Multi-model Combination Housing Price Forecasting Based on Web Search Data

Abstract

In the background of large data, taking the housing price in Xi'an as an example, this paper extracts the relevant search words data from Baidu Index and builds the neural network, support vector machine and random forests model, according to the purchase information inquired by the consumers in Baidu. The gradient boosting decision tree model is established by forecast results of the three single model to achieve a combination of housing prices forecast to determine the final residential sales price index forecast. The result shows that the fitting degree of the combined model is 0.995, and the prediction accuracy is 10.4% higher than the optimal single forecasting model. This method can calculate the new commodity housing price index two weeks in advance than the National Bureau of Statistics. In future studies, this approach will be applied to many types of market forecasts to help companies and consumers make the best decisions.