Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Dataxe2x80x94A Case Study of Shanghai, China 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. Shanghaixe2x80x99s 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 highdensity and highrise 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 landselling and buying, property development and urban environment improvement.

Detecting IntraUrban Housing Market Spillover through a Spatial Markov Chain Model This study analyzed the spillovers among intraurban housing submarkets in Beijing, China. Intraurban spillover imposes a methodological challenge for housing studies from the spatial and temporal perspectives. Unlike the interurban spillover, the range of every submarket is not naturally defined; therefore, it is impossible to evaluate the intraurban spillover by standard timeseries models. Instead, we formulated the spillover effect as a Markov chain procedure. The constrained clustering technique was applied to identify the submarkets as the hidden states of Markov chain and estimate the transition matrix. Using a daybyday transaction dataset of secondhand apartments in Beijing during 2011xe2x80x932017, we detected 16 submarketsregions and the spillover effect among these regions. The highest transition probability appeared in the overlapped region of urban core and Tongzhou district. This observation reflects the impact of urban planning proposal initiated since early 2012. In addition to the policy consequences, we analyzed a variety of spillover xe2x80x9ctypesxe2x80x9d through regression analysis. The latter showed that the xe2x80x9cripplexe2x80x9d form of spillover is not dominant at the intraurban level. Other types, such as the spillover due to the existence of price depressed regions, play major roles. This observation reveals the complexity of intraurban spillover dynamics and its distinct drivingforce compared to the interurban spillover.