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http://ssrn.com/abstract=659881
 
 

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Mortgage Default: Classification Trees Analysis


David Feldman


University of New South Wales - Banking & Finance, Australian School of Business; Financial Research Network (FIRN)

Shulamith Gross


National Science Foundation

December 31, 2004


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 bad 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.

Number of Pages in PDF File: 46

Keywords: Mortgage default, classification and regression Trees, misclassification error

JEL Classification: C12, D12, G21, R29

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Date posted: February 4, 2005  

Suggested Citation

Feldman, David and Gross, Shulamith, Mortgage Default: Classification Trees Analysis (December 31, 2004). Available at SSRN: http://ssrn.com/abstract=659881 or http://dx.doi.org/10.2139/ssrn.659881

Contact Information

David Feldman (Contact Author)
University of New South Wales - Banking & Finance, Australian School of Business ( email )
UNSW Sydney, NSW 2052
Australia
+61 2 9385 5748 (Phone)
+61 2 9385 6347 (Fax)
Financial Research Network (FIRN)
C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia
HOME PAGE: http://www.firn.org.au

Shulamith Gross
National Science Foundation ( email )
4201 Wilson Boulevard
Arlington, VA 22230
United States
Feedback to SSRN


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