Unravelling Airbnb Predicting Price for New Listing

Paridhi Choudhary, Aniket Jain and Rahul Baijal

Papers from arXiv.org

Abstract: This paper analyzes Airbnb listings in the city of San Francisco to better understand how different attributes such as bedrooms, location, house type amongst others can be used to accurately predict the price of a new listing that optimal in terms of the host's profitability yet affordable to their guests. This model is intended to be helpful to the internal pricing tools that Airbnb provides to its hosts. Furthermore, additional analysis is performed to ascertain the likelihood of a listings availability for potential guests to consider while making a booking. The analysis begins with exploring and examining the data to make necessary transformations that can be conducive for a better understanding of the problem at large while helping us make hypothesis. Moving further, machine learning models are built that are intuitive to use to validate the hypothesis on pricing and availability and run experiments in that context to arrive at a viable solution. The paper then concludes with a discussion on the business implications, associated risks and future scope.

Date: 2018-05
New Economics Papers: this item is included in nep-big, nep-pay, nep-tur and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1805.12101

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