Evaluation of spatial data’s impact in mid-term room rent price through application of spatial econometrics and machine learning. Case study: Lisbon

Detalhes bibliográficos
Autor(a) principal: Petkov, Mihail
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/93716
Resumo: Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
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spelling Evaluation of spatial data’s impact in mid-term room rent price through application of spatial econometrics and machine learning. Case study: LisbonPredictive ModelingAmenitiesSupport Vector RegressionSpatial EconometricsHedonic Price ModellingPoints of InterestMid-Term RentDissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesHousehold preferences is a topic whose relevance can be found to dominate the applied economics, but whereas urban economies view cities as production centers, this thesis aims to give importance to the role of consumption. Provision to PoIs might give explanation to what individuals value as an important asset for improvement of their quality of life in a chosen city. As such, understanding short-term rentals and real estate prices have induced various research to seek proof of impacting factors, but analysis of mid-term rent has faced the challenge of being an overlooked category. This thesis consists of an integrated three-steps approach to analyze spatial data’s impact over the mid-term room rent, choosing Lisbon as its case study. The proposed methodology constitutes use of traditional spatial econometric models and SVR, encompassing a large set of proxies for amenities that might be recognized to hold a possible impact over rent prices. The analytical frameworks’ first step is to create a suitable HPM model that captures the data well, so significant variables can be detected and analyzed as a discrete dataset. The second step applies subsets of the dataset in the creation of SVR models, in hopes of identifying the SVs influencing price variances. Finally, SOM clusters are chosen to address whether more natural order of data division exists. Results confirm the impact of proximity to various categories of amenities, but the enrichment of models with the proposed proxies of spatial data failed to corroborate attainment of model with a higher accuracy. (Nüst et al., 2018) provides a self-assessment of the reproducibility of research, and according to the criteria given, this dissertation is evaluated as: 0, 2, 1, 2, 2 (input data, preprocessing, methods, computational environment, results).Henriques, Roberto André PereiraSilva, Joel Dinis Baptista Ferreira daGranell-Canut, CarlosRUNPetkov, Mihail2020-03-03T16:49:57Z2020-02-282020-02-28T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/93716TID:202456951enginfo: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:RCAAP2024-03-11T04:42:00Zoai:run.unl.pt:10362/93716Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:37:49.877449Repositório 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 Evaluation of spatial data’s impact in mid-term room rent price through application of spatial econometrics and machine learning. Case study: Lisbon
title Evaluation of spatial data’s impact in mid-term room rent price through application of spatial econometrics and machine learning. Case study: Lisbon
spellingShingle Evaluation of spatial data’s impact in mid-term room rent price through application of spatial econometrics and machine learning. Case study: Lisbon
Petkov, Mihail
Predictive Modeling
Amenities
Support Vector Regression
Spatial Econometrics
Hedonic Price Modelling
Points of Interest
Mid-Term Rent
title_short Evaluation of spatial data’s impact in mid-term room rent price through application of spatial econometrics and machine learning. Case study: Lisbon
title_full Evaluation of spatial data’s impact in mid-term room rent price through application of spatial econometrics and machine learning. Case study: Lisbon
title_fullStr Evaluation of spatial data’s impact in mid-term room rent price through application of spatial econometrics and machine learning. Case study: Lisbon
title_full_unstemmed Evaluation of spatial data’s impact in mid-term room rent price through application of spatial econometrics and machine learning. Case study: Lisbon
title_sort Evaluation of spatial data’s impact in mid-term room rent price through application of spatial econometrics and machine learning. Case study: Lisbon
author Petkov, Mihail
author_facet Petkov, Mihail
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
Silva, Joel Dinis Baptista Ferreira da
Granell-Canut, Carlos
RUN
dc.contributor.author.fl_str_mv Petkov, Mihail
dc.subject.por.fl_str_mv Predictive Modeling
Amenities
Support Vector Regression
Spatial Econometrics
Hedonic Price Modelling
Points of Interest
Mid-Term Rent
topic Predictive Modeling
Amenities
Support Vector Regression
Spatial Econometrics
Hedonic Price Modelling
Points of Interest
Mid-Term Rent
description Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
publishDate 2020
dc.date.none.fl_str_mv 2020-03-03T16:49:57Z
2020-02-28
2020-02-28T00:00:00Z
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/10362/93716
TID:202456951
url http://hdl.handle.net/10362/93716
identifier_str_mv TID:202456951
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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