dataquick: intelligence solutions to combat short sale fraud

7/30/2019 DataQuick: Intelligence Solutions To Combat Short Sale Fraud

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Intelligence Solutions to CombatShort Sale Fraud

Transforming Information into Intelligence

DataQuick

[email protected]

1-888-299-8787

www.dataquick.com

Data & Analytic Best Practices


2/13

Transforming Information into Intelligencewww.dataquick.com Copyright 2013 DataQuick2

Overview

Te market collapse brought with it an unprecedented number odistressed properties that in most cases require careul dispositionplanning to minimize losses. Short sales have become one o the most

popular and eective strategies to achieve this objective providingborrowers a graceul exit and lenders, servicers and investors a least badresolution. However, with the onslaught o short sales has also come awave o raudulent activity. As short sales have increased in number, sotoo have short sale raudsters. In act, the Financial Crimes EnorcementNetwork reported that in 2012, 10% o the 100,000 suspicious activityreports fled relating to mortgage raud were classifed as short saleraud, up signifcantly rom 2011. In addition, a recent DataQuick studyound that 6.5% o all short sales had some type o suspicious activity.While no such suspicious activity was reported the previous year. Tereare, however, many strategies lenders, servicers, and investors can use tocounter the eorts o raudsters. Some o the most eective approachesleverage advanced data and analytics solutions to identiy likely raudhotbeds and more eectively target and respond to potentially raudulentactivity in real time.

Tis best practices guide will ocus on fve intelligence solutions that canbe deployed now to combat short sale raud:

Understandactivitylevelstoknowmarketswithgreatestfraudpotential

Knowthefraudsterprole

Implementearlywarningtriggers

Knowwhatsrightbeforetheoerismade

Leveragetechnologytoquicklyevaluatetheoer

Introduction Page 2

Best Practice #1 Page 3





Conclusion Page 11

Contents


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Understand Activity Levels to Know Markets with Greatest Fraud

Potential

Tis is the most general o the fve strategies, but it also provides the oundation or all o the other strategies you maydeploy.Quitesimply,itsnecessarytohaveaconstantowofmarketintelligencetounderstandwhereshortsalefraudactivity is most common. You must know where you have to be most vigilant.

DataQuick utilized its RiskFinder Distress product to identiy 205,177 short sales during the past two years in 14 o thelargestU.S.countiestoprovideageneralgeographicactivityroadmap.Avarietyofkeybasetrendsemergedfromthisanalysis

Figure 1 indicates where short sale activity is most common (i.e. where a heightened sense o raud awareness is required).Specifcally, short sales are, by ar, currently most common in Wayne County, MI. O the remaining counties, short salesappear to be more concentrated in Western counties than those in the East, although the short sale craze has clearlysubsided in Clark County, NV.

Source: DataQuick Neighborhood Level HPI

Figure 1

Short Sales as a Percentage of All Sales

Los Angeles, CA Hennepin, MN Miami-Dade, FL

San Diego, CA Maricopa, AZ Broward, FL Clark, NV

26%

38%

20% 19% 19%

15%15%

10%

Wayne, MI

40%

30%

20%

10%

0%

Source: DataQuicks RiskFinder Distress


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able 1 highlights other key trends to consider

Timingsincepeakactivityhelpsexplaintheresultsdescribedabove.Specically,thetimingofpeakactivityhasvariedwidelywithsomecountiessuchasMiami-Dade,FLpeakingmorethan2yearsagowhileothers,suchasLosAngeles, CA, peaking within the last 2-3 quarters.

etimingnaturallydrivesoverallYOYand24-monthchangesinshortsaleactivity.

ShortsalesinWayneCounty,MIarestillontherisewhichexplainswhytheyresuchalargepercentageofall

sales.

ActivityinSanDiegoCounty,CAisupsignicantlyoverthepast2yearsbutonlydownslightlyinthepastyearbecause the peak in this geography occurred just recently.

epeakisadistantmemoryinBrowardCounty,FLwhichexplainswhy24-monthandYOYactivityarebothdown so signifcantly.

Wayne, MI 5% 4% Apr-12 78%

San Diego, CA -2% 15% Aug-12 43%

Los Angeles, CA -3% 5% Oct-12 34%

Maricopa, AZ -24% -4% May-12 31%

Hennepin, MN 18% -22% May-11 26%

Broward, FL -18% -29% Jun-11 18%

Miami-Dade, FL 0% -34% Apr-11 27%

Clark, NV -78% -78% Aug-11 22%

YOY 24-MonthShort Sale Peak Percent of all Distressed Sales

Change in Short Sales

Table 1

Key Short Sale Metrics

Source: DataQuicks RiskFinder Distress


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Know the Fraudster Prole

Understandingwhereshortsalesactivityisconcentratedisagoodstart,butthereismuchmoreintelligencethatcanbeutilized to help pinpoint where you ace the greatest risk.

o more precisely segment the market based on raud potential, DataQuick leveraged its National Property Databaseto identiy suspicious short sales during the past 2 years and then compared these sales to all short sales to determine aproperty-centric raudster profle. Te profle and comparison were defned as ollows:

7,139shortsaleswereidentiedthattookplacebetweenApril2011andApril2013thatalsowerethenre-soldwithin 6 months o the short sale.

516ofthe7,133shortsalesweredeemedsuspiciousbecausethe(original)shortsalepricewaslessthan50%ofmarket value at the time o sale and the subsequent sale was at least 200% higher than the short sale price.

Whenthe516suspiciousshortsaleswerecomparedtothebase7,139shortsales,adeniteproleemerged.

When the two groups are segmented by county, it becomes obvious that there is a greater concentration o suspiciousactivity in Maricopa County, AZ (Figure 2). Specifcally, while Maricopa County, AZ accounted or only 41% o all shortsaleactivity,butitaccountedfor59%ofallsuspiciousactivity.

Geography: Beware of Maricopa County, AZ

Figure 2

Proling Suspicious Short Sales-Geography

Suspicious

All Short Sales

Clark Los Angeles Mariposa San Diego

40%

50%

60%

12%10%

15%

27%

59%

7%

41%

16%

30%

20%

10%

0%

Source: DataQuick National Property Database


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A variety o real estate trends vary based on the property value and suspicious short sale activity is no exception. Figure4 shows a much higher incidence o suspicious activity in lower-priced properties (less than $200,000), a lower thanexpected incidence in moderately- and high-priced properties ($250,000-$750,000), and as-expected activity with the

most expensive properties.

Property Value: Beware of Lower-Valued Properties

Likeanyproperty-relatedanalysis,though,itsimportanttorealizethatyoucantstopatthecountylevelasneighborhood/zip-level trends will vary widely within a county, which will clearly impact how you respond to short sale activity onspecifc properties. Figure 3 uses Maricopa County, AZ as an example o how suspicious activity varies widely betweenzip codes in a specifc geography. Some zip codes report lower than expected suspicious activity while others report higherthan expected levels.

Figure 3

Proling Suspicious Short Sales-Zip Level View, Maricopa County, AZ


85225 85037 85035 85017 89009 85204

4%

5%

6%

7%

3.5%

0.7%

1.6%

3.5%

1.6%

4.3%

4.6%

6.6%

2.7%

1.8%

1.6%

2.2%

3%

2%

1%

0%

Suspicious

All Short Sales

Figure 4

Proling Suspicious Short Sales-Price Band

< $100,000 $200,000-$299,999 $400,000-$499,999 $750,000+

$100,000-$199,999 $300,000-$399,999 $500,000-$749,999

20%

30%

40%

16%

25%

39%

28%

12%

10%

6%5%

3%

19% 19%

9%7%

2%

10%

0%


Suspicious

All Short Sales


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While the discrepancy is not as great as the frst two evaluations, Figure 5 does point to slightly higher than expectedsuspicious activity with multi-amily properties.

Tese three analysis are a sample o the dierent types o evaluations that could be completed to profle the short saleraudster. Specifc approaches will vary based on your requirements and history with short sale raud.

Property Type: Multi-Family Properties Could be a Problem

Figure 5

Proling Suspicious Short Sales-Property Type

Condominium Multi Family

Single Family Residence

40%

60%

80%

90%

70%

50%

30%

10% 9%6%

84% 85%

6%3%

20%

0%


Suspicious

All Short Sales


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Te frst two best practices centered on advanced deployment o intelligence to anticipate where short sale raud islikely to occur and who is more likely to perpetrate this raud. Tis helps guide your overall strategy on where todeployresources,butitsalsocriticalleveragetoolstoeectivelyandecientlyevaluatetheriskoffraudonspecicshort sale oers. Tese tools certainly rely on comprehensive data, but they also utilize advanced analytics anddecisioning to ensure a clear picture o potential raud on every oer.

Automation drives these types o solutions and delivers the beneft o a consistent, accurate, and ast analysis oall transactions. Tat said, the human element should never be taken out o the equation. Instead, the automatedsolutionoptimizesthedeploymentofprecioushumanresourcesbyaggingproblemtransactionsthatrequiretheevaluation o an expert while at the same time reeing review teams rom the drudgery o a ull evaluation on thetransactions thatbased on your business rulesconorm to your standards.

Figure 6 is an example o this type o integrated decisioning tool. Te process leverages a variety o resources tohighlight potential raud very early in the short sale processimmediately ater the property is listed. Tis solution isdeployed as ollows:

Onaveryregularbasis,allloanswithinaportfolioarematchedtoadatabaseofnationwidelistingsthatarecanbe updated daily or weekly.

Implement Early Warning Triggers

Portfolio

No Action

Daily Match Hit

No

Yes

Warning

Nationwide

Listings

Database

Lien & Credit Analysis

Identify all open

liens, CLTV

Evaluate payment

performance

Assess potential

short fall

Apply Fraudster

Profle

Geography

Value

Property Type

Property

Characteristics

Agent

Figure 6

An Early Warning System for Short Sales

Integrated, Automated Decisioning Solution


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Loansaggedasanewlistingarethensubjecttoathoroughevaluation:

erststepinthisevaluationisaLoanandCreditAnalysis.egoaloftheanalysisistoidentifyloansinyour portolio that have a high likelihood o short sale by comparing the listing price to the current propertyprice.

Next,aborrowersmotivationtopotentiallybendtherulesisevaluatedbyidentifyingallliensontheproperty,currentloan-to-value,andtheborrowersperformanceonalltheseliensaswellastheirotheropentradelines. A comprehensive review o all tradelines also helps identiy potential strategic deaulters. All otheseevaluationsarebasedonbusinessrules/thresholdsyouestablishthatareautomaticallydeployedintheprocesstoensuretheresultsoftheevaluationreectyourbusinessrequirements

Oncepotentialshortsalecandidatesareagged,theseloansarethenproledusingcriteriasuchasthosediscussed in the previous section to urther refne short sales into segments o high or low likelihood o raudAgain, this evaluation is automatic and consistently deployed across all loans based on specifc business rulesyou build into the process.

Loansidentiedwithbothhighlikelihoodofshortsaleandhighlikelihoodoffraudcanthenbeescalatedand given to your higher-end review sta or special handling and borrower ollow up.

Implement Early Warning Triggers


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Evenifyouvedecidedwhereyouhavetobemostvigilant,proledthemostcommonoenders,andaggedlistingsashaving a high potential or raud, you still need to make sure you have the right tools in place to evaluate every shortsale oer to determine the likelihood o raud. Te last two best practices do just this.

Fromatacticalstandpoint,itscriticaltoknowthediscountyoushouldexpectonashortsaleinrelationtoanarms-

lengthtransactionideallybeforeanyoersaremade.Luckily,thereareautomatedvaluationmodelsthathavebeendesigned specifcally to calculate discounts expected at dierent stages o deault. Figure 7 leverages this type o tooltoprovidespecicdiscountsthatcanbeexpectedforshortsalesindierentregionsofthecountry.Likemanyoftheother analysis in this guide, there is signifcant variation across the country that must be accounted or when evaluatinganyoer.Andalsolikemostoftheotheranalysisinthisguide,Figure8illustratesthatyoucantrelyoncounty-levelstatistics.ediscountratesforthedierentzipcodesinMiami-DadeCounty,FLclearlyshowthatzip-to-zipvariation must be actored into any decision to accept or reject a short sale and, just as important, the eort to detectpotential raud as quickly and accurately as possible.

Know whats right before the offer is made

Figure 7

Short Sale Discount Rates

Cuyahoga, OH

Wayne, MI

Broward, FL

King, WA

Suffolk, NY

Hennepin, MN

Maricopa, AZ

Queens, NY

San Diego, CA

Kings, NY

Miami-Dade, FL

Clark, NV

Fairfax, VA

Los Angeles, CA

12%

10%

16%

18%

19%

21%

20%

0% 5% 10% 15% 20%

7%

10%

9%

9%

9%

9%

9%


Figure 8

Short Sale Discount Rate Distribution-Miami-

Dade County, FL

11-15%6-10% 16-20% 20+%

16

8

28

17

6

1-5%

30

20

10

0



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Te fnal best practice builds on the last to provide an automated, more comprehensive evaluation o the short saleoerandamorein-depthviewofthepotentialforfraud.Utilizingavarietyofvaluationsources,lendersandinvestorsbeneft rom both a more thorough interrogation o the oer, but also receive more deensible documentation to justiytheir decision to accept or reject an oera critical requirement give the increased incidence o buy back requests.

AsillustratedinFigure9,thedecisionengineunfoldsasfollows:

Aconsensusvalueisdeterminedforthespecicpropertybasedonacustomexpertpanelthatyouselect.epanel can consist o a variety o valuation sources as outlined in Figure 6 below (red panel).

Basedonthedistributionofvalueswithintheexpertpanel,acondencescoreisdevelopedfortheconsensusvalue to allow you understand the level o accuracy associated with the consensus value.

eoerpriceiscomparedtotheconsensusvalueandeitheracceptedorrejectedbasedonatolerancelevelyoudefne. Te tolerance level can vary based on property type, geography, price band and a host o other variables.

Insomecases,clientschoosetoadjusttheconsensusvalue(andensuingcomparisontotheoerprice)byapplyingtheshortsalediscountpercentageforthesubjectpropertysspeciclocation.

Likesomeoftheothersolutionsdiscussedinthisguide,thisprocessisdrivenbyanautomateddecisioningenginewhich provides the confdence that all short sale oers are quickly evaluated in a consistent, accurate ashion.

Leverage technology to quickly evaluate the offer

Figure 9

Automated Short Sale Offer Validation

Use Expert Panel to

develop Consensus

Value

Condence

Score evaluates

distribution of

panel

Compare short sale

offer to Consensus

Value and Expert

Panel

ApplyShort

Sale Discount, if

necessary

Rules-Driven

Decision based on

pre-established

tolerance

Expert Panel

Appraisal Emulation Valuation Models

MLS Valuation Model

Freddie Mac HVE

Tax Assessed Value

HPI Index Value

Hedonic Valuation

Consensus Value w/ Short Sale Discount

Tax Assessed Value

Freddie Mac HVE

Hedonic Valuation

MLS Valuation Model

Appraisal Emulation Valuation Model

Consensus Value-Non Distressed

HPI Index Value

$60,000 $90,000 $120,000


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Shortsaleswillbeasignicantpieceoftherealestatefabricforsometime,anditshighlylikelythataslongasthereare short sales, there will be short sale raud. Te best practices outlined in this guide provide a strong oundation tocombat raud by profling where the risk is greatest and outlining integrated solutions to quickly assess specifc shortsaleoersasearlyintheprocessaspossible.esesolutionsare,however,meanttobejumpingopoints.Itscritical

tointegrateyourownuniquebusinessrequirementsandtheknowledgebaseyouvegainedfromyourownexperiencewithshortsalefraudintothesetypesofsolutionstoensurethatyouvedeployedthemostrelevant,eectivesolutionspossible.

Summary


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Transforming Information into Intelligence

DataQuick

[email protected]

1-888-299-8787

www.dataquick.com

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