Journal article 526 views
An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features
International Journal of Contemporary Hospitality Management, Volume: 35, Issue: 10, Pages: 3592 - 3611
Swansea University Author:
Mohammad Abedin
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1108/ijchm-05-2022-0562
Abstract
Purpose: 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 li...
| Published in: | International Journal of Contemporary Hospitality Management |
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| ISSN: | 0959-6119 0959-6119 |
| Published: |
Emerald
2023
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64223 |
| Abstract: |
Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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. |
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| Keywords: |
Airbnb, Listing price, Ensemble machine learning, Stacking, Explainable AI |
| College: |
Faculty of Humanities and Social Sciences |
| Issue: |
10 |
| Start Page: |
3592 |
| End Page: |
3611 |