House price volatility in China: A pervasive pattern with geographic disparity
Abstract
The booming real estate sector has been regarded as the “gray rhino” risk emerging in China over the past decade. Yet, the house price volatility per se has not been thoroughly examined. Filling the gap in the literature, this paper explores the house price volatility and its determinants for 70 large and medium-sized cities in China, using an extensive monthly data set from 2005 to 2019. We find evidence of significant geographical disparities in both the GARCH effects and the best-fitted volatility specification. Significant GARCH effects are found in 57 cities, among which 40% of cities show a persistent volatility pattern. We also find that both the house price volatility pattern and the associated volatility value are affected significantly by education and healthcare amenities.
Keyword : house price, geographic disparity, volatility, GARCH, model selection, China
How to Cite
Liu, X., Yu, Z., & Li, Y. (2024). House price volatility in China: A pervasive pattern with geographic disparity. International Journal of Strategic Property Management, 28(1), 45–63. https://doi.org/10.3846/ijspm.2024.21096
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Crawford, G. W., & Fratantoni, M. C. (2003). Assessing the forecasting performance of regime-switching, ARIMA and GARCH models of house prices. Real Estate Economics, 31(2), 223–243. https://doi.org/10.1111/1540-6229.00064
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Dolde, W., & Tirtiroglu, D. (1997). Temporal and spatial information diffusion in real estate price changes and variances. Real Estate Economics, 25(4), 539–565. https://doi.org/10.1111/1540-6229.00727
Dolde, W., & Tirtiroglu, D. (2002). Housing price volatility changes and their effects. Real Estate Economics, 30(1), 41–66. https://doi.org/10.1111/1540-6229.00029
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50(4), 987–1007. https://doi.org/10.2307/1912773
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Fang, H., Gu, Q., Xiong, W., & Zhou, L. A. (2016). Demystifying the Chinese housing boom. NBER Macroeconomics Annual, 30(1), 105–166. https://doi.org/10.1086/685953
Fei, P., Ding, L., & Deng, Y. (2010). Correlation and volatility dynamics in REIT returns: Performance and portfolio considerations. The Journal of Portfolio Management, 36(2), 113–125. https://doi.org/10.3905/JPM.2010.36.2.113
Garriga, C., Hedlund, A., Tang, Y., & Wang, P. (2021). Rural-urban migration and house prices in China. Regional Science and Urban Economics, 91, Article 103613. https://doi.org/10.1016/j.regsciurbeco.2020.103613
Germaschewski, Y. (2023). House price volatility in China: Demand versus supply. Economic Inquiry, 61(1), 199–220. https://doi.org/10.1111/ecin.13112
Gil-Alana, L. A., Barros, C., & Peypoch, N. (2014). Long memory and fractional integration in the housing price series of London and Paris. Applied Economics, 46(27), 3377–3388. https://doi.org/10.1080/00036846.2014.929630
Glaeser, E., Huang, W., Ma, Y., & Shleifer, A. (2017). A real estate boom with Chinese characteristics. Journal of Economic Perspectives, 31(1), 93–116. https://doi.org/10.1257/jep.31.1.93
Glaeser, E. L., Kolko, J., & Saiz, A. (2001). Consumer city. Journal of Economic Geography, 1(1), 27–50. https://doi.org/10.1093/jeg/1.1.27
Gong, Y., Boelhouwer, P., & de Haan, J. (2016a). Interurban house price gradient: Effect of urban hierarchy distance on house prices. Urban Studies, 53(15), 3317–3335. https://doi.org/10.1177/0042098015608090
Gong, Y., de Haan, J., & Boelhouwer, P. (2020). Cross-city spillovers in Chinese housing markets: From a city network perspective. Papers in Regional Science, 99(4), 1065–1085. https://doi.org/10.1111/pirs.12512
Gong, Y., Hu, J., & Boelhouwer, P. J. (2016b). Spatial interrelations of Chinese housing markets: Spatial causality, convergence and diffusion. Regional Science and Urban Economics, 59, 103–117. https://doi.org/10.1016/j.regsciurbeco.2016.06.003
Han, H., & Park, J. Y. (2014). GARCH with omitted persistent covariate. Economics Letters, 124(2), 248–254. https://doi.org/10.1016/j.econlet.2014.05.016
Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Journal of Applied Econometrics, 20(7), 873–889. https://doi.org/10.1002/jae.800
Holmes, M. J., & Grimes, A. (2008). Is there long-run convergence among regional house prices in the UK? Urban Studies, 45(8), 1531–1544. https://doi.org/10.1177/0042098008091489
Hossain, B., & Latif, E. (2009). Determinants of housing price volatility in Canada: A dynamic analysis. Applied Economics, 41(27), 3521–3531. https://doi.org/10.1080/00036840701522861
International Monetary Fund. (2011). Global economic prospects and policy challenges. https://www.imf.org/external/np/g20/pdf/070911.pdf
Kim, W., & Linton, O. (2011). Estimation of a semiparametric IGARCH(1,1) model. Econometric Theory, 27(3), 639–661. https://doi.org/10.1017/S0266466610000435
Leamer, E. E. (2007). Housing is the business cycle (NBER Working Paper No. 13428). https://www.nber.org/system/files/working_papers/w13428/w13428.pdf
Lee, C. L., & Reed, R. G. (2014). The relationship between housing market intervention for first-time buyers and house price volatility. Housing Studies, 29(8), 1073–1095. https://doi.org/10.1080/02673037.2014.927420
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