房地产影响因素分析

1、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

2、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   

3、X3     Y 1990 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.

4、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 2674.264 627

5、9.98 476.6 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）   &

6、#160; 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+etDependent Variable: YMethod: Least SquaresDate: 06/05/05   Time: 23:04Sample: 1990 2004Included observations: 15Variable Coefficient Std. Error t-Statistic Prob.  X3 2.537578

7、60;0.590422 4.297908 0.0013X2 0.146495 0.020968 6.986568 0.0000X1 -0.018016 0.035019 -0.514447 0.6171C 33.20929 118.2747 0.280781 0.7841R-squared 0.983094     Mean dependent var 1753.317Adjusted R-squ

8、ared 0.978483     S.D. dependent var 600.9536S.E. of regression 88.15143     Akaike info criterion 12.01917Sum squared resid 85477.42     Schwarz criterion 12.20798Log likelihood -86.14376   

9、;  F-statistic 213.2186Durbin-Watson stat 1.504263     Prob(F-statistic) 0.000000一：多元线性回归               Dependent Variable: YMethod: Least SquaresDate: 06/05/05   Time: 23:05Sample: 199

10、0 2004Included observations: 15Variable Coefficient Std. Error t-Statistic Prob.  X1 0.336010 0.151084 2.223999 0.0445C 792.0169 453.4460 1.746662 0.1043R-squared 0.275612     Mean dependent var 1753.317Ad

11、justed R-squared 0.219889     S.D. dependent var 600.9536S.E. of regression 530.7855     Akaike info criterion 15.51016Sum squared resid 3662533.     Schwarz criterion 15.60457Log likelihood -114.3262 

12、;    F-statistic 4.946171Durbin-Watson stat 0.275870     Prob(F-statistic) 0.044490Dependent Variable: YMethod: Least SquaresDate: 06/05/05   Time: 23:09Sample: 1990 2004Included observations: 15Variable Coefficient Std. Error

13、0;t-Statistic Prob.  X3 5.501779 0.525075 10.47809 0.0000C -486.8605 220.1227 -2.211769 0.0455R-squared 0.894128     Mean dependent var 1753.317Adjusted R-squared 0.885984     S.D. dependent var&

14、#160;600.9536S.E. of regression 202.9191     Akaike info criterion 13.58706Sum squared resid 535290.2     Schwarz criterion 13.68146Log likelihood -99.90293     F-statistic 109.7903Durbin-Watson stat 0.440

15、527     Prob(F-statistic) 0.000000Dependent Variable: YMethod: Least SquaresDate: 06/05/05   Time: 23:10Sample: 1990 2004Included observations: 15Variable Coefficient Std. Error t-Statistic Prob.  X2 0.236347 0.015879 14.8841

16、7 0.0000C 561.9975 88.56333 6.345713 0.0000R-squared 0.944572     Mean dependent var 1753.317Adjusted R-squared 0.940308     S.D. dependent var 600.9536S.E. of regression 146.8243     Akaike

17、 info criterion 12.93992Sum squared resid 280245.9     Schwarz criterion 13.03432Log likelihood -95.04937     F-statistic 221.5384Durbin-Watson stat 0.475648     Prob(F-statistic) 0.000000Dependent Variabl

18、e: YMethod: Least SquaresDate: 06/07/05   Time: 21:42Sample: 1990 2004Included observations: 15Variable Coefficient Std. Error t-Statistic Prob.  X3 2.355833 0.458340 5.139923 0.0002X2 0.150086 0.019157 7.834714 0.0000C 

19、37.56794 114.2991 0.328681 0.7481R-squared 0.982687     Mean dependent var 1753.317Adjusted R-squared 0.979802     S.D. dependent var 600.9536S.E. of regression 85.40783     Akaike info criterion 

20、;11.90961Sum squared resid 87533.98     Schwarz criterion 12.05122Log likelihood -86.32207     F-statistic 340.5649Durbin-Watson stat 1.408298     Prob(F-statistic) 0.000000 得到结果发现，的系数小，然后对与回归可决系数小，相关性差，剔出

21、这个因素。因为价格更多取决于供需关系。修正之后为：Yc+a2*x2+a3*x3et二：多重线性分析：三个表如上：    X2 与X3 存在多重共线性，1.000000  0.876073 0.876073  1.000000Dependent Variable: YMethod: Least SquaresDate: 06/05/05   Time: 23:09Sample: 1990 2004Included observations: 15Variable Coefficient Std.

22、Error t-Statistic Prob.  X3 5.501779 0.525075 10.47809 0.0000C -486.8605 220.1227 -2.211769 0.0455R-squared 0.894128     Mean dependent var 1753.317Adjusted R-squared 0.885984     S.D. depen

23、dent var 600.9536S.E. of regression 202.9191     Akaike info criterion 13.58706Sum squared resid 535290.2     Schwarz criterion 13.68146Log likelihood -99.90293     F-statistic 109.7903Durbin-Watson stat&#

24、160;0.440527     Prob(F-statistic) 0.000000Sample: 1990 2004Included observations: 15Variable Coefficient Std. Error t-Statistic Prob.  X2 0.236347 0.015879 14.88417 0.0000C 561.9975 88.56333 6.345713 0.0000R-s

25、quared 0.944572     Mean dependent var 1753.317Adjusted R-squared 0.940308     S.D. dependent var 600.9536S.E. of regression 146.8243     Akaike info criterion 12.93992Sum squared resid 280245.9  

26、;   Schwarz criterion 13.03432Log likelihood -95.04937     F-statistic 221.5384Durbin-Watson stat 0.475648     Prob(F-statistic) 0.000000由于引入物价指数改善小，所以模型仅一步改进为：Yc+a2*x2et三：异方差检验：   ARCH Test:F-statistic 1.31503

27、1     Probability 0.335173Obs*R-squared 3.963227     Probability 0.265462    Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 06/05/05   Time: 23:46Sample(adjusted): 1993 2004Included observations:

28、 12 after adjusting endpointsVariable Coefficient Std. Error t-Statistic Prob.  C 22737.94 10296.61 2.208295 0.0582RESID2(-1) 0.241952 0.383144 0.631493 0.5453RESID2(-2) -0.327769 0.404787 -0.809734 0.4415RESID2(-3)

29、 -0.273720 0.378355 -0.723449 0.4900R-squared 0.330269     Mean dependent var 16705.23Adjusted R-squared 0.079120     S.D. dependent var 18205.33S.E. of regression 17470.29     Akaike info criter

30、ion 22.63559Sum squared resid 2.44E+09     Schwarz criterion 22.79723Log likelihood -131.8136     F-statistic 1.315031Durbin-Watson stat 1.842435     Prob(F-statistic) 0.335173  临界值 所以无异方差&#

31、160; White Heteroskedasticity Test:F-statistic 0.159291     Probability 0.854522Obs*R-squared 0.387928     Probability 0.823687    Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 06/05/05 

32、60; Time: 23:46Sample: 1990 2004Included observations: 15Variable Coefficient Std. Error t-Statistic Prob.  C 31063.28 22612.20 1.373740 0.1946X2 -5.055754 9.640127 -0.524449 0.6095X22 0.000421 0.000907 0.464605 0.6

33、505R-squared 0.025862     Mean dependent var 18683.06Adjusted R-squared -0.136494     S.D. dependent var 18673.13S.E. of regression 19906.77     Akaike info criterion 22.81236Sum squared resid 4.76E+09

34、0;    Schwarz criterion 22.95397Log likelihood -168.0927     F-statistic 0.159291Durbin-Watson stat 1.357657     Prob(F-statistic) 0.854522  临界值 无异方差。四：自相关分析： 查表的 存在自相关 广义差分法修正：  D

35、ependent Variable: DYMethod: Least SquaresDate: 06/06/05   Time: 00:18Sample(adjusted): 1991 2004Included observations: 14 after adjusting endpointsVariable Coefficient Std. Error t-Statistic Prob.  DX2 0.182086 0.034918 5.214655 0.0002C 23

36、6.5589 63.27388 3.738650 0.0028R-squared 0.693820     Mean dependent var 544.1620Adjusted R-squared 0.668305     S.D. dependent var 148.7133S.E. of regression 85.64840     Akaike info criterion 1

37、1.86994Sum squared resid 88027.77     Schwarz criterion 11.96124Log likelihood -81.08959     F-statistic 27.19263Durbin-Watson stat 1.584278     Prob(F-statistic) 0.000217 得出：回归后可决系数降低，考虑其他方法。 1迭代法：表：

38、 发现可决系数提高，统计量提高， 已经无自相关。结论：YbY（）*（）a2*（x2b*x2（）+et由下表的b=0.681 C=561.9975    a2=0.236347    179.2772 Y*= Y0.681Y（）      X*= x20.681*x2（） Y*=179.2272 +0.2363X*+et  Method: Least SquaresDate: 06/07/05  

39、Time: 20:57Sample(adjusted): 1991 2004Included observations: 14 after adjusting endpointsVariable Coefficient Std. Error t-Statistic Prob.  E2 0.680509 0.177696 3.829624 0.0024C 11.68773 24.88825 0.469608 0.6471R-squared 0.549989&

40、#160;    Mean dependent var 15.32764Adjusted R-squared 0.512488     S.D. dependent var 133.2751S.E. of regression 93.05539     Akaike info criterion 12.03583Sum squared resid 103911.7     Schwarz

41、 criterion 12.12712Log likelihood -82.25081     F-statistic 14.66602Durbin-Watson stat 1.313042     Prob(F-statistic) 0.002397 2改进模型方程（对数法，然后用迭代法）：LL（）（）（LL（） 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。 Dependent Var

42、iable: LYMethod: Least SquaresDate: 06/06/05   Time: 10:24Sample(adjusted): 1991 2004Included observations: 14 after adjusting endpointsConvergence achieved after 7 iterationsVariable Coefficient Std. Error t-Statistic Prob.  LX2 0.586203 0.100243 5.

43、847799 0.0001C 2.525810 0.882350 2.862594 0.0154AR(1) 0.567144 0.220457 2.572589 0.0259R-squared 0.980054     Mean dependent var 7.460096Adjusted R-squared 0.976428     S.D. dependent var 0.351331S.E. of regression 0.053941     Akaike info criterion -2.814442Sum squared resid 0.032006