Chahal, Manager (GIS)

Ms. Chahal received a Bachelor’s degree in Architecture and a Master’s in Computer Programming from IBM

Education, New Delhi. She has over 10 years of experience in geospatial data development, including five

years focused specifically on data design and development. She leads the development of the base map for the

RMS Global Location Module, and has contributed to ZIP-Code data updates in the U.S.




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Steven Chau, Manager Quality Assurance

Steven obtained his Bachelor degrees in Management Information Science and in Finance, minor in Computer

Science from University of Iowa. He has been at RMS for 10 years, in the Quality Assurance Department

working on RiskLink and RiskBrowser testing-related projects. Steven’s main responsibilities are functional and

automation testing.

Hurricane Project Responsibilities: QA testing of RiskLink software components.

Deval Chauhan, Principal Software Engineer

Mr. Chauhan has an MS degree in Computer Science from Illinois Institute of Technology, Chicago. He has

worked as Software Engineer/Developer for more than three years with specialization in GIS technology. Mr.

Chauhan has been with RMS over a year and has worked with the mapping component.

Hurricane Project Responsibilities: Worked on logo and mapping areas of RiskLink.

Han Chen, PhD, Principal Architect

Dr. Chen has an MS in Computer Science from California State University at Hayward and a PhD in Geophysics

from the Institute of Geophysics at SSB in China. For RMS, Dr. Chen has worked in the Research and

Development Division and is primarily responsible for the detailed design and implementation of enhancements

to the RiskLink Detail Loss Model software.

Hurricane Project Responsibilities: Detailed design and implementation of enhancements to the RiskLink

Detail Loss Model software, with an emphasis on optimization.

Tommy Chou, Manager Quality Assurance

Tommy joined RMS in February of 2007. He received a BA in Developmental Studies of Industrial Societies

from the University of California at Berkeley.

Hurricane Project Responsibilities: His primary responsibilities include: certify RMS Catastrophe models on

EGC/HPC and RDP platform. Apply QA principles and methodology to develop test cases and detail test

scenarios for High Performance Computing solutions for RMS core next generation technology; run stress

testing on the HPC solutions stack and determine the impact on performance during stress testing; maintain

and set up testing environments with several hundred compute-node clusters; assist in on-going process

improvement efforts to ensure test planning, execution and reporting is effective and efficient; and analyze and

certify the catastrophe DLM loss and geocoding and hazard lookup results on RMS EGC and RDP platfo rms.

Kay Cleary, Director, Regulatory Practice

Ms. Cleary joined RMS in October of 2006. She has over 25 years experience in Property/Casualty insurance

with a focus on personal property lines catastrophe risk. She has worked in both the public and private sectors,

with stints at Florida’s Office of Insurance Regulation and Florida Citizens Property Insurance Corporation. She

spent 10 years with Allstate at their Research and Planning Center and several years with Aon Re Services.



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Ms. Cleary is an ex-Chair of the American Academy of Actuaries’ Property/Casualty Risk-Based Capital

Committee, was on the Academy Task Force authoring Actuarial Standard of Practice #38 and co-authored

―Reserving for Catastrophes,‖ summarizing a proposal for pre-event tax-deferred catastrophe reserves in the

Fall 2002 Forum. She served on the Florida Commission on Hurricane Loss Projection Methodology 2001-2002.

Ms. Cleary is a Fellow of the Casualty Actuarial Society, a Member of the American Academy of Actuaries and

has a Bachelor of Arts from Northwestern University.

Hurricane Project Responsibilities: Review of model from an actuarial viewpoint and lead contact for RMS

with the Florida Commission on Hurricane Loss Projection Methodologies.

Karishma Dambe, Manager, Software Engineering

Mrs. Dambe has a Bachelor’s Degree in Computer Engineering from Pune University, India and has worked for

software technology companies. For RMS, Mrs. Dambe works on various software components of the RiskLink

product. Her experience includes multithreading, process synchronization and database design.

Hurricane Project Responsibilities: Detailed design and implementation of Detailed Loss Model software

components.

Ravisher Dhillon, Senior Software Engineer

Ravisher has been with RMS in the RMS Newark office for two years on the user interface development team.

His primary responsibilities include working on RiskLink 10.0 refactoring the hazard retrieval process for the

hurricane model, he also designed/implemented a configuration file as a substitute for UI when one was not

available.

Hurricane Project Responsibilities: Refactoring the hazard retrieval process software for the hurricane model.

Mark Dixon, PhD, Lead Modeler

Mark joined RMS in February 2010, in the London model development team. He has worked on the 2011

European Windstorm Model; the RMS tropical cyclone rain model; and is currently responsible for historical

reconstructions both for the RMS Japan Typhoon model upgrade, and the North Atlantic Hurricane model. Prior

to RMS, Mark was at the UK Met Office for 10 years, where he developed data assimilation methods for

numerical weather prediction models. Before this he performed post-doctoral research at the University of

Reading (UK) on extra-tropical cyclones. Mark has a PhD in physics, and has numerous peer-reviewed

publications in data assimilation, meteorology, and condensed-matter physics.

Hurricane Project Responsibilities: Historical reconstructions

Michael Drayton, PhD, Consultant

Dr. Drayton holds a PhD in Applied Mathematics from the University of Cambridge and a first class honors

degree in Civil Engineering from New Zealand. Dr. Drayton is primarily involved in the research and

development of hazard models. Since joining the RMS London office in early 1996 he has worked on the

European windstorm model, the Atlantic hurricane models and the U.K. flood project. He has extensive



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experience of insurance-related hazard modeling and has also worked as a researcher investigating river

flooding and pollution dispersion in the environment. Currently, Dr. Drayton consults to RMS full-time.

Hurricane Project Responsibilities: Review of the hazard model.

Weimin Dong, PhD, Chief Risk Officer

Dr. Dong is a co-founder of RMS. He has over 30 years of industrial, teaching, and research experience

specializing in seismic hazard evaluation and insurance and financial risk assessment. He is the chief architect

of the RMS catastrophe models, and has overseen the company’s research and development efforts since its

inception. Dr. Dong is currently focusing his efforts on further developing the P&C RAROC methodologies,

including the RAROC ASP development and various optimization routines. Prior to founding RMS, Dr. Dong

served as the Director of Earthquake Research for the General Research Institute, Ministr y of Machine Building

in China. Dr. Dong received his PhD from Stanford University, and his Master of Engineering Mechanics from

Shanghai Jiao Tong University. During his career, he has published books, technical reports, and over 100

papers.

Hurricane Project Responsibilities: Advisor on science and technical issues.

Anjali Garg, Technical Specialist

Ms. Garg received her Master’s Degree in Computer Applications (MCA) from the Institute of Management

Studies, Uttar Pradesh, India. She has over 9 years of experience in software designing, development, and

management of software solutions.

Hurricane Project Responsibilities: Involved in software development, focusing on the international

geocoding component in RiskLink and product.

Garrett Girod, Senior Architect

Mr. Girod has a BS degree in Computer Science from Louisiana Tech University. Mr. Girod worked for six years

with a USGS scientist studying the effects of hurricanes on wetlands. Mr. Girod also worked two years for K2

Technologies in the development of Catalyst, a catastrophe loss modeling product. For RMS, Mr. Girod

develops software enhancements and fixes for various aspects of RiskLink.

Hurricane Project Responsibilities: Maintenance of database, analysis settings, and user-interface software

components.

Olga Goldin, QA Engineer

Olga has a Diploma in Power Engineering and in Economics from Azerbaijan University of Oil and Chemistry,

Baku, Azerbaijan. She has about 15 years of extensive experience in software quality assurance for Windows-

based applications. Olga joined RMS in April 1996 as a contractor and became full time employee in September

1996. She is responsible for the testing different aspects of RiskLink and RiskBrowser applications including

user interface, business components, functionalities and databases.

Hurricane Project Responsibilities: Testing of the software related to the hurricane model.



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David Glaubman, Principal Architect

Mr. Glaubman joined RMS in October 2004 as a lead software developer. His responsibilities have included

management of the team responsible for application infrastructure. Prior to joining RMS, he led development of

several financial software products for Barra, Inc. Mr. Glaubman graduated from Northeastern University in

Boston with a BS in Mathematics. He is a member of IEEE and the Association for Computing Machinery

(ACM).

Hurricane Project Responsibilities: Mr. Glaubman is involved in the design and implementation of software

libraries and components used by the loss model engine.

Nathalie Grima, Principal Software Engineer - Web Services

Ms. Grima joined RMS in November 2004 as a financial modeler. Her responsibilities include development and

quality assurance of new financial model related features. Prior to joining RMS, she was a mathematics

graduate student at San Jose State University. Ms. Grima is a graduate of the University of Paris IX Dauphine

with a degree in Mathematics.

Hurricane Project Responsibilities: Ms. Grima is involved in the design, documentation, and quality

assurance of the financial model.

Timothy Hall, PhD, Consultant

Dr. Hall is a Senior Scientist at the NASA Goddard Institute for Space Studies in New York, where he conducts

research in ocean and atmospheric science. He has worked in diverse areas, including upper atmosphere

dynamics, ocean transport and carbon cycle studies. Hall received a PhD in physics from Cornell University and

performed post-doctoral work in atmospheric science at Columbia University in New York and Monash

University in Melbourne, Australia.

Hurricane Project Responsibilities: He worked as a scientific consultant for RMS, helping to construct

components of the North Atlantic tropical cyclone model.

Atin Jain, Senior Software Engineer

Mr. Atin Jain has an MS degree in Physics with specialization in Electronics from Awadhesh Pratap Singh

University Rewa, India and has 7 years industry experience. For RMS, Mr. Atin Jain works on geocoding and

hazard software components of the RiskLink and RiskBrowser products.

Hurricane Project Responsibilities: Development and modification of spatial hazard software component,

design and implementation of upgrades to the geocoding software component.

Pratiksha Kadam, Senior Software Engineer

Ms. Kadam has completed her master of science from University of Central Florida, Orlando, F lorida and

Bachelor of Engineering from Mumbai, India. For RMS, Ms. Kadam has worked on different software

components which include designing new features and bug fixing for RiskLink.

Hurricane Project Responsibilities: Design, implementation and bug fixing for RiskLink software.



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Vidya Karthigeyan, Principal Software Engineer

Mrs. Karthigeyan has a Master of Science in Business Administration degree in Computer Information Systems

from California State University, East Bay and MS in Software Systems from Birla Institute of technology and

Science, Pilani, India. In the past she has worked for Geometric Software Solutions Co. Ltd. in India for 4 years.

At RMS, Mrs. Karthigeyan works on software components of RiskLink product.

Hurricane Project Responsibilities: Enhancements to the software components associated with data

generation for the Hurricane model.

Shree Khare, PhD, Director, Modeling

Shree has been working in the London model development group since 2006, and in that time has worked on a

variety of projects, including hurricane wind model development, uncertainty quantification, catastrophe

response, correlation calibration, and clustering in the North Atlantic hurricane and European windstorm

models. Currently, Shree is leading hazard development for the RMS Japan Typhoon Model upgrade. Prior to

joining RMS, Shree completed two postdoctoral research fellowships: one at the Statistical and Applied

Mathematical Sciences Institute (SAMSI) in Research Triangle Park, NC, and the second at the National Cen ter

for Atmospheric Research (NCAR) in Boulder, CO. Shree has a BS in honors physics from the University of

British Columbia, and a PhD from the atmospheric sciences program at Princeton University with specialization

in ensemble data assimilation. He has numerous peer reviewed journal publications on data assimilation and

risk modeling.

Hurricane Project Responsibilities: Wind model development and clustering.

Joseph Kim, Vice President, Software Engineering

Mr. Kim has a MS in Computer Science from University of Southern California, Los Angeles, Ca, and BS in

Information and Computer Science from UC Irvine, Irvine, CA. He has over 20 years as software engineering

architect and senior technical manager with a successful track record of developing products and building

technology infrastructure to support both start-up and fortune 500 firms. Mr. Kim joined RMS in September

2008. He is responsible for overseeing development of key RMS products, including RiskLink, RiskBrowser,

Data Quality Tool Kit, Risk Manager, and Treaty Manager. He is also responsible for overseeing key new

product development initiatives.

Hurricane Project Responsibilities: Software development management, design and implementation of

software related to the hurricane model.

Swaminathan Krishnamoorthy, Director, Software Engineering

Swami has an MS degree in Computer Applications from the University of Madras, India and has worked for

many well-known software technology companies for nearly 12 years. For RMS, he works on various software

components of the RiskLink product. Swami’s experience includes designing, developing software modules, and

technical lead for software projects.



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Hurricane Project Responsibilities: Detailed design and implementation of secondary modifiers for flood,

vulnerability by floor, vulnerability by height, and alternate BI down time; providing technical direction for hazard

and vulnerability software component development.

Veena Krishnamoorthy, Engineer, Model Certification

Veena received her Master’s of Science in Physics from Madurai Kamaraj University. She has been with RMS

in the analytics QA department for two years working on the financial model QA team.

Hurricane Project Responsibilities: Verified the results with baseline for EUWS, USHU analyses in the

RiskLink 10.0 release.

Punit Kumar, Senior Software Engineer

Punit received his Bachelor’s degree in Computer Science and Engineering from Karnataka University in India.

He has been with RMS for more than three years. His primary responsibilities include developing and designing

the framework for NAHU (WI and Surge). Punit was the key developer of the North Atlantic hurricane model.

Hurricane Project Responsibilities: Development of wind model software framework. Responsibility for

development of event generation, ground-up loss calculation, loss amplification, and hazard retrieval modules.

Tanmay Kumar, Senior Software Engineer

Tanmay received his Masters in Computer Applications from the Allahabad University in India. He has been

with RMS for two years. Tanmay’s primary responsibilities are maintaining and enhancing post analysis treat

edit functionality, performance tuning, and enhancing RDP and EGC platform software.

Hurricane Project Responsibilities: Enhancing and maintaining job distribution and job management

software, and software performance tuning.

Nereida Lark, Director, Program Management

Nereida has a Masters in Computer Information Systems from University of Phoenix. She has extensive

technical experience including software development, technical management and 10+ years in project

management. Nereida joined RMS in November of 2009 and works in the PMO organization.

Hurricane Project Responsibilities: Software Project Management

Thomas Loridan, PhD, Senior Catastrophe Risk Modeler

Thomas received a PhD in physical Geography from King’s College London for his research on boundary layer

modeling in urban environment. Thomas also holds an MSc in Weather, Climate and Modeling from the

University of Reading (UK) and an MSc in applied Mathematics from MATMECA, University of Bordeaux

(France). He joined RMS in December 2011 and has been applying the wind field model and site coefficients

from the hurricane model to the Pacific Basin.

Hurricane Project Responsibilities: Site coefficients, wind field model



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Siyuan (Terry) Liu, Senior Software Engineer

Ms. Liu has an MS in Computer Science from the University of Tennessee and an ME of Computer Science &

Engineering from Beijing University of Aeronautics and Astronautics.

After she graduated, Ms. Liu started working for RMS in 2006. She worked mainly on developing software for

business workflow and application logic modules in RiskBrowser, RiskLink, RiskTools and RiskOnline. Ms. Liu

also worked on database design, programming and maintenance. For example, she has worked on Reinsurance

Platform financial model design and implementation, RiskBrowser Account Access Restriction design and

implementation, RiskLink Windstorm Converter, and Event Rate Converter annual update.

Hurricane Project Responsibilities: Maintains EDM/ RDM database scripts including upgrade and downgrade,

and RiskLink Event Rate Converter annual update.

Sonja Liu, Senior Software QA Engineer

Sonja has a Masters of Computer Engineering from Santa Clara University and Masters of Business

Administration from Schiller International University. She has been working as software analyst and quality

assurance engineer in software technology companies for 10 years. She joined RMS since Dec 2005 and

worked as financial model quality assurance engineer.

Hurricane Project Responsibilities: Testing and validating financial model results.

James Lord, Senior Manager Cloud Operations

Based in California, James manages performance requirements and technology issues for RMS’ software

products. He works with clients in the installation, deployment, and configuration of RMS software and

incorporates client needs into the design of new releases. James came to RMS from a combined technology

and civil engineering background. As vice president of product management and technology at Visual Network

Design, he was responsible for the success of the startup’s enterprise data center management software,

Rackwise. Prior to that, he was a senior structural engineer at URS Corporation. A licensed California civil and

structural engineer, James holds a BS and MS in civil engineering from the University of California Berkeley,

and Carnegie Mellon University, respectively. James also holds the Certified Catastrophe Risk Analyst

(CCRA®) designation.

Hurricane Project Responsibilities: Technology, deployment and performance product management for the

RiskLink software.

Reenal Mahajan, Manager, Software Engineering

Ms. Mahajan has an MS degree in Computer Science from Virginia Tech University, Blacksburg VA and has 6

years industry experience. For RMS, Ms. Mahajan works on geocoding and hazard software components of the

RiskLink product and the RiskOnline web site.

Hurricane Project Responsibilities: Re-architecture of spatial hazard software component, and design and

implementation of upgrades to the geocoding software component.



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Manabu Masuda, P.E., Director, Modeling

Mr. Masuda has a BS and an ME degree in Engineering from Kobe University, and an MS in Civil Engineering

from Stanford University. For RMS, Mr. Masuda has been engaged in multiple risk models including Japan

Earthquake, Mexico Earthquake and China Typhoon. Specifically for the version 11.0 U.S. hurricane update, he

developed the Industrial Facility Model and the Builders Risk Model. He is also responsible for the maintenance

of complex relational databases, client services, and QA of various data layers.

Hurricane Project Responsibilities: Development and QA of the vulnerability module.

Joss Matthewman, PhD, Senior Catastrophe Risk Modeler

Joss joined the RMS London model development team in March 2012. In his role as senior catastrophe risk

modeler Joss has worked on the RMS North Atlantic and North West Pacific tropical cyclone models. Prior to

starting at RMS, Joss held a postdoctoral research associate position in the department of meteorology at the

University of Reading, before spending two years researching physical climate mechanisms as a postdoctoral

scholar at the University of California, Irvine. Joss holds an MSci in Mathematics and a PhD in Applied

Mathematics from University College London.

Hurricane Project Responsibilities: Updating the modeled hurricane landfall statistics.

Rohit P. Mehta, Principal Modeler

Mr. Mehta has a BE degree in Civil Engineering from Delhi College of Engineering, India and an MS in Stati stics

from California State University Hayward. He joined RMS in 2000 and is primarily responsible for

implementation, validations and data management for various models. Prior to joining RMS, he gained four

years experience in the testing, validation, and vulnerability implementation for various models.

Hurricane Project Responsibilities: Implementation, validation, testing, quality assurance, and data

management.

Charles Menun, Consultant to RMS Model Development

Dr. Menun joined RMS as a Lead Vulnerability Engineer in 2005 after spending five years as a faculty member

in the Department of Civil and Environmental Engineering at Stanford University, where his research focused on

the development of probabilistic methods for safety and performance assessment in earthquake engineering.

Prior to joining Stanford, he worked for six years as a licensed structural engineer in Canada, where he

supervised the structural design of residential and commercial high-rise buildings in the Greater Vancouver

area. His responsibilities at RMS include overseeing the development of hurricane and earthquake vulnerability

models. Since July 2009, Dr. Menun has provided his services to RMS as a full -time consultant. Dr. Menun

holds Bachelor's and Master's degrees in Civil Engineering from the University of British Columbia and earned

his doctoral degree in Structural Engineering from the University of California at Berkeley.

Hurricane Project Responsibilities: Dr. Menun was responsible for the development and calibration of the

vulnerability module in RiskLink 11.0.



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Bruce Miller, Senior Director, Software Product Management

Bruce’s charter is to oversee the development of products for the RiskLink software platform. Currently based in

Hawaii, he has been at RMS since 1995, starting as an account manager in the client development organization

focused on our reinsurance clients (primarily the Bermuda market). Bruce then managed the product support

organization when it was centralized in California, and for the last nine years has been in the product

management group. Prior to joining RMS, Bruce was an underwriter and loss control engineer with Kemper

Insurance in the highly protected risk department. He holds a BS in engineering physics from the University of

Colorado.

Hurricane Project Responsibilities: General software product management; oversight of user interface

updates for defining hurricane analysis options; generation of functional specifications.

Nayna Mistry, Senior QA Engineer

Nayna has a BS in Civil Engineering from Gujarat University in India and is an ISTQB Certified QA Engineer.

She joined the RMS Newark office in September 2009. Prior to RMS, Nayna worked as a Software Engineer at

HP for about 10 years. There she worked on SNA on Non Stop Kernel System. She worked about 10 years at

Bechtel Power Corporation for 10 years as Civil Structural Engineer. At RMS she is working as a Senior QA

Engineer. She worked on Model QA-testing RiskLink and RiskBrowser.

Hurricane Project Responsibilities: Finishing RiskTools functional testing project starting from writing test

plan, coordinating with India team and other groups, and executing the test cases.

Venkat Morampudi, Principal Software Engineer

Mr. Morampudi has an MS in Computer Science from the University of Alabama and a B.Tech in Computer

Science & Engineering from Acharya Nagarjuna University, India. After he graduated, he started working for

RMS in 2006. Mr. Morampudi has worked mainly on developing software for business workflow and application

logic modules in RiskLink. He also worked on database design, programming and maintenance.

Hurricane Project Responsibilities: Maintain EDM/RDM database scripts including upgrade and downgrade

of RiskLink databases; developing software for business workflow and application logic modules.

Robert Muir-Wood, PhD, Chief Research Officer

Robert Muir-Wood heads the Model Product Management group within RMS, with the mission to specify the

technical and validation requirements of Catastrophe models. He has more than 20 years of experience in

developing probabilistic catastrophe models covering earthquake, tropical cyclone, windstorm, and flood for

Europe, North America, Australia, and Japan. Author of six books, many scientific publications, and more than

150 articles, he has been the technical lead on a number of catastrophe risk securitization transactions, and is

Lead Author on Insurance, Finance and Climate Change for the 2007 (4th) IPCC Assessment Report. He is also

a member of the OECD High Level Advisory Board of the International Network on Financial Management of

Large-Scale Catastrophes.




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Roopa Nair, Senior Engineer, Model Certification

Roopa has over two years of experience in catastrophe risk model QA. She has done her MS and BS degrees

in Statistics from Delhi University, India. She was involved in the creation of regression datasets for testing in

RiskLink and QA of the tool for the Aggregate Loss Model during its development phases. Roopa was also

involved with the Europe EQ model QA.

Hurricane Project Responsibilities: Roopa was involved in model implementation and QA of geocoding,

hazard and vulnerability files.

Hans Nelsen, Senior Software Architect

Hans has a BA in Philosophy from Creighton University. He has 15 years of both database development and

architecture, ranging from production database administration to logical architectural blueprints for enterprise

information systems. For RMS, Hans is primarily focused upon the high level application and da ta model

designs for the NextGen platform and analytics applications. Hans also uses his extensive performance

background to help enhance the current RiskLink and EGC product lines.

Hurricane Project Responsibilities: Enhance software and database performance.

Matthew Nielsen, Senior Manager, Product Marketing

Mr. Nielsen holds an MS degree in Atmospheric Science from Colorado State University and a BA degree in

Physics from Ripon College in Wisconsin. He supports the product marketing and business development

activities for RMS’ U.S. and Canada climate hazard peril models and derivative products, and has served as

lead contact for RMS in the submission to the Florida Commission on Hurricane Loss Projection Methodologies.

He is a member of the American Meteorological Society (A.M.S.) and has authored and presented technical

papers at several A.M.S. conferences. He has been with RMS since September of 2005.

Hurricane Project Responsibilities: Support of North Atlantic Hurricane Model management.

Geoffrey R. Overton, Geospatial Modeler

Geoffrey has a Diploma in Geography from University of Nebraska, Omaha. For RMS, Mr. Overton develops,

manages, and tests data for the geocoding module and related components.

Hurricane Project Responsibilities: Geocoding data implementation and management, testing of related

software and data issues in support of the hurricane model.

Narvdeshwar Pandey, Senior Engineer, Model Certification

Mr. Pandey has over five years of experience in RMSI. He has completed MS in Future Studies and Planning

from Devi Ahilya University, Indore, India and another MS in Mathematics from Gorakhpur University, India. He

was involved in creating regression dataset for testing in RiskLink, Profile generation and internal tool

development for creating regression dataset. He has also performed model QA for India Earthquake model and

currently involved with Europe EQ model QA.



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Hurricane Project Responsibilities: Mr. Pandey was involved in model implementation and QA of geocoding,

hazard and vulnerability files.

Ghanshyam Parasram, Senior Manager, Geospatial Software, RMSI

Mr. Parasram has a Bachelor’s degree in Mechanical Engineering from Jawahar Lal Nehru Technological

University, India. He has over 10 years of experience in design and development of software applications using

object oriented technologies. Prior to joining RMSI in 2006, Mr. Parasram worked as Software Manager for

Business Services group at RMS, managing software development for the application logic and workflow layer

in RiskLink and RiskBrowser products. Between August 1997 and June 2000, Mr. Parasram worked as a

Development Manager at Liquid Software Inc., building enterprise application integration systems that provide

integration solutions to PeopleSoft and SAP. Prior to that, he worked at CMC India, developing financial

applications for the banking industry. At RMSI, Mr. Parasram's primary role is to manage software and data

development for the international geocoding component in RiskLink and RiskBrowser products.

Hurricane Project Responsibilities: Managing software development for the geocoding component in

RiskLink and RiskBrowser products.

Rupesh Parikh, Senior Vice President, Information Technology

Mr. Parikh is the Vice President of Information Technology and Global Facilities fo r Risk Management Solutions

(RMS) where he has been employed since 2005. Rupesh has been in the IT industry since 1985 and has

worked for EDS, IBM, SGI and Applied Materials with multiple leadership roles around technology. He

graduated in 1985 with a BS in Engineering Sciences – Chemical from the University of California, San Diego.

Hurricane Project Responsibilities: Rupesh is responsible for all the infrastructure and security related to the

building of the model.

Rahul Patasariya, Manager, Quality Assurance

Mr. Patasariya has 9 months of experience in Catastrophe Risk Model QA in RMSI. He graduated in Civil

Engineering from Indian Institute of Technology, Roorkee, India. He was involved in creation of regression

dataset for testing in RiskLink and QA of tool for Aggregate Loss Model during its development phases. He is

currently involved with Europe EQ model QA.

Hurricane Project Responsibilities: Mr. Patasariya was involved in model implementation and QA of

geocoding, hazard and vulnerability files.

Chris Juliana Peter Perianayagam, Senior QA Engineer

Chris has a BS degree in Information Technology and Electronics and Communication Engineering from

Sathyabama Institute of Science and Technology, Chennai and a Masters degree in Information Technology

and Management from Illinois Institute of Technology, Chicago. Chris joined RMS in Feb 2009 as a QA

Software Engineer and is involved in planning, executing and automating tests related to Platform and

Installation verification of the RMS products RiskLink and RiskBrowser. Prior to RMS, Chris did her internship in

JC Consulting Group, Chicago as a Junior Software Engineer involved in the design, implementation and

maintenance of Pension Benefit software for Cook County.



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Hurricane Project Responsibilities: QA for install and platform verification of RMS products RiskLink and

RiskBrowser.

Lekshmi Prakash, Senior QA Engineer

Ms. Prakash has an MS degree in Computer Engineering from San Jose State University and has been working

for RMS for the past two years. She works on providing data access layers for the databases in RiskLink, and

various software components of the next generation platform product. Ms. Prakash’s experience includes

databases, Microsoft WPF and Microsoft WCF.

Hurricane Project Responsibilities: Provide data access layer for the databases used in the project.

Sudha Raghavan, Principal Software Engineer

Sudha is a senior SQL server database administrator and developer at RMS. She is involved in database data

model implementation and database optimization, performance and scalability efforts. Sudha has 11 years of

industry experience working on various database technologies.

Hurricane Project Responsibilities: Data model implementation and database optimization, performance and scalability efforts.

Mohsen Rahnama, PhD, Senior Vice President, Head of Model Development

Mohsen leads the model development team responsible for the creation of catastrophe models at RMS. He has

participated in and been project lead in the development of many RMS models. Mohsen is currently involved

with the translation of all RiskLink models to the NextGen environment as well as the development of new

models using the NextGen simulation based approach. During the past 14 years at RMS, Mohsen was involved

with the development of several major models, including RiskLink 11.0 models, Offshore Platform, IFM and

Builder’s Risk models. He oversaw the development of the 2009 earthquake models for North, Central, and

South America. Mohsen has over 25 years of experience in the field of earthquake engineering, seismic

structural analysis and design, building performance evaluation, catastrophe modeling, and risk assessment. He

earned his MS, Engineer’s degree, and PhD from Stanford University specializing in earthquake and structural

engineering.

Hurricane Project Responsibilities: Advisor on development and upgrade of hurricane vulnerability and

inventory models.

Ambica Rajagopal, PhD, Senior Product Manager

Dr Rajagopal is a member of the Actuarial and Financial Modeling team. Since joining RMS in 2007, she has

been involved in the research and development of new features in the RiskLink financial model as well as the

Simulation Platform. Most recently, she has researched alternatives to the Beta distribution. Prior to joining

RMS, Dr Rajagopal modeled asset-backed securities at KPMG LLP. She holds an MS in Mathematics from

BITS, Pilani, India and PhD in Mathematics from Purdue University, where she studied continuity properties of

symmetric stable processes.

Hurricane Project Responsibilities: Design and development of the RiskLink financial model.



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Edida Rajesh, Assistant General Manager (Modeling)

Rajesh has a Masters of Technology in Geophysics from Andhra University, Visakhapatnam, India and has

been working with RMS / RMSI for over 13.5 years. For RMS, Rajesh works on providing GIS based analysis,

developing hazard data products consumed by both natural catastrophe models and underwriting solutions.

Rajesh’s experience includes analyzing the requirements, project specification, coordination, and providing

industry-standard GIS-based solutions in developing hazard data products.

Hurricane Project Responsibilities: Developing Land Use Land Cover (LULC) data for study area using

Satellite Images and GIS analysis. This data (roughness data) is the base for site coefficients and IED

distribution.

Pranav Raval, Director, Software Engineering

Mr. Raval has an MS degree in Computer Science from Illinois Institute of Technology, Chicago, and has been

working in the field of GIS Software Development for more than 6 years. For RMS, Mr. Raval works on various

GIS software components. Mr. Raval's development experience includes map server, core spatial library,

geocoding components, and job distribution components.

Hurricane Project Responsibilities: Design and implementation of geocoding and job distribution

components.

Venkata (Subba) Ravilisetty, Senior Architect

Mr. Ravilisetty has an MS degree in Computer Information Sciences from University of South Alabama, and a

BS degree in Electrical Engineering from Osmania University, Hyderabad in India. For RMS, Mr. Ravilisetty is

primarily responsible for the detailed design and implementation of software components in the RiskLink

Detailed Loss Model.

Hurricane Project Responsibilities: Detailed design and implementation of software components in the

RiskLink Detailed Loss Model.

Rhoderick Rivera, Senior QA Engineer

Mr. Rivera joined RMS in June of 2005, taking a position as a Configuration Release Engineer. Currently he is

handling order fulfillment and QA duties. He graduated from the University of Illinois, Urbana -Champaign with a

degree in Computer Engineering. Previously he has worked 2 years as a hardware engineer for Arise Computer

and 2.5 years as an account manager at Washington Mutual.

Hurricane Project Responsibilities: Fulfillment of client orders, and quality assurance.

Agustín Rodríguez, Lead Wind Vulnerability Modeler

Mr. Rodríguez joined RMS in July 1999 as a model developer. His responsibilities include development and

implementation of various models, including windstorm, severe convective storm, earthquake, and terrorism.

Mr. Rodríguez joined RMS after earning his MS degree from the University of California at Berkeley and his BS

degree from Stanford University, both in Civil Engineering.



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Hurricane Project Responsibilities: Implementation of U.S. hurricane vulnerability model.

Ricardo Ruiz, Senior Software Engineer

Mr. Ruiz has a BS degree in Computer Science from De La Salle University, Manila and has worked for

software development companies for nearly 13 years. For RMS, Mr. Ruiz works on the distributed processing

functionality of the RiskLink product. Mr. Ruiz’ experience includes distributed, database and object o riented

programming.

Hurricane Project Responsibilities: Maintenance of the distributed processing functionality of RiskLink.

Shraddha Sahay, Manager, Geospatial Software Development

Ms. Sahay has a Bachelor’s of Electrical Engineering from Visvesvaraya Technological University, Karnataka,

India. She has worked for nearly 7 years for software companies developing various enterprise applications. For

RMS, Ms. Sahay works mainly in the geospatial area where she is involved in different aspects of hazard

retrieval for RiskLink.

Hurricane Project Responsibilities: Detailed design and implementation of hazard retrieval for RiskLink.

Majid Khadem Sameni, Principal Software Engineer

Majid has a Master of Science in Mechanical Engineering from University of Waterloo in Canada with a focus on

software development for concurrent systems. He has worked in several world class companies focusing on

primarily software design, architecture, implementation and algorithm design for more than 6 years. He has

been in RMS for about a year and he is currently the development lead for EGC distribution system. He has

expertise in several software development practices including Object-Oriented Design, Design Patterns,

Domain Driven Design, Actor Oriented Design, Event Driven Systems Design, Test Driven Development, Agile

Software Development and Service Oriented Design, Cluster Computing, Cloud Computing. Also, he is expert in

several software technologies including .NET 3.5, WCF, Silverlight, Linq, Linq to Sql, Windows Server, XP, High

Performance Computing, Microsoft SQL Server, CCR, DDS.

Hurricane Project Responsibilities: Development lead for Enterprise Grid Computing job distribution system.

Chris Sams, Senior Geospatial modeler

Joining RMS in 2003, Chris develops RMS geospatial data that covers the entire world. He has also participated

in many RMS catastrophe model implementation features. Chris holds a BA in Geography from the University

Kansas and specialized in geographic information systems, remote sensing and cartography.

Hurricane Project Responsibilities: Chris is the geocoding development liaison to the model development

team.

Pooya Sarabandi, PhD, P.E., Director

Dr. Sarabandi holds a PhD degree in Structural Engineering from Stanford University as well as MS degrees in

Earthquake Engineering and Electrical Engineering. Dr. Sarabandi is a licensed civil engineer in the state of

California and currently serves as a consulting faculty at Stanford University where he established the



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Advanced Global Risk Assessment Laboratory (AGRAL.) Prior to joining RMS in 2007, Dr. Sarabandi was

involved in research and development of wireless sensing devices for measuring structural response of

buildings and bridges, used in early earthquake warning systems as well as structural health monitoring. Prior to

that, he served as a consultant to number of risk and disaster management companies, specializing in

application of remote sensing and statistical inference techniques in modeling and assessing risk to urban area.

Hurricane Project Responsibilities: Dr. Sarabandi was responsible for the business interruption model as well

providing data and requirements for inventory and exposure development in the model.

Pooja Sayal, Manager Model Certification

Ms. Sayal joined RMS, California in December 2008. She graduated in Civil Engineering from Delhi College of

Engineering, New Delhi, India. Before joining RMS, California she had worked for six years at RMS, London

and RMS, India offices in Model Development and Model QA departments. During this re lease her primary

responsibilities include validation of hazard and vulnerability implementation. Her prior experience includes

developing historical storm wind fields and their reconstruction for the previous generation of hurricane model.

She also supported the development of surface roughness data. She had also validated many Earthquake and

Windstorm peril models in earlier releases.

Hurricane Project Responsibilities: Ms. Sayal is involved with validation tests for hurricane hazard and

vulnerability implementation.

Emilie Scherer, PhD, Senior Catastrophe Risk Modeler, Model Development

Dr. Scherer has been involved in various components of the hazard model including historical reconstructions

and model validation. Prior to joining RMS, she conducted postdoctoral research in geophysical fluid dynamics

applied to the atmospheric and oceanic sciences at the Laboratoire de Meteorologie Dynamique in Paris,

France and at the Laboratoire des Ecoulements Geophysiques et Industriels in Grenoble, France. Dr Scherer

received a PhD in Meteorology, Oceanology and Environment from the University Pierre and Marie Curie in

Paris, France.

Hurricane Project Responsibilities: Development of historical footprints and hazard model validation.

Debjani Sen, Director, Technical Publications

Debjani has a Master’s in Liberal Arts from Southern Methodist University, Dallas, Texas. She has worked in the

software industry for over 11 years, providing documentation, online help, and tutorials for enterprise level

software products. Debjani joined RMS in July 2007 and is responsible for the development and delivery of

software documentation, model documentation, and online help for RMS customers.

Hurricane Project Responsibilities: Debjani is responsible for the development and delivery of technical

software documentation, model documentation, and online help.

Neha Shah, Engineer, Model Certification

Neha joined Jonathan Moss’s team within the Quality Assurance Department of RMS in April 2007, after

completing her bachelor’s degree in Applied Mathematics at University of California, Los Angeles. She has



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tested a number of different Financial Model features in few releases of RiskLink and RiskBrowser, and with her

team members, has recently updated the Financial Model Prototype, which is used to test the Event Loss

Tables. Neha is also currently pursuing her MS degree in Biostatistics from California State University, East

Bay.

Hurricane Project Responsibilities: Neha is involved with testing of the financial model features in the 11.0

release and developing of any necessary test tools.

Nilesh Shome, PhD, Director, Model Development

Nilesh joined RMS in 2009 as a director in the model development group. He is involved in developing and

reviewing vulnerability functions of residential and commercial structures for U.S. hurricanes as well as

vulnerability functions of tall buildings for western U.S. earthquakes. Nilesh has more than 10 years of

professional experience in loss estimation of hurricanes, earthquakes, floods, winter storms, and o ther natural

hazard as well as man-made hazards like terrorism. Prior to joining RMS, he managed a number of projects to

develop and update models for earthquakes, hurricanes, and winter storms. He has also worked on several

projects for securitization of risks for earthquakes and hurricanes and a number of world-bank projects to

evaluate risks of different countries for earthquakes and hurricanes. Nilesh has also worked as a consultant to

Federal Emergency Management Agency (FEMA) and Applied Technical Council (ATC). He has authored a

number of publications in international journals and refereed conferences, and is a technical reviewer and editor

of a number of papers for several journals. He received the EERI award for the best journal paper in the year

1998. Nilesh earned his PhD in Structural Engineering from Stanford University.

Hurricane Project Responsibilities: Involved in developing vulnerability functions of residential and

commercial structures.

Maulik Shukla, Manager, Software Development

Mr. Shukla has an MS degree in Computer Science from Illinois Institute of Technology, Chicago. He has

worked with various companies in the capacity of Senior Engineer to Team Lead. At RMS Mr. Shukla works with

the Business Intelligence and Analytics team, leading the data integration efforts for the Next Generation

Platform Initiative.

Hurricane Project Responsibilities: Maulik had provided support to the development team as a Team

Foundation Server Administrator, and has implemented upgrades to the RiskLink exposure data import and

export components.

Bronislava Sigal, Lead Financial Modeler

Ms. Sigal received BS degree (with honors) in Mathematics from Kiev State University in Ukraine and PhD in

Statistics from Stanford University.

She joined RMS in March of 2009.After joining RMS as a part of Model Development team Ms. Sigal worked on

projects related to terrorism, account fire and offshore platform RMS models. Currently Ms. Sigal is a part of a

Financial Modeling group. Prior to joining RMS she worked in the field of catastrophe modeling at K2

Technologies and after that at Stanford University as a biostatistician on stochastic modeling in cancer

research.



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Hurricane Project Responsibilities: Modified documentation and prototype and tested implementation of a

new interaction methodology for loss computation.

Rajesh K. Singh, PhD, P.E., Senior Director, Model Certification

Dr. Singh received his PhD from Stanford University, Master’s degree from the University of British Columbia,

and Bachelor’s degree from IIT Kanpur, all in Civil Engineering. Dr. Singh has worked on the development and

implementation of loss assessment models, design and implementation of engineering databases, and creating

derivative data layers for use with aggregate exposure and reinsurance applications. As a senior director of

analytics QA, Dr. Singh is responsible for quality of the model implementation and analytics with RiskLink. Prior

to RMS, Dr. Singh worked as a design engineer at J. K. M. Associates, a structural engineering consulting f irm

in Vancouver, Canada, on the seismic analysis and design of high-rise buildings. Dr. Singh is a registered

Professional Engineer (P.E.) in California, and a member of the American Society of Civil Engineers.

Hurricane Project Responsibilities: Model implementation and engineering quality assurance.

Ajay Singhal, PhD, Vice President, SW Engineering

Ajay leads the man-made cat and large commercial risk practice at RMS and has more than 10 years of

experience in catastrophe risk assessment for natural and man-made catastrophes. He has been involved in the

development of hazard, vulnerability, and financial models for these perils. At RMS, he has been leading teams

for the development of the various models such as the terrorism model for estimating losses from various man-

made catastrophes, offshore platforms model for hurricane loss analysis to offshore oil & gas platforms, fire risk

analysis for estimating losses from accidental and arson fires, and the fire following earthquake model. Ajay

holds a B.Tech. degree from the Indian Institute of Technology, Madras (India), an MS in Civil Engineering from

Rice University, Houston, Texas, and a PhD in Civil Engineering from Stanford University, California

specializing in probabilistic loss estimation.

Hurricane Project Responsibilities: Managing Financial Model development for RiskLink.

Puja Sinha, Senior Software Engineer

Ms. Sinha has a Bachelor’s degree in Electrical Engineering from Nagpur University, India. She has 5+ years of

experience in software development, which includes around 2 years at RMS. At RMS, Ms. Sinha works on

RiskLink, RiskBrowser, RiskTools and RiskOnline. Her responsibilities include software planning, designing and

implementation.

Hurricane Project Responsibilities: EDM downgrade script.

Michael Smith, PhD, Senior Catastrophe Risk Modeler

Michael joined the RMS London model development team in November 2010. In his role as senior catastrophe

risk modeler Michael’s primary responsibility has been to support the capital markets group’s use of c atastrophe

models to transfer insured risk to capital markets in the form of catastrophe bonds. He has worked on several

different peril regions and his responsibilities include developing automated post-event processes for Paradex –

an index approximating insured industry losses. Michael has also worked on the historical reconstructions and

hazard aggregation for RiskLink 13. Prior to working at RMS, Michael spent five years working as an offshore



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engineer, specializing in offshore installation projects and then finite element analysis for offshore structural

design. Michael holds a BSci in Applied Mathematics from the University of St Andrews and a PhD in Civil

Engineering from the University of Dundee.

Hurricane Project Responsibilities: RiskLink Event Rate Converter annual update.

Jayant Srivastava, Director, SW engineering

Mr. Srivastava has an MS in Computer Science from the Institute of Management and Technology, India. For

RMS, Jayant is managing the Business Services Development Group and develops software enhancements

and fixes for various functionalities of core applications.

Hurricane Project Responsibilities: Primarily, contributing to the job distribution framework for RiskLink.

Beth Stamann, Senior Documentation Specialist

Ms. Stamann joined RMS in August of 1995. She worked within the Client Development Organization until

October 2007 when she moved to the Public Policy Group as Senior Documentation Specialist.

Hurricane Project Responsibilities: Production of RMS Submission.

Philippe Stephan, Chief Technology Officer

Philippe Stephan is the Chief Technology Officer of RMS. He was most recently Head of Business Development

for Sophis, a leading market risk technology vendor. Prior to Sophis, Philippe directed product development as

the CTO of San Francisco based Moody's KMV, the award winning credit risk analytics vendor. Philippe has

also held senior management positions at Internet startups, built derivatives risk management systems for

Societe Generale in Paris, and CA Lazard Financial Product Bank in London. Philippe started his career as a

key contributor to the development of the Eiffel programming language in the early 1990s, after obtaining his

MS in computer science from French Ecole Nationale Superieure des Mines de St Etienne.

Hurricane Project Responsibilities: General management, product management and software development.

Cody Stumpo, Senior Product Manager

Based in California, Cody is responsible for managing the RMS financial model for RMS’ core software products

such as RiskLink and RiskBrowser. Prior to joining RMS, Cody worked in quantitative commercial credit risk

management with Moody’s KMV and as senior catastrophe analyst for the St. Paul Travelers Companies. He

holds a BA in applied mathematics from the University of California, Berkeley and an MS in engineering from

Purdue University.

Hurricane Project Responsibilities: Management of modeled hurricane losses in the financial model.



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Shengjun (John) Su, PhD, Senior Software Engineer

Mr. Su has a PhD degree in Computational Analysis & Modeling from Louisiana Tech Univ. Mr. Su started

working with RMS in 2006, and his duties have mainly focused on development of financial modeling

components. Before joining RMS, Mr. Su worked with UGS for ten months as a software engineer.

Hurricane Project Responsibilities: Enhancement of the Exceedance Probability Engine for RiskLink.

William Suchland, Vice President, Geospatial Development

Mr. Suchland has a BA degree in Geography/Computer Assisted Cartography from the University of

Washington in Seattle, Washington. He has over 25 years of professional experience in software design,

development, and technical project management. Prior to joining RMS in 1996, Mr. Suchland worked for over 15

years as a software developer and software development manager in the geo-demographics industry, building

consumer marketing analysis systems and supporting GIS and mapping capabilities. At RMS, Mr. Suchland's

primary role is manager of geospatial software and data development for the RiskLink and RiskBrows er

products.

Hurricane Project Responsibilities: Management of software design and implementation.

Taronne Tabucchi, Senior Modeler

Taronne supports calibration/validation exercises for the model, mainly responsible for running RiskLink and

processing outputs from the model. She earned a BS in Engineering Science from Smith College and a MS in

Civil Infrastructure Systems from Cornell University, where her course work focused on risk analysis and

earthquake engineering. Taronne joined RMS in June 2007.

Hurricane Project Responsibilities: Calibration/validation of model; running RiskLink and processing outputs

into graphical formats.

Avinash Takale, Senior Software Engineer

Mr. Takale has a Masters of Computer Application from Shivaji University, Maharashtra, India. He has worked

for 7 years for software companies developing various desktop and enterprise applications. For RMS, Mr.

Takale works mainly in the geospatial area where he is involved in different aspects of hazard data

management and retrieval for RiskLink.

Hurricane Project Responsibilities: Implementation of migration of high resolution (spatial) hazard lookup

from C++ to C# .NET and migration of hurricane hazard tabular lookup from MS Access to SQL.

Joel Taylor, Senior Analyst, Model Product Management

Mr. Taylor has a BS degree in Mathematics from Bradley University, Peoria, Illinois. He joined RMS in April

2007. After completing the risk analyst program, he joined the Mitigation and Regulatory Affairs group. Mr.

Taylor participated in post-hurricane reconnaissance visits after Hurricanes Gustav (2008) and Ike (2008).

Hurricane Project Responsibilities: Generation and QA of actuarial, statistical and vulnerability forms.



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Monika Tomar, Project Lead, Application Development

Ms. Tomar completed her Master’s degree in Computer Applications in 2003 from Bundelkhand University,

Jhasi, India. Ms. Tomar has over 5 years of experience in software design and development of software

solutions.

Hurricane Project Responsibilities: Upgrading the RiskLink financial model software.

Yogesh Vani, Manager, Quality Assurance

Yogesh has an MS in Telecommunication Systems from California State University, Hayward. For RMS, he has

worked on RiskLink installation and platform testing. In the past, Yogesh has also worked on Remote

Distributed Processing testing. His responsibilities include testing RiskLink installation across multiple OS

platforms and SQL Server combinations.

Hurricane Project Responsibilities: Testing installation of RiskLink software and DLM data.

Mimi von Kugelgen, Vice President, Program Management

Mimi joined RMS in early 2003 and leads the program management function in model development. Her role is

to coordinate scoping, planning, and delivery of RMS’ multi-year modeling agenda. Mimi played a pivotal role as

the release manager responsible for cross-functional coordination and delivery of the version 9.0 product

release. Mimi earned a BS in genetics from the University of California, Berkeley and holds a PMP designation

from the Project Management Institute.

Hurricane Project Responsibilities: Coordinate product scoping, planning, and delivery.

Yen-Tin Yang, Manager, Model Certification

Ms. Yang received an MS degree in Management Science & Engineering from Stanford University, and an MS

in Structural Engineering and BS in Civil Engineering degrees from National Taiwan University. Ms. Yang joined

RMS in January 2005. She is responsible for model implementation quality assurance and data validation. Prior

to RMS, Ms. Yang worked on product verification at Autodesk, Inc.

Hurricane Project Responsibilities: Model implementation quality assurance, testing, and validation.

Michael Young, Senior Director, Model Product Management

Mr. Young holds a M.Sc. from the University of Western Ontario in Canada where he studied wind loading on

low rise buildings. He was worked in commercial wind tunnel laboratories doing studies on wind loads for a

variety of buildings. Before joining RMS, he worked as a modeler at Applied Research Associates on hurricane

vulnerability risk models. He was involved in the development of the HAZUS-MH software for hurricane risk

assessment and studies on mitigation cost-effectiveness for building codes, such as the 2001 Florida Building

Code and the North Carolina Building Code. Mr. Young has conducted post-hurricane reconnaissance visits

after Hurricanes Bonnie (1998), Isabel (2003), Charley (2004), Frances (2004), Ivan (2004), Jeanne (2004),

Gustav (2008), and Ike (2008). He is a member of the American Society of Civil Engineers and the American

Association of Wind Engineers.



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Hurricane Project Responsibilities: Oversees product specifications for RMS climatic models including the

North Atlantic Hurricane Model. Oversees regulatory certification process.

Ji Zhang, Senior Software Engineer

Ms. Ji Zhang joined RMS in June 2006 as a software engineer in Software Peril Model Services. She is

responsible for software development for several peril models. She has an MS degree in Computer Science

from California State University, East Bay and BS degree in Mathematics from Xiamen University.

Hurricane Project Responsibilities: Maintain, develop and test peril model software.

Christine Ziehmann, PhD, Director, Model Product Management

Dr. Ziehmann received her PhD in meteorology from the Free University of Berlin in 1994 where she also

studied for her bachelor's and master's degrees in meteorology. Dr. Ziehmann joined RMS in 2001 from the

Institute of Physics at the University of Potsdam (Max-Planck-Institute for Nonlinear Dynamics), Germany,

where she held a post doc position with main research interest the predictability of weather and climate and

nonlinear systems in general. Dr. Ziehmann was also a lecturer at the University of Potsdam and previously the

University of Hamburg in theoretical meteorology, atmospheric boundary layer meteorology and non-linear time

series analysis. In October 2007 Dr. Ziehmann was appointed as product manager for the Atlantic Hurricane

model after having various roles in RMS' product management and weather derivatives business units. She is a

member of the German Meteorological Society (DMG).


Appendix C—External Expert Review of Hazard Module


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APPENDIX C—EXTERNAL EXPERT REVIEW OF HAZARD MODULE



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Appendix D—External Expert Review of Vulnerability Module


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APPENDIX D—EXTERNAL EXPERT REVIEW OF VULNERABILITY MODULE



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Appendix E—RiskLink User Interface Screen Shots


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APPENDIX E—RISKLINK USER INTERFACE SCREEN SHOTS

Figure 85: Screen Shot of Model Location Input Form (Part 1)




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Figure 91: Screen Shot of the About RiskLink Screen, Showing Model Name and Version Number

Appendix F—RiskLink Reports Analysis Summary Report for FCHLPM


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APPENDIX F—RISKLINK REPORTS

Figure 92: Analysis Summary Report (Page 1 of 3)



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Appendix F—RiskLink Reports Post Import Summary


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Figure 95: Post Import Summary (Page 1 of 4)



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Appendix F—RiskLink Reports Annual Aggregate Factors Table


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Table 52: Example of Client Output Table Showing Application of Annual Deductible Factors

RiskLink 13.0 (Build 1509)

Signature: _________________________________

Date: _____________________________________

Instructions:

In order to verify that the annual deductible factors have been applied to the model output, clients should report the following results as evidence of appropriate application:

a.) portfolio level occurrence deductible gross AAL and the annual deductible gross AAL, b.) selected return period losses for each of occurrence deductibles and annual deductibles, and c.) for three sample locations in the portfolio, location level occurrence deductible gross AAL and the annual deductible gross AAL.

Client Information:

Client Name:

Model Version:

Annual Deductible Factors Used (AOP/Non-AOP):

Portfolio Level Model Output:

AA

L Portfolio Name

Number of Accounts/Locations

Gross Occurrence Deductible AAL

Gross Annual Deductible AAL

Ratio: (Annual AAL) /

(Occurrence AAL)

10

0

ye

ar

RP

L

100 year RPL with Occurrence Deductible

100 year RPL with Annual Deductible

Ratio: (Annual 100 RPL) /

(Occurrence 100 AAL)

25

0

ye

ar

RP

L

250 year RPL with Occurrence Deductible

250 year RPL with Annual Deductible

Ratio: (Annual 250 RPL) /

(Occurrence 250 AAL)

Location Level Model Output:

Location Identifier Postal Code LOB-Construction

Type HU Deductible

Amount Gross Occurrence

AAL Gross Annual

Deductible AAL Ratio: Annual /

Occurrence