中国房地产市场与宏观经济运行的关联性研究
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摘要
房地产市场不仅与我国经济发展息息相关，并且与国民经济各部门的发展都有着至关重要的联系。房地产价格对于房地产市场具有显著影响，不仅关系到人民生活水平，也对国民经济的稳定发展具有十分重要的作用。我国房地产投资增长率不仅远高于固定资产投资增长率和GDP增长率，同时我国房地产增加值占国民经济总量的比重也在逐年增加，房地产开发投资额占全社会固定资产投资的比重也呈现出日益增长的趋势，房地产业已经成为我国国民经济的支柱性产业之一。
本文运用了许多动态经济计量和时间序列分析方法研究房地产市场相关变量与宏观经济运行之间的关联性问题。我们首先通过对我国房地产价格在经济增长过程中存在的结构转变来了解我国房地产市场的走势及其与我国经济增长之间的对应关系。我们构建双变量结构转变模型，结果发现我国商品房销售价格指数与我国GDP增长率双变量结构转变模型是显著的，从模型的估计结果可以看出我国商品房销售价格指数与我国GDP增长率双变量结构转变模型存在显著的结构转变点。由结构转变点可以将总样本区间划分为4个子样本区间，我们采用子样本区间参数估计方法分别对4个子样本区间分别进行参数估计，发现截距估计值在第一个子样本区间内最大，在第四个子样本区间内最小，随时间发展呈现出逐级递减的趋势，斜率估计值在第一个子样本区间和第四个子样本区间为负数，而在第二个子样本区间和第三个子样本区间为正数，而可以看出解释能力在第三个子样本区间内解释能力最强，在第一个子样本区间内我国商品房销售价格指数对GDP增长率解释能力最弱。
接下来我们应用单位根检验、VAR模型、Granger因果关系检验、脉冲响应函数和方差分解等计量经济学方法，对我国房地产市场与经济增长之间的相互关系，从多方面、多视角进行进一步的综合实证检验与分析，从而试图探寻我国房地产市场与经济增长之间的关联性和影响程度。我们使用GDP增长率和工业增加值增长率两种变量来表征我国经济增长，以商品房销售额同比增长率数据来表征我国房地产市场的发展水平。通过检验我们发现，我国商品房销售额增长率对GDP增长率序列具有较为显著的影响作用，而GDP增长率对商品房销售额增长率的影响则并不十分显著。通过我国商品房销售额增长率与GDP增长率的趋势成分以及波动成分之间的相互影响关系检验我们都发现，我国商品房销售额增长率能够显著影响GDP增长率，而GDP增长率对我国商品房销售额增长率的影响程度依然相对较为微弱。而我们以工业增加值增速来表征经济增长时，得到了相同的结论。
其次，我们通过对我国房地产价格以及通货膨胀率的波动过程对比来了解我国房地产市场的走势及其与我国通货膨胀率之间的对应关系。我们通过利用马尔科夫区制转移模型对我国商品房价格同比增长率数据与我国通货膨胀率数据分别进行计算检验，以判别我国房地产市场和通货膨胀之间的关联与区别。估计结果表明我国商品房价格的月度同比增长率存在明显的两区制特征，即高增长区制和低增长区制，我国商品房价格月度同比增长率在低增长区制的平均持续期为5.1867个月，而在高增长区制的平均持续期为18.1159个月。我国通货膨胀率滞后1阶的马尔科夫区制转移模型的估计结果也表明我国通货膨胀率存在明显的两区制特征，我国通货膨胀率在低增长区制的平均持续期为14.8588个月，而在高增长区制的平均持续期为15.2672个月。可见我国通货膨胀率数据区制转移的次数较少，而在每个区制的持续期则较长。
然后，我们对我国房地产市场与主要宏观经济变量之间的相互关系分别进行实证检验与分析。我们通过检验发现通货膨胀率对我国商品房销售价格增长率的影响较弱，名义利率对我国商品房价格的影响具有一定程度的滞后性，贷款增长率对我国商品房销售价格增长率的影响非常显著，但商品房销售价格增长率对贷款增长率的影响系数较小。方差分解结果表明贷款增长率对商品房销售价格增长率序列的贡献率始终保持在28%左右。投资增长率对我国商品房销售价格增长率的影响较小，Granger因果关系检验表明投资增长率序列不能Granger影响我国商品房销售价格增长率序列，方差分解结果表明我国投资增长率序列对商品房销售价格增长率序列的贡献率很小。我国商品房销售价格增长率对消费增长率的影响系数较小，而消费增长率对我国商品房销售价格增长率的影响较显著。方差分解结果表明消费增长率序列对商品房销售价格增长率序列的贡献率在第10期为4.7799%。
最后，我们对我国房地产市场与我国其他主要市场之间的相互关系分别进行实证检验与分析，从而探寻我国房地产市场与我国其他主要市场之间的关联性和影响程度。我们首先检验我国商品房销售价格与股票市场之间的相关关系，发现滞后一阶和滞后二阶的股票市场对我国商品房销售价格增长率的影响都非常微弱，股票市场对商品房销售价格增长率序列的贡献率也较小。债券市场对我国商品房销售价格增长率的影响较小，债券市场序列能够略微Granger影响我国商品房销售价格增长率序列，方差分解结果表明债券市场对商品房销售价格增长率序列的贡献率在第10期才上升至1.9242%。汇率增长率对我国商品房销售价格增长率具有一定的影响，方差分解结果表明汇率增长率对商品房销售价格增长率序列的贡献率到第10期上升为3.3153%。货币增长率对我国商品房销售价格增长率没有影响，方差分解结果表明货币市场对商品房销售价格增长率序列的贡献率在第2期为0.0601%，然后逐渐下降。商品房销售价格增长率对汽车产量增长率的影响系数非常显著，而汽车产量增长率对我国商品房销售价格增长率几乎没有影响。
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.
本文运用了许多动态经济计量和时间序列分析方法研究房地产市场相关变量与宏观经济运行之间的关联性问题。我们首先通过对我国房地产价格在经济增长过程中存在的结构转变来了解我国房地产市场的走势及其与我国经济增长之间的对应关系。我们构建双变量结构转变模型，结果发现我国商品房销售价格指数与我国GDP增长率双变量结构转变模型是显著的，从模型的估计结果可以看出我国商品房销售价格指数与我国GDP增长率双变量结构转变模型存在显著的结构转变点。由结构转变点可以将总样本区间划分为4个子样本区间，我们采用子样本区间参数估计方法分别对4个子样本区间分别进行参数估计，发现截距估计值在第一个子样本区间内最大，在第四个子样本区间内最小，随时间发展呈现出逐级递减的趋势，斜率估计值在第一个子样本区间和第四个子样本区间为负数，而在第二个子样本区间和第三个子样本区间为正数，而可以看出解释能力在第三个子样本区间内解释能力最强，在第一个子样本区间内我国商品房销售价格指数对GDP增长率解释能力最弱。
接下来我们应用单位根检验、VAR模型、Granger因果关系检验、脉冲响应函数和方差分解等计量经济学方法，对我国房地产市场与经济增长之间的相互关系，从多方面、多视角进行进一步的综合实证检验与分析，从而试图探寻我国房地产市场与经济增长之间的关联性和影响程度。我们使用GDP增长率和工业增加值增长率两种变量来表征我国经济增长，以商品房销售额同比增长率数据来表征我国房地产市场的发展水平。通过检验我们发现，我国商品房销售额增长率对GDP增长率序列具有较为显著的影响作用，而GDP增长率对商品房销售额增长率的影响则并不十分显著。通过我国商品房销售额增长率与GDP增长率的趋势成分以及波动成分之间的相互影响关系检验我们都发现，我国商品房销售额增长率能够显著影响GDP增长率，而GDP增长率对我国商品房销售额增长率的影响程度依然相对较为微弱。而我们以工业增加值增速来表征经济增长时，得到了相同的结论。
其次，我们通过对我国房地产价格以及通货膨胀率的波动过程对比来了解我国房地产市场的走势及其与我国通货膨胀率之间的对应关系。我们通过利用马尔科夫区制转移模型对我国商品房价格同比增长率数据与我国通货膨胀率数据分别进行计算检验，以判别我国房地产市场和通货膨胀之间的关联与区别。估计结果表明我国商品房价格的月度同比增长率存在明显的两区制特征，即高增长区制和低增长区制，我国商品房价格月度同比增长率在低增长区制的平均持续期为5.1867个月，而在高增长区制的平均持续期为18.1159个月。我国通货膨胀率滞后1阶的马尔科夫区制转移模型的估计结果也表明我国通货膨胀率存在明显的两区制特征，我国通货膨胀率在低增长区制的平均持续期为14.8588个月，而在高增长区制的平均持续期为15.2672个月。可见我国通货膨胀率数据区制转移的次数较少，而在每个区制的持续期则较长。
然后，我们对我国房地产市场与主要宏观经济变量之间的相互关系分别进行实证检验与分析。我们通过检验发现通货膨胀率对我国商品房销售价格增长率的影响较弱，名义利率对我国商品房价格的影响具有一定程度的滞后性，贷款增长率对我国商品房销售价格增长率的影响非常显著，但商品房销售价格增长率对贷款增长率的影响系数较小。方差分解结果表明贷款增长率对商品房销售价格增长率序列的贡献率始终保持在28%左右。投资增长率对我国商品房销售价格增长率的影响较小，Granger因果关系检验表明投资增长率序列不能Granger影响我国商品房销售价格增长率序列，方差分解结果表明我国投资增长率序列对商品房销售价格增长率序列的贡献率很小。我国商品房销售价格增长率对消费增长率的影响系数较小，而消费增长率对我国商品房销售价格增长率的影响较显著。方差分解结果表明消费增长率序列对商品房销售价格增长率序列的贡献率在第10期为4.7799%。
最后，我们对我国房地产市场与我国其他主要市场之间的相互关系分别进行实证检验与分析，从而探寻我国房地产市场与我国其他主要市场之间的关联性和影响程度。我们首先检验我国商品房销售价格与股票市场之间的相关关系，发现滞后一阶和滞后二阶的股票市场对我国商品房销售价格增长率的影响都非常微弱，股票市场对商品房销售价格增长率序列的贡献率也较小。债券市场对我国商品房销售价格增长率的影响较小，债券市场序列能够略微Granger影响我国商品房销售价格增长率序列，方差分解结果表明债券市场对商品房销售价格增长率序列的贡献率在第10期才上升至1.9242%。汇率增长率对我国商品房销售价格增长率具有一定的影响，方差分解结果表明汇率增长率对商品房销售价格增长率序列的贡献率到第10期上升为3.3153%。货币增长率对我国商品房销售价格增长率没有影响，方差分解结果表明货币市场对商品房销售价格增长率序列的贡献率在第2期为0.0601%，然后逐渐下降。商品房销售价格增长率对汽车产量增长率的影响系数非常显著，而汽车产量增长率对我国商品房销售价格增长率几乎没有影响。
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.
引文
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