Who’s Getting Paid During the Subprime Crisis?

White Paper

Jennifer Christensen, Senior ConsultantYara Rogers-Silva, Consulting Statistician III

May 2008

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Table of Contents

Executive Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Observation Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Equifax Consumer Modeling Attributes . . . . . . . . . . . . . . . . . . . . 3

Performance Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Statistical Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Study Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Overall Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Payment Behavior by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Mortgage Amount. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Payment Behavior by Risk Score . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Age of Oldest Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Recent Delinquency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Revolving High Credit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Number of Inquiries Within 12 Months . . . . . . . . . . . . . . . . . . . 10

Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Executive Summary

Mortgage delinquencies have been increasing at a rapid pace sincefirst quarter 2006. Much of this delinquency is driven by highermonthly mortgage payments caused by ARMs (adjustable ratemortgages) that have entered their reset period. Anothercontributor is the record amount of equity withdrawal, which hasled to high debt service burdens and more financial pressure onconsumers. If consumers are unable to make all of their monthlypayments, they must choose which obligations they will pay eachmonth, and which they will either pay late, partially or not at all.In some instances, consumers will be forced to choose betweentheir bankcard, auto and mortgage.

Traditional industry consensus is that consumers typically paytheir bills according to a certain hierarchy that puts mortgage first,followed by auto, card and utility bills. Recently, however, therehas been speculation about whether or not there has been a changein this hierarchy such that consumers are prioritizing theirbankcard and auto loan payments over their mortgage payment.The objective of this study is to quantify how prevalent thisreprioritization is and to provide insight into the type of consumerswho exhibit this behavior.

Observations

This study found that 1.2 million consumers opened a mortgagein July 2005 and that 3.4% of them had a 60-day mortgagedelinquency over the next 24 months. Of those that had amortgage delinquency in the 24-month performance period fromJuly 2005 to July 2007, 38% kept their bankcards clean and 62%kept their auto loans clean.

As a means of comparison, Equifax conducted a similar study onmortgages opened in June 2002. That study found that ofconsumers who had a mortgage delinquency in the 24-monthperformance period from June 2002 to June 2004, 26% kept theirbankcards clean and 59% kept their auto loans clean. This showsthat the priority consumers give to auto loan payments has notchanged much over time; however, there is a marked difference inbankcard payment priority.

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“Traditionally, consumerstypically paid mortgages

first, then auto loans, creditcard and utility bills.”

This study used a subset of highly predictive attributes and scores,calculated as of July 2005, to create a profile of consumers whokeep their auto and bankcard loans current over the performancewindow, while struggling to meet their mortgage payments. Theprofile shows that these consumers have:

• More established credit histories.

• More trade lines.

• Better risk scores.

• Lower utilization of revolving lines.

• Fewer recent inquiries.

• Fewer historical delinquencies on their credit histories.

Methodology

Observation Period

The study included all mortgages opened in the U.S. during July2005, with 24 months of performance through July 2007. Thismortgage vintage was selected because it is known to havepoor performance relative to other mortgage vintages. It alsoreflects recent mortgage lending practices, such as the prevalenceof subprime and adjustable rate alternatives.

Equifax Consumer Modeling Attributes

This study makes use of Equifax Consumer Modeling Attributes(CMA Plus). The CMA Plus attributes are a comprehensiveaggregation of consumer credit behaviors, giving Equifaxcustomers a 360-degree view of the consumer credit file. Byevaluating all aspects of the consumer credit file from credithistory and type of accounts to delinquency patterns, publicrecords and consumer-generated inquiries, the CMA Plus utilizesthe full Equifax national consumer credit database to assist inpredicting the future behaviors of consumers. The CMA Plus is apowerful tool that gives insight into consumer credit behaviorpatterns to drive decision making processes across the account lifecycle. CMA Plus attributes are used in this study for attribute-levelanalysis of specific segments of the population.

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Performance Definitions

Figure 1: Performance Definitions

Statistical Testing

The non-parametric Wilcoxon Signed rank test was used toconfirm that the differences in the means of the variables shown inthe paper are indeed statistically significant.

Study Findings

Overall Results

Figure 2 below shows that consumers from the June 2002 mortgagevintage who had clean mortgage performance were more likely tohave an auto or bankcard delinquency than their counterparts fromthe July 2005 vintage. This suggests that, in the past, consumersmay have given higher priority to mortgage payments and lowerpriority to auto and bankcard payments, versus what we see today.

Figure 2: Mortgage Performance v. Auto Loan and Bankcard Performance

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Vintage

% with CleanBankcard

Performance

% withDelinquentBankcard

Performance

% withClean AutoPerformance

% withDelinquent

AutoPerformance

CleanMortgage

2002 92.5% 7.5% 97.0% 3.0%

2005 94.2% 5.8% 97.5% 2.5%

DelinquentMortgage

2002 26.4% 73.6% 59.4% 40.6%

2005 37.9% 62.1% 62.0% 38.0%

Term Definition

Population Mortgages opened in the U.S. during July 2005

PerformanceWindow

24 months from July 2005 - July 2007

MortgageDelinquencyCalculation

60+ days past due over the performance window on anymortgage from the July 2005 vintage. Performance that isless than 60 days past due was considered clean.

AutoDelinquencyCalculation

60+ days past due over the performance window on any autotrade in the credit file with an open status as of July 2005 oropened within 18 months after the mortgage open date.Delinquency less than 60 days past due was considered clean.

BankcardDelinquencyCalculation

60+ days past due over the performance window on anybankcard in the credit file with an open status as of July 2005or opened within 18 months after the mortgage open date.Delinquency less than 60 days past due was considered clean.

Figure 3 shows that consumers in the June 2002 mortgage vintagewith delinquent auto performance were more likely to havebankcard delinquency than consumers in the 2005 vintage (67.5%and 62.1%). This suggests that, in recent times, bankcards arereceiving higher priority from consumers than in the past.Conversely, those in the 2002 vintage with delinquent autoperformance were more likely to have mortgage delinquency thanthose in the 2005 vintage (33.4% vs. 36.2%), suggesting thatmortgage payments have lower priority now than in the past.

In both 2002 and 2005, consumers with a delinquent auto tradelinewere more likely than not to have bankcard delinquency (67.5%and 62.1%). Furthermore, consumers in both vintages withbankcard delinquency were less likely to have delinquent autotrades (24.6% vs. 25.0%). This suggests that consumers place ahigher priority on auto loan payments than bankcard payments.Results also suggest that auto loan payment priority has notchanged much over time, while bankcard payment priority hasbecome more important.

Figure 3: Auto Loan Performance v. Mortgage and Bankcard Performance

Overall, consumers continue to place the highest priority onmortgage payments. Of the consumers with delinquent autopayments in the 2002 vintage, 66.6% kept their mortgage cleancompared to 63.8% in the 2005 vintage. Findings were similar forconsumers with delinquent bankcards in the 2002 and 2005vintages, where 78.7% and 76.7% kept mortgages clean.

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Vintage

% withClean

BankcardPerformance

% withDelinquentBankcard

Performance

% with CleanMortgage

Performance

% withDelinquentMortgage

Performance

Clean Auto2002 92.2% 7.8% 97.8% 2.2%

2005 93.9% 6.1% 97.7% 2.3%

DelinquentAuto

2002 32.5% 67.5% 66.6% 33.4%

2005 37.9% 62.1% 63.8% 36.2%

“Bankcard payment hasbecome more important,while auto loan payment

priority has changedrelatively little.”

Figure 4: Bankcard Performance v. Auto Loan and Mortgage Performance

Payment Behavior By State

This study found that states that had the highest home prices as ofDecember 2005 (the period with peak home prices), followed bylarge price declines during the study's performance window, hadthe highest percentage of consumers who kept their auto andbankcards clean, while letting their mortgage go delinquent. Inthese states, many consumers stretched to purchase expensivehomes, assuming that home prices would continue to rise.Unfortunately, median home price values began to drop starting inthe first quarter of 2006, causing consumers to become "upsidedown" on their mortgages (the condition when the mortgagebalance exceeds the value of the house). With no equity in theirhomes, many of these consumers appeared willing to walk awayfrom their mortgage in order to keep their auto and access totheir credit cards. Figure 5 below shows auto and bankcarddelinquency for consumers who had a mortgage delinquency overthe 24-month performance window in Arizona, California, Florida,Massachusetts, Maryland, and Nevada (states with high homeprice peaks and subsequent falling home prices), compared to therest of the country.

Figure 5: Bankcard and Auto Loan Performance by State

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Population: Consumers with Mortgage Delinquency

State% with Clean

Bankcards

% withDelinquentBankcards

% withClean Auto

Trades

% withDelinquentAuto Trades

AZ, CA, FL, MA,MD, NV

43% 57% 67% 33%

All Other States 36% 64% 60% 40%

Total 38% 62% 62% 38%

Vintage

% withClean AutoPerformance

% withDelinquent

AutoPerformance

% with CleanMortgage

Performance

% withDelinquentMortgage

Performance

CleanBankcard

2002 98.7% 1.3% 99.2% 0.8%

2005 98.7% 1.3% 98.9% 1.1%

DelinquentBankcard

2002 75.4% 24.6% 78.7% 21.3%

2005 75.0% 25.0% 76.7% 23.3%

“With no equity in theirhomes, consumers are more

likely to forfeit mortgagepayments in order to keep

autos and bankcards.”

Mortgage Amount

Figure 6 shows that the mortgage loan amount may influenceconsumer behavior. Consumers with clean bankcard and autoloans have higher average mortgage amounts than those with autoand bankcard delinquencies. Consumers with higher mortgageamounts are likely to have higher mortgage payments and mayfind themselves in an adverse financial position due to ARM resetsor other life events. In many cases, they can simply no longerafford their mortgage payment and need to walk away from theirhome in order to retain their auto and access to credit via theircredit cards.

Figure 6: Mortgage Amount by Mortgage, Bankcard and Auto LoanPerformance

Payment Behavior by Risk Score

Figures 7 and 8 show payment behavior stratified by Beacon 5.0score quartiles at the loan origination time (July 2005). Beacon 5.0is a generic risk score that Equifax co-developed with Fair IsaacCorporation. It predicts the likelihood that a consumer willbecome a serious credit risk within 24 months from scoring. In thethree lowest scoring quartiles, consumers were the most likely tohave auto and bankcard delinquencies. This population is a highcredit risk population and consumers in this score range are likelyto have displayed poor credit payment behavior in the past. Theyare also likely to continue this behavior in the future.

Consumers who fall into the fourth quartile, which representsscores 663 and higher for the population with bankcards and 653and higher for the population with auto trades, are the most likelyto continue to pay their other financial obligations. Even in theevent of mortgage default, this population may be an area ofopportunity, especially for auto lenders, as 72 % of consumers inthis quartile will keep their auto loan clean even in the eventof a mortgage default. Further analysis also demonstrated,unsurprisingly, that those consumers who kept current on theirbankcard and auto tradelines had higher risk scores at observation,compared with those that went delinquent.

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Mortgage BankcardMortgageAmount Mortgage Auto

MortgageAmount

Delinquent Delinquent $185,466 Delinquent Delinquent $177,715

Delinquent Clean $217,737 Delinquent Clean $198,846

Clean Delinquent $178,322 Clean Delinquent $172,206

Clean Clean $214,588 Clean Clean $210,927

Figure 7: Bankcard Performance by BEACON 5.0

Figure 8: Auto Loan Performance by BEACON 5.0

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Age of Oldest Trade

Figure 9 displays the attributes “Age of Oldest Trade,” “Age ofOldest Bankcard Trade,” and “Age of Oldest Auto Trade.” Theseattributes are a measure of length of credit history. Both trade ageattributes show that consumers who maintain their bankcards andauto loans — even if they go delinquent on their mortgage — havelonger credit histories on average than those who do not.Consumers with more established credit histories may recognizethe benefits of maintaining a good credit history, such as morecredit options and more favorable interest rates.

Figure 9: Age of Oldest Trade (months) by Mortgage, Bankcard and Auto LoanPerformance

Recent Delinquency

Figure 10 shows that recent delinquency is correlated with autoand bankcard payment behavior. As expected, consumers withfewer historical delinquencies are more likely to maintain theirauto loans and bankcards going forward. Consumers with previ-ous delinquency may have limited capacity or willingness to paytheir monthly financial obligations as is reflected in the followingtables.

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Mortgage Bankcard

Avg.Age

OldestTrade

Avg. AgeOldest

BankcardTrade Auto

Avg.Age

OldestTrade

Avg. AgeOldest

BankcardTrade

Delinquent Delinquent 145 105 Delinquent 136 99

Delinquent Clean 157 118 Clean 147 110

Clean Delinquent 168 127 Delinquent 157 117

Clean Clean 196 160 Clean 188 153

“Consumers who maintainboth bankcard and auto

loans, even if they defaulton their mortgages,

typically have longer credithistories than those

who do not.”

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Figure 10: Number 30 Days Past Due Within 24 Months by Mortgage,Bankcard and Auto Loan Performance

Revolving High Credit

Figure 11 below shows that the revolving high credit amount iscorrelated with auto and bankcard payment behavior. The avail-able credit amount is a proxy for how credit worthy the customeris. As expected, more creditworthy customers are more likely tokeep their auto loan and bankcards current in the event that theybecome delinquent on their mortgages.

Figure 11: Revolving High Credit by Mortgage, Bankcard and Auto LoanPerformance

Number of Inquiries Within 12 Months

Figure 12 shows that the number of recent inquiries is correlatedwith auto loan and bankcard payment behavior. A flurry of recentcredit inquiries suggests that the consumer is in financial troubleand is seeking additional credit. Recent credit inquiries may alsoindicate that additional lines of credit may have been issued to aconsumer, but have not yet appeared on the consumer's creditreport. This is a telltale sign that the consumer's debt-to-incomeratio may be higher than anticipated.

Mortgage Bankcard

Number30-DayPastDues

Within24 Mos.

Number30-Day

Past DuesWithin 24

Mos.Bankcard

Accts. Auto

Number30-Day

Past DuesWithin 24

Mos.AutoAccts.

Number30-Day

Past DuesWithin 24Mos. Auto

Accts.

Delinquent Delinquent 3.15 1.08 Delinquent 3.80 0.97

Delinquent Clean 2.97 0.74 Clean 3.22 0.87

Clean Delinquent 2.91 1.20 Delinquent 3.89 0.95

Clean Clean 0.65 0.21 Clean 0.88 0.28

Presence ofMortgage 60

DPD Bankcard

High CreditOpen

RevolvingTrades Auto

High CreditOpen

RevolvingTrades

Delinquent Delinquent $22,128 Delinquent $18,925

Delinquent Clean $29,612 Clean $25,698

Clean Delinquent $31,592 Delinquent $25,509

Clean Clean $59,177 Clean $54,587

“As expected, creditworthycustomers are more likelyto keep their auto loansand bankcards current

should they becomedelinquent on their

mortgages.”

Figure 12: Number of Inquiries in 12 Months by Mortgage, Bankcard andAuto Loan Performance

Utilization

Figures 13 and 14 show that consumers with lower revolving creditutilization are more likely to maintain clean bankcard and autoloans in the event of mortgage delinquency. High revolvingutilization may indicate that a consumer is unable to afford theirmonthly credit obligations and may be using credit cards to paytheir bills. This trend is more observable in bankcard paymentbehavior than in auto payment behavior.

Figure 13: Bankcard Performance by Utilization

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Mortgage Bankcard

Number ofInquiries Within

12 Months Auto

Number ofInquiries Within

12 Months

Delinquent Delinquent 6.8 Delinquent 7.5

Delinquent Clean 6.1 Clean 6.5

Clean Delinquent 5.6 Delinquent 6.5

Clean Clean 3.9 Clean 4.2

Figure 14: Auto Loan Performance by Utilization

Conclusions

The purpose of this research paper was to verify an emergingsegment of consumers who maintain their auto and bankcardloans, while letting their mortgage go delinquent. The analysisclearly demonstrated that there are, in fact, consumers who displaythis type of payment behavior. The prevalence of this behavior wassomewhat surprising.

In the event of mortgage delinquency, consumers are more likelyto continue making their auto payment than their bankcardpayments. Of consumers that fall behind on mortgage payments,62% kept their auto loan current, while 38% kept their bankcardscurrent. This is understandable since consumers rely on theirautos to maintain their source of income; you can't drive yourbankcard to work.

The business implication is that traditional assumptions aboutconsumer payment behavior may not bear out over irregulareconomic conditions, such as the recent mortgage industry plight.Of course, this general business implication may not apply equallyto all portfolios. This analysis does demonstrate, however, thatcredit bureau data can help identify consumers who are likely toexhibit payment behavior that is contrary to what is accepted as thetraditional payment hierarchy. Armed with this information,

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financial institutions may be better able to quantify the spillovereffect from mortgage to auto and bankcard, which may lead todifferent portfolio management strategies and likely less dramaticpolicy shifts.

About the Authors:

Jennifer Christensen, Senior Consultant

Jennifer Christensen has been with Equifax for four years and isresponsible for client relationships, including evaluating clientstrategies and recommending appropriate, profitability drivensolutions. Jennifer has expertise in collections, and linemanagement strategy development and implementation.

Prior to joining Equifax, Jennifer was Senior Credit Risk Managerat Metris, where she managed the team that was responsible fordeveloping, evaluating and implementing collections and linemanagement strategies. This team was integral in preparing forfederal financial audits and for responding to their findings.Jennifer brings over 16 years of financial industry experience,including three years as a business analyst at Trans Union andthree years at First Card, where she initially served as pre-approved process manager and then as credit analyst supportingthe account management team.

Yara Rogers-Silva, Statistical Consultant III

Yara Rogers-Silva joined Equifax in January of 2007. She hasworked on a variety of customer engagements representingvarious industries, as well as developing risk models for EquifaxLatin America operations. Previously, Yara was Vice President ofstatistical modeling at SunTrust Banks, where she was responsiblefor the Statistical Modeling of all marketing programs for depositproducts. Prior to SunTrust, Yara worked as a Senior OperationsResearch Consultant at Delta Airlines developing predictivemodels for the maintenance division and managing revenue.

Yara holds an M.S. in Operations Research and Decision Sciencesfrom Rensselaer Polytechnic Institute and a BS in AppliedMathematics from the State University of Campinas, Brazil.

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