Homeownership: Information on Foreclosed FHA-Insured Loans and HUD-Owned
Properties in Six Cities (Letter Report, 10/08/97, GAO/RCED-98-2).

Pursuant to a congressional request, GAO looked at early foreclosures in
Chicago, Illinois; Washington, D.C.; Atlanta, Georgia; Dallas, Texas;
Baltimore, Maryland; and San Bernadino, California focusing on: (1)
comparing early foreclosure rates on Federal Housing Administration
(FHA)-insured single-family loans made in low-, medium-, and high-income
areas nationwide and in the six cities; (2) comparing across income
areas the proportion of loans made in the six cities by FHA-approved
mortgage lenders with and without early foreclosures; (3) factors that
influence early foreclosure rates; and (4) comparing the length of time
the Department of Housing and Urban Development (HUD)-owned
single-family properties remained unsold in low-, medium-, and
high-income areas in the six cities. GAO did not attempt to evaluate the
soundness of mortgage underwriting decisions or the impact of vacant
homes on neighborhood conditions because of the methodological
difficulties that a broad examination of these issues would present.

GAO noted that: (1) GAO's analysis of the FHA-insured single-family
loans made during calendar years 1992 through 1994 nationwide and in the
six cities showed that early foreclosures occurred infrequently but that
early foreclosure rates were higher for low-income areas than for either
medium- or high-income areas; (2) the early foreclosure rate for
low-income areas nationwide was 0.45 percent (i.e., 4.5 early
foreclosures occurring for every 1,000 mortgages insured) compared with
0.30 percent and 0.21 percent for medium- and high-income areas,
respectively; (3) although this pattern prevailed in the six cities,
there were also differences from one city to another; (4) for four of
the cities--Atlanta, Baltimore, Dallas, and Washington, D.C.--lenders
with early foreclosures made a larger proportion of their loans for
properties in low- and medium-income areas and a smaller proportion of
their loans for properties in high-income areas than did lenders that
did not experience early foreclosure; (5) in San Bernadino, however,
lenders with early foreclosures made a smaller proportion of their loans
for properties in low-income areas and a larger proportion of their
loans for properties in high-income areas than lenders without early
foreclosures; (6) in Chicago, lenders with early foreclosures made a
smaller share of their loans in medium-income areas than lenders without
early foreclosures; (7) various factors influence the probability of
early foreclosure; (8) GAO's analysis of the FHA-insured loans made in
calendar years 1992 through 1994 in the six cities indicated that loans
made for homes in poorer census tracts, smaller loans, and loans with
higher loan-to-value ratios or higher interest rates were associated
with higher probabilities of early foreclosure; (9) as of December 31,
1996, HUD held a total of 1,374 properties in its inventory in the six
cities GAO reviewed; (10) GAO's analysis did not identify a pattern in
the median time that these properties remained in HUD's inventory in
different income areas; and (11) however, in five of the six cities and
for the six cities combined, the proportion of properties that had been
in inventory for more than 6 months was greater in low-income areas than
in either medium- or high-income areas.

--------------------------- Indexing Terms -----------------------------

 REPORTNUM:  RCED-98-2
     TITLE:  Homeownership: Information on Foreclosed FHA-Insured Loans 
             and HUD-Owned Properties in Six Cities
      DATE:  10/08/97
   SUBJECT:  Mortgage programs
             Mortgage protection insurance
             Foreclosures
             Loan defaults
             Low income housing
             Insurance losses
             Banking regulation
             Property disposal
             Insurance claims
IDENTIFIER:  FHA Section 203(b) Program
             Mutual Mortgage Insurance Fund
             FHA Section 234(c) Program
             Washington (DC)
             Baltimore (MD)
             Atlanta (GA)
             Dallas (TX)
             Chicago (IL)
             San Bernadino (CA)
             
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Cover
================================================================ COVER


Report to the Chairman, Subcommittee on Housing and Community
Opportunity, Committee on Banking and Financial Services, House of
Representatives

October 1997

HOMEOWNERSHIP - INFORMATION ON
FORECLOSED FHA-INSURED LOANS AND
HUD-OWNED PROPERTIES IN SIX CITIES

GAO/RCED-98-2

Homeownership

(385659)


Abbreviations
=============================================================== ABBREV

  FHA - Federal Housing Administration
  GAO - General Accounting Office
  HUD - Department of Housing and Urban Development
  MMI - Mutual Mortgage Insurance Fund
  MSA - metropolitan statistical area
  SAMS - Single-Family Accounting Management System

Letter
=============================================================== LETTER


B-276286

October 8, 1997

The Honorable Rick A.  Lazio
Chairman, Subcommittee on Housing
 and Community Opportunity
Committee on Banking
 and Financial Services
House of Representatives

Dear Mr.  Chairman: 

Through its Federal Housing Administration (FHA), the Department of
Housing and Urban Development (HUD) provides federally backed
mortgage insurance to hundreds of thousands of homeowners annually. 
However, each year, lenders foreclose on a portion of the FHA-insured
mortgages that go into default and file insurance claims with HUD for
their losses.  With few exceptions, HUD takes ownership of the
foreclosed properties, which generally remain vacant until HUD sells
them.  Critics of FHA contend that the unsound underwriting of
FHA-insured loans in low-income urban communities has contributed to
large numbers of foreclosures and vacant HUD-owned homes in these
areas.  They further contend that these homes remain vacant for long
periods, attracting crime, reducing local property values, and
contributing to neighborhood blight. 

To provide some insights into the concerns raised by FHA's critics,
we examined "early foreclosures"--those occurring within 18 months of
the loan endorsement date.\1 As agreed with your office, we did not
attempt to evaluate the soundness of mortgage underwriting decisions
or the impact of vacant homes on neighborhood conditions because of
the methodological difficulties that a broad examination of these
issues would present. 

We looked at early foreclosures because, according to FHA, they are
an indicator of potentially unsound underwriting practices (e.g.,
lending to unqualified borrowers), whereas foreclosures occurring
later are more likely to result from unforeseen circumstances that
impair the ability of borrowers to make mortgage payments (e.g., job
loss).\2 In addition, we examined the length of time HUD-owned
single-family properties remained unsold.  To provide perspective on
the types of neighborhoods where early foreclosures and unsold
properties may be of greatest concern, we made comparisons across
low-, medium-, and high-income areas.  You requested that we include
Chicago, Illinois, and Washington, D.C., in our analysis, and we
selected four additional cities--Atlanta, Georgia; Baltimore,
Maryland; Dallas, Texas; and San Bernadino, California--because they
provided geographic diversity and had relatively high levels of FHA
loan activity during the past few years.\3

Specifically, you asked us to (1) compare early foreclosure rates on
FHA-insured single-family loans made in low-, medium-, and
high-income areas nationwide and in the six cities; (2) compare
across income areas the proportion of loans made in the six cities by
FHA-approved mortgage lenders with and without early foreclosures;
(3) identify factors that influence early foreclosure rates; and (4)
compare the length of time HUD-owned single-family properties
remained unsold in low-, medium-, and high-income areas in the six
cities. 


--------------------
\1 After making a loan to a borrower, a lender seeks FHA's approval
to insure the loan.  The date when FHA formally approves mortgage
insurance for the loan is termed the "loan endorsement date."

\2 For this report, we considered an early foreclosure to be both a
loan on which the lender foreclosed within 18 months of the loan
endorsement date and a loan on which the lender did not actually
foreclose but on which HUD paid an insurance claim to the lender
within 18 months of the loan's endorsement.  The latter accounted for
about 33 percent of the early foreclosures in our data set and were
part of HUD's mortgage assignment program, which was terminated in
1996.  This program gave a borrower who defaulted on an FHA-insured
loan the opportunity to avoid foreclosure by petitioning HUD to take
assignment (i.e., ownership) of the loan and provide forbearance to
the borrower.  In taking assignment of a loan, HUD paid the mortgage
debt and assumed responsibility for servicing the loan. 

\3 The nationwide data reflect loans made in all of the metropolitan
statistical areas (MSA), which include central cities and surrounding
suburbs, while the data for the six cities reflect loans made within
the formal boundaries of these cities but not loans made in the
surrounding suburbs.  We defined an area's income level as "low" if
the per capita income was at or below 80 percent of the per capita
income for the MSA/city, "medium" if the per capita income was
greater than 80 percent but at or below 120 percent of the
MSA's/city's level, and "high" if the per capita income was greater
than 120 percent of MSA's/city's level. 


   RESULTS IN BRIEF
------------------------------------------------------------ Letter :1

Our analysis of the FHA-insured single-family loans made during
calendar years 1992 through 1994 nationwide and in the six cities
showed that early foreclosures occurred infrequently but that early
foreclosure rates were higher for low-income areas than for either
medium- or high-income areas.\4 The early foreclosure rate for
low-income areas nationwide was 0.45 percent (i.e., 4.5 early
foreclosures occurring for every 1,000 mortgages insured) compared
with 0.30 percent and 0.21 percent for medium- and high-income areas,
respectively.  Although this pattern prevailed in the six cities,
there were also differences from one city to another.  For example,
among the six cities, the early foreclosure rates for low-income
areas ranged from 0.47 percent in Washington, D.C., to 1.45 percent
in Dallas.\5

For four of the cities--Atlanta, Baltimore, Dallas, and Washington,
D.C.-- lenders with early foreclosures\6 made a larger proportion of
their loans for properties in low- and medium-income areas and a
smaller proportion of their loans for properties in high-income areas
than did lenders that did not experience early foreclosure.  In San
Bernadino, however, lenders with early foreclosures made a smaller
proportion of their loans for properties in low-income areas and a
larger proportion of their loans for properties in high-income areas
than lenders without early foreclosures.  Also, in Chicago, lenders
with early foreclosures made a smaller share of their loans in
medium-income areas than lenders without early foreclosures. 

Various factors influence the probability of early foreclosure.  Our
analysis of the FHA-insured loans made in calendar years 1992 through
1994 in the six cities indicated that loans made for homes in poorer
census tracts, smaller loans, and loans with higher loan-to-value
ratios\7 or higher interest rates were associated with higher
probabilities of early foreclosure. 

As of December 31, 1996, HUD held a total of 1,374 properties in its
inventory in the six cities we reviewed.  Our analysis did not
identify a pattern in the median time that these properties remained
in HUD's inventory in different income areas.\8 For example, in
Atlanta the median time in inventory was higher in low-income areas
than in high-income areas, while in Chicago the median time in
inventory was about the same in both of these income areas.  However,
in five of the six cities and for the six cities combined, the
proportion of properties that had been in inventory for more than 6
months was greater in low-income areas than in either medium- or
high-income areas. 


--------------------
\4 We examined loans made during calendar years 1992 through 1994
because HUD's database did not have complete demographic information
for loans made before 1992 and because 1994 was the last full year we
could include in an analysis examining the performance of loans over
an 18-month period.  Approximately 32 percent of the loans for the
six cities were taken out to refinance existing mortgages. 
Comparable data for loans insured by private mortgage insurers were
not available. 

\5 The statements made in this report reflect what we observed in
HUD's data on loans approved during calendar years 1992 through 1994
nationwide and in the six cities.  The foreclosure patterns we
observed may be different from the patterns we might have observed
for loans from a different time period or under different economic
conditions. 

\6 We defined lenders with early foreclosures as lenders with one or
more early foreclosures during the time periods we reviewed. 

\7 This indicator expresses the amount of the loan as a percentage of
the property's value. 

\8 The median is a value in an ordered set of values below and above
which the number of values is equal. 


   BACKGROUND
------------------------------------------------------------ Letter :2

Lenders usually require mortgage insurance when a home buyer has a
down payment of less than 20 percent of the value of the home because
foreclosures are more likely on these loans than on those with higher
down payments.  As the principal provider of federally backed
mortgage insurance, FHA insured 32 percent of the insured mortgages
originated in 1995.  However, FHA fulfills an even larger role in
providing insurance for some groups of borrowers, particularly
low-income home buyers, minorities, and central city residents. 

FHA provides most of its single-family mortgage insurance through the
Section 203(b) program, which covers loans for purchasing a new or
existing one- to four-family home.  The 203(b) program, among other
programs, is supported by the Mutual Mortgage Insurance Fund (MMI
Fund), which is funded by revenue from insurance premiums and
foreclosed property sales.  By law, the fund must meet or endeavor to
meet statutory capital ratio requirements:  that is, it must contain
sufficient reserves to cover the estimated future payments of claims
on foreclosed mortgages and other costs.  Other FHA insurance
programs for single-family home loans include the Section 203(k)
program, for purchasing or refinancing and rehabilitating a home at
least 1 year old, and the Section 234(c) program, for purchasing a
unit in a condominium project. 

A mortgage loan is commonly considered "in default" when the borrower
misses three consecutive monthly payments and a fourth payment is
due.  At that point, foreclosure proceedings against the borrower
become a serious possibility.  In the case of FHA-insured loans, once
the foreclosure process is completed, the lender files an insurance
claim with HUD for its losses (unpaid mortgage balance and interest,
along with the costs of foreclosure and other expenses).  After the
claim is paid, the lender transfers the title to the home to HUD,
which is responsible for managing and selling the property. 
HUD-owned properties generally remain vacant until they are resold. 

At the end of fiscal year 1996, HUD had about 24,700 single-family
properties in its inventory.  The purpose of HUD's property
disposition program is to reduce the inventory of acquired property
in a manner that expands homeownership opportunities, strengthens
neighborhoods and communities, and ensures a maximum return to the
mortgage insurance fund.  Although FHA has always received enough in
premiums from borrowers and other revenues to cover the costs of
foreclosed MMI Fund loans, losses totaled about $12.8 billion in 1994
dollars, or about $24,400 for each foreclosed and subsequently sold
single-family home over the 19-year period ending in 1993. 

To mitigate losses to FHA and hold lenders accountable for the
quality of the loans they make, FHA performs several activities
related to the approval, monitoring, and recertification of mortgage
lenders participating in FHA's programs.  For example, FHA monitors,
by mortgage lender, the percentage of loans in default or on which
FHA has paid the lender a claim.  FHA also conducts on-site reviews
of the loan origination and servicing practices of selected lenders. 
In addition, in 1996, FHA issued guidelines intended to promote the
use of special forbearance plans, mortgage modifications, and other
tools to help FHA borrowers in default remain in their homes whenever
possible and to mitigate losses to FHA resulting from loan
foreclosures. 


   EARLY FORECLOSURE RATES WERE
   HIGHEST IN LOW-INCOME AREAS
------------------------------------------------------------ Letter :3

Nationwide, early foreclosures did not occur for 99.68 percent of the
FHA-insured single-family loans made during calendar years 1992
through 1994.\9 However, early foreclosure rates were higher for
low-income areas than for either medium- or high-income areas.\10
Nationwide, the early foreclosure rate for low-income areas was 0.45
percent (i.e., 4.5 early foreclosures occurring for every 1,000
mortgages insured) compared with 0.30 percent and 0.21 percent for
medium- and high-income areas, respectively.  Federal regulations
require FHA to monitor the performance of FHA-insured loans by
mortgage lender but not by income area.  Consequently, FHA does not
have criteria for determining what would constitute excessively high
early foreclosure rates for low-, medium-, or high-income areas
nationwide or in a specific geographic region. 

Consistent with the nationwide pattern, early foreclosure rates in
the six cities were highest for low-income areas, but these rates and
the proportion of early foreclosures occurring in each income area
varied by city.  Within 18 months, foreclosures occurred on 254 of
the 50,323 loans made in the six cities, for an early foreclosure
rate of 0.50 percent.  For the six cities combined, the early
foreclosure rates for low-, medium-, and high-income areas were 0.80
percent, 0.45 percent, and 0.30 percent, respectively. 

Among the individual cities, the early foreclosure rates for
low-income areas ranged from 0.47 percent in Washington, D.C., to
1.45 percent in Dallas.  For medium-income areas, they ranged from
0.15 percent in Chicago to 1.02 percent in San Bernadino, and for
high-income areas, they ranged from zero percent in Washington, D.C.,
to 0.86 percent in San Bernadino.  San Bernadino had the highest
early foreclosure rate (1.05 percent) for all income areas combined. 
According to HUD and San Bernadino city officials, job losses
associated with military base closings and corporate downsizing have
been a primary cause of foreclosures on FHA-insured mortgages in San
Bernadino.  Chicago had the lowest early foreclosure rate (0.26
percent) for all income areas combined.  Table 1 shows early
foreclosure rates in the six cities by income areas. 



                                Table 1
                
                Early Foreclosure Rates for FHA-Insured
                Loans Made in Calendar Years 1992-94 in
                      Six Cities, by Income Areas

                                     Income level of areas\a
                            ------------------------------------------
City                             Low      Medium        High       All
--------------------------  --------  ----------  ----------  --------
Atlanta                         1.40        0.41        0.23      0.63
Baltimore                       0.78        0.73        0.59      0.66
Chicago                         0.48        0.15        0.12      0.26
Dallas                          1.45        0.79        0.17      0.63
San Bernadino                   1.14        1.02        0.86      1.05
Washington, D.C.                0.47        0.21           0      0.28
Six cities combined             0.80        0.45        0.30      0.50
----------------------------------------------------------------------
\a We defined an area's income level as "low" if the per capita
income was at or below 80 percent of the per capita income for the
city, "medium" if the per capita income was greater than 80 percent
but at or below 120 percent of the city level, and "high" if the per
capita income was greater than 120 percent of city level. 

Source:  GAO's analysis of data from HUD and the Bureau of the
Census. 

For the six cities combined, the percentage of early foreclosures
occurring for low-income areas was disproportionately high relative
to the percentage of loans made for homes in these areas.  As shown
in appendix I, for the six cities combined, low-income areas
accounted for 44.5 percent (113 of 254) of the early foreclosures,
compared with 27.9 percent (14,050 of 50,323) of the loans made.\11

This pattern also held true for the six cities individually.  Among
the six cities, the proportion of early foreclosures occurring for
low-income areas ranged from 8.9 percent (5 of 56 early foreclosures)
in Baltimore to 66.7 percent (6 of 9 early foreclosures) in
Washington, D.C., while the corresponding proportions of loans made
for properties in these areas were 7.5 percent and 39.4 percent,
respectively. 

In two of the six cities--Baltimore and Dallas--the percentage of
early foreclosures for medium-income areas was disproportionately
high relative to the percentage of loans made for homes in these
areas.  For example, in Baltimore, medium-income areas accounted for
46.4 percent of the early foreclosures, compared with 42.2 percent of
the loans made.  In high-income areas in each of the six cities, the
percentage of early foreclosures was smaller than the percentage of
loans made for properties in these areas. 

Appendix I provides additional details on early foreclosure rates in
the six cities. 


--------------------
\9 Early foreclosures also represent a small share of the
foreclosures that will eventually occur.  For example, Price
Waterhouse has forecasted that foreclosures will eventually occur on
6.97 percent of the 30-year fixed-rate mortgages made in fiscal year
1994 that are supported by FHA's MMI Fund. 

\10 We calculated the number of early foreclosures by identifying
loans on which the lender had foreclosed and/or on which FHA had paid
a claim within 18 months of the loan endorsement date.  We divided
this number by the total number of loans to arrive at an early
foreclosure rate. 

\11 Seventy-one early foreclosures--42 fewer than actually
occurred--would have represented a proportionate number of early
foreclosures. 


   LENDERS WITH EARLY FORECLOSURES
   MADE A LARGER SHARE OF THEIR
   LOANS IN LOW- AND MEDIUM-INCOME
   AREAS THAN LENDERS WITHOUT
   EARLY FORECLOSURES
------------------------------------------------------------ Letter :4

For the six cities combined, lenders with early foreclosures made a
larger percentage of their loans for properties in low- and
medium-income areas and a smaller percentage of their loans for
properties in high-income areas than lenders without early
foreclosures.  Lenders with early foreclosures made 30.3, 43.1, and
26.6 percent of their loans for properties in low-, medium-, and
high-income areas, respectively, while the corresponding figures for
lenders without early foreclosures were 24.7, 40.7, and 34.5 percent. 
This pattern also prevailed in four of the individual
cities--Atlanta, Baltimore, Dallas, and Washington, D.C.  In San
Bernadino, however, lenders with early foreclosures made a smaller
proportion of their loans for properties in low-income areas and a
larger proportion of their loans for properties in high-income areas
than lenders without early foreclosures.  Also, in Chicago, lenders
with early foreclosures made a smaller share of their loans in
medium-income areas than lenders without early foreclosures.  The
relative proportions of loans made for properties in the different
income areas of each city by lenders with and without early
foreclosures are shown in table 2. 



                                Table 2
                
                Proportion of FHA-Insured Loans Made in
                Calendar Years 1992-94 for Properties in
                Low-, Medium-, and High-Income Areas in
                Six Cities, by Lenders With and Without
                           Early Foreclosures

                                     Income level of areas\a
                            ------------------------------------------
              Type of
City          lender            Low          Medium          High
------------  ------------  ------------  ------------  --------------
Atlanta       With early        34.9          45.0           20.1
               foreclosures
              Without           24.1          41.5           34.3
               early
               foreclosure
               s
Baltimore     With early        8.4           43.1           48.5
               foreclosures
              Without           6.1           41.0           52.9
               early
               foreclosure
               s
Chicago       With early        36.3          46.7           17.0
               foreclosures
              Without           26.8          47.7           25.6
               early
               foreclosure
               s
Dallas        With early        24.2          35.3           40.5
               foreclosures
              Without           15.4          28.5           56.0
               early
               foreclosure
               s
San           With early        46.8          39.0           14.3
 Bernadino     foreclosures
              Without           49.6          38.6           11.8
               early
               foreclosure
               s
Washington,   With early        40.5          47.5           12.1
 D.C.          foreclosures
              Without           39.0          43.8           17.2
               early
               foreclosure
               s
Six cities    With early        30.3          43.1           26.6
 combined      foreclosures
              Without           24.7          40.7           34.5
               early
               foreclosure
               s
----------------------------------------------------------------------
\a We defined an area's income level as "low" if the per capita
income was at or below 80 percent of the per capita income for the
city, "medium" if the per capita income was greater than 80 percent
but at or below 120 percent of the city's level, and "high" if the
per capita income was greater than 120 percent of the city's level. 

Note:  Percentages may not add to 100 because of rounding. 

Source:  GAO's analysis of data from HUD and the Bureau of the
Census. 

Additional details about differences in lending patterns among
lenders with and without early foreclosures appear in appendix II. 


   SEVERAL FACTORS WERE ASSOCIATED
   WITH EARLY FORECLOSURES
------------------------------------------------------------ Letter :5

For FHA-insured loans made during calendar years 1992 through 1994 in
the six cities we reviewed, we found that the following factors were
associated with early foreclosure rates:  (1) the relative income
level of the census tract where the property was located (expressed
as the ratio of the per capita income for the census tract to the per
capita income for the city), (2) the loan amount, (3) the
loan-to-value ratio, (4) the loan interest rate, and (5) the city
where the property was located.\12 Other things being equal, loans
made for properties in poorer census tracts, smaller loans, loans
with higher loan-to-value ratios, and loans with higher interest
rates were associated with higher probabilities of early foreclosure. 
Our analysis also showed that loans made for homes in San Bernadino
were associated with higher probabilities of early foreclosure,
possibly reflecting the loss of military and defense industry jobs in
the San Bernadino area. 

Our analysis also showed that loans made in poorer census tracts
tended to be smaller and to have higher loan-to-value ratios and
higher interest rates--all factors that increased the probability of
early foreclosure.  The relationship between lower incomes and loans
with these characteristics may partially explain why early
foreclosure rates were higher in low-income areas than in either
medium- or high-income areas.  Nonetheless, the association between
census tract incomes and early foreclosure rates was statistically
significant even after controlling for these other factors. 

We tested additional factors but did not find them to have
statistically significant associations with early foreclosure rates
after accounting for the factors listed above.  These factors were
the race (white or minority) of the borrower, the age of the
borrower, the year of the loan's origination, and the FHA loan
program used (203(b) or other loan program). 

Appendix III provides additional information on the results of our
statistical analysis. 


--------------------
\12 We identified these associations by performing a logistic
regression analysis, a technique used to estimate the individual
influence of each factor while controlling for the influence of the
others.  The associations were significant at the 95-percent
confidence level. 


   LENGTH OF TIME THAT HUD-OWNED
   PROPERTIES REMAINED UNSOLD
------------------------------------------------------------ Letter :6

As of December 31, 1996, HUD held 1,374 single-family properties in
its inventory in the six cities combined.  Among the six cities, the
number of properties in HUD's inventory that remained unsold ranged
from 65 in Atlanta to 471 in Chicago.\13 Our analysis did not
disclose a pattern in the median time that these properties remained
in HUD's inventory in different income areas.  As shown in table 3,
while in Atlanta and Washington the median time in inventory was
higher in low-income areas than in high-income areas, in Baltimore,
Chicago, and San Bernadino, the median time in inventory was about
the same in these areas.  In Dallas, the median time in inventory was
higher in high-income areas than in low-income areas.  According to
HUD officials, the length of time properties remain in HUD's
inventory is greatly affected by the economic conditions in each
city. 



                                Table 3
                
                 Median Months in Inventory for Single-
                  Family Properties in Six Cities That
                Remained Unsold as of December 31, 1996,
                            by Income Areas

                              Income level of areas\a
              --------------------------------------------------------
City              Low          Medium         High           All
------------  ------------  ------------  ------------  --------------
Atlanta           3.7           3.8           0.8            3.7
Baltimore         4.8           4.5           4.8            4.6
Chicago           5.0           4.3           4.9            4.8
Dallas            1.8           3.2           3.0            3.0
San               4.5           2.4           4.5            3.5
 Bernadino
Washington,       8.5           7.6           4.1            8.0
 D.C.
----------------------------------------------------------------------
Note:  We excluded from our analysis properties held off the market
as of May 17, 1997 (the date our data file was created).  HUD may
hold properties off the market while carrying out certain
administrative processes and programs for assisting the homeless, as
well as for other reasons.  However, we were unable to determine
whether included properties had been held off the market for any time
in the past.  In addition, in some cases, we were either unable to
identify the census tract where a property was located or HUD's data
did not provide the date a property entered HUD's inventory. 
Therefore, we excluded these properties from our analysis.  The
percentage of properties in each city that we excluded from our
analysis because of missing information on the census tract or time
in inventory was as follows:  Atlanta, 3 percent; Baltimore, 14
percent; Chicago, 4 percent; Dallas, 5 percent; San Bernadino, 20
percent; and Washington, D.C., 16 percent. 

\a We defined an area's income level as "low" if the per capita
income was at or below 80 percent of the per capita income for the
city, "medium" if the per capita income was greater than 80 percent
but at or below 120 percent of the city's level, and "high" if the
per capita income was greater than 120 percent of the city's level. 

Source:  GAO's analysis of data from HUD and the Bureau of the
Census. 

For the six cities combined and for each of the individual cities
except Dallas, the proportion of properties that had been in
inventory for more than 6 months was greater in low-income areas than
in either medium- or high-income areas.  (See table 4.)



                                Table 4
                
                 Months in Inventory for Single-Family
                 Properties in Six Cities That Remained
                   Unsold as of December 31, 1996, by
                              Income Areas

                                           Income level of areas\a
                                        ------------------------------
                            Months in
City                        inventory      Low  Medium    High     All
--------------------------  ----------  ------  ------  ------  ------
Atlanta                     Less than       19      20       2      41
                             or equal   (61.3%  (64.5%  (66.7%  (63.1%
                             to 6            )       )       )       )
                            Greater         12      11       1      24
                             than 6     (38.7%  (35.5%  (33.3%  (36.9%
                                             )       )       )       )
Baltimore                   Less than       30      52      25     107
                             or equal   (51.7%  (65.0%  (58.1%  (59.1%
                             to 6            )       )       )       )
                            Greater         28      28      18      74
                             than 6     (48.3%  (35.0%  (41.9%  (40.9%
                                             )       )       )       )
Chicago                     Less than      161     107      15     283
                             or equal   (57.1%  (64.1%  (68.2%  (60.1%
                             to 6            )       )       )       )
                            Greater        121      60       7     188
                             than 6     (42.9%  (35.9%  (31.8%  (39.9%
                                             )       )       )       )
Dallas                      Less than       35      56      22     113
                             or equal   (87.5%  (75.7%  (71.0%  (77.9%
                             to 6            )       )       )       )
                            Greater          5      18       9      32
                             than 6     (12.5%  (24.3%  (29.0%  (22.1%
                                             )       )       )       )
San Bernadino               Less than       61      78      23     162
                             or equal   (56.0%  (88.6%  (59.0%  (68.6%
                             to 6            )       )       )       )
                            Greater         48      10      16      74
                             than 6     (44.0%  (11.4%  (41.0%  (31.4%
                                             )       )       )       )
Washington, D.C.            Less than       25      17       5      47
                             or equal   (30.5%  (39.5%  (55.6%  (35.1%
                             to 6            )       )       )       )
                            Greater         57      26       4      87
                             than 6     (69.5%  (60.5%  (44.4%  (64.9%
                                             )       )       )       )
Six Cities Combined         Less than      331     330      92     753
                             or equal   (55.0%  (68.3%  (62.6%  (61.1%
                             to 6            )       )       )       )
                            Greater        271     153      55     479
                             than 6     (45.0%  (31.7%  (37.4%  (38.9%
                                             )       )       )       )
----------------------------------------------------------------------
Note:  See note for table 3. 

\a We defined an area's income level as "low" if the per capita
income was at or below 80 percent of the per capita income for the
city, "medium" if the per capita income was greater than 80 percent
but at or below 120 percent of the city's level, and "high" if the
per capita income was greater than 120 percent of city's level. 

Source:  GAO's analysis of data from HUD and the Bureau of the
Census. 

Additional details about the amount of time HUD-owned properties
remained unsold in the six cities appear in appendix IV. 


--------------------
\13 We were able to match census tract information and valid
time-in-inventory data with 1,232 of the 1,374 properties in HUD's
inventory as of December 31, 1996.  Therefore, we limited our
analysis to these 1,232 properties.  Appendix IV provides additional
details on the number and percentage of properties for which this
match was feasible. 


   AGENCY COMMENTS
------------------------------------------------------------ Letter :7

We provided HUD with a draft of this report for its review and
comment.  Officials who reviewed the report, including a
representative from the Office of the Assistant Secretary for
Housing-Federal Housing Commissioner, stated that they generally
agreed with the report's findings.  HUD also provided several
clarifying comments, which we incorporated into the report as
appropriate. 


   SCOPE AND METHODOLOGY
------------------------------------------------------------ Letter :8

In reporting information relating to early foreclosures on
FHA-insured single-family loans endorsed during calendar years 1992
through 1994 in low-, medium-, and high-income areas nationwide, we
relied on HUD's analysis of the number of loans made, the number of
early foreclosures, and the early foreclosure rates in the three
income areas.  To determine early foreclosure rates for the same
period in the six cities reviewed, we obtained data from HUD's
database on loans insured by FHA in calendar years 1992 through 1994
and merged this information with 1990 census data. 

To further analyze lending and early foreclosure patterns in the six
cities, we divided the lenders into two groups--those with no early
foreclosures and those with one or more early foreclosures during the
periods we reviewed--and compared these groups with respect to the
distribution of the loans they made across income areas.  To obtain
information on factors that contribute to differences in early
foreclosure rates among income areas, we performed an analysis to
show the extent to which certain variables were associated with
differences in the probability of early foreclosure.  Appendix III
provides information on the model we built to estimate relationships
between early foreclosures and factors that contribute to such
foreclosures.  To compare the length of time HUD-owned properties
remained unsold in low-, medium-, and high-income areas in the six
cities, we obtained data from HUD's Single-Family Accounting
Management System (SAMS), which tracks properties acquired and sold
by HUD.  Our analysis focused on single-family properties that
remained in HUD's inventory as of December 31, 1996. 

While we did not independently verify the accuracy or test the
reliability of FHA's data, we performed tests to check the internal
consistency of the data and worked with agency officials to ensure
that we interpreted the data properly.  Appendix V provides
additional details on our scope and methodology. 

We performed our work from December 1996 through September 1997 in
accordance with generally accepted government auditing standards. 


---------------------------------------------------------- Letter :8.1

As agreed with your office, unless you publicly announce its contents
earlier, we plan no further distribution of this report until 7 days
after the date of this letter.  At that time, we will provide copies
to the Secretary of HUD and other interested parties.  We will also
make copies available to others upon request. 

Please call me at (202) 512-7631 if you or your staff have any
questions.  Major contributors to this report are listed in appendix
VI. 

Sincerely yours,

Judy A.  England-Joseph
Director, Housing and Community
 Development Issues


EARLY FORECLOSURE RATES ON LOANS
MADE IN CALENDAR YEARS 1992-94, BY
INCOME AREAS
=========================================================== Appendix I

                                                     San
Income            Baltimor                      Bernadin  Washington  Six cities
level    Atlanta         e   Chicago    Dallas         o      , D.C.    combined
------  --------  --------  --------  --------  --------  ----------  ----------
Low-income areas
--------------------------------------------------------------------------------
Number       786       637     6,805     1,994     2,547       1,281      14,050
 of
 loans
Percen      27.5       7.5      33.0      20.3      48.0        39.4        27.9
 t of
 loans
Number        11         5        33        29        29           6         113
 of
 early
 forec
 losur
 es
Percen      61.1       8.9      62.3      46.8      51.8        66.7        44.5
 t of
 early
 forec
 losur
 es
Early       1.40      0.78      0.48      1.45      1.14        0.47        0.80
 forec
 losur
 e
 rate

Medium-income areas
--------------------------------------------------------------------------------
Number     1,219     3,578     9,691     3,176     2,061       1,457      21,182
 of
 loans
Percen      42.6      42.2      47.1      32.3      38.8        44.8        42.1
 t of
 loans
Number         5        26        15        25        21           3          95
 of
 early
 forec
 losur
 es
Percen      27.8      46.4      28.3      40.3      37.5        33.3        37.4
 t of
 early
 forec
 losur
 es
Early       0.41      0.73      0.15      0.79      1.02        0.21        0.45
 forec
 losur
 e
 rate

High-income areas
--------------------------------------------------------------------------------
Number       856     4,255     4,100     4,665       701         514      15,091
 of
 loans
Percen      29.9      50.2      19.9      47.4      13.2        15.8        30.0
 t of
 loans
Number         2        25         5         8         6           0          46
 of
 early
 forec
 losur
 es
Percen      11.1      44.6       9.4      12.9      10.7         0.0        18.1
 t of
 early
 forec
 losur
 es
Early       0.23      0.59      0.12      0.17      0.86         0.0        0.30
 forec
 losur
 e
 rate

All income areas
--------------------------------------------------------------------------------
Number     2,861     8,470    20,596     9,835     5,309       3,252      50,323
 of
 loans
Percen     100.0     100.0     100.0     100.0     100.0       100.0       100.0
 t of
 loans
Number        18        56        53        62        56           9         254
 of
 early
 forec
 losur
 es
Percen     100.0     100.0     100.0     100.0     100.0       100.0       100.0
 t of
 early
 forec
 losur
 es
Early       0.63      0.66      0.26      0.63      1.05        0.28        0.50
 forec
 losur
 e
 rate
--------------------------------------------------------------------------------
Source:  GAO's analysis of data from HUD and the Bureau of the
Census. 


DATA ON LENDERS WITH AND WITHOUT
EARLY FORECLOSURES ON LOANS MADE
IN CALENDAR YEARS 1992-94, BY
INCOME AREAS
========================================================== Appendix II

                                   Income level of areas
            --------------------------------------------------------------------
                  Low              Medium            High             All
            ----------------  ----------------  --------------  ----------------
City/type             Percen            Percen          Percen            Percen
of lender     Number       t    Number       t  Number       t    Number       t
----------  --------  ------  --------  ------  ------  ------  --------  ------
Atlanta
--------------------------------------------------------------------------------
With early       310    34.9       400    45.0     179    20.1       889   100.0
 foreclosu
 res
Without          476    24.1       819    41.5     677    34.3     1,972   100.0
 early
 foreclosu
 res

Baltimore
--------------------------------------------------------------------------------
With early       432     8.4     2,203    43.1   2,480    48.5     5,115   100.0
 foreclosu
 res
Without          205     6.1     1,375    41.0   1,775    52.9     3,355   100.0
 early
 foreclosu
 res

Chicago
--------------------------------------------------------------------------------
With early     4,918    36.3     6,333    46.7   2,300    17.0    13,551   100.0
 foreclosu
 res
Without        1,887    26.8     3,358    47.7   1,800    25.6     7,045   100.0
 early
 foreclosu
 res

Dallas
--------------------------------------------------------------------------------
With early     1,317    24.2     1,926    35.3   2,208    40.5     5,451   100.0
 foreclosu
 res
Without          677    15.4     1,250    28.5   2,457    56.0     4,384   100.0
 early
 foreclosu
 res

San Bernadino
--------------------------------------------------------------------------------
With early     1,424    46.8     1,186    39.0     434    14.3     3,044   100.0
 foreclosu
 res
Without        1,123    49.6       875    38.6     267    11.8     2,265   100.0
 early
 foreclosu
 res

Washington, D.C.
--------------------------------------------------------------------------------
With early       365    40.5       428    47.5     109    12.1       902   100.0
 foreclosu
 res
Without          916    39.0     1,029    43.8     405    17.2     2,350   100.0
 early
 foreclosu
 res

Six cities combined
--------------------------------------------------------------------------------
With early     8,766    30.3    12,476    43.1   7,710    26.6    28,952   100.0
 foreclosu
 res
Without        5,284    24.7     8,706    40.7   7,381    34.5    21,371   100.0
 early
 foreclosu
 res
--------------------------------------------------------------------------------
Note:  Percentages may not add to 100 because of rounding. 

Source:  GAO's analysis of data from HUD and the Bureau of the
Census. 


GAO'S ECONOMETRIC MODEL USED TO
IDENTIFY FACTORS ASSOCIATED WITH
EARLY FORECLOSURES
========================================================= Appendix III

This appendix describes the econometric model we developed and the
analysis we conducted to estimate the associations between early
foreclosures and several explanatory variables.  The explanatory
variables we tested were the loan-to-value ratio, loan amount,
contract interest rate, city where the property was located, and
neighborhood income.  The equation we estimated used all of the
FHA-insured single-family loans endorsed in calendar years 1992
through 1994 in six cities--Atlanta, Georgia; Baltimore, Maryland;
Chicago, Illinois; Dallas, Texas; San Bernadino, California; and
Washington, D.C.  We excluded loans made for properties within the
metropolitan statistical area (MSA) but outside the city's
boundaries.  We relied on census data to determine the per capita
income of the census tracts in the six cities.  The data we used, our
model, and the results we obtained are discussed in detail in the
following sections. 


   DATA USED IN THIS ANALYSIS
------------------------------------------------------- Appendix III:1

For our analysis, we combined FHA's computerized data from two
separate\1 files of 2,945,252 mortgages endorsed in calendar years
1992, 1993, and 1994.  We then merged the combined FHA data files for
the selected cities with census data to obtain income information for
the census tracts where the loans were made.  From FHA's records, we
obtained information on the initial characteristics of each loan,
such as the year of its endorsement, state and city in which it was
originated, loan-to-value ratio, loan amount, and loan interest rate. 
FHA's files contain information on all of the single-family loans
that FHA insured, including loans for condominium units, loans made
to refinance existing mortgages, rehabilitation loans, and loans
covered under FHA's special risk insurance program.  From the Bureau
of the Census, we obtained data on the aggregate household income and
total population for each of the six relevant MSAs.  We computed the
per capita income for each tract by dividing its aggregate household
income\2 by its total population.  We determined the per capita
income for each city by dividing the aggregate household income for
all of the census tracts within its borders by its total population. 

Within the five states covered by our review (Illinois, Georgia,
Texas, California, and Maryland) and the city of Washington, D.C.,
859,128 loans were made during the 3-year period.  We selected the
loans originated in each of the six cities by first using the county
codes\3 for the appropriate MSA and then identifying the census
tracts that were within the city's borders according to the listing
of tracts supplied to us by an official representative of each city. 
As indicated in table III.1, 399,011 loans were endorsed in the six
MSAs that included the six cities during calendar years 1992 through
1994. 



                              Table III.1
                
                  Number of Loans Made in Six MSAs and
                  Number of Loans Matched With Census
                      Data, Calendar Years 1992-94

                                                    Number and percent
                                                       of loans not
                                                         matched
                                                    ------------------
                                 Total
                             number of   Number of
                              loans in       loans
MSA                                MSA     matched    Number   Percent
--------------------------  ----------  ----------  --------  --------
Atlanta                         83,320      67,091    16,229        19
Baltimore                       54,612      43,468    11,144        20
Chicago                         96,751      80,549    16,202        17
Dallas                         114,534     101,521    13,013        11
San Bernadino                   45,999      37,164     8,835        19
Washington, D.C.                 3,795       3,252       543        14
======================================================================
Total                          399,011     333,045    65,966        17
----------------------------------------------------------------------
We were able to match FHA loans to census records for 83 percent of
the loans (333,045) in the MSA, but not for the remaining 17 percent
(65,966 loans).  Because we used census tract codes to determine if
the loans were within or outside a city, we were not able to
determine what percentage of the 65,966 unmatched loans were within a
city's borders.  We matched 80 percent of the total number of loans
with all six digits of the census tract code and an additional 3
percent with four digits of the census tract code.  The four-digit
match was necessary because of changes to the definitions of some
metropolitan area tracts over time. 

In general, each of the MSAs had hundreds of census tracts, but only
a fraction of them were located within the city's borders.  We
excluded from our analysis 543 loans for properties in Washington,
D.C., because invalid census tract codes made it difficult to obtain
census tract income and population data.  In addition, there were
65,423 loans endorsed in the remaining MSAs that we could not
identify as being within one of the cities because their census tract
codes were invalid.  Another 4,537 loans endorsed in the six states
did not have valid county codes, and we were unable to determine if
they should have been included within one of the MSAs. 

As shown in table III.2, of the 333,045 loans we were able to match
with census tracts, 50,323 were made for properties within the six
cities' borders.  We were able to find the valid census tract income
for virtually all of the 50,323 loans.  In other words, when we
identified a loan as being for a property in one of the six cities,
we were almost always able to determine the total population or
aggregate income for that loan's census tract. 



                              Table III.2
                
                  Number of Loans Made Within the Six
                     Cities, Calendar Years 1992-94

                                                       Number of loans
                                                 identified within the
City                                                     city's border
----------------------------------------  ----------------------------
Atlanta                                                          2,861
Baltimore                                                        8,470
Chicago                                                         20,596
Dallas                                                           9,835
San Bernadino                                                    5,309
Washington, D.C.                                                 3,252
======================================================================
Total                                                           50,323
----------------------------------------------------------------------
Many FHA-insured loans were refinanced during calendar years 1992
through 1994.  Refinanced mortgages\4 accounted for about 32 percent
of the loans in the six cities during the 3-year period we examined. 
Of the loans that were refinanced, about 69 percent had a recorded
loan-to-value ratio of zero, and nearly all of these were streamlined
refinanced mortgages.\5 Because FHA does not require property
appraisals for streamlined refinanced mortgages, the initial
loan-to-value ratios for these loans are unknown. 


--------------------
\1 FHA's A-43 database provides current and historical information on
the mortgage loans that FHA insures.  FHA's F-42 database provides
additional information on characteristics such as the age, race, and
income of FHA borrowers. 

\2 We excluded the income of persons in group quarters and
institutions from our calculation of per capita income.  For the six
cities combined, about 97 percent of the census tracts did not have
persons in group quarters and institutions, and such persons
accounted for less than 10 percent of the population in 76 percent of
the remaining census tracts.  We determined that our classification
of census tracts as low-, medium-, or high-income was not affected by
our exclusion of the income of persons in group quarters and
institutions. 

\3 According to HUD officials, the codes for the state, county, and
census tract are the most important because the metropolitan area can
be identified from these codes (except for split tracts in New
England).  Of the 859,128 loans endorsed in the five states and
Washington, D.C., 4,537 loans did not have an appropriate county
code.  Therefore, we could not tell if these loans were made in the
six cities we reviewed. 

\4 Borrowers often refinance mortgage loans to lower their monthly
principal and interest payments when interest rates decline.  Of the
refinanced mortgages, 89 percent were "streamlined refinanced,"
meaning that the old FHA-insured mortgage loan was repaid from the
proceeds of a new FHA-insured loan using the same property as
security.  Appraisals and credit checks are not required by FHA on
these loans, and borrowers cannot obtain cash from the transaction
except for minor adjustments not exceeding $250 at closing. 

\5 FHA's data did not indicate whether there were any existing second
mortgages on these properties. 


   SPECIFICATION OF THE MODEL
------------------------------------------------------- Appendix III:2

A default on a home mortgage loan may be triggered by unemployment,
divorce, death, or some other event.  Such an event is not likely to
trigger a foreclosure if the owner has positive equity in the home
because the sale of the home with the realization of a profit is
better than the loss of the home through foreclosure.  However, if
the property is worth less than the mortgage, such an event may
trigger a foreclosure. 

We hypothesized that the probability of early foreclosure is
influenced by, among other things, the loan-to-value ratio, the size
of the loan, the loan interest rate, income, and the property's
location.  Because the recorded value of the loan-to-value ratio for
some loans was zero, we added a variable to our analysis to identify
these loans.  We used a logistic regression equation to explore how
foreclosure rates on loans endorsed in calendar years 1992 through
1994 in the six cities varied for each of these factors.  Logistic
regression is a standard procedure for analyzing a dichotomous
dependent variable, such as whether or not an early foreclosure
occurred.  We used the results of our logistic regression to estimate
how the odds of early foreclosure are expected to change with unit
changes in the explanatory factors.  In the logistic regression, we
used deviation coding for categorical variables, such as the city
where the foreclosure occurred.  Therefore, the effect for each
category is compared to the average effect for all of the categories,
rather than to an omitted (or reference) category. 

We tested additional factors but did not find them to be
significantly associated with early foreclosure rates after
accounting for the factors listed above.  These additional factors
were the race of the borrower (white or other), the age of the
borrower, the year of the loan's endorsement (1992, 1993, 1994), and
the loan program used (the MMI Fund's 203(b) program or other loan
program).\6

We were not able to include all of the factors, such as unemployment
rates, that might be related to the probability of early foreclosure
in our analysis.  This was generally because data were not available. 
If we had been able to include these other factors, our results with
respect to the included factors might have been different.  We and
other researchers have estimated the probability of ultimate
foreclosure and have found other factors that have a significant
impact on it.  These factors include the borrower's equity and the
prevailing interest rate at the time of default, lagged unemployment,
the property's location (i.e., urban or rural), whether the borrower
is a first-time homeowner, and the borrower's marital status.  It is
generally agreed that many life-changing events--such as the arrival
of children, divorce, and death--may also be related to the
probability of foreclosure.  However, it should be noted that prior
research has associated these other factors only to ultimate loan
foreclosure, not to early foreclosure. 


--------------------
\6 In HUD's database the age of the borrower was recorded as zero--an
invalid figure--for about 18 percent of the loans.  To compensate for
the missing data, we included in our analysis of age a dummy variable
indicating whether or not the information on age was missing. 
Neither the coefficient for the continuous age variable nor the
coefficient for the dummy variable was significant at the 0.05 level. 
The significance of the variables added were as follows:  race, 0.38;
endorsement year, 0.80; loan program, 0.46; age dummy variable, 0.41;
age as a continuous variable, 0.18. 


      INCOME
----------------------------------------------------- Appendix III:2.1

To determine if early foreclosure rates were different in
lower-income communities, we obtained information on the aggregate
income and the total population for each census tract within the
borders of the cities we studied.  We computed the ratio of the per
capita income for each of the tracts to the per capita income for the
relevant city to obtain the tract-to-city income ratio.  We
anticipated that people living in lower-income tracts might have more
difficulty meeting their mortgage payments than people in
higher-income tracts and that the rate of early foreclosure would,
then, be higher in the lower-income tracts than elsewhere.  Factors
associated with lower-income communities, such as higher unemployment
rates and less stability in employment, could limit the ability of
borrowers to meet their monthly mortgage payments.  Other factors,
such as the greater age of the housing stock or the slower
appreciation of house prices in lower-income communities, could also
affect early foreclosure rates. 


      LOAN-TO-VALUE RATIO
----------------------------------------------------- Appendix III:2.2

The ratio of the loan amount to the value of the property is an
important determinant of whether a loan will end in foreclosure.  The
loan-to-value ratio on the property changes over time because
property values can increase or decrease, and payments reduce the
amount owed on a mortgage.  Because we were examining foreclosures
that occur within 18 months of the loan endorsement date, we
anticipated that the change in the loan-to-value ratio within that
time period would be so small that the initial loan-to-value ratio
would be sufficient to capture the effect of the borrower's equity
percentage on the probability of foreclosure, when the equity
percentage is considered to be 1 minus the loan-to-value ratio. 
Research indicates that borrowers with small amounts of equity (and,
hence, higher loan-to-value ratios), especially those with negative
equity, are more likely than other borrowers to default.\7

FHA's data showed a value of zero for about 22 percent of the loans
in the six cities.  Although almost all of these loans were
refinanced, another 10 percent of the loans were refinanced and had
valid loan-to-value ratios.  FHA does not require an appraisal for
streamlined refinanced loans.  When an appraisal is not performed,
the loan-to-value ratio is unknown.  We have reported that the
probability of foreclosure for FHA-insured refinanced loans differs
from that for other FHA-insured loans,\8 but we did not include a
refinance indicator in the regression.  We did, however, add a
variable to indicate when a loan was missing a loan-to-value ratio. 
We did not separately take into account any further differences that
may result from other characteristics of refinanced loans that did
have valid loan-to-value ratios. 


--------------------
\7 When we discuss the likely effects of one of our explanatory
variables, we are describing the marginal effects of that variable
while holding the effects of other variables constant. 

\8 Mortgage Financing:  FHA Has Achieved Its Home Mortgage Capital
Reserve Target (GAO/RCED-96-50, Apr.  12, 1996)


      INTEREST RATE AND LOAN
      AMOUNT
----------------------------------------------------- Appendix III:2.3

We included the interest rate on the mortgage as an explanatory
variable in the early foreclosure equation.  We expected a higher
interest rate to be associated with a higher probability of early
foreclosure because a higher interest rate causes a higher monthly
payment. 

To obtain insight into the differential effect of relatively larger
loans on the probability of early foreclosure, we used the loan
amount as an explanatory variable.  In our previously cited report,
we pointed out that, other things being equal, larger loans have
lower probabilities of foreclosure than smaller loans.  Different
rates of appreciation in house prices in low- and higher-income
communities may be one factor underlying this phenomenon.  We know
that larger loans are associated with higher-priced homes.  By using
the loan amount as a variable in our equation and holding income
constant, we were testing the relationship between larger loans and
the probability of early foreclosure. 


      CITY WHERE PROPERTY IS
      LOCATED
----------------------------------------------------- Appendix III:2.4

We used variables to indicate the city where the property was
located.  We expected that the coefficients for these variables would
pick up differences in economic conditions within the city that we
could not model explicitly.  Some of these differences may include
changes in the rates of unemployment, house price appreciation, net
migration, and other unknown factors. 


   ESTIMATION RESULTS
------------------------------------------------------- Appendix III:3

The results of our analysis are summarized in table III.3.  In
general, our results are consistent with the economic reasoning that
underlies our model. 



                              Table III.3
                
                   Logistic Regression Summary Table

                                               Odds change factor
                                          ----------------------------
                                                        Confidence
                                                         interval
                                                    ------------------
                                Signific
                                    ance
Variable                         level\a  Estimate
------------------------------  --------  --------  --------  --------
Intercept                           0.00      0.00      0.00      0.00
City                                0.00
Atlanta                             0.49      1.16      0.76      1.76
Baltimore                           0.06      1.33      0.99      1.79
Chicago                             0.00      0.47      0.35      0.62
Dallas                              0.64      1.07      0.80      1.42
San Bernadino                       0.00      2.02      1.51      2.71
Washington, D.C.                    0.20      0.65      0.32      1.32
Income: ratio of the per-           0.00      0.99      0.98      0.99
 capita income for the tract
 to that for the city (ratio x
 100)
Interest rate (percent)             0.02      1.16      1.02      1.32
Loan amount (dollars in             0.02      0.99      0.99      1.00
 thousands)
Loan-to-value ratio                 0.00      1.06      1.03      1.09
 (ratio x 100)
Is the loan-to-value ratio          0.00
 provided in the data equal to
 zero?
Yes                                 0.00     13.89      3.60     53.53
No                                  0.00      0.07      0.02      0.28
Number of observations                                          50,318
----------------------------------------------------------------------
\a We interpreted a value of less than 0.05 as indicating a
statistically significant association between the odds of early
foreclosure and the variable or characteristic.  We did not conclude
that a statistically significant association existed if the value was
more than 0.05. 

\b In logistic regression, the coefficients of the variables are not
easily interpretable.  Therefore, we transformed the original
coefficients into a more interpretable form that we termed the "odds
change factor." Specifically, we raised the natural logarithm base,
e, to the power equal to the value of the original coefficient to
obtain the odds change factor.  Odds change factors estimate the
effect of each variable on the predicted odds of foreclosure.  A
value greater than 1 means that the odds of foreclosure are expected
to increase, while a value less than 1 predicts a decrease in the
odds of foreclosure.  For example, the odds change factor for the
interest rate variable is 1.16, which means that the odds of early
foreclosure increase by 16 percent for each percentage point the
interest rate increases.  Confidence intervals were also calculated
for the original logistic regression coefficients at the 95-percent
confidence level and then transformed into the more interpretable
form.  This means that we would expect the lower and upper bound to
include the true odds change factor 95 times out of 100. 

We found statistically significant associations\9 between increased
rates of early foreclosure and (1) a lower per capita income for a
census tract, (2) higher loan-to-value ratios, (3) higher loan
interest rates, (4) smaller loan amounts, and (5) loans made for
properties located in San Bernadino.  We also found that early
foreclosure was less likely for loans made for properties in Chicago. 

As the per capita income in the census tract in which the property
was located increased relative to the per capita income in the entire
city, the odds of early foreclosure decreased.  For example, the odds
of foreclosure for loans on properties located in areas whose per
capita income was 91 percent of the citywide per capita income were
about 1 percent lower than the odds for properties in areas whose per
capita income was 90 percent of the citywide income.  Larger
mortgages were negatively correlated with the probability of early
foreclosure.  The odds of early foreclosure were estimated to
decrease by about 1 percent for each additional $1,000 borrowed. 

The loan-to-value ratio was significantly and positively correlated
with the odds of early foreclosure.  When the loan-to-value ratio
increased by 1 percentage point, the odds of early foreclosure
increased by about 6 percent.  The odds of early foreclosure for
loans with a loan-to-value ratio of zero--mostly streamlined financed
loans--were about the same as the odds for loans with a loan-to-value
ratio of 90 percent and were about 25 percent lower than the odds for
loans with a loan-to-value ratio of 95 percent.\10

Higher interest rates are associated with an increase in early
foreclosures.  Holding other things constant, an increase of 1
percentage point in the interest rate was found to increase the odds
of early foreclosure by about 16 percent. 

We also found that the odds of early foreclosure differed with the
city being tested.  For example, the odds of early foreclosure were
lower than average for Chicago and about twice as high as the
six-city average for San Bernadino.  We did not obtain statistically
significant results for Atlanta, Baltimore, Dallas, or Washington,
D.C. 


--------------------
\9 We used the 95-percent level of confidence. 

\10 We obtained these results by jointly considering the effects of
the loan-to-value (LTV) ratio and the LTV-equals-zero indicator. 
Because the LTV ratio recorded in FHA's database determined the
values for both of these variables, both coefficients must be
considered. 


TIME IN INVENTORY FOR
SINGLE-FAMILY PROPERTIES IN SIX
CITIES THAT REMAINED UNSOLD AS OF
DECEMBER 31, 1996, BY INCOME AREAS
========================================================== Appendix IV

                                  Income level of areas
         -----------------------------------------------------------------------
               Low              Medium             High               All
         ----------------  ----------------  ----------------  -----------------
Months
in
invento
ry       Number   Percent  Number   Percent  Number   Percent  Number   Percent
-------  -------  -------  -------  -------  -------  -------  -------  --------
Atlanta
--------------------------------------------------------------------------------
Less       19      61.3      20      64.5       2      66.7      41       63.1
 than
 or
 equal
 to 6
Greater     9      29.0       8      25.8       0        0       17       26.2
 than
 6,
 less
 than
 or
 equal
 to 12
Greater     1       3.2       0        0        0        0        1       1.5
 than
 12,
 less
 than
 or
 equal
 to 24
Greater     2       6.5       3       9.7       1      33.3       6       9.2
 than
 24
================================================================================
Total      31      100.0     31      100.0      3      100.0     65      100.0

Baltimore
--------------------------------------------------------------------------------
Less       30      51.7      52      65.0      25      58.1      107      59.1
 than
 or
 equal
 to 6
Greater    17      29.3      22      27.5       9      20.9      48       26.5
 than
 6,
 less
 than
 or
 equal
 to 12
Greater     8      13.8       3       3.8       5      11.6      16       8.8
 than
 12,
 less
 than
 or
 equal
 to 24
Greater     3       5.2       3       3.8       4       9.3      10       5.5
 than
 24
================================================================================
Total      58      100.0     80      100.0     43      100.0     181     100.0

Chicago
--------------------------------------------------------------------------------
Less       161     57.1      107     64.1      15      68.2      283      60.1
 than
 or
 equal
 to 6
Greater    59      20.9      29      17.4       4      18.2      92       19.5
 than
 6,
 less
 than
 or
 equal
 to 12
Greater    27       9.6      10       6.0       0        0       37       7.9
 than
 12,
 less
 than
 or
 equal
 to 24
Greater    35      12.4      21      12.6       3      13.6      59       12.5
 than
 24
================================================================================
Total      282     100.0     167     100.0     22      100.0     471     100.0

Dallas
--------------------------------------------------------------------------------
Less       35      87.5      56      75.7      22      71.0      113      77.9
 than
 or
 equal
 to 6
Greater     5      12.5      11      14.9       4      12.9      20       13.8
 than
 6,
 less
 than
 or
 equal
 to 12
Greater     0        0        2       2.7       0        0        2       1.4
 than
 12,
 less
 than
 or
 equal
 to 24
Greater     0        0        5       6.8       5      16.1      10       6.9
 than
 24
================================================================================
Total      40      100.0     74      100.0     31      100.0     145     100.0

San Bernadino
--------------------------------------------------------------------------------
Less       61      56.0      78      88.6      23      59.0      162      68.6
 than
 or
 equal
 to 6
Greater    25      22.9       6       6.8       8      20.5      39       16.5
 than
 6,
 less
 than
 or
 equal
 to 12
Greater    20      18.3       4       4.5       6      15.4      30       12.7
 than
 12,
 less
 than
 or
 equal
 to 24
Greater     3       2.8       0        0        2       5.1       5       2.1
 than
 24
================================================================================
Total      109     100.0     88      100.0     39      100.0     236     100.0

Washington, D.C.
--------------------------------------------------------------------------------
Less       25      30.5      17      39.5       5      55.6      47       35.1
 than
 or
 equal
 to 6
Greater    24      29.3      10      23.3       2      22.2      36       26.9
 than
 6,
 less
 than
 or
 equal
 to 12
Greater    15      18.3       7      16.3       1      11.1      23       17.2
 than
 12,
 less
 than
 or
 equal
 to 24
Greater    18      22.0       9      20.9       1      11.1      28       20.9
 than
 24
================================================================================
Total      82      100.0     43      100.0      9      100.0     134     100.0

Six cities combined
--------------------------------------------------------------------------------
Less       331     55.0      330     68.3      92      62.6      753      61.1
 than
 or
 equal
 to 6
Greater    139     23.1      86      17.8      27      18.4      252      20.5
 than
 6,
 less
 than
 or
 equal
 to 12
Greater    71      11.8      26       5.4      12       8.2      109      8.8
 than
 12,
 less
 than
 or
 equal
 to 24
Greater    61      10.1      41       8.5      16      10.9      118      9.6
 than
 24
================================================================================
Total      602     100.0     483     100.0     147     100.0    1,232    100.0
--------------------------------------------------------------------------------
Note:  We excluded from our analysis properties held off the market
as of May 17, 1997 (the date our data file was created); however, we
were unable to determine whether included properties had been held
off the market for any time in the past.  In addition, in some cases,
we were either unable to identify the census tract where a property
was located or HUD's data did not provide the date a property entered
HUD's inventory.  Therefore, we excluded these properties from our
analysis.  The percentage of properties in each city that we excluded
from our analysis because of missing information on the census tract
or the time in inventory was as follows:  Atlanta, 3 percent (2 of 67
properties); Baltimore, 14 percent (29 of 210 properties); Chicago, 4
percent (19 of 490 properties); Dallas, 5 percent (8 of 153
properties); San Bernadino, 20 percent (59 of 295 properties); and
Washington, D.C., 16 percent (25 of 159 properties). 

Source:  GAO's analysis of data from HUD and the Bureau of the
Census. 


OBJECTIVES, SCOPE, AND METHODOLOGY
=========================================================== Appendix V

Our objectives were to (1) compare early foreclosure rates on
FHA-insured single-family loans made in low-, medium-, and
high-income areas nationwide and in the six cities; (2) compare
across income areas the proportion of loans made in the six cities by
FHA-approved mortgage lenders with and without early foreclosures;
(3) identify factors that influence early foreclosure rates; and (4)
compare the length of time HUD-owned single-family properties
remained unsold in low-, medium-, and high-income areas in the six
cities. 

In reporting information relating to early foreclosures on
FHA-insured single-family loans endorsed during calendar years 1992
through 1994 in low-, medium-, and high-income areas nationwide, we
relied on HUD's analysis of the number of loans made, the number of
early foreclosures, and the early foreclosure rates in the three
income areas.  To determine early foreclosure rates for the same
period in the six cities reviewed, we obtained data from HUD's
database on loans insured by FHA in calendar years 1992 through 1994
and merged this information with 1990 census data.  Detailed
information on the data we used are provided in the section of
appendix III that discusses the data used in this analysis. 

We defined a census tract's income level as "low" if the per capita
income was at or below 80 percent of the city's per capita income,
"medium" if the per capita income was greater than 80 percent but at
or below 120 percent of the city's level, and "high" if the per
capita income was greater than 120 percent of the city's level. 
Although HUD usually uses the median family income to identify low-,
medium-, and high-income census tracts, we were unable to compute the
median family income for the six cities from the data we extracted
from census records.  We therefore used the per capita income as our
income measure. 

HUD computed early foreclosure rates by income level nationwide for
this report using the average household income as the income measure
for each MSA.  As indicated above, we used the per capita income for
each city as the income measure to calculate early foreclosure rates
by income level for the six cities.  Therefore, our classification of
census tracts as low-, medium-, or high-income may differ from HUD's
classification because (1) the average income for the MSA may differ
from the per capita income for the city, and (2) the per capita
income does not take into account differences in the average
household size among the three income groups.  While our
classification of census tracts differed from HUD's classification,
the relationship between early foreclosure rates and census tract
income levels for both computations was similar. 

We limited our analysis to early foreclosures, that is, to those
occurring within 18 months of the loan endorsement date.  To
determine whether a foreclosure occurred within that time period, we
measured the time elapsed between FHA's endorsement of the loan and
the date the lender foreclosed on the loan.  For this report, we
included in our calculation of early foreclosure rates loans on which
the lender did not actually foreclose but on which FHA paid an
insurance claim to the lender within 18 months of the loan
endorsement date.  We excluded from our calculation of early
foreclosure rates nonconveyance foreclosures, such as instances
during which a foreclosure occurs but an insurance claim is not paid. 
In some cases, early foreclosures may not have been reflected in the
data from HUD that we used because of the lag between the date of the
actual foreclosure and the date it was recorded in HUD's database. 
As a result, our analysis may understate the number of early
foreclosures by the number of these unrecorded cases. 

To further analyze lending and early foreclosure patterns in the six
cities, we divided the lenders into two groups--those with no early
foreclosures and those with one or more early foreclosures during the
periods reviewed--and compared these groups with respect to the
distribution of the loans they made across income areas.  We
determined whether a lender had one or more early foreclosures on a
city-by-city basis.  Therefore, any lender that made loans in more
than one of the six cities could be classified in the group of
lenders with early foreclosures in one city and in the group of
lenders without early foreclosures in another city. 

To obtain information on factors that contribute to differences in
early foreclosure rates among income areas, we performed an analysis
to show the extent to which certain variables were associated with
differences in the probability of early foreclosure.  Appendix III
provides information on the model we built to estimate relationships
between early foreclosures and factors that contribute to such
foreclosures.  In addition, we reviewed the mortgage finance
literature and interviewed officials from HUD's Office of Insured
Single-Family Housing and HUD field office officials in each of the
six cities.  We also interviewed local government officials and
nonprofit housing executives familiar with FHA's role in the real
estate markets in each of the six cities. 

To compare the length of time HUD-owned properties remained unsold in
low-, medium-, and high-income areas in the six cities, we obtained
data from HUD's Single-Family Accounting Management System (SAMS),
which tracks properties acquired and sold by HUD.  Our analysis
focused on single-family properties that remained in HUD's inventory
as of December 31, 1996.  We measured the time in inventory from the
date that HUD acquired the property.  We excluded properties held off
the market as of May 17, 1997 (the date our data extract was
created), but we were unable to determine if the remaining properties
had been held off the market for any time in the past. 

For the six cities reviewed, we matched (both electronically and
manually) the property addresses in SAMS to the addresses in the
Bureau of the Census' street address file to identify corresponding
census tracts.  When an exact match for the zip code and street
address did not exist, we manually selected the closest reasonable
match.  When no reasonable match existed or multiple choices were
possible, we excluded the property from our analysis.  For the six
cities combined, we were able to match about 90 percent (1,232 of
1,374) of the properties in HUD's inventory with a census tract and
data on valid time in inventory. 


MAJOR CONTRIBUTORS TO THIS REPORT
========================================================== Appendix VI

RESOURCES, COMMUNITY, AND ECONOMIC
DEVELOPMENT DIVISION, WASHINGTON,
D.C. 

Karen Bracey
Barbara Johnson
DuEwa Kamara
Robert Procaccini
Chuck Wilson

CHICAGO FIELD OFFICE

Glenn G.  Davis
Dorothy Waniak
Steven Westley


*** End of document. ***