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.

An Integrated Model for Urban Subregion House Price Forecasting: A Multisource Data Perspective Urban housing price is widely accepted as an economic indicator of both business and research interest in urban computing. In this work, we propose an effective and finegrained model for urban subregion housing price predictions. Compared to existing works, our proposal improves the forecasting granularity from citylevel to milelevel in spite of data sparsity and complex factors. The finegrained housing price forecasting has the potential to support a broad scope of applications, ranging from urban planning to housing market recommendations. To achieve that, in this paper, we propose a novel integrated framework, FTD_DenseNet, which incorporates more social and economic features and makes full use of alllevel spatiotemporal features. Specifically, the Kalman Filterbased future expection is firstly involved as an influence factor in our model. Extensive empirical studies on real data show the effectiveness of our proposals.