Abstract
PurposeThis study aims to investigate dynamic relationships among residential housing price indices of ten major Chinese cities for the years 2005–2021.Design/methodology/approachUsing monthly data, this study uses vector error correction modeling and the directed acyclic graph for characterization of contemporaneous causality among the ten indices.FindingsThe PC algorithm identifies the causal pattern and the Linear Non-Gaussian Acyclic Model algorithm further determines the causal path, from which this study conducts innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tiers of cities.Originality/valueThis study suggests that policies on residential housing prices in the long run might need to be planned with particular attention paid to these top tiers of cities.
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