Investigating the impacts of street environment on pre-owned housing price in Shanghai using street-level images

Abstract

Studies considering street environment quality’s impact on housing value were limited to top-down variables such as the green ratio measured from satellite maps. In contrast, this study quantified street views’ impacts on the value of second-hand commodity residential properties in Shanghai based on analysis of street view imagery. (1) It applied computer vision to objectively measure street features from largely accessible street view imagery. (2) Based on the classical urban design measures frameworks, it applied machine learning to evaluate human perceived street quality as street scores systematically, in contrast to the common practice of doing so in a more intuition-based fashion. (3) It further identified important indicators from both human-centered street scores as well as the more objective street feature measures with positive or adverse effects on property values based on a hedonic modeling method. The estimation suggested both street scores and features are significant and non-negligible. For the perceived street scores (from 0-10 scale), neighborhoods with a unit increase in their “enclosure” or “safety” score enjoy price premium of 0.3% to 0.6%. Meanwhile, streets with 10% greater tree canopy exposure are attributable to a 0.2% increase in the property value. This study enriched our current understanding at a micro level of the factors that impact property values from the perspective of the built environment. It introduced human-centered perception of street scores and objective measures of street features as spatial variables into the analysis of neighborhood attribute vectors.

Influence of urban forests on residential property values: A systematic review of remote sensing-based studies

“…Some studies have pointed out that its overhead view provides less connection with residents’ intuitive visual perceptions [ 106 , 117 ]. Among our reviewed studies, some studies since 2018 have incorporated street view images to overcome the limitation of satellite imageries to reconstruct the horizontal view perspective of residents [ 36 , 61 , 63 , 105 , 117 ]. However, it is not without limitations, as it is difficult to find data with specific acquisition times as urban areas go through rapid development and seasonal changes [ 105 ].…”

“…Urban forests characteristics such as tree cover, tree density, tree biomass, leaf area index (LAI), vegetation greenness [ 6 , 10 , 11 , 13 , 16 , [24] , [25] , [26] , [27] , [28] ] and, proximity to and size, type, and condition of forests [ 3 , 9 , 10 , 24 , [29] , [30] , [31] ] were used in hedonic models. Other studies have used vegetation greenness based on the normalized difference vegetation index (NDVI) in hedonic models in relation to housing prices [ 10 , 28 , [32] , [33] , [34] , [35] , [36] ].…”