Spatial Patterns of Housing Prices and Multi-Scale Effects of Its Influencing Factors: A Case Study on the Central City of Fuzhou

Abstract

Housing prices are influenced by various factors, which not only have different intensities but also different scale ranges. As a basic category of geographic research, scale is an important parameter for studying the phenomena, and subdividing the intensity of the effects of different types of influencing factors and spatial scales is of great importance for urban planning. Based on the price data of 3,386 commercial housing districts in the central city of Fuzhou in 2021, we used spatial statistics and the Multiscale Geographically Weighted Regression model to explore the spatial distribution characteristics of housing prices and the varying scale effects of influencing factors. The results show that: 1) The housing price in the central city of Fuzhou presents an "inverted U-shaped" curve in the east-west and north-south directions, with symmetry in the east-west direction and large differences in the north-south direction. The spatial pattern is a multi-center urban hierarchy system of "one main center and two sub-centers," and the neighborhoods with high housing prices show the characteristics of "a tendency to gather around the Minjiang River, school district, shopping malls, hospitals and ecological resources." 2) The factors influencing housing prices have distinct spatially heterogeneous and exclusive bandwidths. Building ages, shopping malls, and primary schools are local factors belonging to the street scale; the Floor Area Ratio (FAR) and hospitals are local factors belonging to the district administrative divisions scale; and the subway station, coach station, and university are global factors belonging to the central city of Fuzhou scale. 3) The strengths of the influencing factors, from largest to smallest, are primary schools, building ages, bus stations, shopping malls, FARs, universities, hospitals, and subway stations. Among them, primary schools, bus stations, FARs, and hospitals are positively correlated with housing prices, whereas building ages, shopping malls, universities, and subway stations are negatively correlated with housing prices. These results can provide theoretical basis for urban planners and policymakers. First, urban planning should be based on the principle of "local adaptation," and the planning layout should be adapted to the spatial and distribution patterns of urban housing prices. Second, urban planning needs to account for the function of functional areas for local factors, whereas global factors need to be evaluated and considered on a large-scale spatial scale. Finally, the strength of the influencing factors is also an important aspect of urban planning. For the influencing factors with strong pulling power, such as primary schools, building ages, and shopping malls, the number of resources allocated and the layout of spatial locations need to be planned rationally, and suitable urban planning solutions need to be proposed to finally achieve the goal of steady economic growth and satisfying residents' willingness to buy houses. This study has some limitations related to data acquisition. The analysis of the temporal trends of the influencing factors should be considered in future studies, as well as account for more influencing factors, such as area, floor, and orientation in future research.

Keywords

• multi-scale geographically weighted regression(mgwr)
• housing prices
• influencing scales
• spatial pattern
• fuzhou