2020 Residential Energy Consumption 

Survey: Household Characteristics 

Technical Documentation Summary

June 2022

(revised March 2023) 

Independent Statistics & Analysis

www.eia.gov

U.S. Department of Energy

Washington, DC 20585
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary i

This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and 

analytical agency within the U.S. Department of Energy. By law, EIA’s data, analyses, and forecasts are 

independent of approval by any other officer or employee of the United States Government. The views 

in this report therefore should not be construed as representing those of the U.S. Department of Energy 

or other federal agencies.
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 2

Contents

Overview and History.................................................................................................................................... 5

Survey Design Elements and Changes .......................................................................................................... 6

Data Products and Revision Policy................................................................................................................ 8

Data products .......................................................................................................................................... 8

Within-cycle data revisions............................................................................................................... 8

Methodological and content changes across survey cycles ............................................................. 8

Frame and Sample Design............................................................................................................................. 9

Frame sources and coverage ................................................................................................................... 9

Sample allocation and sample selection................................................................................................ 10

Completed-case sample size allocation .......................................................................................... 10

Sample selection ............................................................................................................................. 11

Household Survey ....................................................................................................................................... 13

Questionnaire design............................................................................................................................. 13

Data collection methods........................................................................................................................ 14

Phased approach............................................................................................................................. 14

Contact materials............................................................................................................................ 14

Response rate and nonresponse bias.................................................................................................... 15

Editing and data quality......................................................................................................................... 17

Item imputation .................................................................................................................................... 17

Weather and Geographic Data ................................................................................................................... 18

Square footage data.................................................................................................................................... 19

Consumption and expenditure data ........................................................................................................... 19

Weighting and Sampling Error.................................................................................................................... 19

Relative standard errors........................................................................................................................ 20

Confidentiality of Information .................................................................................................................... 20

Appendix A: Comparing preliminary and final main heating fuel,main heating equipment, and water 

heating fuel estimates ................................................................................................................................ 21
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 3

Table of Figures

Figure 1. 2020 Residential Energy Consumption Survey timeline................................................................. 6

Figure 2. Unweighted and weighted self-response rates by state, 2020 RECS ..........................................16
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 4

Table of Tables

Table 1. 2020 RECS relative standard error (RSE) requirements for average fuel consumption..................8

Table 2. Expected completed cases, assumed yield rate, and allocated starting sample for 2020 RECS...10

Table A1. Main space heating fuel in U.S. homes, 2020 RECS preliminary and final estimates...................20

Table A2. Main space heating equipment in U.S. homes, 2020 RECS preliminary and final estimates......21

Table A3. Main water heating fuel in U.S. homes, 2020 RECS preliminary and final estimates...................21 
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 5

Overview and History 

The Residential Energy Consumption Survey (RECS) is a periodic study we conduct that provides detailed 

information about energy usage in U.S. homes. RECS is a multiyear effort consisting of a Household 

Survey, data collection from household energy suppliers, and end-use consumption and expenditures 

estimation. The 2020 RECS is the 15th iteration of the study.

The Household Survey, a voluntary survey, collects data on energy-related characteristics and usage 

patterns at the national and sub-national (for example, state) level from a representative sample of 

housing units. The mandatory Energy Supplier Survey (ESS) collects data on how much electricity, 

natural gas, propane and liquefied petroleum gas (LPG), and fuel oil and kerosene were consumed in the 

sampled housing units during the reference year. It also collects data on actual dollar amounts spent on 

these energy sources. We use engineering-based models to produce consumption and expenditure 

estimates for heating, cooling, refrigeration, and other end uses in all housing units occupied as a 

primary residence in the United States using the data collected from the Household Survey and ESS. 

Figure 1 outlines the 2020 RECS timeline.

The scope and purpose of RECS differ slightly from similar EIA products that report residential energy 

data. RECS samples homes occupied as a primary residence, which excludes secondary homes, vacant 

homes, military barracks, and common areas in apartment buildings. As a result, RECS estimates do not 

represent sector-level totals defined in our other products, but they are best suited for comparisons

across different characteristics of homes within the residential sector.

We collaborated with IMG-Crown and RTI International to conduct the 2020 RECS Household Survey. 

The original version of this document (published June 2022) reflected preliminary 2020 RECS 

household characteristics estimates released in early 2022. We have revised this document to reflect 

updates to household characteristics data since that release. These updates include characteristics 

revisions based on a review of household energy billing data. Researchers who previously 

downloaded the preliminary microdata file or characteristics tables should consider this release to 

be the final, official characteristics data for the 2020 RECS.
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 6

Figure 1. 2020 Residential Energy Consumption Survey timeline

Survey Design Elements and Changes

We instituted a number of survey design revisions, content changes, and variable updates for the 2020 

RECS:

• The target population for the 2020 RECS is all occupied housing units in the 50 states and the 

District of Columbia (DC) that are used as primary residences. Vacant homes, seasonal housing 

units, and group quarters (such as dormitories, nursing homes, prisons, and military barracks)

are excluded. Housing units located on military installations are included.

• We designed the 2020 RECS sample to meet precision requirements for energy consumption for 

all 50 states and DC, with an expected yield of 18,000 to 20,000 completed RECS questionnaires 

from sampled households. For the first time in the program’s history, estimates will be available 

for all 50 states and DC. The larger responding sample size also yields more precise estimates for 

key topics and for emerging technologies, such as electric vehicles.

• We selected housing units for the 2020 RECS using an Address-Based Sample (ABS) design. The 

frame for this sample was a list of residential addresses, based on the U.S. Postal Service’s 

(USPS) Computerized Delivery Sequence file of active mail delivery points. We augmented the 

frame information with supplemental data from the Decennial Census, the American 

Community Survey (ACS), and other sources to allow us to stratify the frame for better statistical 

efficiency and to represent the population of eligible housing units. Non-residential addresses 

were removed from the frame and procedures were implemented to account for special 

situations, such as accounting for non-deliverable and drop-point addresses. 

• The 2020 RECS introduced a completely self-administered design via web and paper 

questionnaire. Before the 2020 RECS, we conducted the study either through in-person 

interviews with trained interviewers at the sampled households or with a combination of inperson and self-administered modes. By eliminating interviewing staff for the 2020 RECS, the 

program could implement several other innovations, including increasing the sample size and 

moving to an unclustered sample design. 

• We added new questions on emerging technologies and usage behavior, and we made some 

questionnaire changes as a result of the COVID-19 pandemic. The 2020 RECS added questions 

about solar capacity and installation, electric vehicle ownership and charging behavior, and 
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 7

smart speakers. Just before data collection, in response to the COVID-19 pandemic, we added 

questions about working from home and whether anyone in the household was participating in

K-12 distance learning or online college courses. 

• A total of 18,496 respondents completed the Household Survey: 73% by web and 27% by paper.

The total unweighted response rate (AAPOR 3)1 was 38.6%, and the total weighted response 

rate was 37.9%. We collected the Household Survey data in two waves: the first wave was from

September through November 2020, and the second wave was from January through April 

2021. 

• We conducted quality control checks and edits for all data to validate the sampled addresses of 

the responding households and to identify and resolve data inconsistencies. This process 

included identifying outliers to numeric items, identifying and resolving logical inconsistencies, 

and recoding write-in responses into established response categories. In addition, after we 

collected consumption and billing data from the ESS, we performed a comprehensive data 

editing process to reconcile the inconsistencies between respondent-reported characteristics 

data and the supplier-reported consumption data.

• To address item nonresponse, we used a hot-deck imputation method for the 2020 RECS. In this 

method, we match a recipient household that has a missing value for the variable being imputed

to a similar donor household that has a response for that variable and use that value to replace 

the missing response. We imputed responses for about 250 Household Survey variables, and the 

median imputation rate was 2.9%.

• We used a new approach for housing unit control totals in weighting for the 2020 RECS To 

ensure the responding sample represented housing units at the national, census region, census 

division, and state levels, we implemented weighting adjustments. These weighting adjustments 

included ineligibility, nonresponse, and poststratification. RECS typically uses control totals from 

the current year’s ACS data for poststratification. Because 2020 ACS one-year data were not 

published, 2020 RECS used a combination of 2020 Decennial Census counts and 2019 ACS 

estimates to calculate the control totals.

• The 2020 RECS sample used the Jackknife Repeated Replication method for variance estimation.

Each RECS estimate has a corresponding relative standard error (RSE). We calculated the RSEs

from the estimated variance using replicate weights, which were determined using the Jackknife 

Repeated Replication method for the 2020 RECS. RSEs are included as a separate tab in each 

published Excel table. Estimates greater than zero with a corresponding RSE of 0.00 indicate a 

variable used as a control total in poststratification. 

• We conducted comprehensive nonresponse bias analysis. Based on the results from the 

nonresponse bias analysis, we identified no major concerns with the data quality of key 2020 

RECS estimates, indicating that the final weighted 2020 RECS estimates are not significantly

different from the target population parameters.

1 AAPOR Response Rate Calculator 4.1
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 8

Data Products and Revision Policy

Data products

We release a variety of RECS products across survey cycles tailored to a wide range of data users. These 

products include:

• Detailed tables of household energy-use estimates across key geographic, structural, and 

demographic variables

• Topic-specific articles and reports

• Data-user webinars

• Microdata files

• Survey methods documentation

Although we release similar products across survey cycles, we change these products from one cycle to 

the next to adapt to changes in the residential energy sector and apply new dissemination methods and 

tools.

The following products are available on the RECS website:

• Data tables for housing characteristics, consumption, expenditures, and end use estimates 

• Today in Energy articles

• Public-Use Microdata File and User Guide

• Webinars

• Topic-specific infographics and articles

RECS products from previous cycles are available on each survey cycle’s Data page or in the archived 

Analysis and Projections page.

Revisions and changes across cycles

Within-cycle data revisions

We execute a series of survey data cleaning, editing, imputation, and coding steps to ensure RECS data 

and estimates meet EIA quality standards. We release preliminary RECS Household Characteristics 

results at the end of the RECS Household Survey phase. After this release, we perform additional quality 

control steps to reconcile Household Characteristics data with energy billing data collected as part of the 

RECS Energy Supplier Survey. This additional quality control process may result in revisions to the 

preliminary Household Characteristics estimates, particularly for main-heating fuel and equipment and 

water-heating fuel. Differences between preliminary and final main space heating fuel estimates are 

discussed in Appendix A.

Methodological and content changes across survey cycles

RECS is a cross-sectional study, with updates to questionnaire content, statistical methods, and 

dissemination strategies from the previous cycle. We do not currently conduct a longitudinal, household 

energy demand study. Each RECS, however, shares content and design elements across survey cycles. 
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 9

The unit of analysis for every RECS cycle is the occupied, primary housing unit. The sample is designed,

using geographic and other stratification methods, using an address-based housing unit frame. Most

survey questions are carried forward from one cycle to the next. Although we encourage RECS users to 

use caution when drawing analysis-based conclusions across RECS cycles, many comparisons are valid 

and statistically sound. 

Major changes to methods or questionnaire content from one cycle to the next are highlighted in 

Technical Documentation reports, special-topic reports (for example, the end-use modeling changes for 

2015), and survey form specifications.

Frame and Sample Design

The target population for the 2020 RECS is all occupied housing units in the 50 states and the District of 

Columbia (DC) that are used as primary residences. Vacant homes, seasonal housing units, and group 

quarters (such as dormitories, nursing homes, prisons, and military barracks) are excluded. Housing 

units on military installations are included. In addition, we benchmark to occupied housing unit totals 

from the ACS. RECS uses the U.S. Census Bureau’s definition of a housing unit, which is a single-family 

home, a unit in a multifamily building, or a mobile home.

Frame sources and coverage

We selected housing units for the 2020 RECS using an Address Based Sample (ABS) design. The frame for 

this sample is a list of residential addresses, based on the U.S. Postal Service’s (USPS) Computerized 

Delivery Sequence (CDS) file of active mail delivery points. The frame file is enhanced with supplemental 

data from the Decennial Census, the ACS, and other sources to allow us to stratify the frame for better 

statistical efficiency and better representation of the population of eligible housing units. The following 

types of addresses were removed: nonresidential addresses, PO Box addresses that are not the onlyway-to-get-mail (OWGM),

2 and drop-point addresses that have a frame indication of more than four 

units. Overall, the expected population coverage of the RECS ABS frame is about 99.6% of the target 

population of housing units. 

Drop points are addresses that serve multiple housing units (drop units) without a unit identifier. These 

addresses posed logistical challenges for the 2020 RECS web- and mail-based data collection because 

the contact protocol included multiple mailings. Because the mailings went to a residential address, not 

a specific respondent name, subsequent mailings at a drop-point address could be received by 

respondents from different housing units. Using a frame indicator for number of units, we excluded

drop-point addresses with four or more units from the sample frame. These units are a small percentage 

(about 0.4%) of the overall drop points, and they are more likely to be group quarters that are out of 

scope for RECS. For the drop-point addresses with fewer than four units, we used a sample substitution 

method.

3 In other words, if a drop-point address was selected, then we would select the nearest non2 OWGM PO Boxes represent addresses at which a resident’s only acceptable form of postal delivery is through a PO Box 

address. See McMichael, J., & Brown, D. (2018). PO Boxes on Address Based Sampling (ABS) frame: Under- or over-coverage or 

both? American Association for Public Opinion Research, Denver, CO. 3 Amaya, A.E (2017). RTI International’s Address-Based Sampling Atlas: Drop points. Research Triangle Park, NC: RTI Press. RTI 

Press Publication No. OP-0047-1712
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 10

drop-point multifamily building with the same number of units as the substitution. A unit in the 

substitute building would be randomly selected as a sampling unit.

Sample allocation and sample selection

Previous RECS cycles used clustered sample designs by grouping housing units into clustered 

geographies to make hiring and deploying in-person interviewers efficient and cost effective. For the 

2020 RECS, introducing an entirely self-administered web and mail design meant that we could use an 

unclustered design, since geographic proximity was no longer necessary for efficient interviewer 

assignments. The design was a single-stage sampling with explicit and implicit stratification. In addition, 

we designed the 2020 RECS sample to meet the residential energy consumption precision requirements

outlined in Table 1, where the fuel consumption data are collected from the ESS.

Table 1. Relative standard error (RSE) requirements for average fuel consumption, 2020 RECS

Geography All fuel totala Electricity Natural gas Fuel oil

United States 1% 1% 1% 3%

Midwest, South, and West regions 2% 2% 2% -

Northeast region 2% 2% 2% 4%

Census divisions 3% 3% 3% -

50-states and District of Columbia 4% - - -

Data source: U.S. Energy Information Administration a Includes electricity, natural gas, fuel oil, and propane

To achieve these statistical requirements and to improve the precision of other key energy-use metrics, 

we targeted an estimated 18,000 to 20,000 completed cases. Compared with previous cycles of RECS, 

the sample allocation for 2020 was more complex due to additional geographic and fuel precision 

requirements. However, the sample selection was simpler with a one-stage sample design, compared 

with a multistage sample design in the previous RECS cycles.

Completed-case sample size allocation

When allocating the completed-case sample size, no single formula could optimize and satisfy all the

precision requirements simultaneously. Therefore, the allocation process was done using a bottom-up 

approach; by optimally satisfying one requirement at a time. The first step was to calculate the 

minimum sample size needed to meet the precision requirement for total energy consumption (in 

British thermal units) at the state level, then check to see if the requirements at higher geographic 

levels, such as the division level, regional level, or the national level, were also satisfied. If they were not, 

then we applied the same optimization process to satisfy the requirements at each geographic level. 

Once we allocated this initial sample, we applied the same approach to ensure the precision 

requirements for each energy fuel source were met.

Originally, we specified proposed precision requirements for propane at the national and census region

levels. These requirements were dropped due to the need for oversampling and significant increase in

sample size. We determined that acceptable precision levels (between 4% and 6% at the region level) 
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 11

and improved quality over previous cycles could be attained for 2020 RECS propane estimates without 

meeting the original regional precision requirements. The estimation of the RSEs with the final allocated, 

completed-case sample size required prior estimation of the means, standard errors, and design effects. 

For the estimates of the means and standard errors used in the RSE formula, we used data from the 

2009 RECS and the 2015 RECS. Because these two datasets did not have complete data in every state, 

we derived the estimates using a pooled sample size from both RECS datasets where the combined 

sample total was at least 30 cases. If the combined sample size was less than 30 cases, then the 

estimates were derived using the average of two different modeled estimates. The design effect, which 

is an unequal weighting effect in this case, was 1.05 to account for eligibility and nonresponse 

adjustments to the equal design weights within each state. 

As a result of the allocation process, the minimum sample needed to meet the precision requirements 

was approximately 10,571 households. However, as mentioned earlier, 18,000 households was the 

target sample for completed cases; therefore, we allocated the remaining 7,429 households to each 

state in proportion to the occupied housing unit distribution of the 2017 ACS. 

In addition, to achieve the number of completed cases allocated for each state, we selected additional 

sample addresses to account for losses due to ineligibility and nonresponse during data collection. We 

determined the starting sample we deployed based on the number of expected completed cases and 

the corresponding assumed yield rate in each state. The yield rate is the proportion of starting sample 

cases that result in a complete, eligible questionnaire based on previous or external information. For 

2020 RECS, we estimated the yield rates based on either data from the 2015 RECS and the National 

Pilot4 or modeled from the self-response rate of the 2013–2017 five-year ACS. Table 2 contains the 

allocated starting sample for each state calculated from the expected completed cases and the yield 

rate. 

Sample selection

For sample selection, the frame was stratified explicitly by state. Within each state, the variables listed 

below were sorted as implicit stratification variables. Next, we used the Chromy’s minimum 

replacement technique (Chromy, 1979) to select housing units systematically within each state:

• International Energy Conservation Code (county level, climate zone from U.S. Department of 

Energy)

• Multifamily dwelling unit indicator (address level, from CDS file)

• Rural-Urban Commuting Area code (census track level, from USDA)

• Zip code

• Carrier route (for mail delivery)

• Walk sequence (mail delivery sort order within carrier route)

• Zip+4 (for addresses that do not have a walk sequence)

4 The National Pilot was a study with a nationally representative sample conducted in 2015 focusing on testing the feasibility of 

the self-administered modes. 
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 12

Table 2. Expected completed cases, assumed yield rate, and allocated starting sample, 2020 RECS

State or district

Expected

completed cases

Assumed 

yield

rate

Allocated

starting 

sample

Alabama 268 0.377 712

Alaska 211 0.384 549

Arizona 506 0.355 1,424

Arkansas 243 0.379 642

California 1,172 0.355 3,299

Colorado 321 0.356 901

Connecticut 315 0.375 841

Delaware 120 0.370 324

District of 

Columbia

194 0.323 600

Florida 676 0.383 1,763

Georgia 430 0.335 1,284

Hawaii 275 0.377 730

Idaho 234 0.399 587

Illinois 505 0.345 1,465

Indiana 355 0.379 938

Iowa 249 0.497 501

Kansas 183 0.397 461

Kentucky 430 0.397 1,083

Louisiana 234 0.360 650

Maine 196 0.386 507

Maryland 321 0.368 873

Massachusetts 553 0.378 1,465

Michigan 366 0.440 832

Minnesota 330 0.481 685

Mississippi 188 0.365 515

Missouri 330 0.421 783

Montana 168 0.403 417

Nebraska 177 0.402 440

Nevada 243 0.354 686

New Hampshire 179 0.398 450

New Jersey 475 0.310 1,534

New Mexico 182 0.339 537

New York 997 0.313 3,190

North Carolina 444 0.328 1,353

North Dakota 292 0.398 734

Ohio 405 0.396 1,023
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 13

Oklahoma 238 0.395 602

Oregon 309 0.444 697

Pennsylvania 637 0.356 1,788

Rhode Island 202 0.373 542

South Carolina 276 0.333 829

South Dakota 167 0.408 410

Tennessee 509 0.414 1,228

Texas 1,033 0.334 3,094

Utah 193 0.440 439

Vermont 217 0.390 556

Virginia 425 0.361 1,177

Washington 405 0.408 993

West Virginia 170 0.400 425

Wisconsin 320 0.425 753

Wyoming 133 0.393 338

Total 18,001 48,649

Household Survey

Questionnaire design

The 2020 RECS Household Survey was designed to be entirely self-administered using either a web or 

paper questionnaire. Both questionnaires were available in English and Spanish. The 2020 RECS 

questionnaire specification is available on the EIA website and consists of the following topical sections: 

• Your home

• Appliances

• Electronics

• Space heating

• Air conditioning

• Thermostats and temperatures

• Water heating

• Lighting

• Energy bills

• Household characteristics

• Energy assistance

• Energy supplier information

Each time we conduct the RECS, we review the content and lessons learned from the previous cycle and 

revise the questionnaire appropriately. The content revisions typically include adding or dropping 

questions to account for household technology changes or to improve response quality. For the 2020 

RECS, new questions included information on:

• All-electric plug-in and hybrid plug-in vehicles and charging 
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 14

• Household solar capacity

• Smart speakers 

• Teleworking and online education at home as a result of the COVID-19 pandemic 

• Information on power outages lasting longer than 24 hours 

To improve response quality, we updated questions on square footage, space heating, and air

conditioning. We also made minor adjustments to the wording of questions to acknowledge potential 

changes in household energy consumption and behaviors related to the COVID-19 pandemic. 

Data collection methods

The RECS Household Survey was voluntary and conducted in two waves: the first wave was September 

to November 2020, and the second wave was January to April 2021. A total of 18,496 eligible 

respondents completed the survey: 72.8% (13,469) responded via web questionnaire, and 27.2% (5,027) 

responded via paper questionnaire. Of the respondents that completed the web questionnaire, 73.4% 

(9,886) used a desktop or laptop computer, 22.2% (2,995) used mobile phones, and 4.4% (587) used 

tablets. Based on survey timing paradata collected without any data transformation, the web survey 

took an average of about 35 minutes to complete, with a median time of 32 minutes. In contrast, for the

2015 RECS, 5,686 households completed the Household Survey using a combination of in-person 

personal interviews, web questionnaires, and paper questionnaires.

Phased approach

The 2020 RECS was planned as a three-phase, responsive-design approach. Phase 1 consisted of 20% of 

the initial starting sample, Phase 2 consisted of 80% of the initial sample, and an optional Phase 3 

provided additional sample to address potential precision and representativeness issues. During data 

collection, we determined that, based on response rates in Phase 1, we needed the additional Phase 3 

sample to meet the targeted completed cases in certain states. To field Phase 3 efficiently, we decided

to release the Phase 3 cases concurrently with Phase 2. This approach contributed to both schedule 

efficiencies and budget optimization. 

An additional benefit to the phased approach was it allowed us to conduct a series of experiments 

during Phase 1 and implement those findings during subsequent phases. Phase 1 included experiments 

to test the effectiveness of two levels of formality on the RECS postcards (using a less formal version 

with colors rather than a more formal black-and-white version) and optimal incentive amounts to 

maximize web response (an additional $10 for web response versus an additional $20 for web

response). Based on the experimental results from Phase 1, neither the increased incentive nor the 

postcard color design made an impact on the web response rate, so we selected the colorful postcard

and the $10 incentive for Phase 2 and Phase 3.

Contact materials

For each phase, we sent a sample of addresses up to six mailings over approximately six weeks:

• Prenotice postcard—sent to all addresses

• First invitation—sent to all addresses

• Thank you or reminder postcard—sent to all addresses

• Second invitation—sent to remaining eligible or open addresses

• Reminder letter—sent to remaining eligible or open addresses

• Third invitation—sent to remaining eligible or open addresses
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 15

Response rate and nonresponse bias

The overall unweighted response rate for the 2020 RECS Household Survey is 38.6%, and the weighted 

response rate is 37.9%. The unweighted response rate was calculated using the American Association for 

Public Opinion Research (AAPOR) formula 3 (AAPOR, 2020): 

RR = I/ (I+R+E)

where I is the number of complete interviews,

5 R is the number of refusal and eligible incompletes, and 

E is the number of eligible cases estimated from cases with unknown eligibility. The E was determined 

based on models for 2020 RECS.

6

The weighted response rate was calculated using the same concept, except now we used the 

corresponding sum of weights in each disposition category.

The response rate for 2020 RECS was lower than that of the 2015 RECS, which had an unweighted 

response rate of 51.2% (weighted response rate was 50.8%). We expected a lower response rate

because we administered the 2015 RECS using both in-person and self-administered modes, and the 

2020 RECS was entirely self-administered. In-person surveys achieve higher response rates than selfadministered surveys because interviewers can build rapport with respondents in person. 

Sampled households in Wisconsin (50.7%), Minnesota (50.4%), and Idaho (49.3%) had the highest

response rates. The states with the lowest response rates were New York (30.4%), New Jersey (31.6%), 

and Texas (33.4%). The unweighted response rate and weighted response rate are the same for each 

state because each case within a state has the same sample selection probability (Figure 2). 

5 Completed interviews include interviews where the respondent did not answer all questions in the survey. The respondent 

must have answered at least 7 out of 10 key RECS questions for the interview to be considered complete. Partially completed 

interviews that did not meet that definition were defined as eligible incompletes. 6 See the Weighting and Sampling Error section for more details.
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 16

Figure 2. Unweighted and weighted self-response rates by state, 2020 RECS

We conducted a comprehensive nonresponse bias study to understand how representative 2020 RECS 

respondents were of the general population or if any subpopulations were underrepresented in the 

responding sample. We compared response rates by sample subgroup and compared the estimates of 

key frame variables between the respondent and nonrespondent groups. Differences in any 

comparisons could indicate potential nonresponse bias. In addition, we compared demographic 

variables to the ACS estimates to assess potential differences between the types of households 

responding to the RECS and the general household population of the United States.

The nonresponse bias study reached the following conclusions about the Household Survey:

• Unit response rates varied across different subgroups, such as housing type, census regions, and 

urban or rural classification. 

• Although statistical tests resulted in statistically significant differences for some characteristics 

variables, the differences were small.

0% 10% 20% 30% 40% 50% 60%

Wisconsin (357)

Minnesota (325)

Idaho (270)

North Dakota (331)

Vermont (245)

Kansas (208)

Oregon (313)

South Dakota (183)

Washington (439)

Nebraska (189)

Montana (172)

Utah (188)

Iowa (286)

Wyoming (190)

South Carolina (334)

Kentucky (428)

Michigan (388)

Colorado (360)

Hawaii (282)

Maine (223)

New Hampshire (175)

West Virginia (197)

Alaska (311)

Missouri (296)

Virginia (451)

District of Columbia (221)

Tennessee (505)

Arizona (495)

North Carolina (479)

New Mexico (178)

Indiana (400)

Rhode Island (191)

Connecticut (294)

Delaware (143)

Oklahoma (232)

Alabama (242)

Arkansas (268)

California (1152)

Mississippi (168)

Nevada (231)

Maryland (359)

Ohio (339)

Massachusetts (552)

Georgia (417)

Illinois (530)

Florida (655)

Pennsylvania (617)

Louisiana (311)

Texas (1016)

New Jersey (456)

New York (904)

state (number of completed cases)
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 17

• Analysis of how well the groups represented the general population found that key 2020 RECS

estimates are statistically similar to ACS and American Housing Survey (AHS) estimates. Most 

comparisons were not statistically different; for variables that were significantly different, the 

differences were small.

• The potential for nonresponse bias in RECS was reduced by applying weighting adjustments, 

which are described in the Weighting and Sampling Error section.

• When comparing estimates within the 2020 RECS or estimates from previous RECS studies, data 

users should use the relative standard errors (RSE) to determine if two estimates are statistically 

different from one another. 

Editing and data quality

We employed several strategies to analyze and improve data quality in the 2020 RECS Household 

Survey. For numeric questions where an accurate response was important for modeling the energy use 

in the household, such as square footage and year of construction, an explicit Don’t Know response was 

available, which led to a categorical follow-up. In addition, range checks were available in the web 

instrument for numeric responses, which reduced the probability of a typographical error. Some 

questions used pictures as a guide in both the web and mail questionnaires, after pretesting indicated 

that the images improved response quality. 

All completed surveys went through a validation process to ensure that the correct sampled households 

responded and that key questions were answered. 

After the validation process, we thoroughly reviewed the data for inconsistent responses, numericresponse outliers, and write-in responses when a respondent chose other as a response. If the review 

indicated that a response was incorrect, it was either changed to a valid response, using deductive 

reasoning, or changed it to missing and then imputed. 

Beginning with the 2015 survey cycle, we introduced an additional quality control step for the RECS 

Household Survey phase. We reviewed characteristics responses for inconsistencies with billing and fuel 

delivery data patterns reported during the ESS phase. For example, we changed a response of electricity

for main heating fuel on the Household Survey to natural gas where ESS data indicated strong winter 

seasonal use in utility-reported natural gas bills for that household. We also reviewed cases where 

households reported solar photovoltaic (PV) generation to ensure ESS-provided electricity billing data 

included both the solar PV generation and the utility-generated consumption. These editing steps 

corrected some measurement errors in the Household Survey, resulting in more accurate main spaceheating fuel, space-heating equipment, and water-heating fuel responses relative to the preliminary 

household data. In addition, these editing steps led to more consistent linkage between characteristics, 

annualized consumption and cost, and modeled end-use estimates. More detail about these changes is 

available in Appendix A.

Item imputation 

Item nonresponse occurs when respondents do not know or refuse to answer a question in the survey or 

when we determine a response is invalid and remove it during editing. Item imputation is the process of 
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 18

filling in the missing responses using a statistical model to produce a complete dataset and to reduce the 

bias associated with item nonresponse.

The 2020 RECS used the hot-deck imputation method. In this method, a recipient case that has a missing 

value for the variable we are imputing is matched with a similar donor case that has a response for that 

variable. We use the donor’s response for that variable to replace the missing response for the recipient 

case. After imputation, final editing reviews ensured questionnaire skip patterns were maintained. For 

the 2020 RECS, we imputed all variables using the Cyclical Tree-Based (CTB)7 hot-deck method. This

method uses classification trees to group recipients and potential donors and uses a weighted,

sequential, hot-deck imputation procedure8 where we use weights to match chosen donors to 

recipients. This method is the same imputation method we used for the 2015 RECS, except for the 

variables that measure square footage, which we imputed using the Predictive Mean Neighborhood 

(PMN)9 hot-deck method in 2015. 

We imputed responses for about 250 Household Survey variables, and the median imputation rate was 

2.9%. Among the household survey variables included on the 2020 RECS public use file, about 240

variables were imputed, and the imputation rate ranged from 0% to 22.1%, with a median imputation 

rate of 2.7%. A total of 69% of the variables had a less than 5% imputation rate; and 87% of the variables 

had a less than 10% imputation rate. The median imputation rate was higher than that of the 2015 RECS

due to the change from partially in-person data collection in 2015 to entirely self-administered in 2020. 

Without an interviewer present, respondents may have been more likely to leave a question blank if 

they were unsure how to answer. The 2020 RECS Household Survey also included more explicit Don’t 

Know response options than in previous cycles.

Weather and Geographic Data

We gather weather and certain geographic indicators from other government agencies to complete the 

characteristics profile of sampled housing units. The daily average temperature, calculated as the 

average of the daily minimum and maximum temperature, is available for a number of weather stations 

within the United States from Climate Data Online (CDO)10 (part of the National Centers for 

Environmental Information [NCEI]. Each sampled RECS housing unit was associated with its nearby 

weather stations, and then we assigned weights to the weather stations based on the horizontal and 

vertical distances between them and the RECS housing unit. We assigned the resulting weighted daily 

average temperatures to the RECS housing units, and then we calculated daily heating degree days 

(HDD) and cooling degree days (CDD) from the weighted temperatures, which are summed to yield

7 Creel, D. V., & Krotki, K. (2006). Creating imputation classes using classification tree methodology. In Proceedings of the Survey 

Research Methods Section, American Statistical Association, Joint Statistical Meeting 2006, pp. 2884–2887. 8 Cox, B. G. (1980). The weighted sequential hot-deck imputation procedure. In Proceedings of the Survey Research Methods 

Section, American Statistical Association, pp.721–726. 9 Singh, A., Grau, E., & Folsom, R. (2004). Imputation and unbiased estimation: Use of centered predictive mean neighborhoods 

method. In Proceedings of the 2004 Joint Statistical Meetings, American Statistical Association, Section on Survey Research 

Methods, Toronto, Ontario, Canada (pp. 4351-4358). Alexandria, VA: American Statistical Association. [Available as a PDF at 

http://www.amstat.org/sections/srms/proceedings/] 10 Formerly known as the National Climatic Data Center (NCDC).
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 19

annualized HDD and CDD values. We also obtained thirty-year HDD and CDD averages11 from the CDO 

data; however, because these normal values were pre-calculated, weighting them was not possible, so 

they reflect the average weather of the nearest station. Building America climate regions and 

International Energy Conservation Code (IECC) climate zones are also assigned to RECS housing units. 

We based these climate regions assignments on a housing unit’s county, but each county’s designation

is ultimately based on its typically observed values of annual HDD, annual CDD, and average humidity 

conditions throughout a year. 

Square Footage Data

Square footage data collection, editing, imputation, and data quality are available in the Square Footage 

Technical Documentation report. 

Consumption and Expenditure Data 

The Energy Supplier Survey (ESS) data collection and estimates produced from the RECS annualization 

and end-use modeling processes are available in the Consumption and Expenditures Technical 

Documentation report.

Weighting and Sampling Error

The 2020 RECS used a single-stage probability design to select a sample of households that represents 

the housing unit population in the United States. To produce population estimates, we weighted the 

sampled housing units to represent all housing units including those not in the sample. First, we 

calculated the base sampling weights, which are the reciprocal of the probability of selection for the 

RECS sample, for each sampled housing unit. We produced the final analysis weights (NWEIGHT) after 

applying various adjustments. In addition, we computed replicate weights for variance estimation 

purposes. 

Similar to the weighting adjustment for the web and mail portions of the 2015 RECS, we calculated the 

2020 RECS final analysis weights by applying eligibility, unit nonresponse, and poststratification 

adjustments to the base weights. The eligibility adjustment consisted of two components: an 

adjustment to unoccupied housing units via a latent-variable technique12 to predict the probability that 

a housing unit is occupied or unoccupied and an adjustment to not-primary housing units via a logistic 

regression model to predict the probability that a housing unit is primary or not-primary. We used the 

Generalized Exponential Model (GEM)13 calibration method for the nonresponse and poststratification 

adjustments. 

The last weighting adjustment, poststratification, improved the quality of the key 2020 RECS estimates 

by benchmarking them to other sources that we assume better represent the full population. The 

11 The most recent available data for the 30-year HDD and CDD averages covers the period between 1981 and 2010. 12 Biemer, P., Murphy, J., & Kott, P. (2016). Estimating mail or web survey eligibility for undeliverable addresses: A latent class 

analysis approach. In JSM Proceedings, pp. 1166–1172. American Statistical Association. 13 Folsom, R. E., & Singh, A. C. (2000). The generalized exponential model for sampling weight calibration for extreme values, 

nonresponse, and poststratification. In Proceedings of the American Statistical Association, Survey Research Methods Section, 

pp. 598–603. Alexandria, VA: American Statistical Association.
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 20

poststratification method used for 2020 RECS differed somewhat from previous RECS. We typically use 

American Community Survey (ACS) estimates from the U.S. Census Bureau as population control totals. 

However, the Census Bureau did not release ACS estimates for 2020 as a result of the COVID-19 

pandemic’s impact on data collection efforts. With no official 2020 ACS estimates, we used an 

alternative strategy to develop control totals for 2020 RECS poststratification. This strategy used a 

combination of 2020 Decennial Census estimates and housing unit occupancy rates from the 2019 ACS. 

The derived control totals we used for 2020 RECS poststratification included state, housing unit type, 

and age of housing unit. We calculated the estimated control totals for housing unit type based on the 

proportional estimates of the 2019 ACS. We modeled the estimated control totals for age of housing 

unit based on the proportional estimates of the 2016 ACS to 2019 ACS.

The final analysis weight for each responding household was the number of households in the 

population that the observation represents. For example, if the analysis weight for a household is 5,000, 

that household represents itself and 4,999 non-sampled households.

Unlike 2015 RECS, which used the Balanced Repeated Replication (BRR) method for replicate weights, 

the 2020 RECS used the Jackknife method for variance estimation because Jackknife is more appropriate 

for a one-stage stratified sample. We constructed 60 Jackknife replicates.

Relative standard errors

Estimates from a sample survey like RECS are not exact; they are statistical estimates with some 

associated sampling error—the result of generating estimates based on a sample rather than conducting 

a census of the entire population. The standard error is a measure of the precision of a particular 

statistic for a characteristic, based on how variable it is in the population and a given sample size. 

Standard errors are used with survey statistics to measure sampling error, construct confidence 

intervals, or perform hypothesis tests. We estimated the standard errors using the Jackknife method 

with a coefficient of 0.983 (59/60 replicates).

The relative standard error (RSE) measures how large the standard error is relative to the corresponding 

statistic; the larger the RSE, the less precise the survey statistic. The RSE is expressed as a percentage 

and is calculated as (standard error/statistic) x 100.

Confidentiality of Information

The 2018 Confidential Information Protection and Statistical Efficiency Act (CIPSEA) protects the privacy 

of respondents of federal surveys, including RECS. Any information we collect that could identify

respondents or their households is kept confidential and used only for statistical purposes. We use

disclosure protection measures before releasing the public-use data files. These measures include 

removing localized geographic information such as addresses and top coding certain variables.

14 These 

disclosure steps mask the data so that the public cannot identify a sampled housing unit or its 

occupants. 

14 See How to Use the 2020 RECS Microdata File for a complete list of top-coded variables.
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 21

Appendix A. Comparing preliminary and final main heating fuel,

main heating equipment, and water heating fuel estimates

To assist Residential Energy Consumption Survey (RECS) data users in determining the impact of the 

additional quality control step (see the Editing and data quality section), the following tables show 

national comparisons of main space heating fuel, main space heating equipment, and main water 

heating fuel estimates, relative standard errors (RSEs), and confidence intervals (CI) for the preliminary 

data release (June 2022) and the final data release (February 2023).

Table A1. Main space heating fuel in U.S. homes, 2020 RECS preliminary and final estimates

Main heating fuel

Preliminary estimate (June 2022) Final estimate (February 2023)

Estimate 

(million 

homes) RSE

95% CI for 

estimate

Estimate 

(million 

homes) RSE

95% CI for 

estimate

Natural gas 56.25 0.97 (55.15, 57.34) 62.71 0.77 (61.75, 63.68)

Electricity 48.89 0.95 (47.96, 49.81) 42.57 0.92 (41.79, 43.35)

Fuel oil or 

kerosene 4.96 3.36 (4.63, 5.29) 4.93 3.50 (4.59, 5.28)

Propane 5.05 4.01 (4.65, 5.46) 5.21 3.76 (4.82, 5.60)

Wood 2.22 5.19 (1.99, 2.46) 2.25 4.88 (2.03, 2.47)

Some other fuel Q (-) (-) Q (-) (-)

Does not use 

heating equipment 6.10 4.53 (5.55, 6.65) 5.79 4.41 (5.28, 6.30)

Data source: U.S. Energy Information Administration, Residential Energy Consumption Survey (RECS)

Note: Q=Data withheld because either the relative standard error (RSE) was greater than 50% or fewer than 10 households 

were in the reporting sample. CI=Confidence interval.

Table A2. Main space heating equipment in U.S. homes, 2020 RECS preliminary and final estimates

Preliminary estimate (June 2022) Final estimate (February 2023)

Main heating 

equipment

Estimate 

(million 

homes) RSE

95% CI for 

estimate

Estimate 

(million 

homes) RSE

95% CI for 

estimate

Central warm-air 

furnace 71.98 0.66 (71.02, 72.93) 74.42 0.58 (73.55, 75.29)

Heat pump 17.75 1.84 (17.10, 18.40) 16.13 1.83 (15.54, 16.72)

Steam or hot water 

system 9.32 2.85 (8.79, 9.85) 9.29 2.89 (8.75, 9.83)

Ductless heat 

pump (mini-split) 1.12 8.38 (0.94, 1.31) 1.06 8.94 (0.87, 1.25)

Built-in electric 

units 8.25 2.74 (7.80, 8.70) 7.65 2.83 (7.21, 8.08)

Built-in oil or gas 

room heater 3.04 5.53 (2.71, 3.38) 3.49 5.37 (3.12, 3.87)

Portable electric 

heaters 3.24 6.15 (2.84, 3.64) 3.03 6.15 (2.65, 3.40)
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 22

Heating stove 

burning wood 1.98 5.76 (1.75, 2.21) 1.94 5.46 (1.73, 2.15)

Some other 

equipment 0.74 10.02 (0.59, 0.89) 0.74 9.92 (0.59, 0. 89)

Does not use 

heating equipment 6.10 4.53 (5.55, 6.65) 5.79 4.41 (5.28, 6.30)

Data source: U.S. Energy Information Administration, Residential Energy Consumption Survey (RECS)

Note: Q=Data withheld because either the relative standard error (RSE) was greater than 50% or fewer than 10 households 

were in the reporting sample. CI=Confidence interval.

Table A3. Main water heating fuel in U.S. homes, 2020 RECS preliminary and final estimates

Main water

heating fuel

Preliminary estimate (June 2022) Final estimate (February 2023)

Estimate 

(million 

homes) RSE

95% CI for 

estimate

Estimate 

(million 

homes) RSE

95% CI for 

estimate

Natural gas 57.96 0.84 (56.98, 58.93) 59.33 0.75 (58.44, 60.23)

Electricity 58.24 0.82 (57.28, 59.19) 57.04 0.80 (56.13, 57.96)

Fuel oil or 

kerosene 4.28 4.49 (3.89, 4.66) 4.12 4.44 (3.76, 4.49)

Propane 2.64 5.20 (2.37, 2.92) 2.62 5.20 (2.35, 2.89)

Some other fuel 0.41 15.84 (0.28, 0.55) 0.41 16.08 (0.28, 0.54)

Data source: U.S. Energy Information Administration, Residential Energy Consumption Survey (RECS)

Note: Q=Data withheld because either the relative standard error (RSE) was greater than 50% or fewer than 10 households 

were in the reporting sample. CI=Confidence interval.

In addition to the changes at the national level (highlighted above), some notable differences exist at 

the state and regional levels as well.

Space heating fuels

Nationally, we estimate about 6.32 million fewer households use electricity for main space heating in 

the final data set. The number of households using natural gas for main space heating increased by 

about 6.46 million homes. This change was not evenly distributed across states—with Arkansas, Arizona, 

California, Colorado, Georgia, Iowa, Kansas, Louisiana, Mississippi, Missouri, New Mexico, Nevada, 

Oklahoma, South Carolina, South Dakota, Tennessee, and Texas increased between 7% and 9% for the 

number of homes using natural gas for space heating. 

Space heating equipment

Regionally, the final data show a reduction in heat pumps used for main space heating in the South 

Census region, from 13.58 million heat pumps in the preliminary data to 12.52 million heat pumps in the 

final data. The largest decreases were in Arkansas, North Carolina, South Carolina, and Tennessee, which 

all had between a 4% and 5% decrease in the number of homes that use heat pumps for main space 

heating. 
U.S. Energy Information Administration | 2020 RECS Technical Documentation Summary 23

The estimate for the number of homes in the South Census region that use electric furnaces for main 

space heating also decreased, from 10.31 million homes in the preliminary data to 9.01 million homes in 

the final data. 

There is a corresponding increase in the number of natural gas furnaces in the South Census region in 

the final data. In the preliminary data, 11.96 million homes used a natural gas furnace for main space 

heating compared with 14.36 million homes in the final data. 

Water heating fuels

Nationally, 1.2 million fewer homes used electricity for their main water-heating fuel in the final data 

when compared to the preliminary data. The largest group of these homes were in California, where the 

preliminary data estimated that 2.78 million households used electricity for water heating compared 

with 2.48 million in the final data.