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Modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the American Housing Survey


Household-level information on central air conditioning (cenAC) and room air conditioning (rmAC) air conditioning and cold-weather thermal comfort are often missing from publicly available housing databases hindering research and action on climate adaptation and air pollution exposure reduction. We modeled these using information from the American Housing Survey for 2003–2013 and 140 US core-based statistical areas employing variables that would be present in publicly available parcel records. We present random-intercept logistic regression models with either cenAC, rmAC or “home was uncomfortably cold for 24 h or more” (tooCold) as outcome variables and housing value, rented vs. owned, age, and multi- vs. single-family, each interacted with cooling- or heating-degree days as predictors. The out-of-sample predicted probabilities for years 2015–2017 were compared with corresponding American Housing Survey values (0 or 1). Using a 0.5 probability threshold, the model had 63% specificity (true negative rate), and 91% sensitivity (true positive rate) for cenAC, while specificity and sensitivity for rmAC were 94% and 34%, respectively. Area-specific sensitivities and specificities varied widely. For tooCold, the overall sensitivity was effectively 0%. Future epidemiologic studies, heat vulnerability maps, and intervention screenings may reliably use these or similar AC models with parcel-level data to improve understanding of health risk and the spatial patterning of homes without AC.

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Fig. 1: Process for developing and applying models predicting central air conditioning (cenAC), room air conditioning (rmAC), and uncomfortably cold indoor winter temperatures (tooCold) for a given parcel.
Fig. 2: Average annual cooling-degree days (CDDs, cumulative number of degree days above 18 C per year), for the 1981–2010 climatological period, in each of the 140 available core-based statistical areas (CBSAs), American Housing Survey, 2003–2013.
Fig. 3: Receiver operating curves (ROCs) for each of the 35 core-based statistical areas (CBSAs) with data available during the training period of 2003–2013 and the out-of-sample prediction period 2015–2017.
Fig. 4: Model-predicted probability (Prob) of air conditioning (AC) by parcel (N=390,668), Detroit, MI, 2016.
Fig. 5: Model-predicted probability (Prob) of air conditioning (AC) by census tract, Detroit, MI, 2016.

Code availability

The R code is available from CJG on request.


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We thank our “Heatwaves, Housing, and Health” community-academic partnership, which includes Marie O’Neill, Larissa Larsen, and Tony Reames of the University of Michigan, Guy Williams and Wibke Heymach of Detroiters Working for Environmental Justice, Justin Schott of EcoWorks, Sara Clark of Southwest Detroit Environmental Vision, Michelle Lee and Rebecca Nikodem of Jefferson East, Inc., and Zachary Rowe of Friends of Parkside for providing the research hypotheses and feedback on preliminary results. Support for this research was provided by grants P30ES017885 (VJB and CJG), K99ES026198 (CJG), and R00ES026198 (CJG) from the National Institute of Environmental Health Sciences, National Institutes of Health and grant 1520803 from the National Science Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Science Foundation.

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Correspondence to Carina J. Gronlund.

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Gronlund, C.J., Berrocal, V.J. Modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the American Housing Survey. J Expo Sci Environ Epidemiol 30, 814–823 (2020).

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  • Climate change
  • Vulnerability
  • Air conditioning

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