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Article

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

1
School of Finance, Zhejiang University of Finance and Economics, Hangzhou 310018, China
2
National School of Development, Southeast University, Nanjing 210000, China
3
Department of Informatics, New Jersey Institute of Technology, Newark, NJ 07102, USA
4
Department of Information Engineering, China University of Geosciences, Wuhan 430074, China
5
School of Economics & Management, Guangxi Normal University, Guilin 541004, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(1), 56; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9010056
Received: 29 November 2019 / Revised: 13 January 2020 / Accepted: 14 January 2020 / Published: 19 January 2020
(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)
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. View Full-Text
Keywords: constrained clustering; housing price; intra-urban spillover; ripple effect; spatial Markov chain constrained clustering; housing price; intra-urban spillover; ripple effect; spatial Markov chain
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MDPI and ACS Style

Zhang, D.; Zhang, X.; Zheng, Y.; Ye, X.; Li, S.; Dai, Q. Detecting Intra-Urban Housing Market Spillover through a Spatial Markov Chain Model. ISPRS Int. J. Geo-Inf. 2020, 9, 56. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9010056

AMA Style

Zhang D, Zhang X, Zheng Y, Ye X, Li S, Dai Q. Detecting Intra-Urban Housing Market Spillover through a Spatial Markov Chain Model. ISPRS International Journal of Geo-Information. 2020; 9(1):56. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9010056

Chicago/Turabian Style

Zhang, Daijun, Xiaoqi Zhang, Yanqiao Zheng, Xinyue Ye, Shengwen Li, and Qiwen Dai. 2020. "Detecting Intra-Urban Housing Market Spillover through a Spatial Markov Chain Model" ISPRS International Journal of Geo-Information 9, no. 1: 56. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9010056

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