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Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM).

Tien Foo Sing1,2, Jesse Jingye Yang2, Shi Ming Yu1

  • 1Department of Real Estate, National University of Singapore, Singapore, Singapore.

The Journal of Real Estate Finance and Economics
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Summary

This study introduces an artificial intelligence automated valuation model (AI-AVM) for Singapore housing prices. The AI-AVM, using boosting tree ensemble, offers accurate and robust predictions for both public and private housing markets.

Keywords:
Automated valuation modelBoostingDecision treeHousing markets

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Real Estate Economics

Background:

  • Accurate housing price prediction is crucial for market stability and investment decisions.
  • Existing valuation models may lack the precision needed for dynamic real estate markets.
  • The Singapore housing market presents unique characteristics influencing property values.

Purpose of the Study:

  • To develop and evaluate an artificial intelligence based automated valuation model (AI-AVM) for predicting Singapore housing prices.
  • To compare the predictive performance of a boosting tree ensemble model against traditional methods.
  • To assess the accuracy and robustness of the AI-AVM for both public and private housing segments.

Main Methods:

  • Utilized a boosting tree ensemble technique for the AI-AVM.
  • Trained models on over 300,000 Singapore housing transactions (1995-2017).
  • Compared AI-AVM performance with decision tree and multiple regression analysis (MRA) models.

Main Results:

  • The boosting AI-AVM demonstrated superior accuracy and robustness compared to other models.
  • The model explained 91.33% (public) and 94.28% (private) of price variances.
  • Achieved low mean absolute percentage errors: 8.55% (public) and 5.34% (private).
  • Out-of-sample forecasting for 2018 sales showed prediction errors between 5% and 9%.

Conclusions:

  • The developed AI-AVM is a highly effective tool for predicting Singapore housing prices.
  • Boosting tree ensemble technique provides a robust and accurate approach for real estate valuation.
  • The AI-AVM shows strong predictive power for both public and private housing markets.