影响四川省房地产业发展的因素分析
“民以食为天，以住为地”，房地产在国民经济生活中起着重要的作用.房地产业的发展对国民经济发展有着巨大而深刻的影响.因此，审慎分析房地产市场的现状及其基本走势,深入探讨影响房地产业发展的主要因素，对促进房地产业事业持续重要.
一、对房地产市场的基本判断
２０００～２００３年间，我国房地产投资连续四年增幅在２０％以上，２００３年房地产投资增长率高达２９．７％，投资规模首次突破１万亿元，占全社会固定资产投资的１８．３％占ＧＤＰ的８．７％。 在投资规模不断增长的同时，商品房的销售率也在稳步上涨。然而，在总体看好的情况下，局部地区投资增长过快、供求结构失衡的问题仍十分突出。除区域性问题外，投资结构也不容忽视。近几年，全国普通住宅销售率连续四年在８０％以上，且不断走高，但高档住宅、经济适用房、办公楼宇和商厦的销售率却都比较低，导致办公楼和商厦的大量闲置。简言之，此轮房地产投资热，有合理的因素，如中国经济的高速增长、城市化进程的加快、居民收入水平的提高、消费的升级换代、大量外资的进入和劳动力的流动形成的市场有效需求等，但是，政府人为造市、无视地区市场环境的盲目大干快上，导致奢靡之风、非理性投资泛滥，由此引发的投资热和结构失衡，对房地产业来说则是贻害长远。
二、选定变量进行计量经济学的分析
我们选定四川省房地产开发总值(单位:亿元)作为被解释变量Ｙ,同时一共选定了八个解释变量:
Ｘ1—四川省每年税收总额(单位:万元)
Ｘ2—四川省每年储蓄存款总额(单位:亿元)
Ｘ3—四川省建筑材料工业品出厂价格指数(单位:％)
Ｘ4—四川省原材料燃料和动力购进价格指数(建筑材料类)(单位:％)
Ｘ5—四川省每人每年可支配收入(单位:元)
Ｘ6—四川省ＧＤＰ(单位:亿元)
Ｘ7—货币供应量(单位:亿元)
Ｘ8—贷款利率(3年-5年期)(单位:％)
年份 Y X1 X2 X3 X4 X5 X6 X7 X8
1900 5.18 256513 698.15 101.2 100 1862.36 890.95 15293.4 11.7%
1991 6.09 323350 813.47 96.4 118.2 2132.56 1016.31 19349.9 11.7%
1992 14.19 486213 904.68 92.4 139.3 2988.21 1177.27 25402.2 11.7%
1993 34.87 658821 988.17 87.2 168.5 3422.17 1486.08 34879.8 11.7%
1994 50.57 869451 1024.21 98.5 215.1 3846.22 2001.41 46923.5 11.7%
1995 79.96 1052013 1148.59 103.7 273.3 4002.91 2504.95 60750.5 11.7%
1996 90.62 1298407 1521.97 140.5 294.4 4406.09 2985.15 76094.9 11.7%
1997 100.26 1525792 1841.22 97.0 354.6 4763.26 3320.11 90995.3 9.9%
1998 120.60 1660286 1984.54 95.3 417.2 5127.08 3580.26 104498.5 7.65%
1999 142.50 1778979 2351.21 94.0 417.2 5477.89 3711.61 119897.9 6.03%
2000 195.97 1946319 2693.17 94.7 444.7 5894.27 4010.25 134610.3 6.03%
2001 268.15 2181466 3123.39 97.3 470.7 6360.47 4421.76 158301.9 6.03%
构造模型: （数据见下页）
Ｙ＝0+1Ｘ1+2Ｘ2+3Ｘ3+4Ｘ4+5Ｘ5+6Ｘ6+7Ｘ7+8Ｘ8
对数据进行分析:
Dependent Variable: Y
Method: Least Squares
Date: 12/22/04 Time: 16:40
Sample: 1990 2002
Included observations: 13
Variable Coefficient Std. Error t-Statistic Prob.
C 9.506190 61.47340 0.154639 0.8846
X1 -0.000108 0.000146 -0.737120 0.5019
X2 -1.32E-06 1.34E-06 -0.981761 0.3818
X3 0.515279 0.894020 0.576362 0.5952
X4 -1.019800 0.958646 -1.063793 0.3474
X5 -0.019462 0.037130 -0.524162 0.6279
X6 -0.154027 0.062463 -2.465914 0.0692
X7 0.007994 0.001154 6.925113 0.0023
X8 9.830840 3.240301 3.033929 0.0386
R-squared 0.995226 Mean dependent var 115.8538
Adjusted R-squared 0.985677 S.D. dependent var 114.4145
S.E. of regression 13.69309 Akaike info criterion 8.277619
Sum squared resid 750.0024 Schwarz criterion 8.668738
Log likelihood -44.80453 F-statistic 104.2251
Durbin-Watson stat 3.388729 Prob(F-statistic) 0.000226
对数据的多重共线性进行分析:
X1 X2 X3 X4 X5 X6 X7 X8
X1 1.000000 0.992449 -0.490698 -0.579667 0.926422 0.942814 0.985242 -0.808622
X2 0.992449 1.000000 -0.482882 -0.568568 0.920679 0.932850 0.975851 -0.819597
X3 -0.490698 -0.482882 1.000000 0.900281 -0.486587 -0.511065 -0.489324 0.424923
X4 -0.579667 -0.568568 0.900281 1.000000 -0.638015 -0.648189 -0.604672 0.455466
X5 0.926422 0.920679 -0.486587 -0.638015 1.000000 0.995972 0.969636 -0.620918
X6 0.942814 0.932850 -0.511065 -0.648189 0.995972 1.000000 0.981303 -0.655603
X7 0.985242 0.975851 -0.489324 -0.604672 0.969636 0.981303 1.000000 -0.749205
X8 -0.808622 -0.819597 0.424923 0.455466 -0.620918 -0.655603 -0.749205 1.000000
可见解释变量间存在多重共线性,对此我们进行修正,采用逐步回归：
Y与X7的拟合效果最好
Dependent Variable: Y
Method: Least Squares
Date: 12/22/04 Time: 16:54
Sample: 1990 2002
Included observations: 13
Variable Coefficient Std. Error t-Statistic Prob.
C -47.59996 17.42865 -2.731133 0.0195
X7 0.001982 0.000178 11.15827 0.0000
R-squared 0.918823 Mean dependent var 115.8538
Adjusted R-squared 0.911444 S.D. dependent var 114.4145
S.E. of regression 34.04795 Akaike info criterion 10.03406
Sum squared resid 12751.89 Schwarz criterion 10.12097
Log likelihood -63.22136 F-statistic 124.5069
Durbin-Watson stat 0.631549 Prob(F-statistic) 0.000000
将其余变量逐一引入的如下几个模型:
引入Ｘ6:
Dependent Variable: Y
Method: Least Squares
Date: 12/22/04 Time: 17:01
Sample: 1990 2002
Included observations: 13
Variable Coefficient Std. Error t-Statistic Prob.
C 24.27906 25.19510 0.963642 0.3579
X6 -0.090727 0.027503 -3.298789 0.0080
X7 0.004151 0.000670 6.196114 0.0001
R-squared 0.961126 Mean dependent var 115.8538
Adjusted R-squared 0.953351 S.D. dependent var 114.4145
S.E. of regression 24.71162 Akaike info criterion 9.451598
Sum squared resid 6106.641 Schwarz criterion 9.581971
Log likelihood -58.43539 F-statistic 123.6208
Durbin-Watson stat 0.917644 Prob(F-statistic) 0.000000
Ｘ6通过检验,引入Ｘ８：
Dependent Variable: Y
Method: Least Squares
Date: 12/22/04 Time: 17:05
Sample: 1990 2002
Included observations: 13
Variable Coefficient Std. Error t-Statistic Prob.
C -80.26395 34.93053 -2.297816 0.0472
X6 -0.143206 0.024342 -5.882998 0.0002
X7 0.005851 0.000676 8.655646 0.0000
X8 11.17218 3.235669 3.452819 0.0072
R-squared 0.983278 Mean dependent var 115.8538
Adjusted R-squared 0.977703 S.D. dependent var 114.4145
S.E. of regression 17.08441 Akaike info criterion 8.761870
Sum squared resid 2626.894 Schwarz criterion 8.935700
Log likelihood -52.95215 F-statistic 176.3999
Durbin-Watson stat 1.513796 Prob(F-statistic) 0.000000
Ｘ8通过检验,引入Ｘ2:
Dependent Variable: Y
Method: Least Squares
Date: 12/22/04 Time: 17:07
Sample: 1990 2002
Included observations: 13
Variable Coefficient Std. Error t-Statistic Prob.
C -50.80815 26.01905 -1.952729 0.0866
X2 -2.24E-06 6.92E-07 -3.237378 0.0119
X6 -0.162752 0.018028 -9.027545 0.0000
X7 0.007402 0.000673 11.00720 0.0000
X8 8.109910 2.448140 3.312683 0.0107
R-squared 0.992761 Mean dependent var 115.8538
Adjusted R-squared 0.989142 S.D. dependent var 114.4145
S.E. of regression 11.92238 Akaike info criterion 8.078435
Sum squared resid 1137.146 Schwarz criterion 8.295723
Log likelihood -47.50983 F-statistic 274.2850
Durbin-Watson stat 2.434411 Prob(F-statistic) 0.000000
Ｘ2通过各项检验,而其他变量未能通过各项检验,以上变量模型拟合度良好:
利用ARCH检验是否存在异方差
ARCH Test:
F-statistic 0.444405 Probability 0.730040
Obs*R-squared 1.818050 Probability 0.611015
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 12/22/04 Time: 17:18
Sample(adjusted): 1993 2002
Included observations: 10 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
C 197.6702 116.5979 1.695315 0.1409
RESID^2(-1) -0.389754 0.411792 -0.946481 0.3804
RESID^2(-2) -0.403048 0.444113 -0.907535 0.3991
RESID^2(-3) -0.091219 0.421512 -0.216410 0.8358
R-squared 0.181805 Mean dependent var 103.1534
Adjusted R-squared -0.227292 S.D. dependent var 136.2191
S.E. of regression 150.9079 Akaike info criterion 13.16039
Sum squared resid 136639.2 Schwarz criterion 13.28143
Log likelihood -61.80196 F-statistic 0.444405
Durbin-Watson stat 1.941317 Prob(F-statistic) 0.730040
Obs*R-squared=1.818050<7.81=x^2 0.05(3)
所以不存在异方差
利用Cochrane-Orcutt迭代法检验
Dependent Variable: Y
Method: Least Squares
Date: 12/22/04 Time: 17:36
Sample(adjusted): 1991 2002
Included observations: 12 after adjusting endpoints
Convergence achieved after 10 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C -46.53201 27.88659 -1.668616 0.1462
X2 -2.54E-06 9.14E-07 -2.775620 0.0322
X6 -0.167869 0.018630 -9.010731 0.0001
X7 0.007674 0.000789 9.719952 0.0001
X8 7.825420 2.701815 2.896357 0.0275
AR(1) -0.307889 0.472444 -0.651695 0.5387
R-squared 0.992656 Mean dependent var 125.0767
Adjusted R-squared 0.986536 S.D. dependent var 114.3435
S.E. of regression 13.26767 Akaike info criterion 8.315390
Sum squared resid 1056.186 Schwarz criterion 8.557844
Log likelihood -43.89234 F-statistic 162.2015
Durbin-Watson stat 2.025073 Prob(F-statistic) 0.000003
Inverted AR Roots -.31
此时 D1<DW=2.025073< 4-D2 所以模型的自相关性得到修正,不存在自相关
最终我们得到的模型为：
Ｙ＝-50.80815－2.24E-06Ｘ2－0.162752Ｘ6＋0.007402Ｘ7＋8.109910Ｘ8
三、经济意义分析
从上述分析可知, Ｘ2—四川省每年储蓄存款总额,Ｘ6—四川省ＧＤＰ,Ｘ7—货币供应量,Ｘ8—贷款利率(3年-5年期)是影响四川省房地产开发总值的最主要的因素。
五.2005年四川房地产业状况分析
1、土地拍卖继续使房地产开发中土地成本上涨，利润降低。
2、建材上涨房产运作压力加大
3、配套设施提高成本上升
4、央行加息房价上升
5、土地增值税的严格征管
6、信贷政策制约房产发展