The Journal of Real Estate Finance and Economics ( IF 1.480 ) Pub Date : 2021-07-13 , DOI: 10.1007/s11146-021-09845-1 Thies Lindenthal 1 , Erik B. Johnson 2
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.
中文翻译:
机器学习、建筑风格和属性值
本文将传统的享乐模型与人类专家的建筑风格分类相结合,机器学习 (ML) 使分类器能够估计建筑物和社区级别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着的价格差异。ML 模型和人类专家的分类之间的比较说明了 ML 分类器在大规模评估模型中至少与人类专家一样可靠的条件。享乐估计表明,建筑风格对价格的影响会因风格不太明确的房产而减弱,我们没有发现复兴对不同价格影响的证据或新建筑的当代建筑。