Quantifying Covid-19 impact on Airbnb hosting: Lisbon as a case study

Detalhes bibliográficos
Autor(a) principal: Merzenich, Justus
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/122672
Resumo: While Covid-19 impact on tourism and the sharing economy has proven to be significant by plenty of previous research, data and tools to recursively measure financial impact are missing in the current state of knowledge. This paper aims at quantifying the disease’s financial impact on Airbnb prices, bookings and hosting revenues with machine learning. The bottom-up approach used predicts a city’s losses at listing level over time and therefore grants leeway to analyzing impact across various dimensions. The city of Lisbon is used to showcase the model’s performance and versatility of results.
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spelling Quantifying Covid-19 impact on Airbnb hosting: Lisbon as a case studyCovid-19AirbnbSharing economyData scienceMachine learningDomínio/Área Científica::Ciências Sociais::Economia e GestãoWhile Covid-19 impact on tourism and the sharing economy has proven to be significant by plenty of previous research, data and tools to recursively measure financial impact are missing in the current state of knowledge. This paper aims at quantifying the disease’s financial impact on Airbnb prices, bookings and hosting revenues with machine learning. The bottom-up approach used predicts a city’s losses at listing level over time and therefore grants leeway to analyzing impact across various dimensions. The city of Lisbon is used to showcase the model’s performance and versatility of results.Han, QiweiRUNMerzenich, Justus2021-08-18T10:16:53Z2021-01-132021-01-042021-01-13T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/122672TID:202741648enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:04:13Zoai:run.unl.pt:10362/122672Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:44:49.378341Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Quantifying Covid-19 impact on Airbnb hosting: Lisbon as a case study
title Quantifying Covid-19 impact on Airbnb hosting: Lisbon as a case study
spellingShingle Quantifying Covid-19 impact on Airbnb hosting: Lisbon as a case study
Merzenich, Justus
Covid-19
Airbnb
Sharing economy
Data science
Machine learning
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Quantifying Covid-19 impact on Airbnb hosting: Lisbon as a case study
title_full Quantifying Covid-19 impact on Airbnb hosting: Lisbon as a case study
title_fullStr Quantifying Covid-19 impact on Airbnb hosting: Lisbon as a case study
title_full_unstemmed Quantifying Covid-19 impact on Airbnb hosting: Lisbon as a case study
title_sort Quantifying Covid-19 impact on Airbnb hosting: Lisbon as a case study
author Merzenich, Justus
author_facet Merzenich, Justus
author_role author
dc.contributor.none.fl_str_mv Han, Qiwei
RUN
dc.contributor.author.fl_str_mv Merzenich, Justus
dc.subject.por.fl_str_mv Covid-19
Airbnb
Sharing economy
Data science
Machine learning
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Covid-19
Airbnb
Sharing economy
Data science
Machine learning
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description While Covid-19 impact on tourism and the sharing economy has proven to be significant by plenty of previous research, data and tools to recursively measure financial impact are missing in the current state of knowledge. This paper aims at quantifying the disease’s financial impact on Airbnb prices, bookings and hosting revenues with machine learning. The bottom-up approach used predicts a city’s losses at listing level over time and therefore grants leeway to analyzing impact across various dimensions. The city of Lisbon is used to showcase the model’s performance and versatility of results.
publishDate 2021
dc.date.none.fl_str_mv 2021-08-18T10:16:53Z
2021-01-13
2021-01-04
2021-01-13T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/122672
TID:202741648
url http://hdl.handle.net/10362/122672
identifier_str_mv TID:202741648
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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