Forecasting real estate prices in Portugal based on a data science approach

Detalhes bibliográficos
Autor(a) principal: Samadani, Sanam
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/10400.5/23098
Resumo: Mestrado Bolonha em Economia Monetária e Financeira
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spelling Forecasting real estate prices in Portugal based on a data science approachreal estatepricespredictionARIMARegressionANNOLSMestrado Bolonha em Economia Monetária e FinanceiraThe evolution of the European residential market is notorious over the last ten years. House prices in the E.U. rose by 30.9 per cent between 2010 and the first quarter of 2021. The prices of homes in Portugal has risen almost 50 per cent during 11 years. Considering this previous argument, I propose the following research question: How to predict real estate prices. In this context, my research aims to analyze the prices’ evolution and understand the main components impacting the price of real estate. First, using the time series analysis, I use ARIMA to analyze the prices of real estate and the number of buildings sold since the first quarter of 2009, almost one year after the great recession in Portugal, until the third quarter of 2020 which was during the COVID-19 pandemic. The model was fitted and the prediction line was accurized with an upward trend. The second approach consists of analyzing the impact of five independent variables on real estate prices. To understand the most relevant components, regression analysis has been performed. I used OLS to analyze the impact of independent variables (crime rate, selected waste rate, tax rate, purchasing power and tourism rate) on real estate prices. Crime rate and tourism are negatively correlated while purchasing power, selected waste rate and tax rate are positively correlated with real estate prices. Then, I compared the accuracy of the result with neural networks and other types of regression analysis. Results were not much better than with linear regression. It is also essential to consider that this approach has some limitations, especially regarding the analysis’s granularity. The data has been collected from INE and PORDATA, the databases of contemporary Portugal, to construct the models and forecast house prices in Portugal.Instituto Superior de Economia e GestãoCosta, CarlosRepositório da Universidade de LisboaSamadani, Sanam2022-01-16T14:18:59Z2021-102021-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.5/23098engSamadani, Sanam (2021). "Forecasting real estate prices in Portugal based on a data science approach". Dissertação de Mestrado, Universidade de Lisboa. Instituto Superior de Economia e Gestão.info: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:RCAAP2023-03-06T14:52:36Zoai:www.repository.utl.pt:10400.5/23098Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:07:23.925799Repositório 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 Forecasting real estate prices in Portugal based on a data science approach
title Forecasting real estate prices in Portugal based on a data science approach
spellingShingle Forecasting real estate prices in Portugal based on a data science approach
Samadani, Sanam
real estate
prices
prediction
ARIMA
Regression
ANN
OLS
title_short Forecasting real estate prices in Portugal based on a data science approach
title_full Forecasting real estate prices in Portugal based on a data science approach
title_fullStr Forecasting real estate prices in Portugal based on a data science approach
title_full_unstemmed Forecasting real estate prices in Portugal based on a data science approach
title_sort Forecasting real estate prices in Portugal based on a data science approach
author Samadani, Sanam
author_facet Samadani, Sanam
author_role author
dc.contributor.none.fl_str_mv Costa, Carlos
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Samadani, Sanam
dc.subject.por.fl_str_mv real estate
prices
prediction
ARIMA
Regression
ANN
OLS
topic real estate
prices
prediction
ARIMA
Regression
ANN
OLS
description Mestrado Bolonha em Economia Monetária e Financeira
publishDate 2021
dc.date.none.fl_str_mv 2021-10
2021-10-01T00:00:00Z
2022-01-16T14:18:59Z
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/10400.5/23098
url http://hdl.handle.net/10400.5/23098
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Samadani, Sanam (2021). "Forecasting real estate prices in Portugal based on a data science approach". Dissertação de Mestrado, Universidade de Lisboa. Instituto Superior de Economia e Gestão.
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.publisher.none.fl_str_mv Instituto Superior de Economia e Gestão
publisher.none.fl_str_mv Instituto Superior de Economia e Gestão
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
repository.mail.fl_str_mv
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