IDEAS home Printed from https://ideas.repec.org/p/arz/wpaper/eres2023_28.html
   My bibliography  Save this paper

See how the land lies: Land valuation using spatial models

Author

Listed:
  • Jacqueline Seufert
  • Geert Goeyvaerts
  • Sven Damen

Abstract

Economists have been advocating for a land tax rather than a reg- ular property tax. There are, however, several challenges to value land for tax purposes. Indeed, data on vacant land transactions are scarce, land and structure are conventionally traded in a bundle and it is hard to capture all factors that determine the value of land. We propose to use a new Bayesian spatial model and apply the model to the uni- verse of vacant and improved land sales from Belgium in 2018. Our results indicate that vacant land prices are substantially more difficult to predict than house prices. However, the predictive performance of the spatial model improves considerably in comparison to a regular linear hedonic approach. Models that combine data from vacant and improved land are unable to improve the predictive accuracy.

Suggested Citation

  • Jacqueline Seufert & Geert Goeyvaerts & Sven Damen, 2023. "See how the land lies: Land valuation using spatial models," ERES eres2023_28, European Real Estate Society (ERES).
  • Handle: RePEc:arz:wpaper:eres2023_28
    as

    Download full text from publisher

    File URL: https://eres.architexturez.net/doc/oai-eres-id-eres2023-28
    Download Restriction: no

    File URL: https://eres.architexturez.net/system/files/P_20230206101430_7873.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Bayesian spatial models; land valuation;

    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arz:wpaper:eres2023_28. 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: Architexturez Imprints (email available below). General contact details of provider: https://edirc.repec.org/data/eressea.html .

    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.