Analysis of sales of vertical residential real estate projects in Goiânia and its influencing factors

• Autor(a) principal: Amaral,Tatiana Gondim do
• Data de Publicação: 2022
• Outros Autores: Kafuri,Roberto Sebba, Oliveira,Marcelo Leite, Kafuri,Matheus Ramos, Medrano,Ronny Marcelo Aliaga
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Gestão & Produção
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2022000100211
• topic: Real Estate Market; Data mining; Big data

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.