房地产影响因素分析.docx
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房地产影响因素分析.docx
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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:
06/05/05Time:
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:
06/05/05Time:
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:
06/05/05Time:
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:
06/05/05Time:
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:
06/07/05Time:
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:
06/05/05Time:
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:
06/05/05Time:
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:
06/05/05Time:
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:
06/06/05Time:
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=561.9975a2=0.236347179.2772
Y*=Y-0.681Y(-1)X*=x2-0.681*x2(-1)
Y*=179.2272+0.2363X*+et
Method:
LeastSquares
Date:
06/07/05Time:
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:
06/06/05Time:
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
270.24
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