New evidence on mental health and housing affordability in cities: A quantile regression approach

Abstract

Unaffordable housing costs are one of the most pressing issues facing our cities, affecting people's health in difficult to measure ways. People's health varies over time and dynamically interacts with experiences of housing. Longitudinal analyses rarely explicitly model these variations. Quantile regression is an underutilised tool for testing associations across the distribution of an outcome. In this paper we apply panel quantile regression to test whether cumulative exposure to unaffordable housing over time has differential impact on mental health, dependent on initial health status. Using an annual longitudinal sample of 20,906 urban Australians (2001–2016), we model mental health outcomes using quantile regression (accounting for being in 10th, 50th, 90th mental health percentile initially). Although traditional fixed-effects models find weak evidence of cumulative effect, quantile regression reveals that individuals with low-median initial mental health were more affected by unaffordable housing, and individuals with high initial mental health appeared to be protected. Our findings suggest quantile regression as a promising method for understanding complex human effects of urban problems, and that policies targeted toward people with the poorest mental health may mitigate the consequences of exposure to unaffordable housing.