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Delineating Housing Submarkets Using Space–Time House Sales Data: Spatially Constrained Data-Driven Approaches

Author

Listed:
  • Meifang Chen

    (School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA)

  • Yongwan Chun

    (School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA)

  • Daniel A. Griffith

    (School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA)

Abstract

With the increasing availability of large volumes of space–time house data, delineating space–time housing submarkets is of interest to real estate agents, homebuyers, urban policymakers, and spatial researchers, among others. Appropriately delineated housing submarkets can help nurture submarket monitoring and housing policy developments. Although submarkets are often expected to represent areas with similar houses, neighborhoods, and amenities characteristics, delineating spatially contiguous areas with virtually no fragmented small areas remains challenging. Furthermore, housing submarkets can potentially change over time along with concomitant urban transformations, such as urban sprawl, gentrification, and infrastructure improvements, even in large metropolitan areas, which can complicate delineating submarkets with data for lengthy time periods. This study proposes a new method for integrating a random effects model with spatially constrained data-driven approaches in order to identify stable and reliable space–time housing submarkets, instead of their dynamic changes. This random effects model specification is expected to capture time-invariant spatial patterns, which can help identify stable submarkets over time. It highlights two spatially constrained data-driven approaches, ClustGeo and REDCAP, which perform equally well and produce similar space–time housing submarket structures. This proposed method is utilized for a case study of Franklin County, Ohio, using 19 years of space–time private house transaction data (2001–2019). A comparative analysis using a hedonic model demonstrates that the resulting submarkets generated by the proposed method perform better than popular alternative submarket creators in terms of model performances and house price predictions. Enhanced space–time housing delineation can furnish a way to better understand the sophisticated housing market structures, and to help enhance their modeling and housing policy. This paper contributes to the literature on space–time housing submarket delineations with enhanced approaches to effectively generate spatially constrained housing submarkets using data-driven methods.

Suggested Citation

  • Meifang Chen & Yongwan Chun & Daniel A. Griffith, 2023. "Delineating Housing Submarkets Using Space–Time House Sales Data: Spatially Constrained Data-Driven Approaches," JRFM, MDPI, vol. 16(6), pages 1-17, June.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:6:p:291-:d:1162787
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    References listed on IDEAS

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    1. Marie Chavent & Vanessa Kuentz-Simonet & Amaury Labenne & Jérôme Saracco, 2018. "ClustGeo: an R package for hierarchical clustering with spatial constraints," Computational Statistics, Springer, vol. 33(4), pages 1799-1822, December.
    2. Yuan, Feng & Wu, Jiawei & Wei, Yehua Dennis & Wang, Lei, 2018. "Policy change, amenity, and spatiotemporal dynamics of housing prices in Nanjing, China," Land Use Policy, Elsevier, vol. 75(C), pages 225-236.
    3. Kopczewska, Katarzyna & Ćwiakowski, Piotr, 2021. "Spatio-temporal stability of housing submarkets. Tracking spatial location of clusters of geographically weighted regression estimates of price determinants," Land Use Policy, Elsevier, vol. 103(C).
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    1. Gladys Elizabeth Kenyon & Dani Arribas-Bel & Caitlin Robinson, 2024. "Extracting Features from Satellite Imagery to Understand the Size and Scale of Housing Sub-Markets in Madrid," Land, MDPI, vol. 13(5), pages 1-23, April.

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