Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression
The main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression model...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/9/6/380 |
| _version_ | 1827715341609861120 |
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| author | Radosław Cellmer Aneta Cichulska Mirosław Bełej |
| author_facet | Radosław Cellmer Aneta Cichulska Mirosław Bełej |
| author_sort | Radosław Cellmer |
| collection | DOAJ |
| description | The main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression models. The spatial heterogeneity of the housing market determinants results in the spatial diversity of the market activity, as well as of real estate prices and values. The main aim of the study was to analyse an effect of these socio-demographic and environmental factors on average housing property prices and on the number of transactions in a spatial approach. In previous research conducted on a national scale, usually all variables were treated in a similar way, i.e., as global or local variables. During the research, an attempt was also made to answer the question of which of the variables adopted for analysis have a local impact on prices and market activity, and which are global. The study was conducted in Poland and used data from the year 2018 on 380 counties (Local Administrative Units). The study showed that determinants both for average prices and for the housing market activity show spatial autocorrelation with high–high and low–low cluster groups. Owing to these models, it was possible to draw specific conclusions on local determinants of flat prices and the market activity in Poland. The study findings have confirmed that they are an extremely effective tool for spatial data analysis. |
| first_indexed | 2024-03-10T19:16:48Z |
| format | Article |
| id | doaj.art-3cda784743184ef3a7bb2b229c89278a |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T19:16:48Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-3cda784743184ef3a7bb2b229c89278a2023-11-20T03:20:05ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-06-019638010.3390/ijgi9060380Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted RegressionRadosław Cellmer0Aneta Cichulska1Mirosław Bełej2Department of Spatial Analysis and Real Estate Market, University of Warmia and Mazury in Olsztyn, 10-720 Olsztyn, PolandDepartment of Spatial Analysis and Real Estate Market, University of Warmia and Mazury in Olsztyn, 10-720 Olsztyn, PolandDepartment of Spatial Analysis and Real Estate Market, University of Warmia and Mazury in Olsztyn, 10-720 Olsztyn, PolandThe main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression models. The spatial heterogeneity of the housing market determinants results in the spatial diversity of the market activity, as well as of real estate prices and values. The main aim of the study was to analyse an effect of these socio-demographic and environmental factors on average housing property prices and on the number of transactions in a spatial approach. In previous research conducted on a national scale, usually all variables were treated in a similar way, i.e., as global or local variables. During the research, an attempt was also made to answer the question of which of the variables adopted for analysis have a local impact on prices and market activity, and which are global. The study was conducted in Poland and used data from the year 2018 on 380 counties (Local Administrative Units). The study showed that determinants both for average prices and for the housing market activity show spatial autocorrelation with high–high and low–low cluster groups. Owing to these models, it was possible to draw specific conclusions on local determinants of flat prices and the market activity in Poland. The study findings have confirmed that they are an extremely effective tool for spatial data analysis.https://www.mdpi.com/2220-9964/9/6/380housing pricesmarket activityspatial analysisgeographically weighted regressionmixed geographically weighted regression |
| spellingShingle | Radosław Cellmer Aneta Cichulska Mirosław Bełej Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression ISPRS International Journal of Geo-Information housing prices market activity spatial analysis geographically weighted regression mixed geographically weighted regression |
| title | Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression |
| title_full | Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression |
| title_fullStr | Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression |
| title_full_unstemmed | Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression |
| title_short | Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression |
| title_sort | spatial analysis of housing prices and market activity with the geographically weighted regression |
| topic | housing prices market activity spatial analysis geographically weighted regression mixed geographically weighted regression |
| url | https://www.mdpi.com/2220-9964/9/6/380 |
| work_keys_str_mv | AT radosławcellmer spatialanalysisofhousingpricesandmarketactivitywiththegeographicallyweightedregression AT anetacichulska spatialanalysisofhousingpricesandmarketactivitywiththegeographicallyweightedregression AT mirosławbełej spatialanalysisofhousingpricesandmarketactivitywiththegeographicallyweightedregression |