The Failure of Models that Predict Failure: Distance, Incentives and Defaults

Abstract

Statistical default models, widely used to assess default risk, are subject to a Lucas critique. We demonstrate this phenomenon using data on securitized subprime mortgages issued in the period 1997--2006. As the level of securitization increases, lenders have an incentive to originate loans that rate high based on characteristics that are reported to investors, even if other unreported variables imply a lower borrower quality. Consistent with this behavior, we find that over time lenders set interest rates only on the basis of variables that are reported to investors, ignoring other credit-relevant information. The change in lender behavior alters the data generating process by transforming the mapping from observables to loan defaults. To illustrate this effect, we show that a statistical default model estimated in a low securitization period breaks down in a high securitization period in a systematic manner: it underpredicts defaults among borrowers for whom soft information is more valuable. Regulations that rely on such models to assess default risk may therefore be undermined by the actions of market participants.