Investigating the Impact of Perceived Micro-Level Neighborhood Characteristics on Housing Prices in Shanghai

Abstract

It is widely accepted that houses in better-designed neighborhoods are found to enjoy a price premium. Prior studies have mainly examined the impact of macro-level neighborhood attributes (e.g., park accessibility using land use data) on housing prices. More recently, research has investigated the micro-level features using street view imagery (SVI) data, though scholars limited the scope to objective indicators such as the green view index and sky view index. The role of subjectively measured street qualities is less discussed due to the lack of large-scale perception data. To provide better explanations of whether and how the micro-level neighborhood environment affects housing prices, this article introduces a framework to collect designers’ perceptions on five subjective urban design perceptions from pairwise SVI rankings in Shanghai with an online visual survey and further predicted through machine learning (ML) algorithms. We also extracted ten important objective features from the scenes. The predictive power of micro-level neighborhood street perceptions (subjective perceptions and objective features) on housing prices was investigated using the hedonic price model (HPM) through ordinary least squares (OLS) and spatial regression, which considers spatial dependence. The findings prove the significance of the value of perceived qualities of the neighborhoods. It reveals that both objective perceived features and subjective perceptions significantly contribute to housing prices; while the objective features show more collective strengths, individual subjective perceptions have more explanatory power, and we argue that these two measures can complement each other. This study provides an important reference for decision makers when selecting street quality indicators to inform city planning, urban design, and community and housing development plans.

Measuring the Spatial-Temporal Heterogeneity of Helplessness Sentiment and Its Built Environment Determinants during the COVID-19 Quarantines: A Case Study in Shanghai

“…The human-eye level perceptions of the residential street qualities include subjectively measured enclosure and complexity, and other objective measures (e.g., view index of the tree, person, etc.) [19][20][21][22] have profound impacts on human behaviors [15,[23][24][25][26] like walkability and running [20,23], biking [21,22], housing prices [27], and crime rates [24]. Therefore, we extracted streetscape perception data (subjective perceptions) from the research of Qiu et al [25], and also added other residential environment data (including plot ratio, greening rate, construction year, etc.)…”

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

“…SVI is an ideal dataset to comprehensively describe the urban environmental variability. For example, it has been used to model buildings (Gurney et al, 2012) including building height (Yan and Huang, 2022), streetscape features (Wang, Liu and Gou, 2022), green and water systems (Jiang, Jiang and Shi, 2020), land use classification (Jain, Meiyappan and Richardson, 2013;Tian, Han and Xu, 2021;Fang et al, 2022), the openness (Xia, Yabuki and Fukuda, 2021), road network (Zhang et al, 2023), mobile monitoring (Sun et al, 2017) and POI (Gao, Janowicz and Couclelis, 2017;Huang et al, 2022;Song et al, 2022;X. Xu, Qiu, Li, Liu, et al, 2022).…”

Identifying Urban Park Events through Computer Vision-Assisted Categorization of Publicly-Available Imagery

“…Although a significant number of studies have been conducted to determine the use of urban parks, the majority of these studies have focused quantitatively on the frequency or intensity of use [8][9][10][11][12][13]. Some emerging studies deploy crowd-sourcing visual survey to effectively collect public opinions (emotions and perceptions) on urban spaces including streetscapes and urban parks [25][26][27][28][29][30][31][32]. Some studies also investigated the demographics of park users [14][15][16], and the periods of time parks are used [14].…”