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1、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 年间，我国房地产投资连续四年增幅在以上，年房地产投资增长率高达，投资规模首次突破万亿元，占全社会固定资产投资的占的。 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
2、续四年在以上，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,同时一共选定了八个解释变量:1四川省每年税收总额(单位:万元)2四川省每年储蓄存款总额(单位
3、:亿元)3四川省建筑材料工业品出厂价格指数(单位:)4四川省原材料燃料和动力购进价格指数(建筑材料类)(单位:)5四川省每人每年可支配收入(单位:元)6四川省(单位:亿元)7货币供应量(单位:亿元)8贷款利率(3年-5年期)(单位:)年份YX1X2X3X4X5X6X7X819005.18 256513 698.15101.21001862.36890.9515293.411.7%19916.09 323350 813.4796.4118.22132.561016.3119349.911.7%199214.19486213904.6892.4139.32988.211177.2725402.21
4、1.7%199334.87658821988.1787.2168.53422.171486.0834879.811.7%199450.578694511024.2198.5215.13846.222001.4146923.511.7%199579.9610520131148.59103.7273.34002.912504.9560750.511.7%199690.6212984071521.97140.5294.44406.092985.1576094.911.7%1997100.2615257921841.2297.0354.64763.263320.1190995.39.9%1998120
5、.6016602861984.5495.3417.25127.083580.26104498.57.65%1999142.5017789792351.2194.0417.25477.893711.61119897.96.03%2000195.9719463192693.1794.7444.75894.274010.25134610.36.03%2001268.1521814663123.3997.3470.76360.474421.76158301.96.03%构造模型: （数据见下页）0+11+22+33+44+55+66+77+88对数据进行分析:Dependent Variable: Y
6、Method: Least SquaresDate: 12/22/04 Time: 16:40Sample: 1990 2002Included observations: 13VariableCoefficientStd. Errort-StatisticProb. C9.50619061.473400.1546390.8846X1-0.0001080.000146-0.7371200.5019X2-1.32E-061.34E-06-0.9817610.3818X30.5152790.8940200.5763620.5952X4-1.0198000.958646-1.0637930.3474
7、X5-0.0194620.037130-0.5241620.6279X6-0.1540270.062463-2.4659140.0692X70.0079940.0011546.9251130.0023X89.8308403.2403013.0339290.0386R-squared0.995226 Mean dependent var115.8538Adjusted R-squared0.985677 S.D. dependent var114.4145S.E. of regression13.69309 Akaike info criterion8.277619Sum squared res
8、id750.0024 Schwarz criterion8.668738Log likelihood-44.80453 F-statistic104.2251Durbin-Watson stat3.388729 Prob(F-statistic)0.000226对数据的多重共线性进行分析:X1X2X3X4X5X6X7X8X1 1.000000 0.992449-0.490698-0.579667 0.926422 0.942814 0.985242-0.808622X2 0.992449 1.000000-0.482882-0.568568 0.920679 0.932850 0.975851
9、-0.819597X3-0.490698-0.482882 1.000000 0.900281-0.486587-0.511065-0.489324 0.424923X4-0.579667-0.568568 0.900281 1.000000-0.638015-0.648189-0.604672 0.455466X5 0.926422 0.920679-0.486587-0.638015 1.000000 0.995972 0.969636-0.620918X6 0.942814 0.932850-0.511065-0.648189 0.995972 1.000000 0.981303-0.6
10、55603X7 0.985242 0.975851-0.489324-0.604672 0.969636 0.981303 1.000000-0.749205X8-0.808622-0.819597 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: YMethod: Least SquaresDate: 12/22/04 Time: 16:54Sample: 1990 2002Included observation
11、s: 13VariableCoefficientStd. Errort-StatisticProb. C-47.5999617.42865-2.7311330.0195X70.0019820.00017811.158270.0000R-squared0.918823 Mean dependent var115.8538Adjusted R-squared0.911444 S.D. dependent var114.4145S.E. of regression34.04795 Akaike info criterion10.03406Sum squared resid12751.89 Schwa
12、rz criterion10.12097Log likelihood-63.22136 F-statistic124.5069Durbin-Watson stat0.631549 Prob(F-statistic)0.000000将其余变量逐一引入的如下几个模型:引入6:Dependent Variable: YMethod: Least SquaresDate: 12/22/04 Time: 17:01Sample: 1990 2002Included observations: 13VariableCoefficientStd. Errort-StatisticProb. C24.2790
13、625.195100.9636420.3579X6-0.0907270.027503-3.2987890.0080X70.0041510.0006706.1961140.0001R-squared0.961126 Mean dependent var115.8538Adjusted R-squared0.953351 S.D. dependent var114.4145S.E. of regression24.71162 Akaike info criterion9.451598Sum squared resid6106.641 Schwarz criterion9.581971Log lik
14、elihood-58.43539 F-statistic123.6208Durbin-Watson stat0.917644 Prob(F-statistic)0.0000006通过检验,引入：Dependent Variable: YMethod: Least SquaresDate: 12/22/04 Time: 17:05Sample: 1990 2002Included observations: 13VariableCoefficientStd. Errort-StatisticProb. C-80.2639534.93053-2.2978160.0472X6-0.1432060.0
15、24342-5.8829980.0002X70.0058510.0006768.6556460.0000X811.172183.2356693.4528190.0072R-squared0.983278 Mean dependent var115.8538Adjusted R-squared0.977703 S.D. dependent var114.4145S.E. of regression17.08441 Akaike info criterion8.761870Sum squared resid2626.894 Schwarz criterion8.935700Log likeliho
16、od-52.95215 F-statistic176.3999Durbin-Watson stat1.513796 Prob(F-statistic)0.0000008通过检验,引入2:Dependent Variable: YMethod: Least SquaresDate: 12/22/04 Time: 17:07Sample: 1990 2002Included observations: 13VariableCoefficientStd. Errort-StatisticProb. C-50.8081526.01905-1.9527290.0866X2-2.24E-066.92E-0
17、7-3.2373780.0119X6-0.1627520.018028-9.0275450.0000X70.0074020.00067311.007200.0000X88.1099102.4481403.3126830.0107R-squared0.992761 Mean dependent var115.8538Adjusted R-squared0.989142 S.D. dependent var114.4145S.E. of regression11.92238 Akaike info criterion8.078435Sum squared resid1137.146 Schwarz
18、 criterion8.295723Log likelihood-47.50983 F-statistic274.2850Durbin-Watson stat2.434411 Prob(F-statistic)0.0000002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-statistic0.444405 Probability0.730040Obs*R-squared1.818050 Probability0.611015Test Equation:Dependent Variable: RESID2Method:
19、Least SquaresDate: 12/22/04 Time: 17:18Sample(adjusted): 1993 2002Included observations: 10 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C197.6702116.59791.6953150.1409RESID2(-1)-0.3897540.411792-0.9464810.3804RESID2(-2)-0.4030480.444113-0.9075350.3991RESID2(-3)-0.0912190.4
20、21512-0.2164100.8358R-squared0.181805 Mean dependent var103.1534Adjusted R-squared-0.227292 S.D. dependent var136.2191S.E. of regression150.9079 Akaike info criterion13.16039Sum squared resid136639.2 Schwarz criterion13.28143Log likelihood-61.80196 F-statistic0.444405Durbin-Watson stat1.941317 Prob(
21、F-statistic)0.730040Obs*R-squared=1.8180507.81=x2 0.05(3)所以不存在异方差利用Cochrane-Orcutt迭代法检验Dependent Variable: YMethod: Least SquaresDate: 12/22/04 Time: 17:36Sample(adjusted): 1991 2002Included observations: 12 after adjusting endpointsConvergence achieved after 10 iterationsVariableCoefficientStd. Err
22、ort-StatisticProb. C-46.5320127.88659-1.6686160.1462X2-2.54E-069.14E-07-2.7756200.0322X6-0.1678690.018630-9.0107310.0001X70.0076740.0007899.7199520.0001X87.8254202.7018152.8963570.0275AR(1)-0.3078890.472444-0.6516950.5387R-squared0.992656 Mean dependent var125.0767Adjusted R-squared0.986536 S.D. dep
23、endent var114.3435S.E. of regression13.26767 Akaike info criterion8.315390Sum squared resid1056.186 Schwarz criterion8.557844Log likelihood-43.89234 F-statistic162.2015Durbin-Watson stat2.025073 Prob(F-statistic)0.000003Inverted AR Roots -.31此时 D1DW=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：-50.808152.24E-0620.16275260.00740278.1099108三、经济意义分析从上述分析可知, 2四川省每年储蓄存款总额,6四川省,7货币供应量,8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
