Real Estate Sales Forecasting with SVM Classification

Abstract

AbstractThe real estate market has a very important role in our society. It has a relationship with development and a person’s fundamental need. So, correct forecasting for sales and demand for real estate is very significant. SVM is a generous type of learning machine which solves classification with limited sample learning, nonlinear classification as well as handle “curse of dimensionality”. SVM has powerful classification capability with the feature selection, kernel selection, and parameter optimization add-on the classification accuracy. This paper focus on real estate sales forecasting and booking scenario on the basis of customer enquiry features. Paper follows the approach of Support Vector Machine (SVM) classification to forecast sales in real state. SVM is a type of machine learning algorithm from this, inference knowledge for prediction of sale. The proposed model helps real estate people to make a decision for the further stage of the construction or launch a new project according to sales and demand. For the classification, data is gathered from the real estate project. SVM classification accuracy is measured with polynomial kernel and feature selection. The optimal solution can be found and forecasting effect can be achieved by SVM classification. The experimental result proves that the SVM has good forecasting capability. Results also identify that how classification in real estate provides the solution for sales forecasting.KeywordsSales forecastingReal estateKernelFeatureSupport vector