Abstract
This paper reports a series of empirical tests that scrutinised the potential effect of market sentiment on China’s housing prices at the city level. The analyses employed unbalanced panel data from 45 large- and medium-sized cities in China for the period of 2011 to 2017. We first constructed the housing market sentiment index using principal component analysis, following which the index was applied in a system GMM estimation to analyse its impact. The results of the dynamic GMM estimation indicated that market sentiment plays a significant role in increasing housing prices in the 45 selected cities. Subsequently, general fixed effect regressions, placebo tests, and Poisson regressions were performed to test the robustness of the dynamic GMM estimation results. All the robustness checks confirmed the positive impact of market sentiment on housing prices. Additionally, we investigated the moderating effects of mobile network coverage, wage rate, and education on the positive relationship between market sentiment and housing prices. It was revealed that mobile network coverage has a positive moderating effect on this link, while wage rate and education have negative moderating effects. Lastly, this study explored the heterogeneity of market sentiment’s effect on housing prices, concluding that although this effect is positive in both first- and second-tier cities, it is significantly stronger in first-tier cities. The research findings are useful for the Chinese government in regulating housing prices by stabilising market sentiment.
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Notes
“Riguangpan” is a vivid description of a scene in which all houses for sale are sold out on the opening day, marking the boom of China’s housing market.
The Baidu search engine generates about 5,650,000 results for the search term "riguangpan" and 4,720,000 results for the search term "panic-buying houses".
Data on annual housing turnover in China was obtained from the CEInet Statistics Database. The annual growth rate was calculated by the authors.
The average growth rate of housing prices in the Chinese mainland's 31 provincial regions from 1999 to 2018 was 463%, while Shanghai’s alone was 686%. Although the growth rate in Shanghai is higher than the average in China, it is not the highest. Jiangxi (730%) and Shaanxi (694%) both reached higher rates than Shanghai. The data on provincial housing prices was derived from the CEInet Statistics Database, while the growth rates of housing prices were calculated by the authors.
MNC is expressed by the number of mobile phone users. Edu is expressed by the number of college students per 10 thousand people.
Housing supply is expressed by floor space of completed housing. Housing demand is expressed by commercial housing sales by floor area.
The list of the first- and second-tier cities is presented in Table 11 of the Appendix.
For example, \(rlNHS_{it}\) and \(rlNHS_{i,t - 1}\) form a group, and so on.
The website address is http://index.baidu.com/v2/index.html#/.
The six indicators are NHS, SHS, NHFSC, LPV, RLTN and LPR.
Four second-tier cities were excluded from the analysis due to a lack of Baidu Index data.
For example,, \(rlNHS_{it}\) and \(rlNHS_{i,t - 1}\) form a group, and so on.
We should have presented 2017’s results; however, due to missing data on some cities in 2016 and 2017, we presented 2015’s results.
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Acknowledgements
This work was supported by Universiti Putra Malaysia under Grant No. GP/2018/9632300.
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YD and CL conceived of and designed the research framework. ML collected the data, and YD conducted the analysis. The original draft was prepared by YD; CL revised the paper. All authors have read the paper and approved the final manuscript.
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Ding, Y., Lee, C. & Lu, M. Does market sentiment push up China’s housing prices? An empirical study based on the data of 45 mainstream cities in China. J Hous and the Built Environ 38, 1119–1147 (2023). https://doi.org/10.1007/s10901-022-09985-7
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DOI: https://doi.org/10.1007/s10901-022-09985-7