通过对专家,机器学习和房地产估值中的混合方法进行比较分析来评估人机协作
Christopher Kmen1,2, Gerhard Navratil3, Markus Kattenbeck3
1Geoinformation Group, TU Wien, 1040, Vienna, Austria. christopher.kmen@geo.tuwien.ac.at.
Scientific reports
|January 17, 2026
概括
机器学习 (ML) 模型与房地产专家在价格预测中的准确性相匹配,大大缩短了估值时间. 将人类专业知识与ML模型相结合,可以获得最高的预测准确度,突出了人类-ML合作的好处.
科学领域:
- 房地产经济学 房地产经济学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 由于市场波动和传统方法的局限性,难以准确地预测房地产价格.
- 人类专家,机器学习 (ML) 和混合估值方法的实证比较很少.
研究的目的:
- 为了比较XGBoost ML模型,人类房地产专家和混合人类-ML方法的预测准确性和效率.
- 评估住宅物业不同估值方法的表现.
主要方法:
- 在维也纳 (2018-2022) 的21,736个房地产交易中训练了一个XGBoost ML模型.
- 与13名专家一起进行了一项实验,在三个条件下评估新公寓:有限的信息,专家方法和ML辅助的专家评估.
主要成果:
- 机器学习模型的准确性与人类专家相提并论,但评估时间显著缩短.
- 混合方法,专家与ML模型合作,导致总体精度最高.
- 使用最先进的方法的人类专家的表现比有限的信息更好.
结论:
- 在房地产估值中,人类-ML协作提供了卓越的准确性和效率.
- 机器学习模型提供了一个有价值的工具,可以增强专家在房地产评估中的判断力.
- 这些发现支持将人工智能整合到传统的房地产估值实践中.
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