In this article, using panel data including 31 provinces in China from 1999 to 2015, I estimated the fundamental price of housing in China based on the fundamentals model. After that, I examined the cointegration relationship between the estimated fundamental price and the actual price of the house. And examined whether there was a bubble in the actual price of the house in China. As a result of the panel cointegration test, there is a cointegration relationship between the actual price and the fundamental price of the house at the national level, northeast region, northern coast, south coast, middle Yellow river area, middle Yangtze area, southwest area, and northwest area. The results show that in these areas, or at the national level, rising house prices are not bubbles. On the other hand, there is no cointegration relationship between the actual price of the house and the fundamental price in the eastern coastal area. The actual price of the house deviates from the fundamental price, and there is a bubble in housing prices in the eastern coastal area. Furthermore, according to the result of the cointegration test of each province, a bubble may exist in housing prices in eight provinces such as Liaoning province, Hebei province, Shandong province, Jiangsu province, Zhejiang province, Hainan province, Henan province, and Shaanxi province. On the other hand, it also became clear that soaring housing prices in other provinces cannot be called bubbles. According to the results of the above panel cointegration analysis, the following policy implications can be derived. That is, the bubble of housing prices in China is a regional phenomenon, not a national one. Therefore, for housing prices in China, rather than adopting uniform policies across China, it is better to take into account regional differences and take into account regional policies that are appropriate for each region.

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This paper explores the location patterns of China’s automobile sector including 11 four-digit industries, using employment weighted K-density function proposed by Duranton and Overman (2005). We construct a detailed micro-geographic dataset based on the firm-level dataset of the 2nd China Economic Census. We classify the firms into SHE (state-holding enterprise), NSE (nonstate-holding enterprise), FFE (foreign-funded enterprise) and DE (domestic enterprise) by their ownership. We find the following mainly. First, in many industries, the NSEs, the FFEs and the DEs are localized at short distances, respectively, while SHEs are not localized in any industry. Second, in almost all industries, the enterprises show random distribution between the SHEs and the NSEs, and also between the FFEs and the DEs. Third, firms of 3720 (vehicle manufacturing) are not localized, and they are also not co-localized with the firms of other industries at short distances. Fourth, the NSEs and the FFEs in 3725 (vehicle parts and accessories manufacturing) show localization pattern at short distances. But they are not co-localized at short distances with the NSEs and the FFEs in the other industries, respectively. Fifth, the NSEs of almost all the industries show co-dispersion patterns with the SHEs of 3725 at short distances. Sixth, the DEs of almost all the industries show co-dispersion patterns with the FFEs of 3725 at short distances, while they show co-localization patterns at long distances, too. Seventh, the SHEs are not colocalized with the NSEs of 3725 at short distances, while the FFEs are not co-localized with the DEs of 3725 at short distances, too.

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