2022
DOI: 10.1108/ijhma-09-2022-0134
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Network analysis of comovements among newly-built residential house price indices of seventy Chinese cities

Abstract: Purpose Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations. Design/methodology/approach Analysis here is facilitated through network analysis together with topolog… Show more

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Cited by 29 publications
(8 citation statements)
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References 96 publications
(116 reference statements)
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“…The second and fifth rows of Figure 1 show histograms of sixty bins and kernel estimates for prices and their first differences, respectively, to visualize data distributions. It could be seen that the data are not normally distributed, which should not be surprising and is generally anticipated for economic and financial time series [262265]. The third and sixth rows of Figure 1 further show quantile-quantile plots of prices and their first differences, respectively, against the standard normal distribution to reveal deviations from normality.…”
Section: Datamentioning
confidence: 67%
“…The second and fifth rows of Figure 1 show histograms of sixty bins and kernel estimates for prices and their first differences, respectively, to visualize data distributions. It could be seen that the data are not normally distributed, which should not be surprising and is generally anticipated for economic and financial time series [262265]. The third and sixth rows of Figure 1 further show quantile-quantile plots of prices and their first differences, respectively, against the standard normal distribution to reveal deviations from normality.…”
Section: Datamentioning
confidence: 67%
“…With these reviews, although not exhaustive, it appears that the neural network model is one of the most useful techniques in terms of constructing price forecasts for agricultural commodities (Bayona-Oré, Cerna, & Tirado Hinojoza, 2021). More specifically, a wide variety of time-series variables that are chaotic and noised could be well forecasted through the neural network model (Karasu, Altan, Bekiros, & Ahmad, 2020; Wang & Yang, 2010; Wegener, von Spreckelsen, Basse, & von Mettenheim, 2016; Xu, 2015, Xu, 2018, Xu, 2018, Xu, 2018; Yang, Cabrera, & Wang, 2010, Yang, Su, & Kolari, 2008), including many different types of economic and financial time series (Xu & Zhang, 2022). 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.…”
Section: Introductionmentioning
confidence: 99%
“…During the past decade, the Chinese housing market has witnessed rapid growth. Undoubtedly, housing price forecasting problems have turned to be one of key concerns of investors and policymakers (Xu and Zhang, 2022g). It is essential to have good understandings of trends and fluctuations of housing prices as they have direct influences on people's investment decisions in the real estate market and choices of cities to reside and work, as well as regulatory agencies' policy analysis, design and implementations.…”
Section: Introductionmentioning
confidence: 99%