Analysis of FHA Single-Family Default and Loss Rates

Previous studies of mortgage risk in both the conventional and FHA sectors have
focused almost exclusively on default behavior and on the factors that lead to default.
This is the approach taken in numerous articles in the professional economics and finance
literature, as well as in nonacademic studies produced by practitioners within the
industry. In addition, recent extensions on FHA mortgage scoring have followed the
main lines of previous research in focusing solely on the default probability as a metric
for risk. In virtually all of this extensive research virtually no attention is given to other
dimensions of loss and to the dollar value of losses in particular; thus, little is known
about dollar loss and its determinants.
This focus on default in the mortgage scoring context means that observable
factors affecting the likelihood of default assume a primary role. Because minorities tend
to have less attractive distributions of factors leading to default, mortgage scoring
systems tend to give minorities less favorable scores than nonminorities, justifying such
patterns with well-reasoned arguments of business necessity. Some observers,
understandably concerned by this racial discrepancy in scoring outcomes, have suggested
that minorities generate smaller dollar losses on average when they default, and thus a
mortgage scoring system relying on dollar losses rather than default alone might improve
minorities’ lot. In addition, a mortgage scoring system that recognizes both the
probability of default and the dollar losses attendant upon default would provide a more
complete, and thus superior, measure of risk that could be used for policy decisions as
well as for underwriting.
The purpose of this paper is to use data on FHA-insured loans from 1992, 1994,
and 1996 to examine the factors that influence both default probabilities and dollar loss
rates, as well as the avenue by which impacts arise. En route we pay special attention to
the possibility that minorities would fare better with a scorecard based in part on dollar
losses. The analysis ranges from simple statistical summaries and descriptive regressions
to more complete and sophisticated statistical analysis.