IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i17p13159-d1231227.html
   My bibliography  Save this article

A Sustainable Price Prediction Model for Airbnb Listings Using Machine Learning and Sentiment Analysis

Author

Listed:
  • Zahyah H. Alharbi

    (Department of Management Information Systems, College of Business Administration, King Saud University, Riyadh 12372, Saudi Arabia)

Abstract

Since 2008, the company Airbnb has brought significant changes to the hospitality industry worldwide. Experiencing remarkable growth, it currently offers over six million listings in 191 countries across one hundred thousand cities. Airbnb has gained immense popularity among travellers seeking accommodations globally. Consequently, Airbnb generates extensive datasets from its listings that contain rich features that have captured the attention of researchers. These datasets offer potentially valuable information that can be extracted to greatly assist individuals and governments in making more informed decisions. Pricing rental properties on Airbnb still presents a challenge for owners, as it directly impacts customer demand. This research aimed to conquer the challenge by developing a sustainable price prediction model for Airbnb listings by incorporating property specifications, owner information and customer reviews. By utilising this model, owners can estimate the expected value of their Airbnb listings. We trained and fine-tuned several machine learning models using an Airbnb listing dataset from Barcelona. Performance evaluation metrics, such as mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE) and R 2 score were then used to compare the models. To enhance the performance of the predictive models, sentiment analysis was used to extract relevant features from customer reviews. Feature importance analysis was also conducted to determine which attributes were the most influential on listing price predictions. The results show that the Lasso and Ridge models outperformed the others considered in the study, with an average R 2 score of 99%. We found that amenities-related features had a negligible impact on all models’ performance. The most significant features found were polarity (positive/negative sentiment), the number of bedrooms, the accommodation’s maximum capacity, the number of beds and the quantity of reviews received by the listing in the past 12 months, respectively. We found that certain room types (categorized as entire home/apartment, private room or shared room) are associated with lower predicted prices.

Suggested Citation

  • Zahyah H. Alharbi, 2023. "A Sustainable Price Prediction Model for Airbnb Listings Using Machine Learning and Sentiment Analysis," Sustainability, MDPI, vol. 15(17), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:13159-:d:1231227
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/17/13159/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/17/13159/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Feifei Tian & Fengzhi Sun & Beibei Hu & Zhitao Dong, 2022. "The Impact on Bed and Breakfast Prices: Evidence from Airbnb in China," Sustainability, MDPI, vol. 14(21), pages 1-15, October.
    2. Chunfang Zhao & Yingliang Wu & Yunfeng Chen & Guohua Chen, 2023. "Multiscale Effects of Hedonic Attributes on Airbnb Listing Prices Based on MGWR: A Case Study of Beijing, China," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    3. Yating Wen & Xiaodong Zhao & Xingguo Li & Yuqi Zang, 2023. "Explaining the Paradox of World University Rankings in China: Higher Education Sustainability Analysis with Sentiment Analysis and LDA Topic Modeling," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
    4. Valentin Kuleto & Milena Ilić & Mihail Dumangiu & Marko Ranković & Oliva M. D. Martins & Dan Păun & Larisa Mihoreanu, 2021. "Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions," Sustainability, MDPI, vol. 13(18), pages 1-16, September.
    5. Ian Sutherland & Kiattipoom Kiatkawsin, 2020. "Determinants of Guest Experience in Airbnb: A Topic Modeling Approach Using LDA," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    6. Yumin Li & Ruiqi Yang & Xiaoman Wang & Jiaming Zhu & Nan Song, 2023. "Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
    7. Emeka Ndaguba & Cina Van Zyl, 2023. "Professionalizing Sharing Platforms for Sustainable Growth in the Hospitality Sector: Insights Gained through Hierarchical Linear Modeling," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    8. Kakar, Venoo & Voelz, Joel & Wu, Julia & Franco, Julisa, 2018. "The Visible Host: Does race guide Airbnb rental rates in San Francisco?," Journal of Housing Economics, Elsevier, vol. 40(C), pages 25-40.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ingrid Gould Ellen & Stephen L. Ross, 2018. "Race and the City," Working Papers 2018-022, Human Capital and Economic Opportunity Working Group.
    2. Gizéh Rangel-de Lázaro & Josep M. Duart, 2023. "You Can Handle, You Can Teach It: Systematic Review on the Use of Extended Reality and Artificial Intelligence Technologies for Online Higher Education," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    3. Zuo, Wenming & Bai, Weijing & Zhu, Wenfeng & He, Xinming & Qiu, Xinxin, 2022. "Changes in service quality of sharing accommodation: Evidence from airbnb," Technology in Society, Elsevier, vol. 71(C).
    4. Villarroel Ordenes, Francisco & Silipo, Rosaria, 2021. "Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications," Journal of Business Research, Elsevier, vol. 137(C), pages 393-410.
    5. Rocsana Bucea-Manea-Țoniș & Luciela Vasile & Rareș Stănescu & Alina Moanță, 2022. "Creating IoT-Enriched Learner-Centered Environments in Sports Science Higher Education during the Pandemic," Sustainability, MDPI, vol. 14(7), pages 1-25, April.
    6. Yongxin Liu & Yiting Wang & Yiwen Lin & Xiaoqing Ma & Shifa Guo & Qianru Ouyang & Caige Sun, 2023. "Habitat Quality Assessment and Driving Factors Analysis of Guangdong Province, China," Sustainability, MDPI, vol. 15(15), pages 1-23, July.
    7. Petter Törnberg & Letizia Chiappini, 2020. "Selling black places on Airbnb: Colonial discourse and the marketing of black communities in New York City," Environment and Planning A, , vol. 52(3), pages 553-572, May.
    8. Adam Pawlicz & Ema Petaković & Ana-Marija Vrtodušić Hrgović, 2022. "Beyond Airbnb. Determinants of Customer Satisfaction in P2P Accommodation in Time of COVID-19," Sustainability, MDPI, vol. 14(17), pages 1-15, August.
    9. Rocsana Bucea-Manea-Țoniş & Oliva M. D. Martins & Radu Bucea-Manea-Țoniş & Cătălin Gheorghiță & Valentin Kuleto & Milena P. Ilić & Violeta-Elena Simion, 2021. "Blockchain Technology Enhances Sustainable Higher Education," Sustainability, MDPI, vol. 13(22), pages 1-21, November.
    10. Jaeger, Bastian & Sleegers, Willem W.A. & Evans, Anthony M. & Stel, Mariëlle & van Beest, Ilja, 2019. "The effects of facial attractiveness and trustworthiness in online peer-to-peer markets," Journal of Economic Psychology, Elsevier, vol. 75(PA).
    11. Nayef Shaie Alotaibi & Awad Hajran Alshehri, 2023. "Prospers and Obstacles in Using Artificial Intelligence in Saudi Arabia Higher Education Institutions—The Potential of AI-Based Learning Outcomes," Sustainability, MDPI, vol. 15(13), pages 1-18, July.
    12. Löw, Franziska & Lüth, Hendrik, 2021. "Quality Signals on Airbnb: A Hedonic Regression Approach," Working Paper 189/2021, Helmut Schmidt University, Hamburg.
    13. Yoon, Sang-Hyeak & Park, Ga-Yun & Kim, Hee-Woong, 2022. "Unraveling the relationship between the dimensions of user experience and user satisfaction: A smart speaker case," Technology in Society, Elsevier, vol. 71(C).
    14. Zhu, Hongrui, 2021. "Why a non-discrimination policy upset Airbnb hosts?," Annals of Tourism Research, Elsevier, vol. 87(C).
    15. Nora Kenworthy & Zhihang Dong & Anne Montgomery & Emily Fuller & Lauren Berliner, 2020. "A cross-sectional study of social inequities in medical crowdfunding campaigns in the United States," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-23, March.
    16. Farajallah, Mehdi & Hammond, Robert G. & Pénard, Thierry, 2019. "What drives pricing behavior in Peer-to-Peer markets? Evidence from the carsharing platform BlaBlaCar," Information Economics and Policy, Elsevier, vol. 48(C), pages 15-31.
    17. Eugene Tan & Teegawende H. Zeida, 2023. "Consumer Demand and Credit Supply as Barriers to Growth for Black-Owned Startups," Opportunity and Inclusive Growth Institute Working Papers 079, Federal Reserve Bank of Minneapolis.
    18. Park, Jeongeun & Yang, Donguk & Kim, Ha Young, 2023. "Text mining-based four-step framework for smart speaker product improvement and sales planning," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    19. Olga Abramova, 2022. "No matter what the name, we’re all the same? Examining ethnic online discrimination in ridesharing marketplaces," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1419-1446, September.
    20. Milica Jevremović & Nada Staletić & Gheorghe Orzan & Milena P. Ilić & Zorica Jelić & Cristina Teodora Bălăceanu & Oana Valeria Paraschiv, 2022. "Predicting User Behaviour Based on the Level of Interactivity Implemented in Blockchain Technologies in Websites and Used Devices," Sustainability, MDPI, vol. 14(4), pages 1-20, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:13159-:d:1231227. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.