 Airbnb Dynamic Pricing Using Machine LearningYuhan Wang ()
A chapter in New Perspectives and Paradigms in Applied Economics and Business, 2024, pp 37-51 from Springer Abstract: Abstract Airbnb is a leading home and apartment-sharing company which have large number of listings. However, how to set an optimal price is a big challenge. While it also involves a large number of data. Thus, we consider dynamic pricing with machine learning techniques to solve this problem. This paper investigates the dynamic pricing on Airbnb using machine learning-based models. Linear regression, random forest, K-nearest neighborhood, AdaBoost Classifier, and Naïve Bayes are trained and tuned on the open dataset of Airbnb listings from New York in 2022. The main contribution of our paper is to find the most suitable price in Airbnb. The resulting models are compared based on R-squared (R2) and root mean square error (RMSE). Experiments show that the random forest model achieves an R2 of 0.997 and an RMSE of 0.038, which is the best model among these five models. The R2 of Naïve Bayes classifier is also ideal, but its RMSE is different from random forest. Keywords: Dynamic pricing; Machine learning; Airbnb (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-49951-7_4 Ordering information: This item can be ordered from DOI: 10.1007/978-3-031-49951-7_4 Access Statistics for this chapter More chapters in Springer Proceedings in Business and Economics from Springer |
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