United States Government Accountability Office

Washington, DC 20548 

December 16, 2009 

The Honorable Carolyn B. Maloney 

Chair 

Joint Economic Committee 

House of Representatives 

The Honorable Charles E. Schumer 

Vice Chairman 

Joint Economic Committee 

United States Senate 

Subject: Loan Performance and Negative Home Equity in the Nonprime 

Mortgage Market 

As we reported to you in July 2009, the number of nonprime mortgage 

originations (including subprime and Alt-A loans) grew rapidly from 2000 through 

2006, a period during which average house prices appreciated dramatically.1

 In 

dollar terms, the nonprime share of mortgage originations rose from about 12 

percent ($125 billion) in 2000 to approximately 34 percent ($1 trillion) in 2006. 

These mortgages have been associated with what was subsequently recognized as 

a speculative housing bubble. As house prices subsequently fell, the subprime and 

Alt-A market segments contracted sharply, and very few nonprime originations 

were made after mid-2007. Borrowers who had obtained nonprime mortgages 

earlier in the decade increasingly fell behind on their mortgage payments, helping 

to push default and foreclosure rates to historical highs. 

Economic conditions and a weak housing market have contributed to the increase 

in troubled loans. In particular, falling house prices have left many borrowers in a 

negative equity position—that is, their mortgage balances exceed the current 

value of their homes. Negative equity makes borrowers more vulnerable to 

foreclosure by, among other factors, limiting their ability to sell or refinance their 

homes in the event they cannot stay current on their mortgage payments. 

To inform congressional decision making about efforts to address problems in the 

mortgage market, you requested that we examine the evolution and condition of 

the market for nonprime loans. On July 28, 2009, we provided you with an interim 

report on certain characteristics of nonprime loans and borrowers, and the 

performance of nonprime mortgages originated from 2000 through 2007 (the last 

year in which substantial numbers of nonprime mortgages were made) as of 

 

1

See GAO, Characteristics and Performance of Nonprime Mortgages, GAO-09-848R (Washington, 

D.C.: July 28, 2009).

Page 1 GAO-10-146R Nonprime Mortgages 
March 31, 2009. This report (1) provides information on the performance of these 

nonprime loans as of June 30, 2009, and describes forecasts made by others of 

future loan performance; and (2) examines the extent of negative home equity 

among nonprime borrowers in selected metropolitan areas and nationwide. In 

addition, enclosure VI describes the preliminary results of our analysis of the 

demographic characteristics of nonprime borrowers—including race and 

ethnicity—whose loans originated in 2005.2

 We identified these characteristics by 

merging loan-level records from two data sources. This report also provides 

supplemental information on the performance of nonprime mortgages by annual 

loan cohort, product type, Census division, state, and congressional district. This 

supplemental information is presented in enclosures I through IV. 

As agreed with your offices, in a final report we will provide information on the 

influence of nonprime loan and borrower characteristics and economic conditions 

on the likelihood of mortgage default and foreclosure. We will also describe the 

features and limitations of primary sources of data on nonprime mortgage 

performance and borrower characteristics. In addition, the final report will 

update information on the performance of nonprime mortgages and provide 

additional analysis of the characteristics of nonprime borrowers. 

To conduct our work, we analyzed data from LoanPerformance’s (LP) Assetbacked Securities database for nonprime loans originated from 2000 through 

2007.3

 The database contains loan-level data on the majority of nonagency 

securitized mortgages in subprime and Alt-A pools.4 For example, for the period 

2001 through July 2007 the LP database contains information covering, in dollar 

terms, an estimated 87 percent of securitized subprime loans and 98 percent of 

securitized Alt-A loans. Research has found that nonprime mortgages that were 

not securitized (i.e., mortgages that lenders held in their portfolios) may have 

different characteristics and performance histories than those that were 

securitized. For 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.5

 We focused our 

analysis on first-lien purchase and refinance mortgages for one- to four-family 

residential units. For certain analyses, we supplemented the LP data with data on

house prices from the Federal Housing Finance Agency (FHFA) and Standard & 

 

2

Our analysis was based on a 2 percent random sample of nonprime mortgages from 2005. 

3

LP is a unit of First American CoreLogic, Incorporated. 

4

Nonagency mortgage-backed securities (MBS), also known as private-label MBS, are backed by 

nonconforming conventional mortgages securitized primarily by investment banks. 

Nonconforming mortgages are those that do not meet the purchase requirements of Fannie Mae or 

Freddie Mac because they are too large or do not meet their underwriting criteria. About 75 

percent of subprime and Alt-A mortgages originated from 2001 through 2007 were securitized. 

5

The LP database has a loan-level indicator for loan class (i.e., subprime or Alt-A), but it is not well 

populated. Therefore, we used the pool-level classification. According to mortgage researchers, 

some of the loans in subprime pools may not be subprime loans, and some of the loans in Alt-A 

pools may not be Alt-A loans. 

Page 2 GAO-10-146R Nonprime Mortgages 
Poor’s (S&P)/Case-Shiller indexes, and data on borrower characteristics from the 

Home Mortgage Disclosure Act (HMDA) data set. 

To examine the recent and expected performance of nonprime mortgages, we 

calculated the number and percentage of 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 as of June 30, 

2009.6 We classified mortgages in default or in the foreclosure process as 

“seriously delinquent.” We also examined mortgage performance by loan cohort, 

loan type, and geographic area, including Census divisions, states, and 

congressional districts.7

 For detailed information on the performance of nonprime 

loans by these geographic areas, see enclosures III and IV. We also reviewed 

select studies containing forecasts of the performance of the mortgage market 

and interviewed the authors of those studies. We focused on four nonproprietary 

forecasts conducted in 2008 or 2009 that we identified through literature searches 

and discussions with industry researchers. 

To examine the extent of negative equity among nonprime borrowers, we used the 

LP data and house price indexes from FHFA and S&P/Case-Shiller. More 

specifically, we used the indexes to adjust the appraised value of each home at 

loan origination to an estimated value as of June 30, 2009. We then subtracted the 

unpaid mortgage balance as of that date from the house value to estimate the 

borrower’s home equity. We used two different indexes for these calculations 

because they offer different advantages. To estimate negative equity nationwide, 

we used the FHFA All-Transactions House Price Index (FHFA index) because it 

provides the broadest geographic coverage.8

 Because the FHFA index likely 

understates average house price declines experienced by nonprime borrowers 

from 2005 through 2008, our estimates of negative equity using this index are also 

likely to be understated. For estimates of negative equity in specific metropolitan 

areas, we used the S&P/Case-Shiller Tiered Price Indices (S&P/Case-Shiller index) 

because it uses data from a broader range of properties than the FHFA index and 

includes separate indexes for homes in different price ranges within a 

 

6

Unless otherwise noted, we treat delinquent loans, loans in default, and loans in the foreclosure 

process as mutually exclusive categories. We considered a loan to have completed the foreclosure 

process if it was in real estate-owned status as of June 30, 2009, or was paid off after being either 

90 or more days delinquent, in the foreclosure process, or in real estate-owned status. 

7

A loan cohort is a group of loans that originated in the same year. For a description of our 

methodology for estimating performance by congressional district, see GAO-09-848R. 

8

The FHFA index, comprising separate indexes for 384 metropolitan areas, is based on sales and 

appraisal data for properties with mortgages purchased or securitized by Fannie Mae or Freddie 

Mac (conforming mortgages). To be eligible for purchase by these entities, loans (and borrowers 

receiving the loans) must meet specified requirements. 

Page 3 GAO-10-146R Nonprime Mortgages 
metropolitan area.9

 See enclosure V for a detailed description of the methodology 

we used to estimate negative equity and key differences between the two sets of 

house price indexes. 

We tested the reliability of the data used in this report by reviewing 

documentation on the process 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 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., from August 2009 through 

November 2009 in accordance with all sections of GAO’s Quality Assurance 

Framework that are relevant to our objectives. The framework requires that we 

plan and perform the engagement to obtain sufficient and appropriate evidence to 

meet our stated objectives and to discuss any limitations in our work. We believe 

that the evidence obtained provides a reasonable basis for our findings based on 

our audit objectives. 

Results in Brief 

The performance of mortgages in the nonprime market segment worsened during 

the second quarter of 2009 (April 1 through June 30, 2009), as the number of loans 

that completed the foreclosure process or became seriously delinquent increased 

from the previous quarter. For example, 147,000 loans completed the foreclosure 

process in the second quarter, an increase of 9 percent from the first quarter, and 

the number of seriously delinquent loans grew by about 48,000, a 4 percent 

increase. The growth in serious delinquencies, coupled with a decline in the total 

number of active loans due to prepayments and completed foreclosures, 

increased the serious delinquency rate from 23 percent to 26 percent during the 

second quarter. Although serious delinquencies were most prevalent among 

subprime borrowers and for adjustable-rate products, serious delinquencies in the 

second quarter of 2009 were growing most rapidly for Alt-A borrowers and fixedrate mortgages. The number of nonprime loans that were seriously delinquent 

rose by approximately 2 percent (16,000) in the subprime market, compared with 

7 percent (32,000) in the Alt-A market. For fixed-rate Alt-A loans, the 

corresponding increase was 11 percent (13,000). Forecasts made by others 

suggest that weaknesses in the nonprime mortgage market will persist, primarily 

due to expected declines in home prices. 

Our analysis of borrowers with active nonprime mortgages originated from 2000 

through 2007 indicates that a substantial proportion had negative equity in their 

homes as of June 30, 2009. Our estimates using the S&P/Case-Shiller index for 16 

metropolitan areas showed that the percentage of borrowers with negative equity 

ranged from about 9 percent (Denver, Colorado) to more than 90 percent (Las 

 

9

The S&P/Case-Shiller index, comprising separate indexes for 17 metropolitan areas, is based on 

sales data for homes purchased with both conforming and nonconforming mortgages. 

Page 4 GAO-10-146R Nonprime Mortgages 
Vegas, Nevada). Our estimates also indicate that in the 16 metropolitan areas we 

reviewed, nonprime borrowers who obtained their mortgages to purchase a home 

were more likely to have negative home equity than those who refinanced their 

mortgages. Using the FHFA index, we estimated that one-quarter of nonprime 

borrowers with active loans nationwide had negative equity in their homes as of 

June 30, 2009. We also found that the incidence of negative equity was highest 

among borrowers who obtained their mortgages in 2005, 2006, and 2007. 

Background 

The nonprime mortgage market has two segments: 

• Subprime—Generally serves borrowers with blemished 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. 

In both of these categories, 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 based on changes in a 

specified index. Specific types of ARMs are prevalent in each market segment. 

“Short-term hybrid ARMs” accounted for most subprime mortgage originations in 

recent years.10 These loans have a fixed interest rate for an initial period 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.11 For an 

initial period of typically 5 years, or when 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. 

Nonprime mortgages, like all mortgages, can fall into any one of several payment 

categories: 

• Current—The borrower is meeting scheduled payments. 

 

10More specifically, short-term hybrid ARMs represented about 70 percent of the subprime 

mortgages originated from 2000 through 2007. 

11Payment-option ARMs accounted for about 17 percent of the Alt-A mortgages originated from 

2000 through 2007. 

Page 5 GAO-10-146R Nonprime Mortgages 
• Delinquent—The borrower has missed one or more scheduled monthly 

payments. 

• Default—The borrower is 90 or more days delinquent. 12 At this point, 

foreclosure proceedings against the borrower become a strong possibility. 

• Foreclosure—The borrower has been delinquent for more than 90 days, and 

the lender has elected to foreclose in what is an often lengthy process with 

several possible outcomes. For instance, 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.” 

The amount of equity a homeowner has in a mortgaged property may influence 

how well the mortgage performs. In general, higher levels of home equity are 

associated with lower probabilities of default and foreclosure. Equity is a 

homeowner’s financial interest in a property, or the difference between the value 

of a property and the amount still owed on the mortgage. Typically, home equity 

increases over time as the mortgage balance is paid down and home values 

appreciate. However, if the home value falls below the amount owed on the 

mortgage, the borrower will be in a position of negative equity. Borrowers with 

nonprime loans may be especially vulnerable to negative equity because they 

typically make small down payments and, as previously discussed, may have loans 

with payment options that defer payment of accrued interest, thereby increasing 

the outstanding loan balance. 

House price appreciation or depreciation in a geographic area is commonly 

measured by changes in a house price index. Such indexes are based on the sales 

prices or appraised values for the same housing units over time. FHFA and 

S&P/Case-Shiller produce two widely used house price indexes that use this 

method. 

The Performance of Nonprime Mortgages Deteriorated into Mid-2009, and 

Continuing Deterioration Appears Likely 

The performance of mortgages in the nonprime market segment worsened during 

the second quarter of 2009 (April 1 through June 30, 2009), as the number of loans 

that completed the foreclosure process or became seriously delinquent grew from 

 

12There is no uniform definition of default across the lending industry. For purposes of this report, 

we use the definition provided. 

Page 6 GAO-10-146R Nonprime Mortgages 
the previous quarter.13 Although serious delinquencies were most prevalent among 

subprime borrowers and for adjustable-rate products, serious delinquencies in the 

second quarter of 2009 were growing most rapidly for Alt-A borrowers and fixedrate mortgages. Forecasts made by others suggest that weaknesses in the 

nonprime mortgage market will persist, primarily due to expected declines in 

home prices. 

Foreclosures and Serious Delinquencies Increased Across Market Segments and 

Product Types

As of June 30, 2009, approximately 1.7 million of the 14.4 million nonprime loans 

(12 percent) originated from 2000 through 2007 had completed the foreclosure 

process (see fig. 1). Of that 1.7 million, about 147,000 loans completed foreclosure 

in the second quarter of 2009, an increase of 9 percent from the first quarter. 

Subprime mortgages accounted for about 89,000, or about 61 percent, of the 

completed foreclosures in the second quarter. Although foreclosures and serious 

delinquencies increased, the number of mortgage prepayments also grew. As of 

June 30, 2009, more than half of nonprime mortgages (54 percent or 7.8 million) 

were prepaid as of June 30, 2009.14 Of that 7.8 million, approximately 154,000 

mortgages were prepaid in the second quarter of 2009, an increase of about 2 

percent from the previous quarter. 

Other measures of the performance of nonprime mortgages have also weakened 

since the first quarter of 2009. Of the 4.9 million nonprime loans still active as of 

the second quarter of 2009, approximately 3.1 million (63 percent) were current 

(i.e., borrowers were meeting scheduled payments), down by about 328,000 (10 

percent) from the previous quarter. During the second quarter, the number of 

seriously delinquent loans—loans either in default or in the foreclosure process—

grew from 1,208,000 to 1,256,000 (4 percent). This growth, coupled with a decline 

in the total number of active nonprime loans due to prepayments and completed 

foreclosures, increased the serious delinquency rate from 23 percent to 26 percent 

during the second quarter.15

 

13As previously noted, the data we used for our analysis do not cover the entire nonprime market 

but do cover the large majority of nonagency securitized mortgages within that market. 

14Because many of these loans were prepaid as a consequence of refinancing, the number of loans 

reported exceeds the number of borrowers. 

15Although defaults and foreclosures also increased in other market segments, the serious 

delinquency rate for the mortgage market as a whole was substantially lower. According to the 

Mortgage Bankers Association, the serious delinquency rate for the broader mortgage market was 

approximately 8 percent as of the end of the second quarter of 2009. 

Page 7 GAO-10-146R Nonprime Mortgages 
Figure 1: Percentage of All Nonprime Loans and All Active Nonprime Loans Originated 

from 2000 through 2007 by Performance Status, as of June 30, 2009 

12%

13%

13% 63%

12%

54% 34%

Source: GAO analysis of LP data.

Completed

foreclosure

process

(1,729,140)

Active

(4,916,670)

Prepaid

(7,778,577)

All nonprime loans Active nonprime loans

In default

(622,000)

In foreclosure

process

(634,167)

Current

(3,080,486)

Delinquent

(580,017)

Note: We considered loans to be delinquent if borrowers were 30 to 89 days late on their mortgage 

payments. We considered loans to be in default if borrowers were 90 or more days late. Percentages in 

graphs may not add to 100 percent due to rounding. 

Although the absolute number of seriously delinquent loans was higher in the 

subprime market than in the Alt-A market, the rate of growth in serious 

delinquencies in the second quarter was higher for Alt-A loans. At the end of the 

second quarter of 2009, about 791,000 subprime loans (31 percent) and 466,000 

Alt-A loans (20 percent) were seriously delinquent. In that quarter, serious 

delinquencies grew by approximately 16,000 loans (2 percent) in the subprime 

market and 32,000 loans (7 percent) in the Alt-A market. 

Additionally, while certain ARM products had the highest serious delinquency 

rates as of the end of the second quarter, the rate of growth in serious 

delinquencies was higher for fixed-rate mortgages in that quarter. For example, in 

the subprime market, 42 percent of short-term hybrid ARMs were seriously 

delinquent as of the end of the second quarter, compared with 16 percent of fixedrate mortgages (see fig. 2). However, while the number of short-term hybrid ARMs 

that were seriously delinquent increased by 1 percent (from about 584,000 to 

587,000) over the quarter, the corresponding increase for subprime fixed-rate 

loans was 7 percent (from about 142,000 to 152,000). 

Page 8 GAO-10-146R Nonprime Mortgages 
Figure 2: Subprime Serious Delinquency Rates as of June 30, 2009, and Growth of 

Seriously Delinquent Subprime Loans in the Second Quarter of 2009 

Source: GAO analysis of LP data.

Total 

subprime

market

Serious

delinquency 

rates Percentage

Growth of seriously delinquent loans in second quarter of 2009

Number

Short-term

hybrid ARMs

Fixed-rate

loans

31% 2%

1%

7%

42%

16%

16,090

3,005

9,955

Note: We considered loans to be seriously delinquent if borrowers were 90 days or more late on their 

mortgage payments or in the foreclosure process. 

In the Alt-A market, 33 percent of payment-option ARMs were seriously 

delinquent as of June 30, 2009, compared with 11 percent of fixed-rate loans (see 

fig. 3). But while the number of payment-option ARMs that were seriously 

delinquent grew by 6 percent (from somewhat over 122,000 to about 130,000), the 

corresponding increase for fixed-rate loans was 11 percent (from about 118,000 to 

about 131,000). 

Figure 3: Alt-A Serious Delinquency Rates as of June 30, 2009, and Growth of Seriously 

Delinquent Alt-A Loans in the Second Quarter of 2009 

20%

33%

11%

Source: GAO analysis of LP data.

Total 

Alt-A

market

Percentage

Growth of seriously delinquent loans in second quarter of 2009

Number

Paymentoption

ARMs

Fixed-rate

loans

7%

6%

11%

32,420

7,435

13,364

Serious

delinquency 

rates

Note: We considered loans to be seriously delinquent if borrowers were 90 days or more late on their 

mortgage payments or in the foreclosure process. 

Page 9 GAO-10-146R Nonprime Mortgages 
Enclosures I and II provide more detailed information about the performance of 

nonprime loans by cohort year and product type. For detailed data on the 

performance of nonprime loans by Census division, state, and congressional 

district see enclosures III and IV. 

Forecasters Predict That the Weak Performance of Nonprime Loans Will Persist, 

Largely Due to Declining Home Prices

The four studies we reviewed that sought to forecast the performance of the U.S. 

mortgage market generally predicted that elevated levels of default and 

foreclosure will persist.16 The studies differed in the methods they used to predict 

future performance, and none focused specifically on the nonprime market, 

although 3 included forecasts of the subprime segment of the nonprime market.17

 

Two studies of the subprime market segment estimated that the number of these 

loans entering foreclosure annually would gradually decline after peaking in 2008 

but would likely remain in the hundreds of thousands per year. For example, a 

Credit Suisse study estimated that 1.9 million subprime loans would enter 

foreclosure between the third quarter of 2008 and the end of 2012.18 (The study 

estimated that about 1.1 million of these foreclosures would occur from 2010 

through 2012.) While none of the four studies we reviewed addressed the Alt-A 

market specifically, the authors told us that default and foreclosure rates in that 

market segment have yet to peak. One author explained this phenomenon by 

noting that Alt-A borrowers often had higher levels of initial equity compared with 

subprime borrowers, which provided a larger cushion against falling home prices. 

Among the factors contributing to future defaults and foreclosures, forecasters 

identified declining home prices as the most important. Additionally, forecasters 

 

16Ellen Schloemer, Wei Li, Keith Ernst, and Kathleen Keest, Losing Ground: Foreclosures in the 

Subprime Market, Center for Responsible Lending (December 2006) and Center for Responsible 

Lending, Updated Projections of Subprime Foreclosures in the United States and their Impact on 

Home Values and Communities (August 2008); Rod Dubitsky, Larry Yang, Stevan Stevanovic, and 

Thomas Suehr, Foreclosure Update: Over 8 Million Foreclosures Expected, Credit Suisse 

(December 4, 2008); Jan Hatzius and Michael Marschoun, Global Economics Paper No. 177,

Goldman Sachs (January 13, 2009); and Shane Sherlund, The Past, Present, and Future of 

Subprime Mortgages, Finance and Economics Discussion Series 2008-63, Federal Reserve Board 

(November 2008). 

17Schloemer and others, Losing Ground: Foreclosures in the Subprime Market, provides a 

cumulative estimate of subprime foreclosures for the nation, state, and metropolitan statistical 

areas. Dubitsky and others, Foreclosure Update: Over 8 Million Foreclosures Expected, provides 

baseline yearly estimates of foreclosure starts for the subprime market and the whole mortgage 

market, as well as estimates of the effects of unemployment and loan modifications on the whole 

mortgage market. Hatzius and Marschoun, Global Economics Paper No. 177, provides quarterly 

estimates of defaults for three house price scenarios for the whole market. Sherlund, The Past, 

Present and Future of Subprime Mortgages, provides yearly estimates of subprime foreclosure 

starts, as well as cumulative performance by loan vintage. 

18Dubitsky and others, Foreclosure Update, 2. 

Page 10 GAO-10-146R Nonprime Mortgages 
indicated that factors affecting the affordability of mortgages—such as 

unemployment, loan recasts, and mortgage modifications—would also affect loan 

performance in coming years. 

House Price Depreciation 

We and others have reported on the strong statistical relationship between 

changes in house prices and the likelihood of mortgage defaults and 

foreclosures.19 Falling house prices can result in negative home equity—that is, a 

mortgage balance that exceeds the current value of the property. Homeowners 

with negative equity may find it difficult to sell or refinance the property to avoid 

foreclosure. They may also have incentives to stop making mortgage payments to 

minimize their financial losses. Prior research suggests that negative equity is a 

necessary, but not sufficient, condition for foreclosure.20 Besides having negative 

equity, borrowers who end up in foreclosure often experience a “trigger event,” 

such as job loss or divorce that reduces their ability to make mortgage payments. 

Three of the four forecasts we reviewed included the assumption that average 

house prices would continue to fall appreciably into 2010, resulting in a higher 

incidence and severity of negative equity among nonprime borrowers.21 For 

example, one forecast assumed cumulative house price depreciation of 8.5 

percent over the 3 year period ending in the last quarter of 2010, while another 

assumed a cumulative 15 percent decline over the 2 year period ending in mid2010. On the basis of data from home purchases, S&P/Case-Shiller and FHFA have 

both reported that average house prices at the national level increased in the third 

quarter of 2009. However, several recent forecasts have projected house price 

declines in 2010.22

 

 

All of the forecasters indicated that house price changes will play a key role in 

future mortgage performance. For example, a study that simulated subprime 

mortgage performance found that the number of subprime defaults was more 

sensitive to house price trends than other explanatory variables. Similarly, the 

authors of a study addressing the entire mortgage market told us that anticipated 

 

19For example, GAO, Home Mortgages: Provisions in a 2007 Mortgage Reform Bill (H.R. 3915) 

Would Strengthen Borrower Protections, but Views on Their Long-term Impact Differ, GAO-09-

741 (Washington, D.C.: July 31, 2009). 

20For example, Christopher Foote, Kristopher Gerardi, and Paul S. Willen, Negative Equity and 

Foreclosure: Theory and Evidence, Public Policy Discussion Paper 08-3, Federal Reserve Board 

(June 5, 2008). 

21The remaining study assumed an overall decrease in house prices but did not indicate specifically 

when prices would stop falling. 

22For example, as of September 2009, IHS Global Insight was projecting a decline in the national 

FHFA house price index through the third quarter of 2010. Additionally, as of October 2009, 

Freddie Mac was projecting a decline in the national S&P/Case-Shiller index through the end of 

2010. 

Page 11 GAO-10-146R Nonprime Mortgages 
house price declines accounted for about 80 percent of the defaults they were 

forecasting. 

Factors Affecting Mortgage Affordability 

Forecasters we spoke with noted several factors affecting the affordability of 

mortgage payments will influence the number of nonprime loans that will end in 

foreclosure over the next few years. These factors include job loss, mortgage 

recasts, and federal loan modification efforts. 

• Job loss—Loss of employment is a common event that can lead to foreclosure 

because of its direct impact on a borrower’s ability to make mortgage 

payments. All of the forecasts we reviewed acknowledged job loss as a 

contributor to mortgage defaults and foreclosures. However, three forecasts 

noted that the impacts of unemployment may be difficult to capture for several 

reasons, including the fact that unemployment data are aggregated and do not 

capture the effects of job losses on individual households. Further, the 

forecasts used older projections of unemployment in their analysis, many of 

which had predicted lower peaks, such as 8 percent unemployment by the end 

of 2009. Some more recent estimates suggest that unemployment rates will 

peak at around 10 percent in 2010, a level that one of the forecasts included in 

its worst-case scenario. As of November 2009, the unemployment rate was 10 

percent. 

• Mortgage recasts—Payment-option ARMs, a common Alt-A product, allow 

borrowers to make minimum payments for an initial period that are lower than 

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. Consequently, 

payment-option ARMs can result in payment shock, especially if the borrower 

was making only the minimum payment. Although none of the forecasts we 

reviewed specifically attempted to model the impact of payment-option ARM 

recasts, the authors told us that recasts would likely lead to additional 

foreclosures for many Alt-A borrowers who may not be able to afford the 

higher payments. Large numbers of payment-option ARMs are scheduled to 

recast beginning in 2010. 

 

• Federal loan modification efforts—Loan modifications involve making 

temporary or permanent changes to the term of the existing loan agreement 

and can include reducing the interest rate charged, extending the loan term, or 

implementing forbearance plans.23 Loan modifications may prevent or delay 

foreclosures on nonprime mortgages by making mortgage payments more 

affordable. Under the Home Affordable Modification Program (HAMP), the 

 

23With a forbearance plan, a lender agrees not to exercise the legal right of foreclosure if the 

borrower agrees to a payment plan that will resolve the borrower’s deficiency for a set period of 

time. 

Page 12 GAO-10-146R Nonprime Mortgages 
Department of the Treasury (Treasury), Fannie Mae, and Freddie Mac will use 

up to $75 billion to encourage loan modifications. The authors of the studies 

we reviewed agreed that loan modifications had the potential to reduce future 

nonprime foreclosures, but some noted the difficulty of predicting an exact 

number. Treasury has estimated that up to 3 to 4 million borrowers who were 

at risk of default and foreclosure could be offered a loan modification under 

HAMP. However, as we reported in July 2009, Treasury’s estimate reflects 

uncertainty created by data gaps and the need to make numerous 

assumptions, and therefore may be overstated.24 

A Substantial Proportion of Nonprime Mortgage Borrowers Have 

Negative Home Equity 

Our analysis of borrowers with active nonprime mortgages originated from 2000 

through 2007 indicates that a substantial proportion had negative equity in their 

homes as of June 30, 2009. Our estimates using the S&P/Case-Shiller Tiered Price 

Indices (S&P Case-Shiller index) for 16 metropolitan areas showed that the 

percentage of borrowers with negative equity ranged from about 9 percent to 

more than 90 percent. Using the FHFA All-Transactions Index (FHFA index), we 

estimated that about one-quarter of nonprime borrowers with active loans 

nationwide had negative equity in their homes as of June 30, 2009.25 

Estimates of Negative Equity in 16 Metropolitan Areas

To estimate the extent of negative equity among nonprime borrowers, we 

compared borrowers’ outstanding balances on first-lien loans with the estimated 

values of their homes as of June 30, 2009.26 Because of data limitations, we could 

not identify borrowers with multiple mortgaged properties. To the extent that 

some borrowers had more than one mortgaged property, our results may 

overstate the actual number of individual borrowers with negative home equity. 

For our estimates of negative equity for specific metropolitan areas, we used the 

S&P/Case-Shiller index—which includes separate indexes for homes in low, 

middle, and high price ranges within a metropolitan area—to adjust the appraised 

value of each home to an updated market value. The S&P/Case-Shiller index is 

available for 17 metropolitan areas. 

 

24GAO, Treasury Actions Needed to Make the Home Affordable Modification Program More 

Transparent and Accountable, GAO-09-837 (Washington, D.C.: July 2009). 

25As previously noted, our estimates using the FHFA index likely understate the extent of negative 

equity. Across the 15 metropolitan areas for which the FHFA and S&P/Case-Shiller indexes use the 

same geographic boundaries, the estimated percentage of borrowers with negative equity was 

nearly two times higher using the S&P/Case-Shiller index compared with estimates using the FHFA 

index. 

26Due to data limitations, our analysis did not account for any second liens that the borrowers had 

on their properties. To the extent that borrowers had second liens, our analysis may understate 

the extent of negative home equity. 

Page 13 GAO-10-146R Nonprime Mortgages 
We estimated the extent of negative home equity for nonprime borrowers with 

active loans in 16 of the 17 metropolitan areas covered by the S&P/Case-Shiller 

index.27 As shown in table 1, we estimate that the metropolitan areas with the 

highest percentage of nonprime borrowers with negative home equity as of June 

30, 2009, were Las Vegas, Nevada (94.3 percent); Phoenix, Arizona (89.4 percent); 

Miami, Florida (85.8 percent); and Minneapolis, Minnesota (80.6 percent). The 

metropolitan areas with the lowest percentages included Denver, Colorado (9.3 

percent) and Portland, Oregon (12.7 percent). It is important to note that the 17 

metropolitan areas covered by the S&P/Case-Shiller index may represent areas 

with higher proportions of negative equity than is generally found across the 

country. Seven of the 17 metropolitan areas are in states (California, Florida, 

Nevada, and Arizona) that in recent years experienced the most dramatic declines 

in house prices. 

Table 1: Estimates of Negative Equity in Selected Metropolitan Areas Using the S&P/CaseShiller Index, as of June 30, 2009 

Metropolitan area 

Number of 

nonprime borrowers 

with active loans 

Estimated number 

with negative equity 

Estimated 

percentage with 

negative equity 

Las Vegas, NV 92,949 87,685 94.3

Phoenix, AZ 131,069 117,185 89.4 

Miami, FL 225,355 193,360 85.8

Minneapolis, MN 49,435 39,841 80.6 

Tampa, FL 85,641 67,343 78.6 

San Diego, CA 86,499 62,160 71.9 

Chicago, IL 128,929 86,523 67.1 

Washington, DC 130,760 83,682 64.0 

Los Angeles, CA 315,289 201,009 63.8

Atlanta, GA 122,302 73,001 59.7 

San Francisco, CA 103,369 61,652 59.6 

New York, NY 295,932 76,204 25.8

Seattle, WA 69,353 17,327 25.0 

Boston, MA 54,844 12,670 23.1 

Portland, OR 42,014 5,323 12.7 

Denver, CO 60,280 5,583 9.3

Total 1,994,020 1,190,548 59.7 

Source: GAO analysis of LP data and S&P/Case-Shiller index. 

Note: As of November 2009, the S&P/Case-Shiller index did not include 2009 data for the Cleveland, Ohio 

metropolitan area. As a result, we did not estimate negative equity for that area. 

Estimates of Negative Equity by Loan and Borrower Type

For the same 16 metropolitan areas examined above, we estimated the extent of 

negative home equity by loan class (subprime or Alt-A), loan purpose, loan 

product, and borrower type (owner occupant or nonowner occupant) using the 

S&P/Case-Shiller index. Our estimates of negative equity are as of June 30, 2009, 

 

27As of November 2009 the S&P/Case-Shiller index did not include 2009 data for the Cleveland, 

Ohio metropolitan area. As a result, we did not estimate negative equity for that area. 

Page 14 GAO-10-146R Nonprime Mortgages 
and are for nonprime borrowers in the 16 metropolitan areas whose loans were 

active as of that date. We found that: 

• Subprime borrowers were more likely than Alt-A borrowers to be in a negative 

equity position. We estimate that about 63 percent of subprime borrowers had 

negative home equity, compared with 57 percent of Alt-A borrowers. 

• Borrowers who obtained a mortgage to purchase a home were more likely to 

have negative equity than those who refinanced an existing loan. Additionally, 

borrowers who refinanced their mortgages to convert their home equity into 

money for personal use (cash-out refinancings) were more likely to have 

negative equity than borrowers who refinanced without taking cash out. More 

specifically, we estimate that 68 percent of borrowers with purchase loans had 

negative home equity, compared with 55 percent of borrowers with cash-out 

refinance loans, and 50 percent of borrowers with no-cash-out refinance loans. 

• Borrowers with adjustable-rate loans were more likely to have negative equity 

than borrowers with fixed-rate loans. For example, we estimate that 80 

percent of borrowers with payment-option ARMs and 75 percent of borrowers 

with short-term hybrid ARMs were in a negative equity position. By 

comparison, an estimated 39 percent of borrowers with fixed-rate mortgages 

had negative home equity. 

• Borrowers who were owner-occupants were somewhat more likely to have 

negative home equity than borrowers who were not owner-occupants (e.g., 

investors). More specifically, we estimate that 60 percent of owner-occupants 

were in a negative equity position, compared with 56 percent of non-owneroccupants. 

Estimates of Negative Equity Nationwide

To estimate the extent of negative home equity nationwide, we used the FHFA 

index because it comprises separate indexes for 384 metropolitan areas covering 

approximately 84 percent of the U.S. population.28 The FHFA index does not 

include data for homes with certain types of financing, including subprime 

mortgages. Partly for this reason, the FHFA index shows more modest declines in 

average house prices from 2005 through 2008, compared with the S&P/CaseShiller index. As a result, our estimates using the FHFA index likely understate 

the extent of negative equity among nonprime borrowers.29 See enclosure V for 

additional information about the major differences between the FHFA and 

 

28We excluded nonprime loans on properties not in these metropolitan areas from our analysis. As 

a result, our estimates cover 4.5 million of the 4.9 million nonprime loans that were active as of 

June 30, 2009. 

29To illustrate, across the 15 metropolitan areas for which the FHFA and S&P/Case-Shiller indexes 

use the same geographic boundaries, the estimated percentage of borrowers with negative equity 

was about 34 percent using the FHFA index and 66 percent using the S&P/Case-Shiller index. 

Page 15 GAO-10-146R Nonprime Mortgages 
S&P/Case-Shiller indexes and how using different indexes can affect estimates of 

negative equity. 

Nationwide, we estimate that 25 percent of the borrowers who obtained nonprime 

mortgages from 2000 through 2007 and whose loans were active as of June 30, 

2009, had negative home equity as of that date. The estimated proportion of 

nonprime borrowers in a negative equity position varied by location. We 

estimated that this proportion ranged from no negative equity in 20 metropolitan 

areas to more than 80 percent in 5 (see fig. 4).30 The 35 metropolitan areas with 

proportions greater than 50 percent were located in five states: Arizona, 

California, Florida, Michigan, and Nevada. 

 

30The five metropolitan areas were Merced, California (87.4 percent); Stockton, California (85.6 

percent); Modesto, California (83.5 percent); Vallejo-Fairfield, California (83.4 percent); and Las 

Vegas-Paradise, Nevada (83.4 percent). 

Page 16 GAO-10-146R Nonprime Mortgages 
Figure 4: Estimated Percentage of Nonprime Borrowers with Negative Home Equity by 

Metropolitan Area Using the FHFA Index as of June 30, 2009 

Source: GAO analysis of LP data and FHFA's All-Transactions Index; MapInfo.

Positive equity

>0 to 9.9%

10 to 19.9%

20 to 29.9%

30 to 39.9%

40 to 49.9%

50% or more

Additionally, we estimate that 5.5 percent of borrowers with active nonprime 

loans had “near negative equity”—that is, home equity of 0 to 5 percent. 

Borrowers with near negative equity face similar challenges to borrowers with 

negative equity when selling or refinancing their homes because mortgage closing 

costs (e.g., lender fees and title charges) are generally between 3 to 5 percent of 

the value of the home. 

Nationwide, we estimate that the total amount of negative equity (i.e., the 

difference between mortgage balances and estimated property values) was about 

$54.8 billion. Among borrowers in a negative equity position, the median borrower 

had negative equity of approximately $36,274. We estimate that 75 percent of 

borrowers in a negative equity position had negative home equity of more than 

$15,615 and 25 percent had negative home equity of more than $67,335. Another 

measure of negative equity is the ratio of the current loan balance to the current 

value of the property (current loan-to-value ratio). A current loan-to-value (LTV) 

ratio of more than 100 percent indicates negative equity in the property, with a 

higher ratio representing greater negative equity as a percentage of the property 

Page 17 GAO-10-146R Nonprime Mortgages 
value.31 As of June 30, 2009, we estimate nearly 63 percent of nonprime borrowers 

with negative home equity had current LTV ratios of 101 to 119 percent, while 

about 5 percent had current LTV ratios of 150 percent or higher (see fig. 5). 

Figure 5: Estimated Current LTV Ratios of Nonprime Borrowers with Active Loans Who 

Had Negative Home Equity as of June 30, 2009 

Percentage of negative equity borrowers

Source: GAO analysis of LP data and FHFA All-Transactions Index.

Current loan-to-value ratio

0

5

10

15

20

25

30

35

40

150

or greater

140 to

149

130 to

139

120 to

129

110 to

119

101 to

109

We also found that borrowers who obtained their loans later in the decade were 

more likely to have negative home equity than borrowers who obtained their 

loans earlier. This pattern reflects the greater home equity that earlier borrowers 

accumulated by paying down their loan balances and experiencing the home price 

appreciation that occurred in most of the country during the first half of the 

decade. We estimate that no more than 1 percent of borrowers with loans that 

originated from 2000 through 2003 and whose loans were still active as of June 30, 

2009, were in a negative equity position as of that date (see fig. 6). In contrast, we 

estimate that 37 percent of borrowers with active loans that originated in 2007 had 

negative home equity as of that date. 

 

31As discussed in our July 2009 report, average LTV ratios at loan origination peaked in 2006 at 

about 86 percent for subprime mortgages and 82 percent for Alt-A mortgages. See GAO-09-848R. 

Page 18 GAO-10-146R Nonprime Mortgages 
Figure 6: Estimated Percentage of Nonprime Borrowers with Active Loans Who Had 

Negative Home Equity as of June 30, 2009, by Loan Origination Year 

Percentage of borrowers with negative equity

Source: GAO analysis of LP data and FHFA All-Transactions Index.

Cohort year

0

5

10

15

20

25

30

35

40

2000 2001 2002 2003 2004 2005 2006 2007

- - - - - 

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 interested congressional parties 

and other interested parties. In addition, the report will be available at no charge 

on GAO’s Web site at http://www.gao.gov. 

If you or your staff 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 enclosure VIII. 

William B. Shear 

Director, Financial Markets and Community Investment 

Enclosures

Page 19 GAO-10-146R Nonprime Mortgages 
Enclosure I 

Page 20 GAO-10-146R Nonprime Mortgages 

Status of Nonprime Loans Originated from 2000 through 2007 by Cohort 

Year and Product Type, as of June 30, 2009 

This enclosure contains the results of our analysis of LoanPerformance (LP) data 

on the status of nonprime mortgages originated from 2000 through 2007, as of 

June 30, 2009. Tables 2 and 3 provide information in percentages and total 

numbers, respectively. 
Enclosure I 

Page 21 GAO

Table 2: Percentage of Nonprime Loans in Different Status Categories by Cohort Year as of June 30, 2009 

Subprime 

ARM Fixed rat

Cohort year Prepaid Current Delinquent In default 

In foreclosure 

process 

Completed 

foreclosure 

process Total 

Cohort 

year Prepaid Current Delinquent In de

2000 80% 3% 1% 1% 0% 15% 100% 2000 72% 6% 2% 2001 83% 3% 1% 1% 0% 11% 100% 2001 72% 9% 2% 2002 83% 5% 2% 1% 1% 9% 100% 2002 73% 13% 3% 2003 82% 8% 1% 1% 1% 7% 100% 2003 66% 22% 3% 2004 73% 14% 3% 2% 2% 7% 100% 2004 53% 32% 5% 2005 47% 28% 5% 4% 5% 11% 100% 2005 35% 40% 8% 2006 23% 32% 8% 10% 10% 16% 100% 2006 22% 43% 11% 2007 16% 37% 11% 16% 11% 10% 100% 2007 14% 46% 14% 1Total 58% 19% 4% 4% 4% 11% 100% Total 51% 29% 6% 

Short-term hybrid ARM Other 

Cohort year Prepaid Current Delinquent In default 

In foreclosure 

process 

Completed 

foreclosure 

process Total Cohort year Prepaid Current Delinquent In de

2000 77% 2% 1% 0% 0% 21% 100% 2000 66% 6% 2% 2001 78% 2% 1% 1% 0% 18% 100% 2001 71% 7% 2% 2002 82% 2% 1% 1% 0% 14% 100% 2002 70% 12% 2% 2003 84% 3% 1% 1% 1% 11% 100% 2003 66% 18% 4% 2004 78% 5% 2% 2% 1% 12% 100% 2004 54% 33% 4% 2005 57% 10% 4% 5% 5% 19% 100% 2005 33% 33% 8% 2006 30% 18% 8% 11% 11% 23% 100% 2006 19% 34% 11% 12007 18% 27% 11% 15% 14% 15% 100% 2007 11% 38% 14% 1Total 62% 9% 4% 5% 4% 17% 100% Total 40% 24% 7% 

Alt-A 

ARM Fixed rat

Cohort year Prepaid Current Delinquent 

In 

default 

In foreclosure 

process 

Completed 

foreclosure 

process Total Cohort year Prepaid Current Delinquent In de

2000 90% 6% 1% 0% 0% 3% 100% 2000 92% 2% 0% 2001 94% 2% 0% 0% 0% 3% 100% 2001 90% 4% 0% 2002 92% 4% 0% 0% 0% 3% 100% 2002 81% 13% 1% 2003 85% 11% 1% 1% 0% 3% 100% 2003 60% 36% 1% 2004 72% 18% 2% 1% 2% 5% 100% 2004 49% 44% 2% 2005 46% 31% 3% 4% 5% 11% 100% 2005 33% 53% 4% 2006 24% 36% 5% 7% 11% 16% 100% 2006 24% 50% 6% 2007 13% 47% 7% 10% 12% 12% 100% 2007 14% 61% 6% Total 48% 28% 3% 4% 6% 10% 100% Total 44% 42% 3% 
Enclosure I 

Page 22 GAO

Payment-option ARM Other 

Cohort year Prepaid Current Delinquent In default 

In foreclosure 

process 

Completed 

foreclosure 

process Total Cohort year Prepaid Current Delinquent In de

2000 97% 2% 0% 0% 0% 1% 100% 2000 85% 5% 1% 2001 94% 4% 0% 0% 0% 1% 100% 2001 84% 4% 1% 2002 92% 7% 0% 0% 0% 0% 100% 2002 84% 6% 1% 2003 85% 11% 1% 1% 1% 1% 100% 2003 75% 20% 1% 2004 77% 15% 2% 2% 2% 2% 100% 2004 67% 27% 1% 2005 54% 23% 3% 7% 6% 8% 100% 2005 37% 31% 4% 2006 26% 34% 6% 11% 11% 12% 100% 2006 19% 36% 7% 2007 12% 47% 8% 12% 13% 9% 100% 2007 9% 49% 8% Total 44% 28% 4% 8% 8% 8% 100% Total 42% 29% 5% 

Source: GAO analysis of LP data. Note: Percentages for ARMs and fixed-rate mortgages do not include balloon mortgages, which account for most of the “other” category. Balloon 

adjustable interest rates. 

Table 3: Number of Nonprime Loans in Different Status Categories by Cohort Year as of June 30, 2009 

Subprime 

ARM Fixed rate 

Cohort year Prepaid Current Delinquent In default 

In foreclosure 

process 

Completed 

foreclosure 

process Total 

Cohort 

year Prepaid Current Delinquent In defa

2000 10,204 384 137 80 29 1,968 12,802 2000 112,149 10,036 2,611 1,542001 7,048 268 89 69 30 975 8,479 2001 130,408 16,662 4,201 2,332002 17,446 979 320 182 137 1,947 21,011 2002 167,995 29,867 5,859 2,982003 35,223 3,283 596 439 339 3,001 42,881 2003 284,032 95,924 13,674 6,42004 38,236 7,258 1,344 1,084 967 3,699 52,588 2004 242,520 146,996 23,207 12,802005 26,324 15,416 2,651 2,352 2,922 6,322 55,987 2005 150,195 171,157 34,222 23,062006 11,210 15,398 3,937 4,643 5,001 7,909 48,098 2006 78,450 153,669 39,451 32,832007 2,412 5,665 1,647 2,509 1,699 1,500 15,432 2007 13,081 42,108 12,997 10,68Total 148,103 48,651 10,721 11,358 11,124 27,321 257,278 Total 1,178,830 666,419 136,222 92,66
Enclosure I 

Page 23 GAO

Short-term hybrid ARM Other 

Cohort year Prepaid Current Delinquent In default 

In foreclosure 

process 

Completed 

foreclosure 

process Total Cohort year Prepaid Current Delinquent In de

2000 189,826 3,875 1,413 1,015 530 51,185 247,844 2000 25,216 2,359 609 2001 246,485 6,769 2,881 2,097 1,114 56,315 315,661 2001 22,559 2,131 550 2002 424,943 11,512 4,533 3,520 2,015 70,103 516,626 2002 12,825 2,246 431 2003 670,733 21,929 8,664 6,973 5,079 84,379 797,757 2003 6,874 1,921 391 2004 1,114,037 66,817 27,042 30,669 20,504 168,972 1,428,041 2004 1,896 1,134 131 2005 1,012,637 182,424 73,282 90,087 89,013 339,339 1,786,782 2005 5,250 5,281 1,255 1,2006 381,560 232,563 98,227 136,406 142,140 298,959 1,289,855 2006 16,412 30,012 9,454 8,2007 35,251 54,504 22,359 29,260 27,054 29,811 198,239 2007 2,928 10,056 3,704 3,Total 4,075,472 580,393 238,401 300,027 287,449 1,099,063 6,580,805 Total 93,960 55,140 16,525 14,

Alt-A 

ARM Fixed rate 

Cohort year Prepaid Current Delinquent In default 

In foreclosure 

process 

Completed 

foreclosure 

process Total 

Cohort 

year Prepaid Current Delinquent 

de

2000 5,277 329 42 15 12 177 5,852 2000 61,367 1,182 119 2001 22,618 587 61 45 49 817 24,177 2001 99,110 4,840 455 22002 44,496 2,020 129 111 91 1,287 48,134 2002 141,852 23,042 1,442 72003 93,110 11,780 692 655 540 2,837 109,614 2003 183,627 110,535 4,041 1,92004 332,729 84,957 7,026 5,810 7,706 21,448 459,676 2004 169,022 152,619 7,754 4,52005 268,556 179,972 19,317 20,503 31,858 61,709 581,915 2005 180,648 292,614 20,616 13,82006 116,993 171,781 25,047 35,300 53,030 77,850 480,001 2006 119,449 248,495 27,959 23,22007 23,799 88,062 12,767 17,827 22,533 22,223 187,211 2007 25,576 109,407 11,188 10,0Total 907,578 539,488 65,081 80,266 115,819 188,348 1,896,580 Total 980,651 942,734 73,574 54,7
Enclosure I 

Page 24 GAO

Payment-option ARM Other 

Cohort year Prepaid Current Delinquent 

In 

default 

In 

foreclosure 

process 

Completed 

foreclosure 

process Total 

Cohort 

year Prepaid Current Delinquent In 

2000 4,086 90 5 6 2 28 4,217 2000 1,228 74 12 2001 643 28 1 1 2 7 682 2001 2,903 141 28 2002 4,216 308 11 4 9 22 4,570 2002 3,242 225 36 2003 6,430 823 96 42 87 93 7,571 2003 7,391 1,938 111 2004 94,931 18,188 2,067 2,662 2,356 2,997 123,201 2004 4,132 1,650 74 2005 167,253 71,096 10,100 20,243 17,707 23,727 310,126 2005 488 405 56 2006 83,870 111,633 19,723 35,713 36,487 38,833 326,259 2006 5,058 9,296 1,795 2007 7,472 28,351 4,799 6,977 7,598 5,322 60,519 2007 640 3,415 579 Total 368,901 230,517 36,802 65,648 64,248 71,029 837,145 Total 25,082 17,144 2,691 

Source: GAO analysis of LP data. Note: Numbers for ARMs and fixed-rate mortgages do not include balloon mortgages, which account for most of the “other” category. Balloon mo

adjustable interest rates. 
Enclosure II 

Page 25 GAO-10-146R Nonprime Mortgages 

Status of Nonprime Loans Originated from 2004 through 2007 by Year and 

Quarter, as of June 30, 2009 

This enclosure contains the results of our analysis of LoanPerformance (LP) data 

on the annual and quarterly status of nonprime mortgages originated from 2004 

through 2007, as of June 30, 2009. Tables 4 and 5 provide information in 

percentages and total numbers, respectively. 
Enclosure II 

Page 26 GAO-10-14Table 4: Percentage of Nonprime Loans Originated in 2004 through 2007 in Different Status Categories as of June 30, 2009 

 2007 Cohort 

 Subprime Alt-A 

Status date 

 Prepaid 

(cumulative) Current Delinquent 

 In 

default 

 In 

foreclosure 

process 

 Completed foreclosure 

process 

(cumulative) Total 

 Prepaid 

(cumulative) Current Delinquent In default 

December 31, 2007 5% 71% 15% 4% 4% 1% 100% 5% 87% 5% 1% December 31, 2008 13% 41% 16% 11% 10% 8% 100% 11% 63% 8% 6% June 30, 2009 16% 34% 12% 14% 12% 12% 100% 13% 53% 7% 8% 

 2006 Cohort 

 Subprime Alt-A 

Status date 

 Prepaid 

(cumulative) Current Delinquent 

 In 

default 

 In 

foreclosure 

process 

 Completed foreclosure 

process 

(cumulative) Total 

 Prepaid 

(cumulative) Current Delinquent In default 

December 31, 2006 7% 78% 11% 2% 2% 0% 100% 6% 90% 3% 0% December 31, 2007 18% 50% 13% 6% 7% 6% 100% 17% 70% 6% 2% December 31, 2008 25% 28% 11% 9% 10% 16% 100% 22% 48% 7% 6% June 30, 2009 27% 24% 9% 10% 10% 20% 100% 24% 41% 6% 7% 

 2005 Cohort 

 Subprime Alt-A 

Status date 

 Prepaid 

(cumulative) Current Delinquent 

 In 

default 

 In 

foreclosure 

process 

 Completed foreclosure 

process 

(cumulative) Total 

 Prepaid 

(cumulative) Current Delinquent In default 

December 31, 2005 7% 83% 8% 1% 1% 0% 100% 6% 92% 2% 0% December 31, 2006 28% 54% 9% 3% 3% 3% 100% 23% 72% 3% 0% December 31, 2007 46% 28% 8% 4% 5% 8% 100% 36% 55% 4% 1% December 31, 2008 51% 19% 6% 5% 5% 15% 100% 40% 43% 4% 3% June 30, 2009 52% 17% 5% 5% 5% 17% 100% 42% 38% 3% 4% 

2004 Cohort 

 Subprime Alt-A 

Status date 

 Prepaid 

(cumulative) Current Delinquent 

 In 

default 

 In 

foreclosure 

process 

 Completed foreclosure 

process 

(cumulative) Total 

 Prepaid 

(cumulative) Current Delinquent In default 

December 31, 2004 6% 86% 6% 1% 1% 0% 100% 5% 93% 2% 0% December 31, 2005 34% 52% 7% 2% 2% 2% 100% 28% 69% 2% 0% December 31, 2006 60% 26% 5% 2% 2% 5% 100% 47% 50% 2% 0% December 31, 2007 69% 16% 4% 2% 2% 8% 100% 58% 37% 2% 1% December 31, 2008 71% 12% 3% 2% 1% 10% 100% 62% 30% 2% 1% June 30, 2009 72% 12% 3% 2% 2% 10% 100% 64% 28% 2% 1% 

Source: GAO analysis of LP data. 
Enclosure II 

Table 5: Number of Nonprime Loans Originated in 2004 through 2007 in Different Status Categories as of June 30, 2009 

2007 Cohort 

Subprime Alt-A 

 Status date 

 Prepaid 

(cumulative) Current Delinquent 

 In 

default 

 In foreclosure 

process 

Completed 

foreclosure 

process 

(cumulative) Total 

 Prepaid 

(cumulative) Current Delinquent 

de

December 31, 2007 16,702 231,732 48,916 14,677 13,944 2,575 328,546 20,524 375,033 21,007 6,4December 31, 2008 44,058 135,117 51,296 37,274 32,844 27,904 328,493 46,120 272,870 35,102 24,9June 30, 2009 51,459 112,333 40,707 45,585 38,755 39,654 328,493 56,164 229,235 29,333 35,4

 2006 Cohort 

 Subprime Alt-A 

 Status date 

 Prepaid 

(cumulative) Current Delinquent In default 

 In 

foreclosure 

process 

 Completed foreclosure 

process 

(cumulative) Total 

 Prepaid 

(cumulative) Current Delinquent 

defa

December 31, 2006 91,519 1,048,577 152,186 27,941 24,897 5,257 1,350,377 58,154 944,997 35,953 3,8December 31, 2007 308,751 882,179 234,132 109,507 132,283 97,330 1,764,182 222,312 919,887 78,744 30,6December 31, 2008 445,971 499,686 198,891 165,708 167,881 285,050 1,763,187 292,837 639,616 97,616 80,0June 30, 2009 468,037 431,642 151,069 182,612 177,706 352,118 1,763,184 317,435 541,205 74,524 96,6

2005 Cohort 

 Subprime Alt-A 

 Status date 

 Prepaid 

(cumulative) Current Delinquent In default 

 In 

foreclosure 

process 

 Completed foreclosure 

process 

(cumulative) Total 

 Prepaid 

(cumulative) Current Delinquent 

defa

December 31, 2005 122,100 1,438,784 140,860 22,174 14,074 2,925 1,740,917 69,322 1,073,961 25,836 2,5December 31, 2006 627,777 1,210,898 210,942 63,499 70,422 59,164 2,242,702 332,527 1,035,468 40,766 7,December 31, 2007 1,051,031 642,437 180,324 99,140 112,349 176,696 2,261,977 513,236 795,044 51,339 20,8December 31, 2008 1,149,945 417,846 138,511 108,054 104,623 332,522 2,251,501 580,024 611,185 59,857 44,6June 30, 2009 1,171,815 374,278 111,410 116,528 108,173 379,118 2,261,322 610,719 544,087 50,089 54,6

 2004 Cohort 

 Subprime Alt-A 

Status date 

 Prepaid 

(cumulative) Current Delinquent 

 In 

default 

 In 

foreclosure 

process 

 Completed foreclosure 

process 

(cumulative) Total 

 Prepaid 

(cumulative) Current Delinquent In default 

December 31, 2004 93,670 1,294,270 97,242 11,653 8,467 1,282 1,506,584 37,168 667,091 12,295 968 December 31, 2005 661,253 1,002,040 142,526 46,016 36,363 32,192 1,920,390 260,603 631,707 19,096 4,375 

December 31, 2006 1,144,949 502,979 100,780 41,053 40,637 91,913 1,922,311 432,908 459,054 16,964 3,830 

December 31, 2007 1,329,079 305,661 69,054 40,021 36,249 149,349 1,929,413 540,156 344,039 16,852 5,921 December 31, 2008 1,368,911 239,764 59,388 41,398 27,984 189,626 1,927,071 576,516 281,900 18,657 10,129 June 30, 2009 1,377,506 222,205 51,724 44,643 29,648 200,565 1,926,291 594,846 257,414 16,921 13,051 

Source: GAO analysis of LP data.Page 27 GAO-10-14
Enclosure III 

Page 28 GAO-10-146R Nonprime Mortgages 

Status of Nonprime Loans Originated from 2000 through 2007 by Census 

Division and State as of June 30, 2009 

This enclosure contains the results of our analysis of LoanPerformance (LP) data 

on the status of nonprime mortgages by Census division and state. The analysis 

covers mortgages originated from 2000 through 2007, as of June 30, 2009. Tables 6 

and 7 provide information in percentages and total numbers, respectively. 
Enclosure III 

Table 6: Percentage of 2000 through 2007 Nonprime Loans in Different Status Categories by State and Census 

Division, as of June 30, 2009 

State 

Market 

segment Prepaid Current Delinquent In default 

In foreclosure 

process 

Completed 

foreclosure 

process Unknown Total 

Connecticut Subprime 62.93% 14.67% 4.56% 3.91% 4.16% 9.69% 0.09% 123,038

 Alt-A 48.26% 36.79% 4.05% 2.86% 4.21% 3.78% 0.06% 44,726 

Maine Subprime 64.01% 14.71% 4.39% 3.00% 4.84% 8.96% 0.09% 36,309 

 Alt-A 48.98% 36.40% 3.54% 2.07% 5.05% 3.90% 0.05% 9,697 

Massachusetts Subprime 67.19% 10.66% 3.49% 4.92% 3.13% 10.45% 0.16% 202,214 

 Alt-A 52.89% 31.27% 3.52% 3.42% 3.82% 4.96% 0.12% 85,212 

New Hampshire Subprime 62.24% 14.69% 5.11% 4.33% 2.06% 11.51% 0.07% 41,429 

 Alt-A 48.73% 36.28% 3.93% 2.81% 1.87% 6.29% 0.10% 15586 

Rhode Island Subprime 70.42% 9.43% 3.10% 2.85% 2.40% 11.61% 0.18% 52814 

 Alt-A 51.74% 30.08% 3.92% 2.92% 3.56% 7.67% 0.10% 15,746 

Vermont Subprime 65.86% 15.93% 4.15% 2.99% 4.31% 6.64% 0.11% 9,754 

 Alt-A 51.93% 37.26% 3.35% 1.79% 3.37% 2.25% 0.05% 3,913

New England Subprime 65.71% 12.37% 3.96% 4.18% 3.38% 10.28% 0.13% 465,558 

Alt-A 50.99% 33.44% 3.72% 3.06% 3.78% 4.90% 0.09% 174,880 

New Jersey Subprime 69.61% 10.20% 3.44% 3.92% 5.55% 7.15% 0.13% 264,675 

 Alt-A 52.84% 30.14% 3.57% 3.22% 6.78% 3.35% 0.10% 143,696 

New York Subprime 59.26% 16.95% 4.71% 5.06% 5.94% 7.99% 0.09% 384,847 

 Alt-A 43.17% 39.45% 4.16% 4.67% 5.52% 2.99% 0.04% 165,959 

Pennsylvania Subprime 53.01% 21.94% 6.21% 5.25% 3.63% 9.90% 0.07% 262,666 

 Alt-A 44.81% 42.87% 3.73% 2.54% 2.74% 3.25% 0.07% 81,596 

Mid Atlantic Subprime 60.46% 16.43% 4.77% 4.78% 5.16% 8.30% 0.10% 912,188 

 Alt-A 47.06% 36.74% 3.86% 3.69% 5.40% 3.18% 0.07% 391,251 

Illinois Subprime 63.36% 11.30% 3.84% 4.15% 3.95% 13.26% 0.14% 450,107 

 Alt-A 52.31% 29.94% 3.34% 3.41% 4.91% 6.02% 0.06% 147,516 

Indiana Subprime 43.63% 18.32% 5.35% 4.59% 4.12% 23.87% 0.12% 176,207 

 Alt-A 39.56% 39.43% 3.60% 2.78% 4.09% 10.52% 0.02% 38,356 

Michigan Subprime 48.97% 12.56% 4.73% 4.82% 1.82% 26.97% 0.13% 373,287 

 Alt-A 37.59% 37.48% 4.33% 3.41% 2.59% 14.57% 0.03% 94,666 

Ohio Subprime 45.49% 17.64% 5.02% 4.58% 4.31% 22.84% 0.12% 319,380 

 Alt-A 36.65% 43.20% 3.76% 2.87% 4.29% 9.19% 0.04% 73,562 

Wisconsin Subprime 63.70% 11.45% 3.81% 3.34% 3.92% 13.67% 0.10% 131,059 

 Alt-A 48.35% 36.45% 3.41% 2.28% 3.88% 5.59% 0.04% 29,600 

East North Central Subprime 53.35% 13.89% 4.51% 4.40% 3.50% 20.22% 0.13% 1,450,040 

 Alt-A 44.10% 35.79% 3.70% 3.16% 4.06% 9.15% 0.05% 383,700 

Iowa Subprime 57.25% 15.49% 4.32% 2.86% 3.31% 16.68% 0.08% 52,626 

 Alt-A 44.39% 43.21% 3.13% 1.45% 2.46% 5.34% 0.02% 10,534 

Kansas Subprime 56.27% 16.81% 4.66% 3.38% 2.11% 16.68% 0.09% 49,747 

 Alt-A 44.44% 44.50% 2.72% 1.51% 1.60% 5.22% 0.01% 16,847 

Minnesota Subprime 60.75% 10.67% 3.28% 3.20% 2.57% 19.41% 0.13% 162,899 

 Alt-A 38.59% 37.84% 3.79% 3.14% 3.77% 12.83% 0.05% 67,315 

Missouri Subprime 55.18% 14.14% 4.89% 4.10% 1.47% 20.11% 0.11% 180,297 

 Alt-A 45.60% 38.83% 3.21% 2.23% 1.45% 8.64% 0.05% 48,821 

Nebraska Subprime 51.68% 19.84% 5.09% 3.90% 2.33% 17.10% 0.07% 29,599 

 Alt-A 41.88% 45.53% 3.08% 2.12% 1.66% 5.74% 0.00% 6,987 

North Dakota Subprime 60.04% 19.78% 4.79% 3.07% 2.40% 9.90% 0.02% 4,464 

 Alt-A 44.63% 46.37% 2.55% 1.19% 1.79% 3.42% 0.05% 1,844 

South Dakota Subprime 57.63% 17.35% 4.71% 2.81% 3.06% 14.42% 0.01% 7,321 

 Alt-A 45.04% 42.85% 2.65% 2.03% 2.32% 5.09% 0.04% 2,418

West North Central Subprime 57.25% 13.84% 4.27% 3.55% 2.19% 18.79% 0.10% 486,953 

 Alt-A 42.15% 39.77% 3.38% 2.47% 2.57% 9.61% 0.04% 154,766 

Delaware Subprime 60.20% 16.92% 5.41% 4.72% 5.06% 7.62% 0.06% 24,643

 Alt-A 47.55% 38.96% 3.67% 2.64% 3.81% 3.31% 0.06% 11,752 

District of Columbia Subprime 71.05% 10.51% 3.30% 3.89% 2.53% 8.56% 0.17% 17,369 

 Alt-A 51.62% 35.67% 3.28% 2.75% 2.40% 4.23% 0.05% 15,458

Florida Subprime 56.24% 13.03% 4.07% 4.83% 10.45% 11.31% 0.08% 935,137 

 Alt-A 37.31% 32.21% 3.93% 5.04% 14.99% 6.45% 0.07% 528,148

Georgia Subprime 48.93% 15.31% 5.85% 6.31% 2.73% 20.77% 0.10% 267,324 

 Alt-A 38.79% 40.27% 4.57% 3.36% 2.73% 10.22% 0.06% 157,912 

Maryland Subprime 70.19% 11.05% 3.86% 4.43% 3.06% 7.30% 0.11% 255,100 

Page 29 GAO-10-146R Nonprime Mortgages 
Enclosure III 

State 

Market 

segment Prepaid Current Delinquent In default 

In foreclosure 

process 

Completed 

foreclosure 

process Unknown Total 

 Alt-A 49.36% 33.73% 3.94% 4.11% 4.40% 4.37% 0.10% 135,828

North Carolina Subprime 53.79% 17.56% 6.22% 5.12% 2.04% 15.17% 0.10% 179,429 

 Alt-A 45.86% 40.80% 3.66% 2.53% 1.81% 5.29% 0.04% 86,516 

South Carolina Subprime 51.05% 18.18% 5.95% 4.19% 3.69% 16.84% 0.11% 94,131 

 Alt-A 46.33% 38.42% 3.47% 2.29% 3.73% 5.70% 0.06% 48,869 

Virginia Subprime 65.27% 13.10% 4.21% 4.19% 1.99% 11.15% 0.09% 213,173

 Alt-A 44.66% 35.19% 3.44% 3.50% 2.93% 10.18% 0.09% 162,456 

West Virginia Subprime 53.22% 19.87% 6.48% 4.90% 2.39% 12.97% 0.18% 16,904 

 Alt-A 40.10% 40.22% 5.11% 2.89% 3.14% 8.41% 0.12% 4,813

South Atlantic Subprime 57.69% 13.82% 4.61% 4.89% 6.30% 12.59% 0.09% 2,003,210 

 Alt-A 41.30% 34.97% 3.90% 4.12% 8.56% 7.08% 0.07% 1,151,752 

Alabama Subprime 50.02% 19.34% 6.65% 6.39% 1.88% 15.68% 0.04% 84,277 

 Alt-A 43.98% 41.21% 3.84% 2.53% 1.66% 6.75% 0.03% 27,191 

Kentucky Subprime 49.53% 18.94% 5.39% 3.96% 3.64% 18.44% 0.10% 71,582 

 Alt-A 41.80% 43.09% 3.43% 2.14% 2.96% 6.57% 0.02% 18,423

Mississippi Subprime 43.35% 20.94% 7.62% 7.62% 2.34% 18.05% 0.08% 52,116 

 Alt-A 41.74% 41.89% 4.42% 2.79% 1.94% 7.18% 0.03% 9,374 

Tennessee Subprime 46.71% 19.50% 6.36% 6.93% 1.77% 18.65% 0.07% 168,260 

 Alt-A 42.00% 43.41% 3.70% 2.54% 1.39% 6.94% 0.02% 46,029 

East South Central Subprime 47.52% 19.56% 6.41% 6.34% 2.23% 17.86% 0.07% 376,235 

 Alt-A 42.47% 42.62% 3.75% 2.49% 1.80% 6.84% 0.02% 101,017 

Arkansas Subprime 47.12% 23.65% 6.53% 5.31% 2.05% 15.31% 0.03% 37,683

 Alt-A 38.59% 47.16% 3.30% 2.39% 1.68% 6.89% 0.00% 11,341 

Louisiana Subprime 52.49% 21.54% 6.42% 5.08% 3.25% 11.12% 0.10% 89,804 

 Alt-A 47.34% 40.58% 3.55% 2.09% 2.50% 3.91% 0.03% 19,371 

Oklahoma Subprime 45.32% 23.79% 5.69% 3.76% 3.54% 17.85% 0.05% 66,156 

 Alt-A 37.99% 49.96% 2.95% 1.46% 2.39% 5.23% 0.01% 18,229 

Texas Subprime 41.67% 28.45% 7.27% 5.05% 2.18% 15.35% 0.04% 569,956 

 Alt-A 37.42% 48.73% 3.30% 2.01% 1.35% 7.18% 0.02% 185,902 

West South Central Subprime 43.53% 26.99% 6.99% 4.95% 2.41% 15.07% 0.05% 763,599 

 Alt-A 38.34% 48.08% 3.29% 1.99% 1.54% 6.74% 0.02% 234,843 

Arizona Subprime 61.22% 11.28% 3.48% 4.78% 3.76% 15.39% 0.08% 300,651 

 Alt-A 46.70% 30.78% 3.39% 3.73% 3.12% 10.84% 0.06% 233,384 

Colorado Subprime 55.20% 14.03% 3.44% 3.12% 2.34% 21.78% 0.09% 188,746 

 Alt-A 46.48% 38.85% 2.63% 1.90% 2.26% 7.84% 0.04% 138,461 

Idaho Subprime 61.54% 14.77% 4.20% 4.02% 3.40% 12.01% 0.04% 38,244 

 Alt-A 47.94% 37.41% 3.09% 2.61% 3.90% 5.03% 0.03% 31,987 

Montana Subprime 64.48% 15.06% 3.91% 3.71% 2.69% 10.13% 0.02% 12,971 

 Alt-A 53.74% 37.09% 2.58% 1.86% 2.02% 2.70% 0.00% 8,848

Nevada Subprime 57.90% 10.47% 3.19% 5.41% 4.60% 18.36% 0.07% 152,571 

 Alt-A 37.47% 30.49% 4.17% 6.30% 7.16% 14.36% 0.05% 152,067 

New Mexico Subprime 64.51% 15.09% 4.12% 2.81% 2.74% 10.60% 0.12% 40,487 

 Alt-A 50.82% 38.34% 2.69% 1.66% 3.30% 3.13% 0.06% 22,322 

Utah Subprime 66.35% 11.77% 3.46% 3.15% 2.40% 12.80% 0.07% 80,321 

 Alt-A 55.47% 31.60% 2.59% 2.08% 2.88% 5.34% 0.04% 56,951 

Wyoming Subprime 65.73% 17.40% 4.25% 2.96% 1.44% 8.21% 0.02% 9,745 

 Alt-A 55.57% 38.10% 2.13% 1.24% 0.75% 2.21% 0.00% 4,517 

Mountain Subprime 60.01% 12.29% 3.49% 4.19% 3.35% 16.59% 0.08% 823,736 

 Alt-A 45.62% 33.23% 3.28% 3.63% 4.37% 9.82% 0.05% 648,537 

Alaska Subprime 63.45% 16.79% 4.54% 3.73% 2.24% 9.23% 0.02% 9,433

 Alt-A 47.68% 41.99% 2.90% 1.73% 1.70% 4.01% 0.00% 3,937 

California Subprime 67.39% 9.34% 2.40% 3.78% 3.21% 13.76% 0.12% 1,745,440 

 Alt-A 48.43% 29.68% 3.37% 5.46% 4.85% 8.18% 0.03% 1,530,336 

Hawaii Subprime 65.82% 17.60% 3.35% 3.66% 4.59% 4.91% 0.07% 42,053

 Alt-A 48.67% 38.89% 3.12% 2.90% 4.42% 1.97% 0.04% 28,459 

Oregon Subprime 63.72% 15.09% 3.67% 3.77% 3.17% 10.51% 0.06% 106,917 

 Alt-A 49.73% 38.12% 2.79% 2.50% 2.80% 4.03% 0.03% 78,473

Washington Subprime 65.58% 13.79% 3.62% 3.85% 2.96% 10.15% 0.05% 204,684 

 Alt-A 50.95% 37.49% 2.99% 2.47% 2.86% 3.22% 0.03% 144,133

Pacific Subprime 66.98% 10.26% 2.61% 3.78% 3.21% 13.05% 0.11% 2,108,527 

 Alt-A 48.69% 30.85% 3.31% 5.04% 4.59% 7.49% 0.03% 1,785,338 

United States Subprime 58.39% 14.38% 4.28% 4.45% 3.97% 14.43% 0.10% 9,390,046 

Page 30 GAO-10-146R Nonprime Mortgages 
Enclosure III 

Prepaid Current Delinquent In default 

In foreclosure 

process 

Completed 

foreclosure 

process Unknown Total 

Market 

State segment 

 Alt-A 45.39% 34.34% 3.54% 4.05% 5.20% 7.41% 0.05% 5,026,084 

Total 

 Nonprime 53.86% 21.34% 4.02% 4.31% 4.40% 11.98% 0.08% 14,416,130 

Source: GAO analysis of LP data. 

Note: Some data were insufficient to classify loans into a status category but these "unknown" loans are included in the total number 

of loans. This table does not include data for Guam, Puerto Rico, and the Virgin Islands. 

Table 7: Number of 2000 through 2007 Nonprime Loans in Different Status Categories by Census Division and State, as of 

June 30, 2009

State 

Market 

segment Prepaid Current Delinquent In default 

In 

foreclosure 

process 

Completed 

foreclosure 

process Unknown Total 

Connecticut Subprime 77,422 18,054 5,608 4,806 5,124 11,918 106 123,038

 Alt-A 21,585 16,455 1,811 1,278 1,881 1,690 26 44,726 

Maine Subprime 23,243 5,341 1,593 1,089 1,758 3,253 32 36,309 

 Alt-A 4,750 3,530 343 201 490 378 5 9,697 

Massachusetts Subprime 135,866 21,560 7,065 9,953 6,325 21,128 317 202,214 

 Alt-A 45,071 26,645 2,996 2,912 3,259 4,229 100 85,212 

New Hampshire Subprime 25,785 6,085 2,115 1,793 855 4,769 27 41,429 

 Alt-A 7,595 5,654 613 438 291 980 15 15,586 

Rhode Island Subprime 37,192 4,979 1,639 1,507 1,269 6,134 94 52,814 

 Alt-A 8,147 4,737 618 460 561 1,207 16 15,746 

Vermont Subprime 6,424 1,554 405 292 420 648 11 9,754 

 Alt-A 2,032 1,458 131 70 132 88 2 3,913

New England Subprime 305,932 57,573 18,425 19,440 15,751 47,850 587 465,558 

 Alt-A 89,180 58,479 6,512 5,359 6,614 8,572 164 174,880 

New Jersey Subprime 184,231 27,004 9,100 10,376 14,702 18,912 350 264,675 

 Alt-A 75,925 43,311 5,134 4,634 9,737 4,818 137 143,696 

New York Subprime 228,070 65,232 18,120 19,475 22,856 30,752 342 384,847 

 Alt-A 71,643 65,464 6,910 7,745 9,163 4,963 71 165,959 

Pennsylvania Subprime 139,227 57,617 16,311 13,780 9,522 26,015 194 262,666 

 Alt-A 36,561 34,977 3,047 2,070 2,239 2,648 54 81,596 

Mid Atlantic Subprime 551,528 149,853 43,531 43,631 47,080 75,679 886 912,188 

 Alt-A 184,129 143,752 15,091 14,449 21,139 12,429 262 391,251 

Illinois Subprime 285,197 50,873 17,279 18,687 17,767 59,676 628 450,107 

 Alt-A 77,168 44,165 4,929 5,035 7,245 8,885 89 147,516 

Indiana Subprime 76,880 32,273 9,431 8,089 7,260 42,066 208 176,207 

 Alt-A 15,175 15,122 1,380 1,065 1,570 4,035 9 38,356 

Michigan Subprime 182,786 46,901 17,668 17,989 6,799 100,669 475 373,287 

 Alt-A 35,589 35,478 4,096 3,229 2,448 13,793 33 94,666 

Ohio Subprime 145,272 56,344 16,025 14,626 13,781 72,944 388 319,380 

 Alt-A 26,962 31,776 2,769 2,114 3,154 6,757 30 73,562 

Wisconsin Subprime 83,480 15,012 4,992 4,383 5,143 17,913 136 131,059 

 Alt-A 14,311 10,790 1,010 674 1,149 1,654 12 29,600 

East North Central Subprime 773,615 201,403 65,395 63,774 50,750 293,268 1,835 1,450,040 

 Alt-A 169,205 137,331 14,184 12,117 15,566 35,124 173 383,700 

Iowa Subprime 30,130 8,151 2,275 1,505 1,742 8,780 43 52,626 

 Alt-A 4,676 4,552 330 153 259 562 2 10,534 

Kansas Subprime 27,994 8,361 2,320 1,680 1,051 8,298 43 49,747 

 Alt-A 7,487 7,497 459 254 269 879 2 16,847 

Minnesota Subprime 98,957 17,374 5,337 5,218 4,185 31,622 206 162,899 

 Alt-A 25,976 25,470 2,548 2,115 2,540 8,634 32 67,315 

Missouri Subprime 99,485 25,495 8,814 7,399 2,654 36,259 191 180,297 

Alt-A 22,260 18,959 1,568 1,089 706 4,217 22 48,821 

Nebraska Subprime 15,298 5,873 1,506 1,153 689 5,060 20 29,599 

 Alt-A 2,926 3,181 215 148 116 401 0 6,987 

North Dakota Subprime 2,680 883 214 137 107 442 1 4,464 

 Alt-A 823 855 47 22 33 63 1 1,844 

South Dakota Subprime 4,219 1,270 345 206 224 1,056 1 7,321 

 Alt-A 1,089 1,036 64 49 56 123 1 2,418

Page 31 GAO-10-146R Nonprime Mortgages 
Enclosure III 

State 

Market 

segment Prepaid Current Delinquent In default 

In 

foreclosure 

process 

Completed 

foreclosure 

process Unknown Total 

West North Central Subprime 278,763 67,407 20,811 17,298 10,652 91,517 505 486,953 

 Alt-A 65,237 61,550 5,231 3,830 3,979 14,879 60 154,766 

Delaware Subprime 14,836 4,170 1,333 1,163 1,246 1,879 16 24,643

 Alt-A 5,588 4,579 431 310 448 389 7 11,752 

District of 

Columbia Subprime 12,340 1,826 573 676 439 1,486 29 17,369 

 Alt-A 7,979 5,514 507 425 371 654 8 15,458

Florida Subprime 525,935 121,841 38,065 45,142 97,683 105,722 749 935,137 

 Alt-A 197,050 170,109 20,746 26,634 79,149 34,088 372 528,148

Georgia Subprime 130,795 40,930 15,643 16,876 7,285 55,516 279 267,324 

 Alt-A 61,260 63,589 7,219 5,308 4,312 16,134 90 157,912 

Maryland Subprime 179,054 28,200 9,846 11,301 7,802 18,613 284 255,100 

 Alt-A 67,038 45,811 5,357 5,583 5,979 5,930 130 135,828

North Carolina Subprime 96,509 31,510 11,162 9,181 3,658 27,228 181 179,429 

 Alt-A 39,678 35,302 3,169 2,191 1,565 4,575 36 86,516 

South Carolina Subprime 48,052 17,109 5,598 3,941 3,478 15,854 99 94,131 

 Alt-A 22,642 18,777 1,694 1,119 1,821 2,786 30 48,869 

Virginia Subprime 139,146 27,922 8,973 8,931 4,252 23,764 185 213,173

 Alt-A 72,554 57,166 5,594 5,690 4,765 16,542 145 162,456 

West Virginia Subprime 8,997 3,358 1,095 828 404 2,192 30 16,904 

 Alt-A 1,930 1,936 246 139 151 405 6 4,813

South Atlantic Subprime 1,155,664 276,866 92,288 98,039 126,247 252,254 1,852 2,003,210 

 Alt-A 475,719 402,783 44,963 47,399 98,561 81,503 824 1,151,752 

Alabama Subprime 42,155 16,296 5,601 5,388 1,587 13,216 34 84,277 

 Alt-A 11,959 11,206 1,043 689 451 1,836 7 27,191 

Kentucky Subprime 35,454 13,555 3,858 2,836 2,604 13,200 75 71,582 

 Alt-A 7,701 7,938 632 394 545 1,210 3 18,423

Mississippi Subprime 22,594 10,914 3,970 3,972 1,217 9,409 40 52,116 

 Alt-A 3,913 3,927 414 262 182 673 3 9,374 

Tennessee Subprime 78,602 32,810 10,704 11,659 2,985 31,378 122 168,260 

 Alt-A 19,332 19,983 1,704 1,168 641 3,193 8 46,029 

East South Central Subprime 178,805 73,575 24,133 23,855 8,393 67,203 271 376,235 

 Alt-A 42,905 43,054 3,793 2,513 1,819 6,912 21 101,017 

Arkansas Subprime 17,755 8,911 2,461 2,002 774 5,768 12 37,683

 Alt-A 4,377 5,348 374 271 190 781 0 11,341 

Louisiana Subprime 47,141 19,344 5,761 4,563 2,916 9,988 91 89,804 

 Alt-A 9,171 7,861 687 404 485 758 5 19,371 

Oklahoma Subprime 29,984 15,739 3,767 2,487 2,343 11,806 30 66,156 

 Alt-A 6,925 9,108 538 267 435 954 2 18,229 

Texas Subprime 237,490 162,136 41,418 28,779 12,401 87,504 228 569,956 

 Alt-A 69,560 90,589 6,134 3,728 2,517 13,341 33 185,902 

West South Central Subprime 332,370 206,130 53,407 37,831 18,434 115,066 361 763,599 

 Alt-A 90,033 112,906 7,733 4,670 3,627 15,834 40 234,843 

Arizona Subprime 184,070 33,918 10,453 14,385 11,318 46,259 248 300,651 

 Alt-A 108,995 71,847 7,914 8,710 10,489 25,289 140 233,384 

Colorado Subprime 104,186 26,477 6,495 5,886 4,415 41,115 172 188,746 

 Alt-A 64,355 53,790 3,645 2,629 3,129 10,862 51 138,461 

Idaho Subprime 23,536 5,648 1,608 1,539 1,302 4,595 16 38,244 

 Alt-A 15,335 11,966 988 834 1,246 1,609 9 31,987 

Montana Subprime 8,364 1,954 507 481 349 1,314 2 12,971 

 Alt-A 4,755 3,282 228 165 179 239 0 8,848

Nevada Subprime 88,344 15,973 4,861 8,253 7,014 28,016 110 152,571 

 Alt-A 56,985 46,361 6,344 9,573 10,881 21,843 80 152,067 

New Mexico Subprime 26,117 6,111 1,670 1,137 1,111 4,292 49 40,487 

 Alt-A 11,345 8,558 600 371 737 698 13 22,322 

Utah Subprime 53,294 9,456 2,778 2,528 1,928 10,280 57 80,321 

 Alt-A 31,593 17,997 1,477 1,184 1,638 3,040 22 56,951 

Wyoming Subprime 6,405 1,696 414 288 140 800 2 9,745 

 Alt-A 2,510 1,721 96 56 34 100 0 4,517 

Mountain Subprime 494,316 101,233 28,786 34,497 27,577 136,671 656 823,736 

 Alt-A 295,873 215,522 21,292 23,522 28,333 63,680 315 648,537 

Alaska Subprime 5,985 1,584 428 352 211 871 2 9,433

Page 32 GAO-10-146R Nonprime Mortgages 
Enclosure III 

State 

Market 

segment Prepaid Current Delinquent In default 

In 

foreclosure 

process 

Completed 

foreclosure 

process Unknown Total 

 Alt-A 1,877 1,653 114 68 67 158 0 3,937 

California Subprime 1,176,213 163,064 41,897 65,942 56,041 240,228 2,055 1,745,440 

 Alt-A 741,079 454,170 51,601 83,496 74,288 125,175 527 1,530,336 

Hawaii Subprime 27,679 7,403 1,407 1,541 1,929 2,063 31 42,053

 Alt-A 13,850 11,067 889 825 1,257 560 11 28,459 

Oregon Subprime 68,127 16,133 3,924 4,036 3,393 11,235 69 106,917 

 Alt-A 39,021 29,910 2,192 1,963 2,194 3,166 27 78,473

Washington Subprime 134,225 28,227 7,411 7,874 6,058 20,784 105 204,684 

 Alt-A 73,434 54,031 4,304 3,567 4,116 4,639 42 144,133

Pacific Subprime 1,412,229 216,411 55,067 79,745 67,632 275,181 2,262 2,108,527 

 Alt-A 869,261 550,831 59,100 89,919 81,922 133,698 607 1,785,338 

United States Subprime 5,483,222 1,350,451 401,843 418,110 372,516 1,354,689 9,215 9,390,046 

 Alt-A 2,281,542 1,726,208 177,899 203,778 261,560 372,631 2,466 5,026,084 

 

Total 

Nonprime 7,764,764 3,076,659 579,742 621,888 634,076 1,727,320 11,681 14,416,130 

Source: GAO analysis of LP data. 

Note: Some data were insufficient to classify loans into a status category but these "unknown" loans are included in the total number 

of loans. This table does not include data for Guam, Puerto Rico, and the Virgin Islands. 

Page 33 GAO-10-146R Nonprime Mortgages 
Enclosure IV 

Page 34 GAO-10-146R Nonprime Mortgages 

Status of Nonprime Loans Originated from 2000 through 2007 by 

Congressional District as of June 30, 2009 

This enclosure contains the results of our analysis of LoanPerformance (LP) data 

on the status of nonprime mortgages by congressional district. The analysis 

covers mortgages originated from 2000 through 2007, as of June 30, 2009. All 

figures reported are estimated. 
Enclosure IV 

Table 8: Estimated Percentage of 2000 through 2007 Active Nonprime Loans in Default and in the 

Foreclosure Process by Congressional District as of June 30, 2009 

State 

Congressional 

district 

Estimated 

number of active 

loans 

Estimated 

percentage 

of active 

loans in 

default 

Estimated 

percentage of 

active loans in 

the foreclosure 

process 

Estimated 

percentage of 

active loans 

that are 

seriously 

delinquent 

Alabama 01 7,831 16.62 5.73 22.35 

 02 4,168 13.60 4.63 18.23

 03 5,231 14.47 4.29 18.77 

 04 3,667 11.87 4.03 15.90 

 05 5,177 10.87 4.15 15.03

 06 8,902 13.75 4.91 18.66 

 07 7,228 16.86 5.12 21.97 

Alaska 00 4,478 9.38 6.16 15.54 

Arizona 01 11,932 11.30 10.01 21.30 

 02 30,366 14.33 13.76 28.09 

 03 22,150 12.90 12.56 25.46 

 04 20,464 19.66 17.15 36.80 

 05 18,041 9.88 10.59 20.47 

 06 28,325 13.40 13.69 27.09 

 07 24,793 14.76 14.05 28.81 

 08 12,915 9.96 6.70 16.66 

Arkansas 01 3,801 10.69 4.65 15.34 

 02 6,882 11.72 4.49 16.22 

 03 5,781 10.25 5.66 15.90 

 04 3,620 12.00 3.55 15.55 

California 01 15,556 10.65 8.80 19.45 

 02 16,851 12.71 11.48 24.19 

 03 25,868 16.48 13.52 30.00 

 04 23,109 12.55 10.05 22.60 

 05 19,357 16.79 15.52 32.32 

 06 18,354 9.08 7.77 16.85 

 07 21,675 16.89 15.20 32.08

 08 8,840 6.85 5.35 12.19 

 09 13,814 13.60 11.96 25.56 

 10 24,462 15.02 12.98 28.01 

 11 28,949 16.65 14.75 31.41 

 12 13,918 9.66 7.67 17.34 

 13 15,745 15.42 13.14 28.56 

 14 13,071 8.17 6.47 14.64 

 15 11,195 11.21 9.40 20.62 

 16 17,392 16.25 13.83 30.08

 17 14,356 15.43 13.67 29.10 

 18 16,536 19.42 18.86 38.29 

 19 20,940 16.51 14.10 30.62 

 20 12,236 15.57 13.30 28.88

Page 35 GAO-10-146R Nonprime Mortgages 
Enclosure IV 

State 

Congressional 

district 

Estimated 

number of active 

loans 

Estimated 

percentage 

of active 

loans in 

default 

Estimated 

percentage of 

active loans in 

the foreclosure 

process 

Estimated 

percentage of 

active loans 

that are 

seriously 

delinquent 

 21 20,715 14.69 11.86 26.56 

 22 27,674 16.12 14.36 30.48

 23 11,541 12.45 10.71 23.16 

 24 24,176 12.45 10.18 22.63

 25 33,661 19.55 17.65 37.20 

 26 20,024 13.63 11.39 25.01 

 27 18,883 16.98 14.87 31.84 

 28 14,886 16.01 14.51 30.52 

 29 12,886 10.80 9.42 20.22 

 30 14,869 9.28 7.71 17.00 

 31 8,865 14.69 12.75 27.43

 32 13,389 15.61 12.87 28.49 

 33 12,868 15.27 11.66 26.94 

 34 10,377 16.26 14.87 31.13

 35 16,775 16.73 13.96 30.69 

 36 13,236 9.14 6.55 15.69 

 37 17,640 17.87 14.28 32.14 

 38 16,850 17.89 14.63 32.52 

 39 15,393 17.08 14.28 31.36 

 40 14,890 14.50 13.21 27.71 

 41 33,190 18.08 16.70 34.77 

 42 21,402 13.67 11.49 25.16 

 43 23,925 21.06 19.14 40.20 

 44 31,125 18.71 16.73 35.44 

 45 35,348 18.34 17.03 35.37 

 46 17,217 11.25 9.24 20.50 

 47 11,421 18.23 18.51 36.73

 48 19,767 11.05 9.49 20.53

 49 28,438 17.38 16.22 33.60 

 50 19,724 10.33 9.32 19.65 

 51 22,934 17.65 15.61 33.26 

 52 18,357 12.75 10.12 22.86 

 53 14,590 10.49 9.99 20.49 

Colorado 01 15,106 8.41 7.81 16.23

 02 15,481 6.78 6.22 13.00 

 03 11,910 7.15 6.17 13.32 

 04 13,854 7.65 7.13 14.78

 05 14,313 7.93 7.06 14.99 

 06 19,896 8.32 6.82 15.14 

 07 15,777 9.34 8.29 17.63

Connecticut 01 9,775 10.38 11.54 21.91 

 02 9,075 11.53 10.73 22.26 

 03 11,924 12.03 13.21 25.24 

Page 36 GAO-10-146R Nonprime Mortgages 
Enclosure IV 

State 

Congressional 

district 

Estimated 

number of active 

loans 

Estimated 

percentage 

of active 

loans in 

default 

Estimated 

percentage of 

active loans in 

the foreclosure 

process 

Estimated 

percentage of 

active loans 

that are 

seriously 

delinquent 

 04 13,212 10.38 14.00 24.38

 05 10,716 11.13 13.51 24.65 

Delaware 00 13,657 10.75 12.40 23.15 

District of Columbia 00 10,337 10.65 7.86 18.50 

Florida 01 12,552 9.37 16.62 25.99 

 02 11,918 8.64 18.86 27.50 

 03 20,242 14.12 23.10 37.22 

 04 16,513 10.94 16.88 27.82 

 05 22,857 11.13 25.20 36.33

 06 16,000 11.62 20.60 32.22 

 07 23,017 11.32 24.77 36.09 

 08 27,549 12.34 29.15 41.49 

 09 22,471 10.17 25.90 36.08

 10 18,603 9.60 24.38 33.98

 11 22,273 11.37 28.83 40.20 

 12 22,899 12.15 26.21 38.37 

 13 21,706 10.65 31.85 42.50 

 14 33,953 12.25 37.64 49.90 

 15 29,211 12.11 31.48 43.59 

 16 23,576 11.90 33.15 45.05 

 17 27,306 14.34 31.41 45.74 

 18 25,541 11.36 35.86 47.22 

 19 27,851 12.03 33.99 46.02 

 20 30,355 12.87 30.79 43.66 

 21 25,063 13.22 32.23 45.44 

 22 27,338 11.06 31.85 42.90 

 23 30,197 13.29 34.42 47.71 

 24 25,800 12.45 25.44 37.88

 25 33,658 13.63 36.39 50.01 

Georgia 01 5,711 12.09 5.79 17.88

 02 3,708 13.01 5.15 18.15 

 03 16,681 14.82 7.52 22.34 

 04 17,514 15.70 8.29 23.99 

 05 14,276 12.65 8.86 21.50 

 06 13,611 9.95 5.99 15.94 

 07 20,814 14.10 7.81 21.90 

 08 8,367 15.51 5.67 21.19 

 09 11,119 12.54 6.55 19.09 

 10 6,953 11.04 5.45 16.50 

 11 14,635 13.30 6.89 20.18

 12 6,158 12.83 5.45 18.29 

 13 21,236 16.25 8.09 24.34 

Page 37 GAO-10-146R Nonprime Mortgages 
Enclosure IV 

State 

Congressional 

district 

Estimated 

number of active 

loans 

Estimated 

percentage 

of active 

loans in 

default 

Estimated 

percentage of 

active loans in 

the foreclosure 

process 

Estimated 

percentage of 

active loans 

that are 

seriously 

delinquent 

Hawaii 01 9,511 7.80 8.75 16.55 

 02 16,781 9.67 14.02 23.69 

Idaho 01 16,613 10.15 11.35 21.51 

 02 8,469 7.97 7.81 15.78

Illinois 01 12,712 16.06 16.42 32.48

 02 20,061 17.59 15.25 32.84 

 03 10,727 16.39 17.61 34.00 

 04 8,583 15.06 20.38 35.43

 05 8,510 14.10 19.07 33.17 

 06 9,018 13.66 15.21 28.86 

 07 11,835 14.46 17.31 31.77 

 08 11,347 13.22 13.60 26.83

 09 6,843 12.88 17.83 30.70 

 10 7,524 11.67 14.15 25.82 

 11 8,308 14.18 12.96 27.14 

 12 5,223 14.31 8.63 22.94 

 13 10,720 13.76 13.91 27.67 

 14 12,057 14.32 17.63 31.95 

 15 3,470 9.48 8.17 17.67 

 16 8,164 12.91 12.46 25.37 

 17 3,458 10.22 8.21 18.42 

 18 3,747 10.78 8.45 19.24 

 19 3,493 11.77 7.81 19.58

Indiana 01 11,537 15.13 12.53 27.65 

 02 8,475 12.02 11.03 23.06 

 03 7,584 11.18 11.77 22.94 

 04 8,077 9.98 12.12 22.10 

 05 9,630 10.04 11.52 21.56 

 06 7,679 10.28 10.58 20.85 

 07 11,521 13.36 12.98 26.33

 08 5,698 12.39 9.46 21.85 

 09 5,852 12.30 10.41 22.71 

Iowa 01 3,400 8.48 9.82 18.29 

 02 3,066 8.26 10.97 19.21 

 03 5,589 9.21 11.88 21.10 

 04 3,302 8.60 10.58 19.17 

 05 3,585 8.77 8.74 17.52 

Kansas 01 2,738 7.58 5.51 13.08

 02 5,173 9.50 6.40 15.89 

 03 8,200 9.91 6.33 16.24 

 04 5,763 7.27 5.54 12.81 

Kentucky 01 3,062 9.61 7.48 17.08

 02 5,267 10.22 8.52 18.74 

Page 38 GAO-10-146R Nonprime Mortgages 
Enclosure IV 

State 

Congressional 

district 

Estimated 

number of active 

loans 

Estimated 

percentage 

of active 

loans in 

default 

Estimated 

percentage of 

active loans in 

the foreclosure 

process 

Estimated 

percentage of 

active loans 

that are 

seriously 

delinquent 

 03 8,485 10.29 12.40 22.69 

 04 6,421 10.74 9.43 20.17 

 05 2,602 8.65 8.12 16.76 

 06 6,481 9.32 9.30 18.62 

Louisiana 01 6,985 10.69 9.02 19.71 

 02 7,056 13.32 10.05 23.37 

 03 5,331 11.96 7.68 19.62 

 04 5,355 12.79 7.29 20.09 

 05 3,779 13.01 6.62 19.63

 06 9,295 11.39 8.09 19.48

 07 4,194 9.48 6.17 15.67 

Maine 01 7,935 9.68 15.35 25.03

 02 6,304 8.22 16.06 24.27 

Maryland 01 11,931 11.65 9.26 20.91 

 02 11,909 14.06 9.34 23.39 

 03 13,801 11.94 9.50 21.44 

 04 21,336 16.63 14.65 31.29 

 05 23,694 16.50 12.68 29.18

 06 11,692 12.86 11.06 23.91 

 07 12,561 12.87 9.92 22.79 

 08 12,869 12.28 12.22 24.50 

Massachusetts 01 6,624 16.71 11.42 28.14 

 02 9,127 17.30 12.39 29.69 

 03 8,176 16.72 13.03 29.75 

 04 7,230 14.90 10.51 25.42 

 05 8,340 16.38 11.86 28.24 

 06 7,338 15.84 11.80 27.64 

 07 6,878 14.27 12.95 27.22 

 08 6,183 13.51 11.67 25.18

 09 10,073 17.63 12.08 29.71 

 10 10,048 14.83 10.96 25.79 

Michigan 01 4,963 10.45 5.63 16.08

 02 6,353 12.44 6.82 19.27 

 03 7,337 12.72 7.06 19.78

 04 5,933 11.45 6.41 17.85 

 05 8,059 17.44 6.07 23.50 

 06 7,274 10.99 6.34 17.34 

 07 8,075 13.29 7.80 21.09 

 08 9,397 12.31 7.03 19.34 

 09 9,571 13.66 7.54 21.19 

 10 8,465 13.04 7.72 20.74 

 11 10,298 14.50 7.28 21.78

 12 12,351 16.34 7.58 23.92 

Page 39 GAO-10-146R Nonprime Mortgages 
Enclosure IV 

Estimated 

percentage of 

active loans 

that are 

seriously 

State delinquent 

Congressional 

district 

Estimated 

number of active 

loans 

Estimated 

percentage 

of active 

loans in 

default 

Estimated 

percentage of 

active loans in 

the foreclosure 

process 

 13 11,742 23.28 5.80 29.08

 14 14,642 24.28 6.25 30.54 

 15 9,916 16.25 7.30 23.56 

Minnesota 01 4,290 9.95 7.96 17.93

 02 11,090 10.96 9.84 20.80 

 03 9,928 11.14 10.53 21.67 

 04 7,739 11.35 12.16 23.50 

 05 9,025 11.65 11.42 23.07 

 06 11,206 12.51 10.53 23.04 

 07 4,130 10.56 7.96 18.52 

 08 7,303 11.09 10.40 21.48

Mississippi 01 7,587 18.82 5.38 24.20 

 02 6,569 19.64 5.85 25.48

 03 4,756 14.77 5.46 20.25 

 04 5,921 13.74 5.86 19.59 

Missouri 01 13,869 17.47 5.64 23.12 

 02 6,476 11.07 4.95 16.03

 03 8,002 12.17 5.19 17.36 

 04 4,770 10.87 4.44 15.30 

 05 12,089 13.26 5.08 18.34 

 06 7,316 11.01 4.47 15.49 

 07 6,261 9.89 5.31 15.19 

 08 3,262 10.46 4.34 14.81 

 09 4,540 10.56 4.55 15.09 

Montana 00 7,134 9.03 7.40 16.43

Nebraska 01 4,019 10.08 6.40 16.47 

 02 6,228 10.84 6.32 17.15 

 03 2,606 8.28 5.73 14.01 

Nevada 01 35,911 17.91 17.60 35.51 

 02 21,702 12.89 13.17 26.06 

 03 51,500 16.64 16.89 33.53

New Hampshire 01 9,398 11.85 6.56 18.42 

 02 8,186 13.25 5.83 19.07 

New Jersey 01 9,631 14.67 16.15 30.82 

 02 12,037 12.91 17.39 30.30 

 03 10,992 12.69 16.35 29.04 

 04 10,748 11.36 16.98 28.34 

 05 8,911 10.26 15.81 26.07 

 06 8,655 11.81 19.99 31.80 

 07 7,509 10.70 17.56 28.25 

 08 9,645 12.82 24.37 37.18

 09 8,605 11.13 20.56 31.69 

 10 10,818 14.88 30.84 45.72 

Page 40 GAO-10-146R Nonprime Mortgages 
Enclosure IV 

Estimated 

percentage of 

active loans 

that are 

seriously 

State delinquent 

Congressional 

district 

Estimated 

number of active 

loans 

Estimated 

percentage 

of active 

loans in 

default 

Estimated 

percentage of 

active loans in 

the foreclosure 

process 

 11 7,338 9.39 15.85 25.24 

 12 8,956 10.67 16.08 26.75 

 13 9,692 12.41 26.58 38.99 

New Mexico 01 8,687 7.70 9.42 17.13

 02 4,707 6.85 8.39 15.23

 03 6,902 7.51 9.16 16.66 

New York 01 18,203 13.42 17.36 30.78

 02 16,002 15.31 21.19 36.51 

 03 12,120 13.40 14.86 28.26 

 04 14,296 15.06 18.88 33.95 

 05 6,853 9.93 11.00 20.93

 06 14,939 16.69 21.26 37.95 

 07 6,712 13.29 16.06 29.35 

 08 3,286 5.80 8.86 14.67 

 09 7,536 11.43 15.15 26.57 

 10 9,659 12.84 25.94 38.79 

 11 5,188 11.64 19.43 31.07 

 12 4,679 11.35 20.50 31.84 

 13 8,777 12.07 12.61 24.68

 14 3,021 4.28 4.90 9.17 

 15 935 8.14 13.11 21.28

 16 1,899 13.95 21.75 35.70 

 17 8,309 13.52 15.25 28.78

 18 8,445 10.55 9.84 20.39 

 19 11,854 12.93 11.33 24.26 

 20 7,986 13.05 10.77 23.82 

 21 6,263 12.48 11.90 24.37 

 22 7,233 13.90 12.93 26.82 

 23 3,348 10.69 8.33 19.03

 24 4,302 10.90 7.45 18.36 

 25 4,617 9.92 8.90 18.82 

 26 4,398 9.78 6.77 16.55 

 27 4,324 10.33 5.98 16.33

 28 5,488 9.98 8.25 18.24 

 29 3,977 10.70 6.46 17.15 

North Carolina 01 3,899 13.63 5.48 19.11 

 02 6,916 12.99 5.75 18.74 

 03 7,454 10.05 5.43 15.48

 04 8,166 10.95 3.98 14.93

 05 5,222 11.89 4.67 16.56 

 06 7,469 12.42 4.96 17.37 

 07 7,357 10.69 5.13 15.81 

 08 7,100 12.04 5.35 17.39 

Page 41 GAO-10-146R Nonprime Mortgages 
Enclosure IV 

Estimated 

percentage of 

active loans 

that are 

seriously 

State delinquent 

Congressional 

district 

Estimated 

number of active 

loans 

Estimated 

percentage 

of active 

loans in 

default 

Estimated 

percentage of 

active loans in 

the foreclosure 

process 

 09 13,339 11.03 5.95 16.99 

 10 6,362 12.30 5.58 17.89 

 11 5,751 9.87 5.15 15.01 

 12 10,609 12.23 6.18 18.42 

 13 7,888 12.30 5.03 17.34 

North Dakota 00 2,287 6.84 6.10 12.99 

Ohio 01 8,848 12.40 10.47 22.88

 02 6,525 10.09 10.36 20.44 

 03 8,154 13.25 12.03 25.28

 04 6,223 11.40 10.80 22.21 

 05 4,986 10.27 11.19 21.46 

 06 4,728 11.38 11.86 23.24 

 07 8,002 11.95 11.13 23.07 

 08 7,281 11.49 11.25 22.74 

 09 8,364 11.85 12.80 24.65 

 10 9,289 11.90 12.15 24.06 

 11 12,105 16.81 13.37 30.18

 12 10,220 11.14 11.21 22.35 

 13 9,136 11.32 13.94 25.26 

 14 7,862 9.63 12.62 22.25 

 15 7,938 10.80 11.65 22.46 

 16 7,246 10.52 11.22 21.74 

 17 8,343 12.36 16.08 28.43

 18 5,139 11.89 9.94 21.83

Oklahoma 01 8,842 8.40 8.36 16.75 

 02 4,676 8.39 8.11 16.49 

 03 4,987 7.68 7.44 15.12 

 04 7,163 7.28 7.13 14.41 

 05 8,981 7.93 8.65 16.58

Oregon 01 12,656 9.34 8.66 17.99 

 02 13,122 10.16 10.27 20.42 

 03 15,537 9.43 8.70 18.13

 04 10,364 8.70 7.22 15.92 

 05 11,992 9.28 8.66 17.94 

Pennsylvania 01 11,475 10.78 8.97 19.76 

 02 10,438 10.87 9.37 20.22 

 03 4,726 11.19 7.39 18.58

 04 6,934 11.50 7.36 18.86 

 05 3,349 10.81 7.12 17.95 

 06 7,613 9.50 8.18 17.68

 07 7,625 10.96 8.07 19.03

 08 7,726 12.08 8.35 20.42 

 09 4,808 11.65 6.85 18.49 

Page 42 GAO-10-146R Nonprime Mortgages 
Enclosure IV 

Estimated 

percentage of 

active loans 

that are 

seriously 

State delinquent 

Congressional 

district 

Estimated 

number of active 

loans 

Estimated 

percentage 

of active 

loans in 

default 

Estimated 

percentage of 

active loans in 

the foreclosure 

process 

 10 6,471 12.30 9.92 22.22 

 11 10,732 12.80 12.31 25.12 

 12 4,821 11.88 7.04 18.94 

 13 8,419 11.04 7.98 19.02 

 14 7,660 12.25 7.02 19.27 

 15 9,350 11.98 8.82 20.80 

 16 5,656 9.85 8.34 18.19 

 17 6,484 10.26 6.98 17.24 

 18 7,839 11.81 6.95 18.77 

 19 7,248 11.75 8.59 20.35 

Rhode Island 01 6,979 11.61 11.00 22.61 

 02 8,648 13.25 12.16 25.42 

South Carolina 01 15,607 8.73 11.80 20.53

 02 11,431 9.67 9.93 19.60 

 03 5,407 8.18 7.61 15.78

 04 7,773 9.39 10.06 19.45 

 05 6,737 10.49 8.13 18.61 

 06 6,535 10.81 8.86 19.66 

South Dakota 00 3,254 7.81 8.60 16.41 

Tennessee 01 5,522 10.99 4.81 15.81 

 02 8,019 13.46 4.78 18.23

 03 8,050 14.98 4.35 19.33

 04 5,596 13.87 4.58 18.44 

 05 11,735 13.47 4.13 17.59 

 06 9,347 14.51 4.38 18.88

 07 11,088 15.24 4.24 19.49 

 08 7,488 18.91 4.07 22.97 

 09 14,673 21.08 4.76 25.84 

Texas 01 4,894 8.20 3.89 12.08

 02 14,442 11.24 4.61 15.84 

 03 13,167 8.46 4.37 12.84 

 04 10,816 8.71 4.78 13.49 

 05 10,516 9.68 4.82 14.50 

 06 13,575 10.40 4.73 15.13

 07 12,313 8.48 4.09 12.56 

 08 10,085 9.17 3.99 13.16 

 09 14,495 10.41 4.40 14.81 

 10 17,656 10.10 4.65 14.75 

 11 4,896 7.17 2.87 10.03

 12 12,459 8.93 4.38 13.32 

 13 4,143 8.28 3.61 11.88

 14 10,494 10.14 4.07 14.21 

 15 7,560 9.89 4.28 14.17 

Page 43 GAO-10-146R Nonprime Mortgages 
Enclosure IV 

Estimated 

percentage of 

active loans 

that are 

seriously 

State delinquent 

Congressional 

district 

Estimated 

number of active 

loans 

Estimated 

percentage 

of active 

loans in 

default 

Estimated 

percentage of 

active loans in 

the foreclosure 

process 

 16 7,915 7.84 3.01 10.85 

 17 7,499 7.72 3.85 11.57 

 18 13,765 10.96 4.53 15.49 

 19 4,701 6.41 3.51 9.91 

 20 9,454 8.51 4.22 12.74 

 21 13,235 7.73 3.91 11.64 

 22 17,942 11.21 4.13 15.34 

 23 10,512 9.31 4.33 13.63

 24 14,037 10.12 4.84 14.95 

 25 10,172 6.76 3.64 10.40 

 26 16,691 8.82 4.52 13.35 

 27 8,693 8.90 3.69 12.58

 28 9,207 10.27 4.59 14.86 

 29 10,642 9.75 3.76 13.51 

 30 13,337 11.84 5.56 17.40 

 31 9,983 7.30 3.80 11.10 

 32 7,731 7.69 4.68 12.37 

Utah 01 11,718 8.28 7.63 15.92 

 02 13,591 9.63 10.06 19.69 

 03 13,593 10.51 9.60 20.10 

Vermont 00 4,423 8.19 12.39 20.57 

Virginia 01 14,151 13.51 7.67 21.18

 02 11,045 9.87 5.31 15.17 

 03 11,481 12.87 5.46 18.33

 04 11,269 12.95 5.64 18.59 

 05 5,834 10.09 4.15 14.24 

 06 5,580 10.55 4.57 15.13

 07 10,766 12.51 6.26 18.77 

 08 11,487 8.91 7.64 16.56 

 09 2,865 9.84 4.56 14.42 

 10 19,925 12.07 9.70 21.77 

 11 18,653 12.99 10.46 23.45 

Washington 01 13,058 8.77 8.75 17.52 

 02 14,488 10.46 8.95 19.42 

 03 14,531 10.60 10.38 20.99 

 04 8,286 7.16 5.15 12.30 

 05 7,793 7.81 6.43 14.24 

 06 14,290 10.57 9.30 19.87 

 07 10,761 7.50 6.97 14.48

 08 16,603 10.91 9.68 20.59 

 09 15,634 12.13 10.30 22.44 

Page 44 GAO-10-146R Nonprime Mortgages 
Enclosure IV 

Estimated 

percentage of 

active loans 

that are 

seriously 

State delinquent 

Congressional 

district 

Estimated 

number of active 

loans 

Estimated 

percentage 

of active 

loans in 

default 

Estimated 

percentage of 

active loans in 

the foreclosure 

process 

West Virginia 01 2,099 9.85 5.80 15.67 

 02 4,001 13.36 8.36 21.72 

 03 2,059 10.87 4.81 15.69 

Wisconsin 01 6,822 11.26 15.20 26.46 

 02 4,437 10.02 13.41 23.44 

 03 3,843 10.33 15.61 25.94 

 04 10,824 14.73 15.48 30.20 

 05 5,165 11.99 13.23 25.21 

06 4,185 10.92 13.29 24.21 

 07 3,720 9.24 14.77 24.03

 08 4,101 10.33 14.36 24.70 

Wyoming 00 4,442 7.72 3.92 11.64 

Source: GAO analysis of LP data. 

Page 45 GAO-10-146R Nonprime Mortgages 
Enclosure V 

Page 46 GAO-10-146R Nonprime Mortgages 

Estimating Negative Home Equity 

To estimate the extent of negative home equity among nonprime borrowers in 

major metropolitan areas, we used loan-level information from 

LoanPerformance’s (LP) Asset-backed Securities database and house price 

indexes from the Federal Housing Finance Agency (FHFA) and S&P/Case-Shiller 

as of June 30, 2009. We also reviewed industry and academic literature concerning 

estimates of negative home equity and the methodologies and data used to 

generate house price indexes. 

Comparison of House Price Indexes

FHFA and S&P/Case-Shiller publish a number of house price indexes covering 

different levels of geography. For the negative equity analysis in this report, we 

used the following indexes: 

• FHFA All-Transactions Index (FHFA index)—Calculated using sales data 

from home purchases and appraisal information from mortgage refinancings. 

FHFA publishes both a national version and separate indexes for 384 

metropolitan areas. 

• S&P/Case-Shiller Tiered Price Indices (S&P/Case-Shiller index)—Calculated 

using sales data from home purchases and available for 17 metropolitan 

areas.32 The indexes for each metropolitan area provide separate house price 

trends for low-, middle-, and high-priced homes within each metropolitan area. 

S&P/Case-Shiller also publishes a national house price index. 

The FHFA and S&P/Case-Shiller indexes use the same basic methodology. The 

methodology measures average price changes for single-family homes (excluding 

new construction, condominiums, and cooperatives) based on sales (or for FHFA, 

sales and refinancings) of the same properties at different points in time. This 

approach requires that each property included in the index be sold or refinanced 

at least twice to form a pair of house values (valuation pairs) from which 

appreciation or depreciation can be measured. The use of repeat transactions on 

the same homes helps to control for differences in the quality of the houses in the 

data. 

 

32Unlike the FHFA indexes, the S&P/Case-Shiller indexes do not always use the same geographic 

boundaries as the Office of Management and Budget’s definitions of metropolitan areas. For 

example, S&P/Case-Shiller’s geographic boundaries are more expansive for the New York City 

metropolitan area and more restrictive for the Chicago metropolitan area. 
Enclosure V 

Although they use a similar methodology, the FHFA and S&P/Case-Shiller indexes 

use different data sources and weighting schemes, which contribute to differences 

in the rates of house price appreciation or depreciation that they show. 33

 

• Data—The FHFA indexes are based on data for homes with conventional, 

conforming mortgages—that is, mortgages purchased or securitized by Fannie 

Mae or Freddie Mac that meet the underwriting guidelines of those agencies. 

As a result, the FHFA indexes do not reflect homes with other types of 

financing. In contrast, the S&P/Case-Shiller indexes are based on data for 

properties with a wider range of financing—including subprime loans, jumbo 

mortgages, and mortgages guaranteed by the Federal Housing Administration 

or Department of Veterans Affairs—as well as mortgages purchased or 

securitized by Fannie Mae and Freddie Mac. Also, as previously noted, the 

FHFA indexes used in this report use both sales and appraisal data, while the 

S&P/Case-Shiller indexes use sales data only.34

• Weights—To limit the influence of atypical changes in the value of individual 

homes, the FHFA and S&P/Case-Shiller indexes assign weights to each 

valuation pair. However, they use different weighting schemes. For example, 

FHFA assigns lower weights than S&P/Case-Shiller to data for homes with 

lengthy periods between valuations. Additionally, the S&P/Case-Shiller indexes 

are value-weighted, meaning that price trends for more expensive homes have 

greater influence on estimated price changes than other homes. In contrast, 

FHFA’s index is unit-weighted and therefore assigns each valuation pair the 

same weight, all other things being equal. 

Because of these and other differences between the FHFA and S&P/Case-Shiller 

indexes, they historically have shown different appreciation and depreciation 

rates. Figure 7 illustrates the differences in the FHFA and S&P/Case-Shiller 

national indexes from the first quarter of 2000 through the second quarter of 2009. 

Particularly in recent years, the S&P/Case-Shiller index shows steeper increases 

and declines in home prices than the FHFA index. 

 

33For a detailed discussion of this issue, see Office of Federal Housing Enterprise Oversight 

(OFHEO), Revisiting the Differences between the OFHEO and S&P/Case-Shiller House Price 

Indexes: New Explanations, (January 2008). 

34FHFA also publishes house price indexes that use sales data only. These “purchase-only” indexes 

are available for the nation as a whole, for each Census division and state, and for 25 metropolitan 

areas. 

Page 47 GAO-10-146R Nonprime Mortgages 
Enclosure V 

Figure 7: Quarterly Changes in House Price Appreciation Rates, First Quarter 2000 to 

Second Quarter 2009 

 

Percentage change

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Source: GAO analysis of FHFA All-Transactions Index (national version) and S&P/Case-Shiller U.S. National Home Price Index.

FHFA All-Transactions national index

S&P/Case-Shiller national index

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

Q2Q1 3 Q2Q1Q4Q 3 Q2Q1Q4Q 3 Q2Q1Q4Q 3 Q2Q1Q4Q 3 Q2Q1Q4Q 3 Q2Q1Q4Q 3 Q2Q1Q4Q 3 Q2Q1Q4Q 3 Q2Q1Q4Q

To illustrate the impact of different house price indexes on estimates of negative 

home equity as of June 30, 2009, we compared estimates we made using the FHFA 

index and the S&P/Case-Shiller index. We limited the comparison to nonprime 

borrowers with active loans in 15 of the 16 metropolitan areas for which both 

indexes had identical geographic coverage.35 Across all 15 metropolitan areas, the 

estimated percentages of nonprime borrowers with negative equity were lower 

using the FHFA index compared with the estimates we made using the S&P/CaseShiller index (see fig. 8). The difference ranged from 8.4 percentage points in the 

Denver, Colorado metropolitan area to 73.1 percentage points in the Minneapolis, 

Minnesota metropolitan area. 

 

35Both indexes also have identical coverage for the Cleveland, Ohio metropolitan area, but as of 

November 2009 the S&P/Case-Shiller index did not include 2009 data for that area. As a result, we 

did not estimate negative home equity for the Cleveland metropolitan area. 

Page 48 GAO-10-146R Nonprime Mortgages 
Enclosure V 

Figure 8: Estimates of Negative Equity in Selected Metropolitan Areas Using the FHFA and 

S&P/Case-Shiller Indexes 

Percentage of borrowers with negative equity

0

20

40

60

80

100

Washington, DC

Tampa, FL

Seattle, WA

San Francisco, CA

San Diego, CA

Phoenix, AZ

Portland, OR

Minneapolis, MN

Miami, FL

Los Angeles, CA

Las Vegas, NV

Chica

Denver, CO

go, IL

Boston, MA

Atlanta, GA

Source: GAO analysis of LP data and data from the FHFA All-Transactions Index and S&P/Case-Shiller Tiered Price Indices.

FHFA All-Transactions Index

S&P/Case-Shiller Tiered Price Indices

Methodology for Estimating Negative Equity

To estimate negative equity nationwide and across metropolitan areas, and for 

certain loan and borrower characteristics, we analyzed LP data and house price 

data from the FHFA index and the S&P/Case-Shiller index. We used LP variables 

for property state and ZIP code, loan origination date, loan origination amount, 

appraised property value at loan origination, and current loan balance. We also 

used LP variables indicating the loan class (subprime or Alt-A), loan purpose, loan 

product, and whether the loan was made to an owner-occupant to disaggregate 

our estimates by loan and borrower characteristic. 

Our overall methodology was as follows. First, using the LP data, we identified 

first-lien loans originated from 2000 through 2007 that were still active (i.e., had 

not prepaid or completed the foreclosure process) as of the end of the second 

quarter of 2009 (June 30). Due to data limitations, our analysis did not account for 

any second liens that the borrowers had on their properties. To the extent that 

borrowers had second liens, our analysis may understate the extent of negative 

home equity. Second, we used Census data files and mapping software to 

associate the records for those loans (which contain the state and ZIP code of the 

mortgaged property) with house price index data (which are available by 

metropolitan statistical area or county). We excluded from our analysis loans for 

properties outside of the geographic areas covered by the house price 

Page 49 GAO-10-146R Nonprime Mortgages 
Enclosure V 

indexes.36 Third, we estimated the extent to which the home associated with each 

loan had changed in value. To do so, we calculated the percentage change in the 

value of the corresponding house price index from the quarter the loan was 

originated through the second quarter of 2009. We then estimated the home’s 

value at the end of the second quarter of 2009 by adjusting the appraised home 

value at loan origination by the percentage change in the house price index. 

Finally, we estimated the borrower’s home equity by subtracting the loan balance 

at the end of the second quarter of 2009 from our estimate of the updated home 

value. When the loan balance exceeded the updated home value, we considered 

the borrower to be in a negative equity position. Because of data limitations, we 

could not identify borrowers with multiple mortgaged properties. To the extent 

that some borrowers had more than one mortgaged property, our results may 

overstate the actual number of individual borrowers with negative home equity. 

To examine the extent of negative equity by loan origination year and nationally, 

we used the FHFA index and LP loan-level data for the 384 metropolitan statistical 

areas (MSA) that the FHFA index covered.37 For each loan that was active as of 

the end of the second quarter of 2009, we estimated the borrower’s home equity as 

of that date using the methodology described above. We then aggregated these 

loan-level estimates to calculate the number and percentage of borrowers with 

negative equity by annual loan cohort and for MSAs nationwide.38 For borrowers 

with negative home equity, we calculated the total and median dollar amount of 

negative equity, as well as the distribution of borrowers across different ranges of 

negative equity (in terms of both dollars and a percentage of the current home 

value). Additionally, we used our loan-level estimates of home equity to calculate 

the number and percentage of borrowers nationwide with near negative equity—

that is, home equity of 0 to 5 percent. 

To estimate the extent of negative home equity in specific MSAs and by loan and 

borrower characteristic, we used the S&P/Case-Shiller index and LP data for 16 of 

the 17 MSAs that the indexes covered. As of November 2009, the S&P/Case-Shiller 

index did not include 2009 data for the Cleveland, Ohio MSA. As a result, we did 

not estimate negative home equity for that MSA. The S&P/Case-Shiller index 

includes separate indexes for homes in different price ranges—low, middle, and 

high. Accordingly, for each loan record, we used the home’s appraised value at 

loan origination to determine the appropriate tiered index with which to update 

the home value. Using the methodology described previously, we estimated the 

borrower’s home equity for each loan that was active as of the end of the second 

 

36For this reason, we excluded 379,230 records (7.7 percent) from our analysis of 384 metropolitan 

areas using the FHFA index. 

37A minimum of 1,000 observations per quarter are required for an MSA to be included in FHFA’s 

index, so the number of MSAs the index includes can fluctuate from quarter to quarter. 

38Our estimates of negative equity nationwide only reflect mortgaged properties in the 384 MSAs 

captured by the FHFA index. These MSAs account for about 84 percent of the U.S. population. 

Page 50 GAO-10-146R Nonprime Mortgages 
Enclosure V 

quarter of 2009. We then aggregated these loan-level estimates to calculate the 

number and percentage of borrowers in each of the 16 MSAs that had negative 

home equity at the end of the second quarter of 2009. We also aggregated the loanlevel estimates by loan class (subprime or Alt-A), loan purpose (purchase, cashout refinance, or no-cash-out refinance), loan product (fixed-rate mortgage, shortterm hybrid adjustable-rate mortgage (ARM), payment-option ARM, long-term 

ARM, and other ARM), and borrower type (owner-occupant or nonowner 

occupant). 

Page 51 GAO-10-146R Nonprime Mortgages 
Enclosure VI 

Page 52 GAO-10-146R Nonprime Mortgages 

 

Demographic Characteristics of Nonprime Borrowers in 2005 

Data limitations have complicated efforts to analyze the demographic 

characteristics of nonprime borrowers, such as race and ethnicity. Existing data 

sets either provide detailed information about nonprime loans but limited 

information about the borrowers (e.g., LoanPerformance data) or have more 

extensive information about borrowers but do not specify which loans are 

nonprime (e.g., Home Mortgage Disclosure Act data). To examine the 

demographic characteristics of nonprime borrowers with loans originated in 2005 

(the peak year for nonprime originations), we extracted a 2 percent random 

sample of records in the LoanPerformance (LP) database and matched them to 

Home Mortgage Disclosure Act (HMDA) records. We achieved a match rate of 

approximately 74 percent, representing about 55,000 records. Of these, about 

35,200 were for subprime loans and about 19,800 were for Alt-A loans. (See 

enclosure VII for a detailed discussion of our methodology.) 

From our analysis of the matched loan records, we estimate that about 67 percent 

of the nonprime borrowers with loans originated in 2005 were White, while 14 

percent were Black or African-American, and 4 percent were Asian.39

Approximately 2 percent of the borrowers were American Indian, Alaska Native, 

Native Hawaiian, or Other Pacific Islander.40 For about 14 percent of the 

borrowers, the HMDA data did not contain information about race. In addition, we 

estimate that 18 percent of nonprime borrowers identified their ethnicity as 

Hispanic or Latino.41 

White borrowers accounted for a smaller estimated proportion of the nonprime 

mortgage market than they did of the mortgage market as a whole, while Black or 

African-American borrowers and Hispanic or Latino borrowers accounted for 

larger proportions. HMDA data for first-lien mortgages originated in 2005 for oneto four-unit properties indicate that approximately 74 percent of the borrowers 

were White and 8 percent were Black or African-American. Slightly more than 12 

percent did not provide information on race. About 11 percent of borrowers in the 

HMDA data identified their ethnicities as Hispanic or Latino. 

As shown in table 9, White borrowers accounted for a higher estimated proportion 

of Alt-A loans (73 percent) than subprime loans (63 percent). The same pattern 

was true for Asian borrowers. In contrast, we estimate that Black or AfricanAmerican borrowers accounted for a higher proportion of subprime loans (17 

39In this report, we use the race and ethnicity categories defined in the HMDA data. As previously 

noted, the LP data we used for our analysis do not cover the entire nonprime market but do cover 

the large majority of nonagency securitized mortgages within that market. 

40In our data tables we combined these racial groups into an “other” category. 

41Individuals who classify themselves as Hispanic or Latino include people of different racial 

backgrounds. 
Enclosure VI 

percent) than Alt-A loans (7 percent). Hispanic or Latino borrowers also 

accounted for a higher estimated percentage of subprime loans (19 percent) than 

Alt-A loans (15 percent), while the reverse was true for non-Hispanic or -Latino 

borrowers, who obtained 66 percent of the subprime loans and 73 percent of the 

Alt-A loans. 

Table 9: Estimated Percentage of Nonprime Borrowers with Subprime and Alt-A Loans, by 

Race and Ethnicity 

Percent 

Borrower category Number of borrowers Subprime Alt-A 

Race 

White 36,721 63 73

Black or AfricanAmerican 

7,423 17 7 

Asian 2,223 3 6 

Other 924 2 2 

Not reported 7,636 15 12 

Ethnicity 

Hispanic or Latino 9,691 19 15 

Non-Hispanic or 

-Latino 

37,797 66 73

Not reported 7,538 14 13

Source: GAO analysis of LP and HMDA data. 

Note: Figures in table are from our analysis of the approximately 55,000 loans for which we were able to 

match LP records with HMDA records. All percentage estimates in this table have 95 percent confidence 

intervals that are within plus or minus 0.7 percentage points of the estimate itself. 

Across all races and ethnicities, most nonprime borrowers obtained a short-term 

hybrid adjustable-rate mortgage (ARM), the most common subprime mortgage 

product. However, higher estimated proportions of Black or African-American 

borrowers and Hispanic or Latino borrowers received short-term hybrid ARMs 

compared with other racial and ethnic groups (see table 10). For example, we 

estimate that about 69 percent of Black or African-American borrowers obtained a 

short-term hybrid ARM, compared with about 52 percent of White borrowers. The 

proportion of borrowers who obtained a payment-option ARM varied 

considerably by racial and ethnic category, ranging from about 3 percent of Black 

or African-American borrowers to 18 percent of Asian borrowers. Across all racial 

and ethnic groups, the estimated proportion of fixed-rate mortgages was more 

even, ranging from 18 percent for Hispanic or Latino borrowers to roughly onequarter or more for White and non-Hispanic or -Latino borrowers. 

Page 53 GAO-10-146R Nonprime Mortgages 
Enclosure VI 

Table 10: Estimated Percentage of Borrowers that Obtained Different Nonprime Loan 

Products, by Race and Ethnicity 

Percent 

Borrower 

category 

Number of 

borrowers

Fixed-rate 

loans

Shortterm 

hybrid 

ARM

Paymentoption 

ARM 

Longerterm ARMa

Other 

ARM

Race 

White 36,721 26 52 9 10 3

Black or 

AfricanAmerican 

7,423 21 69 3 5 1

Asian 2.322 21 44 18 13 4

Other 924 22 55 10 10 3

Not reported 7,636 27 54 8 9 2

Ethnicity 

Hispanic or 

Latino 

9,691 18 63 9 7 2

NonHispanic or 

-Latino 

37,797 27 52 8 10 2

Not reported 7,538 27 54 8 9 2

Source: GAO analysis of LP and HMDA data. 

Note: Figures in table are from our analysis of the approximately 55,000 loans for which we were able to 

match LP records with HMDA records. Percentage estimates by race in this table have 95 percent 

confidence intervals that are within plus or minus 3.2 percentage points of the estimate itself. For ethnicity 

categories, the percentage estimates have 95 percent confidence intervals that are within plus or minus 1.1 

percentage points of the estimate itself. 

a

Longer-term ARMs have interest rates that are fixed for 5, 7, or 10 years before adjusting. 

In a follow-on study to this report, we will expand on this analysis by analyzing a 

larger pool of nonprime borrowers and examining the demographic 

characteristics of borrowers with troubled loans and negative home equity. 

Page 54 GAO-10-146R Nonprime Mortgages 
Enclosure VII 

Page 55 GAO-10-146R Nonprime Mortgages 

Matching LoanPerformance and Home Mortgage Disclosure Act Records 

Data Sources

To describe the race and ethnicity of nonprime borrowers, we matched loan-level 

records from two primary data sources, LoanPerformance’s (LP) Asset-backed 

Securities database and Home Mortgage Disclosure Act (HMDA) data compiled by 

the Federal Financial Institutions Examination Council. The 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.42 In 

contrast, HMDA data contain limited information about loan characteristics and 

nothing about performance, but do provide information on borrowers’ race, 

ethnicity, and 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 should therefore capture most of the loans in the LP 

database. 

While the 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-byloan basis. We will discuss in more detail issues related to data compatibility and 

completeness that affected the matching process we developed. 

To conduct our analysis, we extracted from the LP database a 2 percent random 

sample of loans originated in 2005 for a total of 74,079 loans.43 We selected 2005 

originations because the LP database showed the highest number of nonprime 

originations in that year. Our sample included conventional first-lien purchase and 

refinance loans to owner-occupants, investors, and owners of second homes. We 

excluded loans for units in multifamily structures and for manufactured housing, 

loans in Puerto Rico and the Virgin Islands, and loans with terms other than 15, 

30, or 40 years. 

We used the HMDA data file for 2005. As with the LP data, we focused on first lien 

purchase and refinance loans originated in 2005. We excluded loans in which the 

property type was something other than one- to four-family residential units. 

Because the LP database contained only conventional loans in private label 

 

42The debt-service-to-income ratio is the borrower’s total monthly debt service payments divided 

by monthly gross income. 

43We also included some loans with origination dates in December 2004 or January 2006 if there 

was evidence to suggest those loans might have originated in January 2005 or December 2005, 

respectively, and therefore match to loans in the 2005 HMDA file. We discuss this origination 

month issue later. 
Enclosure VII 

securitizations, we also excluded 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. This process 

resulted in 8,781,084 HMDA loan records. 

Steps Taken to Make the Data Sets Compatible

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 LP loan amount 

to the nearest $1,000 and aggregating the three LP refinance categories into one. 

However, the process was more complicated for origination date and property 

location.44 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 LP records. However,

the originator’s name was useful in assessing the quality of the matches that we 

made using other data elements. 

 

Loan Origination Dates 

We found two issues with the origination date field in the LP data. First, almost 18 

percent of loans in our LP sample had an origination date that was the first day of 

a month.45 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 rather than the 

beginning of each month (see fig. 9). Because of this inconsistency, we relied on 

origination month rather than origination day to match loan records. 

44For privacy reasons, the origination date is omitted from each HMDA record when it is publicly 

released. We requested and obtained the date fields from the Federal Financial Institutions 

Examination Council, which compiles and publishes the HMDA data. 

45This pattern reflects LP’s practice of imputing the origination month for some loans based on the 

month in which the first payment is due. In these cases, LP records the origination date as the first 

day of the imputed origination month. 

Page 56 GAO-10-146R Nonprime Mortgages 
Enclosure VII 

Figure 9: Distribution of Origination Days in the LP and HMDA Data 

Percentage

0

5

10

15

20

Source: GAO analysis of LP and HMDA data.

LP loan records

2005 HMDA loan records

21 3 7654 8 1211109 3 171615141 8 222120191 3 272625242 8 30292

Day of origination month

31

Second, the LP data showed that the first mortgage payment month was generally 

2 months after the origination month but could also be 1 month—mostly for loans 

that originated early in a month—or 3 months—mostly for loans that originated 

later in a month (see fig. 10). 

Page 57 GAO-10-146R Nonprime Mortgages 
Enclosure VII 

Figure 10: Relationship Between Origination Month and First Payment Month in the LP 

Data 

Percentage

0

20

40

60

80

100

Source: GAO analysis of LP data.

First payment 3 months after origination month

First payment 2 months after origination month

First payment 1 month after origination month

21 3 7654 8 1211109 3 171615141 8 222120191 3 272625242 8 30292

Day of origination month

31

To address these issues, we matched some LP loan records twice, once using the 

origination month provided in the LP data and a second time using a replicated LP 

loan record (e.g., same loan amount and purpose) with an adjusted origination 

month. For the replicated records, we moved the origination month back a month 

if the first payment was due the next month and forward if the first payment was 

not due for 3 months. For instance, if a loan originated in April and the first 

payment month was due in May, we adjusted the origination month to March. But 

if a loan originated in April and had a first payment month of July, we adjusted the 

origination month to May. 

Property Location 

The LP and HMDA data provided different geographic identifiers for loans, with 

the LP data providing the ZIP code and the HMDA data the census tract. To 

facilitate record matching based on property location, we related the census tract 

information in the HMDA data to a corresponding ZIP code or ZIP codes in the 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 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 LP records in that ZIP code. All 

Page 58 GAO-10-146R Nonprime Mortgages 
Enclosure VII 

HMDA records in tracts with more than one ZIP code were candidates to match 

LP records in those ZIP codes. 

Matching Methodology

We matched loan records in the LP and HMDA data sets as follows. First, we 

made initial matches by identifying LP and HMDA loans with the same property 

ZIP code (based on the ZIP code-census tract combinations discussed previously), 

origination month, loan amount, and loan purpose. After finding all possible 

HMDA matches for each LP record, we classified these initial matches as either 

one-to-one matches (LP records with one corresponding HMDA record), one-tomany matches (LP records with more than one corresponding HMDA record), or 

nonmatches (LP records with no corresponding HMDA record). One-to-one 

matches accounted for 54.7 percent of our LP data set, one-to-many matches 

accounted for 30.9 percent, and nonmatches accounted for 14.3 percent. 

We believe that the LP records that we were unable to match to HMDA records 

were similar in important respects to LP records that we could match. For 

instance, loans in subprime pools represented 61 percent of the overall LP sample 

and 61.5 percent of matched loans. Purchase loans represented 46.6 percent of the 

overall LP sample and 47.9 percent of matched loans. In terms of geography, state 

shares of unmatched and matched loans were similar. Loans in California 

represented 22.8 percent of the full LP sample and 22.5 percent of matched 

records. Further, subprime borrowers with unmatched records had a median 

credit score of 615, compared with 621 for matched records. Likewise, Alt-A 

borrowers with unmatched LP records and Alt-A borrowers with matched records 

had identical median credit scores of 709. Unmatched LP records in general had 

slightly higher loan amounts, with the differences between matched and 

unmatched Alt-A loan values being a little more pronounced. For instance, the 

median loan amount for unmatched Alt-A records was $259,000, compared with 

$227,250 for matched Alt- A records. This could be related to the somewhat 

greater representation in the set of unmatched LP records of loans in California, 

where house prices and loan amounts were high. 

Quality Checks

We performed three types of quality checks on our initial one-to-one and one-tomany matches. First, we used information about the loan originator—information 

that was included in both the 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. But in more 

than 40 percent of the LP records in our sample, 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 

Page 59 GAO-10-146R Nonprime Mortgages 
Enclosure VII 

Whitaker;” “Taylor, Bean & Whitaker;” “TaylorBean;” “TBW;” and “TBW Mortgage 

Corp.” all referred to HMDA respondent “Taylor, Bean & Whitaker.” For LP loans 

with originator information, we standardized the originator names in the LP data, 

and we used these same originator names for the HMDA data. We compared the 

standardized originator names in matched records. 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 LP record. 

Second, for 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, some typically did not make the 

originator information available. In these cases, if the 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 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 LP data. For an originator-issuer 

combination to be a business relationship, a combination had to appear at least 

five times in our set of initial one-to-one matches and meet one of two criteria. 

Specifically, either the originator must have made 5 percent of the issuer’s 

securitized loans or the issuer had to have securitized 5 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 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 LP and HMDA characterizations of whether the borrower was an 

owner-occupant or not. In some cases, we were able to classify an initial match as 

a robust match if LP and HMDA owner-occupant characteristics matched. Overall, 

we produced robust matches for about 74 percent of the records in our LP data 

set, including about 78 percent of the loans in subprime pools and 69 percent of 

the loans in Alt-A pools (see table 11). 

Page 60 GAO-10-146R Nonprime Mortgages 
Enclosure VII 

Table 11: Results of the Matching Process, LP to HMDA Loan Records 

Initial matches to HMDA 

records 

Robust matches to HMDA 

records 

Market segment 

Number of LP 

records Number Percentage Number Percentage 

Subprime 45,175 39,040 86.4 35,196 77.9 

Alt-A 28,904 24,413 84.5 19,830 68.6 

Total 74,079 63,453 85.7 55,026 74.3 

Source: GAO analysis of LP and HMDA data. 

Page 61 GAO-10-146R Nonprime Mortgages 
Enclosure VIII 

Page 62 GAO-10-146R Nonprime Mortgages 

GAO Contact and Staff Acknowledgments 

GAO Contact: William B. Shear, (202) 512-8678 or shearw@gao.gov

Staff Acknowledgments: In addition to the individual named above, Steve Westley 

(Assistant Director), William Bates, Stephen Brown, Emily Chalmers, Eric 

Charles, DuEwa Kamara, Jamila Kennedy, John McGrail, John Mingus, Colleen 

Moffatt, Marc Molino, Bob Pollard, and Mark Ramage made key contributions to 

this report. 

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