Energy Cost Burdens
for Low-Income and Minority Households
+ Constantine Kontokosta, Vincent Reina, Bartosz Bończak
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.
Of the three primary components of housing affordability—rent, transportation, and utilities—utility costs are the least understood, despite representing a significant opportunity to improve overall affordability without the need for household relocation (Stone, 2006). Excessive utility expenditures fall disproportionately to the lowest income households, who are least able to make energy efficiency investments, thus raising important social and environmental justice concerns that require policymakers and planners to act (Jenkins, McCauley, Heffron, Stephan, & Rehner, 2016). The slow pace of energy retrofits in existing multifamily buildings highlights the systemic investment constraints that result in an underallocation of energy-efficient technologies in housing (Pivo, 2014). Beyond the potential financial benefits for low-income households, energy-efficient investments can reduce carbon emissions and improve occupant health while achieving long-term sustainability goals (Nevin & Jacobs, 2006; Pearsall & Pierce, 2010). Researchers have made important strides in quantifying the magnitude of energy cost burdens (ECBs) on macro and regional levels, but policymakers and planners lack the granular, high-spatial- and temporal-resolution data needed to develop targeted and proactive policies and programs to directly address this issue. Such policies include incentives and mandates for energy efficiency improvements based on measured energy performance, subsidies for specific energy retrofits tied to building characteristics, and affordable housing programs that integrate rental subsidy amounts with ECB estimates.
In this study we present a new methodology to model household ECBs based on actual energy use data for individual buildings across five major U.S. cities and link energy costs to savings opportunities based on specific residential building types and characteristics. Specifically, we a) use high-resolution data to develop small-area estimates (at the level of individual buildings) of ECBs, b) analyze how ECBs vary by building across demographic and income groups, and c) assess the implications of energy retrofit investments on housing affordability. We examine differentials in ECBs across neighborhoods and socioeconomic groups, comparing lower income and wealthier neighborhoods and analyzing racial disparities within income groups. The data we use in this study consist of actual annual building energy consumption for approximately 13,000 multifamily buildings in New York City (NY), Boston (MA), Cambridge (MA), Seattle (WA), and Washington (DC) reported through city energy disclosure laws. We integrate these data with building and land use characteristics, housing subsidy program information, and socioeconomic characteristics to develop a comprehensive building-level data set of energy use and resident attributes. To analyze the potential financial implications of energy retrofit investments for low-income households, we use a unique data set of energy audit reports for approximately 3,000 residential buildings in New York City to estimate economically and technically feasible energy retrofit opportunities and their impact on ECBs. Although the data we use in this study represent a nonrandom sample of buildings and cities, they nonetheless provide a unique opportunity to develop and demonstrate a new method for planners to leverage large energy data sets to more fully understand household cost burdens at higher spatial and temporal resolutions than are currently possible.