Empirical models of mortgage default typically find that the influence of unemployment is negligible compared to other well-known risk factors such as high borrower leverage or low borrower FICO scores. This is at odds with theory, which assigns a critical role to unemployment status in the decision to stop payment on a mortgage. The authors help reconcile this divergence by employing a novel empirical strategy involving simulated unemployment histories to measure the severity of attenuation bias in loan-level estimations of default risk due to a borrower becoming unemployed. Attenuation bias results because individual data on unemployment status is unobserved, requiring that a market-wide unemployment rate be used as a proxy. Attenuation is extreme, with the authors’ results suggesting that the use of an aggregate unemployment rate in lieu of actual borrower unemployment status results in default risk from a borrower becoming unemployed being underestimated by a factor of 100 or more. Correcting for this indicates unemployment is more powerful than other well-known factors such as extremely high leverage orextremely low FICO scores in predicting individual borrower default. The authors’ simulated data indicate that adding the unemployment rate as a proxy for the missing borrower-specific unemployment indicator does not improve the accuracy of the estimated model over the specification without the proxy variable included.
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