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A Synthetic Penalized Logitboost to Model Mortgage Lending with Imbalanced Data

Author

Listed:
  • Jessica Pesantez-Narvaez

    (Universitat de Barcelona)

  • Montserrat Guillen

    (Universitat de Barcelona)

  • Manuela Alcañiz

    (Universitat de Barcelona)

Abstract

Most classical econometric methods and tree boosting based algorithms tend to increase the prediction error with binary imbalanced data. We propose a synthetic penalized logitboost based on weighting corrections. The procedure (i) improves the prediction performance under the phenomenon in question, (ii) allows interpretability since coefficients can get stabilized in the recursive procedure, and (iii) reduces the risk of overfitting. We consider a mortgage lending case study using publicly available data to illustrate our method. Results show that errors are smaller in many extreme prediction scores, outperforming a number of existing methods. Our interpretations are consistent with results obtained using a classic econometric model.

Suggested Citation

  • Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2021. "A Synthetic Penalized Logitboost to Model Mortgage Lending with Imbalanced Data," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 281-309, January.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:1:d:10.1007_s10614-020-10059-5
    DOI: 10.1007/s10614-020-10059-5
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    References listed on IDEAS

    as
    1. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    2. Munnell, Alicia H. & Geoffrey M. B. Tootell & Lynn E. Browne & James McEneaney, 1996. "Mortgage Lending in Boston: Interpreting HMDA Data," American Economic Review, American Economic Association, vol. 86(1), pages 25-53, March.
    3. Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2019. "Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression," Risks, MDPI, vol. 7(2), pages 1-16, June.
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    Cited by:

    1. Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2021. "RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach," Mathematics, MDPI, vol. 9(5), pages 1-21, March.

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    More about this item

    Keywords

    Imbalanced; Boosting; Interpretation; Prediction; Binary;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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