Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach

TL;DR

This paper employs a double machine learning approach with a novel dataset to accurately estimate how local taxes and public services influence property prices, revealing significant effects and heterogeneity across municipalities.

Contribution

It introduces a new empirical strategy combining double machine learning with detailed local data to better identify the effects of taxes and public services on property values.

Findings

Proper controls more than double the estimated impact of income taxes on house prices.

Tax capitalization is stronger in areas with higher municipal competition.

Public services, education, and crime significantly affect house prices.

Abstract

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.