房地产影响因素分析

房地产影响因素分析（背景）2002年以来，我国商品房销售额大幅攀升带动了房地产开发和城市基础设施投资的新一轮高速增长。通过产业链的传递，进而又拉动钢材、有色金属、建材、石化等生产资料价格的快速上涨，刺激这些生产资料部门产能投资的成倍扩张，最后导致全社会固定资产投资规模过大、增速过快情况的出现。房价过快上涨在推动投资增长过快的同时，已经成为抑制消费的重要因素。房地产价格本身呈自然上涨趋势，房价中长期趋势总是看涨。随着我国经济发展，居民可支配收入提高，民间资金雄厚，大量资金需要寻找投资渠道，而股票市场等投资渠道目前又处于低迷状态，这是房地产投资需求不断扩大的经济背景。强劲的CPI上涨说明当前的房价上涨并非孤立，是有其宏观经济背景的。宏观调控能否有效防止局部行业过热出现反弹，其中的关键就是要继续加强和完善对房地产业的调控。（引言）国际上关于房地产有一种普遍的观点:人均收入超过1000美元，房地产市场呈现高速发展阶段。欧美等发达国家基本都经历了这样一个阶段。我们这篇论文，主要探讨房地产影响因素分析，主要从人均收入对房地产长期发展的影响阐述。年份199019911992199319941995199619971998199920002001200220032004X1=建材成本（元/平方米）X2=居民人均收入（元） X3=物价指数 Y=房地产价格（元/平方米）初定模型：Y=c+a1*x1+a2*x2+a3*x3+etDependentVariable:YMethod:LeastSquaresDate:06/05/05Time:23:04Sample:19902004Includedobservations:15VariableCoefficientStd.Errort-StatisticProb.X32.5375780.5904224.2979080.0013X20.1464950.0209686.9865680.0000X1-0.018010.035019-0.5144470.6171C633.20929118.27470.2807810.7841R-squared0.983094Mean1753.31dependentvar7Adjusted0.978483S.D.dependent600.953R-squaredvar6S.E.of88.15143Akaikeinfo12.0191regressioncriterion7Sumsquared85477.42Schwarz12.2079residcriterion8Loglikelihood-86.1437F-statistic213.21866Durbin-Watson1.504263Prob（F-statistic）0.00000stat0

:多元线性回归DependentVariable:YMethod:LeastSquaresDate:06/05/05Time:23:05Sample:19902004Includedobservations:15VariableCoefficieStd.Errort-StatisticntProb.X1C0.336010792.01690.1510842.223999453.44601.7466620.04450.1043R-squared0.275612Mean1753.31dependentvar7Adjusted0.219889S.D.dependent600.953R-squaredvar6S.E.of530.7855Akaikeinfo15.5101regressioncriterion6Sumsquared3662533.Schwarz15.6045residcriterion7Loglikelihood-114.326F-statistic4.9461721Durbin-Watson0.275870Prob(F-statistic)0.04449stat0DependentVariable:YMethod:LeastSquaresDate:06/05/05Time:23:09Sample:19902004Includedobservations:15VariableCoefficieStd.Errort-StatisticProb.ntX35.5017790.52507510.478090.0000C-486.860220.1227-2.21176950.0455R-squared0.894128Mean1753.31dependentvar7Adjusted0.885984S.D.dependent600.953R-squaredvar6

S.E.ofregressionSumsquaredresidLoglikelihoodDurbin-WatsonstatS.E.ofregressionSumsquaredresidLoglikelihoodDurbin-WatsonstatTOC\o"1-5"\h\zcriterion 6535290.2 Schwarz 13.6814criterion 6-99.9029 F-statistic 109.7903 30.440527Prob(F-statistic)0.000000DependentVariable:YMethod:LeastSquaresDate:06/05/05Time:23:10Sample:19902004Includedobservations:15VariableCoefficieStd.Errort-StatisticProb.ntX20.2363470.01587914.884170.0000C561.997588.563336.3457130.0000R-squared0.944572Mean1753.31dependentvar7Adjusted0.940308S.D.dependent600.953R-squaredvar6S.E.of146.8243Akaikeinfo12.9399regressioncriterion2Sumsquared280245.9Schwarz13.0343residcriterion2Loglikelihood-95.0493F-statistic221.53874Durbin-Watson0.475648Prob(F-statistic)0.00000stat 0DependentVariable:YMethod:LeastSquaresDate:06/07/05Time:21:42Sample:19902004Includedobservations:15VariableCoefficieStd.Errort-StatisticProb.ntX3X2C2.3558330.15008637.567940.4583400.019157114.29915.1399237.8347140.3286810.00020.00000.7481R-squared0.982687Meandependentvar1753.317

AdjustedR-squaredS.E.ofregressionSumsquaredresidAdjustedR-squaredS.E.ofregressionSumsquaredresidLoglikelihoodDurbin-WatsonstatTOC\o"1-5"\h\zvar 685.40783 Akaikeinfo 11.9096criterion 187533.98 Schwarz 12.0512criterion 2-86.3220 F-statistic 340.5647 91.408298Prob(F-statistic)0.000000得到结果发现，xl的系数小，然后对y与xl回归可决系数小，相关性差，剔出这个因素。因为价格更多取决于供需关系。修正之后为：Y=c+a2*x2+a3*x3+et二：多重线性分析：三个表如上：X2与X3存在多重共线性，1.000000 0.8760730.8760731.000000DependentVariable:YMethod:LeastSquaresDate:06/05/05Time:23:09Sample:19902004Includedobservations:15VariableCoefficieStd.Errort-StatisticProb.ntX35.5017790.52507510.478090.0000C-486.8605220.1227-2.2117690.0455R-squared0.894128Mean1753.31dependentvar7Adjusted0.885984S.D.dependent600.953R-squaredvar6S.E.of202.9191Akaikeinfo13.5870regressioncriterion6

Sumsquared535290.2Schwarz13.6814residcriterion6Loglikelihood-99.9029F-statistic109.79033Durbin-Watson0.440527Prob(F-statistic)0.00000stat0Sample:19902004Includedobservations:15VariableCoefficieStd.Errort-StatisticProb.ntX20.2363470.01587914.884170.0000C561.997588.563336.3457130.0000R-squared0.944572Mean1753.31dependentvar7Adjusted0.940308S.D.dependent600.953R-squaredvar6S.E.of146.8243Akaikeinfo12.9399regressioncriterion2Sumsquared280245.9Schwarz13.0343residcriterion2Loglikelihood-95.0493F-statistic221.53874Durbin-Watson0.475648Prob(F-statistic)0.00000stat0由于引入物价指数改善小，所以模型仅一步改进为：Y=c+a2*x2+et三：异方差检验：ARCHTest:F-statistic1.315031Probability0.3351730.265462Obs*R-squared3.963227ProbabilityTestEquation:

DependentVariable:RESID八2Method:LeastSquaresDate:06/05/05Time:23:46Sample(adjusted):19932004Includedobservations:12afteradjustingendpointsVariableCoefficieStd.Errort-StatisticProb.ntC22737.9410296.612.2082950.0582RESID八2(-1)0.2419520.3831440.6314930.5453RESID八2(-2)-0.327760.404787-0.8097340.44159RESID八2(-3)-0.273720.378355-0.7234490.49000R-squared0.330269Mean16705.2dependentvar3Adjusted0.079120S.D.dependent18205.3R-squaredvar3S.E.of17470.29Akaikeinfo22.6355regressioncriterion9Sumsquared2.44E+0Schwarz22.7972resid9criterion3Loglikelihood-131.813F-statistic1.3150361Durbin-Watson1.842435Prob(F-statistic)0.33517stat3ARCH=3.963〈临界值7.81473所以无异方差WhiteHeteroskedasticityTest:F-statistic 0.159291 Probability 0.854522Obs*R-squared0.387928 Probability 0.823687TestEquation:

DependentVariable:RESID八2Method:LeastSquaresDate:06/05/05Time:23:46Sample:19902004Includedobservations:15VariableCoefficieStd.Errort-StatisticProb.ntC

X2X2C

X2X2八2-5.055759.640127-0.5244490.609540.0004210.0009070.4646050.6505R-squared0.025862Mean18683.0dependentvar6Adjusted-0.13649S.D.dependent18673.1R-squared4var3S.E.of19906.77Akaikeinfo22.8123regressioncriterion6Sumsquared4.76E+0Schwarz22.9539resid9criterion7Loglikelihood-168.092F-statistic0.1592971Durbin-Watson1.357657Prob(F-statistic)0.85452stat2WHITE=0.3879〈临界值7.81473无异方差。四：自相关分析：DW=0.4756查表的dl=1.077du=1.361存在自相关广义差分法修正：p=1-0.4756/2=0.7622DependentVariable:DYMethod:LeastSquaresDate:06/06/05Time:00:18Sample(adjusted):19912004Includedobservations:14afteradjustingendpointsVariableCoefficieStd.Errort-StatisticProb.ntDX20.1820860.0349185.2146550.0002C236.558963.273883.7386500.0028R-squared0.693820Mean544.162dependentvar0Adjusted0.668305S.D.dependent148.713R-squaredvar3S.E.of85.64840Akaikeinfo11.8699regressioncriterion4Sumsquared88027.77Schwarz11.9612residcriterion4Loglikelihood-81.0895F-statistic27.192693Durbin-Watson1.584278Prob(F-statistic)0.00021stat7得出：回归后可决系数降低，考虑其他方法。1.迭代法：表：发现可决系数提高，F统计量提高，DW=1.5547〉1.361已经无自相关。出人Y—bY(―1)=c*(1—b)+a2*结论：(x2—b*x2(—1))+et由下表的b=0.681C=561.9975 a2=0.236347 179.2772Y*=Y—0.681Y(—1) X*=x2—0.681*x2(-1)Y*=179.2272+0.2363X*+etMethod:LeastSquaresDate:06/07/05Time:20:57Sample(adjusted):19912004Includedobservations:14afteradjustingendpointsVariableCoefficieStd.Errort-StatisticProb.ntE20.6805090.1776963.8296240.0024C11.6877324.888250.4696080.6471R-squared0.549989Mean15.3276dependentvar4Adjusted0.512488S.D.dependent133.275R-squaredvar1S.E.of93.05539Akaikeinfo12.0358regressioncriterion3Sumsquared103911.7Schwarz12.1271residcriterion2Loglikelihood-82.2508F-statistic14.666012Durbin-Watson1.313042Prob(F-statistic)0.00239stat72.改进模型方程(对数法，然后用迭代法)：Ly-bLy(-1)=c*(1—b)+a2*(Lx2-b*Lx2(-1)可决系数很高，F统计量相对1中也有提高，DW=1.81>1.361无自相关。DependentVariable:LYMethod:LeastSquaresDate:06/06/05Time:10:24Sample(adjusted):19912004Includedobservations:14afteradjustingendpointsConvergenceachievedafter7iterationsVariableCoefficieStd.Errort-StatisticProb.nt

LX20.5862030.1002435.8477990.0001C2.5258100.8823502.8625940.0154AR⑴0.5671440.2204572.5725890.0259R-squared0.980054Mean7.46009dependentvar6Adjusted0.976428S.D.dependent0.35133R-squaredvar1S.E.of0.053941Akaikeinfo-2.8144regressioncriterion42Sumsquared0.0