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

Indranil Ghosh, Rabin K. Jana, Mohammad Abedin Orcid Logo

International Journal of Contemporary Hospitality Management, Volume: 35, Issue: 10, Pages: 3592 - 3611

Swansea University Author: Mohammad Abedin Orcid Logo

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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...

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Published in: International Journal of Contemporary Hospitality Management
ISSN: 0959-6119 0959-6119
Published: Emerald 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64223
first_indexed 2023-09-20T15:05:15Z
last_indexed 2024-11-25T14:13:39Z
id cronfa64223
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spelling 2023-09-20T16:05:16.9381675 v2 64223 2023-08-31 An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2023-08-31 CBAE 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. Journal Article International Journal of Contemporary Hospitality Management 35 10 3592 3611 Emerald 0959-6119 0959-6119 Airbnb, Listing price, Ensemble machine learning, Stacking, Explainable AI 30 8 2023 2023-08-30 10.1108/ijchm-05-2022-0562 http://dx.doi.org/10.1108/ijchm-05-2022-0562 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2023-09-20T16:05:16.9381675 2023-08-31T17:23:00.1037085 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Indranil Ghosh 1 Rabin K. Jana 2 Mohammad Abedin 0000-0002-4688-0619 3
title An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features
spellingShingle An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features
Mohammad Abedin
title_short An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features
title_full An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features
title_fullStr An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features
title_full_unstemmed An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features
title_sort An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Indranil Ghosh
Rabin K. Jana
Mohammad Abedin
format Journal article
container_title International Journal of Contemporary Hospitality Management
container_volume 35
container_issue 10
container_start_page 3592
publishDate 2023
institution Swansea University
issn 0959-6119
0959-6119
doi_str_mv 10.1108/ijchm-05-2022-0562
publisher Emerald
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.1108/ijchm-05-2022-0562
document_store_str 0
active_str 0
description 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.
published_date 2023-08-30T20:24:20Z
_version_ 1821347844416602112
score 11.04748

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