dataquick: intelligence solutions to combat short sale fraud
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Intelligence Solutions to CombatShort Sale Fraud
Transforming Information into Intelligence
DataQuick
1-888-299-8787
www.dataquick.com
Data & Analytic Best Practices
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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
Best Practice #2 Page 5
Best Practice #3 Page 6
Best Practice #4 Page 9
Best Practice #5 Page 10
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
Source: DataQuick National Property Database
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%
Source: DataQuick National Property Database
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%
Source: DataQuick National Property Database
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%
Source: DataQuick National Property Database
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
Source: DataQuick National Property Database
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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
1-888-299-8787
www.dataquick.com
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