Sing Tien Foo, Yang Jesse Jingye, Yu Shi Ming
Department of Real Estate, National University of Singapore, Singapore, Singapore.
Institute of Real Estate and Urban Studies, National University of Singapore, Singapore, Singapore.
J Real Estate Financ Econ (Dordr). 2022;65(4):649-674. doi: 10.1007/s11146-021-09861-1. Epub 2021 Nov 5.
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%.
本文运用提升树集成技术开发了一种基于人工智能的自动估值模型（AI-AVM），用于预测新加坡的房价。我们使用了1995年至2017年期间新加坡超过30万笔私人和公共住房交易数据来训练AI-AVM模型。与决策树和多元回归分析（MRA）模型相比，提升模型是对房价进行预测时最稳健、最准确的预测模型。提升AI-AVM模型分别解释了公共住房市场和私人住房市场91.33%和94.28%的价格方差，并且公共住房市场和私人住房市场的平均绝对百分比误差分别保持在8.55%和5.34%。当使用2018年房屋销售样本对AI-AVM进行样本外预测时，预测误差仍保持在5%至9%的窄范围内。
