A few years back, I honestly told journal editors I couldn’t review any more housing voucher studies. The field was crowded, lots of people could review the papers in question, and virtually all of them would be more qualified and more interested than me in housing vouchers. I think vouchers are solid policy, but I”ve run out things to say about them. If we aren’t going to fund them generously, there are hard limits as to what they can do for affordability, granted the wage and housing scarcity environments of large cities. (A true statement about any income support policy.)

Thus it always makes me happy to see housing research that reaches into the greater social and economic context of affordability as a context. Now, I am economically very lucky, but every time I open my utility bill in southern California, I have to take several deep breaths and sit down because my head *swims*. Utilitie are expensive, and while I know my specific context is special, utilties for rental houses can’t be that much less mine. Furthermore, one of the ways that climate change is going to hurt people is through their utility bills, where at least some are going to have to make choices between paying more and letting Grandma struggle during heat waves.

Kontokosta, Reina, and Bonczak take up this question about how utility costs factor into the transportation-housing cost nexus using a dataset that planners don’t tend to use much.

Here is the citation and the link. It’s behind a paywall, but if you ask me or the author, I suspect we can find you a copy of it between the two of us.

Constantine E. Kontokosta, Vincent J. Reina & Bartosz Bonczak (2020) Energy Cost Burdens for Low-Income and Minority Households, Journal of the American Planning Association, 86:1, 89-105, DOI: 10.1080/01944363.2019.1647446

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.