Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

Abstract

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.

Impacts of Street-Visible Greenery on Housing Prices: Evidence from a Hedonic Price Model and a Massive Street View Image Dataset in Beijing

“…Yang [58] demonstrated that a shorter distance from residential developments to the city centre has a significant and positive impact on housing prices in Beijing. In contrast, Xiao et al [26] indicated that a shorter distance from houses to city sub-centres increases Beijing's real estate values. These two studies did not indicate whether the two distances impact Beijing's housing prices simultaneously.…”

“…In addition, we added the number of bus stations and schools within 0.5 km and 1 km road distances into the model (0.5 km: BUS_5H, SUB_5H, and SCH_5H; 1 km: BUS_1T, SUB_1T, and SCH_1T). As indicated by the statistics in Table 1, the densities of bus stations, metro stations, and schools could have an impact on housing prices, since the concentration of amenities can have an impact on the housing prices of residential units [26]. Except for the traffic and educational aspects, several independent variables were used to measure and explain several natural or semi-natural amenities, including PARK_DIS, LAKE_DIS, LAKE_AREA, RIVER_DIS, and HGVI.…”

“…Some famous business districts, such as Dongdan, Xidan, Wangfujing, and Wudaokou, had concentrated commercial facilities and a good ability to attract industry. The fifth ring road area of Beijing is a typical and meaningful study area, so many studies have been conducted here [2,26,[42][43][44]. …”

“…A study by Zheng et al [25] was conducted in 85 cities in China and found that for every 10% decrease in the inflow of air pollutants around the city, housing prices increased by 1.8%. Xiao et al [26] demonstrated that Beijing's housing prices have a negative correlation with the air quality index (AQI). However, there is still very little research on how street greenery affects urban real estate values.…”

Novel Guidance CPS Based on the FatBeacon Protocol

“…Besides, a project has been proposed to give a solution in underwater environments, providing an underwater location system [15] using beacons. Until now, all aforementioned applications and projects have made use of Bluetooth technology for POIs as a sensor dependent on other technologies, such as an Internet connection, to retrieve the information about the place, as shown in [16,17]. This is not the case of the proposal described in this work.…”