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Exploring the meso-determinants of apartment prices in Polish counties using spatial autoregressive multiscale geographically weighted regression

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  • Mateusz Tomal

Abstract

The aim of this study is to identify the meso-determinants of apartment prices in Polish counties using recently developed spatial autoregressive multiscale geographically weighted regression (MGWR-SAR), which can simultaneously deal with spatial dependence, spatial heterogeneity, as well as different spatial scales of modelled processes. Estimation results indicate that MGWR-SAR outperforms other studied models, especially in terms of goodness-of-fit indicators. Moreover, estimates of the above model reveal that relationships between the dependent variable and covariates operate at different spatial scales. Furthermore, among the studied determinants only two, describing unemployment rate and standard of apartments, are significant in all counties.

Suggested Citation

  • Mateusz Tomal, 2022. "Exploring the meso-determinants of apartment prices in Polish counties using spatial autoregressive multiscale geographically weighted regression," Applied Economics Letters, Taylor & Francis Journals, vol. 29(9), pages 822-830, May.
    • Handle: RePEc:taf:apeclt:v:29:y:2022:i:9:p:822-830
    DOI: 10.1080/13504851.2021.1891194
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    Cited by:

    1. Zhenbao Wang & Xin Gong & Yuchen Zhang & Shuyue Liu & Ning Chen, 2023. "Multi-Scale Geographically Weighted Elasticity Regression Model to Explore the Elastic Effects of the Built Environment on Ride-Hailing Ridership," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    2. 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.

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