Housing Markets in the COVID-19 Era: Live from “The Big Apple”

Abstract

As the largest city in the United States, New York is home to more than 8 million people in one of the most densely populated areas of the world. The city is renowned for the allure of its diversity—socioeconomic, national, ethnic, and religious. Global demand for real estate in the city has helped support some of the highest home values. However, the COVID-19 pandemic has upended many facets of ordinary life—employment, consumption, and social interaction. As lifetime New Yorkers seek to relocate—temporarily or otherwise—the housing market has experienced an unprecedented shock. Home sale volume in the city plummeted by nearly 50%, dwarfing drops following the 2008 financial crisis and 9/11, while certain areas of the city have seen more substantial relative price declines. This research leverages economic theory and statistical techniques to lend insights to the different effects of the pandemic on the housing markets. Specifically, this paper, using high-level housing metrics derived from application of machine learning techniques, seeks to answer the critical question of what causal effect rising COVID-19 case counts had on home values and rents and how this effect interacted with time-invariant local differences in affluence, occupation, and population density. Our proposed justification for an association is that, based on research on prior epidemics, housing demand appears to shift based on perception of health risk; furthermore, it is posited that the appeal of highly infected areas was damaged by more concentrated business closures and limited recreational options. We test these hypotheses through implementation of ordinary least squares models that control for omitted variable bias, while being mindful of heteroscedasticity, and ultimately allow us to conclude that higher local case counts do lead to relative housing and rent declines. Finally, we find that the economic burden of rising case counts, with respect to housing, is born unevenly across areas of the city.