On the Economics of Subprime Lending
Amy Crews Cutts and Robert Van Order
January 2004
Freddie Mac Working Paper #04-01
8200 Jones Branch Drive
McLean, VA 22102-3110
www.freddiemac.com
On the Economics of Subprime Lending
Amy Crews Cutts and Robert Van Order
January 2004
Freddie Mac Working Paper
#04-01
This paper was produced for The Credit Research Center at Georgetown University Subprime Lending
Symposium, held at in McLean, VA on September 17, 2002.
Amy Crews Cutts is Deputy Chief Economist Freddie Mac, McLean, VA, and Robert Van Order is
Professor of Economics, George Washington University, Washington, DC, and former Chief International
Economist, Freddie Mac, McLean, VA.
© by Freddie Mac. All rights reserved. Short sections of text, not to exceed two paragraphs, may be
quoted without explicit permission provided that full credit, including © notice, is given to the source.
Any opinions expressed are those of the authors and not necessarily those of Freddie Mac or its Board of
Directors.
This research was conducted while Robert Van Order was Chief International Economist at Freddie Mac.
The authors would like to thank Ralph DeFranco and Sheila Meagher at LoanPerformance, San
Francisco, CA and Craig Watts at Fair, Isaac & Co., San Raphael, CA, for their generous assistance in
providing data tables used in this paper. We also wish to thank Richard Green, David Nickerson, Michael
Fratantoni, and Peter Zorn, and participants at the CRC Subprime Lending Symposium, the FTC
Research Roundtable on the Home Mortgage Market, and the HAAS School of Business Real Estate
Seminar for their comments and suggestions.
Your comments and questions are welcome. Address Correspondence to Amy Crews Cutts, 8200 Jones
Branch Drive MS 484, McLean, VA 22102; (703) 903-2321; [email protected]. Media
Contact: Eileen Fitzpatrick (703) 903-2446; [email protected].
Abstract
U.S. mortgage markets have evolved radically in recent years. An important part of the
change has been the rise of the “subprime” market, characterized by loans with high default
rates, dominance by specialized subprime lenders rather than full-service lenders, and little
coverage by the secondary mortgage market. In this paper we examine these and other “stylized
facts” with standard tools used by financial economists to describe market structure in other
contexts.
We use three models to examine market structure: an option-based approach to mortgage
pricing in which we argue that subprime options are different from prime options, causing
different contracts and prices; and two models based on asymmetric information – one with
asymmetry between borrowers and lenders and one with the asymmetry between lenders and the
secondary market. In both of the asymmetric-information models, investors set up incentives for
borrowers or loan sellers to reveal information, primarily through costs of rejection.
Keywords: asymmetric information, licensing, option-pricing, secondary market,
signaling, subprime mortgage market.
On the Economics of Subprime Lending
Amy Crews Cutts and Robert Van Order
1. Introduction
U.S. mortgage markets have evolved radically in recent years. Innovations in
underwriting, mortgage products, and mortgage funding have expanded mortgage lending and
reduced costs. An important part of the change has been the rise of the “subprime” market. The
subprime market is characterized by loans with high default rates and dominance by specialized
subprime lenders. Indeed, for research and other purposes subprime loans are generally defined
by the characteristics of the lender (a specialized subprime lender) rather than the loan.1
Subprime mortgages are now a little more than ten percent of the mortgage market.
Two characteristics are of special interest for this paper: first, the observation cited above
that subprime is defined by lender rather than loan and that subprime lenders have historically
been separate, specialized institutions, rather than parts of the same institution quoting different
prices for different risks, and second, that the secondary market, which is prominent in most of
the prime market, has done very little purchasing of subprime loans. In this paper we attempt to
explain these and some other “stylized facts” with some standard tools used by financial
economists to describe mortgage markets and financial structure in other contexts. Our
reasoning is inductive rather than deductive, making us tinkers with our economic toolbox. This
is in part because we do not have data that are suitable for rigorous testing; some of our stylized
facts are not much more than hearsay.
The main stylized facts that characterize the subprime market (relative to the prime
market) are:2
1. High interest rates
2. High points and fees
3. Prevalence of prepayment penalties
4. Lending based largely on asset value, rather than borrower characteristics, with
low loan-to-value (LTV) ratios and high rejection rates.
1
For example, subprime loans are identified in Home Mortgage Disclosure Act (HMDA) data by whether they were
originated by a subprime lender; subprime lenders are identified by the U.S. Department of Housing and Urban
Development (HUD) within the list of HMDA reporting institutions.
2
Many of these characteristics are documented in Lax, et al. (2004) and Courchane, et al., (2004); we also provide
some empirical evidence in the sections that follow.
1
5. Specialized subprime lenders who cater only to subprime borrowers and who
have limited access to secondary mortgage markets.
6. Large rate differences between marginally prime and marginally subprime
borrowers.
Some of these characteristics are changing, especially as information improves. As a result
the menu of choices analyzed here is expanding.
Our focus is on whether pricing differences between prime and subprime markets can be
explained by option-based approaches to mortgage pricing and on whether or not historic market
structure can be explained by two asymmetric information models. On the pricing side we argue
that subprime options are different in some ways from prime options, causing different contracts
and prices. In both of the market structure models asymmetric information causes investors to
set up incentives for borrowers or loan sellers to reveal information, primarily through costs of
rejection. In the first model, a combination of signaling and screening leads to a separating
equilibrium in which lenders get borrowers to reveal hidden risks through their
application/rejection strategy and downpayment requirements. In the second model, loan
purchasers use rejection (through quality control sampling) as a device to get sellers to classify
loans for them. While we cannot prove that our models are all that there is to it, we do find that
the enumerated six points are largely consistent with them.
Our models assume that borrowers have better information about themselves than lenders
have about them, and that they act rationally based on that information. If this were true in all
cases, then it would be unlikely that fighting “predatory lending” practices, which are more
concentrated in the subprime market, would be at the forefront of public policy making as it is
today.3 Richardson (2002) explores predatory lending in the context of a market with rational
borrowers seeking to refinance who cannot determine predatory subprime lenders from good,
non-predatory subprime lenders due to weak signals.4 We do not attempt to explain predatory
lending; however, Richardson’s model is consistent with ours, and indeed, could coexist with our
theoretical framework. We do discuss some implications of borrower self-misclassification.
3
For a description alleged predatory practices see, for example, the text of the Predatory Lending Consumer
Protection Act of 2002 (http://www.senate.gov/~banking/chairman.htm) proposed by Senator Paul Sarbanes (DMD).
4
Large disparities between the demographic characteristics of prime and subprime borrowers are well documented.
(see, for example, Lax, et al. (2004), Courchane, et al. (2004) and U.S. Department of Housing and Urban
Development (2000)). The reasons for these disparities, and correlations between them and the mortgage market
structure are not explored here.
2
2. Options and Risks in the Prime and Subprime Segments
We can think of mortgage borrowers as having three options:
1. An option to default, which can be thought of as a put option, giving the borrower the
right to put the house back to the lender in exchange for the loan.
2. A delinquency option, which can be thought of as a fixed rate line of credit that gives
the borrower the right to borrow at the mortgage rate plus penalty.
3. An option to prepay, which can be thought of as a call option, e.g., exercised when
mortgage rates fall, or more broadly, when the borrower can get a better deal.
While option-based models (particularly regarding the first and third options) are well
studied in the finance and real estate literature on prime market loans, little has been done on the
options held by subprime borrowers and how their options differ from those of prime borrowers.
This is in part due to a lack of data. Simply stated, when interest rates fall, prime borrowers will
more or less ruthlessly exercise their option to refinance into a lower rate mortgage and, if home
values fall sufficiently, borrowers will more or less ruthlessly exercise their option to default.5
Subprime borrowers hold these same options, but they are more likely to have difficulty
qualifying for a new mortgage, so they are less likely to refinance when rates fall. However,
they also hold an option to prepay and take out a prime mortgage when their credit condition
improves even if mortgage rates have stayed level or increased. This produces an asymmetry,
characteristic of all options, in that lenders lose the good borrowers but keep the bad ones.
Additionally, subprime borrowers, due to already weaker credit histories, may also place
more value on the option to be delinquent (or less value on the cost of a worse credit rating),
without defaulting, if that option offers a lower cost way to finance their activities. That is, in a
financial emergency borrowing their mortgage payment amount at the mortgage rate inclusive of
the penalty for late payment may be lower cost than entering the unsecuritized debt market for
high-risk borrowers, drawing down assets or raising money informally.
5
See the theory surveys by Hendershott and Van Order (1987), and Kau and Keenan (1995) on mortgage options in
the prime market. Two recent additions to the literature on prepayments are Bennett, et al., (2000) and Ambrose and
Lacour-Little (2001). Prepayments motivated by a move are another option held by borrowers, but this option has
received relatively little attention in studies of prepayment behavior. Clapp et al. (2001) examine prepayments
motivated by moves separately from loan refinances and found statistically strong evidence in support of doing so.
Van Order and Zorn (2000) analyze default and prepayment by location, income and race/ethnicity. See the
references cited within these articles for other excellent studies on the exercising of default and prepayment options
in the prime market.
3
Here we investigate the stylized facts that support differences in the way these options are
exercised and how the market tries to mitigate these risks.
2.1. Default Risk
Option-based default models such as those surveyed in Hendershott and Van Order
(1987) and Kau and Keenan (1995) generally begin with default as a put option on the property
and focus on how the option is exercised when the option is “in the money.” Most analysis and
empirical work (e.g., Deng et al. 2000) focus on both negative equity (the measure of in-themoneyness) and some trigger event as necessary for the option to be “exercised.” The stylized
fact of higher default rates in the subprime segment is generally interpreted as being due to
subprime borrowers being more susceptible to trigger events and/or that they have property that
is more likely to fall in value (e.g., due to neighborhood effects). Hence, credit history is
expected to be especially predictive in conjunction with downpayment (which is the extent to
which the option is out of the money at origination). Thus, higher default rates are often posited
as the reason for higher interest rates in the subprime market, even given a larger downpayment.
The empirical evidence on pricing shown in Table 1 supports this correlation. For 30year, fixed-rate mortgages in the first week of September 2002, the average conventional prime
loan applicant was quoted a 6.14 annualized percentage rate (APR), well below the average APR
of 7.20 percent quoted for the very best, or AA+ quality, subprime loans.6 Thus, marginally
subprime and marginally prime borrowers pay substantially different rates. Subprime loans on
average were quoted a rate of 9.83 percent APR. The riskiest subprime loans, “CC” grade, were
quoted an average APR over 5.5 percentage points higher than the average prime loan.
Although the very best “AA+” and “AA” subprime loans are described similarly to prime
loans, they do not perform like them.7 As of June 30, 2002, the serious delinquency rate for
conventional, prime loans was 0.55 percent and the loss rate was running at just 1 basis point of
original unpaid principal balance (UPB).8 In contrast, AA+ subprime loans had a serious
6
Option One Mortgage Corporation, the source for our subprime interest-rate information, uses unique subprime
grade classifications not commonly used within the subprime market. Their AA+, AA and A grade loans are similar
to what are commonly referred to as Alt-A and A-minus subprime grade loans, and their CC grade loans are similar
to what are commonly referred to as “D” grade subprime loans in the mortgage industry.
7
Table 4 provides a description of how Option One Mortgage Corporation, the basis of our subprime performance
information, defines different subprime loan grades.
8
Serious delinquency is defined as loans 90+ days late or in foreclosure (we do not have comparable data for loans
actually going through foreclosure). Losses are total net cumulative losses including revenues from the sale of
foreclosed properties and are net of mortgage insurance payouts. Delinquency rates are from LoanPerformance, San
4
delinquency rate of 1.36 percent but a loss rate of 5 basis points of UPB – five times the loss rate
of conventional prime loans. FHA loans, which carry a rate similar to conventional prime loans
but have an insurance premium roughly equivalent to paying a 100 basis point higher rate, had a
serious delinquency rate of 4.45 percent with losses running just under 30 basis points, putting
FHA loan performance between AA+ and AA quality subprime loans.9 “C” and “CC” grade
subprime loans had the highest delinquency rates, both exceeding 21 percent, and the highest
loss rates, more than 2.6 percent of UPB, among all loan grades.
We examine the correlation between credit and collateral and loan performance in Table
2. Serious delinquency rates for a sample of loans originated between 1996 and 2001 are shown
for each market segment by borrower credit history as measured by the borrower’s FICO® score
and the loan-to-value ratio (LTV) at origination.10 Since average home values have increased in
every state over this period borrowers generally have an even greater equity stake than our table
demonstrates. The loans are further divided by whether their states’ house-price appreciation has
been stronger or weaker than the national average over the period.
In both the prime and subprime segments the incidence of serious delinquency declines
as FICO scores increase. In the prime segment we also find that as LTV decreases (equity
increases) the rate of serious delinquency decreases. But in the subprime market, the amount of
equity a subprime borrower has in their home is not a deterrent to delinquency. For example, in
all but two subsections in the subprime panel of Table 2, borrowers with high LTVs (equity of 10
percent or less) are less likely to be seriously delinquent than borrowers who have between 10
and 30 percent equity in their homes.11
Differences in delinquency, default and loss rates between the subprime and prime
segments and the relative insensitivity of subprime loan performance to LTV broadly justify
Francisco, CA (updated information based on LoanPerfomance (2002)), and losses are based on Freddie Mac’s
experience as a proxy for all conventional prime loans. See Freddie Mac (2002a).
9
FHA loans are generally viewed as being of prime quality, however, higher delinquency and loss rates are to be
expected relative to conventional prime loans because of the emphasis of the FHA program on creating low-cost
funding for first-time homebuyers. FHA losses are from Weicher (2002); FHA delinquency rates are from the
LoanPerformance, San Francisco, CA.
10
“FICO score” is the common reference for the credit bureau score developed by Fair, Isaac and Co. and came into
widespread use in the mortgage industry in the early 1990’s, followed by the development of automated
underwriting systems in the latter 1990s. Straka (2000) provides similar evidence for loan performance by FICO
score and LTV and the efficacy of automated underwriting.
11
Ideally we would like to examine default (REO) rates rather than serious delinquencies, but such data are not
available at this level of detail. We have less complete comparable data on loans that have gone into foreclosure,
but from the data we have the results look similar.
5
higher rates for subprime loans, even though they generally have larger downpayments. Higher
servicing costs (due to more frequent and longer delinquency spells) for subprime loans also
support higher rates. The evidence above, however, does not indicate how much higher the rates
should be.
2.2. The Delinquency Option
That delinquency rates decline with equity in the prime market but not in the
subprime market suggests that delinquency is a prelude to foreclosure in the prime market, but is
more like a short term borrowing option (line of credit) in the subprime market. This use of the
delinquency option in the subprime market is reinforced by differences in the timing of
delinquency patterns. Generally, in the prime market, the proportion of loans 60-day delinquent
is lower than the share of loans 30-days delinquent, and 90-day delinquencies are lower yet. For
example, in Table 1, prime 30-day delinquencies as of June 30, 2002 were 1.73 percent of loans
outstanding, 60-day delinquencies were 0.31 percent, and 90-day delinquencies were running at
0.28 percent. Only 0.15 percent of prime loans go on to become foreclosed real-estate-owned
(REO) or are disposed of through foreclosure alternatives such as short sales or deed-in-lieu
transfers.
For subprime loans this trend is reversed, with increasing rates the more serious the
delinquency. Again referring to Table 1, subprime 30-day delinquencies were running at 7.35
percent of loans outstanding as of June 30, 2002, 60-day delinquencies were 2.02 percent but 90day delinquencies were running at 4.04 percent – double the 60-day rate!12
Option One Mortgage Corporation (OOMC) (2002a) reports an REO rate for subprime
loans that is less than one-third the foreclosure rate, indicating that most seriously delinquent
subprime borrowers avoid default.13 FHA loans follow a pattern similar to subprime loans.
12
LoanPerformance also provided subprime performance statistics through June 30, 2002: 30 day delinquencies at
3.33%, 60-day delinquencies at 0.96%, 90-day delinquencies at 3.5%, and foreclosures at 4.21 percent. Because
Option One Mortgage Corporation (OOMC) (2002a, 2002b and 2002c) provided statistics on all loan grades, Table
1 reports the OOMC values for all subprime loans.
13
A loan in foreclosure is considered seriously delinquent (as are 90-day delinquent loans). Because it can take
years for foreclosure process to clear, foreclosure rates can exceed 90-day delinquent rates even if borrowers do not
exercise the option to be delinquent without defaulting. Default, in the context of this discussion, occurs when the
lender successfully forecloses on the property or negotiates a foreclosure alternative such as deed-in-lieu transfer or
a short sale. The comparison is not as clean as we would like it because these are flows. Better would be an
analysis of conditional transition rates such as the likelihood of becoming REO conditional on being in foreclosure.
6
Ninety-day delinquency rates can exceed 60-day delinquency rates only if borrowers who
fall behind in their mortgage payments miss two, then three, payments, and then begin to pay
again without making up all of the missed payments immediately, thus remaining 90-days late
for an extended period. Since each period some 60-day delinquent loans will become 90 days
late, the total number of loans 90-days late will exceed that of loans 60-days late under this
scenario. Apparently subprime borrowers tend to exercise the option to take out short- to
medium- term loans from their mortgage lenders in amounts equal to a month or two’s worth of
mortgage payments while prime borrowers do not.
The patterns of subprime delinquencies in the Tables 1 and 2 are more consistent with the
theory of borrowers getting into trouble and borrowing at the subprime mortgage-plus-penalty
rate than as a prelude to default. Presumably, prime borrowers have more to lose from being
delinquent due to their better credit record, and they have better access to credit markets and
wealth to get them through difficult times.14 Thus, the delinquency-short-of-foreclosure option is
less valuable to them.
2.3. Prepayment Options
Given the very high interest rates in the subprime market and the jumps in rates between
grades within the subprime market as well as between the subprime market and the prime
market, subprime borrowers have a strong incentive to refinance their loans if their credit record
improves. Prime borrowers may also have the incentive to refinance when their credit quality
improves but because the room for improvement in credit history is smaller and the default cost
function is relatively flat for borrowers with good credit history the default option is less
valuable than the prepayment option motivated by changes in interest rates. Figure 1 illustrates
the co-movement of prepayment speeds with interest rates. Prime prepayment speeds are very
high when interest rates are relatively low, and when interest rates climb slightly prime
prepayment speeds slow quickly. The subprime market prepayment speeds are relatively
insensitive with respect to interest rates, but were much higher on average over the period.
14
Courchane, et al. (2004), find that prime borrowers have a much larger financial safety net than do subprime
borrowers; specifically prime borrowers report having more savings, greater family resources and better credit
opportunities than do subprime borrowers based on a survey conducted by Freddie Mac in 2001.
7
Indeed, subprime prepayment speeds generally declined over the period June 1998-December
2000 yet interest rates both declined and increased significantly over that same period.15
Table 3 documents that the incentive for subprime borrowers to refinance when their
credit improves can be quick to develop. In an analysis of FICO score migration trends by Fair,
Isaac and Co. based on more than 400,000 individuals with bankcard accounts in the late 1990s,
more than 30 percent of individuals with very low FICO scores (below 600) improved their
scores by 20 points or more within three months. Since high scores are indicative of consistently
good credit management, individuals with initial scores over 700 had a much lower chance of
improving their score by 20 points or more but were also less likely to have their scores fall by
more than 20 points.16 Hence, the stronger credit-score and weaker interest-rate prepayment
propensities suggested by the theory are consistent with the observed small interest-rate elasticity
and the higher average level of prepayment rates of borrowers in the subprime market.
Lenders and investors in the mortgage market have tried to control the optionality
inherent in mortgages by charging rates and fees that compensate them for the credit risk
inherent in the loans they originate and by encouraging the take-up of prepayment penalty
clauses that inhibit a borrower’s ability to prepay the loan. In exchange for the prepayment
limitations, borrowers receive a lower interest rate. Subprime loans are more than three times as
likely as prime loans to have prepayment penalty terms in their mortgage contract, and the refi
lock-outs are usually in effect for two to five years.17 Prepayment-penalty clauses are likely to
be binding constraints on borrowers that have them. For example, for many borrowers who took
out loans in 1998, there was a strong refi incentive due to falling interest rates. Average prime
mortgage rates were 7.48 percent in January 1998, fell to 6.71 percent in March 1999, and rose
back to 7.5 percent in December 1999. OOMC (2002a) reports that among subprime loans
originated in 1998, 10 percent of loans with prepayment penalty clauses prepaid by the 13th
15
Subprime mortgage rates do generally track major benchmarks like the 10-year Treasury Note rate but may not be
as sensitive as prime rates to changes in these benchmarks.
16
According to Fair, Isaac and Co. (2000), roughly 60 percent of the population has a FICO score of 700 or more.
17
A survey of subprime borrowers conducted by Freddie Mac in 2001 (the basis of the Courchane, et al. (2004)
study) shows that 41 percent of subprime first-lien mortgages have prepayment terms but just 12 percent of prime
loans have them. Just 9.6 percent of prepayment lock-out periods are for one year or less and 12.7 percent are for
periods of five or more years. A “best practices” trend in subprime lending started in 2001 has decreased the length
of generally acceptable prepayment lock-out periods from five to three years. See Freddie Mac (2002b) and OOMC
(2002d).
8
month after origination, but more than 20 percent of loans without prepayment penalties prepaid
within 13 months. OOMC’s experience suggests prepayment penalties are effective deterrents.
3. Market Structure and Separating Equilibrium
Having separate prime and subprime markets rather than a single market in which
different risks are priced suggests not only imperfect information, but also asymmetric
information. Models of separating equilibrium, where lenders offer up contracts designed to get
borrowers to reveal information about themselves, are candidates for analyzing such situations.
We assume that there are three types of borrowers. Each borrower knows its type but lenders
don’t, so lenders want to induce borrowers to reveal their risk type. Two sources of separation
devices are entertained: collateral and rejection (i.e., underwriting).
Collateral (downpayment) as a signal of credit risk has been investigated in a variety of
contexts. For instance, Rothschild and Stiglitz (1976) and Bester (1985), in general models, and
Brueckner (2000) and Yezer et al. (1994), in mortgage market models, produce separating
equilibrium models in which it is assumed that supplying collateral is costly, more so for the
riskiest types because they have inside information that they are riskier and as a result want to
put up less of their own money (see Freixas and Rochet (1997) for a survey). On the other hand
research like Harrison et al. (2004) argues that if default is costly to borrowers, riskier borrowers
might signal more risk by offering to make bigger downpayments.
An alternative approach is to assume that the worst borrowers are also likely to be “credit
constrained” in the sense of having low wealth endowments, making raising money for a
downpayment especially costly. A reason for arguing this way is that the worst borrowers are
likely to have had the worst past experience and not have any wealth left or sources from which
to borrow it (alternately it could be assumed that they have higher personal discount rates). In
what follows we appeal to this argument, which leads to the proposition that safer borrowers can
signal their status with higher downpayments.
Underwriting has a more straightforward rationale. If having an application rejected is
costly, then we should expect riskier borrowers to opt for markets with less underwriting, and
this can be a device for separating the riskiest from the safest borrowers (see also Nichols, et al.
2002, and Staten, et al. 1990) because application strategy will be a signal of creditworthiness
made credible by rejection cost (along lines in Spence (1973)). Ben-Shahar and Feldman (2003)
9
present a model similar to ours in which there are two dimensions to selection and signalingscreening exists.
3.1. The Separating Equilibrium Model
Assumptions
There are three, risk-neutral, borrower types: lowest-risk or prime, denoted A, higher-risk or
subprime, denoted B, and extremely high risk, denoted C, and three possible market types:
prime, P, subprime, S and “loan shark,” L.18 The latter is a residual market in which any
borrower can get a loan without underwriting or a downpayment, but at a high interest rate (or
alternately where rates are too high and borrowers opt out). We let i denote borrower type and j
market type. There are two possible equilibrium types: pooling in which there is one market in
which all three borrower types pay the same rate, and separating, where there are three market
types and one rate in each.
The three borrower types choose application strategies that minimize expected costs subject
to pricing constraints. Expected costs are given by the sum of:
1. The rate paid plus the cost of equity used in the downpayment, all multiplied by the
probability of the loan being accepted
2. The cost of having a loan rejected multiplied by the probability of rejection,
3. Underwriting costs, which are paid regardless of whether the loan is accepted or
rejected.
Lenders are also risk neutral, and the equilibrium market structure is assumed to be
characterized by free entry and zero profits.
To simplify the analytics and bring out the role of equity as a signal we assume that
equity has no direct effect on defaults, working only as a signaling device.19 Thus borrowers
have simple expected default costs, dA, dB and dC, respectively. Because C type borrowers have
had especially poor credit histories and are especially risky, collateral is more costly to supply
for C than B, but we assume that it is the same for A and B (or at least the differences are much
18
In the context of our model A, B and C are just names and do not necessarily correspond to any particular grade
of mortgage loans. Similarly, P, S, and L do not necessarily refer to any particular existing market segment.
19
This is not quite as silly as it might appear. We should expect the interest rate to decline as equity increases
because increased equity takes the option to default further out of the money. But the option is a zero sum game,
which affects borrowers and lenders symmetrically, at least in the simplest models. We can consider the rate above
to be net of the option cost, increasing or decreasing because of the added cost of certain risk types, which we could
model as being due to different strike prices. Then the model implies full LTV pricing as long as equity, e, is above
the minimum determined by the model.
10
smaller than those between B and C borrowers). Then, letting Ei (e ) be the cost of equity for the
ith type borrower, we assume
E A (e ) = E B (e ) < EC (e ) , and
(1)
′
′
′
0 < E A (e A ) = E B (eB ) < EC (eC ) .
Underwriting, which is essentially information gathering, has a cost that varies with the
( )
( )
f (u ) be the probability that
amount of time spent, u, doing it. The cost in market j is given by U u j , with U ' u j >0. After
gathering information the underwriters classify borrowers. We let
ij
j
borrower type i will be classified as relatively risky in market type j. Then f AP (u P ) is the
probability that an A or “prime” borrower will be (miss)classified B or “subprime” (and
therefore be rejected) and f BP (u P ) is the probability that a subprime borrower will be (correctly)
classified as subprime in the prime market (and be rejected). More information means better
classification. Thus, it is assumed that in the prime market
′
′
f AP (u P ) < 0 , f BP (u P ) > 0 ,
(2)
″
″
f AP (u P ) > 0 , f BP (u P ) < 0 ,
representing the notion that underwriting helps safer borrowers, hurts riskier ones and there are
diminishing returns to underwriting. We further assume (at least because probabilities are
′
′
bounded) that f AP (u P ) and f BP (u P ) both approach zero as u increases.
Similarly, in the subprime market the Bs have lower credit cost than Cs and
′
′
f BS (u S ) < 0 , f CS (u S ) > 0 ,
(3)
″
″
f BS (u S ) > 0 , f CS (u S ) < 0 ,
where f BS (u S ) > 0 and f CS (u S ) are the probabilities that B and C, respectively, will be
classified as “C” or extreme-risk within the subprime market (and therefore be rejected). We
also assume that f BS ′ (u S ) and f CS ′ (u S ) both approach zero.
For the separating equilibrium model to work there has to be a cost to borrowers of being
rejected, and the expected cost will have to be highest for the highest risks. We assume that the
cost of rejection comes from reapplying and eventually borrowing at a higher rate as well as
incurring further underwriting and delay costs. Assume that these (gross) costs are given by
11
K ij (u j ) . Then the expected net cost of rejection is defined as the probability of rejection times
the difference between K ij (u j ) and the borrowing rate R j plus equity cost Ei (eij ) . This net cost
is given by Tij (u j ), which is assumed to be given by a fixed cost, t j , which is the same for all
borrower types within a market but can differ across markets, times the probability of being
rejected, which is the probability of being classified as a B in the A prime market or as a C in the
B subprime market. Thus, in the subprime segment, B’s expected rejection costs are
TBS (u S ) = t S f BS (u S ) .
Borrowers choose application strategies that minimize total expected cost, which vary by
borrower and market. For borrower type i in market type j expected costs are given by
[
][
]
+ E (e ) + t f (u ) + U (u ) .
Wij (u j , eij , R j ) = R j + Ei (eij ) • 1 − f ij (u j ) + K ij (u j ) f ij (u j ) + U (u j )
= Rj
i
ij
j
ij
j
(4)
j
We assume that underwriting costs U (u j ) are passed along to the borrower, for example through
an application fee, regardless of the lending decision.
Equilibrium
Equilibrium is given by conditions for borrowers minimizing cost, zero profit for lenders
and nonnegative levels of e and u. We begin by looking at equilibrium in the subprime market
assuming that the As have been separated out, and then we move to the prime market and show
how the As are separated out. We first need to show that a pooling equilibrium is not possible,
which is common in models like this one (see Freixas and Rochet, 1997). In particular, a pooling
equilibrium, in which all borrowers are offered the same rate and terms, can be broken by
offering the Bs a slightly better rate in exchange for more equity, which, because equity is less
costly for the Bs, will separate the Bs from the Cs. Hence, if there is to be an equilibrium, it will
have to be a separating one, and only one rate will be charged in each market. We now turn to
characterizing a separating equilibrium that sorts borrowers by specifying a minimum down
payment and an amount of underwriting.
The zero profit condition implies that lenders set rates at breakeven costs. Then letting
D j be the expected default cost in the jth market
Rj = Dj .
(5)
12
Then substituting for R j into Wij (•) , we have
with
Wij (eij , u j | D j ) = D j + Ei (eij ) + Tij (ui ) + U (u j )
(6)
∂Wij (eij , u j | D j ) / ∂eij > 0 and
(7)
∂Wij (eij , u j | D j ) / ∂u j > 0
for ij = BP or CS (i.e., positive effects on costs of e and u for the riskier applicants)
∂Wij (eij , u j | D j ) / ∂eij > 0 and
(8)
∂Wij (eij , u j | D j ) / ∂u j >< 0
for ij = AP or BS (i.e, ambiguous effect of u for the safer applicants).
From (1)
∂WAP (eP , u P | DP ) / ∂eP = ∂WBP (eP , u P | DP ) / ∂eP and
(9)
∂WBS (eS , u S | DS ) / ∂eS < ∂WCS (eS , u S | DS ) / ∂eS .
Furthermore, in a separating equilibrium
DP = d A ,
(10)
DS = d B and
D L = dC
Because ∂Tij (u j ) / ∂u j approaches zero, we expect that ∂Wij (eij , u j | D j ) / ∂u j > 0 if u is
large enough. If we assume for simplicity that Tij (u j ) is the only source of nonlinearity (through
the probability of rejection), then borrower costs become
Wij (e j , u j | D j ) = D j + δ i e j + Tij (u j ) + γu j .
(11)
Then the isocost curves (incorporating the zero profit condition) are of the form
eij = − (D j + T ji (u j ) + γu j ) δ i + ϖ ≡ I ij (u j )
(12)
and their shape depends on the shape of T (•) (and in turn f ij (u j ) ), and the relative sizes of the
δ i . Note that expected rejection costs are indeed higher for riskier borrowers in each market
because while cost given rejection is constant within markets, riskier borrowers are more likely
to be rejected.
13
From (8) we know that for the better borrowers underwriting has an ambiguous effect on
cost, because it increases underwriting cost but also increases the probability of being well
classified, which lowers expected rejection cost. For the worst borrowers underwriting
unambiguously raises cost. Then in both the prime and subprime markets I ij (u j ) is decreasing
in u for the riskier class (the Bs in the prime market and Cs in the subprime), so that their isocost
curves are always downward sloping, and from condition (3), convex to the origin. However, for
the safer class in either market (the As in the prime market and Bs in the subprime) the shape is
ambiguous. Hence, the isocost curves in these cases can be upward sloping, but they are
eventually downward sloping. From (2) and (3) the second derivative for safe borrowers is
positive, so that from (8) the isocost curves are concave to the origin in any case.
A separating equilibrium in the subprime market has to be such that Cs apply only to the
L (or “loan shark”) market and Bs only to the S (or subprime) market, and profits are zero and
costs are minimized. Then for a given level of u and e in the L market, equilibrium requires that
u and e in the B market be enough to keep the Cs from applying in the S market. If e and/or u in
the L market are positive then for any level of e and u in the S market that is a candidate for
equilibrium a lower level of both e and u in the S market will make Cs better off without
affecting Bs. Hence, equilibrium requires that e and u both be zero in the L market, u and e be
set in the S market so they are just enough to keep the Cs out and costs be minimized for Bs.
Then, using (10), the separating equilibrium levels of e and u are determined by solving
Min WBS (e S , u S | d B )
(13)
subject to WCS (eS , u S | d B ) ≥ WCL (0,0 | d C )
This is depicted in Figure 2, with isocost curves shown for Bs and Cs in the subprime
market. The positive slope at low levels of u for the Bs reflects the possibility that the benefits of
underwriting might offset its costs at low levels of underwriting. The tangency solution is given
by g and the equilibrium levels of e (the minimum downpayment) and u are indicated by e S* and
u S* . Because the isocost curves for Cs are convex, the curves for B concave and δ C > δ B , there
can be an interior solution.
If the isocost curves of W BS (e S , u S | d B ) have an upward sloping part there will always
be at least some underwriting, but we cannot rule out a corner solution with no down payment
minimum. As is generally the case in models like this it cannot be proven that this equilibrium
14
exists. In particular if there are few C borrowers then neither underwriting nor downpayment
will be worth their cost in the subprime market, but the pooling equilibrium is still broken for the
reasons given above.
Now we go to the prime market. Assume, as discussed above, that A and B have the
same collateral cost, δ A = δ B , so that collateral reveals nothing about As versus Bs. In this case
the Bs are the riskier type. The solution requires setting e and u in the prime market at levels
high enough to keep the Bs (and Cs) out of the prime market, P. If eS* and u S* are the
equilibrium levels of e and u in the subprime market, then the equilibrium in the P market is
given by solving
Min W AP (e P , u P | d A )
(14)
(
subject to WBP (e P , u P | d A ) ≥ WBS e S* , u S* | d B
)
The isocost curves are similar to those in the subprime market; the only difference in costs is due
to expected rejection costs. From (2) and δ A = δ B , the isocost curves for As are always flatter
than those for Bs. This is depicted in Figure 3. The solution must be a corner solution, at h in
the figure, with no downpayment restrictions in the prime market, and just enough underwriting
to keep the Bs from applying.
3.2. Comments
1. The above describes a separating equilibrium in all three markets: the Cs will only
apply to the L market, the Bs to the S market and the As to the P market.
2. There are three distinct lending markets, but just two mortgage markets; the third
being a loan-shark market, with no downpayment or underwriting.
3. There is less underwriting in the subprime market even though that would appear to
be where it is where it is most “productive.”
4. The subprime market has minimum downpayments, but the prime market does not.20
In both markets there can be LTV pricing (if we introduce the realistic assumption
that LTV directly affects risk) subject to the minimum in the subprime market.
20
This is because
δA = δB .
If
δ A < δ B < δC
but the difference is smaller than in the subprime market, then
there might be a downpayment requirement in the prime market that is smaller than in the subprime market.
15
3.3. Rejections
The model, so far, has no rejections because everyone applies to the right market. Table
1 suggests that there are significant rejections and subprime markets have much higher rejection
rates than prime markets. Our model is consistent with a reasonable model of rejections in
which rejections are mistaken applications, e.g., because applicants do not know their own credit
category or the lenders’ standards. If the prime market is more uniform and/or its participants
more financially sophisticated, then it can have lower rejections relative to subprime, where
borrowers are more likely to be first time borrowers or are likely to be less financially
sophisticated or experienced. 21
3.4. Self-Misclassification
The model requires that people who classify themselves as B or C be, as a group, be
more likely to default than those who classify themselves as A or B, but that need not be true of
all members. Some As and Bs may simply misclassify themselves by overestimating their risk
category, in which case they will cross subsidize borrowers who really are riskier. A benefit of
better underwriting is that such misclassification will be less likely.
4. The Secondary Market and Market Structure
We posited above that the bifurcation of the subprime and prime markets is because the
markets elicit signals from borrowers that lead to a separating equilibrium. The model is of
some historical interest, but the recent trend toward use of the internet to apply for loans and the
advent of banks with subprime subsidiaries that send applications to the subsidiary suggests that
rejection costs may be falling. Here we focus on information asymmetries between types of
lenders rather than between borrowers and lenders, leading to bifurcation between primary and
secondary mortgage markets. It has been posited by Lax, et al. (2000) that subprime borrowers
pay rates that are higher than the differences in prime and subprime default rates would indicate.
We examine why this might be true by allowing the distribution of credit risk types to be
continuous rather than having just three distinct types.
The basic model is that banks originate loans, which are modeled as having put options
on the property securing the loans, and then either hold them or sell them to the secondary
21
Note that this model is complicated by lack correspondence between the D j and d i default rates.
16
market, SM; but they have better information and can select against SM. However, SM has lower
costs. For instance, banks may have better local information than SM, and/or SM may not, for
regulatory reasons, be able to use the information, or while both SM and banks may have access
to borrower credit history (e.g., FICO score), bank underwriting may help predict which low
FICO borrowers will improve (e.g., because the reason for the low score was a temporary
setback, like an illness). Alternatively, there may be lenders who choose not to underwrite, and
they run a loan-shark market.
This model has an equilibrium, which might restrict SM to the worst part of the market,
like the loan-shark segment above, and perhaps to a very small part of it. To obtain a secondary
market like the one we actually observe today, we bring back a version of the costly rejection
model in the previous section by assuming that SM chooses a cutoff point for loan quality, rejects
loans below the cutoff and punishes sellers who deliver bad loans.
4.1. The Model
We begin with a simple, discrete heuristic model with just two types of loans:
“good” in which case default and transaction costs for banks is $5, or bad, in which case cost is
$10. For SM, costs are $1 less than for banks in each case, but SM cannot distinguish good loans
from bad. Assume that banks have two good loans and one bad loan. By offering a guarantee
fee just under $10 the SM can just induce the bank to part with the bad loan, but it will have to
offer $5 to get the two good loans as well. But at $5 it will lose $4 on the bad loan and only
make $2 on the two good ones. The competitive equilibrium (zero profit) is for SM to hold only
the risky loan, and it will have a $9 price. The banks will hold the safe loans, and their price will
be $5. This will continue to be the case if there are three good loans, but if there are four good
loans SM will be indifferent between holding just the bad loan and taking over the entire market.
At five good loans there will be a discrete change in price (to $4 from $9 for bad loans and to $4
from $5 for good loans) and market structure, and SM will suddenly take over the entire market.
However, if one bad loan is added to the available pool or SM’s cost advantage falls to just $.50,
the equilibrium will revert back to one with SM taking only the bad loans.
Hence, equilibrium will depend on the shape of the risk structure as well as the
difference in costs between the two markets. This simple model shows that despite its lower
costs the secondary market can be confined to a small part of the market and that market
structure might be “fragile” in the sense of being sensitive to small changes in cost or risk
17
structure. This suggests incentives for SM to improve information, because while a large part of
the mortgage market has relatively low cost there is a sizeable segment with much higher costs,
which can affect market structure, so there is an incentive to try to separate this risky “subprime”
segment from the prime market.
4.2. Actors and Loans
We assume there are two types of risk neutral financial institutions, banks and the
secondary market, SM. The loans are originated by the banks, which may or may not sell them
to SM. Loans are divided into sub-markets defined by the publicly available information shared
by all loans in the sub-market. For a particular sub-market we assume that there is a fixed supply
of loans given by N .22 The portion of the sub-market held by SM is denoted by s, and the
portion held by the banks is b, so that N = b + s . We assume that the loans are for one period
and are secured by the borrower’s house. The value of the property securing the loan is a
random variable, X. It is assumed that loans take the form of discount bonds that pay $1 at the
end of the period. Interest, credit and other costs are paid by the firm in the form an upfront
discount. At the end of the period the borrower maximizes wealth by paying off the loan if X ≥
$1 and defaulting otherwise.23 Hence, the borrower has a put option, to sell the property back to
the lender at a price equal to the payment due on the loan. All loans are the same size; the only
question is who holds the loans.
Associated with every loan are two costs. The first is the basic cost of funds, which does
not vary with risk. This cost is denoted by c x , x = b, s , and cb > c s , i.e., for reasons discussed
above SM has lower funding costs than banks. The second is default cost, which is the value of
the put option on the property.
4.3. Information Structure
SM has an information disadvantage. We assume that monitoring loans ex ante is
sufficiently costly to SM relative to benefits that it does not take place. We discuss ex post
22
Alternatively, we can assume that loans are determined endogenously, by the banks. What we do not assume is
the possibility of moral hazard in the form of banks originating “bad’ loans solely for the purpose of selling to the
secondary market.
23
It is assumed that fund-raising takes the form of loans rather than equity investment. A reason for this might be
that outside investors cannot verify the level of X in which case (e.g., see Townsend (1979), Gale and Hellwig
(1985) and Krasa and Villamil (2000)) the standard debt contract assumed is optimal (the lender only needs to verify
the state variable if there is a default). One could also appeal to incomplete contracting models as in Hart (1995).
18
monitoring and rejection in the next section. There are two types of information: that which is
known to everyone and that which is known exactly to the banks but only in a distribution sense
to SM, which defines a sub-market. The second type of information is the source of the model’s
information asymmetry.24
Within a sub-market, the relevant information is the expected cost of default. Because
everyone knows at least the distribution of default cost across borrowers everyone can order the
expected costs of default from the safest to the riskiest loans. This ordering produces the offer
curve faced by SM as well as its marginal cost curve, and we assume that the ordering can be
used to represent default costs by the function C( N ) , N ∈ [0, N ] , which gives (increasing)
default costs as we vary the number of loans, N , from the safest ( N = 0) to the riskiest
(N = N ) .
C( N ) comes from the option to default and give up the collateral at the end of the period
if the value of the collateral is less than one. Hence, the value of the option depends on the
distribution function of the level of X at the end of the period, denoted by X 1 , which in turn will
be conditional on the value of X at the beginning of the period, denoted by X 0 . We represent
the value of the option by V ( X 0 ) .
Let F ( X 0 ) be the distribution function of X 0 across borrowers. Then F ( X 0 )
gives the number of borrowers for whom N , is less than or equal to X 0 , and the inverse of
F (•) , G (N ) , N ∈ [0, N ] , ranks borrowers from riskiest to safest according to their level of X 0 .
The information asymmetry is that banks know X 0 for each loan, but SM only knows
F (X 0 ).
Then if V ( X 0 ) and G (N ) are both well-behaved, C ( N ) is given by
C ( N ) = V (G ( N )) .
(15)
We assume that costs accelerate as we move from safest to riskiest loans, which makes
C( N ) convex. The convexity assumption is important, and it is consistent with a wide range of
24
For example, Banks are assumed to know the true value of the underlying collateral, and hence the true loan-tovalue (LTV), of the collateral, but SM knows only the appraisal of the value of any specific collateral, which is
subject to error and manipulation. However, SM knows the distribution of true value given the appraisal. Hence,
the banks can select against the SM by delivering loans with high appraisals relative to value.
19
models, particularly those that model default as an option on the underlying collateral.25 For
instance, Van Order (2003) shows that if ln X (that is, the change in valuation) follows a
random walk, then X 1 has a lognormal distribution, and if traders are risk-neutral (or markets are
sufficiently complete), then V ( X 0 ) can be represented by the Black-Scholes model. Then if
F ( X 0 ) is given by a uniform distribution then C( N ) is convex and looks like the Black-Scholes
representation of the value of a put option as a function of the initial price of the asset on which
the put is written (where N is the initial price), and it is convex in N . If F ( X 0 ) is lognormal or
has an “S” shape, then C( N ) is convex for low levels of N but could be concave at high levels,
for instance if there is a small number of very safe loans. The key property of C( N ) is that it be
convex for low levels of N .
4.4. Behavior
Because banks have full information about risk they find it optimal to engage in riskbased pricing, and assuming perfect competition among banks, the pre-SM price equals marginal
cost of each loan conditional on the index of risk level, N. That is, the up front price charged by
banks, rb (b) , for taking credit risk is given by
rb (b) = cb + C (b) .
(16)
Because SM does not know the quality of any loan it purchases, it charges only one price,
rs (s ) , regardless of quality, and the loans that are purchased by SM are those with cost to banks
greater than rs (s ) . Hence, equation (16) becomes an offer (average revenue) curve for SM, or
(
)
rs ( s ) = cb + C N − s ≡ ARs ,
(17)
As in the heuristic model above, the secondary market starts with the riskiest loan, N , and
can bid it away from the banks by charging a rate, rs (s ) , just under cb + C (N ) ; as SM decreases
rs (s ) , it sweeps in more (successively less risky) loans, and rs (s) is the rate paid by borrowers
for loans purchased by SM.
25
This and more general option-based models can be priced with models like the classic Black and Scholes (1973)
model (e.g., see Kau and Keenan, 1995), given the initial loan-to-value ratio (LTV) of the collateral. It is well
known that the price of such options is a convex function of the value of the collateral. Van Order (2003) discusses
more general models and shows that convexity of C (N ) is consistent with a wide range of option-type models.
20
The cost of adding one more loan to the portfolio of SM is given by the marginal default
cost plus SM’s funding costs, or
MCs = cs + C (N − s ) .
(18)
Total revenue for SM is given by
TRs = cb s + C (N − s ) s ,
(19)
and marginal revenue is given by
MRs = cb + C (N − s ) − C ′(N − s ) s .
(20)
Total cost is
TC s = cs s + ∫
N
N −s
C (θ ) dθ .
(21)
Subtracting total costs from total revenue produces total profits, which are given by
Π s =(cb − cs ) s + C (N − s ) s − ∫
N
N −s
C (θ ) dθ .
(22)
Because the demand and marginal cost curves are downward sloping and parallel,26 price cannot
equal marginal cost, so we cannot have a perfectly competitive equilibrium with all borrowers
paying their marginal cost and SM’s transaction costs.
4.5. Equilibrium
We consider the monopoly solution.27 SM maximizes profit, (22), with respect to s,
s ∈ [0, N ] . Letting SM’s funding advantage be denoted by ∆ = cb − cs > 0 , the profit maximizing
first order condition with respect to changes in s is either
∆ = sC ′( N − s) if 0 ≤ s < N or
(23)
∆ > sC ′( N − s) and s = N .
(24)
Because ∆ is positive and C ′ is bounded s = 0 cannot be a solution. Equation (23) is
equivalent to MRs = MCs , and (17) applies to a corner solution, where SM takes over the entire
market. The monopoly model and solution with C( N ) are depicted in Figure 4. Values of s that
26
That is, the vertical distance between the curves is constant.
Use of the monopoly scenario does not, of course, mean that we believe that the real world SM is a monopoly. It
is simply the easiest model to use in some situations, especially the licensing model that follows. The free-entry,
competitive equilibrium has similar properties.
27
21
satisfy equation (23) occur at the intersections marked A and C, and the value that satisfies
equation (24) is marked E.28
4.6. Comments
1. There may not be an intersection if ∆ is large enough. In that case the economies of
the secondary market completely dominate the selection problems, and s = N .
2. If ∆ is small enough there is at least one interior solution. For the riskiest loan there
is no selection problem – it will always go to SM because of SM’s lower costs.29
3. There can be two intersections. In the monopoly case MRs and MCs can intersect
twice, first at A, the “loan-shark” equilibrium, as in the previous section, where the
secondary market takes the worst of the loans, as is typical of Akerlof (1970)-type
lemons models. It is a local maximum, but the second solution, at C, is a minimum.
4. There is a second possible equilibrium, the corner solution, for N sufficiently large30,
shown in the exhibit in the monopolist case as the point E. Marginal revenue exceeds
marginal cost at E, and E could be more profitable than A. Hence, the monopolist
will either operate as a loan shark based on A, or will serve the entire market at E. In
the free entry case, if N is to the right of D, then competition among SM traders will
force price from E (the monopoly outcome) to F, where N and ACs intersect. Shifts
from interior to corner solutions are discontinuous, implying some instability of
market structure (see Van Order (2003)).
5. The model applies to sub-markets. Within a sub-market SM gets selected against and
has higher default rates than banks have. But this may not hold across sub-markets.
For example markets with steep slopes will have small SM shares, and markets that
are flat will have large shares. Hence, the model is consistent with SM having lower
default rates overall than banks, despite higher default rates in each market.
4.7. Rejections and Market Structure
The solution to this simple model is not entirely satisfactory to SM. It will have a
loan-shark type equilibrium, where the worst is assumed of any loan that is delivered to SM, or
unless ∆ is sufficiently large, an equilibrium that is fragile. Hence, SM is being prevented from
fully-exploiting its lower costs. As is noted above in Tables 1 and 2 the “prime” market is
relatively large and has rather small variation in delinquency rates, and from other data we know
it has rather little variance in default cost. On the other hand, the subprime-high-FICO part of
28
If there is no cost to entry, new entrants into the secondary market can take away SM’s initial business by
undercutting it, eventually driving profits to zero. We get the solution to this by setting equation (15) equal to zero.
This is depicted by the intersections of average revenue and average cost in the exhibit at points B and D.
29
The caveat to this outcome is when there are nontrivial start-up costs for the secondary market. In this case it may
not be profitable for the secondary market to exist at all.
30
That is, where the second intersection of MRs and MCs (or ARs and ACs) occurs to the left of N .
22
the market is smaller and has much larger variation in delinquency rates. This suggests strong
incentives to try to find ways to induce banks to deliver only “prime” loans.
We introduce these incentives by reintroducing rejections, in this case of loans sold by
banks to SM. In particular, we allow SM to reject loans below some quality level, typically
labeled “investment quality.” The historical mechanism for this has been for SM to do quality
control sampling of loans sold to it (by assumption it is too costly to underwrite all loans) and to
require banks to repurchase loans that fail the investment quality test. It is assumed that this is
costly to banks (e.g., because of penalty charges, bad reputation, etc.) and that this leads to a
separating equilibrium, where the banks do not deliver loans below the cutoff. This is equivalent
to providing a “license” to banks that sell good loans, and our model is similar to the licensing
model in Leland (1979). Hence, we assume that SM can carve out a separating equilibrium, and
we analyze how it chooses the cut-off and the implications of that choice.
4.8. A Rejection Model
The contract for the banks specifies a minimum quality standard, N * , which can be
thought of as prime/subprime cutoff, below which loans will be rejected with some probability
and cost, such that banks choose not to violate the contract,31 and we assume that the banks
engage in adverse selection so long as doing so does not violate their licensing contracts with
SM. We assume that the distribution of collateral value, F ( X 0 ) , across properties is s-shaped, so
that, as discussed above, C ( N ) can be concave over some range of the safest loans.
SM chooses N * by maximizing profits with both s and N * as choice variables,
s ∈ (0, N *), N * ∈ (0, N ) . The new profit function is
Π s =(cb − cs ) s + C ( N * − s ) s − ∫
N*
N *− s
C (θ ) dθ .
(25)
The first order conditions for an interior solution (0<s<N*) are
dΠ s
ds
= [∆ − sC ′( N * − s )] = 0 ,
(26)
and
31
Of course it is not quite so simple because banks have the option of cheating and exiting the industry before the
loans can be sent back. Hence, in practice, the licensing contract is enforced by auditing banks and limiting the
number of banks that SM deals with to sellers with long term interests in not cheating.
23
dΠ s
dN *
= sC ′( N * − s ) − [C ( N *) − C ( N * − s )] = 0 .
(27)
For ∆ > 0 neither s nor N * can be 0, so the only corner solution possibility is where
s=N* (SM takes over the entire prime market). Equations (26) and (27) can be rewritten as
C ′( N * − s ) =
(C (N *) − C (N * − s )) ,
s
(28)
and
∆ − [C ( N *) − C ( N * − s )] = 0 .
(29)
Additionally, once N * and s are chosen to satisfy (28) and (29), SM may have additional
profit opportunities within the remaining (loan-shark) segment of the market containing all loans
riskier than N * . In that remaining segment if C ( N ) is convex over the entire range, only (26)
applies. The equilibrium in the risky, loan-shark market is exactly the same as the one where
there is no licensing, the loan-shark equilibrium, with SM holding the riskiest loans in the
remaining segment. Hence, steepness of C( N ) in the riskiest region limits SM share. Neither the
new equilibrium nor the old loan-shark solution exhibits the possibility of fragility that occurred
in the previous model without a quality cutoff.
The equilibrium is depicted in Figure 5. The key condition is that the slope at D equal
BE (the difference between the largest and smallest values of C( N ) ) divided by s, the size of the
market. Note that this condition requires that C( N ) have a concave region; if not the solution is
s = N * , and SM takes over the whole prime market. From (29), SM covers the large
homogeneous (or long flat) part of the market, with the difference between the best and worst
default being equal to ∆ . It does not reach for the safest borrowers because they are few in
number and the one price policy that is optimal for SM implies that to do so would mean
foregoing profits from the more heavily populated middle range.32
The market can have four distinct segments. Two segments have fixed prices: the loanshark part of the market, denoted by s2 , and the prime-quality SM segment, given by s1 ,
characterized by (28) and (29). The subprime lenders operate in the segment N 2 (which could
32
Note that the solution does not maximize market share subject to (29) for SM; doing so (for an interior solution)
would require equating slopes of C( •) at both ends of the market, which cannot be the case given the first order
conditions, which require that the slope at B be steeper than at D.
24
have zero length) with risk based pricing, and prime bank lenders operate along N 1 , which could
also have zero length, again with risk-based pricing.
4.9. Comments
1. The model shows how SM can choose its market by choosing an “investment quality”
cutoff. This market is not the high risk, loan-shark market, but SM can also be in that
market. Its role is limited by the curvature of C( N ) because the range of the market
is limited by ∆ . It is straightforward to see that if C( N ) is very flat then SM will take
over the entire market, but steepness will keep it out of the high-risk end. Intuitively
the steepness of the curve is a measure of the degree of adverse selection. SM
chooses its cutoff in a way that keeps it away from the adverse selection. In the highrisk section SM’s role is limited as before by adverse selection, and the steeper the
curve the less high-risk business it will do. Neither market exhibits the fragility
possible in the model without a quality cutoff. Hence, the investment quality cutoff
produces a type of stability.
2. There can be distinct discontinuous price jumps between the different segments as
shown in Figure 6. For the far right or “super-prime” segment of the market,
N ∈ N − N1 , N , the banks engage in risk-based pricing and prices increase
[
]
continuously from cb + C (N ) (equal to cb in the figure) to P1sm as loan quality
[
]
decreases. The traditional prime segment, s ∈ N * , N − N1 , is covered by SM, which
sm
1
charges everyone the same price, P , and rations its purchases. The third segment
[
]
from the right, the subprime segment, N ∈ s2 , N * , is once again served by the
banks and reverts back to risk-based pricing, but for loans with risk level N * +ε
interest rates will be exactly ∆ more than borrowers at N * . Thus, the difference to
a borrower of being just to the right or left of N * is significant due to the
discontinuity in pricing.
3. The discrete jumps in pricing between market sub-segments coupled with continuous
pricing within a sub-segment are supported by actual market pricing. In Table 4, the
rates for different subprime grades are presented.33 Within each grade, prices vary by
FICO score of the borrower and by LTV. The pricing differentials are much higher
between similar loans in different grades than between slightly different loans within
a grade. For example, for 65% LTV loan with a 580 FICO score, the price jumps
from 8.85 to 9.30 between the A and B subprime grades, but within the A subprime
grade having a slightly worse FICO score, say 560, only raises price to 9.05.
4. Improved information increases the number of identifiable market segments. For
instance, historically the secondary market did not collect credit history for individual
loans but instead did quality control sampling, which did look at credit history on
individual loans and punished (e.g., via repurchase) sellers who delivered low quality
33
These are again from the subprime lender Option One Mortgage Corporation and are based on its naming
conventions for loan grades.
25
loans. Now credit history in the form of FICO scores is available at close to zero
marginal cost, so that market segments can be identified by FICO score range. This
however, does not end the selection problem. For example, as described above, low
FICO scores tend to migrate back up. At least some of this can be predicted with
local information, for instance about whether or not the borrower was unlucky (e.g.,
sick) or is chronically bad at paying debts, and some information that can be used in
selling loans (e.g., some detailed location variables) cannot be used in buying loans.
5. Final Observations
Our study suggests that some standard economic models can explain the main
characteristics of the subprime market. Specifically, option-based models of subprime markets
are consistent with pricing and loan characteristics, particularly after taking into account the
FICO option, i.e., the option to refinance after improved credit history. Similarly, the separating
equilibrium model describing the segmentation between different lenders – those specializing in
prime loans and those specializing in subprime loans – is consistent with sorting borrowers
through signaling mechanisms.
The adverse-selection models presented here describe the stylized history of the
secondary market reasonably well. Fixed pricing and jumps from the prime market to the
subprime market can be explained as a tug-of-war between the lower costs of the secondary
market and the information advantages of the primary market. Improved information and
modeling technology have led to an increased (but still small) role of the secondary market into
riskier markets as well as some pricing on the basis of risk, by expanding the number of market
segments. In these models prices are still constant within classes of risk, making it costly to the
borrower who just misses the cutoff to the next-lowest risk class. However, the borrowers in
each risk class pay equal or lower rates than they would in the absence of the secondary market's
presence in that market segment.
26
References
Ambrose, B. and M. Lacour-Little. 2001. “Prepayment Risk in Adjustable Rate Mortgages
Subject to Initial Year Discounts: Some New Evidence,” Real Estate Economics, 29(2):
305-328.
Akerlof, G. A. 1970. “The Market for ‘Lemons’: Quality Uncertainty and the Market
Mechanism,” Quarterly Journal of Economics, 84: 488-500.
Archer, W. R., P. J. Elmer, D. M. Harrison and D. C. Ling. 2002. “Determinants of Multifamily
Mortgage Default,” Real Estate Economics, 30(3): 445-474.
Bennett, P., R. Peach, and S. Peristiani. 2000. “Implied Mortgage Refinancing Thresholds,”
Real Estate Economics, 28(3): 405-434.
Ben-Shahar, D. and D. Feldman. 2003. “Signaling-Screening Equilibrium in the Mortgage
Market,” Journal of Real Estate Finance and Economics, 26(2).
Bester, H. 1985. “Screening vs. Rationing in Credit Markets with Imperfect Information,”
American Economic Review, 75(4): 850-855.
Black, F. and M. Scholes. 1973. “The Pricing of Options and Corporate Liabilities,” Journal of
Political Economy, 81(3): 637-54.
Brueckner, J. 2000. “Mortgage Default with Asymmetric Information,” Journal of Real Estate
Finance and Economics, 20(3): 251-274.
Chan, Y., and H. Leland. 1982. “Prices and Qualities in Markets with Costly Information,”
Review of Economic Studies, 49(4): 499-516.
Courchane, M., B. Surette, and P. Zorn. 2004. “Subprime Borrowers: Transitions and
Outcomes,” Journal of Real Estate Finance and Economics.
Clapp, J. M., G. M. Goldberg, J. P. Harding, and M. Lacour-Little. 2001. “Movers and
Shuckers: Interdependent Prepayment Decisions,” Real Estate Economics, 29(3): 411450.
Deng, Y. H., J. Quigley and R. Van Order. 2000. “Mortgage Terminations, Heterogeneity and
the Exercise of Mortgage Options,” Econometrica, 68(2): 275-307.
Fair, Isaac and Co. 2000. Understanding your Credit Score. Fair, Isaac: San Raphael, CA.
Freddie Mac. 2002a. Investor Analyst Report: August 2002. Accessed September 6, 2002 at
http://www.freddiemac.com/shareholders/reports.html.
Freddie Mac. 2002b. “Freddie Mac Will No Longer Invest In Subprime Mortgages With
Prepayment Penalty Terms Greater Than Three Years,” March 1, 2002 press release
accessed October 21, 2002 at
http://www.freddiemac.com/news/archives2002/subprime_030102.htm.
Freixas, X. and J. C. Rochet. 1997. Microeconomics of Banking. Cambridge and London: MIT
Press.
Gale, D. and M. Hellwig. 1985. “Incentive-Compatible Debt Contracts: The One-Period
Problem,” Review of Economic Studies, 52(4): 647-63.
Harrison, D. M., T. G. Noordewier and A. Yavas. 2004. “Do Riskier Borrowers Borrow More?”
Real Estate Economics, 32(3) 385-412.
27
Hart, O. 1995. Firms, Contracts, and Financial Structure. New York: Oxford University Press.
Hendershott, P. and R. Van Order. 1987. “Pricing Mortgages: An Interpretation of Models and
Results,” Journal of Financial Services Research, 1(1): 19-55.
Kau, J. B. and D. C. Keenan. 1995. “An Overview of Option-Theoretic Pricing of Mortgages,”
Journal of Housing Research, 6(2): 217-244.
Krasa, S. and A. P. Villamil. 2000. “Optimal Contracts When Enforcement Is a Decision
Variable,” Econometrica, 68(1): 119-134.
Lax, H., M. Manti, P. Raca, and P. Zorn. 2004. “Subprime Lending: An Investigation of
Economic Efficiency,” Housing Policy Debate 15(3) 533-572.
Leland, H. E. 1979. “Quacks, lemons, and Licensing: A Theory of Minimum Quality
Standards,” Journal of Political Economy, 87(6): 1328-1346.
LoanPerformance. 2002. The Market Pulse, 8(2).
Nichols, J., A. Pennington-Cross and A. Yezer. 2002. “Borrower Self-Selection, Underwriting
Costs, and Subprime Mortgage Credit Supply.” Working paper.
Option One Mortgage Corporation (OOMC). 2002a. Loan Performance: June 2002. Accessed
September 6, 2002 at http://www.oomc.com/corp/corp_investor.html.
Option One Mortgage Corporation (OOMC). 2002b. LTV and Pricing Matrix for Legacy
Platinum Plus: Rates Effective September 3, 2002. Accessed September 6, 2002 at
http://www.oomc.com/broker/rate_sheets/Legacy_Plus_Platinum_Pricing_Matrix.pdf
Option One Mortgage Corporation (OOMC). 2002c. Wholesale Rate Sheets (various states):
Rates Effective September 3, 2002. Accessed September 6, 2002 at
http://www.oomc.com/broker/broker_prodprice.html.
Option One Mortgage Corporation (OOMC). 2002d. Best Practices. Accessed October 9, 2002
at http://www.oomc.com/corp/corp_bestpract.html
Richardson, C. 2002. “Predatory Lending and Housing Disinvestment,” paper presented at the
AREUEA Mid-Year Meetings, Washington, DC, May 28-29, 2002.
Rothschild, M. and J. E. Stiglitz. 1976. “Equilibrium in Competitive Insurance Markets: An
Essay on the Economics of Imperfect Information,” Quarterly Journal of Economics,
90(4): 630-649.
Spence, A. M. 1973. “Job Market Signaling,” Quarterly Journal of Economics, 87: 355-374.
Staten, M., O. Gilley and J. Umbeck. 1990. “Information Costs and the Organization of Credit
Markets: A Theory of Indirect Lending,” Economic Inquiry, 28(3): 508-29.
Straka, J. W. 2000. “A Shift in the Mortgage Landscape: The 1990’s Move to Automated Credit
Evaluations,” Journal of Housing Research, 11(2): 207-231.
Townsend, R. M. 1979. “Optimal Contracts and Competitive Markets with Costly State
Verification,” Journal of Economic Theory, 21(2): 265-93.
U.S. Department of Housing and Urban Development. 2000. “Unequal Burden: Income and
Racial Disparities in Subprime Lending in America.” Washington, DC. Accessed at
www.huduser.org/publications/fairhsg/unequal.html
28
U.S. Department of Housing and Urban Development, Office of Housing. 2002. “FHA
Portfolio Analysis: Data as of June 2002.” Accessed October 15, 2002 at
http://www.hud.gov/offices/hsg/comp/rpts/com/commenu.cfm
Van Order, R. 2003. “A Model of Financial Structure and Financial Fragility.” Working paper.
Van Order, R., and P. M. Zorn. 2000. “Income, Location and Default: Some Implications for
Community Lending,” Real Estate Economics, 28(3): 385-404.
Weicher, J. C. 2002. Statement of John C. Weicher before the United States House of
Representatives Committee on Financial Services Subcommittee on Housing and
Community Opportunity, April 24, 2002. Accessed September 2, 2002 at
http://financialservices.house.gov/media/pdf/042402jw.pdf.
Yezer, A., R. F. Phillips, and R. P. Trost. 1994. “Bias in Estimates of discrimination and
Default in Mortgage Lending: The Effects of Simultaneity and Self Selection,” The
Journal of Real Estate Economics, 9(3): 197-215.
29
Table 1. Mortgage market share, interest rates and loan performance by market segment
Subprime
All Prime
Conventional
FHA
Alt-A or A-Minus
AA+
AA
A
82.32
9.43
0.47
3.42
30-Year, Fixed APR Interest Rate**
6.14
6.11
7.20
Denial Rate on Loan Applications***
20.14
11.05
30-Day Delinquency Rates
1.73
60-Day Delinquancy Rates
Market Share and Pricing
All
CC or D Subprime
B
C
1.94
0.87
0.66
0.89
8.25
9.10
9.40
10.60
11.80
12.75
9.83
--
--
--
--
--
--
53.63
7.02
1.86
5.12
7.73
10.15
11.75
10.88
7.35
0.31
2.02
0.43
1.41
2.09
2.74
3.57
2.62
2.02
90-Day Delinquency Rates
0.28
2.69
0.42
2.06
3.93
6.39
8.45
9.95
4.04
Foreclosure Rates
0.27
1.76
0.94
3.82
6.26
9.44
12.55
13.61
6.40
Serious Delinquency Rate (90 days DLQ+FCL)
0.55
4.45
1.36
5.88
10.19
15.83
21.00
23.56
10.44
REO Rate
0.15
0.79
0.53
1.44
1.87
3.21
3.92
4.19
2.14
Loss Rate (% of original UPB)
0.01
0.29
0.05
0.51
1.05
1.64
2.80
2.62
1.10
Share of All 2001 Single Family Mortgage Originations*
Performance****
* Share of all mortgages based on 2001 dollar volume of originations. Sources: Inside B&C Lending (2/11/02), Inside Mortgage Finance (1/25/02), and Option One
Mortgage Corporation (June 2002).
** Interest rates are from the week ended 9/6/2002. Rates are APRs calculated using average points and fees with simple interest rate using the standard APR formula; See
for example www.efunda.com/formulae. Sources: Prime rates are from Inside Mortgage Finance (9/6/02); Subprime rates are from Option One Mortgage Corporation
(OOMC) for Legacy Plus Platinum (AA+) and Legacy (all others) program loans for Colorado and Utah. LTVs are assumed to be 80% in all cases except C and CC
quality loans, which assume 75% and 65% LTVs, respectively. Option One Rate Sheets accessed 9/7/02 at http://www.oomc.com/broker/broker_prodprice.html. Option
One's prices are wholesale; to get retail prices 50 bps were added for average broker compensation.
*** Denial rates from 2000 HMDA and exclude loans with missing action codes, withdrawn applications and closed incomplete files and loan purchases (acquisitions).
HUD's list of identified subprime lenders used to determine the subprime loan denial rate.
**** Prime conventional loan and FHA delinquency rates from LoanPerformance, San Francisco, CA as of June 30, 2002. REO (conveyance foreclosures and foreclosure
alternatives in the form of short or foreclosure-sales and deed-in-lieu foreclosures) and loss rates based on Freddie Mac experience for conventional, conforming loans
(Freddie Mac 2002a). FHA REO rates are from HUD (2002) and FHA loss rates are from Weicher (2002). Subprime performance from Option One Mortgage
Corporation (2002 a). Loss rates are total net cumulative losses including revenues from the sale of foreclosed properties and are net of mortgage insurance payouts but
do not include servicing or other G&A costs; losses should not be confused with profits.
Table 2. Relative loan performance by market segment, FICO score, LTV, and house price appreciation rate
(Percent of loans ever 90+ days delinquent)
High
Medium-High
Medium
Low
Prime Market Segment
Subprime Market Segment
Origination Decision FICO Score
Origination Decision FICO Score
Low
Low
Medium
High Very High
Medium
High Very High
Low House Price Appreciation States
14.10
7.21
3.21
0.99
22.53
11.51
9.58
7.15
10.85
6.50
2.54
0.78
26.95
17.69
12.55
9.35
9.04
5.29
1.87
0.44
29.56
17.80
11.87
11.11
5.64
3.25
1.01
0.25
25.56
12.74
9.94
6.48
High
Medium-High
Medium
Low
12.49
8.41
6.01
4.20
Medium House Price Appreciation States
5.28
2.11
0.68
27.39
4.37
1.64
0.47
24.12
3.02
1.00
0.23
27.42
2.00
0.64
0.15
28.07
13.15
14.74
15.52
12.01
8.27
9.45
11.01
7.25
3.51
4.56
7.39
3.16
High
Medium-High
Medium
Low
12.37
7.05
4.33
3.09
High House Price Appreciation States
4.82
1.82
0.69
20.35
3.46
1.38
0.42
15.45
2.09
0.72
0.18
17.86
1.41
0.46
0.12
18.73
9.46
10.13
9.48
8.60
6.02
7.33
6.98
5.69
3.48
6.33
4.86
3.66
LTV
Notes: FICO Score category: Low (<=620), Medium (>620 and <=660), High (>660 and <=720), Very High (>720);
LTV category: Low (<=70%), Medium (>70% and <=80%), Medium-High (>80 and <90%), High(>=90%); House-price
appreciation (HPA) determined by 5-year growth rates for the period 1Q 2997-1Q2002 using the Freddie Mac
Conventional Mortgage Home Price Index State series -- Low HPA states: AK, HI, ID, ND, NM, NV, UT, WV, WY;
High HPA states: CA, CO, DC, MA, ME, MN, NH, NY. Rates are relative to the rate for prime loans with High FICO
scores, Medium LTVs, and Medium HPA. Loans were originated from 1996-2001 and observed through 2002 Q1.
Source: LoanPerformance, San Francisco, CA.
Table 3. 3-Month credit bureau score migration by score range
Current Observation Score
Less
Migration
550 to 600 to 650 to 700 to 750 &
than
Amount
599
649
699
749
higher
550
2.9%
8.9%
10.4%
6.7%
4.7%
3.3%
Low to – 41
All
5.2%
– 40 to – 21
7.9%
8.8%
6.9%
7.3%
7.7%
7.9%
7.7%
– 20 to + 20
54.4%
50.1%
59.4%
68.2%
71.9%
83.4%
72.8%
+ 20 to + 40
16.3%
15.9%
14.6%
11.9%
11.5%
4.8%
9.7%
+ 41 to high
18.2%
16.1%
8.4%
5.6%
3.9%
0.3%
4.3%
Column Total
100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
100.0%
Notes: Cells show column percentages. Migration is measured over a 90-day period for more
than 400,000 accounts taken from a stratified random sample of bankcard data from the late
1990s. Fair Isaac credit bureau scores provided by Trans Union.
Source: Fair, Isaac & Co., San Raphael, CA.
Table 4. 30-Year fixed-rate mortgage pricing for subprime loans
Credit
Grade
AA
A
B
C
CC
Credit
Score
Mortgage History
680
<80% LTV and [1x30 days late last 12
650
months or 3x30 days late last 24 months]
620
or ≤80% LTV and 1x30 days late last 12
600
months
580
660
<80% LTV and [2x30 days late last 12
months and 0x60 days late last 24
620
months]
or ≤80% LTV and 2x30 days
580
late last 12 months
560
640
<80% LTV and [4x30 days late last 12
600 months or 1x60 days late last 24 months]
or [≤80% LTV and 4x30 or 2x30 or
580
1x60 days late last 12 months]
540
600
6x30 or 1x60 or 1x90 days late last 12
570
months
540
520
580
550
Exceeds "C"
530
500
65%
7.45
7.75
8.20
8.49
8.65
8.20
8.45
8.85
9.05
8.70
9.05
9.30
10.10
10.15
10.49
11.15
11.35
11.60
12.05
12.35
13.05
70%
7.60
7.90
8.35
8.65
8.80
8.35
8.60
9.00
9.20
8.85
9.20
9.49
10.30
10.40
10.75
11.40
11.60
Loan-to-Value Ratio
75% 80% 85%
7.85 8.10 8.60
8.15 8.40 8.90
8.60 8.85 9.35
8.90 9.15 9.65
9.05 9.30 9.80
8.60 8.85 9.35
8.85 9.10 9.60
9.25 9.60 10.00
9.45 9.70 10.49
9.10 9.45 9.95
9.45 9.80 10.30
9.70 10.05 10.49
10.49 10.90
10.90
11.25
11.90
12.10
90%
9.20
9.49
9.90
10.15
10.25
9.95
10.20
10.60
11.30
95%
9.80
10.10
10.30
10.49
Notes: Mortgage History combined with credit score = credit grade. Assumes full documentation, prepayment lock-out of 3
years (additional points of 1.50 otherwise), loan balance of less than $130,000 (discount of 0.50 for larger balances), and a
minimum credit score of 500.
Source: Option One Mortgage Corporation, Colorado and Utah rates effective 9/3/02, accessed off Option One's website at
oomc.com/wholesale/rate_info.html on 9/06/2002.
55
3-Month CPR (%)
50
Interest Rate (%)
30-Year Fixed Mortgage Interest Rate
(right scale)
9.5
9
Subprime Prepayments
(left scale)
45
8.5
40
8
35
7.5
30
7
25
6.5
20
6
Prime Conventional
Prepayments
(left scale)
15
5.5
Jul-03
Jan-03
Jul-02
Jan-02
Jul-01
Jan-01
Jul-00
Jan-00
Jul-99
Jan-99
Jul-98
Jan-98
Jul-97
Jan-97
Jul-96
Jan-96
5
5
Jul-95
10
4.5
Notes: Data on CPRs from LoanPerformance, San Francisco, CA. Interest Rates from Freddie Mac’s Primary
Mortgage Market Survey.
Figure 1. Prime and subprime prepayment trends and mortgage interest rates: 1993-2003.
Equity
WCS0 = WCL (0,0 | d C )
e*S
g
C’s
Isocost
Curves
B’s
Isocost
Curves
0
0
u *S
Underwriting
Figure 2. Equity and underwriting as a separating equilibrium in the subprime
market
Equity
B’s
Isocost
Curves
(
0
WBP
= WBS eS* , u S* | d B
e*P
A’s
Isocost
Curves
=0
0
)
h
u*P
Underwriting
Figure 3: Equity and underwriting as a separating equilibrium in the prime market
$
AR(s)
A
B
AC(s)
D
E
F
MR(s)
0
C
MC(s)
Loan Quality from Low to High
G
N
s
Figure 4. Average and marginal cost and revenue functions for the secondary market.
$
P2sm
∆
A
G
Bank
Default Cost
F
B
P1sm
C
∆
E
D
SM Default
Cost
cb
s2
csm
0
0
N2
s2 N *
s1
∆
N1
N − N1
N
Loan Quality from Low to High
Figure 5. Profit maximization if SM defines “investment quality”: concave & convex case
$
Flat Pricing by
Secondary Market:
“Loan Shark”
Loans
Flat Pricing by
Secondary Market:
Prime Loans
P2sm
A
Risk-Based Pricing by Primary
Market: Subprime Loans
G
H
B
sm
1
P
∆
C
Risk-Based Pricing
by Primary Market:
Ultra-Prime Loans
cb
s2
0
0
N2
s2 N *
s1
N1
N − N1
N
Loan Quality from Low to High
Figure 6. Profit maximization if SM defines “investment quality”: concave & convex case