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

Abstract

Street greenery is a component of urban green infrastructure. By forming foundational green corridors in urban ecological systems, street greenery provides vital ecological, social, and cultural functions, and benefits the wellbeing of citizens. However, because of the difficulty of quantifying people's visual perceptions, the impact of street-visible greenery on housing prices has not been fully studied. Using Beijing, which has a mature real estate market, as an example, this study evaluated 22,331 transactions in 2014 in 2370 private housing estates. We selected 25 variables that were classified into three categories-location, housing, and neighbourhood characteristics-and introduced an index called the horizontal green view index (HGVI) into a hedonic pricing model to measure the value of the visual perception of street greenery in neighbouring residential developments. The results show that (1) Beijing's homebuyers would like to reside in residential units with a higher HGVI; (2) Beijing's homebuyers favour larger lakes; and (3) Beijing's housing prices were impacted by the spatial development patterns of the city centre and multiple business centres. We used computer vision to quantify the street-visible greenery and estimated the economic benefits that the neighbouring visible greenery would have on residential developments in Beijing. This study provides a scientific basis and reference for policy makers and city planners in road greening, and a tool for formulating street greening policy, studying housing price characteristics, and evaluating real estate values.

Daily Accessed Street Greenery and Housing Price: Measuring Economic Performance of Human-Scale Streetscapes via New Urban Data

“…Nevertheless, the attribute belonging to the human-scale streetscape has been neglected in previous studies. Even so, a few related studies have been made at either the neighborhood scale [31] or on the relationship between eye-level greenery and housing prices, but they have ignored the issue of accessibility in people's daily lives [32]. A better understanding of this area, with a broader perspective, would provide important supplement in urban greening policy for urban planners, developers, and decision makers.…”

Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Data—A Case Study of Shanghai, China

“…This method assumes that real estate is heterogeneous and three types of characteristics have significant impacts on housing price, namely structure, neighborhood and location characteristics [31,32]. In empirical research, HPM mainly has three forms, including linear models [24,30], semi-log models [1] and double-log models [33]. However, most studies combine linear regression with HPM to interpret the impact of different independent variables [34,35].…”

Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments

“…Recent years have brought a great interest in applying spatial statistics to hedonic price modelling, which was partially caused by increasing Geographic Information Systems (GIS) development and the applications of big data. The use of GIS tools has been particularly significant for the evaluation of the impact of environmental/spatial attributes in property values [1][2][3]. This has resulted in the introduction of advanced geostatistical methods and Geographically Weighted Regression (GWR) as efficient methodologies for capturing spatial heterogeneity and spatial autocorrelation in housing markets versus multiple regression as a global model [4,5].…”