经济毕业论文房地产影响因素分析.docx
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经济毕业论文房地产影响因素分析
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 X1 X2 X3 Y
1990 2551.736 1510.16 222 704.3319
1991 1111.236 1700.6 233.3 786.1935
1992 590.5998 2026.6 253.4 994.6555
1993 2897.019 2577.4 294.2 1291.456
1994 3532.471 3496.2 367.8 1408.639
1995 3983.081 4282.95 429.6 1590.863
1996 4071.181 4838.9 467.4 1806.399
1997 3527.536 5160.3 481.9 1997.161
1998 2966.057 5425.1 479 2062.569
1999 2818.805 5854 472.8 2052.6
2000 2674.264 6279.98 476.6 2111.617
2001 2830.688 6859.6 479.9 2169.719
2002 2906.16 7702.8 475.1 2250.177
2003 3011.424 8472.2 479.4 2359.499
2004 3441.62 9421.6 495.2 2713.878
X1=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
DependentVariable:
Y
Method:
LeastSquares
Date:
06/05/05 Time:
23:
04
Sample:
19902004
Includedobservations:
15
Variable Coefficient Std.Error t-Statistic Prob.
X3 2.537578 0.590422 4.297908 0.0013
X2 0.146495 0.020968 6.986568 0.0000
X1 -0.018016 0.035019 -0.514447 0.6171
C 33.20929 118.2747 0.280781 0.7841
R-squared 0.983094 Meandependentvar 1753.317
AdjustedR-squared 0.978483 S.D.dependentvar 600.9536
S.E.ofregression 88.15143 Akaikeinfocriterion 12.01917
Sumsquaredresid 85477.42 Schwarzcriterion 12.20798
Loglikelihood -86.14376 F-statistic 213.2186
Durbin-Watsonstat 1.504263 Prob(F-statistic) 0.000000
一:
多元线性回归
DependentVariable:
Y
Method:
LeastSquares
Date:
06/05/05 Time:
23:
05
Sample:
19902004
Includedobservations:
15
Variable Coefficient Std.Error t-Statistic Prob.
X1 0.336010 0.151084 2.223999 0.0445
C 792.0169 453.4460 1.746662 0.1043
R-squared 0.275612 Meandependentvar 1753.317
AdjustedR-squared 0.219889 S.D.dependentvar 600.9536
S.E.ofregression 530.7855 Akaikeinfocriterion 15.51016
Sumsquaredresid 3662533. Schwarzcriterion 15.60457
Loglikelihood -114.3262 F-statistic 4.946171
Durbin-Watsonstat 0.275870 Prob(F-statistic) 0.044490
DependentVariable:
Y
Method:
LeastSquares
Date:
06/05/05 Time:
23:
09
Sample:
19902004
Includedobservations:
15
Variable Coefficient Std.Error t-Statistic Prob.
X3 5.501779 0.525075 10.47809 0.0000
C -486.8605 220.1227 -2.211769 0.0455
R-squared 0.894128 Meandependentvar 1753.317
AdjustedR-squared 0.885984 S.D.dependentvar 600.9536
S.E.ofregression 202.9191 Akaikeinfocriterion 13.58706
Sumsquaredresid 535290.2 Schwarzcriterion 13.68146
Loglikelihood -99.90293 F-statistic 109.7903
Durbin-Watsonstat 0.440527 Prob(F-statistic) 0.000000
DependentVariable:
Y
Method:
LeastSquares
Date:
06/05/05 Time:
23:
10
Sample:
19902004
Includedobservations:
15
Variable Coefficient Std.Error t-Statistic Prob.
X2 0.236347 0.015879 14.88417 0.0000
C 561.9975 88.56333 6.345713 0.0000
R-squared 0.944572 Meandependentvar 1753.317
AdjustedR-squared 0.940308 S.D.dependentvar 600.9536
S.E.ofregression 146.8243 Akaikeinfocriterion 12.93992
Sumsquaredresid 280245.9 Schwarzcriterion 13.03432
Loglikelihood -95.04937 F-statistic 221.5384
Durbin-Watsonstat 0.475648 Prob(F-statistic) 0.000000
DependentVariable:
Y
Method:
LeastSquares
Date:
06/07/05 Time:
21:
42
Sample:
19902004
Includedobservations:
15
Variable Coefficient Std.Error t-Statistic Prob.
X3 2.355833 0.458340 5.139923 0.0002
X2 0.150086 0.019157 7.834714 0.0000
C 37.56794 114.2991 0.328681 0.7481
R-squared 0.982687 Meandependentvar 1753.317
AdjustedR-squared 0.979802 S.D.dependentvar 600.9536
S.E.ofregression 85.40783 Akaikeinfocriterion 11.90961
Sumsquaredresid 87533.98 Schwarzcriterion 12.05122
Loglikelihood -86.32207 F-statistic 340.5649
Durbin-Watsonstat 1.408298 Prob(F-statistic) 0.000000
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 0.876073
0.876073 1.000000
DependentVariable:
Y
Method:
LeastSquares
Date:
06/05/05 Time:
23:
09
Sample:
19902004
Includedobservations:
15
Variable Coefficient Std.Error t-Statistic Prob.
X3 5.501779 0.525075 10.47809 0.0000
C -486.8605 220.1227 -2.211769 0.0455
R-squared 0.894128 Meandependentvar 1753.317
AdjustedR-squared 0.885984 S.D.dependentvar 600.9536
S.E.ofregression 202.9191 Akaikeinfocriterion 13.58706
Sumsquaredresid 535290.2 Schwarzcriterion 13.68146
Loglikelihood -99.90293 F-statistic 109.7903
Durbin-Watsonstat 0.440527 Prob(F-statistic) 0.000000
Sample:
19902004
Includedobservations:
15
Variable Coefficient Std.Error t-Statistic Prob.
X2 0.236347 0.015879 14.88417 0.0000
C 561.9975 88.56333 6.345713 0.0000
R-squared 0.944572 Meandependentvar 1753.317
AdjustedR-squared 0.940308 S.D.dependentvar 600.9536
S.E.ofregression 146.8243 Akaikeinfocriterion 12.93992
Sumsquaredresid 280245.9 Schwarzcriterion 13.03432
Loglikelihood -95.04937 F-statistic 221.5384
Durbin-Watsonstat 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:
Y=c+a2*x2+et
三:
异方差检验:
ARCHTest:
F-statistic 1.315031 Probability 0.335173
Obs*R-squared 3.963227 Probability 0.265462
TestEquation:
DependentVariable:
RESID^2
Method:
LeastSquares
Date:
06/05/05 Time:
23:
46
Sample(adjusted):
19932004
Includedobservations:
12afteradjustingendpoints
Variable Coefficient Std.Error t-Statistic Prob.
C 22737.94 10296.61 2.208295 0.0582
RESID^2(-1) 0.241952 0.383144 0.631493 0.5453
RESID^2(-2) -0.327769 0.404787 -0.809734 0.4415
RESID^2(-3) -0.273720 0.378355 -0.723449 0.4900
R-squared 0.330269 Meandependentvar 16705.23
AdjustedR-squared 0.079120 S.D.dependentvar 18205.33
S.E.ofregression 17470.29 Akaikeinfocriterion 22.63559
Sumsquaredresid 2.44E+09 Schwarzcriterion 22.79723
Loglikelihood -131.8136 F-statistic 1.315031
Durbin-Watsonstat 1.842435 Prob(F-statistic) 0.335173
ARCH=3.963<临界值7.81473
所以无异方差
WhiteHeteroskedasticityTest:
F-statistic 0.159291 Probability 0.854522
Obs*R-squared 0.387928 Probability 0.823687
TestEquation:
DependentVariable:
RESID^2
Method:
LeastSquares
Date:
06/05/05 Time:
23:
46
Sample:
19902004
Includedobservations:
15
Variable Coefficient Std.Error t-Statistic Prob.
C 31063.28 22612.20 1.373740 0.1946
X2 -5.055754 9.640127 -0.524449 0.6095
X2^2 0.000421 0.000907 0.464605 0.6505
R-squared 0.025862 Meandependentvar 18683.06
AdjustedR-squared -0.136494 S.D.dependentvar 18673.13
S.E.ofregression 19906.77 Akaikeinfocriterion 22.81236
Sumsquaredresid 4.76E+09 Schwarzcriterion 22.95397
Loglikelihood -168.0927 F-statistic 0.159291
Durbin-Watsonstat 1.357657 Prob(F-statistic) 0.854522
WHITE=0.3879<临界值7.81473
无异方差。
四:
自相关分析:
DW=0.4756
查表的dl=1.077 du=1.361
存在自相关
广义差分法修正:
ρ=1-0.4756/2=0.7622
DependentVariable:
DY
Method:
LeastSquares
Date:
06/06/05 Time:
00:
18
Sample(adjusted):
19912004
Includedobservations:
14afteradjustingendpoints
Variable Coefficient Std.Error t-Statistic Prob.
DX2 0.182086 0.034918 5.214655 0.0002
C 236.5589 63.27388 3.738650 0.0028
R-squared 0.693820 Meandependentvar 544.1620
AdjustedR-squared 0.668305 S.D.dependentvar 148.7133
S.E.ofregression 85.64840 Akaikeinfocriterion 11.86994
Sumsquaredresid 88027.77 Schwarzcriterion 11.96124
Loglikelihood -81.08959 F-statistic 27.19263
Durbin-Watsonstat 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:
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。
结论:
Y-bY(-1)=c*(1-b)+a2*(x2-b*x2(-1))+et
由下表的b=0.681
C=561.9975 a2=0.236347 179.2772
Y*=Y-0.681Y(-1) X*=x2-0.681*x2(-1)
Y*=179.2272+0.2363X*+et
Method:
LeastSquares
Date:
06/07/05 Time:
20:
57
Sample(adjusted):
19912004
Includedobservations:
14afteradjustingendpoints
Variable Coefficient Std.Error t-Statistic Prob.
E2 0.680509 0.177696 3.829624 0.0024
C 11.68773 24.88825 0.469608 0.6471
R-squared 0.549989 Meandependentvar 15.32764
AdjustedR-squared 0.512488 S.D.dependentvar 133.2751
S.E.ofregression 93.05539 Akaikeinfocriterion 12.03583
Sumsquaredresid 103911.7 Schwarzcriterion 12.12712
Loglikelihood -82.25081 F-statistic 14.66602
Durbin-Watsonstat 1.313042 Prob(F-statistic) 0.002397
2.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:
LY
Method:
LeastSquares
Date:
06/06/05 Time:
10:
24
Sample(adjusted):
19912004
Includedobservations:
14afteradjustingendpoints
Convergenceachievedafter7iterations
Variable Coefficient Std.Error t-Statistic Prob.
LX2 0.586203 0.100243 5.847799 0.0001
C 2.525810 0.882350 2.862594 0.0154
AR
(1) 0.567144 0.220457 2.572589 0.0259
R-squared 0.980054 Meandependentvar 7.460096
AdjustedR-squared 0.976428 S.D.dependentvar 0.351331
S.E.ofregression 0.053941 Akaikeinfocriterion -2.814442
Sumsquaredresid 0.032006 Schwarzcriterion -2.677501
Loglikelihood 22.70109 F-statistic 270.2458
Durbin-Watsonstat 1.810100 Prob(F-statistic) 0.000000
InvertedARRoots .57
DependentVariable:
E1
Method:
LeastSquares
Date:
06/07/05 Time:
21:
00
Sample(adjusted):
19912004
Includedobserv