测量税收和公共服务对房地产价值的影响：一种双重机器学习方法
Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach
摘要：
房价如何响应地方税收和地方公共服务的变化？从奥茨（Oates，1969）开始，人们试图衡量这一点，但由于缺乏地方公共服务控制，这种尝试受到了影响。最近的工作试图通过使用准实验方法来克服这种数据限制。我们重新审视了这个基本问题，但采用了一种不同的经验策略，将Escherzhukov等人（2018）的双重机器学习估计器与2010-2016年期间瑞典所有城市的947个随时间变化的地方特征和公共服务控制的新数据集配对。我们发现，适当地控制地方公共服务和特色控制的地方所得税对房价的影响超过一倍的估计。我们还利用我们的数据集的独特功能，以证明税收资本化是更强的地方市政竞争，提供支持的Tiebout假说的核心含义。最后，我们衡量公共服务，教育和犯罪对房价的影响，以及地方税收对移民的影响。
英文摘要：
How do property prices respond to changes in local taxes and local public services? Attempts to measure this, starting with Oates (1969), have suffered from a lack of local public service controls. Recent work attempts to overcome such data limitations through the use of quasi-experimental methods. We revisit this fundamental problem, but adopt a different empirical strategy that pairs the double machine learning estimator of Chernozhukov et al. (2018) with a novel dataset of 947 time-varying local characteristic and public service controls for all municipalities in Sweden over the 2010-2016 period. We find that properly controlling for local public service and characteristic controls more than doubles the estimated impact of local income taxes on house prices. We also exploit the unique features of our dataset to demonstrate that tax capitalization is stronger in areas with greater municipal competition, providing support for a core implication of the Tiebout hypothesis. Finally, we measure the impact of public services, education, and crime on house prices and the effect of local taxes on migration.
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