Original Article

The Journal of Real Estate Finance and Economics

, Volume 30, Issue 4, pp 369-396

First online:

Mortgage Default: Classification Trees Analysis

  • David FeldmanAffiliated withThe University of New South Wales, School of Banking and Finance, UNSW Email author 
  • , Shulamith GrossAffiliated withThe National Science Foundation and Department of Statistics and Computer Information Systems, Bernard M. Baruch College, The City University of New York

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Abstract

We apply the powerful, flexible, and computationally efficient nonparametric Classification and Regression Trees (CART) algorithm to analyze real estate mortgage data. CART is particularly appropriate for our data set because of its strengths in dealing with large data sets, high dimensionality, mixed data types, missing data, different relationships between variables in different parts of the measurement space, and outliers. Moreover, CART is intuitive and easy to interpret and implement. We discuss the pros and cons of CART in relation to traditional methods such as linear logistic regression, nonparametric additive logistic regression, discriminant analysis, partial least squares classification, and neural networks, with particular emphasis on real estate. We use CART to produce the first academic study of Israeli mortgage default data. We find that borrowers’ features, rather than mortgage contract features, are the strongest predictors of default if accepting icbadli borrowers is more costly than rejecting “good” ones. If the costs are equal, mortgage features are used as well. The higher (lower) the ratio of misclassification costs of bad risks versus good ones, the lower (higher) are the resulting misclassification rates of bad risks and the higher (lower) are the misclassification rates of good ones. This is consistent with real-world rejection of good risks in an attempt to avoid bad ones.

Key Words

mortgage default Classification and Regression Trees misclassification error