Applying data mining algorithms to real estate appraisals: a comparative study
| Autor: | Daniel Henrique Marco Detzel, Thiago Cesar de Oliveira, Lúcio de Medeiros |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
0211 other engineering and technologies 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 021107 urban & regional planning Real estate 02 engineering and technology Data mining Real estate appraisal computer.software_genre General Economics Econometrics and Finance computer Data mining algorithm |
| Zdroj: | International Journal of Housing Markets and Analysis. 14:969-986 |
| ISSN: | 1753-8270 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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