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Default Rates on
Prime and Subprime
Mortgages:
Differences & Similarities

September 2010

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RESEARCH REVIEW

Default rates on prime and subprime
mortgages: differences and similarities
by Gene Amromin and Anna L. Paulson
Introduction and summary
For the past several years, the news
media have carried countless stories
about soaring defaults among subprime
mortgage borrowers. Although concern
over this segment of the mortgage
market is certainly justified, subprime
mortgages only account for about onequarter of the total outstanding
mortgages in the United States. The
remaining 75 percent are prime loans
that are made to borrowers with good
credit, who fully document their income
and make traditional down payments.
While default rates on prime loans are
significantly lower than those on
subprime loans, they are also increasing
rapidly. For example, among prime loans
made in 2005, 2.2 percent were 60
days or more overdue 12 months after
the loan was made (our definition of
default). For loans made in 2006, this
percentage nearly doubled to 4.2
percent, and for loans made in 2007 it
rose by another 20 percent, reaching
4.8 percent. By comparison, the
percentage of subprime loans that had
defaulted after 12 months was 14.6
percent for loans made in 2005, 20.5
percent for loans made in 2006, and
21.9 percent for loans made in 2007. To
put these figures in perspective, only
1.4 percent of prime loans and less than
7 percent of subprime originated in
2002 defaulted within their first 12
months.1 How do we account for these

historically high default rates? How have
recent trends in home prices and
economic conditions affected mortgage
markets? One of the things we want to
consider, specifically, is whether prime and
subprime loans responded similarly to
home price dynamics.
Figure 1, panel A summarizes default
patterns for prime loans; panel B reports
similar trends for subprime loans using
loan-level data from LPS Applied
Analytics. Each line in the figure shows
the cumulative default experience for
loans originated in a given year as a
function of how many months it has been
since the loan was made. Several patterns
are worth noting. First, the performance of
both prime and subprime loans has gotten
substantially worse, with loans made in
2006 and 2007 defaulting at much higher
rates. The default experience among
subprime loans started deteriorating
earlier, with rates being higher for loans
made in 2005 than in 2004. Defaults
among subprime loans are, of course,
much higher than defaults among prime
loans – note the difference in scales of
the two panels. However, the deterioration
in the performance of prime loans
happened more rapidly than it did for
subprime loans. For example, the
percentage of prime loans in default
during their first 12 months grew by 95
percent between 2005 and 2006. Among
subprime loans it grew by a relatively
modest 53 percent.

Home prices clearly play a key role in
households’ ability and desire to honor
their mortgage commitments. One of the
things we consider in this article is
whether performance of prime and
subprime loans responded similarly to
rapid home price appreciation from 2002
to 2005, and the sharp reversal in home
prices beginning in 2006.
In this article, we make use of loanlevel data on individual prime and
subprime loans made between January
1, 2004, and December 31, 2007, to do
two things: 1) analyze loan (and borrower)
characteristics and the default
experience for prime and subprime loans;
and 2) estimate empirical relationships
between home price appreciation, loan
and borrower characteristics, and the
likelihood of default. These estimates
allow us to quantify which factors make
default more or less likely and to examine
how default sensitivity varies over time
and across prime and subprime loans.
By looking at prime and subprime
loans together, we hope to refine the
possible explanations for the ongoing
mortgage crisis.2 Both prime and
subprime loans have seen rising defaults
in recent years, as well as very similar
patterns of defaults, with loans made in
more recent years defaulting at higher
rates. Because of these similarities, it
seems reasonable to expect that a
successful explanation of the subprime
crisis – the focus of most research to

Profitwise News and Views

September 2010

1

RESEARCH REVIEW
date – should also explain the patterns of
defaults we observe in prime mortgages.

Figure 1.A: Cumulative mortgage default rate of prime first-lien loans
(as a function of loan age, by year of origination)
0.12

In this section, we discuss trends in
loan and borrower characteristics, as well
as the default experience for prime and
subprime loans for each year from 2004
through 2007.

0.10

Default Rate

Loan and borrower characteristics

0.08
0.06

Data

0.04

The loan-level data we use come from
LPS Applied Analytics (LPS), which
gathers information from a number of
loan servicing companies.3 The most
recent data include information on 30
million loans, with smaller (but still very
large) numbers of loans going back in
time. The data cover prime, subprime, and
Alt-A loans,4 and include loans that are
privately securitized, loans that are sold
to the GSEs, and loans that banks hold
on their balance sheets.

0.02

The LPS data include a wide array of
variables that capture borrower and loan
characteristics, as well as monthly loan
performance status. In terms of
borrower characteristics, important
variables include the debt-to-income
ratio of the borrower (DTI) and the
borrower’s creditworthiness, measured
by their FICO (Fair Isaac Corporation)
2

Profitwise News and Views

Months since mortgage origination

SOURCE: LPS Applied Analytics.
Figure 1.B: Cumulative mortgage default rate of subprime first-lien loans
(as a function of loan age, by year of origination)
0.40
0.35
0.30
Default Rate

The total number of loans originated
in the LPS data in each year of the
period we study ranges from a high of
6.2 million in 2005 to a low of 4.3
million in 2007. The mortgage servicers
reporting to LPS Applied Analytics give
each loan a grade of A, B, or C, based
on the servicer’s assessment of whether
the loan is prime or subprime. We treat
A loans as prime loans, and B and C
loans as subprime. To make the analysis
tractable, we work with a 1 percent
random sample of prime loans made
between January 1, 2004, and
December 31, 2007, for a total of
68,000 prime loans, and a 10 percent
random sample of subprime loans made
during the same time period, for a total
of 62,000 subprime loans.

0

0.25
0.20
0.15
0.10
0.05
0
Months since mortgage origination

SOURCE: LPS Applied Analytics.
score. 5 Some of the loan characteristics
that we analyze include the loan amount;
whether the loan is a fixed-rate or
variable-rate mortgage; whether the
loan was fully documented; the ratio of
the loan amount to the value of the
home at origination (LTV); whether the
loan was intended for home purchase or
refinancing and, in case of the latter,
whether it involved equity extraction (a
“cash-out refinance”); and whether the
loan was held on the originating bank’s
portfolio, sold to one of the GSEs, or
privately securitized. The outcome
variable that we focus on is whether the
loan becomes 60 days or more past due

September 2010

in the 12 months following origination.
We focus on the first 12 months, rather
than a longer period, so that loans made
in 2007 can be analyzed the same way
as earlier loans, as our data are
complete through the end of 2008.6
We augment the loan-level data with
information on local economic trends and
trends in local home prices. The
economic variable we focus on is the
local unemployment rate that comes from
the U.S. Bureau of Labor Statistics
monthly zip-code-level statistics.
Quarterly data on home prices is
available by metropolitan statistical area

RESEARCH REVIEW
(MSA) from the Federal Housing Finance
Agency (FHFA)—an independent federal
agency that is the successor to the
Office of Federal Housing Enterprise
Oversight (OFHEO) and other
government entities.7 We use the FHFA
all transactions House Price Index (HPI)
that is based on repeat sales information.

Trends in loan and borrower characteristics
Many commentators (see, for
example, Demyanyk and Van Hemert,
2009) have noted that subprime
lending standards became more lax
during the period we study, meaning
that the typical borrower may have
received less scrutiny over time and it
became easier for borrowers to get
loans overall, as well as to get larger
loans. Table 1 summarizes loan
characteristics for each year from
2004 through 2007 for both prime and
subprime mortgages.
Consistent with prior work, we also
document declining borrower quality
over time in the subprime sector. For
example, whereas the average FICO
score for subprime borrowers in 2004
was 617, it had declined to 597 by
2007. 8 By contrast, when we look at
prime loans, the decline in lending
standards is less obvious. The average
FICO score among prime borrowers
was 710 in 2004 and 706 in 2007, a
decline of less than 1 percent.
Our data also allow us to look at the
prevalence of different mortgage
transactions, such as purchases or
refinancings. We are particularly
interested in refinancings that extract
home equity (a cash-out refinance). By
taking out equity in a refinancing, a
household may end up being more
vulnerable to future home price
declines, especially if its new mortgage
has a high loan-to-value ratio. To the
extent that the practice of cash-out
refinancing was common over the
period in our study, the increases in
home prices may be associated with
constant or even increasing leverage
rather than with safer loans and a

bigger cushion against future price
declines. In this way, greater prevalence
of cash-out refinancing transactions
may be indicative of the increasing risk
in the universe of existing loans.
As indicated in table 1, mortgage
servicers assign many refinancing
transactions to the ambiguous category
of “refinancing with unknown cash-out.”
Nevertheless, among prime loans made
in 2004, 12 percent were known to
involve cash-outs. By 2005, this
percentage had risen to about 21
percent, and it remained at this level
through 2007. For subprime loans made
in 2004, 35 percent involved cash-outs;
for those made in 2005, 43 percent; for
those made in 2006, 47 percent; and for
those made in 2007, a staggering 57
percent. Put differently, cash-out loans
accounted for at least 82 percent
(0.575/0.7) of all subprime refinancing
transactions in 2007! Another loan
characteristic that might be an important
determinant of subsequent defaults is
whether the interest rate is fixed for the
life of the contract or allowed to adjust
periodically (as in adjustable-rate
mortgages, or ARMs). When an ARM
resets after the initial defined period
(which may be as short as one year or as
long as seven), the interest rate and,
consequently, the monthly mortgage
payment, may go up substantially. Higher
payments may put enough stress on
some families that they fall behind on
their mortgages. While these loans seem
attractive at first because of low
introductory interest rates (and low initial
payments), they expose borrowers to
additional risk if interest rates go up or if
credit becomes less available in general.
Among subprime mortgages, ARMs
accounted for 73 percent in 2004, 69
percent in 2005, and 62 percent in
2006. By 2007, the ARM share had
fallen to 39 percent, since the availability
of these types of loans declined in the
second half of the year. Importantly,
nearly all subprime ARMs have
introductory periods of three years or
less, which makes borrowers with these

loans very dependent on the ability to
refinance.9 In contrast, loans to prime
borrowers are predominantly made as
fixed-rate contracts (about 75 percent of
all prime loans), and the majority of prime
ARMs have introductory periods of five to
seven years.
One oft-mentioned culprit for the
subprime crisis is the growth of lenders
who followed the “originate-to-distribute
model” (see, for example, Keys et al.,
2010, and Calomiris, 2008). These
lenders sold virtually all of the mortgages
they made, typically to private
securitizers. Because such lenders do
not face a financial loss if these
mortgages eventually default, they have
relatively little incentive to screen and
monitor borrowers. In addition to selling
loans to private securitizers, the lenders
can hold loans on their own portfolios or
sell them to one of the GSEs. However,
only loans that meet certain criteria
(borrower with a FICO score of at least
620, loan value of less than $417,000,
and a loan-to-value ratio [LTV] of 80
percent or less) can generally be sold to
the GSEs. Most subprime loans cannot
be sold to GSEs and must be either
privately securitized or held in portfolio.
The extent of loan securitization is one
of the striking facts in table 1. Recall that
the LPS data comprised loans serviced
by the large mortgage servicers. As a
result, LPS overstates the actual extent
of securitization somewhat, as it is more
common for smaller banks to hold loans
in portfolio and also to service them
internally. That being said, the LPS data
indicate that within the first month of
origination, about half of prime
mortgages made in 2004 remained in
their originators’ portfolios. This figure
declined to about 40 percent in each of
the subsequent years in our data. By
comparison, many fewer subprime loans
were retained by their originators even
for the first month: just over 40 percent
of loans made in 2004 and less than 30
percent made in the following years.
However, by the end of the first year
since origination, the share of originated

Profitwise News and Views

September 2010

3

RESEARCH REVIEW
Table 1: Loan characteristics at origination
Prime Loans
2004

2005

Subprime Loans

2006

2007

2004

2005

2006

2007

% default in first 12 months

2.43%

2.39%

4.33%

4.93%

11.19%

16.22%

23.79%

25.48%

% default in first 18 months

3.90%

3.74%

7.67%

6.86%

15.92%

23.35%

34.91%

33.87%

% default in first 21 months

5.11%

4.91%

10.51%

6.40%

23.35%

31.72%

43.75%

32.15%

HPI growth (12 months since
origination)

13.44%

9.10%

1.94%

-4.19%

13.99%

9.70%

1.52%

-3.94%

HPI growth (21 months since
origination)

20.98%

10.89%

-1.39%

18.85%

11.06%

-2.80%

5.15%

4.70%

4.45%

4.80%

5.28%

4.83%

4.55%

$50,065

$49,486

$48,417

$48,221

$45,980

$44,965

$43,790 $43,817

$173,702

$200,383

$211,052

$205,881

$167,742

$172,316

$179,003 $172,667

Unemployment rate (12
months following orig.)
Median income in zip code
(in $100,000)
Origination amount
FICO

4.81%

710

715

708

706

617

611

607

597

Loan to value ratio

75.92%

74.89%

75.99%

77.75%

79.63%

80.69%

80.40%

80.56%

Debt to income ratio (if
available)

35.95%

37.87%

37.25%

38.74%

39.55%

38.35%

39.78%

40.72%

52.8%

32.1%

27.6%

20.8%

41.0%

30.9%

27.2%

8.0%

Interest rate at origination

5.6%

6.0%

6.7%

6.5%

7.1%

7.5%

8.5%

8.4%

Margin rate (rate increase at
reset for ARMs)

2.3%

2.4%

2.9%

2.7%

5.2%

5.4%

5.5%

5.3%

26.45%

26.04%

23.16%

12.93%

73.31%

69.49%

61.78%

38.92%

reset > 3 yrs

14.52%

13.32%

12.11%

10.38%

1.05%

0.96%

1.93%

6.63%

reset <= 3 yrs

11.93%

12.71%

11.05%

2.55%

72.26%

68.53%

59.85%

32.28%

Prepayment penalty

2.67%

9.82%

10.91%

5.56%

70.98%

75.42%

73.70%

48.52%

Debt to income not available
(fraction of loans)

Fraction of loans that are:
Adjustable rate mortgages
(ARMs)

Purchase loans

44.89%

50.12%

53.33%

49.68%

41.12%

43.47%

40.21%

29.46%

Refinancing loans

40.51%

41.92%

40.70%

45.44%

53.83%

53.65%

57.34%

70.04%

12.19%

20.65%

20.85%

20.97%

35.03%

42.95%

46.59%

57.47%

6.69%

1.93%

1.26%

2.14%

0.27%

0.65%

0.80%

0.16%

21.63%

19.35%

18.59%

22.32%

18.53%

10.05%

9.95%

12.41%

4.90%

7.31%

7.72%

7.15%

2.82%

3.82%

4.24%

3.17%

60.68%

66.50%

66.18%

57.82%

28.89%

24.47%

23.67%

13.40%

Loan sold to GSE

31.10%

34.75%

34.42%

45.76%

3.34%

4.22%

5.96%

32.52%

Loan sold to private
securitizer

18.20%

27.63%

28.25%

12.84%

53.65%

66.51%

64.07%

42.60%

Loan held in portfolio

50.44%

37.61%

37.32%

40.66%

43.01%

29.27%

29.96%

24.88%

Cash out refinancing loans
Refinancing without
cashout
Refinancing with unknown
cashout
Investment property loans
Conforming loan
As recorded at origination

As recorded at 12 months since origination
Loan sold to GSE

74.17%

70.72%

72.30%

82.83%

4.09%

5.76%

8.63%

40.12%

Loan sold to private
securitizer

19.08%

23.73%

23.08%

10.56%

90.92%

91.89%

88.59%

55.06%

Loan held on portfolio

6.75%

5.55%

4.56%

6.40%

4.99%

2.35%

2.78%

4.81%

Number of loans in the
sample

11,604

18,388

15,992

15,039

6,889

20,778

18,189

8,562

SOURCE: FHFA for HPI growth, BLS for unemployment rate and median income, LPS for all other variables.
4

Profitwise News and Views

September 2010

RESEARCH REVIEW
loans kept on portfolio drops to low
single digits for both prime and subprime
mortgages. Not surprisingly, nearly all
subprime mortgages are securitized by
private investors, whereas GSEs
dominate the securitization of prime
mortgages. However, by the second half
of 2007, the private securitization market
had all but disappeared. The GSEs took
up much of the slack, accounting for
about 40 percent of all subprime
securitizations in that year.10

Estimates of default
In this section, we estimate empirical
models of the likelihood that a loan will
default in its first 12 months. This allows
us to quantify which factors make default
more or less likely and to examine how
the sensitivity to default varies over time
and across prime and subprime loans.

Econometric model
Mortgages can have multiple sources
of risk—for example, low credit quality,
high loan-to-value ratios, and adjustable
rates with short introductory periods and
high spreads to the reference rate. To
take into account these and other factors
that might influence default rates, we
estimate a number of multivariate
regression models that allow us to
examine the effect of varying one risk
factor while holding others fixed.
The analysis sample includes loans
that do not default and are observed for
12 months after origination and loans
that default (become 60 or more days
past due) within 12 months of origination.
We drop loans that get refinanced or
transferred within their first 12 months.
While this may bias our results, keeping
early refinanced and transferred loans in
the sample would understate the share
of actual defaults, since by definition
these loans are current for the duration
of their (short) presence in the sample.
Our goal is to evaluate the relative
strength of associations between loan
default and observable borrower, loan,
and macroeconomic characteristics in

different market segments and different
years. To that end, we estimate the
following regressions:
Prob (default within 12 months)i,j,k =
Φ(β1Loani,j,k, β2Borroweri,j,k, β3Econj,k, β4Dk),
(1)
where the dependent variable is an
indicator of whether a loan to borrower i,
originated in an MSA j in state k
defaulted within the first 12 months. We
model this probability as a function of
loan and borrower characteristics, MSAlevel economic variables (unemployment,
home price appreciation, and income),
and a set of state dummy variables (Dk)
that capture additional aspects of the
economic and regulatory environment.
We estimate the model as a standard
maximum likelihood probit with state
fixed effects.
To retain maximum flexibility in
evaluating the importance of covariates
for prime and subprime defaults, we carry
out separate estimations of equation (1)
for prime and subprime loans. To achieve
similar flexibility over time, we further
subdivide each of the prime and
subprime samples by year of origination
(2004 through 2007). Finally, we attempt
to account for unobserved heterogeneity
at the state level by incorporating state
fixed effects in our econometric
specification.
The economic variables include both
the realized growth in the FHFA HPI and
the average realized unemployment rate.
Both of these variables are measured at
the MSA level, and both are computed
over the first 12 months since loan
origination. Consequently, they match the
period over which we are tracking loan
performance. In contrast to all of the
other regressors, this information would
clearly not be available to the analyst at
the time of loan origination. We can think
of the model described in equation 1 as
the sort of analysis one would be able to
do at the end of 2005, after all loans
originated in 2004 had gone through
their first 12 months, and one is able to
observe what happened to home prices

and unemployment rates over the same
period. The same exercise can be
performed for loans originated in 2005
at the end of 2006, for loans originated
in 2006 at the end of 2007, and so on.

Results
The results of the estimation are
summarized in table 2. The first four
columns of data depict estimates for
prime loans originated in each of the four
sample years, and the next four columns
contain the estimates for subprime loans.
The juxtaposition of the data for the two
market segments allows us to easily
compare the importance of certain
factors. The table presents estimates of
the marginal effects of the explanatory
variables, rather than the coefficients
themselves. The marginal effects tell us
how a one unit change in each
explanatory variable changes the
probability that a loan defaults in its first
12 months, holding fixed the impact of
the other explanatory variables.
The defaults of both prime and
subprime loans are strongly associated
with the FICO score, the LTV, and the
interest rate in every estimation year for
each loan type. For instance, an increase
of 100 points in the FICO score of prime
borrowers in 2004 and 2005 is
associated with about a 1.2 percentage
point decrease in default likelihood (the
estimated marginal effect of –0.00012
multiplied by 100). To gauge the strength
of this effect, note that in those years the
baseline rate of default was about 2.2
percent. The point estimates of marginal
effects for 2006 and 2007 increase
about twofold for prime loans, but so
does the baseline sample default rate.
For subprime loans, the estimated
marginal effects are a full order of
magnitude higher, implying that an
improvement in FICO scores generates a
greater decline in subprime defaults, at
least in absolute terms.
Similarly, higher LTV values have a
strong positive association with defaults
for both loan types originated in 2005,
2006, and 2007. For subprime loans, a

Profitwise News and Views

September 2010

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RESEARCH REVIEW
Table 2: Probability of defaulting within 12 months of loan origination (probit regressions with state fixed effects)
Prime Loans

VARIABLES

2004
(1)

2005
(2)

2006
(3)

2007
(4)

2004
(5)

2005
(6)

default_in12

default_in12

default_in12

default_in12

default_in12

default_in12

0.0221

0.0217

0.0423

0.0483

0.1076

0.1572

0.2399

0.2539

-0.00166

-0.00494

-0.137***

-0.00356

-0.183*

-0.168***

-0.447***

-0.0105

(0.0139)

(0.0109)

(0.0294)

(0.0243)

(0.0981)

(0.0500)

(0.0934)

(0.121)

-0.0104

0.222***

-0.0370

0.131

0.218

0.743***

-0.749**

-0.476

Estimation sample mean
HPI growth
Unemployment rate
Median income in zipcode
Origination amount
FICO score
Loan to value ratio (LTV)
Debt to income ratio (0 if
missing)
Missing DTI dummy
Interest rate at origination
ARM w/ reset > 3 yrs
dummy
ARM w/reset < 3 yrs dummy

Cash out refinancing dummy
Purchase loan dummy
Investment property dummy
Conforming loan dummy
GSE-securitized loan dummy
Private label securitized loan
Observations
R-squared

2006
(7)

default_in12 default_in12

(0.0507)

(0.0393)

(0.115)

(0.0968)

(0.324)

(0.239)

(0.346)

(0.503)

-0.00253

-0.00689

-0.0231***

-0.0398

-0.0572***

-0.0942***

-0.0672

(0.00428)

(0.00388)

(0.00772)

(0.00880)

(0.0281)

(0.0215)

(0.0299)

(0.0412)

-0.000122

0.000176

0.000830

0.00190**

0.0135***

0.0160***

0.0247***

0.0331***

(0.000387)

(0.000425)

(0.000806)

(0.000758)

(0.00436)

(0.00341)

(0.00598)

(0.00627)

-0.000120***

-0.000120***

-0.000262***

-0.000318***

-0.000733***

-0.00122***

-0.00131***

-0.00116***

(1.64e-05)

(1.16e-05)

(1.97e-05)

(2.26e-05)

(9.12e-05)

(6.84e-05)

(9.10e-05)

(0.000136)

0.00433

0.0144***

0.0636***

0.0814***

0.0523

0.0820***

0.193***

0.193***

(0.00528)

(0.00500)

(0.0109)

(0.0123)

(0.0368)

(0.0255)

(0.0330)

-0.0477

0.000502

-0.00160

0.00841

0.0343***

0.0876**

0.143***

0.0900**

0.106**

(0.00409)

(0.00350)

(0.00859)

(0.00845)

(0.0399)

(0.0302)

(0.0388)

(0.0446)

0.00248

0.00148

0.00974**

0.0119**

0.0265

0.0593***

0.0183

0.000879

(0.00218)

(0.00187)

(0.00491)

(0.00586)

(0.0191)

(0.0148)

(0.0180)

(0.0258)

0.337***

0.257**

1.351***

1.653***

2.487***

3.092***

4.692***

5.384***

(0.103)

(0.107)

(0.186)

(0.218)

(0.388)

(0.282)

(0.345)

(0.476)

0.00151

-0.00366

-0.00112

0.0358**

0.0288

-0.0328

-0.0956***

0.187

(0.00685)

(0.00228)

(0.00485)

(0.0183)

(0.0609)

(0.0313)

(0.0294)

(0.133)

0.00180

-0.00566***

-0.0140***

0.0462

-0.0345

0.00391

-0.0197

0.203*

(0.00687)

(0.00204)

(0.00364)

(0.0317)

(0.0321)

(0.0200)

(0.0325)

(0.121)

-0.192

0.150

0.322**

-0.165

1.235**

0.776**

1.841***

-2.483

(0.245)

(0.118)

(0.147)

(0.340)

(0.521)

(0.363)

(0.568)

(2.014)

0.00286

0.00374

0.00757

-0.00390

0.00369

0.0109

-0.0189*

0.00180

(0.00572)

(0.00325)

(0.00467)

(0.00476)

(0.00894)

(0.00753)

(0.0111)

(0.0159)

0.00274

0.00445**

0.000601

-0.00157

-0.00985

-0.0186**

-0.0284**

-0.0117

(0.00229)

(0.00222)

(0.00318)

(0.00340)

(0.0110)

(0.00915)

(0.0122)

(0.0164)

0.00290*

0.00222*

0.00552**

0.00604**

0.0243***

0.0415***

0.0856***

0.0729***

(0.00151)

(0.00131)

(0.00244)

(0.00295)

(0.00863)

(0.00602)

(0.00815)

(0.0132)

-0.000422

0.00468

0.000339

0.00159

-0.00612

-0.00102

-0.00164

0.0446
(0.0301)

(0.00305)

(0.00304)

(0.00378)

(0.00486)

(0.0216)

(0.0143)

(0.0163)

-0.00290

-0.00667***

-4.45e-05

0.00166

0.0154

0.0226***

0.0196*

0.0119

(0.00190)

(0.00184)

(0.00288)

(0.00344)

(0.0120)

(0.00864)

(0.0107)

(0.0184)

-0.0113***

-0.00623***

-0.0190***

-0.00352

0.0255

-0.0312

-0.138***

-0.00455

(0.00268)

(0.00225)

(0.00417)

(0.00530)

(0.0271)

(0.0194)

(0.0284)

(0.0294)

-0.00578**

-0.000475

-0.00930**

-0.000801

0.00680

-0.0860***

-0.168***

-0.0619**

(0.00269)

(0.00207)

(0.00424)

(0.00635)

(0.0183)

(0.0148)

(0.0228)

(0.0241)

8,887

15,653

13,941

12,932

5,825

19,356

17,359

8,349

0.2587

0.2364

0.1997

0.1962

0.1138

0.0934

0.0926

0.0745

SOURCE: FHFA for HPI growth, BLS for unemployment rate and median income, LPS for all other variables.
NOTES: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.

6

2007
(8)

-0.00149

Margin rate (0 if FRM)
Prepayment penalty dummy

Subprime Loans

Profitwise News and Views

September 2010

RESEARCH REVIEW
Table 3.A: Average marginal effects from changes in key explanatory variables
Prime

2004 - 2007
VARIABLES

Mean

Std. deviation

CHANGE (+)

Baseline predicted
default rate
HPI growth

FICO score

Loan to value ratio
(LTV)
Debt to income ratio
(if not missing)

4.8

710

76.1

37.7

10.3

62

16.8

14.9

10 ppt

50 points

10 ppt

10 ppt

2004
(1)

2005
(2)

2006
(3)

2007
(4)

default_in12

default_in12

default_in12

default_in12

0.0220

0.0217

0.0422

0.0482

-0.0003

-0.0010

-0.0178***

-0.0006

-1%

-5%

-42%

-1%

-0.0116***

-0.01***

-0.0172***

-0.0204***

-53%

-46%

-41%

-42%

0.0009

0.0031***

0.0113***

0.0141***

4%

14%

27%

29%

-0.0001

-0.0002

0.0009

0.0049***

0%
Interest rate at
origination
Margin rate (0 if
FRM)

6.25%

2.58%

0.81%

1.14%

1%

1%

-1%

2%

10%

0.0105***

0.0062**

0.027***

0.032***

48%

29%

64%

66%

-0.0007

0.0008

0.0018**

-0.0004

-3%

4%

4%

-1%

SOURCE: FHFA for HPI growth, BLS for unemployment rate and median income, LPS for all other variables.
NOTES: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.
rise in LTV generates a stronger
absolute increase in loan defaults. It
must be noted that the effect of the
leverage on the likelihood of default may
be understated by the LTV measure that
we have. A better measure of how
leveraged a borrower is on a given
property would be the combined loanto-value ratio (CLTV) that also takes into
account second-lien loans on the
property. This variable is not available in
the LPS data, however. If the practice of
obtaining such “piggyback loans” is
more prevalent in the subprime market,
then the estimated coefficient for LTV
for subprime loans may be lower than its
true value.
At first glance, the interest rate at
origination is similar to LTV and FICO
score in having a strong statistical and
economic effect on both prime and
subprime loan defaults in each

origination year. What stands out is the
sheer magnitude of the estimated
effects. However, one must be cautious
in interpreting hypothetical marginal
effects of the interest rate. While LTV
and FICO score cover fairly wide ranges
for both prime and subprime loans,
interest rate values are relatively tightly
distributed.11 This means that a
difference of even 1 percent in loan
interest rate makes it look quite
different from loans with otherwise
identical characteristics (e.g., FICO
score, LTV, DTI). In such cases, a likely
explanation is that the lender has
additional information about the credit
quality of the borrower and is charging a
higher interest rate to take into account
additional risk factors – hence, the
strong positive association with eventual
default rates.

There are also a number of notable
differences between the prime and
subprime samples. Perhaps the most
interesting finding is the different
sensitivity of defaults to changes in home
prices. For subprime loans, defaults are
much lower when home price growth is
higher for three out of the four sample
years. This relationship is particularly
striking for 2006 loan originations, many
of which experienced home price
declines over their first 12 months. For
prime loans, 2006 is notable as the only
year of origination in which changes in
home prices are significantly correlated
with loan defaults. These results suggest
that, relative to subprime defaults, prime
defaults have a weaker relationship with
home prices, once key borrower and loan
characteristics (LTV, FICO score, and so
on) are taken into account.

Profitwise News and Views

September 2010

7

RESEARCH REVIEW
Table 3.B: Average marginal effects from changes in key explanatory variables
Subprime

2004 - 2007
VARIABLES

Mean

Std. deviation

CHANGE (+)

baseline predicted
default rate
HPI growth

FICO score

Loan to value ratio
(LTV)
Debt to income ratio
(if not missing)
Interest rate at
origination
Margin rate (0 if
FRM)

5.4

9.6

608

55

80.4

12.6

39.4

10.7

7.93%

1.31%

5.39%

0.72%

10 ppt

50 points

10 ppt

10 ppt

1%

1%

2004
(1)

2005
(2)

2006
(3)

2007
(4)

default_in12

default_in12

default_in12

default_in12

0.1075

0.1571

0.2396

0.2538

-0.0189*

-0.0163***

-0.0406***

-0.0010

-18%

-10%

-17%

0%

-0.0351***

-0.0532***

-0.0581***

-0.0519***

-33%

-34%

-24%

-20%

0.0059

0.0084***

0.0189***

0.0189***

5%

5%

8%

7%

0.0069**

0.0108***

0.0066**

0.0095**

6%

7%

3%

4%

0.0302***

0.0331***

0.0471***

0.0542***

28%

21%

20%

21%

0.0112**

0.0059**

0.0125***

-0.0101

10%

4%

5%

-4%

SOURCE: FHFA for HPI growth, BLS for unemployment rate and median income, LPS for all other variables.
NOTES: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.
The contrast between prime and
subprime loans is even sharper in the
estimated marginal effects on the debtto-income ratio (DTI) and loan margin
rate. The DTI is widely considered to be
one of the main determinants of loan
affordability, since it relates household
monthly income to debt service flows.
The DTI for prime loans is not
significantly correlated with defaults,
except for loans originated in 2007, but it
matters consistently for subprime loans.
The absence of any measurable effects
of DTI even on defaults of prime loans
originated in 2006 can be interpreted as
a sign of the resilience of prime
borrowers who experienced severe
changes in the prices of their homes.
The loan margin rate is one of the key
terms in an ARM contract. It defines the
spread to a reference rate (usually the

8

Profitwise News and Views

London interbank offered rate, or Libor).
At reset, the ARM rate goes up to the
sum of Libor and the loan margin. The
margin is set by the lender, and is often
thought to capture additional aspects of
a borrower’s creditworthiness. This is
consistent with the fact that the margin
rate is, on average, substantially higher
for subprime borrowers (see table 1). We
find that this variable has no association
with defaults among prime loans, with the
exception of loans originated in 2006. In
contrast, defaults on subprime loans
originated in every year except 2007 are
significantly higher for loans with higher
margin rates, all else being equal. This
suggests that, for the subprime borrower,
the margin rate contains additional
information on borrower quality not
reflected in FICO scores and other loan
characteristics. It is also interesting that
the coefficients on ARMs with

September 2010

introductory periods of less than three
years – the most common mortgage
contract in the subprime market – are not
significantly different from zero. This
means that they have the same
correlation with subprime defaults as
fixed-rate mortgages. Put differently,
once loan and borrower characteristics
are accounted for, the choice of a hybrid
ARM is not associated with higher
subprime defaults.

Comparisons across years and across loan types
Since table 2 contains regression
estimates from multiple non-overlapping
samples, the comparison of the relative
importance of the explanatory variables
may be tricky. The distribution of loan
characteristics varies from year to year
and across prime and subprime loans. In
addition, the baseline rates of actual
defaults are quite different across

RESEARCH REVIEW
samples. Because of this, one cannot
simply compare two point estimates and
conclude that a bigger one indicates a
stronger correspondence with defaults.
To compare the economic and relative
importance of the explanatory variables
across the subsamples, we conduct the
following exercise. For each independent
variable, we change its value for each
observation by a specified increment.
Then, we compute the predicted
subsample default rate using estimated
coefficients for each year of origination
and loan type. We compare the new
predicted default to the original one. The
difference between the original
prediction and the new one tells us the
marginal contribution of that variable to
the overall default rate.12 We compare
these figures across years and across
prime loans (table 3, panel A) and
subprime loans (table 3, panel B). For
example, for 2004 prime loans we
increase all FICO scores by 50 points,
predict a new default rate, and compare it
to the old default rate. The difference is
–0.0116 percentage points, or a 53
percent decrease in the likelihood of
default for loans originated in 2004
(column 4, row 2 of table 3, panel A). For
brevity, we look at just six key explanatory
variables: HPI growth, FICO score, LTV,
DTI, interest rate, and loan margin rate.13
The table also reports the means of the
relevant variable, its standard deviation,
and the absolute change that we impose.
We tried to keep the magnitude of the
absolute changes reasonably close to
the standard deviations.
A 10 percentage point increase in
home price appreciation (HPI growth)
substantially lowers default probabilities
(first row of each panel in table 3). This
effect is more consistent for subprime
loans originated in various years, where it
translates to decreases of between 10
percent and 18 percent relative to the
baseline default rate in 2004, 2005, and
2006. For prime loans, the 10
percentage point increase in the HPI has
a big effect only for loans originated in
2006, where the estimates imply that

defaults would have been 1.78 percentage
points, or 42 percent, lower. The effect of
FICO score stands out. A 50-point uniform
increase in FICO scores (row 2 of each
panel) is associated with a 41 percent to
53 percent decline in predicted default
rates relative to the baseline for prime
loans, and a 20 percent to 34 percent
relative decline for subprime loans. The
average marginal effects of the LTV are
greater (in a relative sense) for prime loans
than for subprime loans.14 Finally, higher
interest rates appear to generate
incredible increases in defaults for both
market segments. For instance, a 1
percentage point increase in interest rates
translates into a jump in defaults on 2007
prime loans of more than 3 percentage
points—a rise of 66 percent relative to the
actual default rate. Increasing everyone’s
interest rates by 1 percentage point is
equivalent to a substantial deterioration in
the quality of the borrower pool, and thus
translates into much higher predicted
defaults. As mentioned earlier, DTI and the
margin rate do not have strong
associations with prime mortgage defaults.
In contrast, higher values of these
variables consistently indicate higher
default rates for subprime mortgages.
However, the economic magnitude of
marginal effects of DTI and the margin
rate on defaults (rows 4 and 6 of each
panel) is somewhat muted.

What if market observers foresaw the decline in
home prices?
We turn our attention now to the role of
home prices. We know that home prices
were increasing very rapidly in 2004 and
2005 and began to fall quite dramatically
beginning in 2006. But would it have been
possible to quantify the effect of this
reversal on defaults of both prime and
subprime mortgages in real time? It is also
important to be clear about what
information would have been available to
analysts at different points in time. This will
allow us to get a rough sense of the extent
to which market participants were
“surprised” by the performance of prime
and subprime loans originated in 2006
and 2007.

To do this, let’s conduct the following
thought experiment. Suppose that it is
June 2006 and we are trying to forecast
defaults on prime mortgages originated
earlier that year. The most up-to-date
model of defaults available to us at this
point in time is that of defaults on 2004
originations. (Recall that to estimate this
model, one needs to observe mortgages
for 12 months since origination.) Further
suppose that as astute analysts, we get a
definite sense that house price growth is
slowing down, even though available data
are not picking this up strongly yet. And
so in a fit of pessimism, we conclude that
prices may even decline a touch this year
after growing at 9 percent, on average, in
2005. What would our models tell us
about the default outlook?
The answer is “not much.” In 2004
(and 2005), the models of prime
mortgage performance detected almost
no relationship between house price
growth and defaults. The coefficients on
HPI growth were effectively zero, and so
no forecast of HPI, however dire, would
have rung the alarm bells regarding
prime mortgage defaults.
What an analyst would have had to
realize was that in 2006 prime borrowers
will start reacting to HPI in the same way
as subprime ones. What was needed
then was not a better forecast of housing
prices, but an understanding that the
statistical relationships from the boom
years no longer applied. Detecting the
turning points is never easy, and in this
instance most observers failed abjectly.

Conclusion
We have analyzed the default
experience of prime and subprime loans
originated over the period 2004–07. Like
other studies, we document some decline
in underwriting standards during this
period for both prime and subprime
loans. We also find that characteristics
such as the LTV, FICO score, and interest
rate are important predictors of defaults
for both prime and subprime loans.
However, changes in loan and borrower

Profitwise News and Views

September 2010

9

RESEARCH REVIEW
characteristics are not enough to have
predicted the incredible increase we have
seen in prime and subprime mortgage
defaults. While changes in borrower and
loan characteristics can get us closer to
observed default rates for subprime loans
than they can for prime loans, for both
market segments there were other
factors at work.
Home prices play a very important role
in determining mortgage outcomes; this
became particularly evident for subprime
loans by the end of 2005. For prime
loans, it is only when we analyze data
through the end of 2007 (that is, evaluate
the performance of loans originated in
2006) that we are able to document this
sensitivity. Even very pessimistic
assumptions about the future path of
home prices would not have been
enough to substantially improve
contemporaneous forecasts of prime
mortgage defaults for loans made in
2006 and 2007. In hindsight, of course, it
appears self-evident that the
relationships between HPI growth and
defaults on prime loans might be
different in periods with declining home
prices. Coming up with such revised
estimates in real time would not have
been possible using the available data
from the recent past. It could, perhaps,
have been done by analyzing data that
included earlier episodes of substantial
regional price declines.

Biographies
Gene Amromin is a senior financial
economist in the financial markets
group at the Federal Reserve Bank of
Chicago.
Anna Paulson is a vice president of
the financial economics team in the
economic research department of the
Federal Reserve Bank of Chicago.

10

Profitwise News and Views

Notes
1 These numbers are based on authors’
calculations using data from LPS Applied
Analytics, described later in text.
2 By including prime loans in the analysis,
our intention is to complement the very
informative and extensive literature on
subprime loans that includes, among
others, Bajari, Chu, and Park (2008);
Demyanyk and Van Hemert (2009);
Gerardi, Shapiro, and Willen (2008); and
Mian and Sufi (2009).
3 The servicers included in the data set are
those that participate in the HOPE NOW
Alliance (www.hopenow.com/members.
html#mortgage). This includes some of
the country’s largest home lenders: Bank
of America, Citibank, JPMorgan Chase,
and Wells Fargo.
4 Alt-A loans are a middle category of
loans – more risky than prime and less
risky than subprime. They are generally
made to borrowers with good credit
ratings, but the loans have characteristics
that make them ineligible to be sold to the
GSEs – for example, limited
documentation of the income or assets of
the borrower or higher loan-to-value ratios
than those specified by GSE limits.
5 As Bajari, Chu, and Park (2008)
emphasize, an important feature of the
FICO score is that it measures a
borrower’s creditworthiness prior to taking
out the mortgage. FICO scores range
between 300 and 850. Typically, a FICO
score above 800 is considered very good,
while a score below 620 is considered
poor. As reported on the Fair Isaac
Corporation website (www.myfico.com),
borrowers with FICO scores above 760
are able to take out 30-year fixed rate
mortgages at interest rates that are 160
basis points lower, on average, than those
available for borrowers with scores in the
620–639 range.
6 If we repeat the analysis using alternative
outcome variables and different time
periods (in default after 18 months, in
foreclosure, 30 days or more past, and so
on), the results are very similar.

September 2010

7 As part of the Housing and Economic
Recovery Act of 2008 (HERA), the
Federal Housing Finance Regulatory
Reform Act of 2008 established a single
regulator, the FHFA, for GSEs involved in
the home mortgage market, namely,
Fannie Mae, Freddie Mac, and the 12
Federal Home Loan Banks (see www.fhfa.
gov for additional details).
8 Note that we are looking at a relatively
short period, and other authors document
changes in underwriting criteria that
occurred prior to 2004 (see, for example,
Gerardi et al., 2008).
9 Such mortgages were known as “hybrid
ARMs.” They were also commonly
identified as “2/28” and “3/27” loans,
referring to 30-year ARMs that reset after
two and three years, respectively.
10 In September of 2007 when the private
securitization market had all but shut
down, the GSEs were encouraged by
members of Congress to expand their
portfolios to support the market (see this
correspondence between Senator Charles
E. Schumer (D-NY) and Dennis Lockhart,
the director of OFHEO, at www.ofheo.gov/
newsroom.aspx?ID=383&q1=0&q2=9.
11 Keep in mind that for simplicity, the
analysis uses the actual interest rate at
loan origination and not the difference
between this rate and some reference risk
free rate.
12 Note that this exercise amounts to
computing the average of marginal effects
for individual loans, instead of the marginal
effect at the mean, which is obtained by
multiplying a hypothetical change in an
explanatory variable by its regression
coefficient.
13 In this exercise, the loan margin is
increased only for ARMs, since fixed rate
loans by definition have a zero margin
under all circumstances. Similarly, we
incremented DTI only for those loans that
had non-missing DTI values.
14 As discussed earlier, this may be due to
our inability to accurately account for
piggyback loans.

CONSUMER ISSUES

Alternative small dollar loans:
Creating sound financial products
through innovation and regulation
by Chris Giangreco, Andrea Kovach, and Matt Unrath
Introduction

Review of existing literature

Low- to moderate-income borrowers
need alternatives to payday loans to
meet their short-term credit needs. This
article provides an overview of
consumer demand for smaller loans, and
discusses how and why mainstream
financial institutions should offer less
costly alternatives to traditional payday
loans. A two-year FDIC pilot, a smalldollar loan pool in Baltimore, and
individual case studies suggest that
such lending can be viable and
profitable. The article concludes with
recommendations for how financial
institutions and regulators should
support this effort

There has been a great deal of
research on the payday loan industry.
Various regional banks of the Federal
Reserve System have published papers
examining the nature of payday loan
borrowers and products, and weighed the
effectiveness of industry regulation (see
the Federal Reserve Bank of
Philadelphia’s “Restricting Consumer
Credit Access” and New York’s “Defining
and Detecting Predatory Lending”). The
New America Foundation’s Asset
Building program has written extensively
on the issue, including an often cited
paper on how behavioral sciences can
inform financial services regulation.
Consumer advocacy groups, like the
Center for Responsible Lending and
National Consumer Law Center, have
authored reports on payday lenders (see
“Quantifying the Economic Cost of
Predatory Payday Lending”). The
Woodstock Institute has exposed
regulatory loopholes and published
various reports that highlight the need for
stricter regulation (see “Illinois Payday
Loan Loophole”). Much of this research
has focused on questions about payday
lending regulations; officials, researchers,
and advocates have only recently begun
advocating for alternative products, and
there is likewise little lending data from
which to draw evidence about
effectiveness and profitability.

Many consumers struggling to make
ends meet need small, short-term loans.
Illinois residents received nearly 1.2
million payday loans1 from 2006 to 2008;
however, debate about the utility of this
loan product continues. Payday lenders
claim that they provide a needed service
to those underserved by mainstream
financial institutions. Consumer
advocates cite the predatory pricing of
payday loans, and call for stringent
regulation, especially with respect to
pricing. In an effort to move beyond this
debate and meet consumer demand (with
a less costly product), advocates,
regulators, and financial institutions
should explore the viability of alternative
small-dollar loan products from both a
consumer and business perspective.

Payday lenders, represented by the
Consumer Financial Services

Association of America, defend their
product and service to low- and
moderate-income borrowers. Their
argument, published in reports and
testimonials, is that payday loan terms
compare favorably with alternative
options, like overdraft or late fees. The
Center for Responsible Lending has
encouraged the OCC and other bank
regulators to prohibit banks from
offering products similar to payday
loans. Some larger banks have recently
introduced products such as paycheck
advances, but the charges can amount
to a 120 percent APR or higher. The
National Consumer Law Center
authored a recent report warning that
one should not assume that
“alternatives” to payday loans are
inherently less costly. The authors of
this paper support responsible
alternatives to payday lending with
interest rates capped at 36 percent
APR and amortized loan payments
based on an individual’s ability to repay.

Current landscape of payday
lending
The majority of small-dollar loans
currently available to consumers across
the country are payday loans. Payday
loans are essentially quick cash
advances, usually of $500 or less,
targeted to low- and moderate-income
individuals with limited credit history or
low credit ratings. Payday loan
businesses operate outside of the

Profitwise News and Views

September 2010

11

CONSUMER ISSUES
mainstream financial sector, often
relying on a network of retail storefronts,
where customers can walk in, provide
minimal personal information, and leave
with enough cash to meet their
immediate financial needs. The
“underwriting process” comprises
documenting information from a pay
stub, and the “collateral” is a postdated
check or automatic debit authorization,
which covers the principal borrowed and
interest; the loans often require
borrowers to have a checking account.
Because payday lenders operate with
very little underwriting, they rationalize
high interest rates as necessary to
ensure profit.

1. Small-dollar loan pool pilot
One model for extending small-dollar
loans involves financial institutions
providing capital to a community-based
organization, which uses existing
relationships within a targeted market to
offer loans.

With support from the FDIC and grants
from six financial institutions and one
credit union, Neighborhood Housing
Services of Baltimore (NHS–Baltimore)
created a small-dollar loan pool. NHS–
Baltimore made 80 loans between August
2009 and February 2010, totaling
$60,400. The majority of these 12-month
term loans are $1,000, though some as
small as $500. All of the loans have a
The payday loan industry has seen
competitive APR of 7.99 percent. A typical
enormous growth. In the three years
consumer in this pilot is an Africanbetween 2000 and 2003, national sales
American woman between 40 and 50
volumes quadrupled from $10 billion in
years old, earning about $30,000
2000 to $40 billion in 2003; researchers
put the total costs to consumers for using a annually, who has filed for bankruptcy in
the past but currently holds a bank
payday loan at $4.2 billion annually (King,
account, and plans to use the loan to pay
Parrish and Tanik 2006). According to the
bills. Joan Lok, Community Affairs
Illinois Department of Financial and
Professional Regulation (IDFPR), there are Specialist with the FDIC in Baltimore,
stated that while financial institutions
403 licensed payday lenders operating
contributing to the pool were originally
under the Payday Loan Reform Act in
skeptical of the program, many have come
Illinois. IDFPR found that during the threeto embrace and support it. One goal
year period between February 2006 and
associated with the pilot is the program to
December 2008, 1,194,582 payday loans
earn enough to be self-sustaining. The
were taken out by 204,205 consumers in
FDIC’s Alliance for Economic Inclusion, in
Illinois—an average of 5.9 loans per
consumer at an average annual percentage conjunction with local financial institutions,
is developing similar loan pools in Kansas
rate (APR) of 341 percent (IDFPR 2009).
City and Seattle.

Alternative small-dollar loan
efforts

2. Innovative financial institutions

Consumer advocates, financial
institutions, and regulators have begun
working together to promote and
develop responsible, alternative smalldollar loan products that meet
consumers’ needs and protect them
from usurious lending practices. The
following examples highlight some of
the innovative current strategies in this
market segment.

12

Profitwise News and Views

Banks and credit unions have also
begun developing their own alternative
small-dollar loan programs. In 2002,
North Side Community Federal Credit
Union (North Side CFCU), recognizing
the preponderance of predatory payday
lenders in its community and the impact
high interest debt had on its members,
decided to develop its Payday Alternative
Loan (PAL) program. Loans in the PAL
program are $500, repaid during a sixmonth term, and have an annual
percentage rate of 16.5 percent. If a firsttime borrower has a credit score below

September 2010

600, he or she is required to attend a
free financial education workshop on
understanding credit and meet with a
financial counselor to prepare a personal
budget. In the past seven years, North
Side CFCU has made over 5,000 PALs,
disbursing over $2.5 million in PAL loans
and saving community residents over $3
million in fees and interest from
traditional payday loans.
In July 2008, North Side CFCU
launched its newest alternative loan
product, the “Step-Up” loan, a payday
alternative loan of $1,000 available to
members that have paid off at least five
PALs. No credit check is required and
borrowers can pay back the loan in six
months or one year. Since the launch of
Step-up, North Side has made 527
loans for a total of $527,000. Ed Jacob,
North Side CFCU’s manager (at the
time) stated, “Our goal isn’t to be just a
cheaper payday lender. We want to give
people a path that will help them reach
their financial goals. We want them to
think longer term, and go beyond
needing a $500 loan.”

3. FDIC’s small-dollar loan pilot
The Federal Deposit Insurance
Corporation (FDIC) began a two-year
pilot program for alternative small-dollar
loans in February 2008. At the
program’s conclusion in December
2010, 28 banks with assets from $28
million to $10 billion and offices in 27
states participated. The program aimed
to assess the business practices of the
banks in developing and offering
profitable small-dollar loan programs
alongside other mainstream services.
The FDIC developed the following
guidelines for financial institutions
participating in the pilot:

• Loan amounts of up to $2,500;
• Amortization loan periods of at least

90 days with minimum payments that
reduce the loan principal;

• Annual percentage rates (APR)
below 36 percent;

CONSUMER ISSUES

• No prepayment penalties;
• Origination and/or maintenance fees
limited to the amount necessary to
cover actual costs; and

• An automatic savings component.
The FDIC released final results of the
program in late June 2010 on the
impact and effectiveness on banks’
profitability 2 and long-term customer
relations. In the first year of the program,
banks made over 16,000 loans, for an
aggregate principal balance of $18.5
million. The total amount of loans
charged off in the first year was
$187,378, or 3.4 percent of loans of all
loans originated (Burhouse, Miller, and
Sampson 2009). Banks reported that
job losses and other economic problems
in their market areas led to increased
delinquencies across loan categories
and to a reduction in the pool of
acceptable borrowers. Common factors
cited for operating successful loan
programs included strong senior
management and board support; an
engaged and empowered “champion” in
charge of the program; proximity to
large consumer populations with
demand for small-dollar loans; and, in
rural markets, limited competition.

Benefits of small-dollar loans for
financial institutions
Mainstream financial institutions can
benefit from alternative small-dollar
lending by serving as the pacesetter of
sound and competitive financial practices
for low- and moderate-income clients.
Image improvement – Participation in
initiatives to develop alternative smalldollar loans can help re-position banks
and other mainstream financial
institutions amid the current economic
situation. Luis Ubiñas, president of the
Ford Foundation, said at a meeting in
Brooklyn, New York, in June 2009, “The
economic downturn has tarnished bank
brands; offering innovations and
providing new opportunities to nontraditional customers can help repair the

damage done to the banking industry
brand” (Benjamin 2009).
New customer base – Because of
high demand, financial institutions can
attract new customers by offering an
alternative loan product at competitive
prices. Through such loans, banks and
credit unions can build the financial
skills and knowledge of their customers,
graduating them to more sophisticated
financial products. More than half of the
banks in the FDIC’s small-dollar loan
pilot reported that customers moved to
other bank services after using a smalldollar loan. Most pilot banks opened
deposit accounts for customers who
successfully used a small-dollar loan
product, and some banks transitioned
customers into more sophisticated
products. One participating bank found
that auto loans were a “next step” in
building the lending relations with smalldollar loan customers who successfully
paid off their loan (Burhouse, Miller, and
Sampson 2008).
Leverage advantage in the market –
Banks and credit unions have two
inherent advantages over the payday
loan industry in successfully offering
small-dollar loan programs –
infrastructure and relationships. While
payday loan stores must spend capital
on space, staff, advertising, and more,
banks and credit unions already have
qualified staff, a large network of
physical facilities, and functioning
collection processes. Their ability to
advertise through bank statements and
existing marketing materials helps bring
attention to the product and quickly
draw a market. Banks and credit unions
can build on their relationship with
clients to help determine the type of
loan best suited for a borrower, as well
as streamline the underwriting
process—a necessary step if banks and
credit unions wish to compete with the
present payday loan industry (Burhouse,
Miller, and Sampson 2008). This
underwriting process will help mitigate
delinquency risks. Research from the
Woodstock Institute found that

borrowers who belonged to a financial
institution for more than one year
reported lower delinquency rates
(Williams 2007).
CRA credit – The Federal Financial
Institutions Examination Council recognizes
that small-dollar loans meet an important
credit need of underserved communities
and low- and moderate-income borrowers.
By offering these types of loans or
supporting the development of a smalldollar loan pool, banks can earn
Community Reinvestment Act (CRA) credit.
Such loan products allow borrowers to
avoid high cost credit, and ostensibly serve
the purpose and mission of the CRA.

Recommendations
The case for offering alternative
small-dollar loans through mainstream
financial institutions does not negate
the need for regulation of the industry.
Borrowers and lenders do not enter into
lending contracts on equal footing, in
either financial understanding or
bargaining power (Saunders and Cohen
2004). Regulation must aim to narrow
this divide and protect consumers from
predatory practices and their own
behavior tendencies (Barr, Mullainathan,
and Sharif 2008).
Regulators at the state and federal
level play an important role in
developing alternative small-dollar loans
and assuring proper consumer
protections. The following are
recommendations that state and federal
government offices should follow to
support this effort:

• Support efforts to develop small-

dollar lending pool pilots and study
their effectiveness;

• Support efforts to develop informed
policy on unregulated payday and
consumer installment loans and
provide guidance on features for
small-dollar loan products;

• Encourage responsible alternative
small-dollar loan products;

Profitwise News and Views

September 2010

13

CONSUMER ISSUES

• Build partnerships with alternative

small-dollar lenders – such as credit
unions – to support use of sound,
alternative small-dollar products; and

• Include the offering of responsible

small-dollar loans in CRA examinations
and other regulatory ratings.

In order to meet a recognized
consumer need and provide a beneficial
community service, financial institutions
should begin offering or expand existing
small-dollar credit programs to low- and
moderate-income borrowers. As
demonstrated, these programs can be
profitable and serve an important need.
Community organizations, consumer
advocates, regulatory agencies, and
especially financial institutions each
have an important role to play in
expanding the market of small-dollar
loan products. Lending institutions
should work to foster closer
relationships with borrowers. They are
well positioned to serve these
consumers by encouraging savings and
helping to develop important financial
skills. A strong banking relationship will
help decrease the borrower’s assessed
risk and need for small-dollar loans.
Additional, innovative pilots would
help to expand the availability of smalldollar lending products. Pilots should
follow guidelines to protect consumers
from predatory features and, include
measures aimed at improving individual
financial skills. Lessons from pilot
programs will help additional financial
institutions to create and expand their
own alternative small-dollar loan
programs. The combination of adequate
regulation and innovation will help
create new opportunities for the
development of sound financial products
that meet the ongoing financial needs of
low- and moderate-income consumers.

14

Profitwise News and Views

Works cited:
Barr, Michael S., Sendhil Mullainathan, and
Eldar Shafir. 2008. Behaviorally Informed
Financial Services Regulation. Washington,
DC: New America Foundation, October.
Benjamin, Blair. 2009. Children’s Youth
Savings: Opening Plenary. CFED blog
June 15. Retrieved from http://cfed.org/
childrens_youth_savings/2009/06/
opening-plenary.html on October 14.
Burhouse, Susan, Rae-Ann Miller, and
Aileen G. Sampson. 2009. The FDIC’s
Small-Dollar. Loan Pilot Program. Federal
Deposit Insurance Corporation, FDIC
Quarterly, Vol. 3, No. 2.
Burhouse, Susan, Rae-Ann Miller, and
Aileen G. Sampson. 2008. An introduction
to the FDIC Small-Dollar Loan Pilot
Program. Federal Deposit Insurance
Corporation, FDIC Quarterly, Vol. 2, No. 3.
Illinois Department of Financial and
Professional Regulation. 2009. Illinois
Payday Loan Reform Act: Three Year
Report. Springfield, Illinois: Illinois
Department of Financial and Professional
Regulation, March.
King, Uriah, Leslie Parrish and Ozlem
Tanik. 2006. Financial quicksand: payday
lending sinks borrowers in debt with $4.2
billion in predatory fees every year. Center
for Responsible Lending, November 30.
Saunders, Margot and Alys Cohen. 2004.
Federal Regulation of Consumer Credit:
The Cause or the Cure for Predatory
Lending? Boston, Massachusetts: Harvard
University, Joint Center for Housing
Studies, March.
Williams, Marva. 2007. Cooperative Credit:
How Community Development Credit
Unions are Meeting the Need for
Affordable, Short-Term Credit.” Chicago,
Illinois: Woodstock Institute, May.

September 2010

Notes:
1 It is important to note that the data from
the Illinois Department of Financial and
Professional Regulation only includes
those loans regulated under the Payday
Loan Reform Act of 2005 – defined as
loans less than 120 days in length. There
is no information for loans over 120 days.
Loans longer than 120 days in length are
often considered payday loans, but are not
regulated with the same requirements in
Illinois, thus no data is collected on these
type of loans.
2 A more in-depth report about the question
of profitability for financial institutions is
forthcoming from the Illinois Asset
Building Group in third quarter 2010.

Biographies
Chris Giangreco is a policy
associate at Heartland Alliance for
Human Needs and Human Rights,
where he manages the Illinois Asset
Building Group and advocates for the
promotion of local, state, and federal
policy supporting economic stability for
families and individuals of all incomes.
Giangreco received his PhD from
Loyola University Chicago.
Andrea Kovach is a staff attorney at
Sargent Shriver National Center on
Poverty Law, where she works in the
Community Investment Unit and the
Health Care Unit. Andrea graduated
with honors from Wellesley College and
the University of Illinois College of Law.
Matt Unrath is a policy and advocacy
intern at Heartland Alliance for Human
Needs and Human Rights, where he
assists in policy analysis for the Illinois
Asset Building Group and helps to
administer matched-savings programs
and conducts financial education for
low-income individuals and families.
Matt received his BA in international
studies from Boston College.

AROUND THE DISTRICT

Around the District
Illinois
Illinois launches small business job
creation tax credit
On July 1, 2010, Illinois’ (new $2,500
per job) Small Business Job Creation Tax
Credit became effective. The program is
designed to create jobs at small
businesses across the state. The new tax
credit is part of Illinois’ initiatives to help
employers retain and generate jobs in
Illinois during the current high
unemployment period.
“Small businesses are essential to the
Illinois economy and it’s crucial that state
government find fresh and creative ways
of working with entrepreneurs who will
lead the charge toward economic
recovery,” said Governor Quinn. “This tax
credit will help our small business owners
and operators to grow by creating 20,000
jobs over the next year.”
The $2,500 credit is available to
businesses with 50 or fewer employees
that hire new, full-time Illinois
employees during a 12-month period
that began July 1, 2010. Ninety-five
percent of Illinois businesses have
fewer than 50 employees.
To qualify for the credit, a new job must
be sustained for at least one year and pay
at least $25,000 annually. Eligible
companies can apply for the credit online
and will be issued a tax credit certificate

beginning July 1, 2011. Applications for
the credit may be submitted as soon as a
new, full-time Illinois employee is hired and
begins providing services. The total
amount of credits to be issued is capped
at $50 million.
Calculation of the net increase in the
number of Illinois employees is based on
the employer’s number of Illinois
employees as of June 30, 2010. The
determination of whether an employer has
50 or fewer employees will include all
employees in any location, including those
outside Illinois. Related businesses will be
treated as one business for the
determination.
The legislation passed the General
Assembly unanimously and was signed
by the governor at an event in Chicago
on April 13, 2010. Governor Quinn was
joined at the event by eight small
business owners.
To review the April 13, 2010, press
release, the statute, or frequently asked
questions, visit www.ildceo.net/dceo/
JobsTaxCredit/default.htm.
For a discussion on other programs,
processes, and resources that could
support small businesses and economic
development in your community, contact
the Federal Reserve Bank of Chicago,
Consumer and Community Affairs Unit, at
(312) 322-8232 or via e-mail at
CEDRIC@chi.frb.org.

Indiana
Interagency workshop on CRA
On April 27, 2010, the Federal
Reserve Bank of Chicago’s Community
Affairs Division co-sponsored an
Indiana Interagency Community
Reinvestment Act (CRA) workshop.
This event was held in partnership with
the Federal Deposits Insurance
Corporation, the Office of the
Comptroller of the Currency, and the
Office of Thrift Supervision.
This workshop offered CRA officers
up-to-date information about
developing a CRA Plan; forming
meaningful partnerships; determining
qualified community development
lending, services, and investments;
assessing community needs; and
creating the bank’s personal CRA
Performance Context. Participants in
this event included CRA Officers
representing small, intermediate, and
large financial institutions from Indiana,
Ohio, and Kentucky.
The Community Reinvestment Act
(CRA) of 1977 (12 USC 2901), as
amended, encourages each insured
depository institution covered by the act
to help meet the credit needs of the
communities in which it operates. The
CRA requires that each federal financial
supervisory agency assess the record of
each covered depository institution in

Profitwise News and Views

September 2010

15

AROUND THE DISTRICT
helping to meet the credit needs of its
entire community, including low- and
moderate-income neighborhoods,
consistent with safe and sound
operations; an agency will take that
record into account when deciding
whether to approve an institution’s
application for a deposit facility.
For more information on the CRA,
visit www.federalreserve.gov/
bankinforeg/reglisting.htm.

Iowa
Des Moines neighborhood Finance Corp.
(NFC) reaches $200 million milestone in
lending on 20th anniversary
Celebrating 20 years of service in Des
Moines making loans and home
improvement grants in the city’s low- and
moderate-income neighborhoods, NFC
also celebrated reaching the $200
million milestone in total lending in 2010.
NFC is a member of NeighborWorks®
America, a national organization whose
mission is to revitalize communities.
NeighborWorks® has hailed NFC as a
top generator of loans among its 235
community-based charter members.
NFC’s long-standing goal has been to
preserve neighborhoods. As an effective
intermediary supported by local
government, banks, and neighborhood
associations, NFC serves as a model for
other, similarly oriented organizations
around the country. Aside from purchase
and rehabilitation financing, NFC helps
borrowers qualify for loans to make home
improvements, replace furnaces,
windows, roofs, wiring, and other needed
repairs.
The work of NFC and the lending that
results helps stabilize older
neighborhoods and increase property
values. Other services include a “tool
lending library,” for home owners who
wish to make (some) improvements on
their own. Home buyer counseling – preand post-purchase – is also a key service
provided by NFC.

16

Profitwise News and Views

Michigan

Wisconsin

Re-imagining Detroit

Addressing the Credit Needs of
Wisconsin’s Small Businesses

At a recent Detroit community
development forum, Warren Palmer,
director of Detroit’s Planning and
Development Department, discussed
the “Re-imagining” of Detroit. He
outlined the key ingredients to a
resilient Detroit and sustainable region,
as well as the mayor’s priorities.
Following are paraphrased excerpts
from Palmer’s remarks.
He stated that the Federal
government will need to be a partner in
recovery and revitalization, tailoring
resources specific to the needs of
Detroit and the automotive industry. The
philanthropic and nonprofit sector must
take a more active role, and not work
only at the margins. The leadership of
regional municipalities must look past
historic racial and economic tensions,
find common ground to support regional
policies aimed at sustainability, and
cooperate on strategies and
investments that promote the growth of
the metropolitan area. Municipal
governments have been transferred to
an accountable set of public leaders. He
expressed confidence that they will
work to change the city’s and the
region’s circumstances for the next
generation.

Palmer outlined the mayor’s priorities:

• Removal of blight throughout the city
and alignment with planned
community priorities.

• Focused, near-term investments in

neighborhoods aimed at stabilization
and improvement.

• Strategies to promote job creation in
conjunction with other efforts.

• Coordination of all plans and strategies
into one clear vision for the city
through a public planning process.

September 2010

On May 27, 2010, the Federal
Reserve Bank of Chicago hosted a
roundtable discussion on meeting the
needs of Wisconsin’s small businesses.
Daryll Lund, CEO of Community
Bankers of Wisconsin, helped set the
stage for the discussion. “When you
look at small business lending as a
percentage of assets in Wisconsin
banks, it represents 12 percent of the
assets of community banks but only 4.5
percent of large banks’ assets. Small
business lending at community banks is
a huge portion of our portfolios as we
try to serve our main streets and our
local businesses.”
Eric Ness, Wisconsin district director
of the U.S. Small Business
Administration emphasized community
banks’ role in helping to channel Federal
Recovery Act funds to local businesses
as a unique strength in Wisconsin.
Describing the “American Recovery
Capital (ARC)” loan program, Mr. Ness
said, “Community banks have signed up
(as SBA lenders) just to use this
program. Last year, I had about 154
lenders in this state making loans to
small businesses in the state, and now I
am over 200 lenders making loans to
small businesses in the state.”
The Federal Reserve has held 40
meetings around the country as part of
the “Addressing the Credit Needs of
Small Businesses: A Federal Reserve
System Series.” The Series culminated
with a national meeting at the Federal
Reserve Board of Governors on July 12.
Watch for an upcoming issue of
Profitwise News and Views that will
summarize the series, focusing on the
meetings held in Illinois, Indiana, Iowa,
Michigan and Wisconsin.

CALENDAR OF EVENTS

Calendar of Events
Interagency Community
Development Conference
Cleveland, OH
September 16, 2010
Community bankers will convene at
the Cleveland Fed for a daylong
discussion of the outlook for residential
and small business lending and the
business opportunities afforded by
community development credit
enhancements. Sponsored by the
Federal Reserve Bank of Cleveland,
Federal Deposit Insurance Corporation,
Office of the Comptroller of the
Currency, and Office of Thrift
Supervision.
For more information, visit: www.
clevelandfed.org/Community_
Development/events/20100816_
interagency/SavetheDate.pdf.

Innovations in the disabilities market
Richmond, VA
September 28, 2010
The Federal Reserve Bank of Richmond and The Disability Opportunity Fund
will collaborate on a one day forum to discuss innovative community development
finance opportunities in the disabilities market. Experts from the disabilities and
community development industries will present successful finance models and
explore new capital solutions to meet the tremendous need for asset development
and affordable disability housing.
To register, visit The Disability Opportunity Fund Web site at www.thedof.org. For
questions or more information about the event, call (516) 465-3741. For more
information, visit: www.richmondfed.org/conferences_and_events/community_
development/2010/innovations_disabilities_market_20100928.cfm.

Reclaiming vacant properties
Cleveland, OH
October 13-15, 2010
National Vacant Properties Campaign with its principal planning partner,
Neighborhood Progress, Inc., will be sponsoring this conference to teach policies,
tools, and strategies to catalyze long-term, sustainable revitalization, and allow
peers to share experiences and insights, and become a part of the only national
network focused on building the knowledge, leadership, and momentum to reclaim
vacant and abandoned properties to foster thriving neighborhoods.
Contact Jennifer Leonard with questions about the 2010 Conference, including
sponsorship opportunities at (202) 207-3355, extension 123, or jleonard@
smartgrowthamerica.org.

Profitwise News and Views

September 2010

17

Call for papers: Business, Entrepreneurship, and Economic Recovery
Atlanta, GA – October 26-27, 2010
The Ewing Marion Kauffman Foundation; the Community and Economic
Development division of the Research department and the Labor, Education,
and Health Policy Center at the Federal Reserve Bank of Atlanta; and the
Federal Reserve Bank of Dallas will co-host “Small Business,
Entrepreneurship, and Economic Recovery: A Focus on Job Creation and
Economic Stabilization,” a conference that will take place at the Federal
Reserve Bank of Atlanta.
The goal of the conference is to provide a multidisciplinary approach to
understanding the relationship between small business and entrepreneurship
with economic recovery. For details on the abstract submission guidelines
and conference details, go to www.kauffman.org/sbe.

Call for papers: 2011 Community Affairs Research Conference
Arlington, VA – April 28-29, 2011
The Community Affairs Officers of the Federal Reserve System invite
paper submissions for the seventh annual Federal Reserve Community
Affairs Research Conference. The goal of the conference is to highlight new
research that can directly inform community development policy and practice
in the wake of the deepest recession since the pre-War period. Visit www.
frbsf.org/community/2011ResearchConference for more information on
submission guidelines.

18

Profitwise News and Views

September 2010

CEDRIC’s principal mission is to foster research related to
consumer and economic development issues such as consumer
and small business financial behavior, access to credit, affordable
housing, and community development and reinvestment.
CEDRIC:

• Upcoming Events, Community & Economic Development Research,
• Data Resources on the Web, Federal Reserve Publication,
• Financial Education Research Center, Household & Small Business Data,
• Additional Resources
www.chicagofed.org/cedric

LESLE:

• Lessons Learned (LesLe) Community & Economic Development Case Studies,
• Community Development Institutions, Community Development,
• Finance & General Education, Housing Development,
• Public Infrastructure, Small Business Lending

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