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Predicting mortgage early delinquency with machine learning methods

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  • Chen, Shunqin
  • Guo, Zhengfeng
  • Zhao, Xinlei

Abstract

This paper investigates the performance of thirteen methods for modelling and predicting mortgage early delinquency probabilities. These models include variants of logit models, some commonly used machine learning methods, and variants of ensemble models. We find that heterogenous ensemble methods lead other methods in the training, out-of-sample, and out-of-time datasets in terms of risk classification. Nonetheless, various predictive accuracy performance measures yield different rankings among the thirteen methods and no method consistently dominates in this performance dimension in the training, out-of-sample, and out-of-time data. Lastly, predictive accuracy is a major challenge facing all mortgage early delinquency models, even in the training data.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:290:y:2021:i:1:p:358-372
    DOI: 10.1016/j.ejor.2020.07.058
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    Cited by:

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    3. Margherita Doria & Elisa Luciano & Patrizia Semeraro, 2022. "Machine learning techniques in joint default assessment," Papers 2205.01524, arXiv.org, revised Sep 2023.
    4. Mahsa Tavakoli & Rohitash Chandra & Fengrui Tian & Cristi'an Bravo, 2023. "Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams," Papers 2304.10740, arXiv.org, revised Sep 2023.

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