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Prediction of Loan Rate for Mortgage Data: Deep Learning Versus Robust Regression

Author

Listed:
  • Donglin Wang

    (Middle Tennessee State University)

  • Don Hong

    (Middle Tennessee State University)

  • Qiang Wu

    (Middle Tennessee State University)

Abstract

Mortgage data is often skewed, has missing information, and is contaminated by outliers. When mortgage companies or banks make prediction of note rates for new applicants, robust regression models are usually selected to deal with outliers. In this paper, we utilize deep neural network to predict the loan rate and compare its performance with three classical robust regression models. Two real mortgage data sets are used in this comparison. The results show that deep neural network has the best performance and therefore is recommended.

Suggested Citation

  • Donglin Wang & Don Hong & Qiang Wu, 2023. "Prediction of Loan Rate for Mortgage Data: Deep Learning Versus Robust Regression," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1137-1150, March.
    • Handle: RePEc:kap:compec:v:61:y:2023:i:3:d:10.1007_s10614-022-10239-5
    DOI: 10.1007/s10614-022-10239-5
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    References listed on IDEAS

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    1. 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.
    2. Silvia Magri, 2018. "Are lenders using risk-based pricing in the consumer loan market? The effects of the 2008 crisis," Temi di discussione (Economic working papers) 1164, Bank of Italy, Economic Research and International Relations Area.
    3. Magri, Silvia & Pico, Raffaella, 2011. "The rise of risk-based pricing of mortgage interest rates in Italy," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1277-1290, May.
    4. Marsha J. Courchane, 2007. "The Pricing of Home Mortgage Loans to Minority Borrowers: How Much of the APR Differential Can We Explain?," Journal of Real Estate Research, American Real Estate Society, vol. 29(4), pages 399-440.
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