Abstract
Purpose
This study aims to investigate dynamic relations among office property price indices of 10 major cities in China for the years 2005–2021.
Design/methodology/approach
Using monthly data, the authors adopt vector error correction modeling and the directed acyclic graph for the characterization of contemporaneous causality among the 10 indices.
Findings
The PC algorithm identifies the causal pattern, and the linear non-Gaussian acyclic model algorithm further determines the causal path from which we perform innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tier of cities.
Originality/value
This suggests that policies on office property prices, in the long run, might need to be planned with particular attention paid to the top tier of cities.
Subject
Management Science and Operations Research,Strategy and Management,General Decision Sciences
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