Submitted by Edson Wu (edson_wu1991@hotmail.com) on 2016-05-09T21:45:41Z No. of bitstreams: 1 Dissertation - V20160422.pdf: 3700609 bytes, checksum: fd4bc7b83bbddb6dc18e2b24154d988f (MD5) / Approved for entry into archive by GILSON ROCHA MIRANDA (gilson.miranda@fgv.br) on 2016-05-19T19:50:27Z (GMT) No. of bitstreams: 1 Dissertation - V20160422.pdf: 3700609 bytes, checksum: fd4bc7b83bbddb6dc18e2b24154d988f (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2016-06-06T13:41:31Z (GMT) No. of bitstreams: 1 Dissertation - V20160422.pdf: 3700609 bytes, checksum: fd4bc7b83bbddb6dc18e2b24154d988f (MD5) / Made available in DSpace on 2016-06-06T13:43:11Z (GMT). No. of bitstreams: 1 Dissertation - V20160422.pdf: 3700609 bytes, checksum: fd4bc7b83bbddb6dc18e2b24154d988f (MD5) Previous issue date: 2016-03-30 / 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.