Airbnb Rental Price Prediction Using Machine Learning Models

Founded in 2008 Airbnb has become an attractive alternative within the hospitality industry as an online booking service based in San Francisco, California. It has grown since then and spread throughout the world. Today it is present in over 220 countries and 81,000 cities, has listings in millions, and its revenue in billions. The focus of this paper is to find the best price prediction model using machine learning techniques such as Decision Tree, K-Nearest Neighbors, Extra Trees, Support Vector Machines, Random Forests, and XGBoost, using Airbnb data collected in New York City five boroughs. Experiments are conducted using two datasets. Decision tree-based models; Random Forests, Extra Trees and XGB regressors demonstrated comparable performance with respect to the measured performance metrics.