by Michelle A. Danis & Anthony Pennington-Cross (Office of Federal Housing Enterprise Oversight)

Introduction

Subprime lending in the mortgage market has seen dramatic growth since the early 1990s. The share of total originations that is subprime has risen from 1.4 percent in 1994 to 18.7 percent in 2002. Lenders include both mono-line lenders (subprime only) and larger institutions that provide subprime loans as part of a continuum of alternatives. The securitized market for subprime loans has also been growing, with the securitization rate of subprime home mortgages rising from 31.6 percent in 1994 to 62.5 percent in 2002.‡ While the subprime securitization rate is still below the prime or conventional market rate (73.8 percent in 2002), it has helped bring the subprime market into a form more closely resembling the prime market (Inside Mortgage Finance, 2003).

‡ Securities that include subprime loans are often referred to as Asset Backed Securities (ABS) instead of Mortgage Backed Securities (MBS).

Subprime lending can most easily be characterized as high risk lending, especially as compared to the conventional prime sector of the mortgage market. To compensate for these risks, which include elevated rates of prepayment, delinquency and default, lenders must charge higher risk spreads. The understanding of these risks is of crucial importance to both regulators and lenders. This paper focuses on one of the least studied risks, the delinquency of subprime loans. Delinquent loans increase the costs of servicing, increase losses for any institution guaranteeing timely payments, and impact payments to subordinate tranches. Even if the loans do not terminate, elevated rates of delinquency will impact pricing in the primary and secondary markets.

Motivation

The performance of a mortgage is often characterized by whether the loan has prepaid or defaulted, as well as the loss on any outstanding defaulted balance. Empirical models of these events can then be used to understand the sensitivity of a mortgage to economic conditions, loan type, and borrower information. Estimates of these relationships rely on option pricing techniques that allow the borrower to exercise the option to put the mortgage back to the lender or investor through default or to call the mortgage through prepayment.

Puts and calls can be motivated by financial considerations or by external events. A mortgage is put, at least in its simplest form, when the mortgage outstanding is greater than the value of the property after accounting for costs such as transaction fees. These are often referred to as "ruthless" defaults. Similarly, a mortgage is called and prepaid, typically due to a drop in interest rates, if the gain from doing so outweighs the cost. Subprime borrowers may also put a mortgage when their history of paying financial obligations has improved, thus making lower cost credit available. Beyond the financial motivations, other factors, coined trigger events, have been identified as potential causes of loan defaults and prepayments. Typical trigger events include losing a job, a severe illness, or the breakup of a household. These unanticipated events, which can be either temporary or permanent, will likely change current and future income streams and make it difficult to continue paying a mortgage. Trigger events can lead to both defaults and prepayments depending on the amount of equity in the mortgage and expected income streams.

It is important to remember that the lender and borrower interact once a loan becomes delinquent. It is the outcome of this interaction that determines what status the loan will be in (cure, more delinquency, or termination and type of termination). Therefore, all observed loan outcomes reflect a mixture of lender and borrower objectives.

Before a loan enters foreclosure proceedings or the property becomes owned by the lender, there is a gray area in which a borrower is delinquent. While missing a single payment on a mortgage may violate the mortgage contract or agreement and thus could technically be considered a default, lenders prefer and are usually legally required to allow borrowers to be delinquent over a longer time period before pursuing foreclosure or alternative methods of collecting the debt. It is this time period as a loan moves from being one payment late, to two, and three payments late that is this paper's focal point. Delinquent loans consist of a mix of temporarily delinquency, which will eventually cure, and delinquency that is driven by the standard option motivation to default or prepay.

The subprime mortgage market is a fertile segment of the market to examine delinquency because it includes borrowers who have already shown that they have trouble meeting their financial obligations. Therefore, subprime loans should exhibit high rates of delinquency and default. This should aid in identifying key factors that drive delinquency. These factors could include both time constant and time varying factors. For example, borrowers in ruthless delinquency (delinquency driven by the financial desire to default) may find that by the time delinquency reaches 60 or 90+ days house prices have increased enough to make it no longer financially sensible to go all the way to foreclosure.

This paper uses a large and nationally representative sample of data from LoanPerformance.com (formerly MIC) to examine the monthly status of single-family 30-year fixed-rate subprime mortgages from 1996 through the middle of 2003.

This paper is one of the first examinations of the delinquency of subprime loans. It includes – 1) an examination of subprime mortgage performance using a large nationally representative sample of loans covering multiple lenders and servicers, 2) a model of multiple states of delinquency as well as termination states simultaneously, and 3) a comparison of multinomial logit and nested logit in a hazard model framework.

Background and Literature Review

This paper draws from two lines of literature. The first line is the growing body of research on the subprime mortgage market. The second line focuses on the performance and modeling of mortgages and specifically the delinquency of mortgages.

Subprime

Over the last 10 years the growth in the subprime mortgage market, while substantial, has been uneven. The subprime market rapidly expanded in the mid- and early- 1990s. In 1998, however, the market was hit by two events that caused a liquidity crunch (Temkin, Johnson, and Levy, 2002). First, subprime lenders experienced unexpected losses after high default, delinquency, and prepayment rates occurred. Second, the Russian bond crisis during late 1998 caused investor confidence to decline. The resulting secondary market discipline led to a short time period of retrenchment followed by renewed growth in different market segments. Before 1998, growth in subprime came from a rapid expansion in lending to the riskiest portions of the market. After 1998, which was also associated with consolidation in the industry, growth has come in the least risky portions of the market. This segment is typically referred to as A- lending and includes borrowers with impaired credit histories that are willing to pay a premium over the prime lending rate typically in excess of 290 basis points (Chomsisengphet and Pennington-Cross, 2004a, 2004b).

In fact, subprime lenders provide a menu of mortgage options to borrowers with a variety of impairments. The most typical impairment is poor credit history. Borrowers with worse credit history pay higher premiums and must provide larger down payments to help defray some of the expected losses and expected delinquency of these types of loans. Another segment of the subprime market is referred to as Alt-A, which is short for Alternative A lending. These types of borrowers look just like prime borrowers in terms of credit history and assets to make down payments, but they usually provide limited or no documentation on their income or down payment. As a result, they typically pay a 100 basis point premium (Chomsisengphet and Pennington-Cross, 2004b).

As should be expected given the impairments of subprime borrowers, the Mortgage Bankers Association of America (MBA) reports that in the third quarter of 2002 subprime loans were delinquent 5½ times the rate of conventional mortgages (14.28 versus 2.54 percent). In addition, subprime loans started foreclosure more than 10 times more often (2.08 versus 0.20 percent). There is also evidence that subprime lending is most often used in high-risk locations (Calem, Gillen, and Wachter 2004, and Pennington-Cross 2002). While subprime borrowers also tend to have less knowledge about the mortgage process, a borrower with a subprime mortgage is not necessarily stuck using high cost lending forever. Survey evidence indicates that of subprime borrowers who get another mortgage, 39.6 percent successfully transition into the prime market (Courchane, Surette, and Zorn, 2004).

The growing literature on subprime lending in the mortgage market consistently shows that subprime differs substantially from the prime market on many dimensions. The first and perhaps initially most curious fact regarding subprime is simply that it is segmented from the prime market. Theoretical models have explained this segmentation. Cutts and Van Order (2004) focus on the amount of underwriting a lender should do in each risk classification. They find that the most extensive underwriting will be done on the least risky loan types. Nichols, Pennington-Cross, and Yezer (2004) provide a different segmenting equilibrium in which lenders specialize in either subprime or prime lending. In their model if a lender sets lending standards too close to the entrenched prime market, then application costs become so high that it becomes less costly to lower credit standards and accept more high-risk applicants. This result is driven by the costs associated with processing rejected applications. When a high proportion of applicants are rejected it can overwhelm any benefits associated with the lower risk borrowers. An optimum distance in credit quality space is then found, thus motivating the need for a segmented high-risk or subprime mortgage market.

The literature on the performance of subprime mortgages has focused on default, prepayment, and losses on outstanding defaulted balances. In general, loss severity tends to be higher for high-risk borrowers and high-risk property. These losses tend to be larger even though subprime borrowers tend to put the mortgage earlier than prime borrowers — when it is less in the money to default (Capozza and Thomson, 2004). Research has also found that loans originated by third parties tend to default at elevated rates and that high cost borrowers are less responsive to changing interest rates (Alexander et al 2002, and Pennington-Cross 2003). Evidence from a single subprime lender shows that risks tend to be higher for the higher cost segments in the market because the defaults are more highly correlated. For lower cost segments, such as A- and Alternative-A, subprime loans showed relatively low default correlation rates (Cowan and Cowan 2004).

Delinquency

Traditional option based mortgage-pricing research includes three possible states for a mortgage – 1) current or active, 2) prepaid, or 3) defaulted.‡ This approach typically ignores the fact that lenders are usually legally not allowed to begin foreclosure proceedings until two payments are missed and the third is due. Many options are available to lenders besides foreclosure to recover losses on defaulted loans and it can take a substantial period of time to complete a foreclosure. For example, the main feature of a ruthless default is that it makes financial sense because the mortgage is substantially larger than the value of the property. But, the relevant value of the property is at foreclosure, not when the first or even second payment is missed. Kau and Kim (1994) indirectly discuss this issue by showing that the value of a future default can impact whether an "in the money" default today will be exercised. For example, if house prices continue to drop in the future the value of default will be larger in the future, and the borrower will wait. In a stochastic framework, the larger the variance of house prices the more value there may be in the future, so it is consistent for borrowers that are "in the money" to default to wait.

‡ For a summary of this line or research see Vandell (1995) and Kau and Keenan (1995). Typically these papers consider a loan "defaulted" when the mortgage is terminated through foreclosure or other adverse means.

Ambrose, Buttimer, and Capone (1997) explicitly introduced into the option-pricing framework the delay of foreclosure and the concept that the decision to stop making payments is determined by expected values of the property well into the future (at the foreclosure date). The delay of foreclosure can be interpreted as an increase in the delinquency of the loan, but the model treats the delay of foreclosure as an exogenous variable and therefore, can be used to provide predictions about the probability of default given a foreclosure delay or delinquency time period. For instance, the probability that a loan defaults and becomes delinquent is sensitive to the delay before foreclosure, the loan to value (LTV) ratio at origination, and the variance of house prices. Specifically, longer delays (more expected delinquency) and higher LTVs are associated with higher default probabilities. The response to the variance of house prices in non-linear. In general, as the variance increases the probability of default increases because the probability of negative equity has increased. The direction of this effect can change to negative when there is a very long delay until foreclosure or the lender has no recourse to recover any losses from other assets beyond the value of the house. This is a natural result, because in these circumstances there may be time for the house price to drop even further in the future making a future default more valuable and at the same time the borrower can receive free rent while the loan is delinquent (not paying any mortgage or monthly rent).‡

‡ Another line of literature looks at the time from a default, which is typically defined as being 90+ days delinquent, to resolution of the mortgage. Resolution could include many of the available loss mitigation tools used by lenders such as foreclosure, curing, pre-foreclosure sale or short sale, or even assumption of the mortgage. While some theoretical work has been done most of the work focuses on empirical determinants of the various possible outcomes, typically using multinomial logit in a hazard style framework (Ambrose and Capone1998, Lambrecht et al 2003, Ambrose and Capone 1996, Wang, Young, and Zhou 2002, Lawrence and Arshadi 1995, Phillips and Rosenblatt 1997,Weagley 1988, Geppert and Karels 2001).

Empirical research dating back thirty years has already identified many of the same drivers of delinquency that have been included in more recent options oriented theoretical models. For instance, using sparse data sets from the 1960s and early 1970s, Morton (1975) and Furstenberg (1974) found evidence that the LTV at origination and income of the borrower were important determinants of the delinquency rates. The tenor of the results are remarkably similar in a more recent paper that examined delinquency rates in the United Kingdom (UK) using data from 1983 through 1992. Using a seemingly unrelated regression approach, Chinloy (1995) again found that LTV and income were the most important empirical indicators of delinquency, whether defined as 6-12 months or greater than 12 months delinquent. Clarifying the role of LTVs, Getter (2003) uses the 1998 Survey of Consumer Finances to show that the best tool that borrowers have to avoid becoming late on a mortgage are financial assets, which can be used to cover financial obligations during unexpected periods of financial stress. To the extent that financial assets are correlated with down payments these results are consistent with earlier findings. Using an exponential hazard framework, Ambrose and Capone (2000) find that the probability of being 90+ days delinquent on FHA mortgages during the mid-1990s is sensitive to contemporaneous economic conditions in both the labor and housing markets. Similar to Ambrose and Capone (2000), using individual loan level data Calem and Wachter (1999) estimated the probability of being either 60 days or 90+ days delinquent using individual logits, thus implicitly assuming that the probability of being 60 and 90+ days are independent of each other. They find that credit scores matter for both delinquency categories, but LTV has no effect. Lastly, Baku and Smith (1998) use a case study approach to find that the performance of loans made by nonprofit lenders to low income households is sensitive to the incentive structure internal to the nonprofit agency. In short, the behavior of the lender does have an impact on loan delinquency.

Industry reports have examined transition matrices of subprime loans. In these reports various states of delinquency are collapsed into more aggregate groups, all states are assumed to be independent of each other, the estimation procedure is not reported, and the results cannot be extended beyond the single lender/servicer used in the report (Gjaja and Wang 2004). The results suggest that indicators have different impacts on the extent of delinquency.

This paper expands on this literature by recognizing that there are multiple states of delinquency and that these states are not independent of each other. But, in addition to the need to recognize the importance of various degrees of delinquency, any model must also recognize that loans can also prepay or default and that all these options are best viewed as competing risks.‡

‡ While default has been defined in many different ways, in the empirical work explained in the following sections default is defined as whenever the lender initiates foreclosure proceedings (the acceleration note) or the lender becomes the owner of the property which will be sold to help cover any losses associated with the default and period of delinquency.

Data Description

We utilized data from LoanPerformance (LP, formerly MIC). LP collects data from pools of non-agency publicly placed securitized loans. Static information about individual loans is collected, such as documentation type, origination balance, and purchase price, as well as monthly updated information on loan status. Data on loans originated between January 1996 through May 2003 are included. The database contains information on over 1,000 pools of subprime loans representing over 3,500,000 individual loans.

For our estimations, we choose a random cross-section sample of 100,000 30-year fixed rate loans for owner-occupied property from the LP database. After eliminating loans with missing data, a database of 97,852 observations resulted. The distribution of outcomes in the data is in Table 1.

We matched data from several external data sources to the LP data. First, we matched data on the quarterly change in the OFHEO House Price Index (HPI) and on the standard error of the HPI to the loan data by state. We then matched Bureau of Labor Statistics data on the unemployment rate lagged by one month by state. Finally, we matched data on the prime interest rates on 30-year fixed mortgages from the Freddie Mac's Primary Mortgage Market Survey for the given month and for the origination month and computed the difference. Summary statistics for the data are in Table 2.

Figure 2 shows the 30-day, 60-day and 90+-day delinquency rate by month for our sample from June 1999 to May 2003. The 30-day delinquency rate stays around the average 1.4% during this time period. There is a slight uptrend in the 30-day delinquencies since spring 2002. The 60-day delinquency rate is lower, with an average of 0.5%, and generally lags behind the 30-day delinquency rate. For example, there is a one-half percentage point decline in the 30-day rate in April 2000, followed by a decline of almost a third of percentage point in the 60-day rate in May. The 90+-day delinquency rate closely tracks the 30-day rate and has an average equal to 1.1%.

Comparisons of loan outcomes in our sample to subprime delinquency rates published in the National Delinquency Survey by the Mortgage Bankers Association (MBA) show that our sample consistently has lower delinquency rates. The MBA data on subprime are produced on a quarterly basis and are not representative of the total subprime market.‡ We calculated a quarterly rate from our monthly data by including any occurrence of a particular delinquency status in the quarter in the rate calculation. Thus, a loan could contribute to the 30-, 60-, and 90+-day delinquency rates in a given quarter. Figures 3a-3c show the delinquency rates from the MBA publication and from our survey.

‡ An author's note to the National Delinquency Survey states: "The subprime data represents information from 13 companies including more than 1.5 million conventional loans and are not considered representative of the total subprime market. Therefore, the subprime data should not be cited as representative of industry, regional and state averages."

Table 3 describes mean values of the data in our sample by loan status. Age of the loan, FICO score, change in the HPI, documentation dummy variables, a prepayment penalty indicator, and change in interest rates from origination are all notably different for delinquent and defaulted loans than for those that are current or prepaid. Some trends evident in the data are:

• Delinquent loans are older than those that are current by four to eleven months.
• FICO scores differ by 50 points or more between current/prepaid loans and delinquent/defaulted loans.
• Loans that are current are in geographic areas with smaller changes in house prices.
• Loans that prepay are much less likely to have a prepayment penalty associated with them.
• Delinquent and defaulted loans have experienced a larger change in interest rates since origination.

Econometric analysis will more precisely ascertain the marginal effects of each of these variables on loan status outcomes and are presented in the following section.

Results

This section presents the results of the nested and multinomial results.† In many cases, both the nested and multinomial logit results generate the same direction of effect. But, there are important instances in which the results differ. Instead of presenting the coefficient estimates, which are extremely difficult to interpret, Table 5 presents the one standard deviation elasticity estimates and the Appendix includes sensitivity tests presented in graphical form for all exogenous variables.‡ Table 5 estimates reflect the percent change in the probability of the event occurring, as indicated in each column, holding all other variables at their means. Since these impacts are not symmetric, elasticity estimates for both increasing the variable and decreasing the variable by one standard deviation are presented. Note that the lack of symmetry increases the larger the responsiveness.

† The likelihood function value for the nested logit is -38,165.68 and for the multinomial logit model is -37,270.43.

‡ Coefficient estimates are available from the authors upon request.

When examining the results it is important to remember that this paper defines default as any loan that is in the foreclosure process or the property has become owned by the lender. Other papers occasionally use 90+ days late or the date the distressed property is sold (as real estate owned or at a foreclosure sale) as an alternative definition of default.

Impact of Credit Scores

The impact of credit scores is very strong and most of the results meet expectations. Loans with better credit scores are much more likely to stay current and less likely to enter delinquency or default in both models. This supports the notion that past performance on other financial obligations is a strong indicator of future ability or desire to pay.

The elasticity estimates for the nested logit results indicate that in contrast with Pennington-Cross (2004) we find that borrowers with higher credit scores are less likely to prepay, while the multinomial logit results find the traditional positive relationship. Figure A1 in the Appendix shows a linearly increasing relationship for the multinomial results and a non-linear or upside down U-shaped relationship for the nested logit results. The multinomial and nested results are very similar for credit scores below 630 and show a positive relationship between credit scores and the probability of prepayment. As scores rise above 630 the nested results indicate declining probabilities while the multinomial results continue in a linear fashion. This group of loans with high credit scores is unique because the FICO is sufficient to qualify for a prime loan, yet the borrower obtained a subprime loan. These results likely reflect the uniqueness of the borrowers with these loans and the permanence of their constraining circumstances.

The marginal impact of credit score on the probability of default also differs between nested and multinomial logit. For borrowers with very low credit scores, multinomial logit predicts a probability of default three times as large as nested logit. Nested logit shows the probability of default increasing until approximately a FICO of 600, at which the probability declines. In contrast, multinomial logit shows the probability of default is monotonically decreasing over the range of credit scores.

Financial Incentives

Consistent with expectations, loans that are originated with higher LTVs are more likely to be delinquent and less likely to be current. Serious delinquency (60 and 90 days) is especially sensitive to homeowner equity at origination. The results are not consistent with the predictions of Ambrose, Buttimer, and Capone (1997) that higher LTVs are associated with higher probabilities of default. The results instead find almost no response in the multinomial results and a negative relationship for the nested results. This seems to indicate that original LTV does not in itself provide a good indicator of ruthless default types, but instead the inability or desire to provide a down payment indicates a proclivity to miss payments without the intent of losing the home.

From the prepayment perspective both model results show that loans with higher LTVs, or less equity at origination, are less likely to prepay the mortgage. This result is consistent with prior results in both the prime and subprime mortgage markets, and can be interpreted as indicating that the borrower is constrained in the ability to refinance and move due to low equity.

When house prices are increasing, borrower equity should be growing, making it easier to prepay and less likely to default. In both the multinomial and nested logit models, the probability of delinquency and default decreases when prices increase. However, the nested logit model shows that prepayment is fairly unresponsive to changes in house prices, while multinomial logit shows the expected positive relationship.

To more accurately calculate the equity of a borrower in each month we need to know the value of the property and the outstanding balance of the loan. State level house prices help to proxy for the house value, but there is a substantial dispersion of individual house prices around the mean appreciation rate through time. One way to measure this dispersion is to use estimates of the relationship between the variance of individual house price around the mean appreciation rate and time since the last transaction. We use this information to calculate what is labeled the standard error of house prices for each individual home in the sample. In essence, this provides an estimate of how confident we are that the individual house price has increased at the area rate. If we have little confidence then there is higher probability that the borrower has negative equity in the house. This makes it more likely that the borrower will attempt a ruthless default.‡

‡ While this line of reasoning is commonly used to motivate the importance of equity in a home, it is only valid to the extent that lenders do not attempt to recover losses on defaulted loans from other assets the borrower may have (recourse or deficiency judgment lending). Ambrose, Buttimer, and Capone (1997) show in their model that when a lender has full recourse and fully exercises it that borrowers have no incentive to default.

We do find a strong positive impact of the standard error estimates on the probability of default and 90+ day delinquency in support of this interpretation. However, we find a large disparity between the probabilities of serious delinquency and default between the multinomial logit and nested logit for very large values of the standard error of the house price index. In conjunction with the effects of changes in house price, these results indicate that variation of individual house price appreciation rates and local market conditions are important predictors of default and serious delinquency in the subprime market. The evidence presented so far indicates that subprime loans do respond to the economic and financial incentives to put the mortgage through default.

To measure the volatility of average or typical house prices we include the standard deviation of the detrended OFHEO HPI in each state. Kau and Kim (1994) theorized that when prices are volatile borrowers may wait longer to default, because the value of the option to default may be larger in the future. Ambrose, Buttimer, and Capone (1997) theorized that the result is indeterminate depending on the length of delay (delinquency) and the extent of recourse. The empirical evidence does not find support for a nonlinear relationship.‡ Instead, as volatility increases loans are less likely to default or prepay. Delinquency is fairly unresponsive to the volatility measure, which is consistent with free rent motivations for many delinquent subprime loans. In total, these results provide supporting evidence that the value of delaying default is an important component needed to understand the behavior of subprime loans.

‡ Future research needs to focus on measuring the time to default and the extent that redemption is actually used by lenders to see if the non-linear relationship does exist in empirical models.

Subprime loans are also responsive to changes in interest rates. The elasticity estimates are especially large for the nested logit results. As interest rates drop, the loan is more likely to prepay. This is consistent with the notion that financial considerations are a driver in prepayments even for the financially constrained subprime borrowers.

Trigger Events

Trigger events, as proxied by last month's state unemployment rate, in general do not act as expected. Higher unemployment rates are associated with lower delinquency probabilities. The probability of default is insensitive to local labor market conditions proxied by the state unemployment rate. In addition, worse labor market conditions are also associated with lower prepayment probabilities. In summary, future research needs to come to a more complete understanding of how subprime loans react differently to economic conditions and exogenous trigger events.

Loan Characteristics

Prepayment penalties do tend to reduce prepayments, but are also associated with higher likelihoods of delinquency and default. Loans with limited documentation also are delinquent and default more frequently than full documentation loans. The impact for loans with no documentation is even larger.

We also included a baseline in the estimation. The nested results show a peak in defaults after the first 12 months, while the multinomial logit shows a monotonically decreasing relationship. Both the nested and the multinomial logit models show steady declines in prepayments as the loan ages.

Conclusion

This paper examines the performance of a large national sample of securitized private label loans. It includes multiple lenders and therefore provides one the first broad examinations of the performance of subprime loans. The results reinforce the notion that all loans, including subprime loans, respond to incentives to default and prepay a mortgage. In addition to default and prepayment, the delinquency behavior of subprime loans is examined in an econometric framework that captures all the potential outcomes for the loan.

We find that financial incentives strongly explain subprime loan outcomes. Borrower credit scores are robust predictors of delinquency, default and prepayment and LTV at origination is positively correlated with delinquency. Several measures of housing market conditions indicate that subprime loans are strongly affected by all incentives to become delinquent and default on a mortgage. The change in interest rates affects prepayment, default, and delinquency. In addition, we find that loan characteristics are important determinants of loan outcomes. Prepayment penalties extend the duration of subprime loans, and documentation status is associated with higher delinquency status.

Subprime mortgages represent an increasingly important segment of the securitized mortgage market. These loans are typically more risky than prime mortgages, and are characterized by higher rates of prepayment, delinquency, and default. This research seeks to explain the sources of the higher rates of prepayment, delinquency, and default of subprime mortgages. By better understanding the sources of the risk, subprime lenders can implement better risk management policies.

Source: http://research.stlouisfed.org/wp/2005/2005-022.pdf

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