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Studies of the Airbnb Peer-To-Peer P...

Pan, Qi.

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Studies of the Airbnb Peer-To-Peer Platform.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Studies of the Airbnb Peer-To-Peer Platform./
Author:	Pan, Qi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	131 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-04, Section: A.
Contained By:	Dissertations Abstracts International82-04A.
Subject:	Mass communications. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28023468
ISBN:	9798672162775

Studies of the Airbnb Peer-To-Peer Platform.
Pan, Qi.

Studies of the Airbnb Peer-To-Peer Platform. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 131 p.

Source: Dissertations Abstracts International, Volume: 82-04, Section: A.

Thesis (Ph.D.)--University of Pennsylvania, 2020.

This item must not be sold to any third party vendors.

This dissertation is an empirical study of a peer-to-peer market, Airbnb. Chapter 2 examines the impact of Airbnb on the rental housing market. Airbnb can potentially cannibalize long-term rental and affects rental housing supply and affordability. However, Airbnb also provides extra income for long-term renters. Existing regulations try to improve rental supply, either by prohibiting certain types of apartments from operating on Airbnb or by controlling the maximum nights a listing can be rented. It remains unclear whether these regulations can achieve their goals. Using Airbnb listings data and a novel dataset that allows me to trace the moving history of long-term renters, I build a structural model and simulate the effectiveness of these policies. I find that Airbnb does cannibalize the long-term rental supply market. Airbnb also has a greater negative impact on long-term renters with higher income. The counterfactual results suggest that the first policy is better in improving the welfare of long-term renters than the second policy. Chapter 3 turns from the impact of Airbnb on the long-term rental market to the pricing on the platform. On many e-commerce platforms such as Airbnb, the pricing problems are intrinsically dynamic. However, many sellers on these platforms do not update prices frequently. In this chapter, I develop a dynamic pricing model to study the revenue and welfare implication of automated pricing, which allows sellers to update their prices without manual interference. The model focuses on three factors through which automated pricing influences sellers: price adjustment cost, buyer's varying willingness to pay and inventory structure. In the model, I also take into account competition among sellers. Utilizing a unique data set of detailed Airbnb rental history and price trajectory in New York City, I find that the price rigidity observed in the data can be rationalized by a price adjustment cost ranging from 0.9% to 2.2% of the listed price. Moreover, automated pricing can increase the platform's revenue by 4.8% and the hosts' (sellers') by 3.9%. The renters (buyers) could be either better off or worse off, depending on the length of their stays.

ISBN: 9798672162775Subjects--Topical Terms:

3422380
Mass communications.
Subjects--Index Terms:

Airbnb

Studies of the Airbnb Peer-To-Peer Platform.
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506 $a This item must not be sold to any third party vendors.
520 $a This dissertation is an empirical study of a peer-to-peer market, Airbnb. Chapter 2 examines the impact of Airbnb on the rental housing market. Airbnb can potentially cannibalize long-term rental and affects rental housing supply and affordability. However, Airbnb also provides extra income for long-term renters. Existing regulations try to improve rental supply, either by prohibiting certain types of apartments from operating on Airbnb or by controlling the maximum nights a listing can be rented. It remains unclear whether these regulations can achieve their goals. Using Airbnb listings data and a novel dataset that allows me to trace the moving history of long-term renters, I build a structural model and simulate the effectiveness of these policies. I find that Airbnb does cannibalize the long-term rental supply market. Airbnb also has a greater negative impact on long-term renters with higher income. The counterfactual results suggest that the first policy is better in improving the welfare of long-term renters than the second policy. Chapter 3 turns from the impact of Airbnb on the long-term rental market to the pricing on the platform. On many e-commerce platforms such as Airbnb, the pricing problems are intrinsically dynamic. However, many sellers on these platforms do not update prices frequently. In this chapter, I develop a dynamic pricing model to study the revenue and welfare implication of automated pricing, which allows sellers to update their prices without manual interference. The model focuses on three factors through which automated pricing influences sellers: price adjustment cost, buyer's varying willingness to pay and inventory structure. In the model, I also take into account competition among sellers. Utilizing a unique data set of detailed Airbnb rental history and price trajectory in New York City, I find that the price rigidity observed in the data can be rationalized by a price adjustment cost ranging from 0.9% to 2.2% of the listed price. Moreover, automated pricing can increase the platform's revenue by 4.8% and the hosts' (sellers') by 3.9%. The renters (buyers) could be either better off or worse off, depending on the length of their stays.
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