Network Analysis of Housing Price Comovements of a Hundred Chinese Cities

2022

IJHMA

Self Cite

Purpose This study aims to investigate dynamic relationships among residential housing price indices of ten major Chinese cities for the years 2005–2021. Design/methodology/approach Using monthly data, this study uses vector error correction modeling and the directed acyclic graph for characterization of contemporaneous causality among the ten indices. Findings The 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/value This 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.

“…1. More institutional background about the China Real Estate Index System could be found from Xu and Zhang (2021a, 2021b, 2021c, 2022a, 2022b).…”

Section: Notesmentioning

confidence: 99%

Contemporaneous causality among residential housing prices of ten major Chinese cities

2022

IJHMA

Self Cite

“…Data used are from the China Real Estate Index System (CREIS), which is an analytical platform designed to represent market conditions and development trends of housing markets in major cities in China (Xu and Zhang, 2021b, 2022f). It was originated in 1994, which was initiated by the Development Research Center of the State Council, Real Estate Association and National Real Estate Development Group Corporation (Xu and Zhang, 2022e, 2022h).…”

Section: Data and Wavelet Transformationsmentioning

confidence: 99%

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

2022

IJHMA

Self Cite

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.

“…This fact could stem from the good capability of the neural network model for self-learning (Karasu, Altan, Saraç, & Hacioğlu, 2017, Karasu, Altan, Saraç, & Hacioğlu, 2017) and characterizing nonlinear features (Altan, Karasu, & Zio, 2021; Karasu et al. , 2020; Xu & Zhang, 2022, Xu & Zhang, 2022) in various time series (Xu, 2018; Xu & Zhang, 2021, Xu & Zhang, 2021). Here, we adopt the neural network for the forecasting exercise of the price of yellow corn.…”

Section: Introductionmentioning

confidence: 99%

Yellow corn wholesale price forecasts via the neural network

2023

ECON

Self Cite

PurposeForecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.Design/methodology/approachThe authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.FindingsThe final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.Originality/valueThrough the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.