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Mortgage Default: Classification Trees Analysis

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  • David Feldman

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  • Shulamith Gross

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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. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • David Feldman & Shulamith Gross, 2005. "Mortgage Default: Classification Trees Analysis," The Journal of Real Estate Finance and Economics, Springer, vol. 30(4), pages 369-396, June.
  • Handle: RePEc:kap:jrefec:v:30:y:2005:i:4:p:369-396
    DOI: 10.1007/s11146-005-7013-7
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    References listed on IDEAS

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    Cited by:

    1. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    2. Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 80-98, November.
    3. Krzysztof Dziekoñski, 2016. "Application Of Classification Trees For Comparative Analysis Of Construction Project Manager’S Competencies," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 14(2), pages 40-50, December.
    4. Xudong An & Yongheng Deng & Eric Rosenblatt & Vincent Yao, 2012. "Model Stability and the Subprime Mortgage Crisis," The Journal of Real Estate Finance and Economics, Springer, vol. 45(3), pages 545-568, October.
    5. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
    6. Meleddu, Marta & Pulina, Manuela, 2016. "Evaluation of individuals’ intention to pay a premium price for ecotourism: An exploratory study," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 65(C), pages 67-78.
    7. Thomas G. Calderon, 2008. "Determinants of client-initiated and auditor-initiated auditor changes," Managerial Auditing Journal, Emerald Group Publishing, vol. 23(1), pages 4-25, January.
    8. Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 80-98, November.
    9. Owen P. Hall Jr. & Darrol J. Stanley, 2012. "A comparative modelling analysis of firm performance," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(1), pages 43-56.

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