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Explaining Mortgage Defaults Using SHAP and LASSO

Author

Listed:
  • Belma Ozturkkal

    (Kadir Has University)

  • Ranik Raaen Wahlstrøm

    (Norwegian University of Science and Technology)

Abstract

We utilize machine learning methods to model the credit risk of mortgages in a significant emerging market. For this purpose, we investigate a multitude of variables that explain the characteristics of the loans, the demographics of the borrowers, and macroeconomic factors. We employ SHapley Additive exPlanations (SHAP) values in conjunction with five different tree-based machine learning methods, as well as the least absolute shrinkage and selection operator (LASSO) in conjunction with logistic regressions. Our findings, which are robust across two sampling schemes, reveal that while demographic variables are significant and important, loan-specific and macroeconomic variables are the most crucial in explaining mortgage defaults. As existing literature on mortgage default has primarily focused on advanced markets, we aim to bridge this gap by concentrating on emerging market data. We also share our code, which we hope will encourage others to utilize the methods we have applied.

Suggested Citation

  • Belma Ozturkkal & Ranik Raaen Wahlstrøm, 2025. "Explaining Mortgage Defaults Using SHAP and LASSO," Computational Economics, Springer;Society for Computational Economics, vol. 66(4), pages 3291-3325, October.
  • Handle: RePEc:kap:compec:v:66:y:2025:i:4:d:10.1007_s10614-024-10763-6
    DOI: 10.1007/s10614-024-10763-6
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    References listed on IDEAS

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    1. Christopher L. Foote & Paul S. Willen, 2018. "Mortgage-Default Research and the Recent Foreclosure Crisis," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 59-100, November.
    2. Chen, Shunqin & Guo, Zhengfeng & Zhao, Xinlei, 2021. "Predicting mortgage early delinquency with machine learning methods," European Journal of Operational Research, Elsevier, vol. 290(1), pages 358-372.
    3. 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.
    4. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    5. Butaru, Florentin & Chen, Qingqing & Clark, Brian & Das, Sanmay & Lo, Andrew W. & Siddique, Akhtar, 2016. "Risk and risk management in the credit card industry," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 218-239.
    6. Lin, Emily Y. & White, Michelle J., 2001. "Bankruptcy and the Market for Mortgage and Home Improvement Loans," Journal of Urban Economics, Elsevier, vol. 50(1), pages 138-162, July.
    7. Martin Hellwig, 2009. "Systemic Risk in the Financial Sector: An Analysis of the Subprime-Mortgage Financial Crisis," De Economist, Springer, vol. 157(2), pages 129-207, June.
    8. Andreas G. F. Hoepner & David McMillan & Andrew Vivian & Chardin Wese Simen, 2021. "Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective," The European Journal of Finance, Taylor & Francis Journals, vol. 27(1-2), pages 1-7, January.
    9. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    10. Luigi Guiso & Paola Sapienza & Luigi Zingales, 2013. "The Determinants of Attitudes toward Strategic Default on Mortgages," Journal of Finance, American Finance Association, vol. 68(4), pages 1473-1515, August.
    11. Ashlyn Aiko Nelson, 2010. "Credit scores, race, and residential sorting," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 39-68.
    12. Zanin, Luca, 2020. "Combining multiple probability predictions in the presence of class imbalance to discriminate between potential bad and good borrowers in the peer-to-peer lending market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    13. Periklis Gogas & Theophilos Papadimitriou, 2021. "Machine Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 1-4, January.
    14. Warnock, Veronica Cacdac & Warnock, Francis E., 2008. "Markets and housing finance," Journal of Housing Economics, Elsevier, vol. 17(3), pages 239-251, September.
    15. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    16. Bhattacharya, Arnab & Wilson, Simon P. & Soyer, Refik, 2019. "A Bayesian approach to modeling mortgage default and prepayment," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1112-1124.
    17. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 22, pages 59-82.
    18. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
    19. Kearl, J R, 1979. "Inflation, Mortgages, and Housing," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages 1115-1138, October.
    20. 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.
    21. Pham, Xuan T.T. & Ho, Tin H., 2021. "Using boosting algorithms to predict bank failure: An untold story," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 40-54.
    22. John Y. Campbell & João F. Cocco, 2015. "A Model of Mortgage Default," Journal of Finance, American Finance Association, vol. 70(4), pages 1495-1554, August.
    23. Webb, Bruce G, 1982. "Borrower Risk under Alternative Mortgage Instruments," Journal of Finance, American Finance Association, vol. 37(1), pages 169-183, March.
    24. Apaar Sadhwani & Kay Giesecke & Justin Sirignano, 2021. "Deep Learning for Mortgage Risk [The Subprime Virus]," Journal of Financial Econometrics, Oxford University Press, vol. 19(2), pages 313-368.
    25. Campbell, Tim S & Dietrich, J Kimball, 1983. "The Determinants of Default on Insured Conventional Residential Mortgage Loans," Journal of Finance, American Finance Association, vol. 38(5), pages 1569-1581, December.
    26. Yusuf Emre Akgunduz & H. Ozlem Dursun-de Neef & Yavuz Selim Hacihasanoglu & Fatih Yilmaz, 2021. "Cost of Credit and House Prices," Working Papers 2106, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    27. Chomsisengphet, Souphala & Elul, Ronel, 2006. "Bankruptcy exemptions, credit history, and the mortgage market," Journal of Urban Economics, Elsevier, vol. 59(1), pages 171-188, January.
    28. Stephanie Rauterkus & Grant Thrall & Eric Hangen, 2010. "Location Efficiency and Mortgage Default," Journal of Sustainable Real Estate, Taylor & Francis Journals, vol. 2(1), pages 117-141, January.
    29. Bilgi Yilmaz & Ralf Korn & A. Sevtap Selcuk-Kestel, 2023. "The Impact of Large Investors on the Portfolio Optimization of Single-Family Houses in Housing Markets," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 855-873, February.
    30. Atif Mian & Amir Sufi, 2009. "The Consequences of Mortgage Credit Expansion: Evidence from the U.S. Mortgage Default Crisis," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 124(4), pages 1449-1496.
    31. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
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    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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