  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.
