Energy Cost Burdens for Low-Income and Minority Households: Evidence From Energy Benchmarking and Audit Data in Five U.S. Cities

Problem, research strategy, and findings: Of the three primary components of housing affordability measures—rent, transportation, and utilities—utility costs are the least understood yet are the one area where the cost burden can be reduced without household relocation. Existing data sources to estimate energy costs are limited to surveys with small samples and low spatial and temporal resolution, such as the American Housing Survey and the Residential Energy Consumption Survey. In this study, we present a new method for small-area estimates of household energy cost burdens (ECBs) that leverages actual building energy use data for approximately 13,000 multifamily properties across five U.S. cities and links energy costs to savings opportunities by analyzing 3,000 energy audit reports. We examine differentials in cost burdens across household demographic and socioeconomic characteristics and analyze spatial, regional, and building-level variations in energy use and expenditures. Our results show the average low-income household has an ECB of 7%, whereas higher income households have an average burden of 2%. Notably, even within defined income bands, minority households experience higher ECBs than non-Hispanic White households. For lower income households, low-cost energy improvements could reduce energy costs by as much as $1,500 per year. Takeaway for practice: In this study we attempt to shift the focus of energy efficiency investments to their impact on household cost burdens and overall housing affordability. Our analysis explores new and unique data generated from measurement-driven urban energy policies and shows low-income households disproportionately bear the burden of poor-quality and energy-inefficient housing. Cities can use these new data resources and methods to develop equity-based energy policies that treat energy efficiency and climate mitigation as issues of environmental justice and that apply data-driven, targeted policies to improve quality of life for the most vulnerable urban residents.