Abstract
Diverse urban environmental elements provide health and amenity value for residents. People are willing to pay a premium for a better environment. Thus, it is essential to assess the benefits and values of these environmental elements. However, limited by the interpretability of the machine learning model, existing studies cannot fully excavate the complex nonlinear relationships between housing prices and environmental elements, as well as the spatial variations of impacts of urban environmental elements on housing prices. This study explored the impacts of urban environmental elements on residential housing prices based on multisource data in Shanghai. A SHapley Additive exPlanations (SHAP) method was introduced to explain the impacts of urban environmental elements on housing prices. By combining the ensemble learning model and SHAP, the contributions of environmental characteristics derived from street view data and remote sensing data were computed and mapped. The experimental results show that all the urban environmental characteristics account for 16 percent of housing prices in Shanghai. The relationships between housing prices and two green characteristics (green view index from street view data and urban green coverage rate from remote sensing) are both nonlinear. Shanghai’s homebuyers are willing to pay a premium for green only when the green view index or urban green coverage rate are of higher value. However, there are significant differences between the impacts of the green view index and urban green coverage rate on housing prices. The sky view index has a negative influence on housing prices, which is probably because the high-density and high-rise residential area often has better living facilities. Residents in Shanghai are willing to pay a premium for high urban water coverage. The case of Shanghai shows that the proposed framework is practical and efficient. This framework is believed to provide a tool to inform the decisions of housing buyers, property developers and policies concerning land-selling and buying, property development and urban environment improvement.
Highlights
Urban green spaces, sky and other urban environmental elements can significantly affect the quality of urban life [1,2]
We proposed a new framework for measuring the impacts of urban environmental elements on housing prices in the area within Shanghai’s outer ring
The green view index (GVI), the sky view index (SVI) and the building view index (BVI) were extracted as horizontal-view urban environmental characteristics based on the Baidu street view images using a deep convolutional neural network
Summary
Sky and other urban environmental elements can significantly affect the quality of urban life [1,2]. The rapid development of computer vision provides an efficient method for the information extraction of street view images In this context, a great number of studies have been conducted to measure street-level green [27], estimate the spatiotemporal patterns of urban mobility [28], examine the relationship between street view and perceived safety [29] and assess the visual quality of urban environment [30] in this study, street view data is used to evaluate the relationship between urban environmental elements and housing prices. By combining tree-based ensemble learning regression algorithms and the SHAP model, the complex and nonlinear relationships between most of the environmental elements and housing prices are revealed, which is more elaborate than the results of previous studies.
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