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Open AccessEditor’s ChoiceArticle

Modelling Housing Rents Using Spatial Autoregressive Geographically Weighted Regression: A Case Study in Cracow, Poland

Department of Real Estate and Investment Economics, Cracow University of Economics, Rakowicka 27, 31-510 Cracow, Poland
ISPRS Int. J. Geo-Inf. 2020, 9(6), 346; https://doi.org/10.3390/ijgi9060346
Received: 21 April 2020 / Revised: 4 May 2020 / Accepted: 25 May 2020 / Published: 26 May 2020
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
The proportion of tenants will undoubtedly rise in Poland, where at present, the ownership housing model is very dominant. As a result, the rental housing market in Poland is currently under-researched in comparison with owner-occupancy. In order to narrow this research gap, this study attempts to identify the determinants affecting rental prices in Cracow. The latter were obtained from the internet platform otodom.pl using the web scraping technique. To identify rent determinants, ordinary least squares (OLS) regression and spatial econometric methods were used. In particular, traditional spatial autoregressive model (SAR) and spatial autoregressive geographically weighted regression (GWR-SAR) were employed, which made it possible to take into account the spatial heterogeneity of the parameters of determinants and the spatially changing spatial autocorrelation of housing rents. In-depth analysis of rent determinants using the GWR-SAR model exposed the complexity of the rental market in Cracow. Estimates of the above model revealed that many local markets can be identified in Cracow, with different factors shaping housing rents. However, one can identify some determinants that are ubiquitous for almost the entire city. This concerns mainly the variables describing the area of the flat and the age of the building. Moreover, the Monte Carlo test indicated that the spatial autoregressive parameter also changes significantly over space. View Full-Text
Keywords: housing market; rental price; housing rent; housing price; spatial hedonic model; spatial hedonic pricing model; spatial dependence; spatial heterogeneity; geographically weighted regression; GWR housing market; rental price; housing rent; housing price; spatial hedonic model; spatial hedonic pricing model; spatial dependence; spatial heterogeneity; geographically weighted regression; GWR
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Tomal, M. Modelling Housing Rents Using Spatial Autoregressive Geographically Weighted Regression: A Case Study in Cracow, Poland. ISPRS Int. J. Geo-Inf. 2020, 9, 346.

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