This works examine the responses of housing prices to the monetary policies in various Chinese cities. Thirty-five large and medium sized Chinese cities are classified into six clusters applying the minimum variance c...This works examine the responses of housing prices to the monetary policies in various Chinese cities. Thirty-five large and medium sized Chinese cities are classified into six clusters applying the minimum variance clustering method according to the calculated correlation coefficients between the housing price indices of every two cities.Time difference correlation analysis is then employed to quantify the relations between the housing price indices of the six clusters and the monetary policies.It is suggested that the housing prices of various cities evolved at different paces and their responses to the monetary policies are heterogeneous,and local economic features are more important than geographic distances in determining the housing price trends.展开更多
基金Supported by the Hundred Talent Program of the Chinese Academy of Sciences,the National Natural Science Foundation of China under Grant Nos.71103179 and 71102129Program for Young Innovative Research Team in China University of Political Science and Law, 2010 Fund Project under the Ministry of Education of China for Youth Who are Devoted to Humanities and Social Sciences Research 10YJC630425
文摘This works examine the responses of housing prices to the monetary policies in various Chinese cities. Thirty-five large and medium sized Chinese cities are classified into six clusters applying the minimum variance clustering method according to the calculated correlation coefficients between the housing price indices of every two cities.Time difference correlation analysis is then employed to quantify the relations between the housing price indices of the six clusters and the monetary policies.It is suggested that the housing prices of various cities evolved at different paces and their responses to the monetary policies are heterogeneous,and local economic features are more important than geographic distances in determining the housing price trends.
