| Summary: | 碩士 === 世新大學 === 財務金融學研究所(含碩專班) === 105 === The purpose of this study is to divide Mainland China into the northern region, the central region and the southern region, including first-tier cities such as Beijing, Tianjin, Shanghai, Guangzhou and Shenzhen, second-tier cities such as Hangzhou, Nanjing, Wuhan, Chongqing and Chengdu. We also use Vector Auto-regression (VAR) Models, Forecast Error Variance Decomposition and Impulse –Response Analysis to examine the contagion effects of the house prices between the cities of each region.
Facts prove that the rates of return of house prices in these ten big cities exists sequentially in a stationary state under the ADF Unit Root Test. According to the test result of Vector Auto-regression (VAR) Models, it revealed that in the northern section, the housing prices in first-tier cities Beijing and Tianjin effect each other; in the central section, the house price in first-tier city, Shanghai, effected second-tier cities such as Wuhan, Hangzhou and Nanjing, and vice versa. In addition, the housing prices in second-tier cities Wuhan, Hangzhou and Nanjing effected one another; in the southern section, the house prices in first-tier cities Shenzhen and Guangzhou effect each other and it also happens in second-tier cities Chongqing and Chengdu. Besides that, an Variance Decoposition and Impulse –Response Analysis show the house prices are strongly affected by themselves, and the house prices are impacts each other between the first-tier and second-tier cities but also between second-tier cities.
The housing prices in first-tier and second-tier cities in Mainland China were mutually effected. The conclusion may refer to those who invest in real estate. Investors can observe the house price movement of different cities in particular sections and then predict the housing price changes in the cities that effect each other. This result comes with a strong significance in reality and could be used as a reference to invest in real estate in first-tier and second-tier cities.
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