Understanding the effects of urban perceptions on housing rent using big data and machine learning

Abstract

ABSTRACT Urban perceptions are related to residents’ subjective well-being, activities, settlement choice, and physical and mental health, which are vital for promoting sustainable community development. Landsenses ecology proposes the concepts of physical and psychological perceptions to measure the interaction between humans and the urban environment. However, such perceptions and their quantitative relationships with housing rental prices (HRPs) are still not clear. In this study, a novel urban-perception-oriented hedonic price model (UP-HPM) is proposed based on the construction of a multisource geographic database of Beijing’s rental market with rich information on people-oriented perception metrics, neighborhood environment characteristics, and HRPs. Furthermore, we use advanced machine learning (ML) pipelines to measure residents’ multidimensional urban perceptions and investigate their impact on block-level HRPs and their spatial disparities. We also quantify and compare the contribution of different physical and psychological perceptual attributes to HRPs through UP-HPM. The results show that neighborhood perceived greenery and the use of fences tend to bring about positive psychological perceptions and further affect HRPs. By comparing different combinations of hedonic and perceptual features, the UP-HPM performs well in terms of fitting capacity, spatial description, and interpretability and can inspire humanistic thinking and landsenses planning guidelines for the regulation of the allocation of public rental housing and improvement in block-level physical settings and infrastructure services, thereby providing insights into how best to promote residents’ quality of life and well-being.