That’s the conclusion drawn by a team of graduate students at Stanford, who leveraged machine learning and natural language processing to anticipate future Airbnb rates. To train their price-predicting system, the researchers tapped the public Airbnb data set for New York City, which included 50,221 entries with 96 features in total.

The team tested several price-predicting machine learning techniques, including linear regression, tree-based models, SVR, and neural networks. But they report that the best-performing model - Support Vector Regression (SVR) - achieved an R2 score (a measure of how well the predictions approximate the real data points) of 69% on the test set.

Download: Airbnb Price Prediction Using Machine Learning and Sentiment Analysis