-
Journal of Monetary Economics 53 (2006) 22832298
Risk-based pricing of interest ratesfor consumer loans$
borrowing as a whole either rose less or, sometimes, fell.
r 2006 Elsevier B.V. All rights reserved.
ARTICLE IN PRESS
www.elsevier.com/locate/jme
author and do not necessarily represent those of the Federal
Reserve Board or its staff. I would like to thank
Pierre-Andre Chiappori, Lars Hansen, Erik Hurst and Annette
Vissing-Jorgensen, for their direction and advice. I
also would like to thank the University of Chicago, the National
Science Foundation and the Social Science0304-3932/$ - see front
matter r 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.jmoneco.2005.09.001
Research Council for their nancial support. Of course, all
errors are my own.
E-mail address: [email protected] classification:
D12; E21; E51; G21
Keywords: Consumption; Borrowing; Debt; Consumer credit;
Interest rates; Banking
$Federal Reserve Board, e-mail: [email protected]. The
views presented are solely those of theWendy Edelberg
Received 17 June 2005; received in revised form 15 August 2005;
accepted 12 September 2005
Abstract
By focusing on observable default risks role in loan terms and
the subsequent consequences for
household behavior, this paper shows that lenders increasingly
used risk-based pricing of interest
rates in consumer loan markets during the mid-1990s. It tests
three resulting predictions: First, the
premium paid per unit of risk should have increased over this
period. Second, debt levels should have
reacted accordingly. Third, fewer high-risk households should
have been denied credit, further
contributing to the interest rate spread between the highest-
and lowest-risk borrowers.
For people obtaining loans, the premium paid per unit of risk
did indeed become signicantly
larger after the mid-1990s. For example, for a 0.01 increase in
the probability of bankruptcy, the
corresponding interest-rate increase tripled for rst mortgages,
doubled for automobile loans and
rose nearly six-fold for second mortgages. Additionally, changes
in borrowing levels and debt access
reected these new pricing practices, particularly for secured
debt. Borrowing increased most for the
low-risk households who saw their relative borrowing costs fall.
Furthermore, while very high-risk
households gained expanded access to credit, the increases in
their risk premiums implied that their
-
1. Introduction
ARTICLE IN PRESSW. Edelberg / Journal of Monetary Economics 53
(2006) 228322982284Credit industry literature suggests that by the
early 1980s conventional lenders wereusing credit scores and the
like to automate underwriting standards, but as late as the
early1990s they still simply posted one house rate for each loan
type and continued to rejectmost high-risk borrowers (Johnson,
1992). As data storage costs subsequently fell andunderwriting
technology improved, however, lenders began to use estimates of
default riskto price individual loans. This paper examines both the
extent and consequences of thisincreased use of risk-based pricing
of interest rates in consumer loan markets during themid-1990s.On
the whole, the ndings are in keeping with the predictions that ow
naturally from
these changes. First, for those obtaining loans, the premium
paid per unit of risk becamesignicantly larger over this time
period, with the difference between high- and low-riskborrowers
interest rates nearly doubling for secured loans and increasing for
mostunsecured loans as well. Second, changes in borrowing levels
and access to debt reectedthese new pricing practices, particularly
for secured debt. While lower interest ratesgenerally boosted
borrowing in the late 1990s, the demand for credit increased most
forlow-risk households who saw lower relative borrowing costs.
Third, these changes inpricing practices led to increased credit
access for very high-risk households (again,particularly for
secured debt), but the increase in the very high-risk premium also
causedtheir average borrowing levels to either rise less or, for
some loan types, to fall. Finally,changes in risk-based pricing may
account for one- to three-quarters of the increase in debtlevels
for some secured loan types and may more than account for the
increase in debt useby the highest-risk groups for secured
debt.There has not been much scrutiny of the potential for credit
terms to vary by borrower
risk, let alone empirical examinations of such variance in
terms. On the theoretical side,Geanakoplos has written and
co-written several papers showing the effect of default riskon loan
terms in general equilibrium (some examples are Geanakoplos (2002)
and Dubeyet al. (2003)). Riley (1987) argues StiglitzWeiss style
rationing will not be empiricallyrelevant, as he postulates that
lenders should vary interest rates by risk. However, using1983
mortgage rate data, Duca and Rosenthal (1993) nd no evidence of
such interest ratevariation. My ndings are consistent with Ducas
and Rosenthals, suggesting that risk-based pricing did not become a
signicant factor in credit markets until more than adecade after
1983.Only in the 1990s did improvements in underwriting models and
reductions in data
storage costs became sizeable enough to decrease the costs of
risk-based pricing (Bostic,2002).1 Certain changes in consumer
credit industry practices also spurred investment indeveloping new
underwriting models. Canner and Passmore (1997) explain that in
1995bank regulators began putting greater emphasis on lending in
lower-income neighbor-hoods and to lower-income borrowers in
measuring compliance with the CommunityReinvestment Act. This
increased the protability of developing a technology to lend
tohigher-risk households. Furthermore, Fannie Mae, which previously
bought only low-riskloans and essentially did not vary nancial
terms with loan risk, introduced an improved,
1In addition, Peter McCorkell suggests insufcient data on
defaults made risk-based pricing difcult prior to
1995. He also argues that until the late 1980s, mortgage lenders
simply relied on their constantly appreciatingcollateral to
moderate the costs of default (McCorkell, 2002).
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ARTICLE IN PRESS
Table 1
Interest rate data
Standard deviation by origination year Observations
1989 1995 1998 All years
W. Edelberg / Journal of Monetary Economics 53 (2006) 22832298
2285automated underwriting system in 1995 and began to accept
higher-risk loans subject tosome price discrimination. In 1996,
Fannie Mae and Freddie Mac indicated that loanpackages must include
a credit bureau score (McCorkell, 2002).In the mid-1990s, lenders
could, and did, vary interest rates and issue higher-risk
mortgages (Freeman and Hamilton, 2002). As a result even credit
unions began using risk-based pricing at this time as low-risk
members complained that they were able to get lowerrates at
conventional banks.2 The technology of risk-based pricing made its
way frommortgage loans into other loan types, such as second
mortgages, automobile loans andcredit card loans. For example,
Black and Morgan (1999) nd demographic evidence thatmore high-risk
households gained access to the credit card market in 1995 relative
to 1989.(Indeed, this increased access appears to have been
widespread: average household incomewent up about 20% more than the
average income of those with any debt, pre-1995 versuspost-1995.
Similarly, average education rose about 40% more overall than for
those withdebt.) My results show that loans easily securitized,
such as those mentioned above, havebeen affected the most these
pricing changes, suggesting that secondary loan markets haveplayed
a role in promoting risk-based pricing.
First mortgage ratea 1.16 1.26 1.49b 8,143
Second mortgage 2.21 2.82b 2.63 805
Auto loan 3.58 4.05b 4.53b 5,209
Credit cardc 4.18 4.43b 5.01b 4,007
Other consumer loan 4.47 6.07b 6.69 2,744
Education loan 3.37 4.05b 1.96 997
aOnly 30-year xed rate mortgages are considered.bDifference
between current and preceding year is signicant with
p-valueo0.1.c1983 is used in place of 1989.2. Data
This analysis uses the Surveys of Consumer Finances (SCFs) from
1983 to 1998. Firstand second mortgages, automobile loans, general
consumer loans, credit card loans andeducation loans are
considered. Loans in a category are summed and the highest
interestrate is used.3 Table 1 shows the standard deviations for
three loan originations years: 1989,1995 and 1998 (except for
credit card loans, which substitutes 1983 for 1989 due to
datarestrictions).4 Consistent with the increased use of risk-based
pricing, interest rate variationgenerally increased over time, and
often signicantly. Note that standard deviations of
2This point was made in conversations with the University of
Wisconsin Center for Credit Union Research.3Credit card balances
are considered loans when interest is paid on the balance. Note
that Gross and Souleles
(2001) points out an underreporting of credit card debt in the
SCF, which is problematic only if this
underreporting is signicantly correlated with risk, and this
correlation changes over time.4Sampling weights are used for rst
and second moments. Following Deaton (1997), the data are not
weighted
in the empirical models as coefcients are assumed not to vary
across the population.
-
monthly prime interest rates were similar in 1989 and 1995 and
actually decreased between1995 and 1998. In addition, the table
shows the total observations across the 5 years ofdata for the
various loan categories, anticipating some of the differences in
the resultsrobustness. For its more extensive data on bankruptcy,
the Panel Study of IncomeDynamics (PSID) is also used for the
wealth supplement years of 1984, 1989 and 1994.Total bankruptcies
across all years prior to 1996 is 502, reecting a slightly
lowerbankruptcy rate than in the population, a point made by Fay et
al. (2002).
3. Empirical analysis
ARTICLE IN PRESS
5Maturity does not generally vary meaningfully within a loan
type and was often found to have no real
W. Edelberg / Journal of Monetary Economics 53 (2006)
228322982286signicant effect on interest rates. For example, over
one-half of mortgages have 30-year maturities, and nearly
60% of automobile loans have maturities between 4 and 5
years.6Note that this model essentially does not allow for a
rejection by the lender. However, we can consider a loan
rejected any time RiIip0. For example, if a lender at least
knows the upper bound for a households reservationinterest rate, it
may choose to simply reject a loan rather than offer an interest
rate above this upper bound.
7Dollar values are deated using the CPI. For general consumer
loans, current loan balances are better
predictors than original loan amounts. This may be due to the
more informal nature of these loans. For example,
these loans may be renegotiated more easily so that current
balance is also highly relevant for the terms.8The possible
complication that l may be in part a function of dfor example, ex
ante high-risk people inThe primary goal of this empirical analysis
is to estimate the role default risk plays ininterest rate
determination and also to see if that role has changed over time.
Assume thata household has a reservation interest rate Ri(A,l,Pi),
which is a function of a certain loanamount, A, with collateral to
ensure a recovery rate, l, of the loan balance, and
householdcharacteristics, P. Ii(A,l,di,o,f), the interest rate
offered by the lender, is a function of A, l,default risk, d, the
lenders discount rate, o, and xed costs, f.5 Because the SCF
onlyreports interest rate data for households who successfully
secure loans, selection bias isaccounted for. We can infer that R
is greater than or equal to I for those consumers whohave positive
loan balances. To formalize:
RiA; l;Pi I iA; l; di; o; f Hib ui,ProbRi I i40 FHib.
Hi is a vector of characteristics that helps predict whether the
loan is observed forhousehold i. Ii and Ri, are subscripted i to
allow for an idiosyncratic individual specicshock, eI:
I iA; l; di; o; f Xig i observed when Ri I i40.Note that the
linearity in the equation above assumes lenders are risk neutral or
are
diversied enough to appear risk neutral. Xi is a vector of
characteristics that help predictI.6 X includes direct measures or
proxies, where necessary, for the ve variables, A, l, d, oand f.
First, A is included directly.7 Second, l should be roughly
constant for each type ofnon-collateralized loan and hence captured
by a varying constant term. For collateralizedloans, l should rise
with collateral, and hence the equity in the collateral is
included.8 Third,measures of d are described in detail below.
Fourth, o is assumed constant over a year anddefault may be more
difcult to collect from than low-risk people in defaultis not
considered here.
-
is captured by year dummies.9 Finally, inasmuch as xed costs, f,
are recovered throughthe interest rate, their effect should be
captured by including A.H includes both supply and demand variables
that inuence whether a household holds
a loan. On the supply side, H includes variables that help
predict denial such as second-order polynomials of default risk. To
account for demand, other nancial anddemographic characteristics,
Pi, are included: an age polynomial, marriage status, thenumber of
children, whether the family has a checking account, education, log
of income,net worth, level of assets, race and variables that reect
borrowing attitudes.10 Theattitudinal variables show whether
households consider borrowing to be good, bad, or
zero), unemployment indicator, race, single parent indicator,
and education. For the
ARTICLE IN PRESSW. Edelberg / Journal of Monetary Economics 53
(2006) 22832298 2287unconditional estimation, asset levels are used
in place of debt and home ownershipstatus.13 Signicant time
variation in coefcients is also included. Overall, the
coefcientsare consistent with the bankruptcy literature. A detailed
discussion of these results can befound in Edelberg (2003).
9This should in part reect the required rate of return to those
supplying loanable funds. Ausubel (1991) nds
that credit card issuers earned possibly ve times the ordinary
banking rate of return from 1983 to 1988. Here, no
specic rate of return is imposed, but it is assumed that markets
are competitive.10Racial status may reect preferences for borrowing
and potential lender bias (see Edelberg, 2002).11Numerous variables
are included in H but not in X, such that the demands on the
attitudinal variables as
appropriate instruments are less than they might be. However,
robustness checks were still done. All the
signicant attitudinal variables from the probits were included
in their respective regressions: For example, if an
attitudinal variable signicantly predicted mortgage use, it was
included in the mortgage interest rate equation. In
all cases, most or nearly all of these coefcients in the
interest rate regressions were insignicant.12The imputation of
bankruptcy risk is based on a similar methodology in Jappelli et
al. (1998).13Research used to identify explanatory variables
includes Sullivan et al. (1989), Johnson (1992), Domowitz andokay
and whether they believe borrowing is acceptable in certain
circumstancessuch asfor a loss in income or to buy a house or
jewelry. These variables ensure identication, asthey are excluded
from X. Correlation estimates conrm that these responses are
notsimple functions of borrowers debt portfolios.11
3.1. Default risk
Default risk is comprised of the risks of bankruptcy and
delinquency. The SCF containsno bankruptcy data before 1998, so the
PSID is used to estimate a model of futurebankruptcy, and
bankruptcy risk is imputed for SCF households.12 A household is
denedas bankrupt at time t if it declares bankruptcy during the
period t to t+2. This futuremeasure allows for two forms of
bankruptcy risk: conditional and unconditional on ahousehold
holding debt. Conditional bankruptcy risk is relevant for lenders
assessinginterest rates for loans, and thus will be included in X.
Because the unconditionalbankruptcy risk measure does not include
any debt measures, it is included in H, the vectorof
characteristics predicting whether a household holds a certain
loan. Note a full 14% ofthese bankrupt households have no debt at
time t.In light of the extensive research on bankruptcy, the
following variables are used in a
probit to predict bankruptcy risk: year, age, a checking account
indicator, income, a selfemployment indicator, home ownership
status, unsecured debt, an indicator of whether theratio of
unsecured debt to income exceeds two, net worth (with negative net
worth set toSartain (1999), Gross and Souleles (1999), Sullivan et
al. (2000), and Fay et al. (2002).
-
ARTICLE IN PRESS
Table 2
Probability of default risk
Conditional bankruptcy Unconditional bankruptcy Delinquency
PSID SCFa PSID SCFa SCF
W. Edelberg / Journal of Monetary Economics 53 (2006)
228322982288Counterparts in the SCF that are close to the
bankruptcy determinants in the PSIDare used in order to impute
bankruptcy risk for SCF households. The necessarycorrection of the
standard errors is done following Murphy and Topel (1985). Table
1shows bankruptcy risk in the PSID and imputed risk in the SCF,
which areroughly similar. The bankruptcy risk is quite small: The
90th percentile household withdebt in the SCF still has only a 2.3%
probability of declaring bankruptcy within the next
2years.Delinquency risk is the second measure of default risk. The
SCF reports whether
respondents have been more than 60 days late on a loan payment
in the previous year.14
Nearly 9% of the households report a delinquency, showing it is
much more common thanbankruptcy. A probit is used to determine
delinquency risk using the same determinantsthat are in the
conditional bankruptcy model.15 A selection model is estimated,
asdelinquency data only exists for households with debt. Again, the
attitudinal variablesensure identication. And again, signicant time
variation in coefcients is also included.
Percentiles 1% 0.0 0.0 0.0 0.0 0.1
10% 0.0 0.0 0.0 0.0 1.0
25% 0.2 0.1 0.2 0.1 2.6
50% 0.7 0.6 0.6 0.5 5.6
75% 1.4 1.3 1.2 1.1 11.8
90% 2.2 2.3 1.8 1.7 20.7
99% 5.1 5.3 3.4 3.3 47.9
Mean 1.0 0.9 0.8 0.7 8.9
Standard deviation 1.2 1.4 0.8 0.8 9.8
aBankruptcy risk in the SCF is imputed.Delinquency
riskconditional on holding debtis reported in the last column of
Table 2.Note that the correlation between bankruptcy and
delinquency risk is only 0.35, showingthese are distinct measures
of default risk.
3.2. Putting it all together
We now have three measures of default risk: delinquency risk, g,
conditional bankruptcyrisk, fc, and unconditional bankruptcy risk,
fuc. X and H are dened as follows:
X x; f cI95; f c; gI95; g; H h; f uc; f 2uc.
14As will be clear in the empirical analysis, a good bit of the
information in late payments is indeed used by
lenders in pricing interest rates at loan origination. For every
loan considered, average rates paid are higher for
those who made late payments versus those who had no late
payments, with the differences ranging from a low of
0.2 percentage point for education loans to a high of nearly 2.5
percentage points for automobile loans.15The main results are
robust to using either predicted or actual delinquencies. We might
use actual
delinquencies given rational expectations, as lenders should
predict correctly on average. In addition, if lenders
have superior data, their predictions of delinquency may be
closer to the actual delinquencies than in this analysis.
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ARTICLE IN PRESS
Table 3
Interest rates moments by origination year and risk class over
time
High-risk versus low-risk spread
1989a 1995a 1998a
First mortgage rateb 0.53 0.59 0.69
Second mortgage 2.65 1.75 2.84
Auto loan 1.40 2.42c 3.94c
d c
W. Edelberg / Journal of Monetary Economics 53 (2006) 22832298
2289The indicator variable, I95, will determine whether the role of
default risk in interest ratedetermination changed after 1995. The
vectors x and h contain the remaining variables inX and H,
respectively, aside from default risk. A default risk premium
spread, s, measuresthe difference in interest rates between the
highest- and lowest-risk groups:
s gf c f c;RI95 gf c f c;R gggRI95 gggRh i
gf c f c;LI95 gf c f c;L gggLI95 gggLh i
,
where the gs are the coefcients from the interest rate equation
in the selection model. Theindicator function, I95, determines
whether the risk premium is post- or pre-1995. R and Ldene averages
of the default risk measures using conditional bankruptcy
probabilities: thehighest- and lowest-risk groups are the 20% most
and least likely to declare bankruptcy,respectively.16
Credit card 0.99 1.05 1.22Other consumer loan 0.08 3.03c
4.06
Education loan 0.02 1.30 0.26a1998 spreads computed from 1998
and 1997, 1995 computed from 1995 and 1996, and 1989 computed
from 1988 and 1987 (except for credit card rates which are
computed for single years).bOnly 30-year xed rate mortgages are
considered.cDifference between current and preceding year is
signicant with p-valueo0.1.d1983 is used in place of 1989.4.
Empirical results
Table 3 shows the differences in average interest rates paid by
most and least riskygroups (as dened above) for 1989, 1995 and
1998.17 The clear trend is for the difference torise over time,
consistent with an increased use of risk-based pricing. That the
difference isnot always signicant reects the value of the more
careful and extensive analysis discussedbelow.
16Here, s is calculated using xed risk classespresenting an
economically useful summary of the coefcients ondefault risk. These
premium spreads might change if we looked at the households
actually using certain types of
loans pre- and post-1995, but this would make changes in pricing
practices harder to isolate.17The spreads are taken from interest
rates averaged for a 2-year period in order to have a reasonable
number of
observations for each risk group. 1998 spreads are computed from
1998 and 1997, 1995 spreads are
computed from 1995 and 1996, and 1989 spreads are computed from
1988 and 1987 (except for credit card
spreads which are computed for single years and 1983 is
substituted for 1989). Prime rate volatility is similar
across the time periods, though rates are a little more volatile
from 1997 to 1998 then in the previous periods. For
rst mortgages, only 30-year xed-rate mortgages are used.
-
ARTICLE IN PRESS
Table 4
Default risk premium spreads
Pre-1995 risk premium spread Post-1995 risk premium spread
First mortgage rate* 0.50 0.98
Second mortgage rate* 0.98 3.97
Auto loan rate* 1.08 1.94
W. Edelberg / Journal of Monetary Economics 53 (2006)
228322982290Table 4 shows the default premium spreads for pre-1995
and post-1995.18 For the threetypes of secured loans, spreads at
least nearly double over the sample, with the difference inthe
spreads signicant at the 95% condence level in each case. The
results are mixed forunsecured loans. The spread is positive but
unchanged over the sample for generalconsumer loans, positive and
statistically signicant for credit card loans only post-1995,and
not statistically signicant for education loans before or after
1995.19
These results are quite robust. Default premium spreads were
calculated using actualdelinquencies and then using slightly
different cutoff years. Overall, spreads were a littlesmaller in
the alternative models but still showed the same changes over time
as in the basemodel.20 In addition, the models were estimated using
only conditional bankruptcy anddelinquency predictions, in turn.
While these models generally reect the base modelsresults, there is
value-added from using both measures of default risk. For
example,without delinquency risk, only the rst mortgage spread is
truly consistent with the basemodel. For example, the second
mortgage spread is insignicant post-1995. In addition,the
automobile loan spread does not change over time, and the credit
card spread issignicantly negative pre-1995.Fig. 1 shows these
results graphically for rst mortgages, automobile loans, and
credit
card loans. An interest rate function is plotted against
conditional bankruptcy risk for pre-
General consumer loan rate 1.19 1.08
Credit card rate* 0.53** 1.30Education loan rate 0.03**
0.41**
*Difference is signicant at a 95% condence level.
** Spread is insignicantly different from zero.and post-1995
loan origination dates. For each loan type, interest rates are
predicted by thesignicant measures of default risk, and other
signicant variables are set to their meanvalues for the entire
sample period. The effects of year dummies pre- and post-1995
areaveraged so that the predicted zero default interest rate reects
the average discount rateover the period being considered. In
total, 90% condence bands are also reported.Consistent with Table
4, the slopes are steeper in the post-1995 period, indicating that
thedefault risk premium increased. A comparable gure for second
mortgages is consistent
18Overall, the explanatory variables have the expected signs and
are generally signicant. A more detailed
discussion can be found in Edelberg (2003).19For credit card
loans, state usury laws may have constrained credit card rates more
than other, generally
lower, consumer loan rates in 1983. These laws were rendered
ineffective by a 1978 Supreme Court decision, but
there is evidence that lenders may have been slow to adapt.
Using only 1995 and 1998, the premium is 0.70% in
1995, and it signicantly rises to 1.24% in 1998.20For mortgages,
the inclusion of maturity, xed versus exible interest rates or FHA
loan guarantees does not
signicantly alter the basic default risk premium spread
results.
-
ARTICLE IN PRESSst ra
te
11
10
W. Edelberg / Journal of Monetary Economics 53 (2006) 22832298
2291with those for rst mortgages and automobile loans. For other
consumer loans, the interestrate function is the same in both
periods. The gure for education loans is similar to thecredit card
loan gures, as it shows a at interest rate function pre-1995 and an
upwardsloping function post-1995.
1st m
ortg
age
inte
re
0 0.01 0.02 0.03 0.04 0.05Conditional Bankruptcy Probability
Auto
mob
ile lo
an in
tere
st ra
te
0 0.01 0.02 0.03 0.04 0.05Conditional Bankruptcy Probability
Cred
it ca
rd lo
an in
tere
st ra
te
0 0.01 0.02 0.03 0.04 0.05Conditional Bankruptcy Probability
post 1995 pre 1995post 1995 90% CI pre 1995 90% CI
9
8
7
16
14
12
10
20
18
16
14
12
Fig. 1. Interest rates by bankruptcy risk.
-
The change in the slopes can be summarized by measuring how much
interest rateschange with an increase of 0.01 in bankruptcy risk.
This change more than doubles for rstmortgages, going from 0.16 to
0.38 basis points.21 The change is up nearly ve times forsecond
mortgages and more than doubles for automobile loans. There is no
change in theslope of the interest rate curve for general consumer
loans. Credit card and education loansgo from zero slopes to
changes in interest rates of 0.48 and 0.30, respectively.The
results for secured loans support the hypothesis that lenders
increasingly used risk-
based pricing after 1995. For unsecured loans, credit card loans
are the most robustlyconsistent with the hypothesis. Two potential
reasons may have led to this negative result for
should have increased more (or decreased less) than levels for
high-risk households.22 The
ARTICLE IN PRESSW. Edelberg / Journal of Monetary Economics 53
(2006) 228322982292following selection model is used to estimate
these effects across households:
lnB_ g0 g1O95
X3i1
gi2 fic
X3i1
gi3O95 fic u,
PrB40 f b0 b1Y 95 X3i1
bi2 fiuc
X3i1
bi3Y 95 fiuc yA
!,
21The renancing boom after 1995 should not be driving the
mortgage interest rate results. Loans are compared
by origination year, whether for purchase or renancing. Still,
renancing booms may mean that borrowers who
receive bad shocks cannot renance to the new low rates. This may
lead to less mortgage rate variation pre-1995
(i.e. if only high-risk households hold old loans), but not less
variation as a function of risk.
In addition, mortgage interest rate results are not simply due
to the addition of a subprime market, with little
spread within the prime and subprime markets. Instead of the
implied bimodal distribution, we see a smooth bell-
shaped distribution of mortgage rates post-1995. For example,
50% of the post-1995 rates are between 7% and
8.5%, 20% are between 6% and 7%, and 20% are between 8% and
9%.22The change in the overall level of interest rates over time
has a direct effect. For example, interest rates fell for
all credit card loans, and all risk classes increased credit
card borrowing. However, interest rates fell more for low-education
and general consumer loans. First, of the three unsecured loan
types, credit cardloans have the highest incidence of loan
securitization. As mentioned above, the secondarymarket for loans
may motivate risk-based pricing. Perhaps, lenders of education and
otherconsumer loans have yet to feel the pressures that led to
risk-based pricing. Second, as lenderskeep better track of
borrowers at risk of imminent bankruptcy, default losses may fall
aslenders are more aggressive in obtaining partial payments and
fees (Winton, 1998). Thesefalling default costs would offset the
forces driving the increased use of risk-based pricing.
5. Implications for borrowing
If lenders declined to charge very high-risk households
sufciently high interest ratesbefore the mid-1990s, lending to this
group may have proved signicantly unprotable,and these households
may have been rationed out of the market (Bostic, 2002). With
risk-based pricing, lenders should offer these households debt with
higher interest rates ratherthan reject them. If at least some of
these borrowers have sufciently high reservationrates, debt use
among very high-risk households should rise. Debt levels should
alsochange in reaction to risk-based pricing. Before the mid-1990s,
low-risk borrowers paidrelatively higher rates than their default
risk justied, and high-risk borrowers paid lowerrates. As premiums
adjusted to better reect risk, debt levels among low-risk
householdsrisk borrowers, and this is where we can see the effect
of risk-based pricing.
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ARTICLE IN PRESSwhere B is the debt level for the various
consumer loan types, in 1998 dollars, and A is the vectorof
attitudinal variables. Accounting for changes in the cost of funds,
O95 indicates if the loanorigination year is 1995 or later, and Y95
indicates if the survey year is 1995 or later. The thirddegree
polynomial in bankruptcy risk allows debt use and levels to vary
with default risk, andthe interaction terms measure how debt use
and levels changed across risk classes over time.Fig. 2 shows
predicted debt use and Fig. 3 shows predicted debt levels with 90%
condence
bands. As the top panel of Fig. 2 shows, the very high-risk
households have a higherprobability of holding a rst mortgage after
1995. Low-risk households also have a higherprobability of holding
rst mortgages over time, perhaps as rates fell below some of
theirreservation rates. Conversely, higher interest rates for
high-risk groups lower the probabilityof rst mortgage use for this
group. Consistent with these effects, the increases in
mortgagelevels after 1995 are predicted to fall with default risk,
shown in the top panel of Fig. 2.The increase in the use of
automobile loans and credit cards loans is similar to that for
rst
mortgages, as shown in the lower panels of Fig. 2. However, for
both loan types, thecondence bands are wider, particularly as risk
increases. In addition, the predicted debt levelsfor automobile
loans are quite consistent with the hypothesis, shown in the middle
panel ofFig. 3. Indeed, high-risk households (as opposed to very
high-risk households), which saw nosignicant increase in access but
saw relative borrowing costs rise, are predicted to holdsignicantly
lower levels of automobile debt post-1995. The overall increase in
popularity ofcredit card borrowing overwhelms the effects of
risk-based pricing, and all credit cardborrowers are predicted to
increase debt levels after 1995, shown in the lower panel of Fig.
3.Equivalent gures for the other debt types are not shown. Second
mortgages are only
held by households with rst mortgages, making results on its use
less informative. Otherconsumer loans and education loans showed no
signicant increases in their premiumspreads, so there is little
reason for the hypothesis to hold in these cases, and indeed
noconsistent story emerges from the graphs. In addition, a number
of aggregate debtcategories were considered but are also not shown,
though they bear out the hypothesis.For example, very high-risk
households have a higher probability of holding any debt,post-1995.
Low-risk borrowers increase total debt levels more than high-risk
borrowers do,and in some cases very high-risk borrowers actually
decrease borrowing levels. Finally,consistent with interest rates
falling below or rising above reservation rates, low-risk
(high-risk) households have a higher (lower) probability of holding
any form of debt.
6. Access to debt markets
The increase in the use of debt and debt levels in the 1990s has
been the subject of muchpopular discussion. To isolate the role of
risk-based pricing, a counterfactual of a pre-1995world with the
increased use of risk-based pricing is estimated by the following
model forthe various types of consumer debt, where B is the
borrowing level for each household:
lnB^ g0 g1O95 X4i1
gi2Q5i X4i1
gi3O95Q5i u,
PrB40 f b0 b1Y 95 X10i2
bi2Q10i X10i2
bi3Y 95Q10i yA !
.
For this analysis, default risk quantiles replace continuous
measures of risk: (Q5)i
W. Edelberg / Journal of Monetary Economics 53 (2006) 22832298
2293represents the ith of ve quantiles, and (Q10)i represents the
ith of ten quantiles. Risk is
-
ARTICLE IN PRESS
ebt 1
W. Edelberg / Journal of Monetary Economics 53 (2006)
228322982294t Mor
tgag
e D
0.8
0.6measured this way since the many households with near-zero
default risk should not berepresented by zero. Using zero would
obscure the effects of any changes in the coefcientsfor this risk
group.23
Prob
abilit
y of
1s
0 0.01 0.02 0.03 0.04 0.05Unconditional Bankruptcy
Probability
Prob
abilit
y of
Aut
omob
ile D
ebt
0 0.01 0.02 0.03 0.04 0.05Unconditional Bankruptcy
Probability
Prob
abilit
y of
Cre
dit C
ard
Debt
0 0.01 0.02 0.03 0.04 0.05Unconditional Bankruptcy
Probability
post 1995 pre 1995post 1995 90% CI pre 1995 90% CI
0.4
0.2
0
0.5
0.4
0.3
0.2
0.1
0
1
0.8
0.6
0.4
0.2
Fig. 2. Predicted debt use by bankruptcy risk.
23The preceding sections analysis on the heterogeneous effects
of risk-based pricing across risk groups suggests
which risk groups should be excluded in order to identify this
model. Robustness checks show that the choices
-
ARTICLE IN PRESS
ls200.000
W. Edelberg / Journal of Monetary Economics 53 (2006) 22832298
2295e De
bt L
eve 150.000
100.000Fig. 4 plots the predicted changes for borrowing levels
and debt use for rst mortgagesand all debt for a pre-1995 world
with and without the increased use of risk-based
pricing.Probability of debt use is plotted against bankruptcy risk,
whereas predicted debt levels are
Mor
tgag
0 0.01 0.02 0.03 0.04 0.05Conditional Bankruptcy Probability
Auto
mob
ile D
ebt L
evel
s
0 0.01 0.02 0.03 0.04 0.05Conditional Bankruptcy Probability
Cred
it Ca
rd D
ebt L
evel
s
0 0.01 0.02 0.03 0.04 0.05Conditional Bankruptcy Probability
post 1995 pre 1995post 1995 90% CI pre 1995 90% CI
50.000
0
20.000
15.000
10.000
5.000
2.500
2.000
1.500
1.000
500
Fig. 3. Predicted debt levels by bankruptcy risk.
(footnote continued)
made are quite reasonable. The selection equation uses
additional risk quantiles to better estimate increased debt
use among the very high-risk groups. The tenth quantile is even
further divided into four ner divisions of risk.
-
ARTICLE IN PRESSW. Edelberg / Journal of Monetary Economics 53
(2006) 228322982296plotted against bankruptcy risk quantiles.
Quantiles are used as the signicant changesoccur for households in
the rst quantile, which have nearly zero variation in
bankruptcyrisk.24
Fig. 4. Effects of risk-based pricing.
24These condence intervals reect the prediction error in the
coefcients and not the error associated with the
residual. These plots do not represent genuine forecasts of
levels and use of debt, only the levels and use predicted
by risk-based pricing as summarized by the coefcients. Including
the error associated with the residual generally
makes the condence intervals so large as to include pre- and
post-1995 point estimates.
-
Allowing for the increased use of risk-based pricing in a
pre-1995 world predicts one- tothree-quarters of the actual
increases in debt levels seen across the 1990s. For example,
themodel predicts that risk-based pricing would have added over
$7,000 to the average
ARTICLE IN PRESSW. Edelberg / Journal of Monetary Economics 53
(2006) 22832298 2297mortgage amount excluding any economy-wide
changes (in 1998 dollars). Actualmortgages originated after 1995
versus those originated before 1995 increased about$30,000.
Similarly for automobile loans, the model predicts an increase of
nearly $1500 inthe average loan size, whereas actual automobile
loans increased over $2000. (Figures forautomobile loans are not
shown for brevity, but can be seen in Edelberg (2003).) Themodel
predicts an increase in the average debt burden for households with
debt of nearly$6000 over the mid-1990s. The actual average rose
about $14,000.The model over-predicts the increased use of debt.
For rst mortgages, the model
predicts an increase of nearly 8 percentage points of households
holding mortgages frombefore 1995 to after 1995. The actual
increase was 3 percentage points in the SCF. Forautomobile loans,
the model predicts an increase of nearly 0.5 percentage point
ofhouseholds holding automobile loans, and the actual increase was
only 0.1 percentagepoint. Note that the highest risk group saw much
larger changes. For these households, themodel predicts an increase
of 3.2 percentage points in those holding automobile loans.
Theactual increase was 2.6 percentage points. For all debt, the
model predicts an increase ofalmost 7 percentage points in the
number of borrowers, and the actual increase was 2percentage
points.25
7. Conclusion
Lenders increasingly used risk-based pricing of interest rates
in consumer loan marketsduring the mid-1990s. Risk premium spreads
for secured loans rose over time by asignicant amount. The case for
unsecured loans is less clear. The premium spread forcredit card
loans more than doubled, but education loan and other consumer
loanpremiums are statistically unchanged. The evidence suggests
that variations over time inhouseholds debt levels and use of debt
instruments are consistent with this change inpricing practices.
For example, while very high-risk and very low-risk households
havebeneted from these changes, high-risk households have seen
their relative premiumsincrease and have changed their borrowing in
response.
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Risk-based pricing of interest rates for consumer
loansIntroductionDataEmpirical analysisDefault riskPutting it all
together
Empirical resultsImplications for borrowingAccess to debt
marketsConclusionReferences