> White Paper
Who’s Getting Paid During the Subprime Crisis?
Jennifer Christensen, Senior Consultant
Yara Rogers-Silva, Consulting Statistician III
May 2008
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 since
first quarter 2006. Much of this delinquency is driven by higher
monthly mortgage payments caused by ARMs (adjustable rate
mortgages) that have entered their reset period. Another
contributor is the record amount of equity withdrawal, which has
led to high debt service burdens and more financial pressure on
consumers. If consumers are unable to make all of their monthly
payments, they must choose which obligations they will pay each
month, and which they will either pay late, partially or not at all.
In some instances, consumers will be forced to choose between
their bankcard, auto and mortgage.
Traditional industry consensus is that consumers typically pay
their bills according to a certain hierarchy that puts mortgage first,
followed by auto, card and utility bills. Recently, however, there
has been speculation about whether or not there has been a change
in this hierarchy such that consumers are prioritizing their
bankcard and auto loan payments over their mortgage payment.
The objective of this study is to quantify how prevalent this
reprioritization is and to provide insight into the type of consumers
who exhibit this behavior.
Observations
“Traditionally, consumers
typically paid mortgages
first, then auto loans, credit
card and utility bills.”
2
This study found that 1.2 million consumers opened a mortgage
in July 2005 and that 3.4% of them had a 60-day mortgage
delinquency over the next 24 months. Of those that had a
mortgage delinquency in the 24-month performance period from
July 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 on
mortgages opened in June 2002. That study found that of
consumers who had a mortgage delinquency in the 24-month
performance period from June 2002 to June 2004, 26% kept their
bankcards clean and 59% kept their auto loans clean. This shows
that the priority consumers give to auto loan payments has not
changed much over time; however, there is a marked difference in
bankcard payment priority.
This study used a subset of highly predictive attributes and scores,
calculated as of July 2005, to create a profile of consumers who
keep their auto and bankcard loans current over the performance
window, while struggling to meet their mortgage payments. The
profile 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 July
2005, with 24 months of performance through July 2007. This
mortgage vintage was selected because it is known to have
poor performance relative to other mortgage vintages. It also
reflects recent mortgage lending practices, such as the prevalence
of 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 comprehensive
aggregation of consumer credit behaviors, giving Equifax
customers a 360-degree view of the consumer credit file. By
evaluating all aspects of the consumer credit file from credit
history and type of accounts to delinquency patterns, public
records and consumer-generated inquiries, the CMA Plus utilizes
the full Equifax national consumer credit database to assist in
predicting the future behaviors of consumers. The CMA Plus is a
powerful tool that gives insight into consumer credit behavior
patterns to drive decision making processes across the account life
cycle. CMA Plus attributes are used in this study for attribute-level
analysis of specific segments of the population.
3
Performance Definitions
Term
Definition
Population
Mortgages opened in the U.S. during July 2005
Performance
Window
24 months from July 2005 - July 2007
Mortgage
Delinquency
Calculation
60+ days past due over the performance window on any
mortgage from the July 2005 vintage. Performance that is
less than 60 days past due was considered clean.
Auto
Delinquency
Calculation
60+ days past due over the performance window on any auto
trade in the credit file with an open status as of July 2005 or
opened within 18 months after the mortgage open date.
Delinquency less than 60 days past due was considered clean.
Bankcard
Delinquency
Calculation
60+ days past due over the performance window on any
bankcard in the credit file with an open status as of July 2005
or opened within 18 months after the mortgage open date.
Delinquency less than 60 days past due was considered clean.
Figure 1: Performance Definitions
Statistical Testing
The non-parametric Wilcoxon Signed rank test was used to
confirm that the differences in the means of the variables shown in
the paper are indeed statistically significant.
Study Findings
Overall Results
Figure 2 below shows that consumers from the June 2002 mortgage
vintage who had clean mortgage performance were more likely to
have an auto or bankcard delinquency than their counterparts from
the July 2005 vintage. This suggests that, in the past, consumers
may have given higher priority to mortgage payments and lower
priority to auto and bankcard payments, versus what we see today.
% with
% with
Delinquent
% with Clean Delinquent
% with
Bankcard
Auto
Bankcard
Clean Auto
Vintage Performance Performance Performance Performance
Clean
Mortgage
Delinquent
Mortgage
2002
92.5%
7.5%
97.0%
3.0%
2005
94.2%
5.8%
97.5%
2.5%
2002
26.4%
73.6%
59.4%
40.6%
2005
37.9%
62.1%
62.0%
38.0%
Figure 2: Mortgage Performance v. Auto Loan and Bankcard Performance
4
Figure 3 shows that consumers in the June 2002 mortgage vintage
with delinquent auto performance were more likely to have
bankcard delinquency than consumers in the 2005 vintage (67.5%
and 62.1%). This suggests that, in recent times, bankcards are
receiving higher priority from consumers than in the past.
Conversely, those in the 2002 vintage with delinquent auto
performance were more likely to have mortgage delinquency than
those in the 2005 vintage (33.4% vs. 36.2%), suggesting that
mortgage payments have lower priority now than in the past.
“Bankcard payment has
become more important,
while auto loan payment
priority has changed
relatively little.”
In both 2002 and 2005, consumers with a delinquent auto tradeline
were more likely than not to have bankcard delinquency (67.5%
and 62.1%). Furthermore, consumers in both vintages with
bankcard delinquency were less likely to have delinquent auto
trades (24.6% vs. 25.0%). This suggests that consumers place a
higher priority on auto loan payments than bankcard payments.
Results also suggest that auto loan payment priority has not
changed much over time, while bankcard payment priority has
become more important.
% with
% with
% with
Clean
Delinquent % with Clean Delinquent
Bankcard
Bankcard
Mortgage
Mortgage
Vintage Performance Performance Performance Performance
Clean Auto
Delinquent
Auto
2002
92.2%
7.8%
97.8%
2.2%
2005
93.9%
6.1%
97.7%
2.3%
2002
32.5%
67.5%
66.6%
33.4%
2005
37.9%
62.1%
63.8%
36.2%
Figure 3: Auto Loan Performance v. Mortgage and Bankcard Performance
Overall, consumers continue to place the highest priority on
mortgage payments. Of the consumers with delinquent auto
payments in the 2002 vintage, 66.6% kept their mortgage clean
compared to 63.8% in the 2005 vintage. Findings were similar for
consumers with delinquent bankcards in the 2002 and 2005
vintages, where 78.7% and 76.7% kept mortgages clean.
5
% with
% with
Delinquent % with Clean Delinquent
% with
Auto
Mortgage
Clean Auto
Mortgage
Vintage Performance Performance Performance Performance
Clean
Bankcard
2002
98.7%
1.3%
99.2%
0.8%
2005
98.7%
1.3%
98.9%
1.1%
Delinquent
Bankcard
2002
75.4%
24.6%
78.7%
21.3%
2005
75.0%
25.0%
76.7%
23.3%
Figure 4: Bankcard Performance v. Auto Loan and Mortgage Performance
Payment Behavior By State
“With no equity in their
homes, consumers are more
likely to forfeit mortgage
payments in order to keep
autos and bankcards.”
This study found that states that had the highest home prices as of
December 2005 (the period with peak home prices), followed by
large price declines during the study's performance window, had
the highest percentage of consumers who kept their auto and
bankcards clean, while letting their mortgage go delinquent. In
these states, many consumers stretched to purchase expensive
homes, assuming that home prices would continue to rise.
Unfortunately, median home price values began to drop starting in
the first quarter of 2006, causing consumers to become "upside
down" on their mortgages (the condition when the mortgage
balance exceeds the value of the house). With no equity in their
homes, many of these consumers appeared willing to walk away
from their mortgage in order to keep their auto and access to
their credit cards. Figure 5 below shows auto and bankcard
delinquency for consumers who had a mortgage delinquency over
the 24-month performance window in Arizona, California, Florida,
Massachusetts, Maryland, and Nevada (states with high home
price peaks and subsequent falling home prices), compared to the
rest of the country.
Population: Consumers with Mortgage Delinquency
% with Clean
Bankcards
% with
Delinquent
Bankcards
% with
Clean Auto
Trades
% with
Delinquent
Auto Trades
AZ, CA, FL, MA,
MD, NV
43%
57%
67%
33%
All Other States
36%
64%
60%
40%
Total
38%
62%
62%
38%
State
Figure 5: Bankcard and Auto Loan Performance by State
6
Mortgage Amount
Figure 6 shows that the mortgage loan amount may influence
consumer behavior. Consumers with clean bankcard and auto
loans have higher average mortgage amounts than those with auto
and bankcard delinquencies. Consumers with higher mortgage
amounts are likely to have higher mortgage payments and may
find themselves in an adverse financial position due to ARM resets
or other life events. In many cases, they can simply no longer
afford their mortgage payment and need to walk away from their
home in order to retain their auto and access to credit via their
credit cards.
Mortgage
Bankcard
Mortgage
Amount
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
Mortgage
Auto
Mortgage
Amount
Figure 6: Mortgage Amount by Mortgage, Bankcard and Auto Loan
Performance
Payment Behavior by Risk Score
Figures 7 and 8 show payment behavior stratified by Beacon 5.0
score quartiles at the loan origination time (July 2005). Beacon 5.0
is a generic risk score that Equifax co-developed with Fair Isaac
Corporation. It predicts the likelihood that a consumer will
become a serious credit risk within 24 months from scoring. In the
three lowest scoring quartiles, consumers were the most likely to
have auto and bankcard delinquencies. This population is a high
credit risk population and consumers in this score range are likely
to have displayed poor credit payment behavior in the past. They
are also likely to continue this behavior in the future.
Consumers who fall into the fourth quartile, which represents
scores 663 and higher for the population with bankcards and 653
and higher for the population with auto trades, are the most likely
to continue to pay their other financial obligations. Even in the
event of mortgage default, this population may be an area of
opportunity, especially for auto lenders, as 72 % of consumers in
this quartile will keep their auto loan clean even in the event
of a mortgage default. Further analysis also demonstrated,
unsurprisingly, that those consumers who kept current on their
bankcard and auto tradelines had higher risk scores at observation,
compared with those that went delinquent.
7
Figure 7: Bankcard Performance by BEACON 5.0
Figure 8: Auto Loan Performance by BEACON 5.0
8
Age of Oldest Trade
“Consumers who maintain
both bankcard and auto
loans, even if they default
on their mortgages,
typically have longer credit
histories than those
who do not.”
Figure 9 displays the attributes “Age of Oldest Trade,” “Age of
Oldest Bankcard Trade,” and “Age of Oldest Auto Trade.” These
attributes are a measure of length of credit history. Both trade age
attributes show that consumers who maintain their bankcards and
auto loans — even if they go delinquent on their mortgage — have
longer credit histories on average than those who do not.
Consumers with more established credit histories may recognize
the benefits of maintaining a good credit history, such as more
credit options and more favorable interest rates.
Avg. Avg. Age
Age
Oldest
Oldest Bankcard
Trade
Trade
Avg. Avg. Age
Age
Oldest
Oldest Bankcard
Trade
Trade
Mortgage
Bankcard
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
Auto
Figure 9: Age of Oldest Trade (months) by Mortgage, Bankcard and Auto Loan
Performance
Recent Delinquency
Figure 10 shows that recent delinquency is correlated with auto
and bankcard payment behavior. As expected, consumers with
fewer historical delinquencies are more likely to maintain their
auto loans and bankcards going forward. Consumers with previous delinquency may have limited capacity or willingness to pay
their monthly financial obligations as is reflected in the following
tables.
9
Mortgage
Bankcard
Number
Number 30-Day
30-Day Past Dues
Past
Within 24
Dues
Mos.
Within Bankcard
24 Mos.
Accts.
Auto
Number
30-Day Number
Past Dues 30-Day
Within 24 Past Dues
Mos. Within 24
Auto Mos. Auto
Accts.
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
Figure 10: Number 30 Days Past Due Within 24 Months by Mortgage,
Bankcard and Auto Loan Performance
Revolving High Credit
“As expected, creditworthy
customers are more likely
to keep their auto loans
and bankcards current
should they become
delinquent on their
mortgages.”
Figure 11 below shows that the revolving high credit amount is
correlated with auto and bankcard payment behavior. The available credit amount is a proxy for how credit worthy the customer
is. As expected, more creditworthy customers are more likely to
keep their auto loan and bankcards current in the event that they
become delinquent on their mortgages.
Bankcard
High Credit
Open
Revolving
Trades
Auto
High Credit
Open
Revolving
Trades
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
Presence of
Mortgage 60
DPD
Figure 11: Revolving High Credit by Mortgage, Bankcard and Auto Loan
Performance
Number of Inquiries Within 12 Months
Figure 12 shows that the number of recent inquiries is correlated
with auto loan and bankcard payment behavior. A flurry of recent
credit inquiries suggests that the consumer is in financial trouble
and is seeking additional credit. Recent credit inquiries may also
indicate that additional lines of credit may have been issued to a
consumer, but have not yet appeared on the consumer's credit
report. This is a telltale sign that the consumer's debt-to-income
ratio may be higher than anticipated.
10
Mortgage
Bankcard
Number of
Inquiries Within
12 Months
Auto
Number of
Inquiries 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 12: Number of Inquiries in 12 Months by Mortgage, Bankcard and
Auto Loan Performance
Utilization
Figures 13 and 14 show that consumers with lower revolving credit
utilization are more likely to maintain clean bankcard and auto
loans in the event of mortgage delinquency. High revolving
utilization may indicate that a consumer is unable to afford their
monthly credit obligations and may be using credit cards to pay
their bills. This trend is more observable in bankcard payment
behavior than in auto payment behavior.
Figure 13: Bankcard Performance by Utilization
11
Figure 14: Auto Loan Performance by Utilization
Conclusions
The purpose of this research paper was to verify an emerging
segment of consumers who maintain their auto and bankcard
loans, while letting their mortgage go delinquent. The analysis
clearly demonstrated that there are, in fact, consumers who display
this type of payment behavior. The prevalence of this behavior was
somewhat surprising.
In the event of mortgage delinquency, consumers are more likely
to continue making their auto payment than their bankcard
payments. Of consumers that fall behind on mortgage payments,
62% kept their auto loan current, while 38% kept their bankcards
current. This is understandable since consumers rely on their
autos to maintain their source of income; you can't drive your
bankcard to work.
The business implication is that traditional assumptions about
consumer payment behavior may not bear out over irregular
economic conditions, such as the recent mortgage industry plight.
Of course, this general business implication may not apply equally
to all portfolios. This analysis does demonstrate, however, that
credit bureau data can help identify consumers who are likely to
exhibit payment behavior that is contrary to what is accepted as the
traditional payment hierarchy. Armed with this information,
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financial institutions may be better able to quantify the spillover
effect from mortgage to auto and bankcard, which may lead to
different portfolio management strategies and likely less dramatic
policy shifts.
About the Authors:
Jennifer Christensen, Senior Consultant
Jennifer Christensen has been with Equifax for four years and is
responsible for client relationships, including evaluating client
strategies and recommending appropriate, profitability driven
solutions.
Jennifer has expertise in collections, and line
management strategy development and implementation.
Prior to joining Equifax, Jennifer was Senior Credit Risk Manager
at Metris, where she managed the team that was responsible for
developing, evaluating and implementing collections and line
management strategies. This team was integral in preparing for
federal 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 and
three years at First Card, where she initially served as preapproved process manager and then as credit analyst supporting
the account management team.
Yara Rogers-Silva, Statistical Consultant III
Yara Rogers-Silva joined Equifax in January of 2007. She has
worked on a variety of customer engagements representing
various industries, as well as developing risk models for Equifax
Latin America operations. Previously, Yara was Vice President of
statistical modeling at SunTrust Banks, where she was responsible
for the Statistical Modeling of all marketing programs for deposit
products. Prior to SunTrust, Yara worked as a Senior Operations
Research Consultant at Delta Airlines developing predictive
models for the maintenance division and managing revenue.
Yara holds an M.S. in Operations Research and Decision Sciences
from Rensselaer Polytechnic Institute and a BS in Applied
Mathematics from the State University of Campinas, Brazil.
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