Evaluating objective and perceived ecosystem service in urban context: An indirect method based on housing market

Abstract

Ecosystem service (ES) evaluation is usually based on the stocks of natural resources and their functions. However, the value of ES in the urban area depends on human activities more than the existence of natural resources. This research implements an indirect market method by integrating hedonic housing price model to assess ES in urban context from both objective (remote sensing) and subjective perspectives (street view image). Machine learning tools are employed to investigate the impacts of objective and perceived ES on housing prices based on a case study in Wuxi, China. The analytical results suggest that the contribution of ES to house prices in Wuxi ranges from 0% to 10.85%. Further investigation found that visible trees are the most important ES factor of housing price, more important than the coverage of green space. We also find that the quality of blue-green spaces might modify the value of ES, while the poor landscape design and water pollution in the central urban area made the values of ES low in the housing market. This study proves that the indirect method based on the housing market is helpful in valuing ES in the urban context. The high importance of perceived blue-green spaces in ES encourages more efforts on landscape design rather than only increasing coverage.