利用机器学习估计Airbnb对房价的异质影响:来自科西嘉岛的证据

IF 1.4 3区 经济学 Q3 ECONOMICS
Daniel Brunstein , Georges Casamatta , Sauveur Giannoni
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引用次数: 0

摘要

本研究调查了Airbnb对科西嘉岛房地产价格的影响。利用机器学习技术,我们获得了比传统方法更可靠的结果,并揭示了Airbnb对房地产价值的异质影响。我们的分析显示,Airbnb房源每增加1%,房价就会平均上涨0.21%。有趣的是,与传统的热门旅游目的地和城市地区相比,这种影响在经济欠发达地区更为明显,如内陆城市和偏远的海滨度假胜地。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using machine learning to estimate the heterogeneous impact of Airbnb on house prices: Evidence from Corsica
This study investigates the influence of Airbnb on property prices in Corsica. Leveraging machine learning techniques, we obtain more robust results than those achieved with conventional methods and uncover heterogeneous effects of Airbnb on property values. Our analysis reveals that a 1% increase in Airbnb listings leads to an average 0.21% rise in house prices. Interestingly, this effect is more pronounced in economically less developed regions, such as inland municipalities and remote seaside resorts, compared to traditionally popular tourist destinations and urban areas.
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来源期刊
CiteScore
3.30
自引率
4.20%
发文量
35
期刊介绍: The Journal of Housing Economics provides a focal point for the publication of economic research related to housing and encourages papers that bring to bear careful analytical technique on important housing-related questions. The journal covers the broad spectrum of topics and approaches that constitute housing economics, including analysis of important public policy issues.
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