Mortgage Default: Classification Trees Analysis

Original Article

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 

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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.The University of New South WalesSchool of Banking and Finance, UNSWSydneyAustralia
  2. 2.The National Science Foundation and Department of Statistics and Computer Information Systems, Bernard M. Baruch CollegeThe City University of New YorkArlingtonUSA

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