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The Impact of Local Taxes and Public Services on Property Values

Grodecka, Anna LU and Hull, Isaiah (2019) In Sveriges Riksbank Working Paper Series
Abstract
Attempts to measure the capitalization of local taxes into property prices, starting with Oates (1969), have suffered from a lack of local public service controls. We revisit this vast literature with a novel dataset of 947 time-varying local characteristic and public service controls for all municipalities in Sweden over the 2010-2016 period. To make use of the high dimensional vector of controls, as well as time and geographic fixed effects, we employ a novel empirical approach that modifies the recently-introduced debiased machine learning estimator by coupling it with a deep-wide neural network. We find that existing estimates of tax capitalization in the literature, including quasi-experimental work, may understate the impact of... (More)
Attempts to measure the capitalization of local taxes into property prices, starting with Oates (1969), have suffered from a lack of local public service controls. We revisit this vast literature with a novel dataset of 947 time-varying local characteristic and public service controls for all municipalities in Sweden over the 2010-2016 period. To make use of the high dimensional vector of controls, as well as time and geographic fixed effects, we employ a novel empirical approach that modifies the recently-introduced debiased machine learning estimator by coupling it with a deep-wide neural network. We find that existing estimates of tax capitalization in the literature, including quasi-experimental work, may understate the impact of taxes on house prices by as much as 50%. We also exploit the unique features of our dataset to test core assumptions of the Tiebout hypothesis and to estimate the impact of public services, education, and crime on house prices.
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Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Working paper/Preprint
publication status
published
subject
keywords
Local Public Goods, Tax Capitalization, Tiebout Hypothesis, Machine Learning, Property Prices
in
Sveriges Riksbank Working Paper Series
issue
374
pages
40 pages
publisher
Sveriges Riksbank
language
English
LU publication?
yes
id
f4aaabc7-953d-4bfe-bcbf-7f2b52d2aa50
alternative location
https://www.riksbank.se/globalassets/media/rapporter/working-papers/2019/wp374.pdf
date added to LUP
2019-05-02 11:50:24
date last changed
2019-05-02 13:03:34
@misc{f4aaabc7-953d-4bfe-bcbf-7f2b52d2aa50,
  abstract     = {{Attempts to measure the capitalization of local taxes into property prices, starting with Oates (1969), have suffered from a lack of local public service controls. We revisit this vast  literature with a novel dataset of 947 time-varying local characteristic and public service controls for all municipalities in Sweden over the 2010-2016 period. To make use of the high dimensional vector of controls, as well as time and geographic fixed effects, we employ a novel empirical approach that modifies the recently-introduced debiased machine learning estimator by coupling it with a deep-wide neural network. We find that existing estimates of tax capitalization in the literature, including quasi-experimental work, may understate the impact of taxes on house prices by as much as 50%. We also exploit the unique features of our dataset to test core assumptions of the Tiebout hypothesis and to estimate the impact of public services, education, and crime on house prices.<br/>}},
  author       = {{Grodecka, Anna and Hull, Isaiah}},
  keywords     = {{Local Public Goods; Tax Capitalization; Tiebout Hypothesis; Machine Learning; Property Prices}},
  language     = {{eng}},
  month        = {{05}},
  note         = {{Working Paper}},
  number       = {{374}},
  publisher    = {{Sveriges Riksbank}},
  series       = {{Sveriges Riksbank Working Paper Series}},
  title        = {{The Impact of Local Taxes and Public Services on Property Values}},
  url          = {{https://www.riksbank.se/globalassets/media/rapporter/working-papers/2019/wp374.pdf}},
  year         = {{2019}},
}