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June 20, 2026Journal of Informatics and Web EngineeringOpen Access

Machine Learning-based Prediction of House Sale Prices in Hulu Langat

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Authors

SNSew Lai NgKCKath Moon Yap ChooDADa An

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Overview

Randomized trial shows predictive accuracy in property prices, highlighting house attributes over economic trends.

Key Points

  • This research aims to improve house price prediction in Hulu Langat using machine learning techniques.
  • Developed three model variations: housing attributes only, housing with macroeconomic data, and combined indicators.
  • Trained models including Ensemble Weighted Average, Random Forest, XGBoost, and LightGBM on open-source datasets.
  • Utilized evaluation metrics and SHAP for interpretability of the best-performing model.
  • The Ensemble Weighted Average model on housing data achieved the highest accuracy across evaluation metrics.
  • XGBoost demonstrated the fastest computation time among the models tested.
  • Models including macroeconomic indicators showed poorer performance, suggesting they introduced noise into predictions.

Cite This Study

Ng et al. (2026) studied this question.

synapsesocial.com/papers/6a362d32db0793dc1a535a58https://doi.org/10.33093/jiwe.2026.5.2.7
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Also Consider

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  1. 1A Robust Ensemble-Based Framework for House Price Estimation: Integrating XG-Boost with SHAP and Web Deployment2025
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