Spatial non-stationarity assessments of housing prices in Wuhan based on a TD-GNNWR model
Urban housing prices are influenced by various factors,encompassing macroeconomic conditions,urban planning strategies,and the specific characteristics of housing.These elements play a crucial role in shaping urban planning and development.Nonetheless,the regression analysis depicting the interplay between urban housing prices and their influencing factors reveals significant spatial non-stationarity and intricate nonlinear characteristics.Addressing the limitations of Euclidean distance in delineating spatial proximity for housing price modeling and the challenges encountered by the geographically weighted regression model(GWR)in capturing complex nonlinear features,this study introduces travel duration(TD)as a spatial distance metric and integrates it with a spatially weighted neural network to establish a geographically neural network weighted regression model with travel duration(TD-GNNWR)to estimate housing prices.In an empirical experiment using 2019 second-hand house data in Wuhan,the TD-GNNWR model demonstrates a 16％enhancement in fitting accuracy compared to the GWR model.The TD-GNNWR model notably enhances accuracy within sparsely sampled regions and better mimics their spatial distribution.Moreover,it adeptly captures spatial non-stationarity,offering a more precise elucidation of factors influencing housing prices in Wuhan and the resultant spatial discrepancies stemming from urban zoning.Our findings underscore the comprehensive impact of various factors on housing prices in Wuhan,such as building characteristics,neighborhood attributes,and transportation accessibility.Factors like greening rates,property fees,proximity to primary schools,universities,and public transportation exert substantial influence on housing prices in Wuhan,with varying directions and strengths across different areas,signifying clear spatial differentiation.The TD-GNNWR model clearly elucidates the mechanisms underlying housing price determinants while illustrating the inherent spatial non-stationarity,which is beneficial for urban planning departments and real estate managers in policy formulation,macro-control,urban planning,and investment decision-making.This work can also serve as a valuable reference for tackling challenges in urban analysis and modeling,thereby enriching methodologies within real estate research.
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