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entitled 'Mortgage Financing: HUD Could Realize Additional Benefits 
from Its Mortgage Scorecard' which was released on May 15, 2006.

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United States Government Accountability Office: 

GAO:

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:

GAO Highlights: 

Highlights of GAO-06-435, a report to the Chairman, Subcommittee on 
Housing and Community Opportunity, Committee on Financial Services, 
House of Representatives.

Why GAO Did This Study: 

Along with private mortgage providers, the Department of Housing and 
Urban Developmentís (HUD) Federal Housing Administration (FHA) has been 
impacted by technological advances that began in the mid-1990s and that 
have significantly affected the way the mortgage industry works.  As a 
result, in 2004, FHA implemented Technology Open to Approved Lenders 
(TOTAL) Scorecardóan 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 FHAís approach to developing 
TOTAL and (2) the potential benefits to HUD of expanding its use of 
TOTAL.     

What GAO Found: 

Some of the choices that FHA made during the development process could 
limit TOTALís 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, HUDís 
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.

Table: FHA Could Benefit Significantly More from TOTAL: 

Scorecard Benefits: Past/Present Benefits: Ability to adjust 
underwriting standards; 
Scorecards previously used by FHA: Check;
TOTAL scorecard: Check.
 
Scorecard Benefits: Past/Present Benefits: Majority of loans 
automatically underwritten; 
Scorecards previously used by FHA: Check;
TOTAL scorecard: Check.

Scorecard Benefits: Past/Present Benefits: Faster decisions; 
Scorecards previously used by FHA: Check; 
TOTAL scorecard: Check.

Scorecard Benefits: Past/Present Benefits: Objective underwriting; 
Scorecards previously used by FHA: Check;
TOTAL scorecard: Check.

Scorecard Benefits: Past/Present Benefits: Less paperwork for lenders; 
Scorecards previously used by FHA: N/A;
TOTAL scorecard: Check.

Scorecard Benefits: Past/Present Benefits: More consistent underwriting 
decisions; 
Scorecards previously used by FHA: N/A;
TOTAL scorecard: Check.

Scorecard Benefits: Potential benefits: Ability to react to changes in 
the market; 
Scorecards previously used by FHA: N/A; 
TOTAL scorecard: Check.

Scorecard Benefits: Potential benefits: More control over financial 
condition; 
Scorecards previously used by FHA: N/A;
TOTAL scorecard: Check.

Scorecard Benefits: Potential benefits: Broader customer base; 
Scorecards previously used by FHA: N/A;
TOTAL scorecard: Check. 

Source: GAO. 

[End of table]

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. 

[Hyperlink, http://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. 

[End of section]

Contents:

Letter:

Results in Brief:

Background:

FHA's Approach to Developing TOTAL Was Generally Reasonable, but Some 
of Its Choices Could Limit TOTAL's Effectiveness:

HUD Could Benefit Significantly More from TOTAL:

Conclusions:

Recommendations for Executive Action:

Agency Comments and Our Evaluation:

Appendixes:

Appendix I: Scope and Methodology:

Appendix II: Products That Lenders Can Underwrite with TOTAL:

Appendix III: Comments from the Department of Housing and Urban 
Development:

Appendix IV: GAO Contact and Staff Acknowledgments:

Table:

Table 1: TOTAL Has Generated Added Benefits:

Figure:

Figure 1: FHA's Automated Mortgage Underwriting Process:

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:

United States Government Accountability Office: 
Washington, DC 20548:

April 13, 2006:

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.[Footnote 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.[Footnote 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 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 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 
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 first-time 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 single-family 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.[Footnote 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 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.[Footnote 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.[Footnote 5] More specifically, Freddie Mac implemented its 
Loan Prospector automated underwriting and scorecard tool by 1996, and 
Fannie Mae implemented a similar tool, Desktop Underwriter, in 
1997.[Footnote 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.[Footnote 7]

Between 1998 and 2004, FHA contracted with Unicon Research Corporation 
to develop TOTAL.[Footnote 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.[Footnote 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.

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.[Footnote 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.[Footnote 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.

Figure 1: FHA's Automated Mortgage Underwriting Process:

[See PDF for image] 

Source: GAO and NOVA Development(images). 

[End of figure] 

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.

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 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 
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 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 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.[Footnote 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.[Footnote 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 period were riskier than 
comparable fixed-rate mortgages.[Footnote 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.[Footnote 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.[Footnote 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 
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.[Footnote 17]As 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 
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.[Footnote 18]

Table 1: TOTAL Has Generated Added Benefits:

Scorecard benefits: Ability to adjust underwriting standards: 
Scorecards previously used by FHA: Check; 
TOTAL scorecard: Check.

Scorecard benefits: Majority of loans automatically underwritten: 
Scorecards previously used by FHA: Check;
TOTAL scorecard: Check.

Scorecard benefits: Faster decisions: 
Scorecards previously used by FHA: Check; 
TOTAL scorecard: Check. 

Scorecard benefits: Objective underwriting: 
Scorecards previously used by FHA: Check; 
TOTAL scorecard: Check.

Scorecard benefits: Less paperwork for lenders: 
Scorecards previously used by FHA: N/A; 
TOTAL scorecard: Check.

Scorecard benefits: More consistent underwriting decisions: 
Scorecards previously used by FHA: N/A; 
TOTAL scorecard: Check. 

Source: GAO.

[End of table]

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 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. 
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.[Footnote 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 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 FHA-insured loans that back the securities it 
guarantees.[Footnote 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.

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 routinely 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.

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 Housing- 
Federal 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 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 
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 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 and amount of down payment assistance. We agree that HUD should 
re-analyze 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.

* 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 [Hyperlink, 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 
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,

Signed By:

William B. Shear: 
Director: 
Financial Markets and Community Investment:

[End of section]

Appendix I: Scope and Methodology:

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.

[End of section]

Appendix II Products That Lenders Can Underwrite with TOTAL: 

Table: 

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. 

Source: FHA.

[End of table]

[End of section]

Appendix III: Comments from the Department of Housing and Urban 
Development:

U.S. Department Of Housing And Urban Development: 
Washington, DC 20410- 8000:

Assistant Secretary For Housing-Federal Housing Commissioner:

Mr. William B. Shear: 
Director:
Financial Markets and Community Investments: 
United States Government Accountability Office: 
441 G Street, NW:
Washington, DC 20548:

Dear Mr. Shear:

Thank you for providing the Federal Housing Administration (FHA) the 
opportunity to respond to the report entitled "HUD Could Realize 
Additional Benefits from Its Mortgage Scorecard"(GAO-06-435).

Before addressing the recommendations for executive action, I must 
point out that your report does not convey that developing the TOTAL 
Mortgage Scorecard was an initiative by HUD to modernize its processes 
and improve its delivery to its business partners. To our knowledge, 
neither of the other Federal agencies involved in the mortgage industry 
has undertaken similar efforts to develop loan-level automated risk 
assessment processes. Rural Housing Services in fact sought FHA's 
advice on scorecard building and has adopted TOTAL as its scoring 
engine in its own automated underwriting environment.

TOTAL is working exactly as it was envisioned: it segregates those 
loans where limited underwriting and documentation are required from 
those needing a full review by a qualified individual underwriter. Like 
any recent initiative, and this is only two years old, it takes time to 
determine what changes are warranted. However, the fundamental test of 
a mortgage scorecard's effectiveness is how well it performs in terms 
of distinguishing future claims from non-claims and GAO presents no 
evidence that the scorecard has failed to perform as expected.

Indeed, HUD provided GAO with benchmark information showing that TOTAL 
performed extremely well at sorting future claims from non-claims 
throughout the 1990s using nationally representative random samples of 
FHA loans made in 1992, 1996, and 1997, as well as identifying 
delinquencies for the large universe of 1998 and 1999 (accept and 
refer) FHA loans processed through Freddie Mac's LP for FHA scorecard. 
HUD also provided GAO with results from the ongoing scorecard update 
analyses that confirmed the power of the original TOTAL scorecard to 
separate claims from non-claim defaults when compared to re-estimated 
versions of TOTAL using later nationally representative random samples 
of FHA loans made from 1992 through 1999-the latest year for which HUD 
has information on defaults that occur before the end of the fourth 
year and subsequently claim.

FHA's responses to the individual recommendations are as follows:

GAO Recommendation #1: Develop policies and procedures for the updating 
of TOTAL, including using updated data, testing additional variables, 
exploring hazard model benefits, and test other cut points.

FHA Response:

* Develop Policies and Procedures: FHA does indeed have a formal plan 
for updating TOTAL. FHA has had continuing access to TOTAL's developer, 
Unicon, and implementation contractor, ATS, to accommodate updates 
should the need arise and also has, through HUD's Office of Policy 
Development and Research (PD&R), established formal contracts with 
credit repositories to acquire archive credit data for building 
analysis files for later origination cohorts of FHA loans that are to 
be used in estimating updated models. While the procurement of the 
contracts with the repositories proved to be a protracted effort, it 
was completed and loan cohorts with credit data have been secured in 
support of the scorecard update.

* Updated data: While data from 1992 that included four-year defaults 
that ultimately went to claim within the subsequent 18 months were used 
in estimating the relationship between default and borrower credit and 
loan application factors, that data spanned a wide range of credit 
scores and application factors represented in greater or lesser numbers 
in later cohorts of loans. Benchmarking analyses using later data 
outlined above confirmed the consistent performance of the TOTAL 
scorecard through the years.

* Testing additional variables: Unicon and HUD did test all the 
available variables and included all those that proved empirically 
important for explaining default performance consistent with fair 
lending and ECOA regulation B, which requires the scorecard to be 
empirically derived using statistically sound procedures and does not 
allow for the modification of scorecard coefficients to meet a priori 
expectations. The variables that GAO maintains should have been in 
TOTAL did not survive as empirically important indicators in relation 
to other included variables. FHA is revisiting everything anew as 
required by regulation in the process of re-estimation of TOTAL 
including if the source and amount of gifts for the downpayment should 
be added to the algorithm.

* Exploring hazard model benefits: HUD's selection of a logit model for 
the TOTAL scorecard did not limit HUD with respect to other uses of 
scoring technology. The object of TOTAL was to assess credit worthiness 
of borrowers at application and the logit model was sufficient to that 
purpose and easier to implement. Nothing precludes the use of a hazard 
model for the other purposes enumerated in GAO's report.

* Testing other cut points: While HUD did analysis of cut points and 
their fair lending implications in the context of the 1992 development 
sample, it did not rely solely on the 1992 distribution of loans in 
setting the cut point for TOTAL when it replaced the Freddie Mac and 
Fannie Mae scorecards in 2004. HUD worked with Unicon, Fannie Mae, and 
Freddie Mac using recent and current distributions of loans to obtain a 
cut point score (with an implied maximum default probability) 
consistent with cutpoints already aligned and in use on FHA lending in 
the Fannie Mae and Freddie Mac scorecards. The cut point score does not 
change with shifting distributions of FHA loans. More applications will 
be referred to manual underwriting with distributional shifts toward 
higher risk loans and more applications will be rated accepts with 
shifts to lower risk applications.

GAO Recommendation #2: Explore additional uses of TOTAL and the data in 
it, 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.

FHA Response:

* Implement risk-based pricing: TOTAL was not intended for risk-based 
pricing. However, FHA is exploring how it might be used for that 
purpose. This could prove a lengthy exercise with an unknown outcome as 
TOTAL now operates as an external scorecard component to differing 
automated underwriting systems rather than as an internal component to 
an single integrated system where risk-based pricing could be 
considerably easier to develop and implement.

* Develop new products: If FHA is given authority by Congress to offer 
an array of modem products designed to enhance homeownership 
opportunities, it will certainly explore the benefits that TOTAL may 
present in developing such products.

* Ginnie Mae: TOTAL is not where the universe of credit bureau scores 
on FHA-insured mortgages reside (although most are originally obtained 
via lenders choosing to score the mortgage). However, FHA is exploring 
the legal ramifications of providing Ginnie Mae with credit bureau 
scores from is system of records consistent with credit law.

Finally, I would note that the report contains several errors despite 
our previous meetings, in which we provided clarification. The enclosed 
appendix to this letter provides additional information to address 
these errors.

In closing, I would like to reiterate that FHA will continue to examine 
the performance of its scorecard, and take whatever steps are necessary 
to make it a better tool for assessing risk and reducing the cost to 
lenders that originate mortgages insured by FHA.

Sincerely, 

Signed By:  

Brian D. Montgomery: 
Assistant Secretary for Housing-Federal Housing Commissioner:

[End of section]

Appendix IV: GAO Contact and Staff Acknowledgments:

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.

(250247): 

[End of section]

FOOTNOTES

[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.

[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).

[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. 

[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. 

[10] LTV 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. 

[11] TOTAL 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.

[12] Each 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. 

[13] By contrast, an official from a major lending organization said 
that they used about 200,000 loans to develop their scorecard. 

[14] See 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).

[15] Although 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. 

[16] See 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-SE-
121-0001 (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). 

[17] Lenders are required to obtain the following application 
information: type of mortgage and terms of loan, property information, 
borrower information, and employment information. 

[18] Because TOTAL obtains credit information to automatically assess 
applications for FHA-insured loans, FHA does not require as much 
verification as it does for applications that are manually 
underwritten. 

[19] The 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 single-family 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. 

[20] See GAO-06-9.

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