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 X1 X2 X3 Y1990 2551.736 1510.16 222 704.33191991 1111.236 1700.6 233.3 786.19351992 590.5998 2026.6 253.4 994.65551993 2897.019 2577.4 294.2 1291.4561994 3532.471 3496.2 367.8 1408.6391995 3983.081 4282.95 429.6 1590.8631996 4071.181 4838.9 467.4 1806.3991997 3527.536 5160.3 481.9 1997.1611998 2966.057 5425.1 479 2062.5691999 2818.805 5854 472.8 2052.62000 2674.264 6279.98 476.6 2111.6172001 2830.688 6859.6 479.9 2169.7192002 2906.16 7702.8 475.1 2250.1772003 3011.424 8472.2 479.4 2359.4992004 3441.62 9421.6 495.2 2713.878X1=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+et Dependent Variable: YMethod: Least SquaresDate: 06/05/05 Time: 23:04Sample: 1990 2004Included observations: 15Variable CoefficientStd. Error t-Statistic Prob.X3 2.537578 0.590422 4.297908 0.0013X2 0.146495 0.020968 6.986568 0.0000X1 -0.0180160.035019 -0.514447 0.6171C 33.20929 118.2747 0.280781 0.7841R-squared 0.983094 Meandependent var 1753.317Adjusted R-squared 0.978483 S.D. dependentvar600.9536S.E. of regression 88.15143 Akaike infocriterion12.01917Sum squared resid 85477.42 Schwarzcriterion12.20798Log likelihood -86.14376 F-statistic 213.2186Durbin-Watson stat 1.504263 Prob(F-statistic) 0.00000一：多元线性回归Dependent Variable: YMethod: Least SquaresDate: 06/05/05 Time: 23:05Sample: 1990 2004Included observations: 15Variable CoefficientStd. Error t-Statistic Prob.X1 0.336010 0.151084 2.223999 0.0445C 792.0169 453.4460 1.746662 0.1043R-squared 0.275612 Meandependent var 1753.317Adjusted R-squared 0.219889 S.D. dependentvar600.9536S.E. of regression 530.7855 Akaike infocriterion15.51016Sum squared resid 3662533. Schwarzcriterion15.60457Log likelihood -114.3262 F-statistic 4.946171Durbin-Watson stat 0.275870 Prob(F-statistic) 0.04449Dependent Variable: YMethod: Least SquaresDate: 06/05/05 Time: 23:09Sample: 1990 2004Included observations: 15Variable CoefficientStd. Error t-Statistic Prob.X3 5.501779 0.525075 10.47809 0.0000C -486.8605220.1227 -2.211769 0.0455R-squared 0.894128 Meandependent var 1753.317Adjusted R-squared 0.885984 S.D. dependentvar600.9536S.E. of regression 202.9191 Akaike infocriterion13.58706Sum squared resid 535290.2 Schwarzcriterion13.68146Log likelihood -99.90293 F-statistic 109.7903Durbin-Watson stat 0.440527 Prob(F-statistic) 0.00000Dependent Variable: YMethod: Least SquaresDate: 06/05/05 Time: 23:10Sample: 1990 2004Included observations: 15Variable CoefficientStd. Error t-Statistic Prob.X2 0.236347 0.015879 14.88417 0.0000C 561.9975 88.56333 6.345713 0.0000R-squared 0.944572 Meandependent var 1753.317Adjusted R-squared 0.940308 S.D. dependentvar600.9536S.E. of regression 146.8243 Akaike infocriterion12.93992Sum squared resid 280245.9 Schwarzcriterion13.03432Log likelihood -95.04937 F-statistic 221.5384Durbin-Watson stat 0.475648 Prob(F-statistic) 0.00000Dependent Variable: YMethod: Least SquaresDate: 06/07/05 Time: 21:42Sample: 1990 2004Included observations: 15Variable CoefficientStd. Error t-Statistic Prob.X3 2.355833 0.458340 5.139923 0.0002X2 0.150086 0.019157 7.834714 0.0000C 37.56794 114.2991 0.328681 0.7481R-squared 0.982687 Meandependent var 1753.317Adjusted R-squared 0.979802 S.D. dependentvar600.9536S.E. of regression 85.40783 Akaike infocriterion11.90961Sum squared resid 87533.98 Schwarzcriterion12.05122Log likelihood -86.32207 F-statistic 340.5649Durbin-Watson stat 1.408298 Prob(F-statistic) 0.00000得到结果发现，ｘ１的系数小，然后对ｙ与ｘ１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 0.8760730.876073 1.000000Dependent Variable: YMethod: Least SquaresDate: 06/05/05 Time: 23:09Sample: 1990 2004Included observations: 15Variable CoefficientStd. Error t-Statistic Prob.X3 5.501779 0.525075 10.47809 0.0000C -486.8605220.1227 -2.211769 0.0455R-squared 0.894128 Meandependent var 1753.317Adjusted R-squared 0.885984 S.D. dependentvar600.9536S.E. of regression 202.9191 Akaike infocriterion13.58706Sum squared resid 535290.2 Schwarzcriterion13.68146Log likelihood -99.90293 F-statistic 109.7903Durbin-Watson stat 0.440527 Prob(F-statistic) 0.00000Sample: 1990 2004Included observations: 15Variable CoefficientStd. Error t-Statistic Prob.X2 0.236347 0.015879 14.88417 0.0000C 561.9975 88.56333 6.345713 0.0000R-squared 0.944572 Meandependent var 1753.317Adjusted R-squared 0.940308 S.D. dependentvar600.9536S.E. of regression 146.8243 Akaike infocriterion12.93992Sum squared resid 280245.9 Schwarzcriterion13.03432Log likelihood -95.04937 F-statistic 221.5384Durbin-Watson stat 0.475648 Prob(F-statistic) 0.00000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：ARCH Test:F-statistic 1.315031 Probability 0.335173Obs*R-squared 3.963227 Probability 0.265462Test Equation:Method: Least SquaresDate: 06/05/05 Time: 23:46Sample(adjusted): 1993 2004Included observations: 12 after adjusting endpoints Variable CoefficientStd. Error t-Statistic Prob.C 22737.94 10296.61 2.208295 0.0582RESID^2(-1) 0.241952 0.383144 0.631493 0.5453 RESID^2(-2) -0.3277690.404787 -0.809734 0.4415RESID^2(-3) -0.273720.378355 -0.723449 0.4900R-squared 0.330269 Meandependent var 16705.23Adjusted R-squared 0.079120 S.D. dependentvar18205.33S.E. of regression 17470.29 Akaike infocriterion22.63559Sum squared resid 2.44E+09Schwarzcriterion22.79723Log likelihood -131.8136 F-statistic 1.315031Durbin-Watson stat 1.842435 Prob(F-statistic) 0.335173ＡＲＣＨ＝３．９６３＜临界值７．８１４７３所以无异方差White Heteroskedasticity Test:F-statistic 0.159291 Probability 0.854522 Obs*R-squared 0.387928 Probability 0.823687 Test Equation:Method: Least SquaresDate: 06/05/05 Time: 23:46Sample: 1990 2004Included observations: 15Variable CoefficientStd. Error t-Statistic Prob.C 31063.28 22612.20 1.373740 0.1946X2 -5.0557549.640127 -0.524449 0.6095X2^2 0.000421 0.000907 0.464605 0.6505R-squared 0.025862 Meandependent var 18683.06Adjusted R-squared -0.136494S.D. dependentvar18673.13S.E. of regression 19906.77 Akaike infocriterion22.81236Sum squared resid 4.76E+09Schwarzcriterion22.95397Log likelihood -168.0927 F-statistic 0.159291Durbin-Watson stat 1.357657 Prob(F-statistic) 0.854522ＷＨＩＴＥ＝０．３８７９＜临界值７．８１４７３无异方差。