| Real estate market is closely related not only to economic development and thedevelopment of national economy sectors have a vital link. For the real estate marketreal estate prices have a significant impact, not only to the people’s living standards,but also the stable development of the national economy has a very important role.China’s real estate investment growth rate is not only much higher than the fixed assetinvestment growth and GDP growth, while accounting for the added value of our realestate economy is also growing proportion of the total real estate development andinvestment accounted for in fixed asset investment also showed the proportion of thegrowing trend, real estate has become a pillar industry of China is one of the nationaleconomy.In this paper, a number of dynamic econometric and time series analysis of thereal estate market-related variables and the relationship between macroeconomicissues. We first of our real estate prices in the process of economic growth, structuralchanges to the existing understanding of China’s real estate market trends andeconomic growth in China’s correspondence. We constructed two-variable model ofstructural change and found that sales of commercial housing price index for GDPgrowth of two-variable model of structural change is significant, estimated from themodel results show that sales of commercial housing price index and the structure ofChina’s GDP growth rate of two-variable significant changes in the structure of modeltransition point. By the structural change point total sample interval can be dividedinto4sub-sample interval, we use the sub-sample interval estimation methods aresub-sample interval of4to estimate the parameters, respectively, and found that theintercept estimates range in the first sub-sample of the largest in the The fourthsub-sample of the smallest interval, step by step over time showing a decreasing trend,the slope estimates range in the first and fourth sub-sub-sample of the sample interval is negative, the sample interval in the second and third sub-sub-sample interval ispositive, it can be seen in the third sub-sample explanatory power to explain thestrongest interval, sample interval in the first sub-housing sales price index of GDPgrowth to explain the weakest.Next, we applied unit root test, VAR model, Granger causality test, impulseresponse function and variance decomposition econometric methods, and therelationship between economic growth and the real estate market, in many ways,multi-angle further Comprehensive empirical testing and analysis, which attempts toexplore the real estate market and the relationship between economic growth andimpact. We use the GDP growth rate and growth rate of industrial added value of twovariables to characterize China’s economic growth to year growth rate of real estatesales data to characterize the development level of China’s real estate market.Through testing we found that our rate of growth of the GDP growth rate of realestate sales sequence has more significant influence, while the growth rate of GDPgrowth rates of real estate sales are not very significant. Through our real estate salestrend growth rate and GDP growth volatility component composition and therelationships between the test we found that growth rates of real estate sales cansignificantly affect the GDP growth rate, while the GDP growth rate of real estatesales in China influence the growth rate is still relatively weak. The growth rate ofindustrial added value to our characterization of economic growth, have the sameconclusion.We adopted our real estate prices and the volatility of inflation compared to theprocess of understanding the trend of the real estate market and its relationship withChina the correspondence between the rate of inflation. We use Markov regimeswitching model year growth rate of house price inflation data and the data of testswere calculated to determine the real estate market and the relationship betweeninflation and difference. Our estimation results show that the monthly house pricegrowth year on year there are obvious characteristics of the two-district system, thatsystem of high-growth areas and low-growth areas in the system, my monthly houseprice growth in the low-growth year on year, the average duration of the districtsystem was5.1867months, high-growth areas in the system, the average duration is 18.1159months. China’s inflation rate lagged1-order Markov regime switchingmodel estimation results also show that China’s inflation rate system there areobvious characteristics of the two areas, China’s inflation rate in the low-growth areas,the average duration of the system was14.8588months high-growth areas in thesystem, the average duration is15.2672months. Our data shows the rate of inflationless the number of regime switching, and in the duration of each district system islonger.We study our real estate market and the main macroeconomic variables,respectively, the relationship between the empirical test and analysis. We test showedthat the rate of inflation on real estate sales prices of the impact of weaker growth inthe nominal interest rate of commercial housing prices in China have a certain degreeof lag, the loan growth rate of prices of the impact of sales of commercial property isvery significant, but growth rate of commercial housing sales price impact of loangrowth coefficient is small. Variance decomposition results show that the loan growthrate of real estate sales prices contributed the sequence is always maintained at about28%. Investment growth rate on real estate sales prices of the impact of lower growth,Granger causality test showed that the growth rate of investment is not Grangersequence of growth of commercial housing sales price series, the variancedecomposition results show that the sequence of investment growth rate of real estatesales prices sequence of a small contribution. Growth rate of real estate sales priceson consumption growth rate of the coefficient is small, while the consumption growthrate of real estate sales prices of the impact of more significant growth. Variancedecomposition results show that the consumption growth rate series of real estatesales prices in the contribution rate of the sequence is4.7799%.Finally, our real estate market and other major markets of the relationshipbetween the empirical test and analysis were to explore the Real Estate Market andother major markets in China and the relationship between the degree of influence.We first examined the sales price of real estate and stock market correlation betweenthe found behind the first and second lag of stock market prices of real estate sales arevery weak growth rate of the stock market to real estate sales price growth sequencecontribution rate is also smaller. Bond market growth rate of commercial housing sales price less affected, the bond market can be a little Granger sequence of growthof commercial housing sales price series, the variance decomposition results showthat the bond market, the price growth rate series of real estate sales in thecontribution rate was rose to1.9242percent. Exchange rate on the growth ofcommercial housing sales price has some effect, the exchange rate variancedecomposition results show that the rate of growth of real estate sales pricescontributed to the sequence rose to3.3153%. Money growth rate on the sales price ofreal estate did not affect the variance decomposition results show that the moneymarket rate series of real estate sales prices in the contribution rate is0.0601%, andthen gradually declined. Sales of commercial vehicle production growth rate of pricegrowth in the influence of significant factors, and vehicle production growth rate ofChina’s real estate sales prices had little impact. |
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