Abstract
Many studies have explored the relationship between housing prices and environmental characteristics using the hedonic price model (HPM). However, few studies have deeply examined the impact of scene perception near residential units on housing prices. This article used house purchasing records from FANG.com and open access geolocation data (including massive street view pictures, point of interest (POI) data and road network data) and proposed a framework named “open-access-dataset-based hedonic price modeling (OADB-HPM)” for comprehensive analysis in Beijing and Shanghai, China. A state-of-the-art deep learning framework and massive Baidu street view panoramas were employed to visualize and quantify three major scene perception characteristics (greenery, sky and building view indexes, abbreviated GVI, SVI and BVI, respectively) at the street level. Then, the newly introduced scene perception characteristics were combined with other traditional characteristics in the HPM to calculate marginal prices, and the results for Beijing and Shanghai were explored and compared. The empirical results showed that the greenery and sky perceptual elements at the property level can significantly increase the housing price in Beijing (RMB 39,377 and 6011, respectively) and Shanghai (RMB 21,689 and 2763, respectively), indicating an objectively higher willingness by buyers to pay for houses that provide the ability to perceive natural elements in the surrounding environment. This study developed quantification tools to help decision makers and planners understand and analyze the interaction between residents and urban scene components.
Highlights
Urban scenes can be considered a vital medium for human beings, and it is important to recognize and understand this aspect of cities [1] to ensure the spatial security of urban sustainability [2]
That study explored the relationship between tree crowns and visible greenery at eye level, and the results showed that green view index (GVI) was suitable for analyzing pedestrians’ perceptual patterns [47]
The statistical results indicate that the average GVI values in Beijing and Shanghai are 16.44 and 19.49, respectively, which indicated that pedestrians in Shanghai tend to see more green scenes than those in Beijing
Summary
Urban scenes can be considered a vital medium for human beings, and it is important to recognize and understand this aspect of cities [1] to ensure the spatial security of urban sustainability [2]. Visible greenery can reduce pedestrians’ stress and passive emotions [11] and can soothe hospital patients and shorten their recovery time [12] Places such as urban green parks and open plazas can provide green landscapes and necessary spaces for residents and pedestrians to perform various physical activities [13], which can lower the risks of heart disease, diabetes and obesity [14] and improve people’s physical fitness [15]. Recent studies have suggested that the perception of the thermal environment is connected to the openness of street canyons [18], which have an impact on both ground surface temperature and pedestrians’ walking comfort and physical health [18,19]. Thoroughly studying and quantifying human-level urban environmental perceptions can help promote urban sustainability and public health
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