The rental housing market in Barcelona: a nonparametric analysis
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- dc.contributor.author Cunquero Orts, Josep
- dc.date.accessioned 2021-10-26T13:30:03Z
- dc.date.available 2021-10-26T13:30:03Z
- dc.date.issued 2021
- dc.description Treball de Fi de Grau en Economia. Curs 2020-2021ca
- dc.description Tutors: Gabor Lugosi; María Gundín Castroca
- dc.description.abstract The quality of geolocation services and the amount of publicly available data on the internet have increased substantially in the last decade. This paper studies the Barcelonese rental real estate market by exploiting both geolocation services and user-generated public data. This allows for the creation of a dataset which is later analysed using kernel smoothing methods. The key ndings are that (i) rent prices are approximately log-normally distributed, that (ii) rent prices are lower in areas further away from the city centre, and that (iii) the previous point has to be complemented with a geospatial analysis that accounts for spatial heterogeneity.ca
- dc.format.mimetype application/pdf*
- dc.identifier.uri http://hdl.handle.net/10230/48815
- dc.language.iso engca
- dc.rights This work is licensed under a Creative Commons Attribution 4.0 International Licenseca
- dc.rights.accessRights info:eu-repo/semantics/openAccessca
- dc.rights.uri https://creativecommons.org/licenses/by/4.0/ca
- dc.subject.keyword Kernel smoothingen
- dc.subject.keyword User dataen
- dc.subject.keyword Housing marketsen
- dc.subject.other Treball de fi de grau – Curs 2020-2021ca
- dc.subject.other Econometriaca
- dc.title The rental housing market in Barcelona: a nonparametric analysisca
- dc.type info:eu-repo/semantics/bachelorThesisca