| In recent years, housing prices in China have increased rapidly. The average housing price in China had increased by 43.95% from Spring 2006 to Spring 1999, 5.6 folds of the increasing rate in CPI during that same period. Meanwhile, the amount of home mortgage loans had increased rapidly. The balance of China's home mortgage loans is 1.84 quadrillion at the end of 2005, 43 folds of that at the beginning of 1999.The rapid increase in housing prices has drawn attention of the central government and concerns the public. Economists and policy makers are facing the following questions. 1) What are the trends in the movement of China's housing prices? 2) What are the relationships between housing prices and mortgage loans? They increase synchronously. Are there any causal relationships between the increases of them? If so, which is the cause, which is the result? Answers to the above questions will affect the decision making of housing purchasers, risk analysis of mortgage loans, the formulation of monetary policies. Correct answers to the above questions must be base on scientific knowledge of the relationships between the movement of China's housing prices and mortgage.In this thesis, studies on the movement of housing prices are performed by the housing price time series features. Based on this, in-depth and systematic studies on the relationships between housing prices and mortgage are given through a housing price forcasting model founded on generalized regression neuro network (GRNN) and time varying parameter , multivariate error correction models.Following are the six main contributions of the thesis.1) The background and significance of the topic are analyzed. Literature on housing price forecasting, housing price movement and its determinant factors are reviewed. Drawback in existing research in China on housing prices and their determinant factors are analyzed. Feasible research directions are given.2) Nomal distribution , autocorrelation, stationarity of the time series data of China's housing prices are systematically studied through J-B test, autocorrelation function, and ADF and PP tests. The studies show that the distribution of the time series data of China's housing prices is right-skewed and has peaks. A high degree of autocorrelation and weak mean reversion have observed. The positive autocorrelation coefficient lasts for a long time. There is a long lag in the negative autocorrelation coefficient. When the lag period is at least 9 months, the changes in housing prices in Shanghai and Beijing are mutually related by Granger causal relationship. That means that same factors affect the housing prices of both cities. There is notable autocorrelation in the time series data of China's housing prices. That means that the data of China's housing prices are dependent. And it is possible to forecast the housing prices by historical and current housing prices. It also implies that technical analysis can be applied to housing price forecasting.3) Literature on market efficiency is reviewed. The efficiency of Beijing and Shanghai's housing market is tested. Results show that effective market hypothesis is opposed by the housing market in Shanghai, but housing market in Beijing supports weak effective market hypothesis. There is notable correlation in the time series data of Beijing's housing prices, as well as in that of Shanghai's housing prices. For the capital market, autocorrelation between the prices of securities can be explained by market in efficiency. However, for the housing market, experiments show that only market efficiency along can not explain the autocorrelation of housing prices. Because housing market is different from stock market, research methodology for capital market can not be used to study housing market without modification, even if the housing market is an effective market, because of its special characters, lag in housing supply, unrealistic expectation, restrictions on down-payment, etc. will cause lag of housing price movement.4) ARIMA model for housing price forecast is established based on the characters of the time series data of housing prices in Shanghai. Experiments show that ARIMA model can not provide accurate forecasts and explanation. Because of the limitation of the forecasting ability of ARIMA model, Generalized regression neural network (GRNN) model is used in this thesis for housing price forecasting. Because GRNN network can produce highly nonlinear mapping and fast calculation, it is suitable for simulation of complex system. The relative error of forecasting by GRNN network is 0.37%, that by ARIMA model is 1.96%. That shows that through repeated training with large training set, GRNN can simulate the movement of housing prices in Shanghai better. So GRNN network is more suitable for the forecasting of Shanghai's housing prices than ARIMA model. Of course, the above conclusion is drawn only from the comparison of the two forecasting models for Shanghai's housing prices. whether GRNN model is suitable for forecasting housing prices in other areas in China needs further tests. ARIMA is not superior to GRNN, and vise versa. Which model to use depends on the characters of the system. The forecasting results are better if the model fits the data generation model for the time series of the housing prices and it models the movement of housing prices. Otherwise, a more advanced and complicated model may not produce good forecasting results if it doesn't fit the data generation model for the time series of housing prices.5) The relationships between housing prices and the mortgage and its composition are studied based on the data in Shanghai's market. First, assume that the relationships between housing prices and mortgage are not changed over time. Experiments using the statistical data from August 1996 to August 2006 show that there is no long-term correlation between housing prices and mortgage loan and its components. Second, the casual relation between housing prices and mortgage is examined through standard Granger casual test. Results show that the balance of mortgage loans is the Grange cause of housing prices, not the other way around. The balance of commercial home mortgage loans is the Grange cause of housing prices, not the other way around as well. A close look at the conclusion reveals that Shanghai's housing market is not a stable market. Because of the termination of physical distribution of housing, consumer demand for housing increases. To support the reformulation of housing policies, central bank has developed"regulations for home mortgage loans", and canceled direct control of the size of the loans of commercial banks. Plus more and more fierce competition between commercial banks, it is more and more easily for consumers to get home mortgage loans. Due to the above policy changes, the relationships between housing prices and mortgage changes all the time. The structure of the relation has undergone a series of changes. Although policies for housing reform and housing financing reform will be introduced in a specific time, it is not certain how the market will absorb these policies. In addition, the influence factors are complicated; so it is not possible to analyze the reasons for change via linear functions with dummy variables. Models with time varying parameters are more suitable. So in the third step, the assumptions of constant parameters are deleted. And these constant parameters are replaced by time-varying parameters. Assume the parameters of the relations between housing prices and mortgage is in AR(1) state transition form. The time variable parameter model is estimated by the Kalman filter. Results show that there is correlation changing over time between housing prices and mortgage.Before 2000, there is no obvious relation between housing prices and mortgage. However, after 2000, positive effects from mortgage to housing prices gradually increase and have upper-word trend. Based on comparisons of residuals, mean, and standard deviation, time varying parameter model can achieve better forecasts and describe the relations between housing prices and mortgage better than constant parameter models.Meanwhile, the above experiments prove that Shanghai's housing market features variable structure and further verify why ARIMA model is not suitable for forecasting research on Shanghai's housing prices. More importantly, these experiments results help us recognize a real problem. Currently, public is concerned about the threat of housing bubble against the stability of economy. Few people realize that over rapid financial deregulation and excessive financial support is one of the important factors that contribute to the housing bubble. Prevention of housing bubble should resort to financial policies. Especially during the period when financial policies are undergone changes and uncertain, information are asymmetrical, and financial firms compete with each other and are short-sighted, a large quantity of loans are invested in (speculated on) housing. This directly leads to the turbulence of housing prices.To solve the housing problem, home mortgage is an important tool.However, excessive financial support should be prevented. Therefore, this thesis suggests that the following principles should always be sticked to: housing financing should mainly support consumers to buy their own housing Commercial banks should be required to set differentiated high interest rates for consumers to do housing investment (speculation).Meanwhile, window guidance and supervision of home mortgage business in commercial banks should be enhanced to prevent excessive financial support. In addition, according to our study, housing price is not the Grange cause of the rapid growth of mortgage loans. It demonstrates that the rapid growth of housing mortgage in recent years comes from the growth of mortgage application, not from price increasing of the property mortgaged. And the balance of mortgage is not pushed by high housing prices. Therefore, current mortgage loans are not highly dependent on the property mortgaged and are not high-risk.6) Based on life cycle model, the urbanization index is established using data from Shanghai. Multi-factor relation model of housing prices, per capita disposable income, income expectation (unemployment rate), mortgage loan balance, mortgage interest rate, and urbanization index, etc., is established. Long-term balance relation and short-term impact of housing prices, per capita disposable income, income expectation , mortgage interest rate, and urbanization index, etc are analyzed through E-G two-step cointegration analysis and error correction model. Results show that there is long-term balance relation between these variables. In the long-run, income expectation (unemployment rate), mortgage interest rate, urbanization index, and balance of mortgage loans are main determinant factors of housing price. The degree of impact is as follows. 1% increase in the balance of mortgage loan will lead to 0.17% increase in housing prices; 1% decrease in rural population will lead to 0.17% increase in housing prices; income expectation (unemployment rate) decreasing by 1 percent will lead to housing prices increasing by 0.74%; if mortgage interest rate increases by 1 percent, housing prices will decrease by 0.08%. Among them, income expectation (unemployment rate) and balance of mortgage have greater impact on housing prices. These two variables have impact on housing prices in the short term as well. Error correction coefficient is 14%, which shows that the adjustment rate of Shanghai's housing market is fast. Each month, adjustment is 14%. Impact elastic of two-period lagged changes in housing prices to current housing prices is 0.42; that of three-period lagged changes in housing prices is 0.34, that of four-period lagged changes is 0.20. That shows that the housing price adjustment process is a sticky price adjustment process. Lagged housing price changes are main contribution to short-term fluctuations of housing prices. It proves that housing market doesn't fit the effective market hypothesis. On the housing market, consumer expectations are irrational. Consumers predict future housing prices based on previous housing prices and their movement. During the period when housing prices are increased substantially, bubbles will likely appear, and housing prices may deviate from their long-term value substantially. In addition, current Shanghai's housing market lacks the support of long-term purchase power, and the adjustment rate of housing prices is fast. The"prosperity- recession"cycle of Shanghai's housing market is relatively weak. Government should pay more attention to the housing market to insure a stable growing housing market.Following are the 4 innovative contributions of this thesis.1) Weak-form efficiency experiments show that the market efficiency theory of capital market can not explain the autocorrelation of housing prices. Starting from inherent attributes, this thesis systematically studied the reasons of autocorrelation of housing prices, i.e. lagged adjustment of housing supply and demand , irrational expectation on housing market, restriction on down-payment of housing financing, etc. will lead to lags in housing price adjustment.2) GRNN neural network model is applied to housing price forecasting. Through comparison of its results and that from ARIMA forecasting, we conclude that GRNN model is better than ARIMA model in predicting Shanghai's housing prices, and demonstrate the non-linearity of Shanghai's housing market.3) Time-varying parameter model of the relations between housing prices and housing mortgage are established. we conclude that there are long-term balance relations between housing prices and housing mortgage, and time-varying parameter model can better describe the relations between them than constant parameter models.4) Urbanization index is introduced. Using unemployment rate as the proxy variable of income expectation, multi-factor models of Shanghai's housing prices and per capita disposable income, income expectation (unemployment rate), mortgage loan balance, mortgage rate, and urbanization index, etc., is established. Through E-G two-step cointegration analysis and error correction model, we show that there is long-term balance relation between housing prices and per capita disposable income, income expectation (unemployment rate), mortgage loan interest rate, and urbanization index, etc. There is no obvious correlation between per capita disposable income and housing prices. rbanization and balance of mortgage loans are determinant factors of housing prices. Overall, this thesis draw some new conclusions of the relations between housing prices and mortgage in the area of China's housing price movement. It has quite a great theoretical value and positive practical significance for establishing housing policies and related financial policies, promoting the healthy development of China's housing market.
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