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An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features

Indranil Ghosh (IT and Analytics Area, Institute of Management Technology Hyderabad, Hyderabad, India)
Rabin K. Jana (Operations and Quantitative Methods Area, Indian Institute of Management Raipur, Raipur, India)
Mohammad Zoynul Abedin (Department of Finance, Performance and Marketing, Teesside University International Business School, Middlesbrough, UK)

International Journal of Contemporary Hospitality Management

ISSN: 0959-6119

Article publication date: 15 March 2023

Issue publication date: 30 August 2023




The prediction of Airbnb listing prices predominantly uses a set of amenity-driven features. Choosing an appropriate set of features from thousands of available amenity-driven features makes the prediction task difficult. This paper aims to propose a scalable, robust framework to predict listing prices of Airbnb units without using amenity-driven features.


The authors propose an artificial intelligence (AI)-based framework to predict Airbnb listing prices. The authors consider 75 thousand Airbnb listings from the five US cities with more than 1.9 million observations. The proposed framework integrates (i) feature screening, (ii) stacking that combines gradient boosting, bagging, random forest, (iii) particle swarm optimization and (iv) explainable AI to accomplish the research objective.


The key findings have three aspects – prediction accuracy, homogeneity and identification of best and least predictable cities. The proposed framework yields predictions of supreme precision. The predictability of listing prices varies significantly across cities. The listing prices are the best predictable for Boston and the least predictable for Chicago.

Practical implications

The framework and findings of the research can be leveraged by the hosts to determine rental prices and augment the service offerings by emphasizing key features, respectively.


Although individual components are known, the way they have been integrated into the proposed framework to derive a high-quality forecast of Airbnb listing prices is unique. It is scalable. The Airbnb listing price modeling literature rarely witnesses such a framework.



The authors are grateful to the anonymous reviewers, the Editor-in-Chief and the Associate Editor for their careful evaluation to improve the quality and presentation of the paper.


Ghosh, I., Jana, R.K. and Abedin, M.Z. (2023), "An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features", International Journal of Contemporary Hospitality Management, Vol. 35 No. 10, pp. 3592-3611.



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