Application of SVM Based on Rough Set in Real Estate Prices Prediction
Ting Wang, Yanqing Li, Shu-Fei Zhao · 2008
In an increasingly competitive real estate market, establishing a reasonable price has become an important guarantee for the survival of a real estate Developer in the fierce competition. In order to distinguish from the traditional method of formulating prices, this paper selects rough set (RS) and support vector machine (SVM) algorithms to establish a new mathematical model of pricing on the basis of hedonic price. Selecting the rough set to reduce numbers of price indicators, thus reducing the dimensions of the input space of SVM. When treating the reduced data as the input space of SVM, we find that both the convergence speed and the forecast accuracy are enhanced and the result is fairly good prediction.