Airbnb Price Prediction and Revenue Forecasting using Machine Learning

Abstract

Airbnb is an online marketplace for short-term home and apartment rentals. It offers a service for people to rent out their homes or living space for a short period while they are away or spare space to travellers. While Airbnb is an exciting service to earn some extra cash while renting out their home, it is very challenging for property owners to determine the price of the property as Airbnb determines the price based on the number of guests. This work aims to develop a model with a Machine Learning (ML) framework, which will predict the price of the property and forecast revenue based on other rental information in the locality. Airbnb historical data will be scrapped and used to train two types of Machine Learning models 1) Hedonic Model Regression, and 2) XGBoost and compare their results for best accuracy. These Machine Learning models will later be hosted on a web server. A mobile compatible web client will be able to send queries to the webserver with the address of the property for 1) Property rent price prediction, 2) Periodic revenue forecast, and 3) Features of super-host in the neighbourhood. The final product of this project will be a web application that Airbnb property renters will able to use for the above-mentioned features