Abstract
The housing market in Chinese metropolises have become inflated significantly over the last decade. In addition to an economic upturn and housing policies that have potentially fueled the real estate bubble, factors that have contributed to the spatial heterogeneity of housing prices can be dictated by the amenity value in the proximity of communities, such as accessibility to business centers and transportation hubs. In the past, scholars have employed the hedonic pricing model to quantify the amenity value in relation to structural, locational, and environmental variables. These studies, however, are limited by two methodological obstacles that are relatively difficult to overcome. The first pertains to difficulty of data collection in regions where geospatial datasets are strictly controlled and limited. The second refers to the spatial autocorrelation effect inherent in the hedonic analysis. Using Beijing, China as a case study, we addressed these two issues by (1) collecting residential housing and urban amenity data in terms of Points of Interest (POIs) through web-crawling on open access platforms; and (2) eliminating the spatial autocorrelation effect using the Eigenvector Spatial Filtering (ESF) method. The results showed that the effects of nearby amenities on housing prices are mixed. In other words, while proximity to certain amenities, such as convenient parking, was positively correlated with housing prices, other amenity variables, such as supermarkets, showed negative correlations. This mixed finding is further discussed in relation to community planning strategies in Beijing. This paper provides an example of employing open access datasets to analyze the determinants of housing prices. Results derived from the model can offer insights into the reasons for housing segmentation in Chinese cities, eventually helping to formulate effective urban planning strategies and equitable housing policies.
Highlights
Over the last decade, the housing markets in Chinese metropolises have become inflated to an unprecedented extent
The relatively low variation inflation indicator (VIF) suggested that no severe collinearity existed within the estimated variables
This result indicated that the Eigenvector Spatial Filtering (ESF) model is capable of eliminating the spatial autocorrelation effect in the regression analysis
Summary
The housing markets in Chinese metropolises have become inflated to an unprecedented extent. Due to rapid urbanization and domestic migration, cities are not able to accommodate medical, educational, transportation, and cultural needs [1]. These services or amenities, such as hospitals, schools, and transportation hubs, are distributed unevenly across urban landscapes. The housing markets tend to manifest a clear pattern of spatial heterogeneity in terms of property values. Housing prices vary according to proximity to conveniences, the values of these amenities cannot be quantified. Exploring the correlation between the amenity value and housing prices is the first step to understand the complexities of real estate valuation and to formulate more effective housing policy initiatives [2]. The amenity variables are typically categorized into structural (e.g., size, age of buildings), locational (e.g., availability of public transit, accessibility to the Central Business District [CBD]), and environmental factors (e.g., availability of green spaces, scenic views) near the property [3,4,5,6,7]
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