Housing price forecasting based on genetic algorithm and support vector machine

Authors:
Jirong Gu;Mingcang Zhu;Liuguangyan Jiang
Affiliations:
Key Laboratory of Land Resources Evaluation and Monitoring in Southwest, Sichuan Normal University, Chengdu 610068, China;Land and Resources Department of Sichuan Province, Chengdu 610072, China;Key Laboratory of Land Resources Evaluation and Monitoring in Southwest, Sichuan Normal University, Chengdu 610068, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Accurate forecasting for future housing price is very significant for socioeconomic development and national lives. In this study, a hybrid of genetic algorithm and support vector machines (G-SVM) approach is presented in housing price forecasting. Support vector machine (SVM) has been proven to be a robust and competent algorithm for both classification and regression in many applications. However, how to select the most appropriate the training parameter value is the important problem in the using of SVM. Compared to Grid algorithm, genetic algorithm (GA) method consumes less time and performs well. Thus, GA is applied to optimize the parameters of SVM simultaneously. The cases in China are applied to testify the housing price forecasting ability of G-SVM method. The experimental results indicate that forecasting accuracy of this G-SVM approach is more superior than GM.