Airbnb Pricing Based on Statistical Machine Learning Models

Being one of the largest online accommodation booking platforms, Airbnb has many hosts who are seeking for more proper prices to increase their booking rate. To develop a good pricing prediction model, this paper has employed machine learning models including KNN, MLR, LASSO regression, Ridge regression, Random Forest, Gradient Boosting and XGBoost etc. While past studies on Airbnb pricing have applied quantitative pricing, some face the problems that the models are not robust enough and some face the problem of not training the model plentily. To fill this gap, we give careful consideration in exploratory data analysis to make the dataset more reasonable, apply many robust models ranging from regularized regression to ensemble models and use cross validation and random search to tune each parameter in each model. In this way, we not only select XGBoost as the best model for price prediction with R2 score 0.6321, but also uncover the features which have statistical significance with the target price.