Predictive Modeling of Real Estate Prices Using Machine Learning

• Atílio Cardoso Azevedo UFPA
• Reginaldo Cordeiro dos Santos Filho UFPA

DOI: https://doi.org/10.5753/eniac.2025.13885

Abstract

Determining the value of a property is a complex task due to the wide range of factors that influence its pricing, such as location, size, number of rooms, and other relevant attributes. With the objective of simplifying the pricing process, this article proposes the use of Machine Learning Algorithms capable of estimating a property’s value based on its physical and locational characteristics. The algorithms were trained on a dataset containing online listings of properties for sale in the city of Belém, Pará, during the period from December 2023 to January 2025. The results of this proof of concept indicate that the Random Forest Regressor algorithm achieved the best performance, with an R² of 0.902 and a MAPE of 26%.

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