2015 V43 4 pp. 993–1034DOI 10.11111540-6229.12105RE.docx

2015 V43 4: pp. 993–1034
DOI: 10.1111/1540-6229.12105
REAL ESTATE
ECONOMICS
A Tale of Two Tensions: Balancing Access
to Credit and Credit Risk in Mortgage
Underwriting
Marsha J. Courchane,* Leonard C. Kiefer** and Peter M.
Zorn***
Over the years 2000–2007, mortgage market underwriting
conditions eased in
response to public policy demands for increased
homeownership. This eas-
ing of acceptable credit risk in order to accommodate increased
access to
credit, when coupled with the unanticipated house price
declines during the
Great Recession, resulted in substantial increases in
delinquencies and fore-
closures. The response to this mortgage market crisis led to
myriad changes in
the industry, including tightened underwriting standards and
new market regu-
lations. The result is a growing concern that credit standards are
now too tight,
restricting the recovery of the housing market. Faced with this
history, policy an-
alysts, regulators and industry participants have been forced to

consider how
best to balance the tension inherent in managing mortgage
credit risk without
unduly restricting access to credit. Our research is unique in
providing explicit
consideration of this trade-off in the context of mortgage
underwriting. Using
recent mortgage market data, we explore whether modern
automated under-
writing systems (AUS) can be used to extend credit to
borrowers responsibly,
with a particular focus on target populations that include
minorities and those
with low and moderate incomes. We find that modern AUS do
offer a potentially
valuable tool for balancing the tensions of extending credit at
acceptable risks,
either by using scorecards that mix through-the-cycle and stress
scorecard ap-
proaches or by adjusting the cutpoint—more relaxed cutpoints
allow for higher
levels of default while providing more access, tighter cutpoints
accept fewer
borrowers while allowing less credit risk.
Introduction
U.S. residential mortgage markets changed dramatically during
the past sev-
eral years. In the early 2000s, public policy focused on
expanding credit
access and homeownership and specifically targeted a reduction
in the home-
ownership gap between minority and non-minority households
and between

*Charles River Associates or [email protected]
**Freddie Mac or [email protected]
***Freddie Mac or [email protected]
C© 2015 American Real Estate and Urban Economics
Association
994 Courchane, Kiefer and Zorn
higher and lower income families.1 Relaxation of underwriting
standards, ac-
companied by a surge in subprime lending and an attendant
proliferation of
new products, resulted in many borrowers who could not meet
traditional un-
derwriting standards being able to obtain home mortgages and
achieve home
ownership.
However, the environment changed with the mortgage market
crisis of 2007
and 2008 when the subprime sector collapsed nearly entirely
and delinquency
and foreclosure rates increased throughout the country. In
response, underwrit-
ing standards tightened and legislation was passed imposing
more stringent
regulations on the mortgage industry, particularly the Dodd-
Frank Act reg-
ulations, which introduced both Qualified Mortgage (“QM”) and
Qualified
Residential Mortgage (“QRM”) standards. While providing
assurance that
the performance of recent mortgage originations will reduce the
likelihood of

another housing crisis, this tightening of standards comes at a
significant cost
in terms of access to credit. Balancing the tension between
access to credit
and the management of credit risk remains an ongoing concern.
The rich history of mortgage performance data over this period
offers an
opportunity to better distinguish mortgage programs and
combinations of
borrower and loan characteristics that perform well in stressful
economic en-
vironments from those that do not. The relaxed underwriting
standards of the
2000s provide plentiful performance information on borrowers
who stretched
for credit, but then experienced the stressful post-origination
environment of
declining house prices and rising unemployment. While many of
these loans
performed poorly, a large number performed well. Our goal is to
identify the
characteristics that distinguish between these two groups.
We specifically examine whether the recent data can be used to
create a mod-
ern automated underwriting scorecard that effectively and
responsibly extends
mortgage credit to the general population, and to underserved or
targeted bor-
rowers who reside in low-income communities, make low down
payments
and have poorer credit histories. Our analysis focuses on
mortgage under-
writing, rather than mortgage pricing. This reflects the two-
stage approach

to mortgage lending broadly practiced in the United States—
originators first
underwrite applications to determine whether they qualify for
origination, and
then price the loans that are originated successfully.
1For example, former United States Department of Housing and
Urban Development
(HUD) Secretary Mel Martinez states in 2002 that “The Bush
Administration is
committed to increasing the number of Americans, particularly
minorities, who own
their own homes.”
A Tale of Two Tensions 995
There are four steps necessary to complete this exercise. First,
we empirically
estimate a mortgage delinquency model. Second, we convert the
estimated
delinquency model to an underwriting scorecard for assessing
risk, where
higher scores signify higher risk. Third, we determine a
scorecard value (a
“cutpoint” or risk threshold) that demarcates the marginal risk
tolerance—
score values equal to or below the cutpoint are viewed as
acceptable risk;
score values above the cutpoint are not. Fourth, we process
borrowers through
this prototype of an automated underwriting system. We then
determine the
proportion of the population of mortgage applicants that is
within acceptable

risk tolerances, and the historic performance of these
“acceptable” loans.
The main data we use for this analysis are loan-level
observations from
CoreLogic on mortgages originated in the conventional (prime
and subprime)
and government sectors from 1999 through 2009. For each of
the three market
sectors, we separately estimate the probability that borrowers
will become
90-days or more delinquent on their loans within the first three
years after
origination. Included in the model are standard controls for
borrower and loan
characteristics, as well as for key macroeconomic factors
affecting mortgage
performance post-origination (specifically, changes in house
prices, interest
rates and unemployment rates).
Underwriting scorecards provide ex ante assessments of
mortgage risk at
origination, so creating scorecards requires appropriate
treatment of the
post-origination variables in our estimated models. Two broad
approaches are
possible. One approach attempts to forecast post-origination
variables across
borrower locations and over time. The other approach sets post-
origination
variables to constant values for all borrowers and all time
periods. We use
the latter approach. Specifically, we create two separate
scorecards. The first
scorecard sets post-origination values of house prices, interest

rates and un-
employment rates to their constant long run average levels (a
“through-the-
cycle” scorecard). The through-the-cycle scorecard is inherently
“optimistic”
with respect to credit risk, and therefore reflects a focus on
access to credit.
The second scorecard sets post-origination values of house
prices, interest
rates and unemployment rates to the varying ex post values
defined by the
Federal Reserve in an adverse scenario (a “stress” scorecard) as
defined in the
2014 supervisory stress test for very large banking
organizations.2 The stress
scorecard focuses on “tail” events that are unlikely to occur and
is meant to
prevent crisis outcomes such as those observed during the Great
Recession.
This scorecard therefore represents a focus on credit risk
management.
2See http://www.federalreserve.gov/bankinforeg/stress-
tests/2014-appendix-a.htm.
The next challenge requires choosing appropriate scorecard
cutpoints for
delimiting loans within acceptable risk tolerances. This, in
combination with
the choice of scorecard, is where much of the tension between
credit access
and credit risk resides. Higher cutpoints provide greater access

at the cost of
increasing credit risk; lower cutpoints limit credit risk but
restrict access.
As the choice of a cutpoint is a complicated policy/business
decision, we
provide results for a variety of possible cutpoints, ranging from
a low of a
5% delinquency rate to a high of a 20% delinquency rate. In an
effort to put
forward a possible compromise between access and credit risk,
we explore in
more detail results for alternative cutpoints that are market-
segment-specific;
5% for prime loans, 15% for subprime loans and 10% for
government loans.
We argue that these values represent reasonable risk tolerances
by approxi-
mating the observed delinquency rates in these segments
between 1999 and
2001.
The combination of scorecards and cutpoints creates working
facsimiles of
modern AUS, and we apply these systems to both the full and
target pop-
ulations.3 For this exercise, our “target” population is defined
as borrowers
with loan-to-value (“LTV”) ratios of 90% or above, with FICO
scores of 720
or below or missing, and who are located in census tracts with
median in-
comes below 80% of area median income. This group is
generally reflective
of “underserved” borrowers for whom there is particular policy
concern.

We find that automated underwriting, with a judicious
combination of score-
card and cutpoint choice, offers a potentially valuable tool for
balancing the
tensions of extending credit at acceptable risks. One approach
entails using
scorecards that mix the through-the-cycle and stress scorecard
approaches
to post-origination values of key economic variables. Moving
closer to a
through-the-cycle scorecard provides more focus on access to
credit. Moving
closer to a stress scorecard provides more focus on the control
of risk. The
second approach is to adjust the cutpoint—more relaxed
cutpoints allow for
higher levels of default while providing more access, tighter
cutpoints have
accept fewer borrowers while allowing less credit risk.
Previous Literature
A considerable body of research has examined outcomes from
the mortgage
market crisis during the past decade. Of particular relevance for
this research
3We weight the data using weights based on the proportion of
the target population in
the Home Mortgage Disclosure data (“HMDA”) to ensure that
the target population
in our data is representative of the target population in HMDA.
This allows us to draw
inferences to the full population.

are studies that examine specific underwriting standards and
products that
may be intended for different segments of the population, or
that address the
balancing of access to credit and credit risk.
A recent paper by Quercia, Ding, and Reid (2012) specifically
addresses the
balancing of credit risk and mortgage access for borrowers—the
two tensions
on which we focus. Their paper narrowly focuses on the
marginal impacts of
setting QRM product standards more stringently than those for
QM.4 They
find that the benefits of reduced foreclosures resulting from the
more stringent
product restrictions on “LTV” ratios, debt-to-income ratios
(“DTI”) and credit
scores do not necessarily outweigh the costs of reducing
borrowers’ access
to mortgages, as borrowers are excluded from the market.
Pennington-Cross and Ho (2010) examine the performance of
hybrid and ad-
justable rate mortgages (ARMs). After controlling for borrower
and location
characteristics, they find that high default risk borrowers do
self-select into
adjustable rate loans and that the type of loan product can have
dramatic im-
pacts on the performance of mortgages. They find that interest
rate increases

over 2005–2006 led to large payment shocks and with house
prices declin-
ing rapidly by 2008, only borrowers with excellent credit
history and large
amounts of equity and wealth could refinance.
Amromin and Paulson (2009) find that while characteristics
such as LTV,
FICO score and interest rate at origination are important
predictors of defaults
for both prime and subprime loans, defaults are principally
explained by house
price declines, and more pessimistic contemporaneous
assumptions about
house prices would not have significantly improved forecasts of
defaults.
Courchane and Zorn (2012) look at changing supply-side
underwriting stan-
dards over time, and their impact on access to credit for target
populations
of borrowers.5 They use data from 2004 through 2009,
specifically focusing
on the access to and pricing of mortgages originated for
African-American
and Hispanic borrowers, and for borrowers living in low-income
and minor-
ity communities. They find that access to mortgage credit
increased between
2004 and 2006 for targeted borrowers, and declined
dramatically thereafter.
The decline in access to credit was driven primarily by the
improving credit
mix of mortgage applicants and secondarily by tighter
underwriting standards

4For details of the QRM, see Federal Housing Finance Agency,
Mortgage Market Note
11-02. For details of the QM, see
http://files.consumerfinance.gov/f/201310_cfpb_qm-
guide-for-lenders.pdf.
5See also Courchane and Zorn (2011, 2014) and Courchane,
Dorolia and Zorn (2014).
associated with the replacement of subprime by FHA as the
dominant mode
of subprime originations.
These studies all highlight the inherent tension between access
to mortgage
credit and credit risk. They also stress the difficulty in finding
the “cor-
rect” balance between the two, and suggest the critical
importance of treat-
ing separately the three mortgage market segments—prime,
subprime and
government-insured (FHA)—because of the different borrowers
they serve
and their differing market interactions. The research also
provides some op-
timism that a careful examination of recent lending patterns will
reveal op-
portunities for responsibly extending credit while balancing
attendant credit
risks.
Data

Our analysis uses CoreLogic data for mortgages originated
between 1999
and 2009. The CoreLogic data identify prime (including Alt-A),
subprime
and government loans serviced by many of the large, national
mortgage
servicers. These loan-level data include information on
borrower and loan
product characteristics at the time of origination, as well as
monthly updates
on loan performance through 2012:Q3. Merged to these data are
annual
house price appreciation rates at a ZIP code level from the
Freddie Mac
Weighted Repeat Sales House Price Index, which allow us to
update borrower
home equity over time.6 We prefer this house price index to the
FHFA’s,
as the latter are not available at the ZIP code level. The
CoreLogic data
do not provide Census tract information, so we use a crosswalk
from ZIP
codes to 2000 Census tracts.7 We also merge in county-level
unemployment
rates from the Bureau of Labor Statistics, which are seasonally
adjusted by
Moody’s Analytics.8 Finally, we include changes in the
conventional mortgage
market’s average 30-year fixed mortgage (“FRM”) rate reported
in Freddie
Mac’s Primary Mortgage Market Survey.9
The CoreLogic data are not created through a random sampling
process and
so are not necessarily representative of the overall population,
or our target

6While these data are not publicly available, the metro/state
indices can be found
which are available at:
http://www.freddiemac.com/finance/fmhpi/.
7Missouri Census Data Center, available at:
http://mcdc.missouri.edu/
websas/geocorr12.html.
8The unemployment rate is from the BLS Local Area
Unemployment Statistics
(http://www.bls.gov/lau/).
9These data are available publicly at:
http://www.freddiemac.com/pmms/pmms30.htm.
population. This is not a problem for estimating our
delinquency model, but it
does create concern for drawing inference with our scorecards.
To address this
potential concern, we apply appropriate postsample weights
based on HMDA
data to enhance the representativeness of our sample. We
develop weights by
dividing both the HMDA and the CoreLogic data into
categories, and then
weight so that the distribution of CoreLogic loans across the
categories is the
same as that for HMDA loans. The categories used for the
weighting are a
function of loan purpose (purchase or refinance), state, year of
origination and
loan amount. Because we rely on a postsample approach and
cannot create

categories that precisely define our target population, our
weighting does not
ensure representativeness of the CoreLogic data for this group.
Nevertheless,
it likely offers a significant improvement over not weighting.
We also construct a holdout sample from our data to use for
inference. This
ensures that our estimated models are not overfitted. The
holdout sample was
constructed by taking a random (unweighted) sample of 20% of
all loans in
our database. All summary statistics and estimation results
(Tables 1 and 2
and Appendix) are reported based on the unweighted 80%
estimation sample.
Consistent with our focus on identifying responsible credit
opportunities, we
restrict our analysis to first lien, purchase money mortgage
loans. Summary
statistics for the continuous variables used in our delinquency
estimation are
found in Table 1. Table 2 contains summary statistics for the
categorical
variables.
As shown in Table 1, the average LTV at origination is 97% for
government
loans. This is considerably higher than for the prime market,
where first
lien loans have LTVs less than 80%, on average.10 We also
observe the
expected differences in FICO scores, with an average FICO
score in the prime
sector of 730, 635 for subprime and 674 for government loans.
The prime

market loan amount (i.e., unpaid principal balance, or UPB, at
origination)
averages $209,000 with the government loan amount the lowest
at a mean of
$152,000. The mean value in the subprime population is below
that for prime
at $180,000. DTI ratios do not differ much between prime and
government
loans, and the DTI for subprime is unavailable in the data. As
DTI is a key
focus in the efforts of legislators to tighten underwriting
standards, we use it
when available for estimation. The equity measures post-
origination reflects
the LTV on the property as house prices change in the area.
All three markets faced significant house price declines, as
captured by the
change in home equity one, two or three years after origination.
For all three
10The mean LTV for subprime mortgages is surprisingly low at
83%, although this
likely reflects the absence of recording second lien loans, which
would lead to a higher
combined LTV.
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2.
72
%
2.
72
%
Table 2 � Summary statistics for categorical (class) variables
(80% estimation

sample)—statistics not weighted.
All Prime Subprime Government
ARM 12.60% 48.50% 4.72% 14.91%
Balloon 0.39% 4.91% 0.05% 0.82%
FRM-15 7.68% 1.61% 1.16% 5.63%
FRM-30 68.25% 22.31% 90.14% 67.79%
FRM-Other 4.48% 1.71% 2.73% 3.81%
Hybrid 6.59% 20.97% 1.20% 7.04%
Other 41.05% 33.13% 43.57% 40.71%
Retail 33.70% 21.20% 22.12% 29.87%
Wholesale 25.25% 45.67% 34.31% 29.42%
Full Documentation 29.83% 49.38% 41.80% 34.52%
Missing 38.89% 18.44% 42.30% 37.35%
Not Full Documentation 31.27% 32.19% 15.90% 28.13%
Owner Occupied 83.43% 85.88% 91.84% 85.48%
Not Owner Occupied 16.57% 14.12% 8.16% 14.52%
Condo 13.82% 7.75% 6.97% 11.70%
Single Family 86.18% 92.25% 93.03% 88.30%
mortgage market segments, post-origination equity measures
(post-origination
estimated LTV) averaged over 90%. Post-Origination
unemployment rates are
highest, on average, in the geographies with government loans,
although the
differentials among market segments fell after three-year post-
origination.
Table 2 presents the summary statistics for the categorical
(class) variables
in our sample. Some expected results emerge. The subprime
segment has the
largest share of loans originated through the wholesale channel
at 45.7%,

while the wholesale share for the prime segment was only
25.2%. Nearly half
(48.5%) of subprime loans were “ARM” loans, while only
22.3% of subprime
loans were the standard 30-year FRM product. In contrast,
69.1% of prime
loans were 30-year FRMs and an additional 7.8% were 15-year
FRMs. Nearly
all of the government loans (91.2%) were 30-year FRMs. The
documentation
figures are somewhat surprising, with nearly half (49.4%) of
subprime loans
indicating full documentation. The low share of full
documentation loans in
the prime sector (about 30%) likely reflects the inclusion of
Alt-A loans,
which are defined to be prime loans in the CoreLogic data.11
In our analyses, we focus on access to credit and credit risk
outcomes for all
borrowers. However, many homeownership and affordable
lending programs
11Historically, Alt-A loans were originated through prime
lenders, offering their more
credit worthy customers a simpler origination process.
focus more narrowly on assessing opportunities for responsibly
extending
mortgage credit to borrowers with low down payments and poor
credit his-
tories, or who are otherwise underserved by the prime market

(“target pop-
ulation”). As a result of long standing public policy objectives
focused on
the value of homeownership, both government insured mortgage
programs
(such as FHA) and the GSEs have long held missions to meet
the needs of
underserved borrowers, including low income, minority and
first-time home-
buyers.12 Programs meeting this mission are tasked with
balancing access to
credit for borrowers with any attendant increases in credit risk.
Therefore, aside from our focus on the opportunities provided to
the full pop-
ulation of borrowers, we also provide an analysis of scorecard
outcomes for a
specific target population. We define this target population as
borrowers who
receive first lien, purchase money mortgages on owner-occupied
properties
located in census tracts with median incomes below 80% of the
area median
income, with FICO scores less than or equal to 720 and with
LTV ratios
greater than or equal to 90%.
Limiting our analysis to borrowers who live in lower income
census tracts is
especially constraining, as many borrowers with high LTVs and
lower FICO
scores live elsewhere. However, our data lack accurate income
measures, and
public policy considerations encourage us to include an income
constraint in
our definition of the target population.13 As a consequence,

loans to target
borrowers account for a small percentage of the total loans
made during our
period of study (roughly 4%). We can be assured, however, that
our target
population is composed of borrowers who are an explicit focus
of public
policy.
Figure 1 provides a graphical illustration of the HMDA-
weighted distribution
of target population loans in our sample across the three market
segments.
The dramatic shift over time in the share going to the
government sector is
obvious, as is the reduction in the number of loans originated to
the target
population by all three segments, combined, post-crisis.
12Both the Community Reinvestment Act (CRA) and the
Federal Housing Enterprises
Financial Safety and Soundness Act of 1992 (the 1992 GSE Act)
encouraged mortgage
market participants to serve the credit needs of low- and
moderate-income borrowers
and areas.
13For example, GSE affordable goals are stated with respect to
low- and moderate-
income borrowers and neighborhoods.
Figure 1 � Target population by year and market segment
(weighted).

Analysis
Our analysis begins with the estimation of mortgage
performance models over
the crisis period. We use loan-level origination data from 1999
through 2009
to estimate models of loans becoming 90-days or more
delinquent in the first
three years after origination. These models include standard
borrower and loan
characteristics at origination, as well as control variables
measuring changes
in house prices, unemployment rates and interest rates post-
origination. They
also include several interaction terms for the borrower, loan and
control
variables.
We then use our estimated delinquency models to specify two
underwrit-
ing scorecards—a through-the-cycle scorecard and a stress
scorecard.14 We
next apply various cutpoints (risk thresholds) to our scorecards
to define
levels of acceptable risk. By definition, loans with risk scores
(delinquency
probabilities) at or below the cutpoint are assumed to be within
appropriate
(acceptable) risk tolerances.
The scorecard and cutpoint combinations provide working
prototypes of an
AUS. Our final step applies these prototypes to the full and
target populations
and assesses the results.

Estimating the Models
We estimate three separate delinquency models based on an
80% sample
of first-lien, purchase money mortgage loans in our data.
Separate models
14Additional scorecards constructed using “perfect foresight”
and macroeconomic
forecasts are available from the authors upon request.
were estimated for prime loans (including Alt-A loans),
subprime loans and
government loans, using an indicator provided in the CoreLogic
data to as-
sign each loan to its appropriate segment.15 We estimate
separate models for
each market sector because we believe that there is clear market
segmenta-
tion in mortgage lending. In the conventional market the
lenders, industry
practices, market dynamics and regulatory oversight have
differed between
the prime and subprime segments.16 A similar distinction exists
between the
conventional and government segments—the latter focuses on
first-time bor-
rowers and lower income households. Moreover, acceptable risk
tolerances
will necessarily vary across segments, as may concerns
regarding access to

credit.
Our process differs from the typical construction of
underwriting …
Part I
a
The Subprime Market Takes Off
The astonishing thing about the subprime crisis is that
something so small wreaked so much havoc. Subprime loans
started out as just a pocket of the U.S. home loan
market, then mutated like a virus into a crisis of global
proportions. Along the way,
brokers, lenders, investment banks, rating agencies, and—for a
time—investors made
a lot of money while borrowers struggled to keep their homes.
The lure of money
made the various actors in the subprime food chain ever more
brazen and, with each
passing year, subprime crowded out safe, prime loans, putting
more homeowners at
risk of losing their homes and ultimately pushing the entire
world economy to the
edge of a cliff.
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2
a

The Emergence of the Subprime Market
Abusive subprime lending burst into public consciousness in
2007, but its legacy dated back years. As early as the 1990s,
consumer advocates were reporting pred-
atory lending in lower-income neighborhoods. This early period
was the fi rst iteration
of subprime lending. Only later did subprime loans morph into
products that ulti-
mately brought down the fi nancial system.
FROM CREDIT RATIONING TO CREDIT GLUT
To trace the emergence of subprime lending, we have to begin
with the home mort-
gage market in the 1970s. Back then, mortgage lending was the
sleepy province of
community thrifts and banks. Banks took deposits and plowed
them into fi xed-rate
loans requiring down payments of 20 percent. Consumers
wanting mortgage loans
went to their local bank, where loan offi cers helped them fi ll
out paper applications.
The applications then went to the bank’s back offi ce for
underwriting. Using pencils
and adding machines, underwriters calculated loan-to-value and
debt-to-income
ratios to determine whether the applicants could afford the
loans. In addition, under-
writers drew on their knowledge of the community to assess
whether the customers
were “good folk” who would repay their loans.
Banks kept their loans in their portfolios and absorbed the loss
if borrowers
defaulted. Knowing that they bore the risk if loans went bad,

lenders made conserva-
tive lending decisions. They shied away from applicants with
gaps in employment, late
payments on bills, and anything less than solid reputations in
the community. People
of modest means could rarely obtain loans because their
incomes were too low and
they couldn’t afford the high down payments. For people of
color, obtaining credit was
even harder. Many lenders refused to serve African-American
and Hispanic borrowers
at all, even when they had high incomes and fl awless credit
histories.
16 • PART I THE SUBPRIME MARKET TAKES OFF
Deregulation
Just as mortgage lending was conservative, so was regulation.
Throughout most of the
1970s, federal and state governments imposed interest rate caps
on home mortgages.
Some states banned adjustable-rate mortgages (ARMs), loans
with balloon pay-
ments, and prepayment penalties, which are charges for refi
nancing loans or paying
them off early. These regulations had the effect of limiting or
delaying opportunities
for homeownership.1
The interest rate restrictions and bans on certain types of

mortgages did not last
forever. From 1972 to 1980, the average interest rate on thirty-
year fi xed-rate mort-
gages rose from 7.38 percent to 13.74 percent a year.2 These
high rates hurt lend-
ers and borrowers alike. Mortgage lending and real estate sales
declined. In states
where market interest rates exceeded the state’s interest rate
cap, some lenders stopped
fi nancing home mortgages altogether. To add insult to injury,
depositors were fl ocking
to withdraw their money from banks to invest in money market
funds, which offered
higher returns because they were not subject to interest rate
caps on bank accounts.
The outfl ow of deposits meant banks had less money to lend,
further curtailing the
availability of mortgage loans.
Eventually, as the banking industry faltered and real estate sales
dried up, Congress
took action to dismantle the regulatory apparatus. First, it
passed a law in 1980 elim-
inating interest rate caps on fi rst-lien home mortgages. Then, in
1982, it permitted
loan products other than fi xed-rate, fully amortizing loans.
Overnight new products
sprung up, including ARMs, balloon payment loans, and reverse
mortgages.3 Con-
gress, in a sweeping move, also overrode state and local
provisions that were inconsis-
tent with the 1980 and 1982 laws.4
Deregulation addressed the immediate pressures facing banks.
The abolition of
interest rate caps allowed banks and thrifts to charge market

rates of interest. At the
same time, the proliferation of new loan products broadened the
array of loans avail-
able to borrowers. Borrowers who knew they would only be in
their homes for a few
years could opt for low-interest loans with a fi ve-year balloon
to be paid when they
sold their homes. Other borrowers were attracted to ARMs
offering initial interest
rates below the rates on fi xed-rate mortgages. Many of these
borrowers planned to
refi nance later if fi xed-rate loans dropped in price.
Deregulation was not all good news. Without the constraint of
interest rate caps,
lenders were free to charge exorbitant interest rates. They also
had carte blanche to dream
up an endless menu of exotic loan products that borrowers had
no hope of understanding.
Technological Advances
Starting in the 1980s, technological innovation also transformed
the home mortgage
market and paved the way for subprime lending. Lenders, in the
past, had been
extremely careful about borrowing decisions. They had erred on
the side of caution
because they did not know how to calculate the risk that
borrowers would default.
When underwriting loans, they had used rules of thumb to help
ensure repayment,
such as a total debt-to-income ratio of 36 percent, a 20 percent
down payment, and
three months of savings in the bank.
When the mainframe computer arrived on the scene, lenders

could suddenly ana-
lyze vast stores of data on borrowers and their credit histories.
Statisticians began
CHAPTER 2 THE EMERGENCE OF THE SUBPRIME
MARKET • 17
using the power of computing to identify the factors that best
predicted whether
borrowers would make their mortgage payments. They used
these factors to develop
models for determining the risk that individual borrowers would
default. The models
were called automated underwriting and were dubbed AU. With
AU, loan offi cers and
brokers could take information from the loan applications of
potential borrowers and
run it through a computer program to determine the applicants’
default risk and their
eligibility for loans.
AU dashed a number of hoary maxims about traditional loan
underwriting. Out went
requirements that borrowers make down payments of 20 percent
and have savings equal
to three months of expenses. Out, too, went an insistence on
pristine credit records, low
debt-to-income ratios, and full documentation of income. The
old-fashioned under-
writing rules and underwriters’ seat-of-the-pants judgment gave

way to fancy statisti-
cal models, giving lenders the confi dence to lend to borrowers
with damaged credit or
no credit history at all.
Equally important, AU made underwriting quick and cheap. In
the “old days,” it
took weeks to get a loan approved. With AU, lenders could
shorten the underwriting
period to seconds. New Century Financial, now a bankrupt
lender that approved loans
through a call center, advertised: “We’ll give you loan answers
in just 12 seconds.” AU
not only saved time. It also saved money. AU software reduced
underwriting costs by
an average of $916 per loan.5
The mortgage fi nance giants Fannie Mae and Freddie Mac set
the gold standard
for AU systems with their Desktop Underwriter and Loan
Prospector programs for
prime loans. Later, Fannie Mae designed a program called
Custom DU, which was
supposed to automate the underwriting of subprime loans. Other
companies designed
their own AU models for subprime mortgages.6
Although automated underwriting was a valuable innovation, it
had downsides,
especially when it came to subprime loans. One problem was
that many models
assumed that housing prices in the United States would go up
indefi nitely, which
was an unfounded and foolish assumption. AU systems also had
a garbage in, garbage
out problem. AU is only as good as the data that are entered.

For example, if a broker
entered false information, by infl ating borrowers’ income or
the value of their property,
the computerized assessment of the borrowers’ risk would come
out wrong.
When it came to subprime loans, there was even greater reason
to question the
reliability of automated underwriting. AU was originally
developed for the prime
market, using decades of data on the performance of prime
loans. There was scant
evidence, however, that these models yielded accurate results
for subprime loans
because there was little historical data on subprime loans.
Despite these problems,
AU gave the appearance of reliable underwriting, which was
enough to embolden
the market.
Securitization
Perhaps the biggest factor contributing to the subprime boom
was the securitization
of home mortgages. Securitization quietly entered the scene in
the 1970s. The idea
behind securitization is ingenious: bundle a lender’s loans,
transfer them to a legally
remote trust, repackage the monthly loan payments into bonds
rated by rating agen-
cies, back the bonds using the underlying mortgages as
collateral, and sell the bonds to

investors. It is a bit more complicated than this description
suggests; we save the nitty-
gritty of securitization for the next chapter.
The roots of securitization date back to the 1930s, when
Congress established the
Federal National Mortgage Association (Fannie Mae) as a
federal agency to increase
the money available for home mortgages. Initially, Fannie Mae
purchased FHA-
insured mortgages and in the process replenished the funds that
lenders had on hand
to make home mortgages.7 Thirty years later, Congress spun
Fannie Mae off into
a government-sponsored entity (GSE) and created a new GSE,
the Federal Home
Mortgage Corporation (Freddie Mac). Both securitized
mortgages and eventually
became private sector companies owned by shareholders. The
government exempted
the GSEs from state and local taxes. In exchange, Fannie and
Freddie agreed to meet
affordable housing goals set by the U.S. Department of Housing
and Urban Develop-
ment (HUD). This public mission meant that Fannie and Freddie
had two masters to
serve: their shareholders and the government.
The way that GSE securitizations work is that lenders originate
mortgage loans
that they sell to the GSEs. Only loans that meet Fannie’s and
Freddie’s underwriting

standards and that fall below a certain dollar threshold are
accepted for securitization
by the GSEs. In the industry, these loans are called “conforming
loans.” Once they
acquire the loans, the GSEs package them into pools. Those
pools then issue bonds
backed by the loans. As part of the bond covenants, Fannie and
Freddie guarantee
investors that they will receive their bond payments on time
even if the borrowers
default on their loans.8
Seeing the success of GSE securitization, investment banks and
other fi nancial
institutions wanted in on the game. Fannie and Freddie had
captured most of the
prime mortgage market, but had not yet tapped subprime
mortgages for securitiza-
tion. This set the wheels in motion for “private label”
securitization of subprime loans.
Private label is the term used for any securitization other than
those orchestrated by
one of the GSEs. Some private-label securitizations were done
by lenders. For exam-
ple, Countrywide Financial Home Loans, one of the largest
subprime lenders, pack-
aged and securitized the loans it originated. More often,
however, subprime loans were
securitized by Wall Street investment banks. By 2006, up to 80
percent of subprime
mortgages were being securitized.9
Securitization revolutionized home mortgage fi nance by
wedding Wall Street with
Main Street. It tapped huge new pools of capital across the
nation and abroad to

fi nance home mortgages in the United States. Lenders, in a
continuous cycle, could
make loans, sell those loans for securitization, and then plow
the sales proceeds into a
new batch of loans, which in turn could be securitized.
Securitization also solved an age-old problem for banks. In the
past, banks had held
home mortgages until they were paid off, which meant they
were fi nancing long-term
mortgage loans with short-term demand deposits. This “lending
long and borrowing
short” destabilized banks. If interest rates rose, banks had to
pay depositors rates that
exceeded the interest rate borrowers were paying on older
mortgages. And if interest
rates dropped, borrowers would refi nance to less expensive
loans. This “term mis-
match” problem was a direct cause of the 1980s savings and
loan crisis. Securitization
solved that problem by allowing banks to move mortgages off
their books in exchange
for upfront cash.
MARKET • 19
It was not only banks that benefi ted from the advent of
securitization. All of a sud-
den, thinly capitalized entrepreneurs could become nonbank

mortgage lenders. They
fi nanced their operations not with deposits, but by borrowing
money to fund loans,
which they paid back as they sold the loans for securitization.
The new lenders oper-
ated free from costly and time-consuming banking regulation
and fl ew under the radar
by making loans through brokers. Many had no physical
presence in the communities
where they operated and were anonymous unless borrowers read
the fi ne print.
By the time everyone was toasting the millennium, subprime
lending was poised to
take off. Soon what had been a credit drought would become a
glut of credit.
Macroeconomic and Public Policy Factors
Macroeconomic forces also helped spawn the subprime boom.
Ironically, two fi nancial
busts helped clear the way for subprime lending’s phenomenal
growth in the 2000s.
One of those grew out of the Asian fl u. In July 1997, the Asian
fi nancial crisis ignited
in Thailand, driving down the value of assets and currencies
throughout Southeast
Asia. In a domino effect, the crisis reduced the demand for oil,
which contributed to a
fi nancial crisis in Russia the following year. After Russia
defaulted on its debt, fearful
investors began dumping both Asian and European bonds. The
crisis spread to the
United States when Long-Term Capital Management (LTCM), a
highly leveraged
hedge fund that made its money through arbitrage on bonds, lost
money and experi-

enced crippling redemptions. The Federal Reserve Board (the
Fed) orchestrated a
private bailout of LTCM of over $3.5 billion. With LTCM’s
collapse, the bond mar-
kets erupted in chaos, briefl y paralyzing private-label
securitization and resulting in a
liquidity crunch. Several subprime lenders found themselves
unable to raise working
capital, and ultimately their businesses failed.10
During the same period, the dot-com bubble was swelling. In
2000, the bubble
burst and stock values plunged. By August 2001, the S&P 500
Index was off 26 per-
cent from its former high. Then on September 11, 2001,
terrorists attacked the United
States. As the country grieved, the faltering economy attempted
to revive, only to sus-
tain another body blow in December 2001 when Enron fi led for
bankruptcy. As one
corporate scandal after another came to light, confi dence in the
stock market crum-
bled. The S&P 500 dropped another 15 percent and the country
slid into a recession.
Throughout it all, the housing and credit markets were a beacon
of hope for the
economy. Alan Greenspan, the chairman of the Federal Reserve
Board, seized on
mortgage loans and other consumer credit as the way out of the
slump. In mid-2000,
the Fed exercised its “Greenspan put” and slashed interest rates,
causing housing prices
to grow at a steady clip of 10 percent a year nationally. After
the 9/11 attacks, with
the recession in full swing, the Fed ordered further rate cuts in

order to jump-start the
economy. Between August 2001 and January 2003, the Fed
chopped the discount rate
from 3 percent to 0.75 percent. This series of cuts drove down
interest rates on prime
loans. The cuts also made it possible for subprime lenders to
borrow money at low
rates, charge high rates to borrowers who couldn’t qualify for
prime loans, and make
money on the spread when they sold the loans.11
Low interest rates answered President Bush’s post-9/11 call for
Americans to go
shopping. Suddenly spending money became patriotic, and many
consumers fi nanced
their purchases with credit cards that charged exorbitant interest
and late fees. Too
often, families converted their credit card debt into mortgage
debt or refi nanced their
homes to pull out equity. As Greenspan noted, “Consumer
spending carried the econ-
omy through the post-9/11 malaise, and what carried consumer
spending was hous-
ing.”12 Programs advertising “Your Home Pays You Cash”
urged people to borrow
against their homes. Companies also promoted the idea that
credit was the way to live

the good life. Citibank spent $1 billion on a “live richly”
campaign designed to lure
people into home equity loans. PNC ads for second mortgages
showed a wheelbarrow
with the slogan, “The easiest way to haul money out of your
house.”13
The constant message was that people should feel good about
using credit. Debt,
which used to be considered embarrassing and a sign of poor
discipline, had stopped
being shameful. As a sign of this cultural shift, between 2001
and 2007, overall house-
hold debt grew from $7.2 trillion to $13.6 trillion, a 10 percent
increase each year.14
The Fed under Greenspan not only kept interest rates low, but
also refused to
intervene to protect consumers despite growing evidence of
abusive mortgages. Like-
wise, Congress and federal regulatory agencies were unmoved
by stories of defrauded
consumers. The dominant ideology was that if there were
problems with mortgage
lending, the market would solve them. In addition, if consumers
were taking on credit
they couldn’t afford, that was their choice and their problem.
The market’s job was to
offer consumers choices, and consumers’ job was to take
personal responsibility for
the choices they made. On the corporate side, responsibility
meant maximizing the
bottom line for the benefi t of shareholders, without regard for
the consequences of
abusive lending to consumers or society.

These dynamics coincided with a huge federal push for
homeownership. This
push began in the mid-1990s under President Bill Clinton, when
HUD coordi-
FIGURE 2.1.
U.S. President George W. Bush makes remarks on home
ownership at the
Department of Housing and Urban Development. (Luke Frazza/
AFP/ Getty
Images).
MARKET • 21
nated a public-private partnership designed to increase
homeownership.15 When
President George W. Bush came into offi ce in 2001, he went
further, advocating
that everyone should own a home as part of his vaunted
“Ownership Society” ini-
tiative. In response, HUD increased its pressure on Fannie Mae
and Freddie Mac
to fi nance an ever greater number of mortgages to people with
modest incomes
and to borrowers of color. The Bush administration embraced
subprime loans
as the key to growth in homeownership. By 2004, even the chief
counsel of the
Offi ce of the Comptroller of the Currency, Julie Williams, was

lauding “the rise
of the subprime segment . . . in advancing homeownership,
especially for minority
Americans.”16
Ultimately, the forces of technology, fi nancial engineering, and
public policy
converged to fuel the growth of the subprime market. Starting in
2000 the subprime
market grew exponentially, capturing 36 percent of the
mortgage market at its height in
2006, up from 12 percent in 2000, before crashing and infecting
the world economy.17
PREDATORY LENDING
The fi rst iteration of subprime lending—coined predatory
lending—began in the 1990s
and was targeted at people who historically had been unable to
get loans. Some had
blemishes on their credit or limited credit histories that made
them ineligible for
prime credit with its stiff underwriting standards. Others were
eligible for prime loans,
but did not know how to go about applying for credit or,
because of past discrimina-
tion, mistrusted banks. These people were ready prey for a new
class of brokers and
lenders, who targeted unsophisticated borrowers.
In these early days, mortgage brokers were small-time
operators, soliciting bor-
rowers over the phone or door-to-door like Fuller Brush
salesmen of yore, armed
with a menu of loan products from various lenders. Lenders
back then were often

small fi nance companies that generated money for loans
through warehouse lines of
credit. Some lenders worked solely with brokers, but many had
storefronts where they
took applications directly. One of the early entrants was
Citigroup, which bought the
Baltimore subprime lender Commercial Credit and later
renamed it CitiFinancial,
CitiFi for short.
Finding potential borrowers and getting them to commit to loans
was the key to
success. Existing homeowners were the most frequent targets
because they had equity
and were easy to identify through property records, unlike
prospective homeowners.
Brokers and lenders perfected marketing strategies to fi nd
naïve homeowners and
dupe them into subprime loans. Some hired “cold callers” who
would contact home-
owners to see if they were interested in a new mortgage. The
cold callers got paid a
few hundred dollars for each successful call. Brokers and
lenders also used municipal
records to identify prospects. They scoured fi les at city offi ces
to fi nd homes with
outstanding housing code violations, betting that the
homeowners needed cash to
make repairs. They read local obituaries to identify older
women who had recently lost
their husbands, surmising that widows were fi nancially
gullible. They also identifi ed
potential borrowers through consumer sales transactions. For
example, in Virginia,
Bennie Roberts, who could neither read nor write, bought a side
of beef and over 100

pounds of other meat from a roadside stand on credit from the
notorious subprime
lender Associates First Capital. In talking with Mr. Roberts to
arrange the consumer
loan, the loan offi cer from Associates learned that Mr. Roberts
had no mortgage on
his home. He soon convinced his new client to take out a loan
using the client’s home
equity. Associates refi nanced that mortgage ten times in four
years. The principal after
the refi nancings was $45,000 of which $19,000 was paid to
Associates in fees.18
High fees were not the only thing that typifi ed predatory loans.
Interest rates, too,
could be astronomical. In 2000, the Baltimore City Paper told
the story of the Pulleys,
who had overextended themselves with credit card debt. In
1997, the Pulleys “were bar-
raged with letters and calls from mortgage lenders offering to
consolidate [their] exist-
ing mortgage . . . and all their other debts into a new loan,”
which would supposedly
save them $500 per month. “Needing the cash and not well-
versed in such dealings,”
the Pulleys made a deal with Monument Mortgage for an
adjustable rate mortgage

loan with an annual interest rate that increased every six months
up to 19.99 percent.19
Some brokers and lenders had understandings with real estate
agents and home
improvement contractors to refer homeowners to them for loans.
This network also
worked in reverse, when mortgage brokers received kickbacks
for suggesting contrac-
tors to borrowers who were seeking loans for home repairs.
These referrals generated
good money for everyone except the borrowers, who ultimately
paid for the referrals
out of the loan proceeds or through up-front fees.
Shady contractors who helped homeowners fi nance repairs
were rife. In Cleveland,
Ruby Rogers had a mortgage-free home she had inherited from
her uncle. Citywide
Builders, a contractor, helped her obtain a loan through
Ameriquest Mortgage to
update the home. Over six months, the contractor arranged
repeated refi nancings of
Ms. Rogers’ loan until the principal hit $23,000. Of that
amount, Ms. Rogers only
saw $4,500. Meanwhile, Citywide Builders walked off the job
after doing $3,200 of
work on the house. Ms. Rogers was left with a leaking roof,
peeling tiles, warped wall
paneling, and a hole in the wall. After Citywide Builders went
bankrupt, Ameriquest
sued Ms. Rogers for foreclosure.20
Brokers and lenders also targeted black and Latino
neighborhoods, where they
knew credit had been scarce and demand for loans was high. As

electronic databases
of consumers became more sophisticated, lenders could
“prescreen for vulnerability,”
picking out people they could most easily dupe.21 Loan offi
cers at one lender report-
edly referred to neighborhoods with a high percentage of
borrowers of color as “never-
never land.’”22
For homeowners, the arrival of brokers and lenders offering
them credit seemed
like manna from heaven. Some lenders even invoked heaven in
luring borrowers.
Gospel radio station Heaven 600 AM aired advertisements for
refi nance loans
through Promised Land Financial. To help brokers win
customers’ confi dence, First
Alliance Mortgage Company, nicknamed FAMCO, had brokers
watch movies to
help them understand borrowers’ points of view. They were
instructed to watch Boyz
N the Hood to experience inner-city life and Stand and Deliver
to get a feel for His-
panic borrowers.23
Loan offi cers and brokers were trained to make customers feel
that they were act-
ing in their best interests, even going so far as to provide
attorneys to “represent” par-
ticularly leery borrowers.24 They were told to “establish a
common bond . . . to make
the customer lower his guard.” Suggested common bonds
included family, jobs, and

MARKET • 23
pets.25 Clueless that they were being targeted, residents
welcomed the salespeople who
befriended them into their homes. There the pitchmen would ply
them with offers of
loans to fi x a sagging porch, pay for a child’s education, or buy
a car. One borrower,
whose loan was fl ipped multiple times, said, “Everyone was
just so buttery and nice.”26
Some brokers were people that borrowers knew through work or
church. For many
people, especially those who had been victims of redlining in
the past, working with
someone familiar or recommended felt safer than going to a
bank. Often that was a
mistake. The head deacon of the Message of Peace Church in
South San Francisco
allegedly used his position to exploit Brazilian immigrants who
were parishioners at
his church, by encouraging them to fi nance their home
purchases through him. After
they placed their trust in the deacon, he completed their loan
applications, reportedly
falsifi ed documents, and agreed to terms on their behalf. One
borrower said that when
she uncovered what the deacon had done, he threatened to
report her …

The Government’s Role in
the Evolving Mortgage Market
IVpart
10-2564-0 ch10.indd 315 5/14/14 11:06 AM
Belsky, E. S., Herbert, C. E., & Molinsky, J. H. (Eds.). (2014).
Homeownership built to last : Balancing access, affordability,
and
risk after the housing crisis. Retrieved from
http://ebookcentral.proquest.com
Created from apus on 2020-04-27 14:40:55.
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10-2564-0 ch10.indd 316 5/14/14 11:06 AM
Belsky, E. S., Herbert, C. E., & Molinsky, J. H. (Eds.). (2014).
Homeownership built to last : Balancing access, affordability,
and
risk after the housing crisis. Retrieved from
http://ebookcentral.proquest.com

2015 V43 4 pp. 993–1034DOI 10.11111540-6229.12105RE.docx

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2015 V43 4 pp. 993–1034DOI 10.11111540-6229.12105RE.docx