Determining spatial heterogeneity and influencing factors in housing prices with geographically weighted regression method: A case of Erzurum

Understanding the dynamics of the real estate market has emerged as a pivotal concern in urban economics for ensuring sustainable land management and effective investment strategies. The spatial heterogeneity of housing market determinants gives rise to variations in market activity and significant spatial differences in property values. However, global regression models are constrained in their ability to capture this heterogeneity and the spatial autocorrelation of housing prices. The aim of this study is to identify the spatial variability of housing prices and the potential factors influencing them. To this end, the study employs the Geographically Weighted Regression (GWR) model, which enables the analysis of spatial heterogeneity. In addition to conventional structural variables (floor area, age, heating type, number of floors, and floor level), measurable indicators, including network-based accessibility metrics (connectivity, betweenness, and closeness), distance to the central business district, and the remotely sensed Normalized Difference Vegetation Index (NDVI), are integrated into the model. The findings reveal the complexity of the housing market in Erzurum, showing that newly developing peripheral areas form high-priced clusters that reshape the center-periphery dynamics. While structural variables, including floor area and building age, emerge as dominant factors across the city, environmental determinants vary considerably by location. It is noteworthy that network-based accessibility metrics are critical infrastructural variables that shape market heterogeneity. NDVI highlights the decisive role of vegetation density and accessible and functional urban green spaces in determining housing values. In conclusion, this study offers novel insights into the role of environmental and infrastructural metrics in real estate research and provides guidance for policymakers in regulating housing values and designing more sustainable urban planning strategies.

Keywords: Geographical information system, GWR, housing price, spatial autocorrelation, spatial heterogeneity.