Do School Districts Affect NYC House Prices? Identifying Border Differences Using a Bayesian Nonparametric Approach to Geographic Regression Discontinuity Designs

Abstract

What is the premium on house price for a particular school district? To estimate this in New York City we use a novel implementation of a geographic regression discontinuity design (GeoRDD) built from Gaussian processes regression (kriging) to model spatial structure. With a GeoRDD, we specifically examine price differences along borders between “treatment” and “control” school districts. GeoRDDs extend RDDs to multivariate settings; location is the forcing variable and the border between school districts constitutes the discontinuity threshold. We first obtain a Bayesian posterior distribution of the price difference function, our nominal treatment effect, along the border. We then address nuances of having a functional estimand defined on a border with potentially intricate topology, particularly when defining and estimating causal estimands of the local average treatment effect (LATE). We test for nonzero LATE with a calibrated hypothesis test with good frequentist properties, which we further validate using a placebo test. Using our methodology, we identify substantial differences in price across several borders. In one case, a border separating Brooklyn and Queens, we estimate a statistically significant 20% higher price for a house on the more desirable side. We also find that geographic features can undermine some of these comparisons. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.