House price information flows among some major Chinese cities: linear and nonlinear causality in time and frequency domains

Abstract

Purpose With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019. Design/methodology/approach The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework. Findings The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect. Originality/value Results here should be of use to policymakers in certain policy analysis.

Yellow corn wholesale price forecasts via the neural network

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Office property price index forecasting using neural networks

“…The collected data go through multiple crosscheck processes before use (Xu and Zhang, 2023b, g). The index value of Beijing in December 2000, which is 1,000, is used as the base period index across the 10 cities (Xu and Zhang, 2022f).…”

Spatial-Temporal Analysis of Residential Housing, Office Property, and Retail Property Price Index Correlations: Evidence from Ten Chinese Cities

“…Although time series models, such the vector autoregression, are effective econometric instruments for revealing spillover processes, the outcomes may be susceptible to model assumptions (Zhang, Ji, Zhao and Horsewood, 2021;Xu and Zhang, 2022a). For a big dataset, dimensionality may also place restrictions on estimations using vector autoregression (Zhang and Fan, 2019;Xu and Zhang, 2023a).…”