Housing-Price Prediction in Colombia using Machine Learning

Daniel Otero Gomez, Miguel Angel Correa Manrique, Omar Becerra Sierra, Henry Laniado, Rafael Mateus C and David Andres Romero Millan

No w85z2, OSF Preprints from Center for Open Science

Abstract: It is a common practice to price a house without proper evaluation studies being performed for assurance. That is why the purpose of this study provide an explanatory model by establishing parameters for accuracy in interpretation and projection of housing prices. In addition, it is intentioned to establish proper data preprocessing practices in order to increase the accuracy of machine learning algorithms. Indeed, according to our literature review, there are few articles and reports on the use of Machine Learning tools for the prediction of property prices in Colombia. The dataset in which the research is built upon was provided by an existing real estate company. It contains near 940,000 items (housing advertisements) posted on the platform from the year 2018 to 2020. The database was enriched using statistical imputation techniques. Housing prices prediction was performed using Decision Tree Regressors and LightGBM methods, thus deriving in better alternatives for house price prediction in Colombia. Moreover, to measure the accuracy of the proposed models, the Root Mean Squared Logarithmic Error (RMSLE) statistical indicator was used. The best cross validation results obtained were 0.25354±0.00699 for the LightGBM, 0.25296 ±0.00511 for the Bagging Regressor, and 0.25312±0.00559 for the ExtraTree Regressor with Bagging Regressor, and it was not found a statistical difference between their performances.

Date: 2020-09-02
New Economics Papers: this item is included in nep-big, nep-cmp, nep-isf and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:w85z2

DOI: 10.31219/osf.io/w85z2

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