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

arXiv: https://arxiv.org/abs/2203.14751 · HTML: https://mlsanity.com/paper/2203.14751/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 109 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Nonparametric regression using deep neural networks with ReLU activation function](https://mlsanity.com/paper/1708.06633/) (2017)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Double/Debiased/Neyman Machine Learning of Treatment Effects](https://mlsanity.com/paper/1701.08687/) (2017)
- [Deep Neural Networks for Estimation and Inference](https://mlsanity.com/paper/1809.09953/) (2018)
- [Uniform Inference in High-dimensional Dynamic Panel Data Models](https://mlsanity.com/paper/1501.00478/) (2015)

## Abstract (arXiv)

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.