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Exploring XAI techniques for enhancing model transparency and interpretability in real estate rent prediction: A comparative study

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  • Lenaers, Ian
  • De Moor, Lieven

Abstract

Black-box artificial intelligence (AI) models are popular in real estate research, but their lack of interpretability raises concerns. To address this, explainable AI (XAI) techniques have been applied to shed light on these models. This paper presents a comparative study of six global XAI techniques on a CatBoost model for Belgian residential rent prediction. Results show that while some techniques offer substitute insights, others provide complementary perspectives on the model's behavior. Employing multiple XAI techniques is crucial to comprehensively understand rents drivers which contributes to transparency, interpretability, and model governance in the real estate industry, advancing the adoption of (X)AI.

Suggested Citation

  • Lenaers, Ian & De Moor, Lieven, 2023. "Exploring XAI techniques for enhancing model transparency and interpretability in real estate rent prediction: A comparative study," Finance Research Letters, Elsevier, vol. 58(PA).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pa:s1544612323006785
    DOI: 10.1016/j.frl.2023.104306
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    References listed on IDEAS

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    1. Ian Lenaers & Kris Boudt & Lieven De Moor, 2023. "Predictability of Belgian residential real estate rents using tree-based ML models and IML techniques," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 17(1), pages 96-113, April.
    2. W.J. McCluskey & M. McCord & P.T. Davis & M. Haran & D. McIlhatton, 2013. "Prediction accuracy in mass appraisal: a comparison of modern approaches," Journal of Property Research, Taylor & Francis Journals, vol. 30(4), pages 239-265, December.
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