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

Author

Listed:
  • Tien Foo Sing

    (National University of Singapore
    National University of Singapore)

  • Jesse Jingye Yang

    (National University of Singapore)

  • Shi Ming Yu

    (National University of Singapore)

Abstract

This paper develops an artificial intelligence based automated valuation model (AI-AVM) using the boosting tree ensemble technique to predict housing prices in Singapore. We use more than 300,000 private and public housing transactions in Singapore for the period from 1995 to 2017 in the training of the AI-AVM models. The boosting model is the best predictive model that produce the most robust and accurate predictions for housing prices compared to the decision tree and multiple regression analysis (MRA) models. The boosting AI-AVM models explain 91.33% and 94.28% of the price variances, and keep the mean absolute percentage errors at 8.55% and 5.34% for the public housing market and the private housing market, respectively. When subject the AI-AVM to the out-of-sample forecasting using the 2018 housing sale samples, the prediction errors remain within a narrow range of between 5% and 9%.

Suggested Citation

  • Tien Foo Sing & Jesse Jingye Yang & Shi Ming Yu, 2022. "Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM)," The Journal of Real Estate Finance and Economics, Springer, vol. 65(4), pages 649-674, November.
    • Handle: RePEc:kap:jrefec:v:65:y:2022:i:4:d:10.1007_s11146-021-09861-1
    DOI: 10.1007/s11146-021-09861-1
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    References listed on IDEAS

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