Research On The Correlation Between Real Estate And Inflation In China

Since 1998 the monetary reform of housing in china, real estate market entered a new round of prosperity by driving of commercial bank starting the mortgage. Real estate is a fixed asset, but the investment growth rate is always higher than other fixed asset investment rate, and soaring housing prices caused widespread concern in the whole society, it relations with the people's livelihood and social harmony.Inflation is the major problem which impact national economic development and social stability. After the founding of New China, inflation gradually fade out of sight with the establishment of a socialist planned economy. Until the reform and opening up, especially the implementation of dual pricing system, which makes the problem of inflation become the focus of attention once again.In this context, this paper take the relationship of real estate prices and inflation as the research object, trying to obtain impact mechanism along with effect size between real estate prices and inflation by applying standard analysis and empirical analysis.The first chapter is an introduction. The first section of this chapter describes the research background and significance; and the second section is the literature review which summarize current research and analysis of domestic and foreign about the correlation of real estate prices and inflation; Last the third section introduce study ideas, methods and structure of this essay.The second chapter describes the real estate price fluctuations and inflation relationship. First of all, it provides a definition of concept to real estate and inflation in the first quarter, and the classification of inflation, causes and economic effects are introduced; then in the second quarter, it analyses how asset price fluctuate during inflation from the wealth effect, Tobin's Q effect and financial accelerator effect three aspects; Finally in the third quarter, how inflation can affect real estate prices were analyzed both from a cost and credit.The third chapter describes the research method and theoretical model of this essay. In testing the relationship between variables, the traditional methods are generally assumed that the variable time series is stationary, but encountered in practice most of the economic and financial data are non-stationary time series, it is difficult to adopt the kind of sequence known information to master the randomness of time series as a whole. This chapter mainly introduces a number of analytical methods processing non-stationary time series. Section I describes the non-stationary time series unit root test and co-integration test, non-stationary series can be a smooth differential operator sequence and obtain stationary time series which is called single whole sequence, where the number of differential (ie the sequence number of unit roots) is one entire order. Used in this article unit root test is ADF test. Co-integration depicts relationships between the balance and smooth of two or more variables, in other words, the dependent variable can be explained by a linear combination of variables. But a necessary condition for co-integration is a single integer variable with the same order. Section II describes the vector error correction model (VEC). Co-integration relationship only show probably exist the long-run equilibrium relationship between variables, but can not determine the short-term dynamic relationship between variables, and vector error correction model reflect the long-run equilibrium between the variables and the relationship between short-term fluctuations. It is reputed that the VEC model is a constrained VAR co-integration model, used with Co-integration of non-stationary time series modeling. Section III introduce Granger causality test, Granger causality test is essentially testing whether a variable's lagged variable can be introduced into the equation of other variables. If a variable is lagged effecting by other variables, they are called Granger causality. Fourth section presents the impulse response function and variance decomposition. Impulse response function analysis effect when an error term, or subject to some shock model the dynamics of the system. Variance decomposition structure depicts the impact of each change in endogenous variable contribution, use this to further evaluate the importance of the impact of different structures.The fourth chapter is empirical analysis about real estate prices and inflation correlation in China, and this chapter is a key part of the paper. This variable which is selected in this essay are real estate prices, inflation, output and interest rates, and respectively take housing sales price index, the consumer price index, industrial added value and the inter-bank lending rates as an alternative to the variable studied. First, the data obtained X12 seasonal adjustment and dealing with the common logarithm, then obtaining the entire sequence of all first-order one by unit root test, and established the vector error correction model by co-integration test, Real estate property price fluctuations and inflation rates between the two-way causality in china which is obtained by the Granger causality test. In the last place the image of impulse response function and variance decomposition is given. Draw a conclusion: China's real estate price fluctuation and inflation have some relevance. Manifested in the short term, real estate price fluctuations on inflation is feeble, or even zero. But in the long term, real estate price volatility will be increased gradually affection on the macro-economy , that is with real estate prices, the price level will gradually rise, so the real estate price volatility are meaningful for forecasting future inflation.