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-------------------------Indexing Terms------------------------- 
REPORTNUM:   GAO-06-435		

TITLE:     Mortgage Financing: HUD Could Realize Additional 
Benefits from Its Mortgage Scorecard

DATE:   04/13/2006 
				                                                                         
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GAO-06-435

     

     * 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
     * Contents
          * Results in Brief
          * Background
          * FHA's Approach to Developing TOTAL Was Generally Reasonable, but
            Some of Its Choices Could Limit TOTAL's Effectiveness
               * The Process FHA and Its Contractors Used to Develop TOTAL
                 Was Generally Reasonable
               * Some Development and Implementation Choices Could Limit
                 TOTAL's Effectiveness
                    * Data Not Current
                    * No Plan for Regular Updates
                    * Limited Sample of Loans Used for Development and
                      Testing
                    * Excluded Important Variables
                    * Limited Logit Model
          * HUD Could Benefit Significantly More from TOTAL
               * FHA Could Realize Additional Benefits Using TOTAL
               * Private Sector Organizations Benefit from Using Scorecards
                 in a Variety of Ways
               * Implementing Private Sector Scorecard Practices Could
                 Provide Additional Benefits for FHA
               * Providing Data Used by TOTAL Could Offer Additional Benefits
                 to Ginnie Mae
          * Conclusions
          * Recommendations for Executive Action
          * Agency Comments and Our Evaluation
     * Scope and Methodology
     * Products That Lenders Can Underwrite with TOTAL
     * Comments from the Department of Housing and Urban Development
     * GAO Contact and Staff Acknowledgments
     * PDF6-Ordering Information.pdf
          * Order by Mail or Phone
     * cov1&2.pdf
          * 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

Contents

Table

Figure

April 13, 2006Letter

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
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

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

o did not include all the important variables that could help explain
expected loan performance,

o selected a type of model that limits the uses to which the scorecard can
be put, and

o 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.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.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

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.

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.

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.

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:

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

o choosing a scorecard model by conducting various tests,

o validating the scorecard to ensure that it is stable (i.e., consistently
produces reasonable results), and

o 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.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

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 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.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.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 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.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.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 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.

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:

o 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

o 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:

o 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.

o 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.

o 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.

o 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.

o 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,

o 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.

o HUD stated that it planned to determine the benefits that TOTAL could
present in developing new products, if given the authority from Congress.

o 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 .

If you or your staff have any questions about this report, please contact
me at (202) 512-8678 or [email protected] . 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

Scope and MethodologyAppendix I

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.

Products That Lenders Can Underwrite with TOTALAppendix II

Source: FHA.

Comments from the Department of Housing and Urban DevelopmentAppendix III

GAO Contact and Staff AcknowledgmentsAppendix IV

William B. Shear (202) 512-8678

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)

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

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Highlights of GAO-06-435 , a 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

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 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)
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,

           o  did not develop a formal plan for updating TOTAL on a regular
           basis,
           o  did not include all the important variables that could help
           explain expected loan performance, and
           o  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

Source: 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
*** End of document. ***