Spatial Differentiation and Influencing Factors of Second-Hand Housing Prices: A Case Study of Binhu New District, Hefei City, Anhui Province, China

The multiscale geographic weighted regression (MGWR) model obtains different influence scales of various variables better than the classical geographic weighted regression (GWR) model. This paper studies the price characteristics of second-hand residential transactions in Binhu New District taking advantage of the hedonic price model and MGWR model and draws the following conclusions. (1) There are obvious spatial positive correlation and spatial heterogeneity in the price of second-hand housing in Binhu New District. (2) The number of bedrooms, area, age of the house, and the distance to the nearest school have small effect on the scale, so they have strong spatial heterogeneity. The decoration status and floor are global scale variables, and their spatial heterogeneity is weak. (3) The number of bedrooms, orientation, decoration status, floor, and building structure all positively affect house prices, while area, house age, the distance to the nearest subway station, and the distance to the nearest school negatively affect house prices. Among all factors, the distance to the nearest school is the most important factor affecting house prices, followed by the number of bedrooms and then followed by the distance to the nearest subway station and area, while the orientation, floor, building structure, and decoration conditions have less impact, and the house age has the weakest impact.