Arquitetura híbrida inteligente para classificação de liquidez imobiliária urbana em leilões
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da Uninove |
Texto Completo: | http://bibliotecatede.uninove.br/handle/tede/2791 |
Resumo: | The role of liquidity in the real estate market has attracted attention in the financial literature because of its strong impact on the economy and the sectors it covers. The liquidity of a property is an indicator of the speed or the degree of ease with which properties are traded, traded and converted into monetary value. Much of the information on these properties is available in large internet databases. If on the one hand, access to real estate data is not a problem, extracting knowledge from these databases is. Knowledge Discovery in Data Bases (KDD) systems are applied as a solution for the extraction of knowledge in decision making in a risky condition in the real estate business, since it is uncertain to establish a limit for this negotiation. Decisions occupying a central space in organizations become more complex under conditions of uncertainty. This implies that to meet the demand for success and quality of decisions, a decision-making process must be established that will have as central elements, the scenarios of these decisions, the alternatives and their impacts. Therefore, the following general objective was defined: to evaluate intelligent techniques and develop an Intelligent Hybrid Architecture (AHI) for classifying urban real estate liquidity in auctions, supporting the decision making process with a Double Impact and Probability Matrix (MPID). To achieve this goal, a series of experiments were conducted applying intelligent techniques to an actual database on an Auction site, containing auctioned and non-armed properties, in the years 2016 to 2020, collected randomly. The evaluation of intelligent techniques for data mining such as Randon Forest (RF), Decision Tree and Multilayer Perceptron Neural Network (MLP), determined the most promising techniques and most adherent to the real estate data collected, in joint action with AHI. The main characteristic of AHI is its ability to predict discount values, exposure time, number of bids and the classification of the end product. Therefore, the proposed model is capable of predicting and classifying the liquidity of auction properties through the enrichment of the database, reducing the decision bias for the classification of real estate liquidity in auctions. The synergy between AHI and MPID made it possible to map the threats and also the opportunities in this sector. A new concept called bidding edge was created in this work, which determines the convergence of real bids for a property. The evaluation of the extracted knowledge is useful and can be applied in the auction and banking sectors. The solution developed reached 75% Score, training the AHI RF technique with the number of standard trees in the "randomForest" library with 500 trees. |
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Napolitano, Domingos Marcio Rodrigueshttp://lattes.cnpq.br/0433818215929535Napolitano, Domingos Marcio Rodrigueshttp://lattes.cnpq.br/0433818215929535Gaspar, Marcos Antoniohttp://lattes.cnpq.br/3809285940688486Librantz, Andre Felipe Henriqueshttp://lattes.cnpq.br/3569470521730110Chalco, Jesús Pascual Menahttp://lattes.cnpq.br/4727357182510680Sassi, Renato Joséhttp://lattes.cnpq.br/8750334661789610http://lattes.cnpq.br/3392758888478198Albano, Marcelo Ferreira2021-12-02T15:00:01Z2020-12-17Albano, Marcelo Ferreira. Arquitetura híbrida inteligente para classificação de liquidez imobiliária urbana em leilões. 2020. 138 f. Dissertação( Programa de Pós-Graduação em Informática e Gestão do Conhecimento) - Universidade Nove de Julho, São Paulo.http://bibliotecatede.uninove.br/handle/tede/2791The role of liquidity in the real estate market has attracted attention in the financial literature because of its strong impact on the economy and the sectors it covers. The liquidity of a property is an indicator of the speed or the degree of ease with which properties are traded, traded and converted into monetary value. Much of the information on these properties is available in large internet databases. If on the one hand, access to real estate data is not a problem, extracting knowledge from these databases is. Knowledge Discovery in Data Bases (KDD) systems are applied as a solution for the extraction of knowledge in decision making in a risky condition in the real estate business, since it is uncertain to establish a limit for this negotiation. Decisions occupying a central space in organizations become more complex under conditions of uncertainty. This implies that to meet the demand for success and quality of decisions, a decision-making process must be established that will have as central elements, the scenarios of these decisions, the alternatives and their impacts. Therefore, the following general objective was defined: to evaluate intelligent techniques and develop an Intelligent Hybrid Architecture (AHI) for classifying urban real estate liquidity in auctions, supporting the decision making process with a Double Impact and Probability Matrix (MPID). To achieve this goal, a series of experiments were conducted applying intelligent techniques to an actual database on an Auction site, containing auctioned and non-armed properties, in the years 2016 to 2020, collected randomly. The evaluation of intelligent techniques for data mining such as Randon Forest (RF), Decision Tree and Multilayer Perceptron Neural Network (MLP), determined the most promising techniques and most adherent to the real estate data collected, in joint action with AHI. The main characteristic of AHI is its ability to predict discount values, exposure time, number of bids and the classification of the end product. Therefore, the proposed model is capable of predicting and classifying the liquidity of auction properties through the enrichment of the database, reducing the decision bias for the classification of real estate liquidity in auctions. The synergy between AHI and MPID made it possible to map the threats and also the opportunities in this sector. A new concept called bidding edge was created in this work, which determines the convergence of real bids for a property. The evaluation of the extracted knowledge is useful and can be applied in the auction and banking sectors. The solution developed reached 75% Score, training the AHI RF technique with the number of standard trees in the "randomForest" library with 500 trees.O papel de liquidez, no mercado imobiliário, tem atraído atenção na literatura financeira por conta de seu forte impacto na economia e pelos setores em que ela abrange. A liquidez de um imóvel é um indicador de velocidade ou o grau de facilidade com que as propriedades são negociadas, comercializadas e convertidas em valor monetário. Boa parte das informações desses imóveis estão disponíveis em grandes bases de dados pela internet. Se por um lado, o acesso a dados de imóveis não é um problema, extrair conhecimento dessas bases é. Os sistemas de descoberta de conhecimento Knowledge Discovery in Data Bases (KDD) são aplicados como uma solução para que a extração do conhecimento na tomada de decisão em condição de risco no ramo imobiliário, uma vez que é incerto estabelecer um limite para essa negociação. As decisões ocupando um espaço central nas organizações, tornam-se mais complexas em condições de incerteza. Isto implica que para atender a demanda pelo sucesso e qualidade das decisões, deve-se estabelecer um processo decisório que terá como elementos centrais, os cenários destas decisões, as alternativas e seus impactos. Logo, definiu-se o seguinte objetivo geral: avaliar técnicas inteligentes e desenvolver uma Arquitetura Híbrida Inteligente (AHI) para classificação de liquidez imobiliária urbana em leilões, apoiando o processo de tomada de decisão com Matriz de Probabilidade e Impacto Dupla (MPID). Para atingir este objetivo foi realizada uma série de experimentos aplicando técnicas inteligentes a uma base de dados reais num site de Leilões, contendo imóveis arrematados e não arrematados, no intervalo de anos de 2016 a 2020, coletados de forma aleatória. A avaliação de técnicas inteligentes para mineração dos dados como Randon Forest (RF), Árvore de Decisão e Rede Neural Artificial Multilayer Perceptron (MLP), determinou as técnicas mais promissoras e mais aderentes aos dados de imóveis coletados, na atuação conjunta com AHI. A principal característica da AHI é a capacidade de prever valores de descontos, tempo de exposição, número de lances e a classificação do arremate. Logo, o modelo proposto é capaz de prever e classificar a liquidez dos imóveis de leilão através de enriquecimento da base de dados, diminuindo o viés da decisão para a classificação de liquidez imobiliária em leilões. A sinergia da AHI com a MPID, possibilitou mapear as ameaças e também as oportunidades nesse setor. Foi criado nesse trabalho um novo conceito denominado borda de lances, que determina a convergência de lances reais para um imóvel. A avaliação do conhecimento extraído é útil e pode ser aplicado nos setores de leilão e bancário. A solução desenvolvida alcançou Score de 75%, treinando a técnica RF da AHI com o número de árvores padrão da biblioteca “randomForest” com 500 árvores.Submitted by Nadir Basilio (nadirsb@uninove.br) on 2021-12-02T15:00:01Z No. of bitstreams: 1 Marcelo Ferreira Albano.pdf: 3040475 bytes, checksum: 74161369a3025d5e05739ff3d183ef03 (MD5)Made available in DSpace on 2021-12-02T15:00:01Z (GMT). No. of bitstreams: 1 Marcelo Ferreira Albano.pdf: 3040475 bytes, checksum: 74161369a3025d5e05739ff3d183ef03 (MD5) Previous issue date: 2020-12-17application/pdfporUniversidade Nove de JulhoPrograma de Pós-Graduação em Informática e Gestão do ConhecimentoUNINOVEBrasilInformáticaarquitetura híbridaimóveisleilãodecisãoriscoshybrid architecturereal estateauctiondecisionrisksCIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAOArquitetura híbrida inteligente para classificação de liquidez imobiliária urbana em leilõesSmart hybrid architecture for classification of urban real estate liquidity in auctionsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis8930092515683771531600info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da Uninoveinstname:Universidade Nove de Julho (UNINOVE)instacron:UNINOVEORIGINALMarcelo Ferreira Albano.pdfMarcelo Ferreira Albano.pdfapplication/pdf3040475http://localhost:8080/tede/bitstream/tede/2791/2/Marcelo+Ferreira+Albano.pdf74161369a3025d5e05739ff3d183ef03MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://localhost:8080/tede/bitstream/tede/2791/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede/27912021-12-02 13:00:01.325oai:localhost: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Biblioteca Digital de Teses e Dissertaçõeshttp://bibliotecatede.uninove.br/PRIhttp://bibliotecatede.uninove.br/oai/requestbibliotecatede@uninove.br||bibliotecatede@uninove.bropendoar:2021-12-02T15:00:01Biblioteca Digital de Teses e Dissertações da Uninove - Universidade Nove de Julho (UNINOVE)false |
dc.title.por.fl_str_mv |
Arquitetura híbrida inteligente para classificação de liquidez imobiliária urbana em leilões |
dc.title.alternative.eng.fl_str_mv |
Smart hybrid architecture for classification of urban real estate liquidity in auctions |
title |
Arquitetura híbrida inteligente para classificação de liquidez imobiliária urbana em leilões |
spellingShingle |
Arquitetura híbrida inteligente para classificação de liquidez imobiliária urbana em leilões Albano, Marcelo Ferreira arquitetura híbrida imóveis leilão decisão riscos hybrid architecture real estate auction decision risks CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO |
title_short |
Arquitetura híbrida inteligente para classificação de liquidez imobiliária urbana em leilões |
title_full |
Arquitetura híbrida inteligente para classificação de liquidez imobiliária urbana em leilões |
title_fullStr |
Arquitetura híbrida inteligente para classificação de liquidez imobiliária urbana em leilões |
title_full_unstemmed |
Arquitetura híbrida inteligente para classificação de liquidez imobiliária urbana em leilões |
title_sort |
Arquitetura híbrida inteligente para classificação de liquidez imobiliária urbana em leilões |
author |
Albano, Marcelo Ferreira |
author_facet |
Albano, Marcelo Ferreira |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Napolitano, Domingos Marcio Rodrigues |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/0433818215929535 |
dc.contributor.referee1.fl_str_mv |
Napolitano, Domingos Marcio Rodrigues |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/0433818215929535 |
dc.contributor.referee2.fl_str_mv |
Gaspar, Marcos Antonio |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/3809285940688486 |
dc.contributor.referee3.fl_str_mv |
Librantz, Andre Felipe Henriques |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/3569470521730110 |
dc.contributor.referee4.fl_str_mv |
Chalco, Jesús Pascual Mena |
dc.contributor.referee4Lattes.fl_str_mv |
http://lattes.cnpq.br/4727357182510680 |
dc.contributor.referee5.fl_str_mv |
Sassi, Renato José |
dc.contributor.referee5Lattes.fl_str_mv |
http://lattes.cnpq.br/8750334661789610 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/3392758888478198 |
dc.contributor.author.fl_str_mv |
Albano, Marcelo Ferreira |
contributor_str_mv |
Napolitano, Domingos Marcio Rodrigues Napolitano, Domingos Marcio Rodrigues Gaspar, Marcos Antonio Librantz, Andre Felipe Henriques Chalco, Jesús Pascual Mena Sassi, Renato José |
dc.subject.por.fl_str_mv |
arquitetura híbrida imóveis leilão decisão riscos |
topic |
arquitetura híbrida imóveis leilão decisão riscos hybrid architecture real estate auction decision risks CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO |
dc.subject.eng.fl_str_mv |
hybrid architecture real estate auction decision risks |
dc.subject.cnpq.fl_str_mv |
CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO |
description |
The role of liquidity in the real estate market has attracted attention in the financial literature because of its strong impact on the economy and the sectors it covers. The liquidity of a property is an indicator of the speed or the degree of ease with which properties are traded, traded and converted into monetary value. Much of the information on these properties is available in large internet databases. If on the one hand, access to real estate data is not a problem, extracting knowledge from these databases is. Knowledge Discovery in Data Bases (KDD) systems are applied as a solution for the extraction of knowledge in decision making in a risky condition in the real estate business, since it is uncertain to establish a limit for this negotiation. Decisions occupying a central space in organizations become more complex under conditions of uncertainty. This implies that to meet the demand for success and quality of decisions, a decision-making process must be established that will have as central elements, the scenarios of these decisions, the alternatives and their impacts. Therefore, the following general objective was defined: to evaluate intelligent techniques and develop an Intelligent Hybrid Architecture (AHI) for classifying urban real estate liquidity in auctions, supporting the decision making process with a Double Impact and Probability Matrix (MPID). To achieve this goal, a series of experiments were conducted applying intelligent techniques to an actual database on an Auction site, containing auctioned and non-armed properties, in the years 2016 to 2020, collected randomly. The evaluation of intelligent techniques for data mining such as Randon Forest (RF), Decision Tree and Multilayer Perceptron Neural Network (MLP), determined the most promising techniques and most adherent to the real estate data collected, in joint action with AHI. The main characteristic of AHI is its ability to predict discount values, exposure time, number of bids and the classification of the end product. Therefore, the proposed model is capable of predicting and classifying the liquidity of auction properties through the enrichment of the database, reducing the decision bias for the classification of real estate liquidity in auctions. The synergy between AHI and MPID made it possible to map the threats and also the opportunities in this sector. A new concept called bidding edge was created in this work, which determines the convergence of real bids for a property. The evaluation of the extracted knowledge is useful and can be applied in the auction and banking sectors. The solution developed reached 75% Score, training the AHI RF technique with the number of standard trees in the "randomForest" library with 500 trees. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020-12-17 |
dc.date.accessioned.fl_str_mv |
2021-12-02T15:00:01Z |
dc.type.status.fl_str_mv |
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info:eu-repo/semantics/masterThesis |
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masterThesis |
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publishedVersion |
dc.identifier.citation.fl_str_mv |
Albano, Marcelo Ferreira. Arquitetura híbrida inteligente para classificação de liquidez imobiliária urbana em leilões. 2020. 138 f. Dissertação( Programa de Pós-Graduação em Informática e Gestão do Conhecimento) - Universidade Nove de Julho, São Paulo. |
dc.identifier.uri.fl_str_mv |
http://bibliotecatede.uninove.br/handle/tede/2791 |
identifier_str_mv |
Albano, Marcelo Ferreira. Arquitetura híbrida inteligente para classificação de liquidez imobiliária urbana em leilões. 2020. 138 f. Dissertação( Programa de Pós-Graduação em Informática e Gestão do Conhecimento) - Universidade Nove de Julho, São Paulo. |
url |
http://bibliotecatede.uninove.br/handle/tede/2791 |
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por |
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600 |
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dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Informática e Gestão do Conhecimento |
dc.publisher.initials.fl_str_mv |
UNINOVE |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Informática |
publisher.none.fl_str_mv |
Universidade Nove de Julho |
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