Disparities in affecting factors of housing price: A machine learning approach to the effects of housing status, public transit, and density factors on single-family housing price
Disparities in affecting factors of housing price: A machine learning approach to the effects of housing status, public transit, and density factors on single-family housing price
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DOI：
10.1016/j.cities.2023.104432
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发表时间：
2023-06-10
期刊：
影响因子：
6.7
通讯作者：
Farahi，Arya
中科院分区：
文献类型：
--
作者：
Chen，Yefu;Jiao，Junfeng;Farahi，Arya
对于哪些特征决定了房价，已经有了深刻的见解。这些特征反映了两个不同的方面：与住宅本身相关的特征和与位置和周围区域相关的特征。然而，很少有研究准确地研究这些影响在不同条件下的社区之间的差异和异质性。此外，缺乏针对中等密度情况的研究，这些情况最近引起了房地产市场的担忧。这项研究旨在通过分析不同经济和种族/民族条件的社区之间的差异来填补这一研究空白。通过机器学习方法，我们比较了住房状况、公共交通服务和周围环境因素在七种条件下的影响差异。结果表明，经济条件的异质性可能比种族/民族条件更显著。通过比较分析，我们呼吁政策制定者在房价分析中需要采取差异化的视角，未来的研究应该考虑不同社区之间影响的差异。
Profound insights have been gained into which characteristics determine housing prices. These characteristics reflect two different aspects: those which are correlated with the dwelling itself and those which are correlated with the location and the surrounding area. However, few studies precisely looked at the disparities and heterogeneity in these effects across neighborhoods with varied conditions. Also, there lacks studies focusing on the moderate-density cases where housing markets have drawn concerns recently. This study aims to fill this research gap by analyzing these disparities across neighborhoods with different economic and racial/ethnic conditions. Through machine learning approaches, we compare the disparities in the impacts of housing status, public transit services, and surrounding environment factors under seven conditions. Results indicate that the heterogeneity in economic conditions could be more significant than racial/ethnical conditions. Through comparison analysis, we call policymakers to need to adopt differentiated perspectives on housing price analysis, and future studies should consider the disparities in the impacts across neighborhoods.