Housing Prices Prediction with a Deep Learning and Random Forest Ensemble

  • Bruno Afonso Universidade Federal de São Paulo
  • Luckeciano Melo Instituto Tecnológico da Aeronáutica
  • Willian Oliveira Universidade Federal de São Paulo
  • Samuel Sousa Universidade Federal de São Paulo
  • Lilian Berton Universidade Federal de São Paulo


The development of a housing prices prediction model can assist a house seller or a real estate agent to make better-informed decisions based on house price valuation. Only a few works report the use of machine learning (ML) algorithms to predict the values of properties in Brazil. This study analyzes a dataset composed of 12,223,582 housing advertisements, collected from Brazilian websites from 2015 to 2018. Each instance comprises twenty-four features of five different data types: integer, date, string, float, and image. To predict the property prices, we ensemble two different ML architectures, based on Random Forest (RF) and Recurrent Neural Networks (RNN). This study demonstrates that enriching the dataset and combining different ML approaches can be a better alternative for prediction of housing prices in Brazil.

Palavras-chave: Housing prices prediction, Machine Learning, Ensemble, Random Forest, Deep Learning, Recurrent Neural Networks


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AFONSO, Bruno; MELO, Luckeciano; OLIVEIRA, Willian; SOUSA, Samuel; BERTON, Lilian. Housing Prices Prediction with a Deep Learning and Random Forest Ensemble. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 389-400. DOI: https://doi.org/10.5753/eniac.2019.9300.