Spatial impact of air pollutants on housing prices: Data visualisation and spatial Durbin approaches

Abstract

The negative impact of economic activities has been aggravated by the development of the world economy, technological progress, rapid growth and utilisation of natural resources. People are paying more attention to environmental pollution and have higher requirements for the quality of real estate. This paper analyses the spatial spillover of air pollutants on real estate prices in the main Beijing urban area from 2018 to 2022 using the Moran index, linear regression model, and spatial Durbin model. The results show that: 1) there is a significant price spatial aggregation feature per Moran's index; 2) the ordinary least square and spatial Durbin mode models’ results indicate that SO2, NO2, and temperature in air pollutants have a significant negative spillover effect on real estate prices and the ordinary least square model underestimates this effect; 3) PM2.5 and O3 have adverse spillover effects on surrounding real estate prices, but they are insignificant; 4) The number of bathrooms, floors, and bedrooms significantly positively affects surrounding real estate prices. However, the OLS model underestimated 3 and 4's impact to a certain extent.