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Erschienen in:
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2024 | OriginalPaper | Buchkapitel

Airbnb Dynamic Pricing Using Machine Learning

verfasst von : Yuhan Wang

Erschienen in: New Perspectives and Paradigms in Applied Economics and Business

Verlag: Springer Nature Switzerland

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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.

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Vorheriges Kapitel On the Links Between Forecasting Performance and Statistical Features of Time Series Applied to the Cash Flow of Self-Employed Workers
Nächstes Kapitel Monte Carlo Value-At-Risk and Expected Shortfall of Efficient-Frontier Investment Portfolios: Testing Gaussian Versus Vine Copulas and Normal Versus Empirical Marginals
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Metadaten
Titel
Airbnb Dynamic Pricing Using Machine Learning
verfasst von
Yuhan Wang
Copyright-Jahr
2024
Buch
New Perspectives and Paradigms in Applied Economics and Business

Print ISBN: 978-3-031-49950-0

Electronic ISBN: 978-3-031-49951-7

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-3-031-49951-7_4