DOI: 10.3390/su151713159 ISSN:

A Sustainable Price Prediction Model for Airbnb Listings Using Machine Learning and Sentiment Analysis

Zahyah H. Alharbi
  • Management, Monitoring, Policy and Law
  • Renewable Energy, Sustainability and the Environment
  • Geography, Planning and Development
  • Building and Construction

Since 2008, the company Airbnb has brought significant changes to the hospitality industry worldwide. Experiencing remarkable growth, it currently offers over six million listings in 191 countries across one hundred thousand cities. Airbnb has gained immense popularity among travellers seeking accommodations globally. Consequently, Airbnb generates extensive datasets from its listings that contain rich features that have captured the attention of researchers. These datasets offer potentially valuable information that can be extracted to greatly assist individuals and governments in making more informed decisions. Pricing rental properties on Airbnb still presents a challenge for owners, as it directly impacts customer demand. This research aimed to conquer the challenge by developing a sustainable price prediction model for Airbnb listings by incorporating property specifications, owner information and customer reviews. By utilising this model, owners can estimate the expected value of their Airbnb listings. We trained and fine-tuned several machine learning models using an Airbnb listing dataset from Barcelona. Performance evaluation metrics, such as mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE) and R2 score were then used to compare the models. To enhance the performance of the predictive models, sentiment analysis was used to extract relevant features from customer reviews. Feature importance analysis was also conducted to determine which attributes were the most influential on listing price predictions. The results show that the Lasso and Ridge models outperformed the others considered in the study, with an average R2 score of 99%. We found that amenities-related features had a negligible impact on all models’ performance. The most significant features found were polarity (positive/negative sentiment), the number of bedrooms, the accommodation’s maximum capacity, the number of beds and the quantity of reviews received by the listing in the past 12 months, respectively. We found that certain room types (categorized as entire home/apartment, private room or shared room) are associated with lower predicted prices.

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