Lost in the Modeling Stage: A Comparative Analysis of Machine Learning Models for Real Estate Data

ABSTRACT Machine learning dominates automated property valuation, yet comprehensive comparisons of predictive models remain scarce. This study compares 28 rent prediction models using 79,735 Belgian residential rental properties from 2022. Predictive performance is evaluated with traditional and alternative metrics for train data, test data, and across deciles. The results confirm that tree‐based ensemble models outperform others, with stacking and averaging yielding superior results at a higher computational cost. Furthermore, middle‐range rents show better predictive accuracy than extremes. Traditional and alternative metrics provide consistent findings. These insights aid real estate stakeholders seeking to enhance their expert systems for real estate price modeling.