Predicting Airbnb Rental Prices Using Multiple Feature Modalities
This work addresses the need for accurate price prediction for Airbnb hosts and customers, but it is incremental as it applies existing multimodal methods to a specific domain.
The paper tackled the problem of predicting Airbnb rental prices by using geolocation, temporal, visual, and natural language features, resulting in a reliable and accurate algorithm for this regression task.
Figuring out the price of a listed Airbnb rental is an important and difficult task for both the host and the customer. For the former, it can enable them to set a reasonable price without compromising on their profits. For the customer, it helps understand the key drivers for price and also provides them with similarly priced places. This price prediction regression task can also have multiple downstream uses, such as in recommendation of similar rentals based on price. We propose to use geolocation, temporal, visual and natural language features to create a reliable and accurate price prediction algorithm.