IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i10p431-d1487363.html
   My bibliography  Save this article

Explainable Machine Learning for Fallout Prediction in the Mortgage Pipeline

Author

Listed:
  • Preetam Purohit

    (Embrace Home Loans, Inc., Middletown, RI 02852, USA)

  • Amit Verma

    (Craig School of Business, Missouri Western State University, St. Joseph, MO 64507, USA)

Abstract

This study examines mortgage loan fallout using data provided by a leading financial institution. By accurately predicting mortgage loan fallout, lenders can protect their bottom line, maintain financial stability, and contribute to a healthier economy. The paper employs various machine learning models to predict mortgage fallout based on loan, market, property, and borrower characteristics. A large dataset of locked mortgage applications from a major U.S. lender was analyzed. The random forest model demonstrated superior predictive efficiency and stability. To understand the factors influencing mortgage fallout, the SHAP method, along with empirical analysis with logistic regression, was utilized to identify key determinants. The paper discusses the implications of these findings for mortgage lenders and future research.

Suggested Citation

  • Preetam Purohit & Amit Verma, 2024. "Explainable Machine Learning for Fallout Prediction in the Mortgage Pipeline," JRFM, MDPI, vol. 17(10), pages 1-20, September.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:10:p:431-:d:1487363
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/10/431/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/10/431/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jin Man Lee & Kiat Ying Seah & James Shilling, 2023. "The Impact of Commitment on Pending Home Sales in Entrapping Situations," International Real Estate Review, Global Social Science Institute, vol. 26(4), pages 491-520.
    2. repec:ire:issued:v:26:n:04:2023:p:489-518 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:17:y:2024:i:10:p:431-:d:1487363. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.