Quantifying Covid-19 impact on Airbnb hosting: Lisbon as a case study
Autor(a) principal: | |
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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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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 |
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info:eu-repo/semantics/openAccess |
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openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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