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1、房地产企业的利润总额与其收入构成的相关性研究以2007年房地产企业营业数据为例一、数据选择本文选择的数据为2007年全国31个省、自治区和直辖市的截面数据，研究各省区房地产企业2007年的利润总额与其收入构成（商品房销售收入、房屋出租收入和土地转让收入）之间的相关性。表1.2007年中国31省区房地产企业营业利润与收入构成表地区经营总收入土地转让收商品房屋房屋出租营业(2007)入销售收入收入利润北京228799731034695204644798101622241125天津5595697236431495684240158527771河北465258332753454924478632949
2、64山西175534713018158924023475-48201内蒙古376620379413740967572286935辽宁876174068877857502020793471886林2399140023617511669070445黑龙江3224432503130882064450259512上海287629169341642363122717869895511776江苏2170207016301920929900619541921361浙江186776045600418186819655051972895安徽530582246854510276317277272033福建8118
3、966112461740100744425946495江西34755461979433003383995316988山东121544411299701163161963053999041河南609031545874593902914200583482湖北510895232707481057117629544947湖南5044767203876457954734215-1631广东32702297752014293145796075745498023广西353156871191340331916504191231海南1184764213921104830129097482重庆7494978906
4、84691671156993420912四川9780001114589908055835132659611贵州16710338652157616414690-49016云南308889257408296859726305142347西藏1702020162858704132212陕西276280574422698491511353444甘肃91943174758643142212027719青海305942196301373175714362宁夏861927355283897915698-3520新疆20209301140197273124446109486单位：万元数据来源：中国统计年鉴二、
5、模型构造现在，我们来分析各省区房地产企业营业利润受其收入构成的影响。首先，我们构造模型的回归方程：Profiti=CCq+aiSalei+a2Renti+aiLandi+i接下来，把相关数据输入EVieWs,用普通最小二乘法求解，得道样本回归方程：PROFIT=-247265.451472+0.131961941353*SALE+2.05919332068*RENT-1.03829711341*LANDDependentVariable:PROFITMethod:LeastSquaresDate:12/08/09Time:19:01Sample:131Includedobservations:
6、31CoefficientStd.Errort-StatisticProb.SALE0.1319620.0149758.8123150.0000RENT2.0591930.4130794.9849900.0000LAND-1.0382970.662320-1.5676680.1286C-247265.596291.37-2.5678880.0161R-squared0.934357Meandependentvar786003.8AdjustedR-squared0.927064S.D.dependentvar1392977.S.E.ofregression376197.8Akaikeinfoc
7、riterion28.63353Sumsquaredresid3.82E+12Schwarzcriterion28,81856Loglikelihood-439.8197Hannan-Quinncriter.28.69385F-statistic128.1057Durbin-Watsonstat2.003651Prob(F-Statistic)0.000000通过上图可以发现，回归方程的拟合度较好，D-W检验也很好，不存在一阶序列相关。但是土地转让收入的参数估计量的P值与t检验值都不显著，于是我们再来检验解释变量间的多重共线性：CovarianceAnalysis:OrdinaryDate:1
8、2/08/09Time:20:23Sample:131Includedobservations:31CovarianceCorrelationPROFITLANDSALESRENTPROFIT1.88E+121.000000LAND2.99E+116.83E+100.8350521.000000SALES9.39E+121.55E+125.56E+130.9188910.7952921.000000RENT4.01E+118.03E+101.78E+121.21E+110.8402160.8824520.6843751.000000通过上图可以看出，土地转让收入与商品房销售收入和房屋出租收入之
9、间存在近似共线性，于是结合上面的P值和t值检验，可以去掉引起共线性的变量“土地转让收入”，从而得到新的回归方程：Profiti=a0+aiSalei+a2Renti+i同样，把相关的数据输入EVieWs,用普通最小乘法得到样本回归方程为：PROFIT=-237492.569606+0.118864183784*SALE+1.56375792454*RENTDependentvariable:PROFITMethod:LeastSquaresDate:12/08/09Time:20:35Sample:131Includedobservations:31CoefficientStd.Errort-
10、StatisticProb.SALE0.1188640.0127479.3251410.0000RENT1.5637580.2728375.7314660.0000C-237492.698558.67-2.4096570.0228R-squared0.928382Meandependentvar786003.8AdjustedR-squared0.923267S.D.dependentvar1392977.SE.ofregression385865.3Akaikeinfocriterion28,65613Sumsquaredresid4.17E+12Schwarzcriterion28.794
11、90Loglikelihood-441.1700Hannan-Quinncriter.28.70137F-Statistic181.4824Durbin-Watsonstat1.979213Prob(F-Statistic)0.000000从上图可以看出，各参数估计量的P值和t检验值都很好，D-W检验也很漂亮地接近2,方程总体的拟合度也较佳，接下来异方差检验，这里我们采用的是怀特检验：HeteroskedasticityTest:WhiteF-Statistic372.7994Prob.F(5,25)0.0000Obs*R-squared30.58973Prob.Chi-Square(三)00
12、000ScaledexplainedSS102.6637Prob.Chi-Square(三)0000TestEquation:DependentVariable:RESID2Method:LeastSquaresDate:12/08/09Time:21:04Sample:131Includedobservations:31CoefficientStd.Errort-StatisticProb.C1.85E+091.84E100.1004180.9208SALE-4551.0926628.202-0.6866260.4986SALE2-9.38E-050.000344-0.2730800.787
13、0SALE*RENT0.0868280.0216414.0122220.0005RENT1943309.410161.54.7379110.0001RENTa2-2.1778950.091214-23.876800.0000R-squared0.986765Meandependentvar1.34E+11AdjustedR-squared0.984119S.D.dependentvar3.92E+11S.E.ofregression4.94E+10Akaikeinfocriterion52.25696Sumsquaredresid6.10E+22Schwarzcriterion52.53451Loglikelihood-803.9829Hannan-Quinnenter.52.34744F-Statistic372.7994Durbin-Watsonstat2.658628Prob(F-Statistic
