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

Real estate management and valuation, 2023-09, Vol.31 (3), p.67-74 [Peer Reviewed Journal]

ISSN: 2300-5289 ;EISSN: 2300-5289 ;DOI: 10.2478/remav-2023-0022

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  • Title:
    Review of Clustering Methods Used in Data-Driven Housing Market Segmentation
  • Author: Skovajsa, Štěpán
  • Subjects: clustering algorithms ; data-driven segmentation ; housing market analysis ; housing market segmentation ; R31
  • Is Part Of: Real estate management and valuation, 2023-09, Vol.31 (3), p.67-74
  • Description: 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.
  • Publisher: Sciendo
  • Language: English
  • Identifier: ISSN: 2300-5289
    EISSN: 2300-5289
    DOI: 10.2478/remav-2023-0022
  • Source: Walter De Gruyter: Open Access Journals
    ROAD: Directory of Open Access Scholarly Resources
    DOAJ Directory of Open Access Journals
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