Machine Learning, Architectural Styles and Property Values

Thies Lindenthal, Erik B. Johnson
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引用次数: 9

Abstract

This paper couples a traditional hedonic model with architectural style classifications from human experts and machine learning (ML) enabled classifiers to estimate sales price premia over architectural styles, both at the building and the neighborhood-level. We find statistically and economically significant price differences for houses from distinct architectural styles across an array of specifications and modeling assumptions. Comparisons between classifications from ML models and human experts illustrate the conditions under which ML classifiers may perform at least as reliable as human experts in mass appraisal models. Hedonic estimates illustrate that the impact of architectural style on price is attenuated by properties with less well-defined styles and we find no evidence for differential price effects of Revival or Contemporary architecture for new construction.
机器学习,架构风格和属性值
本文将传统的享乐模型与人类专家的建筑风格分类和机器学习(ML)分类器相结合,以估计建筑物和社区层面的建筑风格的销售价格溢价。我们发现,在一系列规格和建模假设中,不同建筑风格的房屋在统计和经济上存在显著的价格差异。机器学习模型和人类专家分类之间的比较说明了机器学习分类器在大规模评估模型中至少可以像人类专家一样可靠的条件。Hedonic估计表明,建筑风格对价格的影响被风格不太明确的物业所减弱,我们没有发现复兴或当代建筑对新建筑的不同价格影响的证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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