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

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...

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Bibliographic Details
Published in:The journal of real estate finance and economics 2022-11, Vol.65 (4), p.649-674
Main Authors: Sing, Tien Foo, Yang, Jesse Jingye, Yu, Shi Ming
Format: Article
Language:English
Subjects:
Artificial intelligence
Automation
Economics
Economics and Finance
Financial Services
Housing costs
Housing market
Housing prices
Intelligence
Prediction models
Prices
Public housing
Regional/Spatial Science
Regression analysis
Transactions
Valuation
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Summary: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%.
ISSN:0895-5638
1573-045X
1573-045X
DOI:10.1007/s11146-021-09861-1