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Mortgage Default: Classification Trees Analysis

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Author Info

  • 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

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    File URL: http://hdl.handle.net/10.1007/s11146-005-7013-7
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    Bibliographic Info

    Article provided by Springer in its journal The Journal of Real Estate Finance and Economics.

    Volume (Year): 30 (2005)
    Issue (Month): 4 (June)
    Pages: 369-396

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    Handle: RePEc:kap:jrefec:v:30:y:2005:i:4:p:369-396

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    Web page: http://www.springerlink.com/link.asp?id=102945

    Related research

    Keywords: mortgage default; Classification and Regression Trees; misclassification error;

    References

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    1. Sanders, Anthony B, 2002. "Government Sponsored Agencies: Do the Benefits Outweigh the Costs?," The Journal of Real Estate Finance and Economics, Springer, vol. 25(2-3), pages 121-27, Sept.-Dec.
    2. Vassilis Lekkas & John M. Quigley & Robert Order, 1993. "Loan Loss Severity and Optimal Mortgage Default," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 21(4), pages 353-371.
    3. Yongheng Deng, . "Mortgage Termination: An Empirical Hazard Model with Stochastic Term Structure," Working Papers _002, University of California at Berkeley, Econometrics Laboratory Software Archive.
    4. Quigley, John M., 1993. "Explicit Tests of Contingent Claims Models of Mortgage Defaults," Department of Economics, Working Paper Series qt3df5357v, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    5. Yongheng Deng & John M. Quigley & Robert Van Order, . "Mortgage Terminations, Heterogeneity and the Exercise of Mortgage Options," Zell/Lurie Center Working Papers 322, Wharton School Samuel Zell and Robert Lurie Real Estate Center, University of Pennsylvania.
    6. Dennis R. Capozza & Dick Kazarian & Thomas A. Thomson, 1998. "The Conditional Probability of Mortgage Default," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 26(3), pages 259-289.
    7. Ambrose, Brent W & Sanders, Anthony B, 2003. "Commercial Mortgage-Backed Securities: Prepayment and Default," The Journal of Real Estate Finance and Economics, Springer, vol. 26(2-3), pages 179-96, March-May.
    8. Ambrose, Brent W & Buttimer, Richard J, Jr & Capone, Charles A, 1997. "Pricing Mortgage Default and Foreclosure Delay," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(3), pages 314-25, August.
    9. Kuhnert, Petra M. & Do, Kim-Anh & McClure, Rod, 2000. "Combining non-parametric models with logistic regression: an application to motor vehicle injury data," Computational Statistics & Data Analysis, Elsevier, vol. 34(3), pages 371-386, September.
    10. Karolyi, G Andrew & Sanders, Anthony B, 1998. "The Variation of Economic Risk Premiums in Real Estate Returns," The Journal of Real Estate Finance and Economics, Springer, vol. 17(3), pages 245-62, November.
    11. Ambrose, Brent W & Capone, Charles A, Jr & Deng, Yongheng, 2001. "Optimal Put Exercise: An Empirical Examination of Conditions for Mortgage Foreclosure," The Journal of Real Estate Finance and Economics, Springer, vol. 23(2), pages 213-34, September.
    12. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. " Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-91, March.
    13. Ambrose, Brent W & Buttimer, Richard J, Jr, 2000. "Embedded Options in the Mortgage Contract," The Journal of Real Estate Finance and Economics, Springer, vol. 21(2), pages 95-111, September.
    14. Dennis R. Capozza & Dick Kazarian & Thomas A. Thomson, 1997. "Mortgage Default in Local Markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 25(4), pages 631-655.
    15. Kau, James B, et al, 1992. "A Generalized Valuation Model for Fixed-Rate Residential Mortgages," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 24(3), pages 279-99, August.
    16. Kerry D. Vandell, 1993. "Handing Over the Keys: A Perspective on Mortgage Default Research," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 21(3), pages 211-246.
    17. Kau James B. & Keenan Donald C. & Kim Taewon, 1994. "Default Probabilities for Mortgages," Journal of Urban Economics, Elsevier, vol. 35(3), pages 278-296, May.
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    Cited by:
    1. 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.
    2. Evžen Kocenda & Martin Vojtek, 2009. "Default Predictors and Credit Scoring Models for Retail Banking," CESifo Working Paper Series 2862, CESifo Group Munich.

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