POTENTIALS AND LIMITATIONS OF WEB SCRAPING DATA FOR MAPPING URBAN PROPERTY PRICES
DOI:
https://doi.org/10.14393/RCG249668395Keywords:
Real estate market, Land price, Big data, Price mapAbstract
While the availability of online data has increased, the processing and evaluation of this data give rise to discussions regarding the scientific instruments that enable the measurement of critical data characteristics while minimizing inherent inconsistencies related to the subject. This article analyzed the representativeness of data obtained through web scrapping from advertisements on two nationally recognized websites. It applied a data refinement technique to property sales data and examined its proportion concerning the municipal residential real estate registry. The method proposed the utilization of web-available data retrieved through the technique of online data scraping. The approach focused on average prices during reference periods, as well as on the assessment of the potentialities and limitations of big data, along with the spatial concentration mapping of real estate market prices categorized by type and spatialized by neighborhood. The research concluded that the Olx website dataset exhibited lower completeness and volume than Imovelweb (Iw) yet demonstrated greater diversity regarding spatial coverage of properties, including mapping the distribution of average per-square-meter prices by neighborhood.
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Copyright (c) 2023 Thaís Góes de Souza, Vivian de Oliveira Fernandes, Julio César Pedrassoli, Fernanda Doracy Rocha Fonseca
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