Differences In Urban Housing Price Fluctuations And The Chain Reaction

House price dynamics will influence not only the dwelling quality and living level, but also family wealth. It is related to national economy development and social harmonious. In recent ten years, with the reform of housing allotment system, Chinese housing market has gotten rapid development. At the same time, house price has run up sharply. Compared in the whole country, there are higher price levels and more rapid growth in some coastal cities to which people always pay more attention. The house price in inland cities is lower in the mass, but some have shown strong chasing tendency. Then, why the house price rise more rapidly in costal cities? What are the factors and reasons that cause the house dynamics? Weather or not the house dynamics is affected by psychological expectation? At present, some scholars have studied the factors of house price levels in Chinese cities and some have made empirical research about speculative characters of house price. But few focused on the difference of house price dynamics among cities. The empirical research in different countries and districts have inconsistent conclusion about ripple effect. Some reportage in China alleged that there were "linkage" and "by turns" among prices in different cities. But normative empirical research is absent. This paper tries to investigate the difference of house price dynamics among cities and examine the ripple effect in Chinese housing market.After studying abroad and home studies on housing price and price dynamics, this paper constructed an analytical frame to examine house price dynamics through autocorrelation and spatial correlation and explained the causing mechanism about it. Collecting data of house prices and city economy in Chinese 35 metropolitans since 1995, the paper made an empirical study about the difference and relevancy of house price dynamics in our country. In the research of dynamic difference, based on the factors analysis of house price level, constructing similar and normal error correction model, the paper studied the determinant of short-run dynamics, and analyzed the reason of difference in autocorrelation and mean reversion. In the research of ripple effect, the paper constructed national and local error correction model of house price. According to different reaction to national fundamentals, and the departure trend relative to national average house price, the paper investigated if there was ripple effect. With the cointegration and Granger test for several hotspot cities, the paper examined the interaction of house prices. In order to better explain the diversity of the macro- economic phenomenon, the author made a survey for psychology and behavior in three representative housing markets. In October2007, we put out 400 pieces of questionnaire respectively in Hangzhou, Wuhan and Wuxi, trying to know the price expectation, buying motivation, opinion about housing market and the reason of price appreciation, and to analyze the formation of price expectation.The paper research obtains main conclusions as follows: (1)The house price dynamics in Chinese 35 metropolitans is determined together by the dynamics of fundamentals and short-run adjustment dynamics. With the method of direct regress model and normal error correction model, the paper drew consistent conclusion. House price in Chinese cities is determined by income, population, construction cost and density in the long run. Besides the effect of fundamental dynamics, the house price dynamics is influenced by shot-run adjustment dynamics, including house price changes autocorrelation and the mean reversion to the fundamentals.(2)There is obvious intercity difference in shot-run adjustment dynamics. The autocorrelation of house price changes is notable in coastal cities but unapparent in inland cities. There is strong mean reversion tendency in inland. Considering the interaction of fundamentals and adjustment dynamics, the paper examined the possible reasons for autocorrelation and mean reversion. In cities with higher income and bigger density, the autocorrelation of house price dynamics is stronger. The more the income grows, the bigger is the mean reversion; the more the cost grows, the smaller is the mean reversion.(3)There is weak ripple effect in Chinese cities. The paper divided 35 metropolitans into six parts i.e. East, North, South, West, Northeast and the Center. The deviations of regional house price from the national average are stationary—the deviations show no long-run trends-- in five regions except the East. But the short-run coefficients surely exhibit distinct spatial patterns. House price are more responsive to income changes and rate changes in the East than the national average. The money supply has a more positive effect on house price in the North. House price in the Center, South, Northeast and west are less responsive to construction cost than average, and the least is in the West. The ripple effect shows weak linkage with centers more than one.(4)The spatial interactions exist among house price in different cities. The paper used seasonal residential index (China Real Estate Index System) for Beijing, Shanghai, Tianjin, Guangzhou, Shenzhen and Chongqing to construct vector error correction (VEC) model. The results of co integration and Granger test indicated the existence of spatial interaction. But the diffusion is not obviously related to contiguous. To judging with these six cities, the house price diffuses from the east coastal to the west inland and between hotspot cities. The possible reasons for diffusion include capital transfer, information transmits, expectation and differences in regional structure, and the last two are more important.(5)Cities with stronger autocorrelation in house price changes are also those with leading dynamics spatially. The regions with leading dynamics in ripple effect are East, North and South, most are coastal cities with strong autocorrelation in price changes. The consistency is because of their common ground. Good economic fundamentals and amenities are necessary condition for leading growth and bases for long-run expectation. And investment consciousness and backward expectation are main momentum for shot-run positive autocorrelation and leading dynamics. With the survey of micro psychology and behavior for three representative cities i.e. Hangzhou, Wuhan and Wuxi, the paper found that the formation for price expectation reflects the price changes autocorrelation and is consistent with ripple effect.(6)We should fully aware the intercity difference in house price dynamics about time and extent when enacting policies. The key for the coastal to development healthily is to prohibit speculation, to orient public opinion accurately, and to increase market transparence. For the inland, it is both important to not be embroiled in suppress and to not be hoped catching up with leading cities disregarding fund-mental. The policy maker in central government should consider the cyclical phases in different cities and can't treat underway inland market as same as boom coastal market. Local government should prevent overspectulation in the rising up. Both capital transfer and information transmission are important in autocorrelation and spatial correlation. To prevent drastic dynamics, we should prevent the smart money from flowing wantonly. The news should report the risk warning of housing market.Compared with previous research findings, the main innovation of this paper is manifested in the following aspects:(1) The paper comprehensively studied the causing mechanism of house price dynamics through constructing a research frame with two dimensions—autocorrelation and spatial correlation. Then, it made empirical research for the difference and linkage of house price dynamics in Chinese 35 metropolitans. Some house price research in China focused on dynamics in certain hotspot cities, and some research the general factors of house price. Few paid attention to intercity difference in price dynamics and even fewer studied difference and linkage of house price dynamics simultaneously. Using panel data cointegration and error correction model, this paper analyzed short-run adjustment dynamics beyond fundamentals and leading dynamics deviating from national average, and found that there were really nexus between the two. Leading dynamic cities are usually those coastal cities with strong autocorrelation. The comprehensive study for house price dynamics is useful to rich research of abnormal house price dynamics and house price bubbles, becoming an important complement to present research in our country. The tentative findings will provide reference and experience for future research.(2)This paper used a method of integrating macro econometric model and micro psychology survey in house price dynamics research. Comparing with some studies which directly called autocorrelation as speculation and bubble, and some made subjective explanation for ripple effect, the paper did more detailed. First, it added interaction items of fundamentals and autocorrelation in price dynamics error correction model, detecting the city factors which impact the strength of autocorrelation and mean reversion. In the study of ripple effect, the paper made qualitative analysis for possible reasons. Then, through the survey for house buying psychology and behavior in representative cities, the paper made an anatomy for the formation of price expectation with 1132 effective questionnaires, and drew consistent conclusions with macro econometric analysis. The paper found that expectation is important reason for house price dynamics at present in China. The method of macro econometric analysis accompanied with homochronous survey for several cities and the manner of open question for attitude are unwonted in housing research in China.(3)The paper made empirical research for ripple effect in Chinese housing market through coefficient heterogeneity and spatial interaction. Owing to the price rising in many cities in recent years, there are some sayings about "linkage"and "rise by turns" which haven't been approved by normal research. The paper examined price deviation from average for the shock of fundamentals through constructing structure model for city's and national house price and judge the existence of "ripple" with it. On the other hand, the paper did cointegration and Granger test for representative cities to examine spatial interaction. The two dimensions both indicate that there is weak ripple effect in Chinese housing market. The character is to some extent different from foreign countries. Ripple effect has more than one center and the diffusion exists mainly between hotspot cities and from the coastal to inland, not related to contiguity. The method and the conclusion have referenced value for future research.Restricted to time, ability and data collection, this paper has some shortcomings waiting for improvement in future. Possible development is to use more effective test method for stationarity and cointegration with the extension of time series and improvement of panel data methodology. In addition, the relationship of autocorrelation and spatial correlation and the reason behind them are worthy to be further explored.