<?xml version="1.0" encoding="UTF-8"?><feed xmlns="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
<title>RP / PPA - Dissertação</title>
<link href="https://hdl.handle.net/10438/18120" rel="alternate"/>
<subtitle/>
<id>https://hdl.handle.net/10438/18120</id>
<updated>2021-12-04T04:05:44Z</updated>
<dc:date>2021-12-04T04:05:44Z</dc:date>
<entry>
<title>Learning in peer-to-peer markets: evidence from Airbnb</title>
<link href="https://hdl.handle.net/10438/16568" rel="alternate"/>
<author>
<name>Wu, Edson An An</name>
</author>
<id>https://hdl.handle.net/10438/16568</id>
<updated>2018-08-23T13:13:26Z</updated>
<published>2016-01-01T00:00:00Z</published>
<summary type="text">Learning in peer-to-peer markets: evidence from Airbnb
Wu, Edson An An
Peer-to-peer markets are highly uncertain environments due to the constant presence of shocks. As a consequence, sellers have to constantly adjust to these shocks. Dynamic Pricing is hard, especially for non-professional sellers. We study it in an accommodation rental marketplace, Airbnb. With scraped data from its website, we: 1) describe pricing patterns consistent with learning; 2) estimate a demand model and use it to simulate a dynamic pricing model. We simulate it under three scenarios: a) with learning; b) without learning; c) with full information. We have found that information is an important feature concerning rental markets. Furthermore, we have found that learning is important for hosts to improve their profits.
</summary>
<dc:date>2016-01-01T00:00:00Z</dc:date>
</entry>
</feed>
