针对基于人工智能的自动化估值模型 (AI-AVM) 的增强型树集
Tien Foo Sing1,2, Jesse Jingye Yang2, Shi Ming Yu1
1Department of Real Estate, National University of Singapore, Singapore, Singapore.
概括
本研究介绍了新加坡住房价格的人工智能自动化估值模型 (AI-AVM). 使用增强树木组合的AI-AVM,为公共和私人住房市场提供了准确和强大的预测.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 房地产经济学 房地产经济学
背景情况:
- 准确的房价预测对于市场稳定和投资决策至关重要.
- 现有的估值模型可能缺乏动态房地产市场所需的精度.
- 新加坡房地产市场具有独特的特点,影响房地产价值.
研究的目的:
- 开发和评估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.
- 为了比较一个提升树组合模型与传统方法的预测性能.
- 评估AI-AVM对公共和私人住房领域的准确性和稳定性.
主要方法:
- 在AI-AVM中使用了增强树组合技术.
- 在超过30万个新加坡住房交易 (1995-2017年) 中训练模型.
- 将AI-AVM性能与决策树和多重回归分析 (MRA) 模型进行比较.
主要成果:
- 与其他模型相比,增强AI-AVM表现出卓越的准确性和稳定性.
- 该模型解释了91.33% (公共) 和94.28% (私人) 的价格差异.
- 实现了较低的平均绝对百分比误差:8.55% (公共) 和5.34% (私人).
- 2018年销售额的样本外预测显示预测错误在5%至9%之间.
结论:
- 开发的AI-AVM是预测新加坡房价的高效工具.
- 增强树组合技术为房地产估值提供了强大而准确的方法.
- 人工智能-AVM显示出对公共和私人住房市场的强大预测能力.
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