Journal article 215 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: Abedin Abedin
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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...
Published in: | International Journal of Contemporary Hospitality Management |
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ISSN: | 0959-6119 0959-6119 |
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Emerald
2023
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URI: | https://cronfa.swan.ac.uk/Record/cronfa64223 |
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v2 64223 2023-08-31 An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-08-31 BAF 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 Accounting and Finance COLLEGE CODE BAF 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 Abedin Abedin 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 Abedin 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_***_Abedin Abedin |
author |
Abedin Abedin |
author2 |
Indranil Ghosh Rabin K. Jana Abedin 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 |
2023-08-30T16:05:15Z |
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1777569435104575488 |
score |
11.004418 |