Nonprime Mortgages: Analysis of Loan Performance, Factors Associated with Defaults, and Data Sources
Published: 2010-08-24T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: The surge in mortgage foreclosures that began in late 2006 and continues today was initially driven by deterioration in the performance of nonprime (subprime and Alt-A) loans. Nonprime mortgage originations increased dramatically from 2000 through 2006, rising from about 12 percent ($125 billion) of all mortgage originations to about 34 percent ($1 trillion). The nonprime market contracted sharply in mid-2007, partly in response to increasing defaults and foreclosures for these loans. This report (1) provides information on the performance of nonprime loans through December 31, 2009; (2) examines how loan and borrower characteristics and economic conditions influenced the likelihood of default (including foreclosure) of nonprime loans; and (3) describes the features and limitations of primary sources of data on nonprime loan performance and borrower characteristics, and discusses federal government efforts to improve the availability or use of such data. To do this work, GAO analyzed a proprietary database of securitized nonprime loans and Home Mortgage Disclosure Act data, and reviewed information on mortgage data sources maintained by private firms and the federal government.
section_title: What GAO Found
paragraphs: The number of active nonprime loans originated from 2000 through 2007 that were seriously delinquent (90 or more days late or in the foreclosure process) increased from 1.1 million at the end of 2008 to 1.4 million at the end of 2009. Serious delinquency rates were higher for certain adjustable-rate products common in the subprime and Alt-A market segments than they were for fixed-rate products. The number of nonprime loans that were 90 or more days late grew throughout 2009, accounting for most of the overall growth in the number of serious delinquencies. By comparison, the number of active loans in the foreclosure process grew in the first half of the year, and then began to decline somewhat. Additionally, 475,000 nonprime mortgages completed the foreclosure process during 2009. The persistently weak performance of nonprime loans suggests that problems in the nonprime market will not be resolved quickly, and underscores the importance of federal efforts to assist distressed borrowers and prevent a recurrence of the aggressive lending practices that helped precipitate the foreclosure crisis. In addition to performance differences between mortgage products, GAO found across product types that house price changes, loan amount, the ratio of the amount of the loan to the value of the home, and borrower credit score were among the variables that influenced the likelihood of default on nonprime loans originated from 2004 through 2006. In addition, loans that lacked full documentation of borrower income and assets were associated with increased default probabilities, and the influence of borrowers' reported income varied with the level of documentation. GAO found that borrower race and ethnicity were associated with the probability of default, particularly for loans used to purchase rather than to refinance a home. However, these associations should be interpreted with caution because GAO lacks data on factors that may influence default rates and that may also be associated with race and ethnicity, such as borrower wealth and first-time homebuyer status. Existing sources of data on nonprime mortgages contain a range of information to support different uses. While these data sources offer some similar elements, they vary in their coverage of loan, property, and borrower attributes. The data sources generally lack information on certain attributes that could help inform policy decisions or regulatory efforts to mitigate risk. For example, first-time homebuyers are not identified in any of the data sources, limiting the ability of analysts to compare the marginal effect of prior homeownership experience on default probabilities. In addition, most of the data sources do not cover the entire nonprime mortgage market. Ongoing federal efforts have the potential to provide data that may not have some of the constraints of the existing sources. For example, officials from the Board of Governors of the Federal Reserve System and Freddie Mac are collaborating on a pilot project to develop a publicly available National Mortgage Database, which would compile data on a representative sample of outstanding mortgages and provide more comprehensive data than are currently available.
section_title: What GAO Recommends
paragraphs: GAO makes no recommendations in this report.

section_title: Letter
paragraphs: The surge in mortgage foreclosures that began in late 2006 and continues  today was initially driven by deterioration in the performance of nonprime  (subprime and Alt-A) loans. Nonprime mortgage originations increased  dramatically from 2000 through 2006, rising from about 12 percent   ($125 billion) of all mortgage originations to about 34 percent ($1 trillion).  The nonprime market contracted sharply in mid-2007, partly in response to  increasing default and foreclosure rates for these mortgages. As economic  conditions deteriorated in 2008 and 2009, growing numbers of  borrowers—including those with both nonprime and prime loans— entered foreclosure, exacerbating stresses in the mortgage and housing  markets.
Researchers and policymakers have sought to understand the causes of  the foreclosure crisis and develop policy responses to reduce foreclosures  and prevent similar crises in the future. However, data limitations have  complicated efforts to analyze the nonprime mortgage market, in part  because no one database provides complete information on the features  and performance of nonprime loans and the characteristics of borrowers.  Furthermore, questions have been raised about whether timely access to  more comprehensive information on the nonprime mortgage market could  have helped federal banking regulators anticipate the foreclosure crisis or  respond to it more quickly and effectively.
To inform congressional oversight and decision making about efforts to  address problems in the mortgage market, you requested that we examine  the evolution and condition of the nonprime market segment. In prior  reports, we discussed certain characteristics of nonprime loans and  borrowers; the performance of nonprime mortgages as of March 31, 2009,  and June 30, 2009; the extent of negative equity among nonprime  borrowers in selected metropolitan areas and nationwide as of June 30,  2009; and the proportion of nonprime borrowers with negative equity and  seriously delinquent loans, by state, from 2006 through 2009. This report  (1) provides information on the performance of nonprime loans through  December 31, 2009; (2) examines how loan and borrower characteristics  and economic conditions influenced the likelihood of default and  foreclosure of nonprime loans; and (3) describes the features and  limitations of primary sources of data on nonprime loan performance and  borrower characteristics, and discusses federal government efforts to  improve the availability or use of such data. An electronic supplement to  this report provides additional information on the performance of  nonprime mortgages by annual loan cohort, product type, Census division,  state, and congressional district as of December 31, 2009.
To examine the recent performance of nonprime mortgages, we used data  from CoreLogic LoanPerformance’s (CoreLogic LP) Asset-Backed  Securities Database for nonprime loans originated from 2000 through 2007  (the last year in which substantial numbers of nonprime mortgages were  made). The CoreLogic LP database contains loan-level data on a large  majority of nonagency securitized mortgages in subprime and Alt-A pools.
For the purposes of our analysis, we defined a subprime loan as a loan in a  subprime pool and an Alt-A loan as a loan in an Alt-A pool. We focused  our analysis on first-lien purchase and refinance mortgages for one- to  four-family residential units. For the nonprime market as a whole, and for  the subprime and Alt-A market segments, we calculated the number and  percentage of nonprime mortgages that were in different performance  categories—for example, current (up to date on payments); delinquent   (30 to 89 days behind); in default (90 or more days behind); in the  foreclosure process; or having completed the foreclosure process—at the  end of each quarter from December 31, 2008, through December 31, 2009,  the most recent quarterly data that we could analyze within the time frame  of our review. We classified mortgages in default or in the foreclosure  process as “seriously delinquent.” We also examined mortgage  performance as of December 31, 2009, by loan cohort; product type; and  geographic areas, including Census divisions, states, and congressional  districts. These latter analyses are reported in detail in the electronic  supplement to this report.
To analyze the influence of loan and borrower characteristics and  economic conditions on the performance of nonprime loans, we  developed a statistical model to estimate the relationship between relevant  variables and the probability of loan default or foreclosure within 24  months after the borrower’s first payment. We define a loan as being in  default or foreclosure if it was delinquent by at least 90 days, in the  foreclosure process (including loans identified as in real-estate-owned  status), paid off after being 90-days delinquent or in foreclosure, or already  terminated with evidence of a loss. We analyzed nonprime loans originated  from 2004 to 2006, using records from the CoreLogic LP database that we  matched to records in the Home Mortgage Disclosure Act (HMDA)  database compiled by the Federal Financial Institutions Examination  Council (FFIEC) from information reported by lenders. Combining the  information in these two data sources yielded a data set with loan-level  information on loan characteristics (mortgage type and key mortgage  terms); loan performance (payment status at particular times); and certain  borrower characteristics (such as borrower race, ethnicity, reported  income, and credit score). In addition, we used the Federal Housing  Finance Agency’s (FHFA) house price indexes (HPI) for metropolitan  areas to incorporate data on house price appreciation. We also used  employment data from the Bureau of Labor Statistics (BLS) and Census  tract-level data from the 2000 Census to control for various economic  conditions and neighborhood characteristics. Appendix I contains  additional information on the methodology for this statistical model.
To identify sources of data on nonprime loans and borrowers, we  reviewed research literature on mortgage markets and interviewed  knowledgeable private sector and federal agency officials. For data  sources that are national in scope, provide loan-level information on  nonprime loans, and are widely available for free or a fee, we reviewed  database documentation and related research and interviewed agency and  company officials to determine the scope and features of each data source.  We also collected and reviewed similar documentation for data on loans  insured by the Department of Housing and Urban Development’s (HUD)  Federal Housing Administration (FHA) because borrowers served by FHA  earlier in the decade had some similar characteristics to subprime  borrowers. We also used our review of documentation and research and  interviews to identify limitations in data availability and federal  government efforts to address them or to improve the use of such data.
We tested the reliability of the data used in this report by reviewing  documentation on the process that the data providers use to collect and  ensure the reliability and integrity of their data, and by conducting  reasonableness checks on data elements to identify any missing,  erroneous, or outlying data. We also interviewed CoreLogic LP  representatives to discuss the interpretation of various data fields. We  concluded that the data we used were sufficiently reliable for our  purposes.
We conducted this engagement in Washington, D.C., and Chicago, Illinois,  from December 2009 through August 2010 in accordance with generally  accepted government auditing standards. Those standards require that we  plan and perform the audit to obtain sufficient, appropriate evidence to  provide a reasonable basis for our findings and conclusions based on our  audit objectives. We believe that the evidence obtained provides a  reasonable basis for our findings and conclusions based on our audit  objectives.
subsections: section_title: Background
paragraphs: The nonprime mortgage market has two segments:    Subprime: Generally serves borrowers with blemished or limited credit  histories, and the loans feature higher interest rates and fees than prime  loans.
Alt-A: Generally serves borrowers whose credit histories are close to prime,  but the loans have one or more high-risk features, such as limited  documentation of income or assets or the option of making monthly  payments that are lower than would be required for a fully amortizing loan.
Of the 14.5 million nonprime loans originated from 2000 through 2007,   9.4 million (65 percent) were subprime loans and 5.1 million (35 percent)  were Alt-A loans.
In both of these market segments, two types of loans are common: fixed- rate mortgages, which have unchanging interest rates, and adjustable-rate  mortgages (ARM), which have interest rates that can adjust periodically on  the basis of changes in a specified index. Specific types of ARMs are  prevalent in each market segment. “Short-term hybrid ARMs” accounted  for 70 percent of subprime mortgage originations from 2000 through 2007  (see fig. 1). These loans have a fixed interest rate for an initial period (2 or  3 years) but then “reset” to an adjustable rate for the remaining term of the  loan. In the Alt-A segment, “payment-option ARMs” are a common  adjustable-rate product, accounting for 17 percent of Alt-A mortgage  originations from 2000 through 2007. For an initial period of typically   5 years, or until the loan balance reaches a specified cap, this product  provides the borrower with multiple payment options each month,  including minimum payments that are lower than what would be needed  to cover any of the principal or all of the accrued interest. After the initial  period, payments are “recast” to include an amount that will fully amortize  the outstanding balance over the remaining loan term.
Several payment categories describe the performance of mortgages,  including nonprime mortgages:    Current: The borrower is meeting scheduled payments.
Delinquent: The borrower is 30 to 89 days behind in scheduled payments.
Default: The borrower is 90 days or more delinquent. At this point,  foreclosure proceedings against the borrower become a strong possibility.
In the foreclosure process: The borrower has been delinquent for more  than 90 days, and the lender has elected to foreclose in what is often a  lengthy process. The loan is considered active during the foreclosure  process.
Completed the foreclosure process: The borrower’s loan terminates and  foreclosure proceedings end with one of several possible outcomes. For  example, the borrower may sell the property or the lender may repossess  the home.
Prepaid: The borrower has paid off the entire loan balance before it is  due. Prepayment often occurs as a result of the borrower selling the home  or refinancing into a new mortgage.
In this report, we describe mortgages in default or in the foreclosure  process as “seriously delinquent.”
As we have stated in previous reports, a combination of falling house  prices, aggressive lending practices, and weak economic conditions have  contributed to the increase in troubled mortgages. For example, in 2009,  we noted that falling house prices had left a substantial proportion of  nonprime borrowers in a negative equity position—that is, their mortgage  balances exceeded the current value of their homes—limiting their ability  to sell or refinance their homes in the event they could not stay current on  their mortgage payments. Additionally, we reported that an easing of  underwriting standards and wider use of certain loan features associated  with poorer loan performance contributed to increases in mortgage  delinquencies and foreclosures. These features included mortgages with  higher loan-to-value (LTV) ratios (the amount of the loan divided by the  value of the home at loan origination), adjustable interest rates, limited or  no documentation of borrower income or assets, and deferred payment of  principal or interest. Also, in some cases, mortgage originators engaged in  questionable sales practices that resulted in loans with onerous terms and  conditions that made repayment more difficult for some borrowers.  Furthermore, rising unemployment has contributed to mortgage defaults  and foreclosures because job loss directly affects a borrower’s ability to  make mortgage payments.
The foreclosure crisis has imposed significant costs on borrowers,  neighborhoods, and taxpayers. For example, vacant and foreclosed  properties have contributed to neighborhood blight and reduced property  values in many communities. Additionally, foreclosures affecting minority  populations and the high incidence of subprime lending to members of  these groups have heightened concerns that these groups have received  disparate treatment in mortgage lending. In light of these costs and  concerns, Congress and federal agencies have taken a number of steps to  address and prevent a recurrence of ongoing problems in the mortgage  market. These efforts include programs to modify or refinance the loans of  distressed borrowers and legislation to strengthen mortgage-lending  standards and prevent mortgage originators from steering borrowers into  high-risk or high-cost mortgages.
section_title: Nonprime Loan Performance Deteriorated through the End of 2009 and Varied by Market Segment, Product Type, Cohort Year, and Location
subsections: section_title: The Worsening Performance of Nonprime Loans Was Reflected in Increases in Serious Delinquencies
paragraphs: As of December 31, 2009, 63 percent of the 14.50 million nonprime loans  originated from 2000 through 2007 (the last year in which substantial  numbers of nonprime mortgages were made) was no longer active. Fifty  percent of the nonprime loans originated during this period had prepaid,  and 13 percent had completed foreclosure (see fig. 2).
Among the 4.59 million nonprime loans that remained active as of the end  of 2009, about 16 percent was in default (90 or more days late) and about  14 percent was in the foreclosure process, for a total serious delinquency  rate of 30 percent (see fig. 3). About 12 percent was in a less serious  stage of delinquency (30 to 89 days late), and the remaining 58.5 percent  was current.
The performance of nonprime mortgages originated from 2000 through  2007 deteriorated from the end of 2008 through the end of 2009. At the end  of 2009, 1.38 million active nonprime loans were seriously delinquent,  compared with 1.10 million at the end of 2008. Over the 12-month period,  the serious delinquency rate rose from 21 percent to 30 percent. About  three-quarters of the year-over-year change in the number of serious  delinquencies was due to an increase in defaults, while the remainder was  due to an increase in loans in the foreclosure process. As shown in figure  4, the number of active nonprime loans in default grew each quarter, with  the largest increases occurring in the third and fourth quarters of 2009. By  comparison, the number of active nonprime loans in the foreclosure  process grew in the first two quarters of the year, held almost steady in the  third quarter, and declined in the last quarter of 2009. The decline in the  number of loans in the foreclosure process may be attributable to  decisions by lenders not to begin foreclosure proceedings on defaulted  loans.
In addition, among all nonprime loans originated from 2000 through 2007,  the cumulative percentage that had completed the foreclosure process  increased from 10 percent at the end of 2008 to 13 percent at the end of  2009. About 475,000 nonprime loans completed foreclosure in 2009, or  roughly 119,000 per quarter. Most (63 percent) of the 759,000 decline in the  number of active loans in 2009 was attributable to loans completing  foreclosure, rather than to prepayments.
section_title: Loan Performance Varied by Market Segment, Product Type, Cohort Year, and Location
paragraphs: In 2009, the performance of nonprime loans differed between the  subprime and Alt-A market segments and, within each segment, among  product types (fixed-rate mortgages versus ARMs). Nonprime loan  performance also varied by the year of loan origination (cohort year) and  by location.
subsections: section_title: Loan Performance by Market Segment and Product Type
paragraphs: In general, the subprime market segment performed worse than the Alt-A  segment in 2009.
Of the 2.76 million subprime loans that were active at the end of 2008,   10 percent (267,000) completed foreclosure in 2009. By comparison,   8 percent (208,000) of the 2.59 million Alt-A loans that were active at the  end of 2008 completed foreclosure in 2009.
Cumulatively, 15 percent (1.41 million) of subprime loans originated from  2000 through 2007 had completed foreclosure as of December 31, 2009,  compared with 9 percent (474,000) of Alt-A loans.
Among active loans at the end of 2009, 36 percent (858,000) of subprime  loans were seriously delinquent, compared with 23 percent (517,000) of  Alt-A loans.
However, Alt-A loans accounted for 55 percent (152,000) of the 277,000  year-over-year increase in the number of seriously delinquent loans.
Within the subprime and Alt-A market segments, loan performance varied  by product type. As we stated in a previous report, serious delinquency  rates were higher for certain adjustable-rate products common in the  subprime and Alt-A market segments than they were for fixed-rate  products or the market as a whole. Although many nonprime borrowers  with adjustable-rate loans fell behind on their mortgages before their  payments increased, the higher serious delinquency rates for these  products may partly reflect the difficulties some borrowers had in making  their payments when their interest rates reset to higher levels or when  their monthly payments recast to fully amortizing amounts. In the  subprime market segment, the serious delinquency rate for short-term  hybrid ARMs was 48 percent at the end of 2009, compared with 21 percent  for fixed-rate mortgages and 36 percent for all active subprime loans (see  fig. 5). The serious delinquency rate increased by 11 percentage points for  short-term hybrid ARMs in 2009, compared with 8 percentage points for  fixed-rate mortgages and 10 percentage points for all active subprime  loans. However, the year-over-year increase in the number of fixed-rate  mortgages that were seriously delinquent (over 62,000) was greater than  the corresponding increase among short-term hybrid ARMs (over 47,000),  even though short-term hybrid ARMs were more prevalent than fixed-rate  mortgages among subprime loans.
In the Alt-A segment, the serious delinquency rate at the end of 2009 was  higher for payment-option ARMs (38 percent) than for fixed-rate  mortgages (15 percent) and active Alt-A mortgages as a whole (23 percent)  (see fig. 6). The serious delinquency rate increased by 14 percentage  points for payment-option ARMs in 2009, compared with 7 percentage  points for fixed-rate mortgages and 9 percentage points for all active Alt-A  mortgages. Although the serious delinquency rate grew faster for payment- option ARMs than for fixed-rate mortgages, the year-over-year increase in  the number of seriously delinquent loans was greater for fixed-rate  mortgages (about 63,000) than for payment-option ARMs (over 36,000),  reflecting the preponderance of fixed-rate mortgages in the Alt-A market  segment.
Nonprime mortgages originated from 2004 through 2007 accounted for  most of the distressed loans at the end of 2009. Of the active subprime  loans originated from 2000 through 2007, 94 percent of those that were  seriously delinquent as of December 31, 2009, were from those four  cohorts. In addition, loans from these cohorts made up 77 percent of the  subprime loans that had completed the foreclosure process. This pattern  was more pronounced in the Alt-A market, where 98 percent of the loans  that were seriously delinquent as of December 31, 2009, were from the  2004 through 2007 cohorts. Similarly, 95 percent of the Alt-A loans that had  completed the foreclosure process were from those cohorts.
Also, within each market segment, the percentage of mortgages  completing the foreclosure process generally increased for each  successive loan cohort (see fig. 7). Within 3 years of loan origination,  5 percent of subprime loans originated in 2004 had completed the  foreclosure process, compared with 8 percent of the 2005 cohort and  16 percent each of the 2006 and 2007 cohorts. Among Alt-A loans,  1 percent of the 2004 cohort had completed the foreclosure process within  3 years of origination, compared with 2 percent of the 2005 cohort,  8 percent of the 2006 cohort, and 13 percent of the 2007 cohort.
This trend is partly attributable to a decline in the appreciation of or an  absolute decline in house prices in much of the country beginning in 2005  and worsening in subsequent years. This situation made it more difficult  for some borrowers to sell or refinance their homes to avoid default or  foreclosure. In addition, borrowers who purchased homes but came to  owe more than the properties were worth, had incentives to stop making  mortgage payments to minimize their financial losses. The deterioration in  loan performance for the successive cohorts may also reflect an increase  in riskier loan and borrower characteristics over time, such as limited  documentation of borrower income and higher ratios of debt to household  income.
section_title: Loan Performance by Location
paragraphs: The proportion of active nonprime loans that were seriously delinquent as  of December 31, 2009, varied across the states. Four states—Florida,  Illinois, Nevada, and New Jersey—had serious delinquency rates above  35 percent at the end of 2009. Seven states had serious delinquency rates  between 30 and 35 percent; 9 states had serious delinquency rates between  25 and 30 percent; and 19 states had serious delinquency rates between 20  and 25 percent. The remaining 12 states had serious delinquency rates of  less than 20 percent, including Wyoming’s rate of 15 percent, which was  the lowest in the country. Detailed data on the performance of nonprime  loans by cohort year and location, as well as by market segment and  product type, are available in the electronic supplement to this report.
section_title: House Price Changes and Certain Loan and Borrower Characteristics Were Associated with Default Rates
paragraphs: House price changes and loan and borrower characteristics, such as loan  amount, combined LTV (CLTV) ratio, and borrower credit score, were  among the variables that we found influenced the likelihood of default on  nonprime loans originated from 2004 through 2006, the peak years of  nonprime mortgage lending. In addition, nonprime loans that lacked full  documentation of borrower income and assets were associated with  increased default probabilities, and the influence of borrowers’ reported  income varied by product type, loan purpose, and the level of  documentation. For purchase loans in particular, borrower race and  ethnicity were associated with the probability of default. However, these  associations should be interpreted with caution because we lack data on  factors—such as borrower wealth, first-time homebuyer status, and  employment status—that may influence default rates and that may also be  associated with race and ethnicity.
subsections: section_title: Description of our Statistical Model
paragraphs: Prior research has shown that various loan, borrower, and economic  variables influence the performance of a mortgage. We developed a  statistical model to examine the relationship between such variables and  the probability of a loan defaulting within 24 months after the borrower’s  first payment. We focused on the probability of a loan defaulting within  24 months as our measure of performance because a large proportion of  nonprime borrowers had hybrid ARMs and prepaid their loans (e.g., by  refinancing) within 2 years. For the purposes of this analysis, we defined a  loan as being in default if it was delinquent by at least 90 days, in the  foreclosure process (including loans identified as in real-estate-owned  status), paid off after being 90 days delinquent or in foreclosure, or already  terminated with evidence of a loss.
We developed the statistical model using data on nonprime mortgages  originated from 2004 through 2006. To include more information on  borrower demographics (i.e., race, ethnicity, and reported income) than is  available in the CoreLogic LP data, we matched CoreLogic LP records to  HMDA records. Although we matched about three-quarters of the  CoreLogic LP loans, and the loans that we could match were similar in  important respects to the loans that we could not match, our estimation  results may not be fully representative of the securitized portion of the  nonprime market or the nonprime market as a whole. (See app. II for  additional information on our matching methodology.)
We produced separate estimates for the three most prevalent nonprime  loan products: (1) short-term hybrid ARMs, representing 51 percent of  nonprime loans originated during this period; (2) longer-term ARMs— those with interest rates that were fixed for 5, 7, or 10 years before  adjusting (11 percent of originations); and (3) fixed-rate mortgages  (27 percent of originations). For each product type, we produced separate  estimates for purchase and refinance loans and for loans to owner- occupants and investors. Twenty-four months after the first loan  payment, default rates were highest for short-term hybrid ARMs and,  across product types, were generally higher for purchase loans than  refinance loans. Appendix I provides additional information about our  model and estimation results.
section_title: Across Product Types, Changes in House Prices Influenced Default Probabilities
paragraphs: Consistent with prior research, we found that lower rates of house price  appreciation or declines in house prices were strongly associated with a  higher likelihood of default for each product type and loan purpose. To  illustrate the role of this variable, we estimated the default probability  assuming house price changes that resembled the actual patterns in  certain metropolitan areas, all else being equal. For example, for short- term hybrid ARMs used for home purchases, house price appreciation of  25 percent in the 1st year of the loan and then 20 percent in the 2nd year was  associated with about a 5 percent estimated default probability, all else  being equal (see fig. 8). Assuming instead that house prices stayed about  level in the 1st year of the loan and then dropped by about 10 percent in the  2nd year, the estimated default probability for short-term hybrid ARM  purchase loans increased by about 26 percentage points, to 31 percent.
These two scenarios approximate the actual house price changes in Los  Angeles beginning in early 2004 and mid-2005, respectively, and are  emblematic of a number of markets in which a period of substantial house  price growth was followed by a period of decline. Assuming that house  prices rose by a modest 2 percent per year—approximating the pattern in  a number of midwestern markets—the estimated default probability was  about 22 percent. As shown in figure 8, the influence of house prices  changes on estimated default probabilities was greater for short-term  hybrid ARMs than for other mortgage products.
House price changes may also reflect broader economic trends, thereby  affecting the precision of estimated impacts of other broad economic  variables, such as employment growth, on mortgage defaults. In our  model, we included a variable for state-level employment growth and  noted that the variable was positively correlated with the variable for  house price changes. With that in mind, we found that for purchase and  refinance loans of all product types, lower rates of employment growth  were associated with somewhat higher estimated default probabilities. For  example, for short-term hybrid ARM purchase loans, moving from a 4  percent employment growth rate over 24 months to a zero percent  employment growth rate was associated with about a 1 percentage point  increase in estimated default probabilities. For each of the other product  types and loan purposes, the corresponding change was between 1 and 2  percentage points.
section_title: Loan Amount, CLTV Ratio, and Credit Score Also Were Associated with the Likelihood of Default for All Product Types
paragraphs: In general, we found that higher loan amounts, higher CLTV ratios, and  lower credit scores also were strongly associated with higher likelihoods  of default. For example:    Loan amount: For each product type and loan purpose, we estimated the  default probability assuming a loan amount near the 25th percentile for that  product and purpose and compared this with the estimated default  probability assuming a loan amount near the 75th percentile for that  product and purpose. For short-term hybrid ARMs used for home  purchases, moving from a loan amount of $125,000 to $300,000 was  associated with a 6 percentage point increase in estimated default  probability, all else being equal (see fig. 9). A similar pattern held across  product types, with a larger effect for purchase loans than refinance loans.
CLTV ratio: For each product type and loan purpose, we estimated the  default probability assuming a CLTV ratio close to the 25th percentile for  that product and purpose and compared this with the estimated default  probability assuming a CLTV ratio close to the 75th percentile for that  product and purpose. For short-term hybrid ARMs used for home  purchases, moving from a CLTV ratio between 80 and 90 percent to a  CLTV ratio of 100 percent or more was associated with a 10 percentage  point increase in estimated default probability, all else being equal (see  fig. 9). For short-term hybrid ARMs used for refinancing, moving from a  CLTV ratio of less than 80 percent to a CLTV ratio of 90 percent was  associated with a 7 percentage point increase in estimated default  probability. For the other product types, the effects of increasing the CLTV  ratio were smaller for both purchase and refinance loans.
Borrower credit score: For each product type and loan purpose, we  estimated the default probability assuming a borrower credit score near  the 75th percentile for that product and purpose and compared this with  the estimated default probability assuming a loan amount near the 25th  percentile for that product and purpose. For short-term hybrid ARMs used  for home purchases, moving from the higher credit score to the lower one  was associated with a 10 percentage point increase in estimated default  probability, all else being equal (see fig. 9). For the other product types  (whether for home purchase or refinancing), the effects were smaller.
We also found that the difference between the loan’s initial interest rate  and the relevant interest rate index (interest rate spread) had a significant  influence on estimated default probabilities, which is generally consistent  with other economic research showing a positive relationship between  higher interest rates and default probabilities for nonprime mortgages.  Across product types and loan purposes, the interest rate spread had a  statistically significant influence on estimated default probabilities. For  example, for short-term hybrid ARMs, moving from a spread of 3.0 percent  (near the 25th percentile for that product) to a spread of 4.5 percent (near  the 75th percentile) was associated with about a 4 percentage point  increase in default probability for purchase and refinance loans, all other  things being equal.
We also estimated the effect of the debt-service-to-income (DTI) ratio at  origination and found that for all product types, this variable did not have  a strong influence on the probability of default within 24 months. This  relatively weak association, based on the DTI ratio at origination, could  differ from the impact of changes to the DTI ratio after origination due, in  part, to changes in borrower income or indebtedness. For example, a  mortgage that is affordable to the borrower at origination may become  less so if the borrower experiences a decline in income or takes on  additional nonmortgage debt.
section_title: Level of Income Documentation Influenced Default Probabilities, and Associations between Income and Defaults Varied by Product Type, Loan Purpose, and Documentation Level
paragraphs: Loans originated with limited documentation of borrowers’ income or  assets became prevalent in the nonprime mortgage market, particularly in  the Alt-A market segment. We found that documentation of borrower  income and assets influenced the probability of default of nonprime loans  originated from 2004 through 2006. For purchase and refinance loans of all  product types, limited documentation of income and assets was associated  with a 1 to 3 percentage point increase in the estimated probability of  default, all other things being equal. Our results are generally consistent  with prior research showing an association between a lack of  documentation and higher default probabilities.
Because our data indicated that borrowers with full documentation loans  had different reported risk characteristics (e.g., credit score, CLTV ratio,  and reported income) than borrowers with limited documentation loans,  we more closely explored the relationship between documentation level  and default for short-term hybrid ARMs (the most common nonprime  product) taking these differences into account. On average, short-term  hybrid ARM purchase loans with limited documentation went to  borrowers with higher credit scores, higher reported incomes, and  somewhat lower CLTV ratios, compared with borrowers who had full  documentation loans. To account for these differences, we estimated  default probabilities separately for borrowers with full and limited  documentation loans, using the mean credit score, reported income, and  CLTV ratio values specific to each group. Using this method, the expected  default probability for the limited documentation group was 3 percentage  points lower than for the full documentation group, reflecting their better  reported risk characteristics. However, in reality, borrowers with limited  documentation loans had a 5 percentage point higher default rate than  borrowers with full documentation loans. The differences between the  estimated and actual default probabilities for these borrowers suggest that  the reported risk characteristics—particularly income—may be misstated,  or that other unobserved factors may be associated with the use of the  limited documentation feature. For example, mortgage originators or  borrowers may have used the limited documentation feature in some cases  to overstate the financial resources of borrowers and qualify them for  larger, potentially unaffordable loans. In addition, borrowers who used the  feature could have experienced decreases in their income after loan  origination, thereby making it more difficult for them to stay current on  their payments.
We also found that the influence of borrowers’ reported income varied by  product type and loan purpose and, in some cases, depended on whether  the loan had full documentation. For example, for short-term hybrid ARMs  used for home purchases and refinancing, moving from $60,000 to  $100,000 in reported income was associated with an 1 percentage point  decrease in the estimated default probability for loans with full  documentation, all else being equal (see fig. 10). However, for loans with  limited documentation, the same change in reported income was  associated with a slight increase (0.2 percentage points) in estimated  default probability for purchase loans and a small decrease  (0.5 percentage points) for refinance loans. For fixed-rate mortgages used  for purchase and refinancing, moving from $60,000 to $100,000 in reported  income was associated with small decreases in estimated default  probabilities for both full and limited documentation loans, although the  decreases were slightly smaller for loans with limited documentation. For  longer-term ARMs, moving from the lower to the higher income level  generally did not affect the estimated default probabilities for purchase or  refinance loans, regardless of the level of documentation.
section_title: Associations between Race and Ethnicity and the Likelihood of Default Varied by Product Type and Loan Purpose, but Other Unobserved Variables May Help to Explain these Associations
paragraphs: Some researchers and market observers have noted that the foreclosure  crisis has hit minority borrowers particularly hard. We found that, for  certain product types and loan purposes, reported race and ethnicity were  associated with the probability of default for nonprime mortgages. Not  controlling for other variables, black or African-American borrowers had  higher 24-month default rates across product types than white borrowers,  especially for purchase loans. For example, for short-term hybrid ARMs,  black or African-American borrowers had about a 12 percentage point  higher default rate than white borrowers for purchase loans and about a  2 percentage point higher default rate for refinance loans (see fig. 11).  Additionally, Hispanic or Latino borrowers (of all races) generally had  higher default rates than (non-Hispanic) white borrowers. For example,  Hispanic or Latino borrowers had about an 8 percentage point higher  default rate than white borrowers for short-term hybrid ARM purchase  loans and about a 2 percentage point higher default rate for refinance  loans. For fixed-rate refinance loans, however, Hispanic borrowers had  essentially the same default rate as white borrowers.
Various factors may help to explain some of the observed differences in  the default rates between racial and ethnic groups. Across product types,  black or African-American borrowers had lower average credit scores and  reported incomes than white and Hispanic or Latino borrowers. Also,  black or African-American borrowers generally were more likely than  white borrowers to have CLTV ratios of 90 percent or more. For short- term hybrid ARMs and longer-term ARMs, black or African-American and  Hispanic or Latino borrowers were less likely to have loans that originated  in 2004, when house price appreciation was still strong in many parts of  the country. In addition, Hispanic or Latino borrowers had a higher  incidence of limited documentation loans and were concentrated in  California, where house price declines in a number of areas were  particularly severe.
Controlling for these variations, we found that the differences in estimated  default probabilities by racial and ethnic group were still significant but  considerably smaller than the actual observed differences (i.e., the  differences without the statistical controls in place). Taking short-term  hybrid ARMs used for home purchases as an example, when we estimated  default probabilities by racial and ethnic group holding the other variables  in our model to the mean values for each group, we found that the  estimated default probability for black or African-American borrowers  was about 7 percentage points higher than for white borrowers, compared  with the observed 12 percentage point difference that we have previously  discussed (see fig. 12). Using the same assumptions, the corresponding  default probability for Hispanic or Latino borrowers was about  4 percentage points higher than for white borrowers. For short-term  hybrid ARMs used for refinancing, black or African-American borrowers  had only about a 1 percentage point higher estimated default probability  than white borrowers, while Hispanic or Latino borrowers had about the  d about the  same estimated default probability as white borrowers.  same estimated default probability as white borrowers.
Inferences drawn from these statistical results should be viewed with  caution because we lack data for variables that may help to explain the  remaining differences in estimated default probabilities between  borrowers of different racial and ethnic groups. Unobserved factors that  may influence the likelihood of default may also be associated with race  and ethnicity. For example:    First-time homebuyer: We could not determine which nonprime  borrowers were first-time homebuyers, but other evidence suggests that  members of minority groups are disproportionately first-time  homebuyers. To the extent that black or African-American and Hispanic  or Latino borrowers with purchase loans were disproportionately first- time homebuyers, their higher estimated default probabilities may partly  reflect limited experience with the risks and costs of homeownership. As  shown in figure 12, we found that the differences in estimated default rates  between racial and ethnic groups were much smaller for nonprime  refinance loans—which, by definition, exclude first-time homebuyers— than they were for purchase loans.
Employment status: We did not have data on the employment status of  nonprime borrowers, but unemployment rates are generally higher for  black or African-American and Hispanic or Latino workers than for white  workers.  The higher estimated default probabilities that we found for  black or African-American and Hispanic or Latino borrowers may reflect  that nonprime borrowers from minority groups were disproportionately  affected by unemployment in recent years.
Wealth: Although we obtained data on reported income by matching  CoreLogic LP and HMDA records, we did not have information on  nonprime borrowers’ savings or other assets, which may affect their ability  to keep up with their mortgage payments if faced with job loss or other  unexpected changes in income or expenses. However, according to the  Survey of Consumer Finances, nonwhite and Hispanic families generally  are less likely to save or hold financial assets than non-Hispanic white  families. Furthermore, the median value of assets for nonwhite and  Hispanic families having financial assets is dramatically less than for non- Hispanic white families.
Origination channel or lender steering to higher-cost or riskier loans:  We did not have data on whether the nonprime loans were originated by  mortgage brokers (intermediaries between borrowers and lenders) or  directly by a lender’s retail branch, or how the loans were marketed to the  borrowers. Some evidence suggests that broker-originated loans were  associated with higher default rates and that, at least in some markets,  minority families were more likely to access the mortgage market through  brokers rather than through retail lenders. In addition, some researchers  and market observers have raised concerns that some nonprime loan  originators used questionable marketing tactics in lower-income and  minority neighborhoods. Such practices may have led borrowers to take  out higher-cost or riskier loans than necessary, which may have increased  their probability of default.
section_title: Available Nonprime Mortgage Data Sources Provide Useful Information but Have Constraints That May Be Addressed, in Part, by Ongoing Efforts
subsections: section_title: Several Private and Public Sector Data Sources Cover Nonprime Loans
paragraphs: Mortgage market participants, financial regulators, investors, and public  policy analysts use mortgage data for a variety of purposes. Some of the  broad uses of such data include monitoring and modeling the performance  of mortgages and mortgage-backed securities, assessing the soundness of  financial institutions with mortgage-related holdings, and examining fair  lending and consumer protection issues. For example, in a 2009 report, we  used loan-level mortgage data to assess the implications of proposed  mortgage reform legislation on consumer protections and on the  availability of mortgage credit. Existing sources of data on nonprime  mortgages contain a range of information to support these different uses.  Loan-level data with broad national coverage of the nonprime market  segment are available from several sources: four mortgage databases  (three maintained by private firms and one by the federal government) and  two major credit reporting agencies. For comparison, we also reviewed  information on a HUD database of FHA-insured mortgages, because the  borrower populations served by FHA and the nonprime market earlier in  the decade had some similarities (e.g., relatively low credit scores) and the  database is rich in detail.
CoreLogic LP Asset-Backed Securities (ABS) Database: A private sector  database of nonprime loans that contains information on nonagency  securitized mortgages in subprime and Alt-A pools. The data are supplied  by a number of different parties, including loan servicers; broker-dealers;  and security issuers, trustees, and administrators.
CoreLogic LP Loan Level Servicing (LLS) database: A private sector  database of prime, nonprime, and government-guaranteed mortgages that  contains data supplied by participating loan servicers. The mortgages  include loans in agency and nonagency securitizations and loans held in  lenders’ portfolios.
Lender Processing Services (LPS) Loan Level Data: Similar to the LLS  database, this private sector database contains data supplied by  participating loan servicers on prime, nonprime, and government- guaranteed mortgages, including loans in agency and nonagency  securitization and loans held in lenders’ portfolios.
Consumer credit file data: Two national credit reporting agencies—both  private firms—provide anonymous data from consumer credit files that  include information on prime, nonprime, and government-guaranteed  mortgages.
FFIEC HMDA data: A federal government database that contains  information reported by lenders on about 80 percent of all mortgages  funded each year, including nonprime loans.
HUD Single Family Data Warehouse (SFDW): A federal government  database with information on mortgages insured by FHA.
Among the data sources that include nonprime mortgages, the private  databases and extracts of credit file data can be licensed or purchased for  a fee. Recent HMDA data can be acquired at no charge. Some of these data  may be subject to use restrictions determined by the provider. The private  companies and credit reporting agencies update data on a daily or monthly  basis and provide the updated data to users within 1 month or upon  request. HMDA data are updated annually with a lag of 9 months.
While these data sources currently offer some similar data elements, the  sources vary in their coverage of loan, property, and borrower attributes.  In part, this variation reflects the different primary purposes of the data  sets. For example, the HMDA database is intended to provide the public  with loan data that can assist in identifying potential risks for  discriminatory patterns to help enforce antidiscrimination laws and  evaluate bank community reinvestment initiatives. Accordingly, the HMDA  data provide relatively detailed information about mortgage borrowers but  no information about the performance of the loans. By contrast, the  CoreLogic LP and LPS databases offer performance data to support the  benchmarking and analysis of loans or mortgage-backed securities. Figure  13 presents some of the available data elements, with a focus on data that  may assist in evaluating the probability of mortgage default and  differences in mortgage outcomes across demographic groups. All of the  nonprime data sources report on loan amount. The sources vary in their  coverage of other loan attributes, such as mortgage type and performance  status. All of the nonprime data sources report the property location at the  ZIP code or Census-tract level, while coverage of other property attributes,  such as property type and appraised value, varies. In the category of  borrower attributes, all but one of the nonprime data sources provide  borrower credit score at loan origination and owner-occupancy status.  Among the nonprime data sources, only the HMDA data and credit  reporting agency data provide additional demographic information on  borrowers.
Several other sources of mortgage data provide useful information about  the mortgage market, including nonprime loans, but do not provide loan- level detail or, in some cases, lack broad national data coverage. For  example, the Mortgage Bankers Association’s National Delinquency  Survey provides quarterly summary statistics on the performance of the  overall mortgage market and different market segments, including  subprime loans. RealtyTrac offers data on the number of properties in  some stage of the foreclosure process but not data on all active loans.  Additionally, federal banking regulators and the government-sponsored  enterprises produce free or comparatively low-cost data that are typically  aggregated and only cover mortgages within their regulatory jurisdiction.
section_title: Limitations in Data Sources Constrain Analysis of Some Aspects of Nonprime Mortgages
paragraphs: Although the selected data sources that include nonprime mortgages  contain important loan, property, and borrower characteristics, the  sources have a number of constraints. First, the data sources generally  lack information on certain attributes that could help inform policy  decisions or regulatory efforts to mitigate risk, including the following:    Loan attributes: Although three of the five nonprime data sources provide  information on the initial interest rates of the mortgages (and, in some  cases, how those interest rates can change over the life of the loan), they  do not provide information on other mortgage costs, such as points and  fees paid at loan closing. For example, one study that found no evidence  of adverse pricing of subprime loans by race, ethnicity, or gender noted  that an important caveat to the analysis was the lack of data on points and  fees. Consequently, data users have limited ability to evaluate the  influence of loan costs on default probabilities or to examine fair lending  concerns regarding loan pricing. In addition, while the CoreLogic LP LLS  and LPS Loan Level Data databases indicate whether a mortgage was  originated by a broker or directly by a lender’s retail branch, the other data  sources do not. As we have previously noted, some research has suggested  associations between origination channel and mortgage performance.
Borrower attributes: A number of borrower characteristics that may be  associated with default risk generally does not appear in the nonprime  data sources we reviewed. For example, first-time homebuyers are not  directly identified in any of the nonprime data sources, limiting the ability  of analysts to compare the marginal effect of prior homeownership  experience on default probabilities. (By comparison, SFDW identifies first- time homebuyers with FHA-insured mortgages and contains data on loan  performance.) In addition, none of the nonprime data sources contain  information on borrower wealth (savings and other assets), a factor that  could affect a borrower’s ability to continue making mortgage payments in  times of economic stress. With the exception of the credit reporting  agencies, the data sources also do not always directly provide information  on the amount of borrowers’ other mortgage debt (second liens), which  may constrain accurate assessment of the relationship between home  equity and default. Similarly, data on nonmortgage credit obligations are  unavailable, except from the credit reporting agencies, which may limit  researchers’ understanding of how borrowers’ total debt burden affects  the mortgages they obtain and their ability to meet mortgage obligations.  Also, the data sources lack information on borrower life events that may  influence the probability of mortgage default, such as job loss or divorce.
A second type of constraint is that analysts may not be able to generalize  their results to the entire nonprime market because certain data sources  do not cover all segments of the market and some mortgage originators,  securitizers, or servicers do not contribute information. For example, the  CoreLogic LP ABS database contains information on a large majority of  nonprime mortgages that were securitized but not those that lenders hold  in their portfolios. As we have previously noted, researchers have found  that nonprime mortgages that were not securitized may have less risky  characteristics than those that were securitized. Private sector databases  that contain information on both securitized and nonsecuritized mortgages  (CoreLogic LP LLS and LPS Loan Level Data) cover the majority of the  market but do not provide complete market coverage because not all  servicers contribute information to the databases. Similarly, because  mortgage originators located outside of metropolitan areas are not  required to report their loan information, the HMDA data do not capture  many mortgages made in rural areas. By contrast, the credit reporting  agencies have broader market coverage but lack data on key mortgage  attributes, such as loan type and purpose.
The third constraint we identified is that the existing nonprime data  sources cannot readily be combined to create a single database with a  more comprehensive set of variables. Merging data sources enables  researchers to more thoroughly analyze lending patterns and factors  influencing loan performance. However, due to competition and privacy  concerns, the selected data sources either elect not to provide or are  restricted from providing certain key fields that could be used to merge  databases, such as the property address. For example, to match loan  records in the CoreLogic LP ABS database and HMDA data, we relied in  part on loan origination date fields that are not publicly released due to  privacy concerns. Even with the origination date fields, we could not  match all of the CoreLogic LP records to HMDA records.
Finally, a user of existing data sources may have the ability to track some  specific loans over time but may not easily track a specific borrower or  property. Tracking a specific borrower or property over time would  provide insights into mortgage outcomes throughout a homeownership  experience, even if a borrower refinances into a new mortgage.
section_title: Ongoing Federal Efforts May Address Some Constraints in Mortgage Data Sources
paragraphs: Ongoing federal efforts could provide data on the entire mortgage market  that potentially would not have some of the constraints that we identified  in the existing sources of mortgage data. First, officials from the Board of  Governors of the Federal Reserve System (Federal Reserve Board) and  Freddie Mac are collaborating on a pilot project to develop a publicly  available National Mortgage Database (NMDB). The officials are exploring  the feasibility of developing a federally funded, loan-level, and  representative database of first-lien mortgages designed to address  mortgage-related policy, finance, and business concerns. NMDB would  compile data on a representative sample of outstanding mortgages from a  national credit reporting agency, supplement those data by matching  records to existing mortgage databases (such as the HMDA data), and  obtain data unavailable in any existing databases through a survey of  borrowers. Since NMDB would include data from a variety of sources, it  would provide more comprehensive data on the first-lien mortgage market  than are currently available. If implemented, the combined database would  contain loan-level information on (1) mortgage terms; (2) mortgage  performance from origination to termination; (3) borrowers’ other credit  circumstances over the life of the loan; (4) borrower demographics; and  (5) other borrower attributes, such as key life events and shopping  behavior.
Second, the Dodd-Frank Wall Street Reform and Consumer Protection Act  provides for additional compilation of HMDA data, such as borrower age  and credit score, loan origination channel, and—as the Bureau of  Consumer Financial Protection deems appropriate—a unique identifier for  the loan originator and a universal loan identifier. Additionally, the act  includes the creation of a publicly available Default and Foreclosure  database that would include Census tract-level information on the number  and percentage of mortgages delinquent for more than 30 and 90 days,  real-estate-owned properties, mortgages in the foreclosure process,  mortgages with negative equity, and other information. If implemented, the  universal loan identifier could facilitate matching among mortgage  databases, and the HMDA data would become more comprehensive.
section_title: Observations
paragraphs: The growth of the nonprime market earlier in this decade was  accompanied by a shift toward increasingly risky mortgage products.  Nonprime loans provided homeownership and refinancing opportunities  that may have benefited many households. However, many nonprime  loans had features or were underwritten to standards that made them  vulnerable to default and foreclosure, particularly in recent years when  house prices began to stagnate and decline and economic conditions  eroded more broadly. As a result, millions of nonprime borrowers have  lost their homes or are in danger of doing so. These issues have particular  salience for minority borrowers, who have experienced particularly high  default rates.
The persistently weak performance of nonprime mortgages suggests that  loan performance problems in the nonprime market will not be resolved  quickly, and underscores the importance of federal efforts to assist  distressed borrowers and prevent a recurrence of the aggressive lending  practices that helped precipitate the foreclosure crisis. As lawmakers seek  to reform mortgage lending practices, they will need to consider how their  efforts may affect consumer protections, the availability of mortgage  credit, and progress toward the goal of sustainable homeownership.
Data on the performance of nonprime loans and on the borrowing and  lending practices associated with them can help analysts and  policymakers assess the potential effects of proposed reforms and  evaluate the results of their implementation. Although extensive data are  available on nonprime loans, no one data source is comprehensive.  Existing data sources can be combined with effort, but even then certain  data that could inform understanding of the nonprime market—such as  total mortgage costs and first-time homebuyer status—are not readily  available. Having access to a more comprehensive set of data might have  enhanced the ability of researchers, regulators, and investors to monitor  lending practices, evaluate mortgage performance, and assess the  mortgage outcomes for different groups of borrowers. Ongoing federal  efforts, including the NMDB pilot project, may improve the quality and  availability of mortgage market data going forward.
As agreed with your offices, unless you publicly announce its contents  earlier, we plan no further distribution of this report until 30 days from the  report date. At that time, we will send copies of this report to the  appropriate congressional committees and other interested parties. In  addition, the report will be made available at no charge on GAO’s Web site  at http://www.gao.gov.
If you or your staffs have any questions about this report, please contact  me at (202) 512-8678 or shearw@gao.gov. Contact points for our Offices of  Congressional Relations and Public Affairs may be found on the last page  of this report. Key contributors to this report are listed in appendix III.
section_title: Appendix I: Description of the Econometric Analysis of Nonprime Mortgage Default Probabilities
paragraphs: This appendix describes the econometric model we developed to examine  the relationship between variables representing loan attributes, borrower  characteristics, and economic conditions and the probability of a  nonprime loan entering default within 24 months after the first loan  payment. Certain loan attributes and borrower characteristics have been  associated with a higher risk of mortgage default. For example, lower  down payments, lower borrower credit scores, and limited documentation  of borrowers’ income and assets have been cited as increasing the risk of  default. Economic conditions, such as house price changes, have also been  associated with default risk.
Since minority borrowers have accounted for a larger share of the  nonprime mortgage market than the mortgage market as a whole,  associations between race and ethnicity and nonprime mortgage  performance also are of interest. However, data limitations have  complicated efforts to analyze the demographic characteristics of  nonprime borrowers, such as race, ethnicity, and income. Existing data  sets either provide detailed information about nonprime loans but limited  information about the borrowers (e.g., CoreLogic LoanPerformance  (CoreLogic LP) data) or provide more extensive information about  borrowers but not about loan performance over time (e.g., Home Mortgage  Disclosure Act (HMDA) data). To include information on the demographic  characteristics of nonprime borrowers in our model, we matched records  in the CoreLogic LP data to HMDA records. For securitized first-lien  nonprime loans originated from 2004 through 2006, we achieved a match  rate of approximately 73 percent, representing about 6.9 million records.  (App. II contains a more detailed discussion of our methodology.)
Of all the CoreLogic LP records that we matched to HMDA records, we  used those for which the associated property was located in an area  covered by the Federal Housing Finance Agency’s (FHFA) house price  indexes (HPI) for metropolitan areas, approximately 92 percent of loans.  Based on each associated property’s state and Census tract, we also  incorporated employment data from the Bureau of Labor Statistics (BLS)  and data from the 2000 Census to control for various economic conditions  and neighborhood characteristics. For each loan, we determined the  performance status 24 months after the month of the first payment. We  defined a loan as being in default if it was delinquent by at least 90 days, in  the foreclosure process (including loans identified as in real-estate-owned  status), paid off after being 90-days delinquent or in foreclosure, or already  terminated with evidence of a loss.
We separately analyzed the three most prevalent types of nonprime loans:  short-term hybrid adjustable-rate mortgages (ARM) (ARMs with initial 2-  or 3-year fixed-rate periods followed by frequent interest rate  adjustments); fixed-rate mortgages; and other longer-term ARMs (ARMs  with initial 5-, 7-, or 10-year fixed-rate periods). For each product type, we  estimated default probabilities for purchase money loans separately from  loans for refinance, and for each product type and loan purpose, we  examined separately loans made to owner-occupants and investors. Our  primary reason for examining performance by product type, loan purpose,  and occupancy status is that borrower incentives and motivations may  vary for loans with different characteristics and purposes. For example,  because of their early, frequent, and upward interest rate adjustments,  short-term hybrid ARMs provide a stronger incentive for a borrower to  exit earlier from a mortgage as compared with fixed-rate mortgages or  longer-term ARMs. Also, an investor may not react the same way as an  owner-occupant may react when facing similar economic circumstances.
We estimated separate default models for each mortgage product type,  although the general underlying structure of the models was similar. We  used a logistic regression model to explain the probability of loan default,  based on the observed pattern of actual defaults and the values of  variables representing loan attributes, borrower characteristics, and  economic conditions (see table 1). Some variables describe conditions at  the time of mortgage origination, such as the loan-to-value (LTV) ratio, the  borrower’s credit score, and the borrower’s reported income. Other  factors influencing loan performance vary over time in ways that can be  observed, or at least approximated. For example, greater house price  appreciation (HPA) contributes to greater housing equity, thus reducing  the probability that a borrower, if facing financial distress, views  defaulting on a loan as a better option than prepaying. More generally,  greater house price appreciation creates equity that may induce a  borrower to prepay, which eliminates any default risk that would remain if  the loan were active. Some potentially significant determinants of  mortgage default, such as job loss or illness, are not available for inclusion  in our model. In addition, we lack data on certain factors—such as  borrower wealth and first-time homebuyer status—that could be  especially relevant to explaining actual loan performance.
Tables 2 through 4 provide information on the number of loans and mean  values for each of the product types for which we estimated default  probabilities. Short-term hybrid ARMs were the most prevalent type of  mortgage, and purchase loans were more prevalent than refinance loans,  except among fixed-rate mortgages. Default rates were highest for short- term hybrid ARMs and generally higher for purchase loans than for  refinance loans, except for fixed-rate and longer-term ARM loans to  investors.
The results of our analysis are presented in tables 5 through 8. We ran 12  regressions: separate owner-occupant and investor regressions for  purchase and refinance loans of three product types (short-term hybrid  ARMs, fixed-rate mortgages, and longer-term ARMs). For short-term  hybrid ARMs, the most prevalent product type, we present the results for  purchase and refinance loans to owner-occupants (table 5) and investors  (table 6). For the other product types, we present the results for purchase  and refinance loans to owner-occupants only (tables 7 and 8); the results  for investors were substantively similar. We present coefficient estimates  as well as a transformation of the coefficients into a form that can be  interpreted as the marginal effect of each variable on the estimated  probability of default. This marginal effect is the calculation of the change  in the estimated probability of default that would result if a variable’s  standard deviation were added to that variable’s mean value, while all  other variables are held at their mean values. This permits a comparison of  the impact of different variables within and across product types. In  general, HPA, loan amount, CLTV ratio, and FICO score had substantial  marginal effects across different product types and loan purposes.  Specifically, lower HPA, higher loan amount, higher CLTV ratio, and lower  FICO scores were associated with higher likelihoods of default. The  observed effects for DTI ratio were smaller. Documentation of borrower  income and assets and a loan’s interest rate spread over the applicable  Treasury rate had substantial marginal effects. Limited documentation and  higher interest rate spreads were associated with higher default  probabilities.
section_title: Appendix II: Matching CoreLogic LoanPerformance and Home Mortgage Disclosure Act Records
subsections: section_title: Data Sources
paragraphs: To describe the race, ethnicity, and reported income of nonprime  borrowers, we matched loan-level records from two primary data  sources—CoreLogic LoanPerformance’s (CoreLogic LP) Asset-Backed  Securities Database and Home Mortgage Disclosure Act (HMDA) data  compiled by the Federal Financial Institutions Examination Council  (FFIEC). The CoreLogic LP database provides extensive information  about the characteristics and performance of securitized nonprime  mortgages. However, it contains relatively little information about  borrowers, providing only credit scores and debt-service-to-income ratios.  In contrast, HMDA data contain limited information about loan  characteristics and nothing about performance, but they do provide  information on borrowers’ race, ethnicity, and reported income. HMDA  data are estimated to capture about 80 percent of the mortgages funded  each year and cover all major market segments, including nonprime loans.  HMDA data, therefore, should capture most of the loans in the CoreLogic  LP database.
While the CoreLogic LP and HMDA data emphasize different kinds of loan  and borrower information, they do have some information in common.  These common data items—including loan amount, loan purpose, loan  origination date, property location, and loan originator—allow the two  data sets to be matched on a loan-by-loan basis. Using the methodology  that we developed in previous work, we matched records from the  CoreLogic LP database for loans that were originated from 2004 through  2006 to HMDA data files for those same years. We focused on loan  originations from this period because there were large numbers of  nonprime originations in those years.
The CoreLogic LP data set that we used for the matching process  contained records for 9,292,684 loans. The data set included records for  conventional first-lien purchase and refinance loans to owner-occupants,  investors, and owners of second homes. The data excluded records for  loans for units in multifamily structures, and for manufactured housing;  loans in Guam, Puerto Rico, and the Virgin Islands; and loans with terms  other than 15, 30, or 40 years.
The HMDA data set that we used for the matching process contained  records for 24,227,566 loans. As with the CoreLogic LP data, we focused  on first-lien purchase and refinance loans. The HMDA data set excluded  loans for properties other than one- to four-family residential units.  Because the CoreLogic LP database contained only conventional loans in  private label securitizations, we also excluded from the HMDA data set  loans that involved government programs—such as mortgages guaranteed  by the Federal Housing Administration or the Department of Veterans  Affairs—and conventional loans that were indicated as sold to Fannie  Mae, Freddie Mac, Ginnie Mae, or Farmer Mac.
section_title: Steps Taken to Make the Data Sets Compatible
paragraphs: Matching the loan records from the two data sources required us to make  the common data items compatible. We were able to use a straightforward  process for the loan amount and purpose that required only rounding the  CoreLogic LP loan amount to the nearest $1,000 and aggregating the three  CoreLogic LP refinance categories into one category. However, the  process was more complicated for origination date and property location.  We determined that the name of the loan originator was not particularly  useful for making initial matches of loan records because this information  was missing for a substantial percentage of the CoreLogic LP records.  However, the originator’s name was useful in assessing the quality of the  matches that we made using other data elements.
subsections: section_title: Loan Origination Dates
paragraphs: About 15 percent of the loans in our CoreLogic LP data set had an  origination date that was the 1st day of a month. This distribution pattern  was inconsistent with the distribution of origination days in HMDA, which  showed a much more even pattern throughout the month, with an increase  in originations toward the end of each month rather than the beginning of  each month. Because of this inconsistency, we relied on the origination  month rather than the origination month and day to match loan records.
section_title: Property Location
paragraphs: The CoreLogic LP and HMDA data provided different geographic  identifiers for loans, with the CoreLogic LP data providing the ZIP code  and the HMDA data providing the Census tract. To facilitate record  matching on the basis of property location, we related the Census tract  information in the HMDA data to a corresponding ZIP code or ZIP codes in  the CoreLogic LP data, using 2000 Census files and ZIP code boundary  files from Pitney Bowes Business Insight. Using mapping software, we  overlaid Census tract boundaries on ZIP code boundaries to determine the  proportion of each Census tract’s area that fell within a given ZIP code  area. For each Census tract, we kept all ZIP codes that accounted for at  least 5 percent of that tract’s area. About 60 percent of the Census tracts  were associated with only one ZIP code (meeting the 5 percent threshold),  and almost all Census tracts (97.5 percent) included no more than four ZIP  codes. When a Census tract was associated with only one ZIP code, all  HMDA records in that Census tract were candidates to match CoreLogic  LP records in that ZIP code. All HMDA records in tracts with more than  one ZIP code were candidates to match CoreLogic LP records in those ZIP  codes.
section_title: Matching Methodology
paragraphs: We matched loan records in the CoreLogic LP and HMDA data sets as  follows. First, for each loan origination year (2004, 2005, and 2006), we  made initial matches by identifying CoreLogic LP and HMDA loans with  the same property location, origination month, loan amount, and loan  purpose. After finding all possible HMDA matches for each CoreLogic LP  record, we classified these initial matches as either one-to-one matches  (CoreLogic LP records with one corresponding HMDA record), one-to- many matches (CoreLogic LP records with more than one corresponding  HMDA record), or nonmatches (CoreLogic LP records with no  corresponding HMDA record). One-to-one matches accounted for about 55  percent of the loans in our CoreLogic LP data set, one-to-many matches  accounted for about 25 percent, and nonmatches accounted for about 15  percent. Our match rates were highest for 2004 originations, about 85  percent, and lowest for 2006 originations, about 82 percent.
The quality of the matches was particularly important because we were  examining statistical relationships between borrower characteristics and  loan performance. To provide reasonable assurance that the matches were  robust, we performed three types of quality checks on our initial one-to- one and one-to-many matches. First, we used information about the loan  originator—information that was included in both the CoreLogic LP and  HMDA data. The HMDA data clearly identified loan originators—referred  to as “HMDA respondents”—using a series of codes that corresponded to a  list of standardized originator names. However, in more than 40 percent of  the CoreLogic LP records in our data set, the originator name was marked  as not available. In other cases, the originator was listed by a generic term,  such as “conduit,” or was an entity that appeared to be involved in the  securitization process but was not necessarily the originator. Originators  that were listed were often referred to in a number of ways—for example,  “Taylor Bean,” “Taylor Bean Whitaker,” “Taylor, Bean & Whitaker,”  “TaylorBean,” “TBW,” and “TBW Mortgage Corp.” all referred to the HMDA  respondent “Taylor, Bean & Whitaker.” For CoreLogic LP loans with  originator information, we standardized the originator names in the  CoreLogic LP data, and we used these same originator names for the  HMDA data. We compared the standardized originator names in matched  records and if the standardized names matched, we classified the match as  a robust match, and deleted any other HMDA records that might have  matched to that CoreLogic LP record.
Second, for CoreLogic LP loans with no originator name, we examined the  relationship between the HMDA loan originator and the issuer of the  securities associated with the loan. Many institutions, such as  Countrywide and Ameriquest, originated and securitized large numbers of  nonprime loans. While some of these institutions identified themselves as  the originator of a loan, others typically did not make the originator  information available. In these cases, if the CoreLogic LP securitizer  matched the HMDA originator, we classified an initial match as a robust  match. If the issuer did not originate substantial numbers of nonprime  loans, or also relied on other originators to provide loans for its  securitizations, we developed criteria to check for evidence of business  relationships between the issuer and various originating institutions. This  check had two components. First, if within the CoreLogic LP data set we  identified an originator-issuer combination, we defined that combination  as a business relationship. Second, we considered combinations of  originators from the HMDA data and issuers from the CoreLogic LP data.  For an originator-issuer combination to be a business relationship, a  combination had to appear at least 250 times in our set of initial one-to-one  matches, or meet one of two additional criteria. Specifically, if the  combination appeared at least 100 times, the originator must have made 10  percent of the issuer’s securitized loans, or if the combination appeared at  least 50 times, the issuer had to have securitized 33 percent of the loans  made by the originator. We classified initial matches for which such  business relationships existed as robust matches.
Additionally, if none of these tests resulted in a robust match, we  examined the loan origination day in the CoreLogic LP and HMDA data  sets. If the days matched exactly, we classified an initial match as a robust  match. Finally, for some one-to-many matches that shared originator,  issuer, or business relationship characteristics, we examined the  CoreLogic LP and HMDA characterizations of whether the borrower was  an owner-occupant. In some cases, we were able to classify an initial  match as a robust match if CoreLogic LP and HMDA owner-occupant  characteristics matched. Overall, we produced high-quality matches for  about 73 percent of the records in our CoreLogic LP data set, including  about 75 percent of the loans originated in 2004, about 73 percent of the  loans originated in 2005, and about 72 percent of the loans originated in  2006 (see table 9).
A potential concern with constructing a data set using a matching process  is that records that do not match may differ systematically from records  that do match, thereby making it difficult to make inferences from the  matched data. However, we believe that the CoreLogic LP records that we  were unable to match to HMDA records were similar in important respects  to CoreLogic LP records that we could match. For example, loans in  subprime pools represented 61.5 percent of the overall CoreLogic LP  sample, and 62.3 percent of matched loans. Purchase loans represented  44.8 percent of the overall CoreLogic LP data set, and 46.0 percent of  matched loans. In terms of geography, state shares of unmatched and  matched loans were similar. Loans in California represented 23.1 percent  of the full CoreLogic LP data set and 22.5 percent of matched records.  Furthermore, nonprime borrowers with matched and unmatched records  had similar FICO scores. For example, subprime borrowers with matched  records had median FICO scores of 617, 620, and 617 for loans originated  in 2004, 2005, and 2006, respectively; the corresponding scores for  subprime borrowers with unmatched records were 617, 617, and 615.  Likewise, Alt-A borrowers with matched records had median FICO scores  of 708, 709, and 703 for 2004, 2005, and 2006, respectively; the  corresponding scores for Alt-A borrowers with unmatched records were  706, 707, and 702. In addition, as shown in table 10, for each loan  origination year and mortgage product type, median initial interest rates  were identical or similar for borrowers with matched and unmatched  records.
section_title: Appendix III: GAO Contact and Staff Acknowledgments
subsections: section_title: GAO Contact
section_title: Staff Acknowledgments
paragraphs: In addition to the individual named above, Steve Westley (Assistant  Director), William Bates, Jan Bauer, Stephen Brown, Julianne Dieterich,  DuEwa Kamara, John McGrail, John Mingus, Marc Molino, Bob Pollard,  and Jennifer Schwartz made key contributions to this report.