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

Ian Lenaers & Lieven De Moor

Intelligent Systems in Accounting, Finance and Management2025https://doi.org/10.1002/isaf.70019article
AJG 1
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.1002/isaf.70019

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@article{ian2025,
  title        = {{Lost in the Modeling Stage: A Comparative Analysis of Machine Learning Models for Real Estate Data}},
  author       = {Ian Lenaers & Lieven De Moor},
  journal      = {Intelligent Systems in Accounting, Finance and Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1002/isaf.70019},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.