Applying data mining algorithms to real estate appraisals: a comparative study

Thiago Cesar de Oliveira, Lúcio de Medeiros and Daniel Henrique Marco Detzel

International Journal of Housing Markets and Analysis, 2021, vol. 14, issue 5, 969-986

Abstract: Purpose - Real estate appraisals are becoming an increasingly important means of backing up financial operations based on the values of these kinds of assets. However, in very large databases, there is a reduction in the predictive capacity when traditional methods, such as multiple linear regression (MLR), are used. This paper aims to determine whether in these cases the application of data mining algorithms can achieve superior statistical results. First, real estate appraisal databases from five towns and cities in the State of Paraná, Brazil, were obtained from Caixa Econômica Federal bank. Design/methodology/approach - After initial validations, additional databases were generated with both real, transformed and nominal values, in clean and raw data. Each was assisted by the application of a wide range of data mining algorithms (multilayer perceptron, support vector regression, K-star, M5Rules and random forest), either isolated or combined (regression by discretization – logistic, bagging and stacking), with the use of 10-fold cross-validation in Weka software. Findings - The results showed more varied incremental statistical results with the use of algorithms than those obtained by MLR, especially when combined algorithms were used. The largest increments were obtained in databases with a large amount of data and in those where minor initial data cleaning was carried out. The paper also conducts a further analysis, including an algorithmic ranking based on the number of significant results obtained. Originality/value - The authors did not find similar studies or research studies conducted in Brazil.

Keywords: Algorithms; Statistics; Real estate appraisal; Data mining; Weka; Caixa Econômica Federal (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eme:ijhmap:ijhma-07-2020-0080

DOI: 10.1108/IJHMA-07-2020-0080

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