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Consulting Corner
The Competing Risks of Mortgage Prepayment
and Default
by Kyle G. Lundstedt, Ph.D.
The Increasing Relevance of Credit Risk to Mortgage
Investors
There is an enormous amount of credit risk in the mortgage sector. Government
statistics show that over $3.8 trillion in single-family residential
(SFR) mortgages were originated in 2003. Even a very small percentage
credit loss on such an enormous asset class would have serious repercussions
for the U.S. economy.
For many years, however, credit risk has been of little concern to investors
in mortgage securities. Historically, the housing government-sponsored
enterprises (GSEs) - Fannie Mae, Freddie Mac, and Ginnie Mae -have provided
significant if not total protection from credit risk for well over half
of the mortgages originated in the U.S. each year. The remaining credit
risk traditionally was held in portfolio as whole loans by financial
institutions such as banks and thrifts. Nonetheless, in the last ten
years, mortgage investors have seen the emergence of a significant secondary
market for "non-agency" mortgage-backed securities (MBS),
mortgage-related asset-backed securities (ABS), and the collateralized
mortgage obligations (CMOs) derived from there. As a result, fixed income
investors now must concern themselves with measuring and understanding
credit risk, in addition to the traditional market and prepayment risks
for mortgages.
In this article, we describe some commonly used tools for measuring
credit risk. We discuss the pros and cons of these tools and then describe
a methodology called "competing risks" models. We discuss
how prepayment and default constitute competing risks in the context
of mortgages and give some examples of why mortgage investors might
consider this methodology for use in evaluating "non-agency"
MBS and ABS.
Types of Consumer Default Models
There is a long history of attempts to quantify the amount of credit
risk in mortgages. One might categorize these attempts (Lundstedt 2004)
into four types of methodology:
- Scoring models
- Roll Rate or Migration models
- Life-of-loan Loss/Actuarial models
- Option-based Structural models >>>
