Energy Efficiency And Commercial-Mortgage Valuation

Paper Written By Dwight Jaffee, Richard Stanton and Nancy Wallace

http://www.law.berkeley.edu/files/bclbe/DOE_Valuation_9.25.12.pdf

January 17, 2012Presentation by Matthew Kwatinetz1 EEB HUBModerated and Discussed by Scott Muldavin

1Mr. Kwatinetz is presenting this paper in absence of the authors. Every effort has been made to accurately convey the intention and conclusion of the authors. Any errors, omissions are Mr. Kwatinetz’ alone and do not reflect the work of the authors.

Executive Summary (1)• Commercial Office Buildings Are Intense Users of Energy• Building Energy Intensity Creates Different Cost Structure

– For Landlord and/or Tenant• Energy Prices are Volatile• Non-EE Buildings Are More Exposed to Volatility

– Have more loan default risk– Are more expensive per square foot (SF)– Should be less attractive to investors and have different loan terms

• Yet Existing Loan Practices Provide No Incentive for EE• This Paper Proposes a Method for Lenders Explicitly Taking

Energy Risk (and level of EE) into Mortgage Underwriting

Executive Summary (2)• Standard Underwriting Seeks to Avoid Default• Manages Risks– Interest Rate Dynamics, Market Pricing Dynamics– Default Risk, Pre-Payment Risk

• Authors Extend Underwriting to Manage Risks– Electrical and Gas Pricing Dynamics– Location Dynamics

• Result: Building Loans Are Differentiated on EE

Traditional Mortgage Underwriting

Traditional Mortgage UnderwritingPrimary Goal: Avoid Default

• Risk Management– Interest Rates, Market Pricing/Rents– Default, Pre-Payment

• Variables– Principle, Rate, Maturity, Amortization Schedule

• Metrics– Loan to Value (LTV): Sized by Market/Risk (65%)– Debt Service Coverage Ratio (DSCR): How much does

annual net income (NOI) cover annual amortized debt service payment? (1.25)

Underwriting: Cash FlowsYear 1 Year 2 Year 3 Year 4 Year 5

Gross potential revenue $408,000 $416,160 $424,483 $432,973 $441,632 Revenue Growth Estimate 2.0% 2.0% 2.0% 2.0% 2.0%Vacancy 5.0% 5.0% 5.0% 5.0% 5.0%Rental Value of Vacancy $20,400 $20,808 $21,224 $21,649 $22,082

Subtotal GROSS POTENTIAL REVENUE $387,600 $395,352 $403,259 $411,324 $419,551

Collection Loss & Concessions $27,132 $27,675 $28,228 $28,793 $29,369 Management Fee $17,054 $17,395 $17,743 $18,098 $18,460

Net Rental Income $324,034 $330,514 $337,125 $343,867 $350,744

Expense Recovery $116,280 $60,489 $61,699 $62,933 $64,191 Effective Gross Income (EGI) $440,314 $391,003 $398,823 $406,800 $414,936

Operating Expenses $116,280 $120,978 $123,397 $125,865 $128,383

Net Operating Income (NOI) $324,034 $270,025 $275,426 $280,934 $286,553

Underwriting: NNN (Triple Net)Year 1 Year 2 Year 3 Year 4 Year 5

Gross potential revenue $408,000 $416,160 $424,483 $432,973 $441,632 Revenue Growth Estimate 2.0% 2.0% 2.0% 2.0% 2.0%Vacancy 5.0% 5.0% 5.0% 5.0% 5.0%Rental Value of Vacancy $20,400 $20,808 $21,224 $21,649 $22,082

Subtotal GROSS POTENTIAL REVENUE $387,600 $395,352 $403,259 $411,324 $419,551

Collection Loss & Concessions $27,132 $27,675 $28,228 $28,793 $29,369 Management Fee $17,054 $17,395 $17,743 $18,098 $18,460

Net Rental Income $324,034 $330,514 $337,125 $343,867 $350,744

Expense Recovery $116,280 $60,489 $61,699 $62,933 $64,191 Effective Gross Income (EGI) $440,314 $391,003 $398,823 $406,800 $414,936

Operating Expenses $116,280 $120,978 $123,397 $125,865 $128,383

Net Operating Income (NOI) $324,034 $270,025 $275,426 $280,934 $286,553

Underwriting NNN: DSCR Year 1 Year 2 Year 3 Year 4 Year 5

Net Rental Income $324,034 $330,514 $337,125 $343,867 $350,744

Expense Recovery $116,280 $120,978 $123,397 $125,865 $128,383

Effective Gross Income (EGI) $440,314 $451,492 $460,522 $469,732 $479,127

Operating Expenses $116,280 $120,978 $123,397 $125,865 $128,383

Net Operating Income (NOI) $324,034 $330,514 $337,125 $343,867 $350,744

Debt Service Payment ($116,280) ($116,280) ($116,280) ($116,280) ($116,280)

Debt Service Coverage Ratio (DSCR) 1.24 1.26 1.29 1.31 1.34

Cash Flow After Debt Service $61,822 $68,302 $74,913 $81,655 $88,532

Underwriting NNN: Expenses ExternalYear 1 Year 2 Year 3 Year 4 Year 5

Net Rental Income $324,034 $330,514 $337,125 $343,867 $350,744

Expense Recovery $116,280 $120,978 $123,397 $125,865 $128,383

Effective Gross Income (EGI) $440,314 $451,492 $460,522 $469,732 $479,127

Operating Expenses $116,280 $120,978 $123,397 $125,865 $128,383

Net Operating Income (NOI) $324,034 $330,514 $337,125 $343,867 $350,744

Debt Service Payment ($116,280) ($116,280) ($116,280) ($116,280) ($116,280)

Debt Service Coverage Ratio (DSCR) 1.24 1.26 1.29 1.31 1.34

Cash Flow After Debt Service $61,822 $68,302 $74,913 $81,655 $88,532

Traditional Underwriting Risks• Uncertainty of NOI– Managed by setting LTV, DSCR

• Default Options– Managed by Hazard Rates (Conditional Probability of

Exercise)• Pre-Payment of Borrower– Managed with Penalties or Lock-Out

• Interest Rate & Market Dynamics– Can Be Simulated with Probability Weighted Cash

Flows and Monte Carlo

What About Energy Risks?• Energy Related Shocks Occur– Consumption Shocks– Shock on Energy Factor Inputs

• Shocks Effect Level and Volatility of NOI – Should Effect Value Since They Influence Default Risk

• No Risk Adjustment For EE Means– EE Buildings Treated Same as Non-EE– No Adjustment in Mortgage Pricing for EE– Lack of Ability to Price Risk Mitigation

Geography of Energy Risk in the U.S.

U.S. Electrical Power System• Three Major Networks– Eastern, Western, Texas

• Pricing Is Effected By– Electricity Hub Sales– Nodal Structure of Natural Gas– Geography of Population Centers

• Limited Hubs for Electrical Auctions• There is No National Market for Electrical Pricing– Considerable Difference per Region

U.S. Electrical Power SystemThree Major Networks

U.S. Natural Gas Market• Benchmarked to a Single Hub• Henry Hub (Erath, Louisiana)– Center of NYMEX Pricing of Gas Contract Futures– Interconnects with 9 Inter-State Pipelines– Interconnects with 4 Intra-State Pipelines

• No National Market for Gas Pricing– Considerable Difference per Region

Gas v. Electric: Volatility DifferencesEven Though Gas Is Primary Input

U.S. Energy Pricing Conclusions• Energy Can Be Up to 30% Total Costs (BOMA)– But Lenders Do Not Underwrite Energy Exposure

• Differences in Gas & Electrical Volatility– Despite Gas as Fundamental input of Electric

• Differences Across Three Electrical Networks• Differences Between Hubs (Within Networks)• No National Market for Energy Pricing

Underwriting Mortgage Energy Risk

Problem: Measurement of EEMust Measure Consumption and Volatility

• Accurate Underwriting Difficult/Labor Intensive• Utility Bills Sometimes Available: No Algorithm– Need to relate to building systems, tenant meters, relative

occupancy, equipment commissioning. • Energy Star and Portfolio Manager– Lenders Cannot Use Energy Star Score to Predict Level and

Volatility of Energy Consumption• Option 1: Regression– Data Insufficient, Buildings Heterogeneous

• Option 2: Simulation– Requires Detailed Data on Features/Systems

Benchmarking Is Best OptionCalculates EUIs/Peer Group Then uses EnergyIQ

• CBECS (Non-CA Buildings)– 5,215 Samples Across country, Statistically Extended

• CEUS (CA Buildings)– 2,790 Stratified Random Sample in CA– Utility Area, Climate Region, Building Type, EUI

• Peer Groups (Each contains at least 20 buildings)– Building Type, Size, Geographic Region (9 Census, 7 CA)

• Limitation 1: EUI Estimates Do not Account for Relative EE – Building Asset/Operations Data Not Available – All Buildings in a given region, size have the same EUI

• Limitation 2: No Differences in Climate Within Regions

Testing: Mortgage Valuation ProcessHull-White, Georgian Brownian Motion (GBM)

(1) Acquire Data

(2) Calibrate to Distribution

(3) Monte Carlo Simulation Matched to Known Values

Electrical Calibration: ERCOT-EasternSignificant Heterogeneity Between Hubs and Between Networks

Natural Gas CalibrationSignificant Times Series Variation in Shape and Level vs Maturity

Part I: Building Specific Rental Drift

• Simulate 10,000 paths with Monte Carlo– Rent, interest rates, gas prices, electricity prices

• Calculate Monthly NOI Along Each Path• Discount Each Path’s Cash Flows to Present

then Average All Paths• Continue Until Result Matches Known

Origination Value

Part II: Solve for Mortgage Value• Follow Similar Process as Part I– 10,000 Paths, Calculate Monthly CFLO– Discount back and Average Across All paths

• Two Significant Differences– Empirical Hazard Model to Model Default Option– Value Estimated At Every Date Along Path to

Match LTV Exposure of Default Risk

Valuation Application: Three Sims1. Value Loan Without Default Risk– But including dynamics of interest rates, rents and

energy prices

2. Value Loans With Default Risk, but Without Energy– Traditional Mortgage process using only rent and interest

rate dynamics and default option

3. Value Loans with Default Risk and With Energy– Proposed New Model

Valuation Results For Sample

• Inclusion of Energy Generates Mortgage Values 8.89% Below Those of Traditional Modeling Approach

• Reductions Larger for Larger Buildings• Valuation Reductions Larger for Larger LTV Ratios• Ignoring Energy Would Lead to Significant Mispricing

Conclusions / Implications

Authors' Conclusions• Energy Is A Local Market – Energy Costs Are Significant Portion of OpEx Costs

• Underwriting Can Incorporate Energy Risk– Location and Level of EE– Standard Engineering Reports– Existing Benchmark Tools

• Method Leads to an 8.5% Reduction In Mispricing• Adaptable for Actual Market Applications– Considerable Difference per Region

Presenter/My Comments• Market Applicability Mainly NNN Securitization– <<18% of US Energy Use (Other Markets, Leases)

• Some Sample/Data Issues But Should Not Discard• 2002-07 Vintage, CBECS

• Theoretical Conclusions Still Powerful– Prices EE Effectively with Currently Available Data

• Must Be Done Regionally⁻ But Same Method Can Be Applied Everywhere

• NOI Extension: Water/Sewer• CR/Risk Extension: Market Penetration of EE

My Suggested Next Steps for EE• Standardize the PCA and Tailor to EE• Clear, Standardized Energy Efficiency Scoring (IMT)– Convert from LEED Subset and USGBC Data– Difference between EUI and EE

• Develop Standardized Coefficients for Cities/Ctys– Like Cap Rates– Capture Regional Energy Level and Volatility– Trailing Twelve Months Averages or Other Approximation

• Equity Faster than Debt

Questions/Discussion

Matthew KwatinetzManaging Partner

QBL Partners(206) 391 – 0131(212) 729 – 3489

matthew@qblre.com

Appendix Slides(Discussion Only Not Presented)

Stages of Adjusted Modeling Process1. Monthly Data Assembled

– US Interest Rates, Electricity Forward Prices, Gas Futures

2. Interest Rate Fit to a Hull-White Process and Gas/Electricity Fit to Exponential Hull-White Processes.– Fit to Exactly Match Observed Term Structure on Monthly Frequency

3. Stage 1: Calculate Long Run Mean (“Drift”) of Building’s Market Rent Dynamic– Use Fitted Dynamics of Interest Rates, Energy Forward Prices– Assumes that Process follows a Geometric Brownian Motion (GBM)– GBM such that the estimated process exactly matches the observed building

price at the origination of the mortgage.

4. Stage 2: Four Factor Model Monte Carlo Simulation– Value the Mortgage Contract Cash Flows and Embedded Default Option– Factors: Interest Rates, Natural Gas forward prices, Electricity hub Forward

prices, Building specific rental price dynamic

Property Condition Assessments (PCA)• Required Engineering Report

– Not Used for Energy Forecast– Not Used For Appraisal (Precedes)

• Analyzes 10 Systems In Two Phases– Site Inspection + Data Analysis

• Systems Analyzed– Site (Topology, Drainage, retaining Walls, Paving, Curbing), Lighting, Envelope,

Structural (Foundation and Framing), Interior Elements (Stairways, Hallways, common Areas), Roofing Systems, Mechanical (HVAC), Plumbing & Electrical, Vertical Transportation, Life Safety/ADA/Compliance/AQ

• No Standardized Format– Costs $15k-$100k– Hard To Translate in General– Harder to Translate for Level and Volatility of Energy Use– No Cost of Capital Adjustments for EE or Energy Defficiency

Benchmarking Results

Result: Western Sample Significant Heterogeneity Between Hubs and Between Networks

Result: Dated Across HubsCross-sectional Differences in Electrical Exposure Across Regions

Volatility by Maturity (Western Hubs)Level of Volatility Higher In Short Maturity Contracts

Volatility by Maturity (Eastern/ERCOT)Level of Volatility Higher In Short Maturity Contracts

Natural Gas Implied VolatilitiesComparable to electricity hubs

Exceed both interest rate & office rents volatility

Valuation Strategy Conclusions• Empirical Default Hazard Model

– Statistically Significant Positive Coefficients– Loans Default when 10Y US Treasury Is Different than Loan coupon – Loans Default when the value of the loan relative to the value of the

building is high• Empirical Building Value Estimator

– Uses CoStar (brokers), Trepp (securitized) Data (Combined Data Set)– 10,000 Paths, Calculate Monthly CFLO– Estimator explains about 68% of observed variance in building prices– Log of natural gas and electricity prices have a positive effect on log price– Consumption levels have a negative effect on price

Summary Stats For Sample

Points Required To Price at Par

• 18.8 Points Charged Would Normalize Risk/Option Pricing • More Likely Overall Terms (LTV, DSCR) Would Change

Using Mortgages To Force Reduction