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）xx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 * 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 xx 2830.688 6859.6 479.9 2169.719 xx 2906.16 7702.8 475.1 2250.177 xx 3011.424 8472.2 479.4 2359.499 xx 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 Dependent Variable: Y Method: Least Squares Date: 06/05/05 Time: 23:04 Sample: 1990 xx Included observations: 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 Mean dependent var 1753.317 Adjusted R-squared 0.978483 S.D. dependent var 600.9536 S.E. of regression 88.15143 Akaike info criterion 12.01917 Sum squared resid 85477.42 Schwarz criterion 12.20798 Log likelihood -86.14376 F-statistic 213.2186 Durbin-Watson stat 1.504263 Prob(F-statistic) 0.000000

一：多元线性回归 Dependent Variable: Y Method: Least Squares Date: 06/05/05 Time: 23:05 Sample: 1990 xx Included observations: 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 Mean dependent var 1753.317

Adjusted R-squared 0.219889 S.D. dependent var 600.9536 S.E. of regression 530.7855 Akaike info criterion 15.51016 Sum squared resid 3662533. Schwarz criterion 15.60457 Log likelihood -114.3262 F-statistic 4.946171 Durbin-Watson stat 0.275870 Prob(F-statistic) 0.044490

Dependent Variable: Y Method: Least Squares Date: 06/05/05 Time: 23:09 Sample: 1990 xx Included observations: 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 Mean dependent var 1753.317 Adjusted

R-squared 0.885984 S.D. dependent var 600.9536 S.E. of regression 202.9191 Akaike info criterion 13.58706 Sum squared resid 535290.2 Schwarz criterion 13.68146 Log likelihood

-99.90293 F-statistic 109.7903 Durbin-Watson stat 0.440527 Prob(F-statistic) 0.000000

Dependent Variable: Y Method: Least Squares Date: 06/05/05 Time: 23:10 Sample: 1990 xx Included observations: 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 Mean dependent var 1753.317 Adjusted R-squared

0.940308 S.D. dependent var 600.9536 S.E. of regression 146.8243 Akaike info criterion 12.93992 Sum squared resid