Understanding the Impacts of Public Facilities on Residential House Prices: Spatial Data-Driven Approach Applied in Hangzhou, China

Abstract

Housing is fundamental to livelihood. House prices are not only affected by house quality, but also closely related to the location. In order to recognize the urban real estate market, it is meaningful to quantitatively analyze the impact of location on house prices. This paper aims to investigate the relationship between house price and location. First, an inverse distance weighted (IDW) method is applied to recognize the spatial distribution of house prices. Second, spatial auto-correlation analysis is applied to uncover the spatial pattern of house prices. Finally, a geographic weighted regression (GWR) model is used to analyze the extent and spatial heterogeneity of the impacts of location elements on house prices quantitatively. The main area in Hangzhou is used as a case study area. Crawler and remote sensing technology are utilized to fetch the house prices and five public facilities revealing location, including subway stations, schools, hospitals, green space, and business centers. The study finds that house prices have evident spatial heterogeneity and spatially agglomerate. Ranking the five public facilities according to the impacts they have on house prices, we reveal that school > subway station > green space > business center > hospital. Schools and subway stations are the most important location factors on house prices. Schools, subway stations, and green space all have a positive effect on house prices in the study area, whereas business centers and hospitals have a negative effect. The research unfolds the relationship between public facilities and house prices and provides support for improving the distribution of spatial resource and sophisticated urban management.