University of Isfahan Urban Economics 2588-4867 11 1 2026 03 21 Analysis of Spatial Factors Affecting Housing Prices in Tehranpars Neighborhood, Tehran Analysis of Spatial Factors Affecting Housing Prices in Tehranpars Neighborhood, Tehran 89 100 30467 10.22108/ue.2026.147497.1337 FA Fatemeh Chabik Msc student of Geography and Urban Planning, Department of Geography and Urban Planning, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran Habibollah Fasihi Associate Professpr, Department of Geography and Urban Planning, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran. Taher Parizadi Associate Professpr, Department of Geography and Urban Planning, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran. Journal Article 2025 11 18 This article aimed to analyze the spatial differences in housing prices in the Tehranpars neighborhood and their relationship with 12 spatial factors. Housing data, which served as the dependent variable, were obtained from 54 real estate offices to create a point layer in GIS. Information on the 12 independent variables was gathered through field observations and calculations based on shapefile data from the 2016 Iranian National Census. To analyze the data, zoning maps were created using the Interpolation-IDW (Inverse Distance Weighting) function. The Moran index and Pearson correlation were calculated, and a geographically weighted regression (GWR) model was implemented. Findings showed that housing prices follow a clustered distribution pattern, with higher prices in the geometric center of the neighborhood and decreasing prices toward the east and west. Among the 12 independent variables, three factors—per capita residential infrastructure, the percentage of resilient houses, and [third factor]—showed a strong positive correlation, while one factor (the percentage of housing with an area of 75 square meters or less) showed a strong negative correlation with housing prices. The weighted regression analysis showed that three independent variables—population density, distance from the nearest park, and per capita residential infrastructure—have a significant spatial relationship with housing prices. This article aimed to analyze the spatial differences in housing prices in the Tehranpars neighborhood and their relationship with 12 spatial factors. Housing data, which served as the dependent variable, were obtained from 54 real estate offices to create a point layer in GIS. Information on the 12 independent variables was gathered through field observations and calculations based on shapefile data from the 2016 Iranian National Census. To analyze the data, zoning maps were created using the Interpolation-IDW (Inverse Distance Weighting) function. The Moran index and Pearson correlation were calculated, and a geographically weighted regression (GWR) model was implemented. Findings showed that housing prices follow a clustered distribution pattern, with higher prices in the geometric center of the neighborhood and decreasing prices toward the east and west. Among the 12 independent variables, three factors—per capita residential infrastructure, the percentage of resilient houses, and [third factor]—showed a strong positive correlation, while one factor (the percentage of housing with an area of 75 square meters or less) showed a strong negative correlation with housing prices. The weighted regression analysis showed that three independent variables—population density, distance from the nearest park, and per capita residential infrastructure—have a significant spatial relationship with housing prices. https://ue.ui.ac.ir/article_30467_84a06252a9e856d88ece0352adc4da01.pdf