Multiscale analysis of human social sensing of urban appearance and its effects on house price appreciation in Wuhan, China

Abstract

• Explore the effect of human social sensing of urban appearance on the house price appreciation rate with consideration of temporal and scale heterogeneity. • Use machine learning with big data containing 34 variables. • Provide strategies to achieve sustainable smart urban layout plan. City managers seek to achieve people-oriented sustainable city development, which requires a clear understanding of socioeconomic effects of citizens’ perceptions of urban appearance. Traditional studies have investigated effects of urban appearance but have ignored the perception of place, which depends on people's unique social experiences. We distinguish “house” and “home” and propose an integrated model to test the prediction effect of combining conventional perception and social perception variables on house price appreciation. We establish a dataset that includes factors, e.g., housing structures, visual quality, and human physical perception. Then, we use machine learning models to extract features from multisource data, investigate the price appreciation of 1,032 houses in Wuhan from 2015 to 2020 and use multiscale geographically weighted regression (MGWR) to discuss spatial dependence. Finally, we perform a cluster analysis to understand the combined effects. The results show that human physical perception has the highest effect, the visual quality of urban streets has the highest impact in places where highly educated people gather, and the impact of service facilities is greatest in economically underdeveloped areas. Our findings provide novel insights into the interlinkages between human social sensing and appreciation rates, which can be efficiently applied to build sustainable smart cities.