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House Market Prediction Using Machine Learning

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  • NicuÈ™or-Andrei ANDREI

    (The Bucharest University of Economic Studies, Romania)

Abstract

This study compares tree-based machine learning algorithms for predicting Bucharest residential apartment prices. Using a dataset from March 2025, comprehensive preprocessing—including imputation, categorical encoding, and feature engineering (e.g., distance to public transport)—was applied. Models were optimized via grid search with 5-fold cross-validation and evaluated using RMSE, MAE, and R². Results show XGBoost outperforms Random Forest and Decision Tree models across all metrics.

Suggested Citation

  • NicuÈ™or-Andrei ANDREI, 2025. "House Market Prediction Using Machine Learning," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(1), pages 55-64.
    • Handle: RePEc:aes:dbjour:v:16:y:2025:i:1:p:55-64
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