The regression analysis and prediction of Real estate added value based on genetic algorithm

Author

Zhao, Yanli ; Jia, Shuangshuang

Author_Institution

Sch. of Manage., Harbin Univ. of Commerce, Harbin, China

fYear

2011

fDate

8-11 Jan. 2011

Firstpage

944

Lastpage

946

Abstract

Total productive value of Real estate is a crucial part of the Service Industry, which directly affects the value of GDP. It is significant to predict the added value of the total productive value of Real estate, by historical observed data and dynamic regression equations. Compare dynamic regression equations with genetic algorithm to regression equation from exponential regression and linear regression. The predicted results through genetic algorithm method get closer to the true value than the other two methods. Meanwhile, the result also points out the added value of the total productive value of Real estate with genetic algorithms in the next years.

Keywords

genetic algorithms; regression analysis; dynamic regression equations; exponential regression; genetic algorithm; linear regression; productive value; real estate added value; regression analysis; service industry; Analytical models; Biological system modeling; Computational modeling; Equations; Industries; Mathematical model; Predictive models; egression equation; genetic algorithms; real estate added value;

fLanguage

English

Publisher

ieee

Conference_Titel

Management Science and Industrial Engineering (MSIE), 2011 International Conference on

Conference_Location

Harbin

Print_ISBN

978-1-4244-8383-9

Type

conf

DOI

10.1109/MSIE.2011.5707566

Filename

5707566