[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IDEAS home Printed from https://ideas.repec.org/a/taf/jpropr/v39y2022i4p338-364.html
   My bibliography  Save this article

House price prediction with gradient boosted trees under different loss functions

Author

Listed:
  • Anders Hjort
  • Johan Pensar
  • Ida Scheel
  • Dag Einar Sommervoll
Abstract
Many banks and credit institutions are required to assess the value of dwellings in their mortgage portfolio. This valuation often relies on an Automated Valuation Model (AVM). Moreover, these institutions often report the models accuracy by two numbers: The fraction of predictions within $$ \pm 20\% $$±20% and $$ \pm 10\% $$±10% range from the true values. Until recently, AVMs tended to be hedonic regression models, but lately machine learning approaches like random forest and gradient boosted trees have been increasingly applied. Both the traditional approaches and the machine learning approaches rely on minimising mean squared prediction error, and not the number of predictions in the $$ \pm 20\% $$±20% and $$ \pm 10\% $$±10% range. We investigate whether introducing a loss function closer to the AVMs actual loss measure improves performance in machine learning approaches, specifically for a gradient boosted tree approach. This loss function yields an improvement from $$89.4\% $$89.4% to $$90.0\% $$90.0% of predictions within $$ \pm 20\% $$±20% of the true value on a data set of $$N = 126{\kern 1pt} 719$$N=126719 transactions from the Norwegian housing market between 2013 and 2015, with the biggest improvements in performance coming from the lower price segments. We also find that a weighted average of models with different loss functions improves performance further, yielding $$90.4\% $$90.4% of the observations within $$ \pm 20\% $$±20% of the true value.

Suggested Citation

  • Anders Hjort & Johan Pensar & Ida Scheel & Dag Einar Sommervoll, 2022. "House price prediction with gradient boosted trees under different loss functions," Journal of Property Research, Taylor & Francis Journals, vol. 39(4), pages 338-364, October.
  • Handle: RePEc:taf:jpropr:v:39:y:2022:i:4:p:338-364
    DOI: 10.1080/09599916.2022.2070525
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/09599916.2022.2070525
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/09599916.2022.2070525?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. João A. Bastos & Jeanne Paquette, 2024. "On the uncertainty of real estate price predictions," Working Papers REM 2024/0314, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.

    More about this item

    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:taf:jpropr:v:39:y:2022:i:4:p:338-364. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RJPR20 .

    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.