Human settlement value assessment from a place perspective: Considering human dynamics and perceptions in house price modeling. (November 2021) - Kang, Yuhao; Zhang, Fan; Gao, Song; Peng, Wenzhe; Ratti, Carlo
Abstract: A better formalization of place - where people live, perceive, and interact with others - is crucial for understanding socioeconomic environment and human settlement. The widely used hedonic pricing model for houses was proposed from the perspective of space, focusing mostly on static house structural information and objective built environment factors. However, the value of house settlement is not only determined by its spatial settings, but also varies from one place to another with different cultures, human dynamics, human perceptions and social interactions. In this work, we introduce a place-oriented hedonic pricing model (P-HPM) that incorporates human dynamics and human perceptions of places to understand human settlement. As an empirical study, we employ a large volume of house price data in Boston and Los Angeles, including detailed house and locational amenity information. Besides, we take the hourly number of visits to places as a proxy of human mobility patterns, and obtain human perceptions of places extracted from large-scale street-view images using deep learning. The results show that the P-HPM outperformed the traditional HPM significantly in these two cities. Moreover, through a geographically weighted regression analysis and the Monte Carlo test, we find that the impacts of the proposed place-related variables on house prices are stable across space. Our results provide new insights into the assessment of human settlement values by incorporating the role of place using multi-source big geo-data. Highlights: Place-based variables are important for human settlement evaluation. A place-oriented hedonic pricing model with human dynamics and perceptions of places Impacts of the proposed place-related variables on house prices are stable across space. Deriving human perceptions of places from street-level imagery using deep learning