House Market Prediction Using Machine Learning

Nicușor-Andrei Andrei
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Nicușor-Andrei Andrei: The Bucharest University of Economic Studies, Romania

Database Systems Journal, 2025, vol. 16, issue 1, 55-64

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.

Keywords: House market; Machine learning; Price prediction; Tree-based algorithms; XGBoost (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aes:dbjour:v:16:y:2025:i:1:p:55-64

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