Deciphering house prices by integrating street perceptions with a machine-learning algorithm: A case study of Xi'an, China

Abstract

A comprehensive understanding of house prices and their factors provide insights into the demand for housing while helping policymakers implement measures to manage the housing market. Traditional studies either focus more on linear relationships and ignore complex, non-linear influences or consider neighborhood amenities but lose sight of the streetscape. This study aims to enrich the literature by integrating street-perception characteristics with an interpretable machine-learning technique for modeling house prices. Specifically, street-view images were semantically segmented to quantify street-perception characteristics from five perspectives: greenness, openness, enclosure, walkability, and imageability. By combining the determinants of community attributes and living convenience, 17 explanatory variables were fed into a gradient-boosting decision tree (GBDT) model to estimate housing prices. The results reveal that the model significantly outperforms the linear model (R2 increased by 47.87 %). Additionally, an improvement of 26.15 % (R2) was observed when street-perception characteristics were incorporated. Moreover, complicated non-linear relationships and interaction effects are discussed by visualizing partial dependence plots (PDPs). These findings offer nuanced guidance for improving the neighborhood environment to promote urban equity and develop a sustainable housing market.