Research on Different Factors Affecting Airbnb Housing Prices

Gengyuan Zeng; Ningning Huo; Xinhe Zhang

doi:10.54254/2754-1169/2024.25061

Advances in Economics, Management and Political SciencesOpen access

Research on Different Factors Affecting Airbnb Housing Prices

Research Article

Open Access

Research on Different Factors Affecting Airbnb Housing Prices

Gengyuan Zeng ^1* Ningning Huo ², Xinhe Zhang ³

¹ Macau University of Science and Technology

² The Ohio State University

³ The Ohio State University

^*Corresponding author: 18759575497@163.com

Published on 11 July 2025

AEMPS Vol.202

ISSN (Print): 2754-1177

ISSN (Online): 2754-1169

ISBN (Print): 978-1-80590-263-8

ISBN (Online): 978-1-80590-264-5

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Abstract

To study the different factors affecting Airbnb housing prices, this research uses the data from the Airbnb platform and evaluates key determinants influencing listing prices on the Airbnb website. The study employs exploratory data analysis (EDA) and linear regression analysis to analyze the impact of these variables on Airbnb housing prices. The research finding reveals that accommodations, bedrooms, and beds have a significant correlation with housing prices. Insights coming out of this research are able to provide valuable perspectives to Airbnb hosts and its housing rental business. It is suggested that focusing on these factors will enhance Airbnb’s pricing strategies and its overall market performance.

Keywords:

Airbnb, Exploratory Data Analysis (EDA), Linear Regression Analysis, Pricing Strategies

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References

[1]. Xie, K., & Mao, Z. (2017). The impacts of quality and quantity attributes of airbnb hosts on listing performance. International Journal of Contemporary Hospitality Management, 29(9), 2240-2260. doi: https: //doi.org/10.1108/IJCHM-07-2016-0345

[2]. Glusac, E. (2016) Hotels vs. Airbnb: Let the Battle Begin. New York Times. Retrieved from https: //www.proquest.com/newspapers/hotels-vs-airbnb-let-battle-begin/docview/1809640061/se-2

[3]. WEED, & JULIE. (2015). Hotels view airbnb as hardly a threat, for now. New York Times, 164(56864), B4-B4.

[4]. Worzala, E., Lenk, M., Silva, A. (1995) An Exploration of Neural Networks and Its Application to Real Estate Valuation. Journal of Real Estate Research, 10: 185–201. https: //doi.org/10.1080/10835547.1995.12090782.

[5]. Nguyen, N., Cripps, A. (2001) Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks. The Journal of Real Estate Research, 22: 313-336. Retrieved from https: //www.proquest.com/scholarly-journals/predicting-housing-value-comparison-multiple/docview/200301026/se-2.

References

[2]. Glusac, E. (2016) Hotels vs. Airbnb: Let the Battle Begin. New York Times. Retrieved from https: //www.proquest.com/newspapers/hotels-vs-airbnb-let-battle-begin/docview/1809640061/se-2

[3]. WEED, & JULIE. (2015). Hotels view airbnb as hardly a threat, for now. New York Times, 164(56864), B4-B4.

Cite this article

Zeng,G.;Huo,N.;Zhang,X. (2025). Research on Different Factors Affecting Airbnb Housing Prices. Advances in Economics, Management and Political Sciences,202,37-49.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of the 3rd International Conference on Financial Technology and Business Analysis

ISBN: 978-1-80590-263-8(Print) / 978-1-80590-264-5(Online)

Editor: Ursula Faura-Martínez

Conference website: https://2024.icftba.org/

Conference date: 13 June 2025

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.202

ISSN: 2754-1169(Print) / 2754-1177(Online)

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