Impact of households- mortgage debt on consumption and prediction of households- debt payment issues in the Spanish economy

Detalhes bibliográficos
Autor(a) principal: Rodríguez, Beatriz Vidal
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/10362/144712
Resumo: This paper aims at analysing the impact of households mortgage debt on consumption, as well as the default behaviour of Spanish households over the last 18 years, using the data collected in the Survey of Household Finances (EFF). For this purpose ,a regression model is created, where consumption is expressed mainly as a function of several households social economic characteristics and financial positions, finding out that changes in consumption are highly negatively sensitive to changes in house-holds’ mortgage debt; whereas income and possession of real and financial assets are positively associated to changes in households’ consumption. Besides, three different machine learning algorithms are implemented to predict when households face debt payment issues. After evaluating and comparing the performance of each of them, it is concluded that both the Nearest Neigh bors and Classification Tree approaches are the best ones.
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spelling Impact of households- mortgage debt on consumption and prediction of households- debt payment issues in the Spanish economyMachine learningHousehold consumptionFinancial crisisData analyticsSpainMortgage debtLoan defaultDomínio/Área Científica::Ciências Sociais::Economia e GestãoThis paper aims at analysing the impact of households mortgage debt on consumption, as well as the default behaviour of Spanish households over the last 18 years, using the data collected in the Survey of Household Finances (EFF). For this purpose ,a regression model is created, where consumption is expressed mainly as a function of several households social economic characteristics and financial positions, finding out that changes in consumption are highly negatively sensitive to changes in house-holds’ mortgage debt; whereas income and possession of real and financial assets are positively associated to changes in households’ consumption. Besides, three different machine learning algorithms are implemented to predict when households face debt payment issues. After evaluating and comparing the performance of each of them, it is concluded that both the Nearest Neigh bors and Classification Tree approaches are the best ones.Gianinazzi, VirginiaRUNRodríguez, Beatriz Vidal2022-10-14T14:10:41Z2022-01-122021-12-172022-01-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/144712TID:203063120enginfo: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:RCAAP2024-03-11T05:24:33Zoai:run.unl.pt:10362/144712Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:51:42.831156Repositório 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 Impact of households- mortgage debt on consumption and prediction of households- debt payment issues in the Spanish economy
title Impact of households- mortgage debt on consumption and prediction of households- debt payment issues in the Spanish economy
spellingShingle Impact of households- mortgage debt on consumption and prediction of households- debt payment issues in the Spanish economy
Rodríguez, Beatriz Vidal
Machine learning
Household consumption
Financial crisis
Data analytics
Spain
Mortgage debt
Loan default
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Impact of households- mortgage debt on consumption and prediction of households- debt payment issues in the Spanish economy
title_full Impact of households- mortgage debt on consumption and prediction of households- debt payment issues in the Spanish economy
title_fullStr Impact of households- mortgage debt on consumption and prediction of households- debt payment issues in the Spanish economy
title_full_unstemmed Impact of households- mortgage debt on consumption and prediction of households- debt payment issues in the Spanish economy
title_sort Impact of households- mortgage debt on consumption and prediction of households- debt payment issues in the Spanish economy
author Rodríguez, Beatriz Vidal
author_facet Rodríguez, Beatriz Vidal
author_role author
dc.contributor.none.fl_str_mv Gianinazzi, Virginia
RUN
dc.contributor.author.fl_str_mv Rodríguez, Beatriz Vidal
dc.subject.por.fl_str_mv Machine learning
Household consumption
Financial crisis
Data analytics
Spain
Mortgage debt
Loan default
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Machine learning
Household consumption
Financial crisis
Data analytics
Spain
Mortgage debt
Loan default
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description This paper aims at analysing the impact of households mortgage debt on consumption, as well as the default behaviour of Spanish households over the last 18 years, using the data collected in the Survey of Household Finances (EFF). For this purpose ,a regression model is created, where consumption is expressed mainly as a function of several households social economic characteristics and financial positions, finding out that changes in consumption are highly negatively sensitive to changes in house-holds’ mortgage debt; whereas income and possession of real and financial assets are positively associated to changes in households’ consumption. Besides, three different machine learning algorithms are implemented to predict when households face debt payment issues. After evaluating and comparing the performance of each of them, it is concluded that both the Nearest Neigh bors and Classification Tree approaches are the best ones.
publishDate 2021
dc.date.none.fl_str_mv 2021-12-17
2022-10-14T14:10:41Z
2022-01-12
2022-01-12T00:00:00Z
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/10362/144712
TID:203063120
url http://hdl.handle.net/10362/144712
identifier_str_mv TID:203063120
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
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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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