Quantifying the drivers of residential housing demand – an interpretable machine learning approach

IF 1.3 Q3 BUSINESS, FINANCE
Marcelo Cajias, Joseph-Alexander Zeitler
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引用次数: 0

Abstract

PurposeThe paper employs a unique online user-generated housing search dataset and introduces a novel measure for housing demand, namely “contacts per listing” as explained by hedonic, geographic and socioeconomic variables. Design/methodology/approachThe authors explore housing demand by employing an extensive Internet search dataset from a German housing market platform. The authors apply state-of-the-art artificial intelligence, the eXtreme Gradient Boosting, to quantify factors that lead an apartment to be in demand.FindingsThe authors compare the results to alternative parametric models and find evidence of the superiority of the nonparametric model. The authors use eXplainable artificial intelligence (XAI) techniques to show economic meanings and inferences of the results. The results suggest that hedonic, socioeconomic and spatial aspects influence search intensity. The authors further find differences in temporal dynamics and geographical variations.Originality/valueTo the best of the authors’ knowledge, it is the first study of its kind. The statistical model of housing search draws on insights from decision theory, AI and qualitative studies on housing search. The econometric approach employed is new as it considers standard regression models and an eXtreme Gradient Boosting (XGB or XGBoost) approach followed by a model-agnostic interpretation of the underlying effects.
量化住宅需求的驱动因素——一种可解释的机器学习方法
本文采用了一个独特的在线用户生成的住房搜索数据集,并引入了一种新的住房需求测量方法,即由享乐、地理和社会经济变量解释的“每套房源的联系人”。设计/方法/方法作者通过使用来自德国住房市场平台的广泛的互联网搜索数据集来探索住房需求。作者运用了最先进的人工智能技术“极限梯度提升”(eXtreme Gradient Boosting)来量化导致公寓供不应求的因素。作者将结果与其他参数模型进行了比较,并找到了非参数模型优越性的证据。作者使用可解释的人工智能(XAI)技术来显示结果的经济意义和推论。结果表明,搜索强度受享乐、社会经济和空间因素的影响。作者进一步发现了时间动态和地理变化的差异。原创性/价值据作者所知,这是同类研究中的首例。住房搜索的统计模型借鉴了决策理论、人工智能和住房搜索的定性研究的见解。所采用的计量经济学方法是新的,因为它考虑了标准回归模型和极端梯度增强(XGB或XGBoost)方法,然后是对潜在效应的模型不可知的解释。
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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来源期刊
CiteScore
3.10
自引率
7.70%
发文量
18
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