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Spatio-temporal stability of housing submarkets. Tracking spatial location of clusters of geographically weighted regression estimates of price determinants

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  • Kopczewska, Katarzyna
  • Ćwiakowski, Piotr

Abstract

This paper fills the gap in rich housing literature by testing the spatio-temporal stability of real estate submarkets. We start with standard Geographically Weighted Regression (GWR) estimation of the hedonic model on point data, and we cluster model coefficients to detect housing submarkets. We check spatio-temporal stability - we add novelty by comparing if clusters move over space or stay in the same place. We rasterise surface and apply the Rand Index and Jaccard Similarity to check if clusters assigned to raster cells yield stable spatial structure. This approach allows for quantitative assessments of how much determinants of price are stable over time and space. The same mechanism applied to standard errors of GWR coefficients is a good test of the spatio-temporal stability of local heteroscedasticity. A Case study of apartments' transactions in Warsaw-Poland for the 2006–2015 period, evidences relatively high spatio-temporal stability.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:lauspo:v:103:y:2021:i:c:s0264837721000156
    DOI: 10.1016/j.landusepol.2021.105292
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    Cited by:

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    2. Łaszkiewicz, Edyta & Heyman, Axel & Chen, Xianwen & Cimburova, Zofie & Nowell, Megan & Barton, David N, 2022. "Valuing access to urban greenspace using non-linear distance decay in hedonic property pricing," Ecosystem Services, Elsevier, vol. 53(C).
    3. Yunzi Yang & Yuanyuan Ma & Hongzan Jiao, 2021. "Exploring the Correlation between Block Vitality and Block Environment Based on Multisource Big Data: Taking Wuhan City as an Example," Land, MDPI, vol. 10(9), pages 1-23, September.
    4. Mateusz Tomal & Marco Helbich, 2023. "A spatial autoregressive geographically weighted quantile regression to explore housing rent determinants in Amsterdam and Warsaw," Environment and Planning B, , vol. 50(3), pages 579-599, March.
    5. Brzezicka Justyna, 2022. "The Application of the Simplified Speculative Frame Method for Monitoring the Development of the Housing Market," Real Estate Management and Valuation, Sciendo, vol. 30(1), pages 84-98, March.
    6. 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.
    7. Mateusz Tomal & Marco Helbich, 2022. "The private rental housing market before and during the COVID-19 pandemic: A submarket analysis in Cracow, Poland," Environment and Planning B, , vol. 49(6), pages 1646-1662, July.

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    More about this item

    Keywords

    Geographically weighted regression; Spatio-temporal stability; Spatial location; Housing valuation; Submarkets; Data-driven clusters;
    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
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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