Commercial Mortgage Defaults: Proportional Hazards Estimation Using Individual Loan Histories

Abstract

This paper examines the theory of commercial mortgage default and tests it using a data set of 2,899 loan histories provided by a major multi‐line insurance company. A default model is estimated which relates subsequent default incidence and timing to contemporaneous loan term, borrower, property and economic/market conditions. Maximum likelihood estimation is used to estimate a hazard function predicting conditional probability of default over time. Results confirm many expected default relationships, in particular the dominance of loan terms and property value trends over time in affecting default. The effectiveness of the model in discriminating between “good” and “bad” loans is explored. Implications for underwriting practice and credit risk diversification are noted. Finally, suggestions are made for extending these results in pricing applications.

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