LCE  Vol.6 No.2 , June 2015
An Empirical Research on Interactive Relationship of Urban Housing Prices in China: Analysis of Six Major Cities
Abstract: This paper applies non-linear granger causality test and impulse response function method to analyze the spillover effect of housing prices fluctuation among six major Chinese cities, namely, Beijing, Shanghai, Guangzhou, Shenzhen, Tianjin and Chongqing. Results indicate that fluctuation of urban housing prices in the short term is a wide range of positive spillover effect, and then the effect will gradually disappear. The spillover effect of housing prices fluctuation and cities’ space distance do not necessarily exist relationship; at the same time, Shanghai housing price fluctuation has a great influence on other cities generally. Accordingly, relevant policy suggestions are put forward.
Cite this paper: Zhang, Q. and Mei, D. (2015) An Empirical Research on Interactive Relationship of Urban Housing Prices in China: Analysis of Six Major Cities. Low Carbon Economy, 6, 64-72. doi: 10.4236/lce.2015.62008.

[1]   Holmans, A.E. (1990) House Prices: Changes through Time at National and Sub-National Level. Department of the Environment, London.

[2]   Hui, H.-C. (2010) House Price Diffusions across Three Urban Areas in Malaysia. International Journal of Housing Markets and Analysis, 3, 369-379.

[3]   Holly, S., Hashem Pesarana, M. and Yamagata, T. (2010) A Spatio-Temporal Model of House Prices in the USA. Journal of Econometrics, 158, 160-173.

[4]   De Bandt, O., Barhoumi, K. and Bruneau, C. (2010) The International Transmission of House Price Shocks. Springer, Berlin, 129-158.

[5]   Lee, C.-C. and Chien, M.-S. (2011) Empirical Modelling of Regional House Prices and the Ripple Effect. Urban Studies, 48, 2029-2047.

[6]   Simo-Kengne, B.D., Bittencourt, M. and Gupta, R. (2012) House Prices and Economic Growth in South Africa: Evidence From Provincial-Level Data. Journal of Real Estate Literature, 20, 97-117.

[7]   Lean, H.H. and Smyth, R. (2013) Regional House Prices and the Ripple Effect in Malaysia. Urban Studies, 50, 895-922.

[8]   Ferrari, E. and Rae, A. (2013) The Spatiality of Housing Market Volatility and Selective Migration. Town Planning Review, 84, 107-125.

[9]   Liu, N. and Roberts, D. (2013) Counter-Urbanisation, Planning and House Prices: An Analysis of the Aberdeen Housing Market Area, 1984-2010. Town Planning Review, 84, 81-105.

[10]   Zhu, B., Füss, R. and Rottke, N.B. (2013) Spatial Linkages in Returns and Volatilities among U.S. Regional Housing Markets. Real Estate Economics, 41, 29-64.

[11]   Liang, Y.-F. and Gao, T.-M. (2007) Empirical Analysis on Real Estate Price Fluctuation in Different Provinces of China. Economic Research Journal, 8, 133-142.

[12]   Hong, T., Xi, B. and Gao, B. (2007) Co-Movement of Real Estate Prices and Spatial Diffusion of Bubbles: Evidence from 35 Metropolis in China from 2000 to 2005. Statistical Research, 24, 64-67.

[13]   Li, Y.-Y. (2014) Demand Driving and Ripple Effects of Housing Prices Rising: On Strategies to Deal with the Problems of Housing Price. China Economic Quarterly, 2, 443-464.

[14]   Wang, J.-Y. and Liu, X.-L. (2014) Residential Fundamental Value, Bubble Component and Regional Spillover Effect. China Economic Quarterly, 4, 1283-1302.

[15]   Liang, Y.-F. and Xing, C.-S. (2012) An Application Research on Dynamic Factor Model of Housing Prices Fluctuation: Based on 26 Cities in China. Mathematics in Practice and Theory, 24, 7-16.

[16]   Baek, E.G. and Brock, W.A. (1992) A Nonparametric Test for Independence of a Multivariate Time Series. General Information, 2, 137-156.

[17]   Hiemstra, C. and Jones, J.D. (1994) Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation. Journal of Finance, 49, 1639-1664.