经管之家送您一份
应届毕业生专属福利!
求职就业群
感谢您参与论坛问题回答
经管之家送您两个论坛币!
+2 论坛币
关于房价问题的初步分析<p></p>引言:近改革开放20多年来,从来没有哪一个行业像房地产业这样盛产亿万富翁,各种富豪排行榜上,房地产富豪连年占据半壁江山;“中国十大暴利行业”中,房地产业每年都是“第一名”。是什么造就了这样的状况。房地产的问题,在开发商,ZF,购房者三者来看,就是一场完完全全的博弈。而这场博弈的焦点则是房价问题。如果说开发商与ZF之间的博弈是围绕“土地”这个关键词,那么整个房地产市场则在价格上开展了新一轮的对峙。先是开发商与购房者在房价涨跌上僵持不下;再有开发商与ZF之间的土地成本论;最后则是关于房地产是否归为暴利行业的争执,“价格”成了市场关注的焦点。而对于房价的构成因素,至今仍然是不透明的。公布房价成本成为另ZF极为头疼的一件事。房价成本是一个非常复杂的集合体,并且项目间差异性较大,同时还有软资产、品牌等组成部分,特别是现在的商品房,追求品质、功能完善以及个性化成本构成越来越难衡量。 <p></p>写作目的:通过对一系列影响房价的基本因素的分析,了解对其主要因素和次要因素。并对这些因素进行统计推断和经济意义上的检验。选择拟和效果最好的最为结论。在一定层面上分析房地产如此暴利的因素。当然笔者的能力有限,并不能全面的分析这一问题。仅仅就几个因素进行分析。<p></p>写作方法:理论分析及计量分析方法,将会用到Eviews软件进行帮助分析。<p></p>关键词:房价成本 拟合优度<p></p>现在我们以2003年的数据,选取30个省市的数据为例进行分析。在Eviews软件中选择建立截面数据。现在我们以2003年的数据,选取31个省市的数据为例进行分析。令Y=各地区建筑业总产值。(万元)X1=各地区房屋竣工面积。(万平方米)X2=各地区建筑业企业从业人员。(人)X3=各地区建筑业劳动生产率。(元/人)X4=各地区人均住宅面积。(平方米)X5=各地区人均可支配收入。(元) <p>数据如下:</p>&nbsp;<p></p>Y<p></p>X1<p></p>X3<p></p>X2<p></p>X4<p></p>X5<p></p>&nbsp;12698521<p></p>&nbsp;4254.800<p></p>&nbsp;569767.0<p></p>&nbsp;129961.0<p></p>&nbsp;24.77140<p></p>&nbsp;13882.62<p></p>&nbsp;5208402.<p></p>&nbsp;1465.800<p></p>&nbsp;238957.0<p></p>&nbsp;147063.0<p></p>&nbsp;23.09570<p></p>&nbsp;10312.91<p></p>&nbsp;7799313.<p></p>&nbsp;4748.300<p></p>&nbsp;989317.0<p></p>&nbsp;70048.00<p></p>&nbsp;23.16710<p></p>&nbsp;7239.060<p></p>&nbsp;5401279.<p></p>&nbsp;1313.300<p></p>&nbsp;591276.0<p></p>&nbsp;89151.00<p></p>&nbsp;22.99680<p></p>&nbsp;7005.030<p></p>&nbsp;2576575.<p></p>&nbsp;1450.700<p></p>&nbsp;265953.0<p></p>&nbsp;61074.00<p></p>&nbsp;20.05310<p></p>&nbsp;7012.900<p></p>&nbsp;10170794<p></p>&nbsp;3957.100<p></p>&nbsp;966790.0<p></p>&nbsp;82496.00<p></p>&nbsp;20.23510<p></p>&nbsp;7240.580<p></p>&nbsp;3469281.<p></p>&nbsp;1626.800<p></p>&nbsp;303837.0<p></p>&nbsp;77486.00<p></p>&nbsp;20.70590<p></p>&nbsp;7005.170<p></p>&nbsp;4401878.<p></p>&nbsp;2181.300<p></p>&nbsp;441518.0<p></p>&nbsp;68033.00<p></p>&nbsp;20.49200<p></p>&nbsp;6678.900<p></p>&nbsp;11958034<p></p>&nbsp;3609.200<p></p>&nbsp;505185.0<p></p>&nbsp;153910.0<p></p>&nbsp;29.34530<p></p>&nbsp;14867.49<p></p>&nbsp;27949354<p></p>&nbsp;17730.00<p></p>&nbsp;2727006.<p></p>&nbsp;100569.0<p></p>&nbsp;24.43530<p></p>&nbsp;9262.460<p></p>&nbsp;31272779<p></p>&nbsp;16183.90<p></p>&nbsp;2429352.<p></p>&nbsp;127430.0<p></p>&nbsp;31.02330<p></p>&nbsp;13179.53<p></p>&nbsp;6227073.<p></p>&nbsp;4017.600<p></p>&nbsp;910691.0<p></p>&nbsp;66407.00<p></p>&nbsp;20.75480<p></p>&nbsp;6778.030<p></p>&nbsp;5493441.<p></p>&nbsp;2952.100<p></p>&nbsp;553611.0<p></p>&nbsp;108288.0<p></p>&nbsp;30.29870<p></p>&nbsp;9999.540<p></p>&nbsp;3593356.<p></p>&nbsp;2750.900<p></p>&nbsp;574705.0<p></p>&nbsp;70826.00<p></p>&nbsp;22.61980<p></p>&nbsp;6901.420<p></p>&nbsp;14813618<p></p>&nbsp;9139.800<p></p>&nbsp;2072530.<p></p>&nbsp;60728.00<p></p>&nbsp;24.48080<p></p>&nbsp;8399.910<p></p>&nbsp;6345217.<p></p>&nbsp;3433.600<p></p>&nbsp;932901.0<p></p>&nbsp;66056.00<p></p>&nbsp;20.20090<p></p>&nbsp;6926.120<p></p>&nbsp;8729958.<p></p>&nbsp;4840.800<p></p>&nbsp;1048763.<p></p>&nbsp;81761.00<p></p>&nbsp;22.90280<p></p>&nbsp;7321.980<p></p>&nbsp;8188402.<p></p>&nbsp;4969.700<p></p>&nbsp;1119106.<p></p>&nbsp;74553.00<p></p>&nbsp;24.42580<p></p>&nbsp;7674.200<p></p>&nbsp;15163242<p></p>&nbsp;8105.000<p></p>&nbsp;1492820.<p></p>&nbsp;101932.0<p></p>&nbsp;24.93280<p></p>&nbsp;12380.43<p></p>&nbsp;2818466.<p></p>&nbsp;1721.600<p></p>&nbsp;353700.0<p></p>&nbsp;77472.00<p></p>&nbsp;24.17320<p></p>&nbsp;7785.040<p></p>&nbsp;394053.0<p></p>&nbsp;121.5000<p></p>&nbsp;61210.00<p></p>&nbsp;55361.00<p></p>&nbsp;23.43200<p></p>&nbsp;7259.250<p></p>&nbsp;5862095.<p></p>&nbsp;4939.600<p></p>&nbsp;817997.0<p></p>&nbsp;69432.00<p></p>&nbsp;25.72440<p></p>&nbsp;8093.670<p></p>&nbsp;12253374<p></p>&nbsp;8784.600<p></p>&nbsp;2070534.<p></p>&nbsp;59748.00<p></p>&nbsp;26.35850<p></p>&nbsp;7041.870<p></p>&nbsp;2122907.<p></p>&nbsp;980.3000<p></p>&nbsp;293310.0<p></p>&nbsp;72152.00<p></p>&nbsp;18.19430<p></p>&nbsp;6569.230<p></p>&nbsp;3967957.<p></p>&nbsp;2248.700<p></p>&nbsp;522470.0<p></p>&nbsp;69238.00<p></p>&nbsp;24.92940<p></p>&nbsp;7643.570<p></p>&nbsp;293427.0<p></p>&nbsp;121.3000<p></p>&nbsp;36593.00<p></p>&nbsp;73205.00<p></p>&nbsp;19.92990<p></p>&nbsp;8765.450<p></p>&nbsp;4404362.<p></p>&nbsp;1580.000<p></p>&nbsp;410311.0<p></p>&nbsp;93212.00<p></p>&nbsp;21.75050<p></p>&nbsp;6806.350<p></p>&nbsp;2236860.<p></p>&nbsp;1327.200<p></p>&nbsp;449409.0<p></p>&nbsp;46857.00<p></p>&nbsp;21.11380<p></p>&nbsp;6657.240<p></p>&nbsp;747325.0<p></p>&nbsp;242.9000<p></p>&nbsp;101501.0<p></p>&nbsp;61046.00<p></p>&nbsp;19.10550<p></p>&nbsp;6745.320<p></p>&nbsp;1080546.<p></p>&nbsp;578.7000<p></p>&nbsp;88225.00<p></p>&nbsp;61459.00<p></p>&nbsp;22.25500<p></p>&nbsp;6530.480<p></p>&nbsp;3196774.<p></p>&nbsp;1450.800<p></p>&nbsp;203375.0<p></p>&nbsp;95835.00<p></p>&nbsp;20.78110<p></p>&nbsp;7173.540<p></p>先用Eviews软件进行White检验:<p></p>White Heteroskedasticity Test:<p></p>F-statistic<p></p>2.779810<p></p>&nbsp;&nbsp;&nbsp; Probability<p></p>0.049670<p></p><p></p>Obs*R-squared<p></p>26.27412<p></p>&nbsp;&nbsp;&nbsp; Probability<p></p>0.156948<p></p><p></p>&nbsp;<p></p>&nbsp;<p></p>&nbsp;<p></p>&nbsp;<p></p>&nbsp;<p></p><p></p>Test Equation:<p></p><p></p>Dependent Variable: RESID^2<p></p><p></p>Method: Least Squares<p></p><p></p>Date: 12/22/05&nbsp;&nbsp; Time: 21:50<p></p><p></p>Sample: 1 31<p></p><p></p>Included observations: 31<p></p><p></p>Variable<p></p>Coefficient<p></p>Std. Error<p></p>t-Statistic<p></p>Prob.&nbsp; <p></p><p></p>C<p></p>6.08E+12<p></p>2.29E+13<p></p>0.265539<p></p>0.7960<p></p><p></p>X5<p></p>1.64E+08<p></p>3.88E+09<p></p>0.042370<p></p>0.9670<p></p><p></p>X5^2<p></p>87293.54<p></p>453712.3<p></p>0.192398<p></p>0.8513<p></p><p></p>X5*X4<p></p>38067124<p></p>3.56E+08<p></p>0.106810<p></p>0.9171<p></p><p></p>X5*X3<p></p>1363.555<p></p>6160.070<p></p>0.221354<p></p>0.8293<p></p><p></p>X5*X2<p></p>-17464.36<p></p>50393.75<p></p>-0.346558<p></p>0.7361<p></p><p></p>X5*X1<p></p>-453312.2<p></p>1215201.<p></p>-0.373035<p></p>0.7169<p></p><p></p>X4<p></p>-9.71E+11<p></p>1.83E+12<p></p>-0.531486<p></p>0.6067<p></p><p></p>X4^2<p></p>4.28E+10<p></p>6.46E+10<p></p>0.661720<p></p>0.5231<p></p><p></p>X4*X3<p></p>-1905048.<p></p>1949296.<p></p>-0.977301<p></p>0.3515<p></p><p></p>X4*X2<p></p>-19010403<p></p>17319142<p></p>-1.097653<p></p>0.2981<p></p><p></p>X4*X1<p></p>4.23E+08<p></p>4.15E+08<p></p>1.020801<p></p>0.3314<p></p><p></p>X3<p></p>-13869460<p></p>34509844<p></p>-0.401899<p></p>0.6962<p></p><p></p>X3^2<p></p>41.81843<p></p>22.62540<p></p>1.848296<p></p>0.0943<p></p><p></p>X3*X2<p></p>517.0981<p></p>231.1954<p></p>2.236628<p></p>0.0493<p></p><p></p>X3*X1<p></p>-14772.93<p></p>8469.467<p></p>-1.744258<p></p>0.1117<p></p><p></p>X2<p></p>1.51E+08<p></p>3.45E+08<p></p>0.438853<p></p>0.6701<p></p><p></p>X2^2<p></p>2050.261<p></p>1851.410<p></p>1.107405<p></p>0.2940<p></p><p></p>X2*X1<p></p>-67170.59<p></p>50453.24<p></p>-1.331343<p></p>0.2126<p></p><p></p>X1<p></p>7.80E+08<p></p>6.17E+09<p></p>0.126430<p></p>0.9019<p></p><p></p>X1^2<p></p>1246362.<p></p>746355.0<p></p>1.669932<p></p>0.1259<p></p><p></p>R-squared<p></p>0.847552<p></p>&nbsp;&nbsp;&nbsp; Mean dependent var<p></p>1.17E+12<p></p><p></p>Adjusted R-squared<p></p>0.542656<p></p>&nbsp;&nbsp;&nbsp; S.D. dependent var<p></p>1.78E+12<p></p><p></p>S.E. of regression<p></p>1.21E+12<p></p>&nbsp;&nbsp;&nbsp; Akaike info criterion<p></p>58.69986<p></p><p></p>Sum squared resid<p></p>1.46E+25<p></p>&nbsp;&nbsp;&nbsp; Schwarz criterion<p></p>59.67127<p></p><p></p>Log likelihood<p></p>-888.8478<p></p>&nbsp;&nbsp;&nbsp; F-statistic<p></p>2.779810<p></p><p></p>Durbin-Watson stat<p></p>1.809921<p></p>&nbsp;&nbsp;&nbsp; Prob(F-statistic)<p></p>0.049670<p></p><p></p>结果显示为没有异方差。<p></p>DW值为1.809921,没有自相关。<p></p>做多重共线性检验:<p></p>&nbsp;<p></p>X5<p></p>X4<p></p>X3<p></p>X2<p></p>X1<p></p>X5<p></p>&nbsp;1.000000<p></p>&nbsp;0.686513<p></p>&nbsp;0.279851<p></p>&nbsp;0.836241<p></p>&nbsp;0.418307<p></p>X4<p></p>&nbsp;0.686513<p></p>&nbsp;1.000000<p></p>&nbsp;0.477886<p></p>&nbsp;0.540881<p></p>&nbsp;0.538697<p></p>X3<p></p>&nbsp;0.279851<p></p>&nbsp;0.477886<p></p>&nbsp;1.000000<p></p>&nbsp;0.125029<p></p>&nbsp;0.960871<p></p>X2<p></p>&nbsp;0.836241<p></p>&nbsp;0.540881<p></p>&nbsp;0.125029<p></p>&nbsp;1.000000<p></p>&nbsp;0.271375<p></p>X1<p></p>&nbsp;0.418307<p></p>&nbsp;0.538697<p></p>&nbsp;0.960871<p></p>&nbsp;0.271375<p></p>&nbsp;1.000000<p></p>&nbsp;<p></p>可以看出有多重共线性。<p></p>数 97<p></p>数 97得的的的<p></p>采取逐步回归法:<p></p>第一次回归,我们可以根据T检验值和可决系数看出:X1的效果最好:<p></p>Dependent Variable: Y<p></p>Method: Least Squares<p></p>Date: 12/22/05&nbsp;&nbsp; Time: 21:16<p></p>Sample: 1 31<p></p>Included observations: 31<p></p>Variable<p></p>Coefficient<p></p>Std. Error<p></p>t-Statistic<p></p>Prob.&nbsp; <p></p>X1<p></p>1651.403<p></p>87.67703<p></p>18.83508<p></p>0.0000<p></p>C<p></p>903234.0<p></p>502408.2<p></p>1.797809<p></p>0.0826<p></p>R-squared<p></p>0.924432<p></p>&nbsp;&nbsp;&nbsp; Mean dependent var<p></p>7446408.<p></p>Adjusted R-squared<p></p>0.921826<p></p>&nbsp;&nbsp;&nbsp; S.D. dependent var<p></p>7227629.<p></p>S.E. of regression<p></p>2020815.<p></p>&nbsp;&nbsp;&nbsp; Akaike info criterion<p></p>31.93824<p></p>Sum squared resid<p></p>1.18E+14<p></p>&nbsp;&nbsp;&nbsp; Schwarz criterion<p></p>32.03076<p></p>Log likelihood<p></p>-493.0427<p></p>&nbsp;&nbsp;&nbsp; F-statistic<p></p>354.7601<p></p>Durbin-Watson stat<p></p>1.930762<p></p>&nbsp;&nbsp;&nbsp; Prob(F-statistic)<p></p>0.000000<p></p>&nbsp;<p></p>依次<p></p>21得加入X2,X3,X4,X5:<p></p>可得,加入X2后的效果最好:<p></p>Dependent Variable: Y<p></p>Method: Least Squares<p></p>Date: 12/22/05&nbsp;&nbsp; Time: 21:16<p></p>Sample: 1 31<p></p>Included observations: 31<p></p>Variable<p></p>Coefficient<p></p>Std. Error<p></p>t-Statistic<p></p>Prob.&nbsp; <p></p>X2<p></p>60.57577<p></p>9.136899<p></p>6.629795<p></p>0.0000<p></p>X1<p></p>1547.354<p></p>57.83197<p></p>26.75604<p></p>0.0000<p></p>C<p></p>-3711880.<p></p>765709.2<p></p>-4.847637<p></p>0.0000<p></p>R-squared<p></p>0.970594<p></p>&nbsp;&nbsp;&nbsp; Mean dependent var<p></p>7446408.<p></p>Adjusted R-squared<p></p>0.968493<p></p>&nbsp;&nbsp;&nbsp; S.D. dependent var<p></p>7227629.<p></p>S.E. of regression<p></p>1282914.<p></p>&nbsp;&nbsp;&nbsp; Akaike info criterion<p></p>31.05893<p></p>Sum squared resid<p></p>4.61E+13<p></p>&nbsp;&nbsp;&nbsp; Schwarz criterion<p></p>31.19771<p></p>Log likelihood<p></p>-478.4134<p></p>&nbsp;&nbsp;&nbsp; F-statistic<p></p>462.0886<p></p>Durbin-Watson stat<p></p>2.098685<p></p>&nbsp;&nbsp;&nbsp; Prob(F-statistic)<p></p>0.000000<p></p>&nbsp;<p></p>再<p></p>21得DE 加<p></p>2入X3,X4,X5<p></p>加入X3,回归:<p></p>Dependent Variable: Y<p></p>Method: Least Squares<p></p>Date: 12/26/05&nbsp;&nbsp; Time: 10:09<p></p>Sample: 1 31<p></p>Included observations: 31<p></p>Variable<p></p>Coefficient<p></p>Std. Error<p></p>t-Statistic<p></p>Prob.&nbsp; <p></p>X1<p></p>1392.586<p></p>243.1554<p></p>5.727144<p></p>0.0000<p></p>X2<p></p>64.15614<p></p>10.72532<p></p>5.981748<p></p>0.0000<p></p>X3<p></p>0.924103<p></p>1.409311<p></p>0.655713<p></p>0.5176<p></p>C<p></p>-4115494.<p></p>988624.2<p></p>-4.162850<p></p>0.0003<p></p>R-squared<p></p>0.971055<p></p>&nbsp;&nbsp;&nbsp; Mean dependent var<p></p>7446408.<p></p>Adjusted R-squared<p></p>0.967838<p></p>&nbsp;&nbsp;&nbsp; S.D. dependent var<p></p>7227629.<p></p>S.E. of regression<p></p>1296176.<p></p>&nbsp;&nbsp;&nbsp; Akaike info criterion<p></p>31.10765<p></p>Sum squared resid<p></p>4.54E+13<p></p>&nbsp;&nbsp;&nbsp; Schwarz criterion<p></p>31.29268<p></p>Log likelihood<p></p>-478.1686<p></p>&nbsp;&nbsp;&nbsp; F-statistic<p></p>301.9308<p></p>Durbin-Watson stat<p></p>2.037807<p></p>&nbsp;&nbsp;&nbsp; Prob(F-statistic)<p></p>0.000000<p></p>加入X4,回归:<p></p>Dependent Variable: Y<p></p>Method: Least Squares<p></p>Date: 12/26/05&nbsp;&nbsp; Time: 10:09<p></p>Sample: 1 31<p></p>Included observations: 31<p></p>Variable<p></p>Coefficient<p></p>Std. Error<p></p>t-Statistic<p></p>Prob.&nbsp; <p></p>X1<p></p>1569.186<p></p>66.74467<p></p>23.51029<p></p>0.0000<p></p>X2<p></p>64.04945<p></p>10.56258<p></p>6.063810<p></p>0.0000<p></p>X4<p></p>-69455.16<p></p>102797.7<p></p>-0.675649<p></p>0.5050<p></p>C<p></p>-2476469.<p></p>1985261.<p></p>-1.247428<p></p>0.2230<p></p>R-squared<p></p>0.971083<p></p>&nbsp;&nbsp;&nbsp; Mean dependent var<p></p>7446408.<p></p>Adjusted R-squared<p></p>0.967870<p></p>&nbsp;&nbsp;&nbsp; S.D. dependent var<p></p>7227629.<p></p>S.E. of regression<p></p>1295550.<p></p>&nbsp;&nbsp;&nbsp; Akaike info criterion<p></p>31.10668<p></p>Sum squared resid<p></p>4.53E+13<p></p>&nbsp;&nbsp;&nbsp; Schwarz criterion<p></p>31.29171<p></p>Log likelihood<p></p>-478.1536<p></p>&nbsp;&nbsp;&nbsp; F-statistic<p></p>302.2316<p></p>Durbin-Watson stat<p></p>2.298423<p></p>&nbsp;&nbsp;&nbsp; Prob(F-statistic)<p></p>0.000000<p></p>加<p></p>21得DE 入<p></p>X5,回归:<p></p>Dependent Variable: Y<p></p>Method: Least Squares<p></p>Date: 12/26/05&nbsp;&nbsp; Time: 10:10<p></p>Sample: 1 31<p></p>Included observations: 31<p></p>Variable<p></p>Coefficient<p></p>Std. Error<p></p>t-Statistic<p></p>Prob.&nbsp; <p></p>X1<p></p>1511.624<p></p>60.28105<p></p>25.07627<p></p>0.0000<p></p>X2<p></p>39.25698<p></p>15.77525<p></p>2.488517<p></p>0.0193<p></p>X5<p></p>316.7476<p></p>193.7661<p></p>1.634691<p></p>0.1137<p></p>C<p></p>-4428358.<p></p>863348.9<p></p>-5.129279<p></p>0.0000<p></p>R-squared<p></p>0.973242<p></p>&nbsp;&nbsp;&nbsp; Mean dependent var<p></p>7446408.<p></p>Adjusted R-squared<p></p>0.970269<p></p>&nbsp;&nbsp;&nbsp; S.D. dependent var<p></p>7227629.<p></p>S.E. of regression<p></p>1246240.<p></p>&nbsp;&nbsp;&nbsp; Akaike info criterion<p></p>31.02907<p></p>Sum squared resid<p></p>4.19E+13<p></p>&nbsp;&nbsp;&nbsp; Schwarz criterion<p></p>31.21410<p></p>Log likelihood<p></p>-476.9506<p></p>&nbsp;&nbsp;&nbsp; F-statistic<p></p>327.3477<p></p>Durbin-Watson stat<p></p>1.861895<p></p>&nbsp;&nbsp;&nbsp; Prob(F-statistic)<p></p>0.000000<p></p>我们<p></p>21得DE发现加入X3,X4,X5的效果都不好,T检验都不充分。<p></p>于是我们只保留X1,X2再回归,得:<p></p>Dependent Variable: Y<p></p>Method: Least Squares<p></p>Date: 12/22/05&nbsp;&nbsp; Time: 21:16<p></p>Sample: 1 31<p></p>Included observations: 31<p></p>Variable<p></p>Coefficient<p></p>Std. Error<p></p>t-Statistic<p></p>Prob.&nbsp; <p></p>X2<p></p>60.57577<p></p>9.136899<p></p>6.629795<p></p>0.0000<p></p>X1<p></p>1547.354<p></p>57.83197<p></p>26.75604<p></p>0.0000<p></p>C<p></p>-3711880.<p></p>765709.2<p></p>-4.847637<p></p>0.0000<p></p>R-squared<p></p>0.970594<p></p>&nbsp;&nbsp;&nbsp; Mean dependent var<p></p>7446408.<p></p>Adjusted R-squared<p></p>0.968493<p></p>&nbsp;&nbsp;&nbsp; S.D. dependent var<p></p>7227629.<p></p>S.E. of regression<p></p>1282914.<p></p>&nbsp;&nbsp;&nbsp; Akaike info criterion<p></p>31.05893<p></p>Sum squared resid<p></p>4.61E+13<p></p>&nbsp;&nbsp;&nbsp; Schwarz criterion<p></p>31.19771<p></p>Log likelihood<p></p>-478.4134<p></p>&nbsp;&nbsp;&nbsp; F-statistic<p></p>462.0886<p></p>Durbin-Watson stat<p></p>2.098685<p></p>&nbsp;&nbsp;&nbsp; Prob(F-statistic)<p></p>0.000000<p></p>&nbsp;<p></p>得出<p></p>数 97回归函数为:<p></p>Y=1547.354 X1+60.57577 X2-3711880 结论:我们总认为房产总价值与许多成分有关,其实在最后我们看到并不是这样。但现实中房价成本具有相当大的难度。不管是资金成本很难简单地以招拍挂价格进行测算,还是融资成本比较难核算。而且房地产的利润要以综合成本衡量。种种原因构成了房价成本确定的难度。而房产行业的暴利,开发商的暴利是来源于开发商的阶层优越感和特殊占有地位,而与之相对的是老百姓的阶层卑微感和相对剥削感。房地产业的暴利如果继续维持,考验的不仅是中国经济的稳定,更是老百姓忍耐的限度。而且这种房产的暴利行为导致了从2003年10月开始的通货膨胀,并造成了中国越来越大的金融风险。我国房价的公开将会采取怎么样的方式,笔者将和大家一起拭目以待。 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <p></p><p></p>&nbsp;<p></p>&nbsp;<p></p>&nbsp;<p></p>参考资料:http://www.stats.gov.cn/tjsj/ndsj/yb2004-c/indexch.htm<p></p>http://www.xinhuanet.com<p></p>
扫码加我 拉你入群
请注明:姓名-公司-职位
以便审核进群资格,未注明则拒绝
|