Pricing Analytics of the Sharing Economy in Lodging—A Case Study of the Airbnb Online Marketplace

DOI

10.2991/aebmr.k.191217.145How to use a DOI?

Keywords

Sharing Economy, House Rental, Airbnb, Big Data Analysis, Econometrics

Abstract

This paper studies the pricing strategy of Airbnb’s online marketplace. Through the analysis of Airbnb’s publicly available listing data over time, we use econometric methods including statistical regression and time series analysis to understand how the Airbnb listings’ pricing is affected by their geofigureical locations, room-specific features (room type, number of beds, etc.), customer reviews and potential seasonality effects. We aim at gaining insights into Airbnb’s pricing strategy and providing observations and guidance on the operations of the two-sided markets and more broadly, the proliferating sharing economy. This study applies econometrics and big data analysis techniques to a practical setting. We use the Python’s Pandas package to conduct basic data analysis and visualization, and then we employ regression analysis and time series analysis to analyze the impact of various factors on pricing and make price predictions. In order to make the prediction more accurate, we use resampling method to get the average coefficient of the regression. Lastly, we draw conclusions from our analysis and provide practical insights and understanding of the Airbnb platform and the sharing economy it represents.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yiming Peng
PY  - 2019
DA  - 2019/12/20
TI  - Pricing Analytics of the Sharing Economy in Lodging—A Case Study of the Airbnb Online Marketplace
BT  - Proceedings of the 2019 International Conference on Economic Management and Cultural Industry (ICEMCI 2019)
PB  - Atlantis Press
SP  - 817
EP  - 838
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.191217.145
DO  - 10.2991/aebmr.k.191217.145
ID  - Peng2019
ER  -