Analysis of sales of vertical residential real estate projects in Goiânia and its influencing factors
Main Author: | |
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Publication Date: | 2022 |
Other Authors: | , , , |
Format: | Article |
Language: | eng |
Source: | Gestão & Produção |
Download full: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2022000100211 |
Summary: | Abstract Several topics were analyzed to produce this article, such as Evaluation Engineering, Data Modeling, Data Mining and Big Data. Although the literature on these theories is extensive, there is no record of their simultaneous use in favor of conducting an analysis of the real estate market in a city or region. The present work aims to use these theories to elaborate and implement quantitative criteria which group real estate projects according to their sale speed in order to classify them as high or low demand in the market. Thus, a database was used to develop the proposal, containing the number of units sold from 268 real estate developments in Goiânia with a launch date between January 2016 to December 2019, recorded month by month, generating a total of 4746 entries in the database. Data Mining and Big Data techniques were used to determine the database to perform the research and enable direct comparison between the analyzed enterprises. It was possible to accurately define the greatest market opportunities by studying the characteristics of the number of bedrooms, private square footage, price per square meter, apartment price and location. |
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Analysis of sales of vertical residential real estate projects in Goiânia and its influencing factorsReal Estate MarketData miningBig dataAbstract Several topics were analyzed to produce this article, such as Evaluation Engineering, Data Modeling, Data Mining and Big Data. Although the literature on these theories is extensive, there is no record of their simultaneous use in favor of conducting an analysis of the real estate market in a city or region. The present work aims to use these theories to elaborate and implement quantitative criteria which group real estate projects according to their sale speed in order to classify them as high or low demand in the market. Thus, a database was used to develop the proposal, containing the number of units sold from 268 real estate developments in Goiânia with a launch date between January 2016 to December 2019, recorded month by month, generating a total of 4746 entries in the database. Data Mining and Big Data techniques were used to determine the database to perform the research and enable direct comparison between the analyzed enterprises. It was possible to accurately define the greatest market opportunities by studying the characteristics of the number of bedrooms, private square footage, price per square meter, apartment price and location.Universidade Federal de São Carlos2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2022000100211Gestão & Produção v.29 2022reponame:Gestão & Produçãoinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCAR10.1590/1806-9649-2022v29e020info:eu-repo/semantics/openAccessAmaral,Tatiana Gondim doKafuri,Roberto SebbaOliveira,Marcelo LeiteKafuri,Matheus RamosMedrano,Ronny Marcelo Aliagaeng2022-03-18T00:00:00Zoai:scielo:S0104-530X2022000100211Revistahttps://www.gestaoeproducao.com/PUBhttps://old.scielo.br/oai/scielo-oai.phpgp@dep.ufscar.br||revistagestaoemanalise@unichristus.edu.br1806-96490104-530Xopendoar:2022-03-18T00:00Gestão & Produção - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.none.fl_str_mv |
Analysis of sales of vertical residential real estate projects in Goiânia and its influencing factors |
title |
Analysis of sales of vertical residential real estate projects in Goiânia and its influencing factors |
spellingShingle |
Analysis of sales of vertical residential real estate projects in Goiânia and its influencing factors Amaral,Tatiana Gondim do Real Estate Market Data mining Big data |
title_short |
Analysis of sales of vertical residential real estate projects in Goiânia and its influencing factors |
title_full |
Analysis of sales of vertical residential real estate projects in Goiânia and its influencing factors |
title_fullStr |
Analysis of sales of vertical residential real estate projects in Goiânia and its influencing factors |
title_full_unstemmed |
Analysis of sales of vertical residential real estate projects in Goiânia and its influencing factors |
title_sort |
Analysis of sales of vertical residential real estate projects in Goiânia and its influencing factors |
author |
Amaral,Tatiana Gondim do |
author_facet |
Amaral,Tatiana Gondim do Kafuri,Roberto Sebba Oliveira,Marcelo Leite Kafuri,Matheus Ramos Medrano,Ronny Marcelo Aliaga |
author_role |
author |
author2 |
Kafuri,Roberto Sebba Oliveira,Marcelo Leite Kafuri,Matheus Ramos Medrano,Ronny Marcelo Aliaga |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Amaral,Tatiana Gondim do Kafuri,Roberto Sebba Oliveira,Marcelo Leite Kafuri,Matheus Ramos Medrano,Ronny Marcelo Aliaga |
dc.subject.por.fl_str_mv |
Real Estate Market Data mining Big data |
topic |
Real Estate Market Data mining Big data |
description |
Abstract Several topics were analyzed to produce this article, such as Evaluation Engineering, Data Modeling, Data Mining and Big Data. Although the literature on these theories is extensive, there is no record of their simultaneous use in favor of conducting an analysis of the real estate market in a city or region. The present work aims to use these theories to elaborate and implement quantitative criteria which group real estate projects according to their sale speed in order to classify them as high or low demand in the market. Thus, a database was used to develop the proposal, containing the number of units sold from 268 real estate developments in Goiânia with a launch date between January 2016 to December 2019, recorded month by month, generating a total of 4746 entries in the database. Data Mining and Big Data techniques were used to determine the database to perform the research and enable direct comparison between the analyzed enterprises. It was possible to accurately define the greatest market opportunities by studying the characteristics of the number of bedrooms, private square footage, price per square meter, apartment price and location. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2022000100211 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2022000100211 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1806-9649-2022v29e020 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Universidade Federal de São Carlos |
publisher.none.fl_str_mv |
Universidade Federal de São Carlos |
dc.source.none.fl_str_mv |
Gestão & Produção v.29 2022 reponame:Gestão & Produção instname:Universidade Federal de São Carlos (UFSCAR) instacron:UFSCAR |
instname_str |
Universidade Federal de São Carlos (UFSCAR) |
instacron_str |
UFSCAR |
institution |
UFSCAR |
reponame_str |
Gestão & Produção |
collection |
Gestão & Produção |
repository.name.fl_str_mv |
Gestão & Produção - Universidade Federal de São Carlos (UFSCAR) |
repository.mail.fl_str_mv |
gp@dep.ufscar.br||revistagestaoemanalise@unichristus.edu.br |
_version_ |
1750118208132612096 |