The Ripple Effect and Spatiotemporal Dynamics of Intra-Urban Housing Prices at the Submarket Level in Shanghai, China

Abstract

The ripple effect of housing price movements between cities has been extensively investigated, but there are relatively few studies on this topic within a metropolitan context, especially at the submarket level. This paper describes the use of ripple effect theory to examine the diffusion process and convergence of intra-urban housing prices at the submarket level in Shanghai, an emerging global city in China. The analysis is based on directed acyclic graphs, local indicators of spatial association time-paths, and a recently developed convergence test. The empirical results of grouping analysis identify 25 submarkets in Shanghai, and the diffusion of housing prices between these submarkets is found to be caused by both geographical and economic proximities. There is also a complex recursive process of price spillovers from high- to low-priced submarkets, and vice versa, which contributes to the spiraling local housing prices. Housing prices diverge across all submarkets, and the whole market can be divided into three convergence clubs. Finally, these convergence clubs have a circular structure with a degree of continuity. This study broadens our knowledge of the price interrelationship among housing submarkets at the intra-urban level. These findings have profound implications for urban planners, policy makers, and local residents.

Testing for overall and cluster convergence of housing rents using robust methodology: evidence from Polish provincial capitals

“…The empirical research to date in this area is not very extensive, and to the best of the author's knowledge only several such analyses have been developed. In particular, studies on the presence of convergence clubs in the residential market have been conducted in the USA (Kim and Rous 2012 ; Apergis and Payne 2012 , 2019 , 2020 ; Montañés and Olmos 2013 ), China (Meng et al 2015 ; Lin et al 2015 ; Zhang et al 2017 ; Hu et al 2020 ), the UK (Montagnoli and Nagayasu 2015 ; Holmes et al 2019 ), Spain (Blanco et al 2016 ), Australia (Awaworyi Churchill et al 2018 ), South Africa (Apergis et al 2015 ) and Poland (Matysiak and Olszewski 2019 ; Tomal 2019a , 2020a ). It should be noted that almost all of the above studies analysed the convergence of sales prices in the residential market.…”

Analysis of the Spatiotemporal Heterogeneity of Housing Prices’ Association in China: An Urban Agglomeration Perspective

“…Second, differently from the previous studies that demonstrated monocentricity for spillover effects of regional housing prices [67,68], a polycentric structure of the network was observed, and central cities in each urban agglomeration were identified by the block model analysis. Plus, the impulse responses on other cities' housing prices caused by central cities showed different characteristics in terms of intensity and breadth, but all persisted in the lag period.…”

Contemporaneous causality among residential housing prices of ten major Chinese cities

“…Teye and Ahelegbey (2017) incorporate the DAG into the Bayesian vector auto-regressive framework (Ahelegbey et al , 2016) to explore spatio-temporal relations among housing prices in twelve provinces of The Netherlands and find that Noord-Holland was most predominant during 1995Q1–2005Q2 while Drenthe became most central during 2005Q3–2016Q1. Hu et al (2020) use the DAG to analyze the diffusion process of intra-urban housing prices at the sub-market level in Shanghai, China and find that geographical and economic proximities contribute to the process. They also determine a complex recursive process of price spillovers from high- to low-priced sub-markets and vice versa, which contributes to spiraling local housing prices.…”