Honors Theses and Capstones

Date of Award

Spring 2020

Project Type

Senior Honors Thesis

College or School

PAUL

Department

Decision Sciences

Program or Major

Information Systems and Business Analytics

Degree Name

Bachelor of Science

First Advisor

Christopher Glynn

Abstract

Many sharing-economy companies like Airbnb have their profits rely on the dynamic pricing market that they participate in. Airbnb hosts can set their own price based on what they deem the market will buy. Recent research argues that almost all hosts fail to maximize their potential profit due to poorly pricing their listing (Gibbs et al., 2018). While previous studies have looked at how specific variables effect the price of an Airbnb listing, this study aims to be the first to group variables separately into two distinct categories based on the host’s ability to control that variable. Looking at two U.S. cities with developed Airbnb markets, this study aims to use linear regression analysis to determine the significance that variables inside of the host’s control have on price versus variables outside of the host’s ability to control. The results show that variables within the host’s control appear to have more of an impact on price versus variables outside of the host’s control. Also, when variables inside and outside of the host’s control are combined, they prove most accurate when predicting the price of a listing.

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