Natural Attraction and Driving Forces: Analyzing the Impact of Parkland on Residential Prices and Typological Difference*

Exploring the driving mechanism of parks and green spaces on the spatial heterogeneity of housing prices helps to rationalize the allocation pattern of social and environmental resources and the housing economic market, and to optimize the spatial pattern of green facilities in human habitat. The study integrates Shenzhen housing samples and their price transaction data, park green space and other related facilities data, and by using Ordinary Least Squares (OLS) Geographically Weighted Regression Model (GWR), compares and analyses the sequence of effects of architectural characteristics, location characteristics, neighborhood characteristics and green space characteristics on housing prices, and analyses the relationship between park green space proximity and housing prices, and the types of differences in their effect mechanisms. The results show that the driving effect of overall park green space proximity on housing prices shows the concentration effect of high-level development areas; the proximity of different types of park green space drives housing prices as urban parks > natural parks > community parks, and urban parks have higher economic value. The results of this study suggest that future land resource planning should pay more attention to the economic drive of ecological space and effectively improve the efficiency of social resource services.