IDEAS home Printed from https://ideas.repec.org/a/spr/anresc/v74y2025i1d10.1007_s00168-025-01365-7.html
   My bibliography  Save this article

Boosting the accuracy of property valuation with ensemble learning and explainable artificial intelligence: The case of Hong Kong

Author

Listed:
  • Lin Deng

    (The Hong Kong University of Science and Technology)

  • Xueqing Zhang

    (The Hong Kong University of Science and Technology)

Abstract

This paper proposes a novel three-level ensemble learning model to boost the accuracy of property valuation. Compared with current machine learning models, our ensemble learning strategies perform better. Explainable artificial intelligence methods are applied to identify significant housing price determinants. The results show that most features have nonlinear relationships with price. The property’s age and geographic coordinates are the most important features and the density and diversity of urban amenities positively relate to price. The number of building blocks or housing units shows a threshold effect on price, and a price premium of higher public transit accessibility is witnessed. Implications for future property development and urban planning are provided regarding building rehabilitation and renewal, property development preparation and urban center identification, land use planning and architectural design, integrated development of metropolitan amenities, and transit-oriented development.

Suggested Citation

  • Lin Deng & Xueqing Zhang, 2025. "Boosting the accuracy of property valuation with ensemble learning and explainable artificial intelligence: The case of Hong Kong," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 74(1), pages 1-28, March.
    • Handle: RePEc:spr:anresc:v:74:y:2025:i:1:d:10.1007_s00168-025-01365-7
    DOI: 10.1007/s00168-025-01365-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00168-025-01365-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00168-025-01365-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    JEL classification:

    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

    Statistics

    Access and download statistics

    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:spr:anresc:v:74:y:2025:i:1:d:10.1007_s00168-025-01365-7. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.