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Review of Clustering Methods Used in Data-Driven Housing Market Segmentation

Author

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  • Skovajsa Štěpán

    (Institute of Forensic Engineering, Brno University of Technology, Purkyňova 464/118, 612 00 Brno, Czech Republic)

Abstract

A huge effort has already been made to prove the existence of housing market segments, as well as how to utilize them to improve valuation accuracy and gain knowledge about the inner structure of the entire superior housing market. Accordingly, many different methods on the topic have been explored, but no universal framework is yet known. The aim of this article is to review some previous studies on data-driven housing market segmentation methods with a focus on clustering methods and their ability to capture market segments with respect to the shape of clusters, fuzziness and hierarchical structure.

Suggested Citation

  • Skovajsa Štěpán, 2023. "Review of Clustering Methods Used in Data-Driven Housing Market Segmentation," Real Estate Management and Valuation, Sciendo, vol. 31(3), pages 67-74, September.
    • Handle: RePEc:vrs:remava:v:31:y:2023:i:3:p:67-74:n:3
    DOI: 10.2478/remav-2023-0022
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    References listed on IDEAS

    as
    1. Bourassa, Steven C. & Hamelink, Foort & Hoesli, Martin & MacGregor, Bryan D., 1999. "Defining Housing Submarkets," Journal of Housing Economics, Elsevier, vol. 8(2), pages 160-183, June.
    2. Usman Hamza & Lizam Mohd & Adekunle Muhammad Usman, 2020. "Property Price Modelling, Market Segmentation and Submarket Classifications: A Review," Real Estate Management and Valuation, Sciendo, vol. 28(3), pages 24-35, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    clustering algorithms; housing market analysis; housing market segmentation; data-driven segmentation;
    All these keywords.

    JEL classification:

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

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