Selection of optimal window size and ensemble methods for rental price estimation with XGBoost: Evidence from Buenos Aires
Date
2024?
Authors
Sosa, Juan Bautista
relationships.isContributorOfPublication
Margaretic, Paula
Sosa Escudero, Walter
Journal Title
Journal ISSN
Volume Title
Publisher
Universidad de San Andrés. Departamento de Economía
Abstract
In the context of forecasting rental prices of residential properties, the fact that the relationship
between rents and property characteristics may be subject to significant changes over time can hinder
the predictive performance of models that do not adjust to these changes. Previous studies have
tested the accuracy of different statistical models in predicting rent prices, but none has focused
on how the definition of the estimation window of observations used in the training stage can
affect predictive performance. This paper explores the impact of selecting different window sizes
in a forecasting experiment of rent prices in the City of Buenos Aires from 2020 to 2022 using
the machine learning algorithm XGBoost. The results obtained for out-of-sample one-month-ahead
forecasts indicate that the decision to use an expanding window or rolling windows is not trivial in
terms of the achieved predictive performance. Among a set of rolling window sizes, the model with
a size of 6 months yields the lowest error in terms of root mean square error and mean absolute
error. Additionally, three methods that dynamically combine the predictions of models with different
window sizes are tested. An ensemble method that outputs a weighted average of the predictions
using time-varying weights based on the inverse of previous forecasting errors emerges as the superior
strategy, producing a mean absolute percentage error of 19.7%. Overall, these results underscore
the need to take into account the temporal dimension when selecting observations for training if the
goal is to maximize out-of-sample predictive performance.
Description
Fil: Sosa, Juan Bautista. Universidad de San Andrés. Departamento de Economía; Argentina.