a

GAO

United States Government Accountability Office

Report to the Chairman, Subcommittee 

on Housing and Community Opportunity, 

Committee on Financial Services, House 

of Representatives

April 2006 MORTGAGE 

FINANCING

HUD Could Realize 

Additional Benefits 

from Its Mortgage 

Scorecard

GAO-06-435
What GAO Found

United States Government Accountability Office

Why GAO Did This Study

Highlights

Accountability Integrity Reliability

April 2006

MORTGAGE FINANCING 

HUD Could Realize Additional Benefits 

Highlights of GAO-06-435, a report to the from Its Mortgage Scorecard 

Chairman, Subcommittee on Housing and 

Community Opportunity, Committee on 

Financial Services, House of 

Representatives 

Along with private mortgage 

providers, the Department of 

Housing and Urban Development’s 

(HUD) Federal Housing

Administration (FHA) has been 

impacted by technological 

advances that began in the mid1990s and that have significantly 

affected the way the mortgage

industry works. As a result, in 

2004, FHA implemented

Technology Open to Approved

Lenders (TOTAL) Scorecard—an 

automated tool that evaluates the 

majority of new loans insured by 

FHA. However, questions have 

emerged about the effectiveness of

TOTAL. Given these concerns, you 

asked GAO to evaluate the way the 

agency developed and uses this

new tool. This report looks at (1) 

the reasonableness of FHA’s 

approach to developing TOTAL and

(2) the potential benefits to HUD of

expanding its use of TOTAL. 

What GAO Recommends

To improve how HUD uses and 

benefits from TOTAL, GAO 

recommends that the Secretary of

HUD (1) develop policies for 

updating TOTAL, including using 

updated data, testing additional 

variables, and exploring the

benefits of alternative modeling

approaches, and (2) explore 

additional uses of TOTAL. HUD 

did not explicitly agree or disagree 

with our recommendations but 

indicated that it was taking some 

steps to update TOTAL and explore

different uses for it. 

Some of the choices that FHA made during the development process could 

limit TOTAL’s effectiveness, although overall the process was reasonable. 

Like the private sector, FHA and its contractor used many of the same 

variables, as well as an accepted modeling process, to develop TOTAL. 

However, the data that FHA and its contractors used to develop TOTAL were 

12 years old by the time FHA implemented the scorecard, and the market 

has changed significantly since then. Also, FHA, among other things, 

• did not develop a formal plan for updating TOTAL on a regular basis, 

• did not include all the important variables that could help explain 

expected loan performance, and 

• selected a type of model that limits how the scorecard can be used. 

Despite potential problems with TOTAL, HUD could still see added benefits 

from it. As a result of TOTAL, FHA lenders and borrowers have seen two 

new benefits--less paperwork and more consistent underwriting decisions. 

However, FHA could gain additional benefits if, like private lenders and 

mortgage insurers, it put TOTAL to other uses (see table). These uses

include relying on TOTAL to help inform general management decision 

making, price products based on risk, and launch new products. Adopting 

these scorecard uses from the private sector could potentially generate three

other benefits for FHA, including the ability to react to changes in the 

market, more control over its financial condition, and a broader customer 

base. Additionally, HUD’s Government National Mortgage Association, a 

government corporation that guarantees securities of federally insured or 

guaranteed mortgage loans, could use credit scores that are used by TOTAL 

to help improve the transparency of the secondary mortgage market. 

FHA Could Benefit Significantly More from TOTAL 

Scorecard benefits 

Scorecards previously

used by FHA 

TOTAL 

scorecard 

Past/present benefits 

Ability to adjust underwriting standards X X

Majority of loans automatically underwritten X X

Faster decisions X X

Objective underwriting X X

Less paperwork for lenders X 

More consistent underwriting decisions X 

Potential benefits 

Ability to react to changes in the market X 

More control over financial condition X 

Broader customer base X 

Source: GAO.

www.gao.gov/cgi-bin/getrpt?GAO-06-435.

To view the full product, including the scope 

and methodology, click on the link above. 

For more information, contact William B. 

Shear, (202) 512-8678, shearw@gao.gov. 
Page i GAO-06-435 Mortgage Financing

Contents

Letter 1

Results in Brief 2

Background 4

FHA’s Approach to Developing TOTAL Was Generally Reasonable, 

but Some of Its Choices Could Limit TOTAL’s Effectiveness 8

HUD Could Benefit Significantly More from TOTAL 15

Conclusions 20

Recommendations for Executive Action 21

Agency Comments and Our Evaluation 21

Appendixes

Appendix I: Scope and Methodology 26

Appendix II: Products That Lenders Can Underwrite with TOTAL 27

Appendix III: Comments from the Department of Housing and Urban 

Development 28

Appendix IV: GAO Contact and Staff Acknowledgments 31

Table Table 1: TOTAL Has Generated Added Benefits 16

Figure Figure 1: FHA’s Automated Mortgage Underwriting Process 8
Contents

Page ii GAO-06-435 Mortgage Financing

Abbreviations

ARM adjustable-rate mortgage

CLUES Countrywide Loan Underwriting Expert System

ECOA Equal Credit Opportunity Act

FHA Federal Housing Administration

Ginnie Mae Government National Mortgage Association 

HUD U.S. Department of Housing and Urban Development

LTV loan-to-value ratio

MGIC Mortgage Guaranty Insurance Corporation

MMI Mutual Mortgage Insurance Fund

TOTAL Technology Open to Approved Lenders 

This is a work of the U.S. government and is not subject to copyright protection in the 

United States. It may be reproduced and distributed in its entirety without further 

permission from GAO. However, because this work may contain copyrighted images or 

other material, permission from the copyright holder may be necessary if you wish to 

reproduce this material separately.
Page 1 GAO-06-435 Mortgage Financing

United States Government Accountability Office

Washington, D.C. 20548

A

April 13, 2006 Letr

The Honorable Robert W. Ney

Chairman, Subcommittee on Housing 

and Community Opportunity

Committee on Financial Services

House of Representatives

Dear Mr. Chairman:

Since its inception in 1934, the Department of Housing and Urban 

Development’s (HUD) Federal Housing Administration (FHA) has provided 

mortgage insurance for nearly 33 million properties, often for low-income, 

minority, and first-time homebuyers. Along with private mortgage 

providers, FHA has been impacted by technological advances that began in 

the mid-1990s and that have significantly affected the way the mortgage 

industry works. Among the most important of these innovations are the 

automated underwriting systems that mortgage providers now use to 

process loan applications. 1

 With automated underwriting, lenders enter 

information on potential borrowers into electronic systems that contain an 

evaluative formula, or algorithm, called a scorecard. The scorecard uses a 

variety of variables that include the borrower’s characteristics (credit score 

and cash reserves, for example) and loan characteristics to calculate the 

applicants’ creditworthiness.2

In the mid-1990s, Freddie Mac and Fannie Mae developed the first 

automated underwriting systems and scorecards—Freddie Mac’s Loan 

Prospector and Fannie Mae’s Desktop Underwriter—that could be used to 

evaluate applications for FHA-insured loans and inform FHA’s underwriting 

standards. However, these two systems’ scorecards sometimes generated 

conflicting results for the same borrower. In part because FHA did not have 

access to these systems’ proprietary scorecards, the agency chose to 

1

Underwriting refers to a risk analysis that uses information collected during the origination 

process to decide whether to approve a loan. Different mortgage providers may have 

different underwriting standards. 

2

Credit scores, which assign a numeric value to a borrowers’ credit history, have become a 

popular tool in assessing applications for loans. They are often called “FICO scores” 

because most scores are produced with software developed by Fair Isaac Corporation. 

FICO scores generally range from 300 to 850, with higher scores indicating better credit 

history. The lower the credit score, the more compensating factors lenders might require to 

approve a loan, such as a higher down payment or greater borrower reserves.
Page 2 GAO-06-435 Mortgage Financing

replace them with its own. In addition, HUD wanted to modernize its 

processes and improve its delivery to its business partners. Between 1998 

and 2004, FHA worked with HUD’s contractor, Unicon Research 

Corporation, to develop and implement the Technology Open to Approved 

Lenders (TOTAL) scorecard. Since 2004, FHA and its lenders have used 

TOTAL to evaluate applications for FHA-insured loans and inform 

underwriting standards.

Recently, questions have emerged about the effectiveness of TOTAL 

Scorecard, as well as concerns that FHA has not fully explored all possible 

uses of this new tool. Given these concerns, you asked us to evaluate the 

way the agency developed and uses this new tool. This report looks at (1) 

the reasonableness of FHA’s approach to developing TOTAL and (2) the 

potential benefits to HUD of expanding its use of TOTAL.

To assess the reasonableness of FHA’s approach to developing TOTAL, we 

reviewed agency documents and interviewed officials from HUD and 

Unicon Research Corporation to determine (1) the process used to develop 

TOTAL, (2) the reliability of the analysis used to evaluate it, and (3) the 

methods FHA used to establish policies on cut points (i.e., the points of 

separation within a population of mortgage scores that divide applications 

that are accepted from those that are not). To assess the benefits to FHA of 

expanding its use of TOTAL, we reviewed existing research on the uses and 

benefits of scorecards and interviewed private sector companies, 

academics, and HUD officials about these issues. We compared FHA’s use 

of TOTAL with the private sector’s use of scorecards in order to determine 

whether FHA could benefit from any private sector practices. We also 

examined the extent to which opportunities exist for FHA to extend the use 

of TOTAL, and the data it utilizes, throughout HUD by sharing information 

with other HUD offices that could benefit from it. Appendix I contains 

details of our scope and methodology, and appendix II contains 

information on the products that lenders can underwrite with TOTAL. We 

conducted our work in Washington, D.C., between April 2005 and February 

2006 in accordance with generally accepted government auditing 

standards. 

Results in Brief Some of the choices FHA made during the development process could 

affect TOTAL’s effectiveness, although overall the process was reasonable. 

Like the private sector, FHA and its contractor used variables that reflected 

borrower and loan characteristics to create TOTAL, as well as an accepted 

modeling process to test the variables’ accuracy in predicting default. As a 
Page 3 GAO-06-435 Mortgage Financing

result, FHA and its contractors were able to create a scorecard similar to 

those used by private sector organizations. However, certain choices made 

while TOTAL was being developed and implemented could limit its 

effectiveness. For example, the data that FHA and its contractors used to 

develop TOTAL were 12 years old by the time FHA implemented the 

scorecard. The market has changed significantly since 1992, in part 

because many borrowers have lower credit scores and receive down 

payment assistance. FHA’s TOTAL does not take these market changes into 

account. In addition, among other things, FHA

• did not develop a formal plan for updating TOTAL on a regular basis, 

• did not include all the important variables that could help explain 

expected loan performance, 

• selected a type of model that limits the uses to which the scorecard can 

be put, and 

• did not base cut points on the loan data used to develop TOTAL.

HUD could see more benefits from TOTAL scorecard by expanding its use 

of this tool. As a result of TOTAL, FHA lenders and borrowers have seen 

two added benefits—less paperwork and more consistent underwriting 

decisions. Private lenders and mortgage insurers, however, put their 

scorecards to other uses, relying on them to help inform general 

management decision making, price products based on risk, launch new 

products, as well as regularly updating them. By increasing their use of 

scorecards, these lenders and brokers not only reduce application time and 

see more consistent results from underwriters but also are able to broaden 

their customer base and improve their financial performance. Adopting 

these “best practices” from the private sector could generate similar kinds 

of benefits for FHA. Additionally, HUD’s Government National Mortgage 

Association (Ginnie Mae), which guarantees the timely payment of 

principal and interest on securities issued by private institutions and 

backed by pools of federally insured or guaranteed mortgage loans, could 

use credit scores utilized by TOTAL to improve the transparency of the 

secondary market for securities backed by FHA-insured loans. 

To improve how HUD uses and benefits from TOTAL, we recommend that 

the Secretary of HUD develop policies and procedures for regularly 

updating TOTAL and explore additional uses of TOTAL and the credit data 

it utilizes. In comments on a draft of the report, HUD did not explicitly 
Page 4 GAO-06-435 Mortgage Financing

agree or disagree with our recommendations but indicated that it was 

taking some steps to update TOTAL and explore different uses for it.

Background Congress established FHA in 1934 under the National Housing Act (Pub. L. 

No. 73-479) to broaden homeownership, protect and shore up lending 

institutions, and stimulate employment in the building industry. FHA’s 

single-family programs insure private lenders against losses from borrower 

defaults on mortgages that meet FHA criteria and that are made primarily 

to low-income, minority, and first-time homebuyers of properties with one 

to four housing units. In 2004, some 77.5 percent of FHA loans went to firsttime homebuyers, and 35 percent of these loans went to minorities. FHA 

insures most of its single-family mortgages under its Mutual Mortgage 

Insurance Fund (MMI Fund), which is supported by borrowers’ insurance 

premiums. 

FHA insures a variety of mortgages that cover initial home purchases, 

construction and rehabilitation, and refinancing. Its primary program is 

Section 203(b), the agency’s standard product for single-family dwellings. 

As the mortgage industry has developed products such as adjustable-rate 

mortgages (ARM), FHA has followed suit and now insures ARMs on singlefamily properties. FHA insures a variety of refinancing products, including 

mortgages designed to promote energy efficiency. Finally, it insures 

specialty mortgages, such as the Hawaiian Home Lands mortgage, which 

enables eligible native Hawaiians to obtain insurance for a mortgage on a 

homestead lease granted by the Department of Hawaiian Home Lands.

Despite the products it insures, the number of loans FHA insures each year 

has fallen dramatically since 2000, largely because lending for conventional 

mortgage products (i.e., mortgages with no federal insurance or guarantee) 

has grown much more rapidly since the late 1980s than mortgages insured 

by government entities such as FHA and the Department of Veterans 

Affairs.3

 As conventional markets have grown, so has the private sector’s 

use of automated underwriting systems, which has streamlined the 

application process and allowed lenders to more quickly assess the risk of 

loans. FHA began approving specific automated underwriting systems for 

lenders in 1996 in an effort to streamline its manual underwriting process. 

When it began delegating underwriting tasks to approved lenders in the 

3

See GAO, Housing Finance: Ginnie Mae Is Meeting Its Mission but Faces Challenges in a 

Changing Marketplace, GAO-06-9 (Washington, D.C.: Oct. 31, 2005).
Page 5 GAO-06-435 Mortgage Financing

1980s, lenders manually underwrote loans before submitting the loan 

applications and required documentation to an FHA field office for 

approval. Once automated underwriting systems for FHA lending came 

into use, “direct endorsement lenders” (i.e., lenders certified by HUD to 

underwrite loans and determine their eligibility for FHA mortgage 

insurance without obtaining prior review) could streamline the loan 

application process by bypassing some documentation requirements.4

According to FHA officials, automated underwriting has allowed FHA to 

reduce the amount of time needed to approve insurance for a loan from 

several days to 1 day.

The key to automated underwriting is a mortgage scorecard algorithm that 

attempts to objectively measure the borrower’s risk of default quickly and 

efficiently by examining the data that has been entered into the system. To 

underwrite a loan, lenders first enter into the electronic system data such 

as application information and credit scores. A scorecard compares these 

data with specific underwriting criteria (e.g., cash reserves and credit 

requirements) using a mathematical formula. Because the scorecard 

electronically analyzes each variable, it can quickly predict the likelihood 

of default. According to FHA officials, this process not only reduces 

underwriting time but also decreases the amount of documentation needed 

to assess the borrower’s credit risk. 

Private mortgage insurers, such as United Guaranty and Mortgage 

Guaranty Insurance Corporation (MGIC), were among the first to develop 

mortgage scorecards in the early 1990s. Beginning in the mid-1990s, 

Freddie Mac and Fannie Mae began to create their own automated 

underwriting systems and scorecards to evaluate conventional loans for 

purchase.5

 More specifically, Freddie Mac implemented its Loan Prospector 

automated underwriting and scorecard tool by 1996, and Fannie Mae 

4

Direct endorsement lenders underwrite the large majority of FHA loans. 

5

Fannie Mae and Freddie Mac are government-sponsored enterprises that purchase 

mortgages from lenders across the country, financing their purchases by borrowing or 

issuing securities backed by the mortgages (mortgage-backed securities). Most of the 

mortgages they purchase are conventional mortgages. 
Page 6 GAO-06-435 Mortgage Financing

implemented a similar tool, Desktop Underwriter, in 1997.6 Experience 

with these scorecards prompted Freddie Mac in 1998 and Fannie Mae in 

1999 to develop versions of these scorecards for FHA that lenders first used 

to automatically underwrite FHA-insured loans. Both entities used 

performance data on FHA-insured loans as part of the loan data used to 

create the FHA versions of their scorecards.

However, while FHA cooperated in the development of Freddie Mac’s and 

Fannie Mae’s scorecards for FHA-insured loans, they were nonetheless 

proprietary to those entities, and some important details (e.g., the 

weighting of the variables) were withheld from FHA. In addition, the two 

scorecards sometimes yielded contradictory results for the same borrower. 

As a result, FHA decided to replace the Loan Prospector and Desktop 

Underwriter scorecards and develop its own scorecard that would provide 

uniform outcomes.7

Between 1998 and 2004, FHA contracted with Unicon Research 

Corporation to develop TOTAL.8

 Direct endorsement lenders now use 

TOTAL in conjunction with automated underwriting systems that meet 

FHA standards—Loan Prospector, Desktop Underwriter, and Countrywide 

Loan Underwriting Expert System (CLUES)—to determine the likelihood 

of default.9

 Although TOTAL can determine the credit risk of a borrower, it 

does not reject a loan; FHA requires lenders to manually underwrite loans 

that are not accepted by TOTAL to determine if the loan should be accepted 

or rejected. 

6

In addition to Fannie Mae’s and Freddie Mac’s automated underwriting systems and 

scorecards, other major lenders we spoke with, such as Countrywide, also have tools that 

they use internally to score conventional loans. These lending companies use TOTAL in 

conjunction with external automated underwriting systems, such as Loan Prospector and 

Desktop Underwriter, to underwrite FHA-insured loans. 

7

HUD rescinded lenders’ authority to use the Loan Prospector and Desktop Underwriter 

scorecards to underwrite FHA-insured loans once TOTAL Scorecard was implemented in 

2004. However, lenders can continue to use Loan Prospector and Desktop Underwriter 

automated underwriting systems in conjunction with TOTAL scorecard to underwrite loans.

8

Fair Isaac Corporation was a subcontractor to Unicon in this effort. Although Unicon was 

the primary contractor FHA used to help develop TOTAL, FHA also contracted with other 

firms to assist with TOTAL’s implementation.

9

CLUES is another automated underwriting system developed by Countrywide that lenders 

can use in conjunction with TOTAL to underwrite FHA-insured loans. 
Page 7 GAO-06-435 Mortgage Financing

FHA’s automated mortgage underwriting process starts at the time that the 

borrower meets with and submits information to the direct endorsement 

lender for loan prequalification (see fig.1). First, the direct endorsement 

lender enters the application variables, such as the applicant’s loan-to-value 

ratio (LTV) and debt, into the automated underwriting system.10 Second, 

the automated underwriting system electronically “pulls” the additional 

credit data required to score the loan, which includes any bankruptcy and 

foreclosure information and credit scores. Third, the automated 

underwriting system transmits the data to TOTAL, which evaluates the 

information and recommends whether the loan should be “referred” or 

“accepted.” A “refer” recommendation requires that the direct endorsement 

lender manually underwrite the loan.11 An “accept” recommendation means 

that the loan does not have to be manually underwritten to determine the 

borrower’s creditworthiness and, accordingly, that less documentation will 

be required to process it. For example, borrowers whose loans are 

accepted do not have to verify their employment history if they have 

already met certain conditions, such as providing confirmation of current 

employment. An accepted application must go through an additional series 

of credit checks, or overrides, to ensure that it meets all of FHA’s 

underwriting standards. If the loan does not pass the series of additional 

credit checks, the application can still be downgraded to a “refer” for 

manual underwriting. Once the loan is processed through the credit 

checks, the automated underwriting system then sends the decision in a 

feedback document that the lender uses to continue processing the loan 

application. 

10LTV is the relationship between the loan amount and the value of the property (the lower 

of the appraised value or sales price) expressed as a percentage of the property's value. 

11TOTAL may refer a loan that was initially accepted if certain conditions are found (e.g., the 

loan would represent an excessive debt burden to the borrower or the borrower has 

experienced bankruptcy or foreclosure) that trigger an override of the initial decision.
Page 8 GAO-06-435 Mortgage Financing

Figure 1: FHA’s Automated Mortgage Underwriting Process

FHA’s Approach to 

Developing TOTAL 

Was Generally 

Reasonable, but Some 

of Its Choices Could 

Limit TOTAL’s 

Effectiveness 

FHA’s approach to developing TOTAL was generally reasonable, but some 

of the decisions made during the development process could ultimately 

limit the scorecard’s effectiveness. Like the private sector, FHA and its 

contractor followed an accepted process, using a variety of variables that 

took into account such items as credit history and economic conditions. As 

a result, TOTAL is similar to private sector scorecards. But TOTAL’s 

effectiveness could be limited by some of the choices that were made 

during the development process, including the fact that (1) the data FHA 

and its contractor used were 12 years old by the time TOTAL was 

implemented, (2) FHA has not developed policies and procedures for 

updating TOTAL, and (3) the benchmark analysis for determining TOTAL’s 

predictive capability may have been inadequate.

R

Direct

endorsement

lender

Automated

underwriting

system

Borrower

Loan

inquiry

Input of underwriting

variables

Underwriting and credit

variables

Refer or

accept/approve

decision

Credit bureaus

provide credit variables

Overrides

Feedback

document

(approve/

refer)

A

R

TOTAL approve/accept decision

TOTAL refer decision (lender will have to manually underwrite loan)

Loan application

Feedback document containing TOTAL decision

Sources: GAO and Nova Development (images).

A

A

A

Manual

underwriting

FHA TOTAL

scorecard

R

R
Page 9 GAO-06-435 Mortgage Financing

The Process FHA and Its 

Contractors Used to 

Develop TOTAL Was 

Generally Reasonable

Scorecards are typically developed and maintained using data with specific 

characteristics and an accepted modeling process. The data—such as, 

variables that reflect credit histories and loan information—are typically 

several years old and are drawn from samples of borrowers whose 

characteristics resemble those of the borrowers whom the scorecard will 

assess. The process used in the private sector to develop the scorecard 

itself typically has four components: 

• identifying the variables that best predict the likelihood of default, 

• choosing a scorecard model by conducting various tests, 

• validating the scorecard to ensure that it is stable (i.e., consistently 

produces reasonable results), and 

• determining the appropriate cut point for separating loans that will be 

accepted from those that will be referred for manual underwriting.

Once the scorecard is complete, many private sector organizations plan for 

and conduct ongoing analyses and generate reports to monitor and update 

their scorecards. Analyses that help in updating scorecards include 

measuring changes in the population of borrowers, the quality of the 

portfolio, and the scorecard’s effectiveness. Organizations may conduct 

these analyses on a monthly and quarterly basis, and they may also 

supplement these analyses with more in-depth reviews. 

In developing TOTAL, FHA’s contractor Unicon followed the four-step 

process. First, it identified variables using data primarily for loans that FHA 

had endorsed (i.e., approved for mortgage insurance) in 1992. In 1998, 

when Unicon began developing TOTAL, FHA chose to use 1992 loan data, 

which would reflect the characteristics of FHA borrowers and be 

“seasoned,” or old enough, to provide a sufficient number of defaults that 

could be attributed to a borrower’s poor creditworthiness. The 1992 sample 

of endorsed loans included 9,867 loans that did not result in a claim default 

and 4,818 that did. Unicon tested the variables’ ability to predict claim 

default. Unicon determined that a number of variables, such as credit, LTV 

ratio, and cash reserves should be included in TOTAL. To determine the 

best type of credit variable for FHA’s purposes to include in TOTAL, Unicon 

and its subcontractor Fair Isaac Corporation used 1994 and 1996 credit 

data to test various credit models and confirm the results. These models 

included those that measured borrowers’ credit using only credit scores 

and more complex models that were based on individual credit 
Page 10 GAO-06-435 Mortgage Financing

characteristics rather than on a credit score. Based on this analysis, FHA 

decided that the standard FICO credit score was a reasonable credit 

variable to include in the scorecard. 

Second, Unicon tested various versions of statistical models suitable for 

developing scorecards. These were variations on two types of models, 

“logit” and “hazard.” Both models predict the probability of default based 

on predictive variables that are weighted according to their statistical 

importance, although the hazard model can predict default over multiple 

time periods. FHA officials stated that, based on Unicon’s analyses, both 

models’ predictive capability were about equal. FHA chose the logit model, 

claiming that it was easier to implement and that its estimates were easier 

to interpret. 

Third, Unicon tested the stability of the model by estimating it against a 

sample of loans from 1992 that had not been included in the original 1992 

data. In addition, Unicon tested the model’s stability over time by checking 

whether the determinants of defaults occurring within 2 years were similar 

for the 1992 and 1994 application years. Both stability tests, according to 

documents provided by FHA, suggested that the model did not materially 

change over the 2-year period. In addition, FHA performed a benchmark 

analysis by comparing the performance of TOTAL with previously used 

scorecards—the FHA versions of Freddie Mac’s Loan Prospector and 

Fannie Mae’s Desktop Underwriter—to determine the model’s precision. 

According to documents provided by FHA, TOTAL slightly outperformed 

the other scorecards.

Finally, FHA worked with Unicon, Freddie Mac, and Fannie Mae to 

determine a cut point for TOTAL that would enable the agency to quickly 

accept the majority of loan applications so that lenders could focus their 

manual underwriting on the marginal, potentially riskier borrowers. This 

cut point was based partly on a 1996 analysis that Freddie Mac, in 

consultation with FHA, conducted on the version of the Loan Prospector 

scorecard developed for FHA. According to HUD officials, it was also 

consistent with cut points that had previously been used before TOTAL was 

implemented. The current cut point allows the agency to accept 65 to 70 

percent of the loan applications automatically and refer the remainder.

In a 2001 report, a consulting firm—KPMG LLP—that reviewed documents 

relating to the development of TOTAL concluded that FHA adequately 

supported most of its development decisions. The report focused on the 
Page 11 GAO-06-435 Mortgage Financing

data used, the type of model selected, the determination of cut points, and 

FHA’s benchmark analysis. 

Some Development and 

Implementation Choices 

Could Limit TOTAL’s 

Effectiveness

Although FHA and its contractor used a reasonable and generally accepted 

practice for developing TOTAL, some of the choices made during that 

process could affect FHA’s ability to maximize its use of the scorecard. 

Data Not Current By the time TOTAL was implemented in 2004, the loans in the development 

sample were 12 years old. Best practices call for scorecards to be based on 

data that are representative of the current mortgage market—specifically, 

relevant data that are no more than several years old. FHA officials told us 

that the relationship between TOTAL’s predictive variables and FHA 

borrowers’ tendency to default had not changed significantly since 1992 

and that they believed the data were still useful. However, since 1992, 

significant changes have occurred in the mortgage industry that have 

affected the characteristics of those applying for FHA-insured loans. These 

changes include generally lower credit scores, increased use of down 

payment assistance, and new mortgage products that have allowed 

borrowers who would previously have needed an FHA-insured loan to seek 

conventional mortgages. As a result, the relationships between borrower 

and loan characteristics and the likelihood of default may also have 

changed. For example, the statistical relationship between the LTV ratio 

and the likelihood of default may be different for borrowers who receive 

down payment assistance than for those who do not. 

No Plan for Regular Updates As noted earlier, when TOTAL was implemented in 2004, FHA officials 

believed that the 1992 loan sample used to develop the scorecard still 

provided an adequate basis for assessing new loan applications. The 

agency’s subsequent analyses of TOTAL using samples of FHA-insured 

loans throughout the 1990s indicate that, for years tested, the scorecard has 

performed consistently in separating loans that resulted in insurance 

claims from those that did not. As a result, HUD did not update TOTAL 

either before it was deployed or subsequently. However, best practices 

implemented by private entities and reflected in guidance from a bank 

regulator call for having formal policies to ensure that scorecards are 

routinely updated. Frequent updating of scorecards ensures that they 

reflect changes in consumer behaviors and thus continue to accurately 

predict the likelihood of default. In September 2004, FHA awarded another 

contract to Unicon to, among other things, update TOTAL by 2007. In 
Page 12 GAO-06-435 Mortgage Financing

addition, HUD indicated that, through its contractors, it has the capacity to 

update TOTAL should the need arise and has contracts for acquiring credit 

data to support an update of the scorecard. However, FHA has not 

developed policies and procedures for updating TOTAL on a regular basis.

Limited Sample of Loans Used 

for Development and Testing

Another potential shortcoming that could affect TOTAL’s effectiveness is 

the fact that FHA used only endorsed loans to develop TOTAL. Because the 

data did not cover all of the possible outcomes of applying for a loan 

(rejection, for example), the results could be biased. Therefore, TOTAL will 

likely assess a population of applications with generally poorer overall 

credit quality than the original population used to develop the scorecard 

and thus may not be as effective in evaluating applicants with poorer 

credit. In addition, because the sample of loans that was used to develop 

TOTAL differed from the total population of loan applications, the selection 

and weighting of the variables in the scorecard could be less than optimal. 

For the riskier applications, the predictive variables and associated 

weightings might differ from those TOTAL currently uses. FHA officials 

stated that, at the time TOTAL was being developed, they did not have 

another choice in the data used. However, updating TOTAL using 

information on marginal loans that were referred by the scorecard, but 

ultimately endorsed for FHA insurance, could help mitigate the bias 

problem.

Similarly, using cut points that were based only on endorsed loans at the 

time TOTAL was developed—in this case, loans that were originated using 

the Loan Prospector scorecard—could mean that a higher percentage of 

loans that are likely to default would be accepted rather than referred for 

manual underwriting. That is, a sample of endorsed loans does not include 

loans that have been rejected and thus does not represent the total 

population of loans. As previously noted, the current cut point allows FHA 

to accept 65 to 70 percent of the total population of loan applications and 

that percentage could include riskier loans—riskier loans that the sample 

did not represent because they were referred by Loan Prospector and 

ultimately rejected. Furthermore, because FHA’s selection of cut points 

was not based on analysis of loans accepted by TOTAL, but rather on loans 

accepted by Loan Prospector, the cut points may prove to be less useful for 

FHA as it attempts to manage and understand its risk. KPMG LLP—the 

consulting firm that reviewed TOTAL’s development in 2001—raised similar 

concerns.

We also found that, similar to the sample of loans used to develop TOTAL, 

the sample FHA used to perform the 1996 benchmark analysis of TOTAL 
Page 13 GAO-06-435 Mortgage Financing

consisted only of endorsed loans, rather than a broader sample that 

included the riskiest loans. Partly because other loan data were not readily 

available, Unicon benchmarked TOTAL against a sample of loans 

originated using the Loan Prospector scorecard. This sample consisted 

primarily of loans that had been accepted by the scorecard and endorsed 

for FHA insurance. However, because all models perform slightly 

differently (i.e., each scorecard will mistakenly accept certain high-risk, or 

“bad” loans), using a prescreened sample of loans could limit the accuracy 

of the benchmark analysis.12 The potential effect on the benchmark 

analysis was to suggest that TOTAL outperformed Loan Prospector. 

However, using a sample of loans that had not been prescreened by Loan 

Prospector might have yielded somewhat different results that would have 

more accurately represented TOTAL’s predictive capabilities.

Excluded Important Variables While TOTAL includes many of the variables included in other mortgage 

scoring systems, it does not include a number of important variables 

included in other systems. For example, the systems used by Fannie Mae 

and Freddie Mac may assign higher risks to adjustable rate loans than to 

fixed-rate loans. ARMs are generally considered to be higher risk than 

otherwise comparable fixed-rate mortgages, because borrowers are 

subject to higher payments if interest rates rise. Further, other scoring 

systems often include indicators for property type (single-family detached, 

two- to four-unit, or condominiums, for example). FHA indicated that these 

variables were not included in TOTAL because the risk associated with 

them did not differ significantly in the 1992 data used to estimate the 

model. However, the 1992 data set was fairly small—fewer than 15,000 

loans—and only about 16 percent of it consisted of ARMs.13 In addition, 

condominiums and multiunit properties are a small component of FHA’s 

business. The modeling effort may have failed to find significant effects for 

these variables simply because of the small numbers of loans with these 

characteristics in the development sample. Previous research by FHA 

contractors on larger samples of FHA loans found that ARMs from this 

12Each institution may define a “bad” loan uniquely. FHA defines a bad loan as a loan 

resulting in an insurance claim that could be attributed to a borrower’s poor 

creditworthiness, rather than subsequent general economic reverses, location-based market 

effects, or other things unrelated to the individual borrower. 

13By contrast, an official from a major lending organization said that they used about 200,000 

loans to develop their scorecard. 
Page 14 GAO-06-435 Mortgage Financing

period were riskier than comparable fixed-rate mortgages.14 The fact that 

FHA’s scoring system does not consider the extra risk inherent in ARMs or 

distinguish between different types of properties, while competitors’ 

systems do, could have important consequences. If marginal applications 

that are ARMs or multiunit properties are rejected by competitors’ systems, 

but accepted by FHA’s, then FHA’s share of these riskier loans may 

increase. Finally, FHA does not include the source of the down payment in 

its scorecard.15 However, research by HUD contractors, HUD’s Inspector 

General, and us have all identified the source of a down payment as an 

important indicator of risk, and the use of down payment assistance in the 

FHA program has grown rapidly over the last 5 years.16 For example, as we 

reported in November 2005, FHA-insured loans with down payment 

assistance have higher delinquency and insurance claim rates than do 

similar loans without such assistance.

Limited Logit Model FHA chose a logit rather than a hazard model as a basis for TOTAL and, 

therefore, potentially limited the variety of uses to which the scorecard can 

be put. While a logit model predicts the probability of default for a specific 

point in time, a hazard model, as previously noted, predicts the probability 

of default over multiple time periods. Because a hazard model captures the 

dynamic between time and loan performance, HUD could use it to project 

cash flows over time and estimate profitability. In addition, a hazard model 

more readily accepts and analyzes recent data, and FHA could update a 

scorecard developed from this model with recent origination data as often 

as it needs. Moreover, with a relatively current scorecard, FHA could 

monitor market changes and TOTAL’s effectiveness at predicting defaults in 

the current climate. Despite the added capabilities of a hazard model, FHA 

14See Technical Analysis Center, Inc., An Actuarial Review of the Federal Housing 

Administration Mutual Mortgage Insurance Fund for Fiscal Year 2004 (Fairfax, VA: Oct. 

19, 2004).

15Although private sector scorecards do not generally include this variable, other mortgage 

industry participants are generally more restrictive than FHA—for instance, they do not 

allow down payment assistance from sellers, even through nonprofit organizations. 

16See Technical Analysis Center, Inc., An Actuarial Review of the Federal Housing 

Administration Mutual Mortgage Insurance Fund for Fiscal Year 2004 (Fairfax, VA: Oct. 

19, 2004); Concentrance Consulting Group, An Examination of Down Payment Gift 

Programs Administered by Non-profit Organizations (Washington, D.C.: Mar. 1, 2005); 

HUD IG, Final Report of Nationwide Audit Down Payment Assistance Programs, 2000-SE121-0001 (Washington, D.C.: Mar. 21, 2000); and GAO, Mortgage Financing: Additional 

Action Needed to Manage Risks of FHA-Insured Loans with Down Payment Assistance, 

GAO-06-24 (Washington, D.C.: Nov. 9, 2005). 
Page 15 GAO-06-435 Mortgage Financing

officials stated that the logit model was sufficient for TOTAL’s intended 

purpose because TOTAL was only intended to be used to rank order 

applications for FHA-insured loans based on the likelihood of default. 

HUD Could Benefit 

Significantly More 

from TOTAL

FHA uses TOTAL Scorecard in much the same way as its two earlier 

scorecards—to inform underwriting standards and assess loan applications 

against those standards. TOTAL has produced more consistent 

underwriting results and, for some lenders, has streamlined the approval 

process and reduced paperwork. Private sector organizations use their 

scorecards more broadly, relying on them to assess risk, help launch new 

products, and broaden their customer base, as well as updating them 

regularly. FHA could realize similar types of benefits from TOTAL to help 

the agency serve low- and moderate-income borrowers while ensuring its 

financial soundness. In addition, the credit data used by TOTAL could help 

to improve the transparency of the secondary market for FHA-insured 

loans. 

FHA Could Realize 

Additional Benefits Using 

TOTAL 

FHA used TOTAL to test variables and identify the most predictive ones, 

which the agency then used to inform its underwriting standards. 

Therefore, TOTAL enables FHA to adjust its underwriting standards, if 

needed, based on analyses of current market conditions—something that 

Desktop Underwriter and Loan Prospector did not readily allow because 

FHA did not have direct access to them. In addition, FHA directs lenders to 

use TOTAL to assess loan applications by entering information that 

corresponds to certain variables.17As with the previous scorecards, the 

only lenders that can directly interface with TOTAL and input loan 

application data into the scorecard via automated underwriting systems are 

direct endorsement lenders. Direct endorsement lenders can assess most 

FHA loan products with TOTAL (see app. II). 

As described in table 1, FHA’s current use of TOTAL has provided 

additional benefits over previous scorecards, such as less paperwork for 

lenders and more consistent underwriting decisions. Loan Prospector and 

Desktop Underwriter had, among other things, helped speed up the 

application process and provided an opportunity to base approvals on 

17Lenders are required to obtain the following application information: type of mortgage and 

terms of loan, property information, borrower information, and employment information. 
Page 16 GAO-06-435 Mortgage Financing

objectively determined variables. TOTAL continues these benefits and, in 

addition, has generated two others. First, as noted earlier, the previous 

scorecards did not always provide consistent underwriting decisions—that 

is, at times the results of their assessments differed, which resulted in the 

same loan being accepted by one scorecard and referred by the other. As a 

result, certain loans had to be approved manually, through potentially 

subjective decision making. TOTAL limits the number of loans that need to 

be approved manually because it provides consistent automatic 

underwriting decisions. Second, lenders that use TOTAL do not have to 

provide as much documentation for the accepted loans they underwrite as 

lenders that do not use TOTAL. For example, these lenders do not have to 

obtain or submit verification of rent, and the requirements for proof of 

income employment and assets are less stringent.18

Table 1: TOTAL Has Generated Added Benefits 

Source: GAO.

Private Sector 

Organizations Benefit from 

Using Scorecards in a 

Variety of Ways

As noted earlier, the key to successfully using a scorecard is ensuring that it 

is updated so that it can provide accurate and useful information. Updated 

scorecards can provide a number of benefits because of the variety of 

potential uses. Private sector organizations we spoke with said that their 

scorecards had produced the same benefits as TOTAL, including reducing 

loan origination times, and enhancing consistency and objectivity in the 

underwriting process. In addition, private sector organizations use their 

18Because TOTAL obtains credit information to automatically assess applications for FHAinsured loans, FHA does not require as much verification as it does for applications that are 

manually underwritten. 

Scorecard benefits

Scorecards previously 

used by FHA TOTAL scorecard

Ability to adjust underwriting standards X X

Majority of loans automatically 

underwritten X X

Faster decisions X X

Objective underwriting X X

Less paperwork for lenders X

More consistent underwriting decisions X
Page 17 GAO-06-435 Mortgage Financing

scorecards to help inform general management decision making, set prices 

based on risk, and launch new products. To inform general management 

decision making, private sector organizations compare the scorecards’ 

actual results with its predictions to, for example, set cut points and 

redirect underwriting resources from relatively low-risk cases to more 

marginal borrowers. To set risk-based prices, private sector organizations 

use scorecards to rank the relative risk of borrowers and price products 

according to that ranking. For instance, mortgage insurers may use FICO 

scores as a basis for reducing insurance premiums for low-risk borrowers. 

Finally, to help launch new products, these lenders may use scorecards to 

balance risk and compensating factors. For example, a product with a more 

flexible LTV could be offered to borrowers with characteristics such as a 

strong credit history. 

As a result of these uses, private lenders have been able to broaden their 

customer base and improve their financial performance. Expanding their 

product offerings based on a greater understanding of risk allows lenders 

to broaden their customer base. Lenders told us that their scorecards had 

allowed them to underwrite some borrowers who would have been 

rejected using manual underwriting and to develop products to better serve 

borrowers who were at a greater risk of default. One official noted that the 

scorecard had provided a greater understanding of the individual 

borrower’s risk and that, as a result, borrowers who would previously have 

been considered for subprime loans were now rated at a higher level of 

eligibility. In addition, lenders reported being able to reduce personnel 

costs because the organizations were writing fewer loans manually. 

Ultimately, these lenders said that they were able to maximize their profits 

because of the streamlining and cost reductions the scorecards provided. 

Implementing Private 

Sector Scorecard Practices 

Could Provide Additional 

Benefits for FHA

FHA could see additional benefits from TOTAL if it implemented some 

private sector practices. By routinely monitoring and updating TOTAL, for 

instance, FHA could better anticipate, understand, and react to changes in 

the marketplace. FHA could also exercise more control over its financial 

condition by using the scorecard to help (1) project estimated insurance 

claims and adjust cut points and (2) institute its proposal for risk-based 

pricing of the agency’s mortgage insurance products. FHA could also use 

TOTAL to aid its efforts to develop new products for underserved 

borrowers. 

FHA could better anticipate, understand, and react to changes in the 

marketplace if, like the private sector, it routinely updated TOTAL. 
Page 18 GAO-06-435 Mortgage Financing

Updating the scorecard as new data become available could help ensure 

that changes in consumer behavior are reflected in the model, which can be 

affected by changes in products and other trends. By routinely comparing 

the scorecard’s actual results to its predictions, FHA could ascertain 

whether TOTAL was effectively predicting default risk and make any 

necessary changes to the variables. In addition, FHA could use TOTAL to 

more accurately determine the performance of new loans, which HUD 

currently monitors on an ad hoc basis, to inform policy discussions on the 

creation and revision of FHA products.

FHA could exercise more control over its financial condition, specifically 

its credit subsidy costs and financial soundness, by using the scorecard’s 

default predictions to project estimated claims and adjust cut points if 

necessary.19 In order to project estimated insurance claims, FHA would 

need to combine the variables’ weights estimated in the scorecard 

development process with projections of interest and house price 

appreciation rates, as is done in FHA’s actuarial studies. Based on its 

projections, FHA could then determine how much risk it could or should 

tolerate and make adjustments, if necessary, to the cut points and thus to 

the numbers and types of loans it automatically accepted and referred for 

manual underwriting. For example, if FHA raised the cut point, TOTAL 

would accept fewer high-risk loans (i.e., loans more likely to result in an 

insurance claim), thereby lowering FHA’s claim rate. Conversely, by 

lowering the cut point, TOTAL would accept more high-risk loans, and the 

agency would experience a higher claim rate.

TOTAL could also aid HUD’s efforts to implement risk-based pricing of its 

mortgage insurance products. In its fiscal year 2007 budget submission, 

HUD proposed legislation that would allow the agency to replace its 

current insurance premium structure, where most borrowers pay the same 

premium regardless of their default risk, to a risk-based structure where 

borrowers would pay higher or lower premiums depending on their default 

risk. HUD believes that risk-based pricing would allow the agency to 

19The credit subsidy cost is the net present value of the estimated long-term cost to the 

federal government of extending or guaranteeing credit (through FHA mortgage insurance), 

calculated over the life of the loan and excluding administrative costs. Federal agencies are 

required to estimate these costs as part of the annual budget process. FHA’s main singlefamily mortgage insurance program is supported by the MMI Fund, which is financed 

through mortgage insurance premiums and currently operates at a profit. Since 1990, the 

financial condition of the fund has been assessed by measuring the economic value of the 

fund—its capital resources plus the net present value of future cash flows—and the related 

capital ratio—the economic value as a percent of the fund’s insurance-in-force. 
Page 19 GAO-06-435 Mortgage Financing

charge more competitive mortgage insurance premiums, attract and retain 

relatively low-risk borrowers, and exercise more control over its credit 

subsidy costs. HUD plans to set premiums based on an assessment of 

borrowers’ credit histories, LTVs, and debt-to-income ratios. However, it 

has not fully explored the potential of using TOTAL—especially a version 

that includes additional variables, such as down payment assistance—

which is capable of evaluating risk in a more comprehensive way, for this 

purpose. 

In its budget submissions for fiscal years 2006 and 2007, HUD also 

proposed legislative changes that would allow FHA to develop new 

mortgage insurance products for low- and moderate-income borrowers 

(loans with lower down payment requirements, for example). HUD 

believes that its traditional customers would be better served by these new 

products than some of the high-cost, nonprime products offered in the 

conventional market. To the extent that FHA develops these products, it 

could use TOTAL to help identify alternatives that it previously may have 

believed posed too much risk, given the expected profit, when its lenders 

manually underwrote loans. 

Providing Data Used by 

TOTAL Could Offer 

Additional Benefits to 

Ginnie Mae

HUD’s Ginnie Mae—which guarantees the timely payment of principal and 

interest on securities issued by private institutions and backed by pools of 

federally insured or guaranteed mortgage loans—could benefit from the 

credit data used by TOTAL. As we reported in October 2005, Ginnie Mae 

has taken steps to disclose more information to investors about the FHAinsured loans that back the securities it guarantees.20 However, unlike 

many conventional securitizers, Ginnie Mae does not disclose credit 

information—for example, summarized credit score data—for its loan 

pools. Disclosing such information is important because investors can use 

it to more accurately model prepayment rates. According to a Ginnie Mae 

official, prior to the implementation of TOTAL in 2004, the credit scores 

associated with FHA-insured loans were not available within HUD. 

Because borrowers’ credit scores are used by TOTAL, Ginnie Mae has 

expressed interest in obtaining this information and summarizing it for 

investors. 

20See GAO-06-9.
Page 20 GAO-06-435 Mortgage Financing

Conclusions Although FHA has helped to provide financing for nearly 33 million 

properties, its share of the single-family market has steadily decreased over 

time. Many of these potential borrowers—typically, first-time homebuyers 

with minimal cash for down payments and lower than average credit 

scores—may have been lost to conventional lenders. These lenders have 

been, in part, able to provide conventional mortgages to these borrowers 

with the increased use of scorecards—the evaluative component of 

automated underwriting systems—that have enabled them to target the 

traditional FHA borrower that poses the least amount of risk. If that is the 

case, the effect on FHA is that it has started to serve more high-risk 

borrowers. To enhance its understanding of risk posed by its borrowers, 

FHA has adopted automated underwriting and developed its own 

scorecard.

FHA followed an accepted process in developing TOTAL and has already 

seen significant benefits from the scorecard. Because TOTAL has the same 

types of capabilities as private sector scorecards, FHA has the option to 

use and benefit from TOTAL in many different ways as do private sector 

organizations. Specifically, FHA could use TOTAL to help compete in the 

marketplace, manage risk, and serve its mission for borrowers. TOTAL’s 

capabilities are important to FHA, in part, because as it begins to insure 

more inherently risky loans, such as loans with down payment assistance, 

it needs to understand the risks they pose to the FHA insurance fund and 

manage those risks.

However, the potential benefits of TOTAL cannot be realized without 

ensuring that TOTAL is regularly updated and exploring additional uses of 

TOTAL. For example, by not developing and implementing policies and 

procedures for rountinely updating TOTAL, it may become less reliable 

and, therefore, less effective at predicting defaults. In addition, as a result 

of not exploring additional uses of TOTAL, FHA will not receive all of the 

types of benefits seen by private sector organizations. These additional 

uses include applying TOTAL to proposed initiatives—such as risk-based 

pricing and the development of new products—which may help strengthen 

the FHA insurance fund and reach additional borrowers. Finally, FHA has 

not taken steps to share credit scores utilized by TOTAL with Ginnie Mae, 

which could use the information to help improve the transparency of the 

secondary mortgage market. 
Page 21 GAO-06-435 Mortgage Financing

Recommendations for 

Executive Action

To improve how HUD uses and benefits from TOTAL, we recommend that 

the Secretary of HUD take the following two actions:

• develop policies and procedures for updating TOTAL on a regular basis, 

including using updated data, testing additional variables, exploring 

hazard model benefits, and testing other cut points; and

• explore additional uses of TOTAL and the credit data it utilizes, 

including to help adjust cut points, implement risk-based pricing, 

develop new products, and enable Ginnie Mae to disclose more 

information about securities backed by FHA-insured loans.

Agency Comments and 

Our Evaluation

We provided HUD with a draft of this report for review and comment. HUD 

provided comments in a letter from the Assistant Secretary for HousingFederal Housing Commissioner (see app. III). HUD made two general 

observations about the report and provided specific comments on our 

recommendations. First, HUD said the report did not convey the fact that 

developing TOTAL was a HUD initiative to modernize its processes and 

improve its delivery to business partners. Our draft report did discuss 

HUD’s rationale for implementing TOTAL and the scorecards that preceded 

it. It also discussed the benefits of these scorecards to FHA lenders, 

including less paperwork and quicker approval of mortgage insurance. 

However, in response to HUD’s comments, we added language to the report 

that further describes HUD’s motivation for developing TOTAL. 

Second, HUD said that TOTAL was working exactly as envisioned (i.e., 

segregating loans requiring limited underwriting and documentation from 

those requiring a full review by an individual underwriter) and that the 

draft report presented no evidence that the scorecard had failed to perform 

as expected. HUD also indicated that the agency had provided us with 

information and analysis based on FHA loan data from the 1990s, showing 

that TOTAL performed well in separating loans that resulted in insurance 

claims from those that did not. Our draft report did not state or intend to 

suggest that TOTAL was not fulfilling its intended function or was not 

working as well as expected. In fact, the report pointed out that TOTAL had 

continued the benefits of previous scorecards while generating others. At 

the same time, our draft report identified opportunities for HUD to improve 

TOTAL so that it could become a more effective tool for assessing and 

managing risk. For example, HUD could improve TOTAL by updating it to 

reflect recent changes in the mortgage market, such as the substantial 
Page 22 GAO-06-435 Mortgage Financing

growth in the percentage of FHA-insured loans with down payment 

assistance.

HUD did not explicitly agree or disagree with our recommendation that it 

should develop policies and procedures for updating TOTAL, including 

using updated data, testing additional variables, exploring hazard model 

benefits, and testing other cut points. HUD indicated that it was taking 

steps to address some aspects of our recommendation but not others, as 

follows: 

• HUD said that it had a formal plan for updating TOTAL, access to 

TOTAL’s development and implementation contractors to accommodate 

updates should the need arise, and contracts for acquiring credit data to 

support an update of the scorecard. As our draft report discussed, HUD 

had a contract to update TOTAL by 2007. However, best practices 

implemented by private entities and reflected in guidance from a bank 

regulator call for having formal policies to ensure that scorecards are 

routinely updated. HUD’s current plan calls for one update to be 

completed by 2007 (7 years after HUD finalized the scorecard model) 

and has no provision for subsequent updates. Accordingly, we continue 

to believe that HUD should develop policies and procedures for 

updating TOTAL on a regular basis.

• HUD acknowledged that it had used 1992 data to develop TOTAL but 

stated that the data spanned a wide range of credit scores and 

application factors represented in greater or lesser numbers in later 

cohorts of loans. We disagree that the 1992 loan data sufficiently 

represents later cohorts of loans and thus continue to believe that HUD 

should use more current loan data to update TOTAL. As our draft report 

stated, significant changes have occurred in the mortgage industry since 

1992 that have affected the characteristics of those applying for FHAinsured loans. These changes include generally lower credit scores, 

increased use of down payment assistance, and new mortgage products 

that have allowed borrowers who would have previously needed an 

FHA-insured loan to seek conventional mortgages.

• HUD said that in developing TOTAL, the agency and Unicon tested all 

the available variables and included those that were empirically 

important, consistent with Equal Credit Opportunity Act (ECOA) 

regulations (which, among other things, set forth rules for evaluating 

credit applications). HUD also said that it intends to re-analyze all 

available variables, including, as our draft report suggested, the source 
Page 23 GAO-06-435 Mortgage Financing

and amount of down payment assistance. We agree that HUD should reanalyze all available variables and incorporate them into TOTAL, 

consistent with ECOA requirements. Our draft report stated that HUD’s 

analysis of certain variables, such as loan and property type, may not 

have found significant effects simply because of the small numbers of 

loans in HUD’s sample that were ARMs or were for condominiums or 

multiunit properties. HUD could conduct future analyses with greater 

statistical reliability if it were to use larger samples of loans, as major 

private lending organizations do.

• HUD stated that because TOTAL was designed to assess the 

creditworthiness of borrowers, the logit model was sufficient for that 

purpose. However, HUD also acknowledged that a hazard model could 

be used for the purposes enumerated in our draft report. Accordingly, 

we continue to believe that HUD should explore the benefits of a hazard 

model.

• HUD said that it did not rely solely on a 1992 sample of loans in setting a 

cut point for TOTAL and that it worked with Unicon, Fannie Mae, and 

Freddie Mac, using recent distributions of loans, to obtain a cut point 

that was consistent with the ones already in use for FHA lending. Our 

draft report did not state that HUD relied solely on a 1992 sample of 

loans. Rather, it indicated that the cut point was based partly on a 1996 

analysis that Freddie Mac performed in consultation with FHA. 

However, in response to this comment, we added additional language to 

the report describing how HUD determined the cut point. HUD did not 

address the fundamental issue raised in our draft report—that the 

limitations of its original analysis suggest that the agency should test 

additional cut points. We continue to believe that HUD should test other 

cut points based on analysis of loans accepted by TOTAL.

HUD did not explicitly agree with our recommendation that it should 

explore additional uses of TOTAL, such as using it to help adjust cut points, 

implement risk-based pricing, develop new products, and enable Ginnie 

Mae to disclose more information about securities backed by FHA-insured 

loans. However, the actions HUD said it plans to take are consistent with 

our recommendation. Specifically, 

• HUD said that while TOTAL was not intended for risk-based pricing, the 

agency planned to explore how TOTAL might be used for that purpose. 
Page 24 GAO-06-435 Mortgage Financing

• HUD stated that it planned to determine the benefits that TOTAL could 

present in developing new products, if given the authority from 

Congress.

• HUD said that it was exploring the legal ramifications of giving Ginnie 

Mae the credit scores obtained using TOTAL. HUD also provided a 

technical correction, which we addressed in our final report, concerning 

how it stores these credit scores. 

Finally, HUD stated that the draft report contained several errors and that 

these errors had been previously pointed out in meetings with us. Where 

appropriate, we made technical corrections and clarifications in response 

to HUD’s written comments and comments provided by a HUD official at a 

March 2006 meeting to discuss our findings. However, we found that many 

of these comments, rather than correcting any errors, merely provided 

additional levels of detail that were unnecessary for the purpose of this 

report.

As agreed with your office, unless you publicly announce the contents of 

this report earlier, we plan no further distribution until 30 days from the 

date of this letter. At that time, we will send copies to the Chairman and 

Ranking Member of the Senate Committee on Banking, Housing, and Urban 

Affairs; the Chairman and Ranking Member of the House Committee on 

Financial Services; and the Ranking Member of the Subcommittee on 

Housing and Community Opportunity. We also will send copies to the 

Secretary of Housing and Urban Development and other interested parties 

and make copies available to others upon request. In addition, this report 

will be available at no charge on the GAO Web site at http://www.gao.gov.
Page 25 GAO-06-435 Mortgage Financing

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 Office of 

Congressional Relations and Public Affairs may be found on the last page 

of this report. Key contributors to this report are listed in appendix IV. 

Sincerely yours,

William B. Shear

Director, Financial Markets and 

Community Investment
Page 26 GAO-06-435 Mortgage Financing

Appendix I

Scope and Methodology AAppenednxidexiIs

To assess the reasonableness of the Federal Housing Administration’s 

(FHA) approach to developing Technology Open to Approved Lenders 

(TOTAL), we reviewed agency documents and interviewed the Department 

of Housing and Urban Development (HUD) and contractor officials to 

determine (1) the process and data used to develop TOTAL, including how 

FHA identified and evaluated scorecard variables; (2) the reliability of the 

analysis used to evaluate TOTAL’s effectiveness in predicting defaults; and 

(3) how FHA established policies on cut points and overrides. In addition, 

we reviewed industry literature and interviewed private sector officials 

from large (based on volume) lending and private mortgage insurance 

organizations to determine the extent to which FHA’s development of 

TOTAL is consistent with private sector practices.

To assess the benefits to FHA of expanding its use of TOTAL, we reviewed 

existing research on the uses and benefits of scorecards and interviewed 

private sector companies, academics, and HUD officials about these issues. 

We also determined how FHA and lenders use TOTAL by reviewing 

relevant agency guidance and reports and interviewing FHA officials and 

private lenders. In doing this work, we looked for any ways that FHA and 

lenders are using TOTAL differently than the scorecards TOTAL replaced. 

We compared FHA’s use of TOTAL with the private sector’s use of 

scorecards and determined whether FHA could benefit from any private 

sector practices that it has not already adopted. We also identified any 

opportunities that may exist for FHA to share information with other HUD 

offices that could benefit from TOTAL. 

We conducted our work in Washington, D.C., between April 2005 and 

February 2006 in accordance with generally accepted government auditing 

standards.
Page 27 GAO-06-435 Mortgage Financing

Appendix II

Products That Lenders Can Underwrite with 

TOTAL ApendxiI

Source: FHA. 

Loan purpose Purchase money mortgage

Construction-to-permanent mortgage

Regular refinance with credit qualifying

Cash-out refinances up to 85 percent of the appraised value

Streamline refinance

Credit qualifying assumptions

FHA insurance 

products

Section 203(b)—Mortgage insurance for one- to four-family homes

Section 203(h)—Single-family mortgage insurance for disaster 

 victims

Section 234(c)—Mortgage insurance for condominium units

Section 203(k)—Rehabilitation mortgage insurance

Section 251—Insurance for adjustable-rate mortgages 

Energy efficient mortgages

Section 247—Hawaiian Home Lands 

Types of 

properties 

covered

Single-family dwellings of one- to four-family living units

Manufactured homes meeting FHA’s property requirements for Title 

 II mortgage insurance

Units in low- and high-rise condominium projects

Types of 

mortgages 

covered

Fixed-rate mortgages

Adjustable-rate mortgages 
Page 28 GAO-06-435 Mortgage Financing

Appendix III

Comments from the Department of Housing 

and Urban Development ApendxiI
Appendix III

Comments from the Department of Housing 

and Urban Development

Page 29 GAO-06-435 Mortgage Financing
Appendix III

Comments from the Department of Housing 

and Urban Development

Page 30 GAO-06-435 Mortgage Financing
Page 31 GAO-06-435 Mortgage Financing

Appendix IV

GAO Contact and Staff Acknowledgments ApendxiIV

GAO Contact William B. Shear (202) 512-8678

Staff 

Acknowledgments 

In addition to the individual named above, Steve Westley, Assistant 

Director; Triana Bash; Austin Kelly; Mamesho MacCaulay; John McGrail; 

Mitch Rachlis; Rachel Seid; and Grant Turner made key contributions to 

this report.

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