Remotely sensed urban environmental indices and their economic implications

Abstract Numerous studies reveal that urban favorable amenities potentially contribute to housing prices, but we still lack proper indices to quantify intangible urban disservices and lack the understanding of their economic effects. We attempt to develop remotely sensed indices to reflect these unfavorable and intangible urban disservices. Taking Wuhan in central China as an example, we propose the Thermal Environment Index (TEI) and the Vegetation Coverage Index (VCI) to characterize the urban environment based on Landsat images and examine their influences on housing prices using a hedonic price model. We build the hedonic price model using the spatial lag regression between housing prices and explanatory variables, including the proposed environmental indices, locational variables, and apartment structural variables. The spatial regression shows that the floor area ratio, floor height, proximity to business centers, and road accessibility exert significant and positive influences on housing prices whereas the TEI and the VCI have significant and negative influences on housing prices. A one-percent increase in the TEI will decrease housing prices by approximately 55 RMB/m 2 in 2010. We further investigate the differences between housing prices inside heat islands of different levels or types and outside heat islands, confirming our findings with the results of hedonic modeling. This study shows the potential of developing a remote sensing index to measure intangible urban disservices and exploring their economic implications.