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Over these years, China's real estate industry has achieved a rapid development. However, high house prices have become a hidden danger for the economy. Based on previous studies, this article selects three representative variables to build the vector error correction model (VECM) to test the impact of monetary policy on house price fluctuations Empirically. The result shows that there is a long-term stable relationship between monetary policy and house prices, and the monetary policy has a significant impact on house prices. To achieve the objectives of lowering house prices, central bank could combine administrative measures with the appropriate monetary policies. Introduction With the process of marketization, house prices in China have become too high for normal people to purchase. As we know, if the house prices are excessively high, not only the economic system will be affected, but also the society may be disrupted. As a result, taking measures to lower the house prices is an urgent task. In China, the main measures to regulate and control house prices is the combination of monetary policies, fiscal policies and administrative policies, among which the monetary policies is the most important. Based on the previous researches, this paper starts with the theoretical analysis of monetary policy’s influence on house prices, and then selects some representative variables to build vector error correction model(VERM) to find out the effectiveness of monetary policy to regulate house prices. The Theoretical Analysis of Monetary Policy’s Influence on House Prices The real estate industry is a typical capital intensive industry, as one of the major financial policies, monetary policy does have some influence on house prices. We can see the monetary policy’s influence on the supply-demand relationship in the real estate market from three different aspects: interest rate, credit and supply of money. Empirical Analysis Based on the VECM Model A. Variable selection and preliminary Data Processing As said in the theoretical analysis, this paper selects three typical variables: the inter-bank offered rate(R), broad money supply(M2) and total RMB loans(LOAN) as explanatory variables, chooses price index of real estate(HPI) as explained variable. And all variables are monthly data from January 2000 to December 2011. To eliminate the effect of inflation, we divided HPI by CPI. Through the seasonal test, we can find that there are seasonal characteristic in HPI, M2 and LOAN under the significance level of 0.1%. To eliminate the effect, we adjusted those variables by means of Census X12. Meanwhile, to reduce heteroscedasticity, we take the logarithm of all variables, and recorded them as LR, LM2, LLOAN and LHPI.This paper uses Eviews 6.0 to do the empirical analysis. B. Stationarity test To test whether there is cointegration relationship in time series, we should test the non-station first. This paper applied ADF method to do unit root test. And we get lag intervals for endogenous 2nd International Conference on Science and Social Research (ICSSR 2013) © 2013. The authors Published by Atlantis Press 588 according to AIC. The following table shows the results of the unit root test. Table 1 Results Of Series Unit Root Test VARIABLE Test For Unit Root In Lever Test For Unit Root In First Difference ADF 5% critical value P ADF 5% critical value P LR -2.9969 -2.8817 0.0376 \ \ \ LM2 -1.8485 -3.4416 0.6758 -11.7934 -2.8818 0.0000 LLOAN -1.7049 -3.4425 0.7441 -4.6830 -2.8823 0.0002 LHPI -2.4463 -2.8818 0.1311 -8.5190 -2.8818 0.0000 It can be seen from the table above that under the significant level of 5%, each variable is non-stationary except LR. However, those non-stationary variables would be stationary after we take the log, which means that they are all first order integration, so we can build VECM for all the variables. C. Cointegration test There are two methods to do the co-integration test: E-G test and Johansen test, and this paper applied Johansen test to do co-integration test. The results of the co-integration test are shown as below. Table 2 Results Of Cointegration Test Hypothesized No. of CE(s) Eigenvalue Trace Statistic 0.05 Critical Value Prob.** None * At most 1 0.176194 0.077663 48.45889 20.93647 47.85613 29.79707 0.0438 0.3616 Trace test indicates 1 cointegrating eqn(s) at the 0.05 level From the table, we can know that there is a cointegration relationship between the four variables. And from the unit root test of the cointegration sequence we can know the sequence of the cointegration equation is stationary, that means there are co-integration relationship between the four variables. And the equation is as follows: LHPI_SA=0.015939 LLOAN-0.013771 LM2-0.184200LR+et. (1) D. Granger Causality Test To analysis the relationship between monetary policy and house prices, we should find out whether there is causal relationship between them.We can conclude from the granger causality test that: (1)LR, LM2,LLOAN is the Granger cause of LHPI, which means that house prices would be influenced by interest rate, supply of money and credit. (2)LM2 is the Grange cause of LHPI, which suggests that there may be connected effect between supply of money and total credit. (3)LR is the Grange cause of the other three variables, which means that interest rate is the most powerful factor that have influence on house prices. E. Impulse response and Variance decomposition To examine that monetary policy’s influence on house prices more intuitively, we applied impulse response and variance decomposition to do a further analysis. Fig. 1 shows the impulse

Empirical analysis of the effects of monetary policy on house prices��Based on the VECM model