Leveraging Multi-Modality Data to Airbnb Price Prediction

摘要

Airbnb is a short-term housing rental platform, which is gaining tremendous popularity among tourists. A reasonable prediction of Airbnb rentals could help customers make the best choices and provide homeowners with an indicator of prices to reach maximum benefits. In this paper, a variety of data are combined as inputs into machine learning algorithms and natural language processing framework to construct a reliable price prediction model for Airbnb rentals. This research shows: 1) customer reviews, house features, and geographical data can be used as predictive factors for Airbnb rentals; 2) using multimodality data in price forecast performs higher accuracy compared with single-type data.