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

Daijun Zhang,Xiaoqi Zhang,Shengwen Li,Xinyue Ye,Qiwen Dai,Yanqiao Zheng

doi:10.3390/ijgi9010056

Abstract

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.

Highlights

The externalities and spillover effect in housing markets have attracted growing scholarly interest [1,2,3,4,5,6,7]
We found that in the housing market of Beijing, there are 16 robust housing submarkets among which price spillover occured during the observation period 2011 October–2017 October
Because in this study, Beijing is only taken as an example to demonstrate the analytic power of the proposed Makarov model and constrained clustering method, it would be misleading to present too many details on the background of the city; interested readers can find comprehensive introductions to Beijing’s housing market by themselves in the references [61,62]

Summary

Introduction

The externalities and spillover effect in housing markets have attracted growing scholarly interest [1,2,3,4,5,6,7]. Meen [16] provides convincing economic explanations for the driving forces of spillover and summarizes four major mechanisms by which the housing price spillover can occur: migration, equity transfer, spatial arbitrage, and spatial patterns. Most studies support the mechanisms, such as the spatial arbitrage and spatial patterns, which generate the “ripple”-form spillover with the spatial continuous pattern. While little evidence could support the migration and equity transfer mechanisms, they can potentially lead to the spillover with spatially discontinuous pattern

Results

Discussion

Conclusion