Detecting Intra-Urban Housing Market Spillover through a Spatial Markov Chain Model

This study analyzed the spillovers among intra-urban housing submarkets in Beijing, China. Intra-urban spillover imposes a methodological challenge for housing studies from the spatial and temporal perspectives. Unlike the inter-urban spillover, the range of every submarket is not naturally defined; therefore, it is impossible to evaluate the intra-urban spillover by standard time-series 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 day-by-day transaction dataset of second-hand apartments in Beijing during 2011−2017, we detected 16 submarkets/regions 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 “types” through regression analysis. The latter showed that the “ripple” form of spillover is not dominant at the intra-urban level. Other types, such as the spillover due to the existence of price depressed regions, play major roles. This observation reveals the complexity of intra-urban spillover dynamics and its distinct driving-force compared to the inter-urban spillover.

Abstract licence: CC BY

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