Taxi activity as a predictor of residential rent in New York City

Abstract

Real estate developers and investors have a vested interest in discovering new techniques for estimating the direction and magnitude of changes in residential rent within a neighborhood. This study hypothesizes, and finds evidence, that taxi activity is a proxy for changing income and neighborhood quality as well as an indicator of gentrification. Novel research is performed to determine if taxi activity is a significant predictor of rents in New York City at the neighborhood level. Nine OLS regression models are created using data about 1,466,234,991 taxi pickups and drop-offs, median rent, and median income across 188 neighborhoods in New York City in the years of 2010-2015. In all nine models, taxi activity is found to be a statistically significant predictor of rent at 99% confidence. This study finds that a I standard deviation positive shock in taxi drop-offs will result in a 0.009% 0.155% higher rent the next year on average.