Forecasting house price index with social media sentiment: A decomposition–ensemble approach

Abstract

Social media sentiment influences housing market trading and policy‐making in China. To explore the multiscale relationship between social media sentiment and house price index (HPI) and improve prediction performance, a sentiment‐based decomposition–ensemble approach is proposed for HPI forecasting. In this approach, five steps, that is, sentiment analysis for massive Weibo textual reviews about house prices, data decomposition for bivariate time series integrated by HPI and the sentiment index (SI), data smoothing for high‐frequency components, component reconstruction for all individual modes, and all components prediction and ensemble, are involved. For verification, the National‐level and two city‐level house price indices are used as the sample data. The empirical results illustrate that the proposed approach can achieve better performance than all considered benchmark models at multi‐step‐ahead prediction horizons, indicating that it can be used as an effective tool for HPI forecasting.