Hedonic Pricing Model, Random Forest and Artificial Neural Network: Comparison for Real Estate Price Prediction in Lisbon

Detalhes bibliográficos
Autor(a) principal: Fonseca, Pedro Gonçalo Varela Braz Mendes da
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/149240
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
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spelling Hedonic Pricing Model, Random Forest and Artificial Neural Network: Comparison for Real Estate Price Prediction in LisbonReal EstateHouse Price PredictionMachine LearningHedonic Pricing ModelRandom ForestArtificial Neural NetworkDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe real estate market is a complex industry with many variables affecting the prices of transactions. Formerly, understanding this market could be restricted by the lack of data or tools. However, with the current developments in the Artificial Intelligence field, three machine learning models (Hedonic Pricing Model, Random Forest and Artificial Neural Network) are deployed and set side by side on the topic of predicting the price per square meter of housing properties, using real estate data from Lisbon, Portugal. Overall, model evaluation reveals that the Random Forest was the best performing model, followed by the Artificial Neural Network and the Hedonic Model. These can predict the selling price per square meter accurately and learn the importance of certain features. Furthermore, the models implemented allow for users in the real estate paradigm to accurately and objectively assess the selling price or asking price of a dwelling, as well as estimate the valuation of certain features of a property.Neto, Miguel de Castro Simões FerreiraJardim, João Bruno Morais de SousaRUNFonseca, Pedro Gonçalo Varela Braz Mendes da2023-02-15T16:57:10Z2023-01-252023-01-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/149240TID:203228570enginfo: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:RCAAP2024-03-11T05:31:09Zoai:run.unl.pt:10362/149240Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:40.714126Repositório 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 Hedonic Pricing Model, Random Forest and Artificial Neural Network: Comparison for Real Estate Price Prediction in Lisbon
title Hedonic Pricing Model, Random Forest and Artificial Neural Network: Comparison for Real Estate Price Prediction in Lisbon
spellingShingle Hedonic Pricing Model, Random Forest and Artificial Neural Network: Comparison for Real Estate Price Prediction in Lisbon
Fonseca, Pedro Gonçalo Varela Braz Mendes da
Real Estate
House Price Prediction
Machine Learning
Hedonic Pricing Model
Random Forest
Artificial Neural Network
title_short Hedonic Pricing Model, Random Forest and Artificial Neural Network: Comparison for Real Estate Price Prediction in Lisbon
title_full Hedonic Pricing Model, Random Forest and Artificial Neural Network: Comparison for Real Estate Price Prediction in Lisbon
title_fullStr Hedonic Pricing Model, Random Forest and Artificial Neural Network: Comparison for Real Estate Price Prediction in Lisbon
title_full_unstemmed Hedonic Pricing Model, Random Forest and Artificial Neural Network: Comparison for Real Estate Price Prediction in Lisbon
title_sort Hedonic Pricing Model, Random Forest and Artificial Neural Network: Comparison for Real Estate Price Prediction in Lisbon
author Fonseca, Pedro Gonçalo Varela Braz Mendes da
author_facet Fonseca, Pedro Gonçalo Varela Braz Mendes da
author_role author
dc.contributor.none.fl_str_mv Neto, Miguel de Castro Simões Ferreira
Jardim, João Bruno Morais de Sousa
RUN
dc.contributor.author.fl_str_mv Fonseca, Pedro Gonçalo Varela Braz Mendes da
dc.subject.por.fl_str_mv Real Estate
House Price Prediction
Machine Learning
Hedonic Pricing Model
Random Forest
Artificial Neural Network
topic Real Estate
House Price Prediction
Machine Learning
Hedonic Pricing Model
Random Forest
Artificial Neural Network
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
publishDate 2023
dc.date.none.fl_str_mv 2023-02-15T16:57:10Z
2023-01-25
2023-01-25T00:00:00Z
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/10362/149240
TID:203228570
url http://hdl.handle.net/10362/149240
identifier_str_mv TID:203228570
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
repository.mail.fl_str_mv
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