Multifamily Building Study Pilot: 

Methods, Findings, and Lessons 

Learned

December 2022

Independent Statistics & Analysis

www.eia.gov

U.S. Department of Energy

Washington, DC 20585
U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot 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 U.S. Government. The views in 

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

federal agencies.
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Contents

Executive Summary....................................................................................................................................... 2

Introduction .................................................................................................................................................. 3

Pilot Objectives............................................................................................................................................. 3

Frame and Sample Selection......................................................................................................................... 3

Questionnaire Design.................................................................................................................................... 6

Contact-Tracing and Data Collection Protocols............................................................................................ 8

Results and Analysis of Data Collection ...................................................................................................... 10

Results and Analysis—Select Building Characteristics................................................................................. 12

Lessons Learned.......................................................................................................................................... 20

Frame and sampling .............................................................................................................................. 20

Contact information .............................................................................................................................. 20

MBS questionnaire ................................................................................................................................ 20

Contact tracing....................................................................................................................................... 21

Contact protocols and response rates................................................................................................... 22

Survey mode .......................................................................................................................................... 22

Multi-building responders..................................................................................................................... 22

Figures

Figure 1. Example of survey question with adaptations for web and phone administration ...................... 8

Tables

Table 1. Multifamily Building Study (MBS) pilot sample allocation.............................................................. 5

Table 2. Response rates overall and by geography .................................................................................... 10

Table 3. Response rates by building size and geography ........................................................................... 11

Table 4. Distribution of number of computer-assisted telephone instrument (CATI) contact attempts by 

geography ................................................................................................................................................... 11

Table 5. Distribution of number of computer-assisted telephone instrument (CATI) contact attempts by 

building size ................................................................................................................................................ 12

Table 6. Multifamily Building Study (MBS) respondent distribution of building characteristics by 

geography ................................................................................................................................................... 13

Table 7. Prevalence of common areas by number of residential units...................................................... 14

Table 8. Prevalence of commercial space by geography............................................................................ 15

Table 9. Respondent distribution of metering and billing characteristics by geography...........................16

Table 10. Respondent distribution of metering and billing characteristics by building type.....................18

Table 11. Respondent distribution of metering and billing characteristics by building size ......................19
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Executive Summary

We designed the Multifamily Building Study (MBS) pilot to assess the feasibility of collecting data on the 

characteristics of multiunit residential buildings, such as apartments, condominiums, and co-ops. The 

target population was multifamily and multifamily mixed-use buildings that were more than 50% 

residential by floor space or square footage and that contained five or more residential units. For each 

selected building, we attempted to identify and interview individuals most knowledgeable about the 

building’s energy-related characteristics and energy use. We collected data from July to October 2021. 

Overall, the unweighted MBS response rate was 31.4%. 

We conducted the MBS pilot in four geographical areas chosen because of their estimated high 

proportion of units in multifamily buildings, their inclusion of large and small cities and rural areas, and 

their spread across the four census regions: the state of New York; California; the Denver Metropolitan 

Statistical Area (MSA); and Harris County, Texas. We selected a total sample of 859 building addresses 

from the 2020 Residential Energy Consumption Survey (RECS) Household Survey sample, which is an 

address-based sample of housing units. The sample was stratified by the four geographic areas and 

building size (number of housing units). 

We designed the MBS questionnaire to collect building-level characteristics related to energy 

consumption. The questionnaire asked about: 

• Building structure

• Characteristics and uses of different spaces

• Fuels and equipment used for major end uses

• Billing and metering information 

The survey took on average about 20 minutes per building to complete. We programmed the survey 

instrument as both a computer-assisted web interview (CAWI, or web) and computer-assisted telephone 

instrument (CATI, or phone). The web instrument was also mobile optimized. 

The 859 selected buildings from the starting sample were assigned to a contact-tracing task. We

reviewed results for each address returned from common internet search methods. These results

included street views, apartment websites, rental postings, property records, and realty websites. 

After we completed contact-tracing efforts, we contacted individuals in the sampled buildings using a 

standard contact protocol. Although CATI interviewers did not complete many CATI surveys (14 of 269 

surveys), they were successful in tracking down additional contact information, particularly email 

addresses, when speaking with building contacts over the phone. They also gained cooperation from 

respondents; multiple contact attempts by the same interviewer over time built trust and rapport. 

At the end of data collection, we received 269 completed questionnaires, where about 95%, or 255 

interviews, were completed via web and 5%, or 14 interviews, were completed by phone. The overall 

unweighted response rate was 31.4%; responses varied by region and building size. The Denver MSA 

had the highest response rate (36.7% of buildings responded), and New York had the lowest response 

rate (29.2% of buildings responded). Larger buildings, with more than 140 units, were also more likely to 

respond (37% responded). 
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Introduction

We identified multifamily and mixed-use buildings as a significant gap in our building energy 

consumption program, which includes the Residential Energy Consumption Survey (RECS) and the 

Commercial Buildings Energy Consumption Survey (CBECS). The RECS sample includes individual units in 

multifamily buildings; it excludes common areas and amenities in multifamily buildings and commercial 

spaces in multifamily mixed-use buildings. The CBECS sample includes buildings with some multifamily 

units but only those that dedicate most of their square footage to commercial purposes.

In its 2012 report on EIA’s energy consumption surveys, the Committee on National Statistics (CNSTAT) 

recommended we pursue a “whole building” supplement to RECS to “address the data gap related to 

multiunit residential buildings.” Since the release of that report, stakeholders and data users have 

indicated that estimates of energy use at the building level for multifamily and mixed-use buildings are 

important for building benchmarking programs and evaluating efficiency and other energy-related 

policies. To begin addressing this data need, we piloted a Multifamily Building Study (MBS) in 

conjunction with the 2020 RECS data collection. 

Pilot Objectives

We worked with both internal and external stakeholders to develop a set of pilot objectives, taking into 

account timing and resource constraints: 

• Objective 1: Develop a defined contact protocol for the multifamily and mixed-use building 

population. 

• Objective 2: Develop an instrument to collect characteristics data for multifamily and mixed-use 

buildings. The building characteristics we collect should support future modeling of end uses and 

consumption. 

• Objective 3: Conduct research on how to develop a frame for the multifamily and mixed-use 

building population. 

We designed and conducted the MBS in collaboration with IMG-Crown and RTI International, who were 

are also contracted by EIA to conduct the 2020 RECS Household Survey.

Frame and Sample Selection

The MBS pilot was conducted in four geographical areas that were chosen because they include a high 

proportion of units in multifamily buildings, they represent large and small cities and rural areas, and 

they represent different climate regions. The sample frame was restricted to addresses that geocode to 

these selected geographies. The four areas are: 

• New York (state)

• California 

• Denver-Aurora-Lakewood, Colorado, Metropolitan Statistical Area (Denver MSA)

• Harris County, Texas (Houston and surrounding areas)
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

In addition, we also considered local energy policy around benchmarking in the selection process; three 

of the four locations selected have laws in place for multifamily building energy benchmarking. Harris 

County, Texas had no local energy policies identified for multifamily building energy benchmarking at 

the time of the study.

We derived the MBS pilot’s sample frame from individual housing unit addresses sampled in the 2020 

RECS Household Survey, which included both respondents and nonrespondents, in the selected 

geographic areas. We selected the Household Survey sample from RTI International’s Address-Based 

Sample (ABS) frame, which was derived from the U.S. Postal Service’s Computerized Delivery Sequence 

(CDS) file of active mail delivery points. We identified multifamily buildings using building addresses that 

appeared in the ABS frame multiple times with different unit numbers. We created a count of how many 

distinct unit numbers were present at a single building address to create an indicator for number of 

units. To create the sampled building address for the MBS pilot, the unit numbers were stripped from 

the RECS sampled address. Drop point addresses were not included in this pilot because those addresses 

were not included in the 2020 RECS sample. 

The MBS pilot building address frame was limited to city-style addresses and focused on buildings with 

an estimated count of five or more units. The pilot focused on buildings with five or more units because 

of the higher likelihood that these buildings would have common areas, amenities, or commercial 

spaces in mixed-use buildings. We identified 2,306 RECS individual-unit addresses as the basis for 

deriving the building-level MBS pilot frame in the four geographic areas (Table 1). 

Our target starting sample for the pilot was 750 buildings, allocated equally among the four

geographies. In addition, the sample was intended to be allocated across various building-size categories 

(for example, 5 to 20 units, 21 to 55 units, etc.) However, because sample selection prioritized building 

addresses provided by RECS respondents, we did not perfectly achieve our intended allocation. The final 

sample selection, which included 774 building addresses, was allocated to the MBS starting sample in 

the following priority order: 

1. RECS respondents who provided contact information for someone knowledgeable about their 

entire building who could answer questions about energy use in common areas

2. RECS respondents who did not provide contact information

3. RECS nonrespondents who responded to a short nonresponse follow-up (NRFU) survey 

4. A simple random sample of RECS nonrespondents selected to meet the target MBS sample 

allocation shown in Table 1

We later added an additional 85 addresses, bringing the total starting sample to 859 cases. The 

additional 85 addresses were selected to increase the percentage of cases that could be fielded in 

computer-assisted telephone interview (CATI) data collection. Online tracing of the initial 774 addresses 

resulted in a higher-than-anticipated count of cases with no phone contact information. Cases with no 

phone contact information were fielded in a mail-only protocol, which at the time of planning, we 

expected to yield a lower response rate. This expectation was later confirmed in analysis of the data 

collection protocols.
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Table 1. Multifamily Building Study (MBS) pilot sample allocation

MBS pilot 

location

Building size 

category

Total 

MBS 

frame

MBS Sample

Target 

MBS 

sample 

allocation

Achieved 

MBS 

sample 

allocation

RECS 

respondents 

with contact 

information

RECS 

respondents 

without 

contact 

information

RECS NRFU 

respondents

RECS 

nonrespondents

Overall 2,306 197 411 8 243 750 859

New York

(state)

< 5 units* 11 2 9 0 0 - 11

5–20 units 306 18 56 0 2 60 76

21–55 units 267 23 45 0 1 60 69

56–140 units 316 21 50 2 2 65 75

> 140 units 228 17 43 1 6 65 67

California

< 5 units* 17 9 8 - - - 17

5–20 units 304 25 50 - - 60 75

21–55 units 200 18 39 1 13 60 71

56–140 units 168 17 35 1 18 65 71

> 140 units 147 11 26 1 29 65 67

Harris 

County,

Texas

< 5 units* - - - - - - 0

5–20 units 13 - 2 - 1 - 3

21–55 units 13 - 1 - - - 1

56–140 units 40 3 2 - 32 65 37

> 140 units 132 14 20 - 57 65 91

Denver 

Metropolitan 

Statistical 

Area

< 5 units* 2 - 2 - - - 2

5–20 units 39 4 5 2 24 30 35

21–55 units 33 6 4 - 22 30 32

56–140 units 29 4 7 - 16 30 27

> 140 units 41 5 7 - 20 30 32

Data source: U.S. Energy Information Administration, Multifamily Building Study

Notes: PO boxes were included only if the respondent provided a building address and reported that their home was within a 

building with five or more units (that is, TYPEHUQ = 5); cases were excluded if the secondary address information indicated the 

address was a pier, slip, space, lot, or trailer.

NRFU=nonresponse follow-up

RECS=Residential Energy Consumption Survey

* Cases included because RECS or RECS NRFU respondents indicated residing in a building with five or more units, but our frame 

estimates indicate the building as having fewer than five units
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Questionnaire Design 

We designed the MBS questionnaire to collect building-level characteristics related to energy 

consumption. We asked about the building structure, characteristics and use of different spaces, fuels 

and equipment used for major end uses, and billing and metering information, among other questions. 

These characteristics were identified based on the subject-matter expertise of the RECS and CBECS 

teams as well as stakeholder input. Where possible, the MBS questionnaire borrowed questions directly 

from the RECS or CBECS instrument or from other federal surveys focusing on multifamily housing. For 

topics where an existing question was not identified, we developed a new question. 

Time and resource constraints limited extensive pretesting as an option for the MBS pilot, so we

obtained feedback through two main pretesting activities—expert review of the questionnaire by survey 

methodologists and an expert panel review with representatives from large multifamily housing 

property management companies and advocacy organizations. We incorporated feedback from both of 

these activities into the final MBS pilot questionnaire. 

The questionnaire collected building information from respondents on the following topics: 

Structural characteristics

• Information on the campus (if 

applicable)

• Square footage

• Percentage of residential space

• Year of construction

• Number of floors and elevators

• Conditioned spaces

• Roof construction

• Wall construction

• Percentage of building covered in glass 

and windows

• If the building had undergone 

renovations, and if so, what types of 

renovations

• If the building has a green certification

Residential space

• Number of units and types (by number 

of bedrooms)

• Whether the building consists of 

apartments or condos

• Ownership structure

• Percentage occupied

Commercial spaces

• Percentage of commercial floorspace

• Commercial activities (if applicable)

Common areas

• Types of common areas

• Percentage of common area floorspace

• Presence of common area kitchen

• Characteristics of indoor parking areas 

• Presence of electric vehicle chargers

• Presence of pool and pool 

characteristics

• Common area or in-unit laundry

• Common area lighting

Heating

• Central or individual units

• Central heating equipment and fuels

• Equipment and fuels used for heating in 

units

• Commercial spaces and common areas

• Heating control
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Air-conditioning

• Central or individual units

• Central air-conditioning equipment and 

fuels

• Equipment and fuels used for airconditioning in units

• Commercial spaces and common areas

• Air-conditioning control

Water heating

• Central or individual units

• Central water heating fuels

• Fuels used for water heating in units

• Commercial spaces and common areas 

Cooking

• Fuels used for cooking in units

• Commercial spaces and common areas

Generation

• Presence of solar panels and backup 

generation equipment

Metering and billing

• Building master metering

• Individual metering for electricity and 

natural gas

• Payment responsibility for electricity or 

natural gas

We designed the MBS questionnaire to take an average of about 20 minutes per building. We 

programmed the survey instrument as both a computer-assisted web interview (CAWI, or web) and a 

computer-assisted telephone instrument (CATI, or phone). The web instrument was also mobile 

optimized. 

We made changes to the survey instrument based on mode of administration, mainly to accommodate 

the differences between a self-administered (web) versus an interviewer-administered (phone) survey. 

For example, although only select questions in the web instrument displayed a Don’t Know option, every 

question in the phone instrument needed to include a Don’t Know or Refused option for the interviewer

to be able to move on to the next question. We also slightly changed wording to accommodate the 

difference between a person reading the question to themselves and seeing the possible answer 

categories in the web instrument versus having the question and answer categories read to the 

respondent in the phone instrument. We’ve included an example question with mode differences in 

Figure 1. The web mode text is in the top row, and the phone text is in the bottom row. For the phone 

instrument, capitalized text indicated information for the phone interviewer, but it was not read to the 

respondent. 
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Figure 1. Example of survey question with adaptations for web and phone administration 

RENV, RENV_NADKREF

ASK All respondents

Excluding original construction, in what year was the last major renovation to the building (valued at 

$10,000 per unit or more) completed? 

________________

{ALLOW RANGE 1700 – 2021}

-3 Not applicable – Building has not had major renovations

-4 Don’t know

Excluding original construction, do you know in what year the last major renovation to the building, 

valued at $10,000 per unit or more, was completed? If there have been no major renovations or you do 

not know the year of the last major renovation, please let me know. 

IF NECESSARY: AND, IN WHAT YEAR WAS THAT RENOVATION COMPLETED?

________________

{ALLOW RANGE 1700 – 2021}

-3 NOT APPLICABLE – BUILDING HAS NOT HAD MAJOR RENOVATIONS

-4 DON’T KNOW

-5 REFUSED

Contact-Tracing and Data Collection Protocols

We assigned the 859 selected buildings from the starting sample to a contact-tracing task. We reviewed 

results for each address that were returned from common internet search methods. These results 

included street views, apartment websites, rental postings, property records, and realty websites. From 

these sources, we collected the following information: 

• Complex name, phone, email, and mailing address (if different than building address)

• Contact person name, phone, and email 

• Management company name, phone, email, and mailing address

Of the starting sample, 87% of buildings had a phone number, which was the preferred method of 

making contact with the building respondent, and 13% (112 buildings) did not have a phone number and 

were assigned a mail-only protocol. Only 3 of 112 mail-only cases had an email address, and one of 

those was found to be ineligible (a single-family detached home), so an email-only protocol was not 

pursued. During data collection, 24 of the 112 cases were flagged for in-person field tracing. An inperson field tracer visited the address to obtain contact information for the building, but the field tracer
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

did not conduct the survey. If the field tracer ended up speaking directly to the best point of contact, 

they mentioned that a survey request would be forthcoming.

In addition, we found an email address for 30% of buildings through online tracing. Unlike phone 

numbers obtained through tracing efforts, where the probability of finding a phone number increased as 

building size increased, the likelihood of finding an email address for a building was not related to

building size. 

After contact-tracing efforts were complete, we made contact with sampled buildings using a standard 

contact protocol. The protocol included a pre-notice postcard for buildings with phone information, who 

would be contacted by a phone interviewer. A mailed survey invitation, which included web login 

information, was sent to cases with no phone contact information available. If the phone interviewer 

was able to get a valid email address, a series of emails, including login and password information and 

reminder follow-ups were sent. At predetermined dates during data collection, buildings who had not 

yet completed the survey were sent reminders via USPS, UPS, and FedEx. Additional email and letter 

follow-up proved important for a productive MBS data collection. 

Although CATI interviewers did not complete many CATI surveys (14 of 269 completed surveys were 

CATI mode), they successfully collected additional contact information, particularly email addresses, 

when speaking with building contacts over the phone. Interviewers also gained the cooperation of 

respondents; multiple contact attempts by the same interviewer over time built trust and relationships.

Several buildings shared common property management companies. After online tracing, we identified 

17% (148 buildings) of the sample to have shared management among 53 property management 

companies. Respondents commonly noted that they needed permission from corporate to participate in 

the survey; however, when we reached out to corporate contacts, they would often tell us to contact 

the buildings directly for the kind of information the survey requested. Custom corporate outreach that 

did not rely on the set CATI script or invitation email language was useful for larger companies in our 

sample; however, we did not see much utility in custom outreach for the smaller companies. It was 

more efficient to work cases without tailoring the protocol based on a commonly shared corporate 

contact. Of the companies with a corporate contact, 24% of companies completed surveys for each of 

their buildings selected, 37% completed surveys for only some of the buildings selected, and 39% did not 

complete surveys for any of their buildings. 

Future MBS data collections should continue to test possible corporate engagement strategies. Direct 

outreach from EIA staff or from a government (“.gov”) email address could add legitimacy, for example. 

Although contacting corporate first in the case of smaller companies did not yield higher response rates, 

it is likely still important to manage possible linkages across the sample. For example, several 

interviewers contacting a single company about multiple addresses on different phone calls could 

increase confusion and refusals. Interviewers need to be aware of possible linkages so they can 

capitalize on them if a common reporter is willing to report for multiple addresses. 
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Results and Analysis - Data Collection

At the end of data collection, we received 269 completed questionnaires. The majority of those 

interviews (95%, or 255 interviews) were completed via web, and 5% (14 interviews) were completed by 

phone. The following sections will highlight some key analysis on both the data collection process and 

building characteristics. 

Note, these results are unweighted and not representative of the multifamily building population 

nationally or in the selected geographic areas. 

The overall unweighted response rate was 31.4%, and responses varied by geography and building size

(Tables 2 & 3). The Denver MSA had the highest response rate (36.7% of buildings responded), and New 

York had the lowest (29.2%). Larger buildings with more than 140 units were also more likely to 

respond, at 37%. We selected Harris County, Texas, specifically for its lack of building benchmarking 

targets for multifamily buildings to see if this affected response rates. However, the response rate in 

Harris County was very similar to response rates in both New York and California. 

Table 2. Response rates overall and by geography

Geography Sample size1

Number of MBS 

respondents Response rate

Combined 858 269 31.4%

New York (state) 298 87 29.2%

California 301 93 30.9%

Harris County, Texas 131 42 32.1%

Denver Metropolitan 

Statistical Area 128 47 36.7%

Data source: U.S. Energy Information Administration, Multifamily Building Study

1 Excludes cases with a final disposition status of ineligible.

MBS=Multifamily Building Study
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Table 3. Response rates by building size and geography

Building size: 

frame

Geography

Combined New York California

Harris County, 

Texas

Denver 

Metropolitan 

Statistical Area 

SS1 #R RR SS1 #R RR SS1 #R RR SS1 #R RR SS1 #R RR

< 5 units2 30 8 26.7% 11 1 9.1% 17 7 41.2% 0 0 0.0% 2 0 0.0%

5–20 units 189 48 25.4% 76 24 31.6% 75 19 25.3% 3 0 0.0% 35 5 14.3%

21–55 units 173 43 24.9% 69 17 24.6% 71 15 21.1% 1 0 0.0% 32 11 34.4%

56–140 units 209 75 35.9% 75 21 28.0% 71 25 35.2% 36 15 41.7% 27 14 51.9%

≥ 141 units 257 95 37.0% 67 24 35.8% 67 27 40.3% 91 27 29.7% 32 17 53.1%

Data source: U.S. Energy Information Administration, Multifamily Building Study

SS = sample size; #R = number of Multifamily Building Study respondents; RR = response rate

1 Excludes cases with a final disposition status of ineligible.

2 Includes RECS respondents who indicate residing in a building with five or more units, contradicting frame estimates that a 

building has fewer than five units. 

CATI contact attempts ranged from 0 to a maximum of 26. Buildings with 1 to 10 CATI contact attempts 

produced more respondents than nonrespondents. However, as the number of contact attempts 

needed for a building increased beyond 10, the conversion rate declined to less than 4% for buildings 

with 16 or more CATI contact attempts. Among the 92 buildings where no CATI contact attempts were 

made, 8.7% resulted in a completed interview. The average number of CATI contact attempts per 

building was 10.2 with little variation by geography (Table 4). On average, larger buildings had a greater 

number of contact attempts than smaller buildings (Table 5), which is likely related to the quality and 

amount of phone contact information available. Larger buildings may also require more attempts to 

identify and contact a knowledgeable respondent due to more organizational layers in larger buildings.

Table 4. Distribution of number of computer-assisted telephone instrument (CATI) contact attempts 

by geography

CATI contact attempts

Geography N Total Mean Median Minimum Maximum

Combined 859 8,774 10.2 11 0 26

New York 298 3,082 10.3 12 0 26

California 301 2,828 9.4 11 0 26

Harris County, Texas 132 1,574 11.9 12 0 21

Denver Metropolitan 

Statistical Area 128 1,290 10.1 11 0 21

Data source: U.S. Energy Information Administration, Multifamily Building Study
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Table 5. Distribution of number of computer-assisted telephone instrument (CATI) contact attempts 

by building size

CATI contact attempts

Building size: frame N Total Mean Median Minimum Maximum

< 5 units1 30 307 10.2 11.5 0 26

5–20 units 189 1,516 8.0 8 0 24

21–55 units 173 1,766 10.2 11 0 23

56–140 units 210 2,123 10.1 12 0 22

≥ 141 units 257 3,062 11.9 12 0 26

Data source: U.S. Energy Information Administration, Multifamily Building Study 1 Includes RECS respondents who indicate residing in a building with five or more units, contradicting frame estimates that a 

building has fewer than five units.

Results and Analysis—Select Building Characteristics

Approximately 82% of all buildings responding to the MBS were run exclusively as apartment rentals, an 

additional 10% of the buildings were condominiums, and 8% were owned as a cooperative (Table 6). 

This distribution varied by geography. In New York, only 69% of responding buildings were apartment 

rentals, and nearly 25% were cooperatives. Conversely, more than 95% of the buildings in Harris County, 

Texas, were apartment rentals, and none of the buildings that responded to the MBS were cooperatives. 

The distribution of respondents by building size, as measured by both number of units and square 

footage, is fairly evenly distributed across categories. Although RECS respondents or the frame 

calculation indicated that some buildings had 5 or more units in the building, there were still 5 buildings 

with fewer than 5 units that responded to the survey (Table 6). 
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Table 6. Multifamily Building Study (MBS) respondent distribution of building characteristics by 

geography

Characteristic

Geography

Combined New York California Harris County, 

Texas

Denver 

Metropolitan 

Statistical Area

N % N % N % N % N %

Residential type

Apartment 

rentals 219 81.7% 60 69.0% 81 87.1% 40 95.2% 38 82.6%

Condominiums 27 10.1% 6 6.9% 11 11.8% 2 4.8% 8 17.4%

Cooperatives 22 8.2% 21 24.1% 1 1.1% 0 0.0% 0 0.0%

Residential units1

< 5 units 5 1.9% 2 2.4% 2 2.2% 0 0.0% 1 2.2%

5–20 units 68 26.0% 21 24.7% 35 37.6% 6 15.8% 6 13.0%

21–55 units 38 14.5% 11 12.9% 12 12.9% 3 7.9% 12 26.1%

56–140 units 68 26.0% 29 34.1% 21 22.6% 8 21.1% 10 21.7%

≥ 141 units 83 31.7% 22 25.9% 23 24.7% 21 55.3% 17 37.0%

Square footage

≤ 5,000 15 6.0% 4 5.2% 7 8.0% 3 7.5% 1 2.3%

5,001–10,000 30 12.0% 10 13.0% 17 19.3% 2 5.0% 1 2.3%

10,001–25,000 39 15.7% 9 11.7% 19 21.6% 5 12.5% 6 13.6%

25,001–50,000 36 14.5% 10 13.0% 8 9.1% 3 7.5% 15 34.1%

50,001–

100,000 33 13.3% 13 16.9% 10 11.4% 3 7.5% 7 15.9%

100,001–

200,000 33 13.3% 16 20.8% 11 12.5% 5 12.5% 1 2.3%

200,001–

500,000 39 15.7% 6 7.8% 8 9.1% 16 40.0% 9 20.5%

> 500,000 24 9.6% 9 11.7% 8 9.1% 3 7.5% 4 9.1%

Data source: U.S. Energy Information Administration, Multifamily Building Study (MBS)

Note: Distribution is based on nonmissing values for each characteristic.

1 Number of residential units is based on MBS responses to number of apartment units (NUMAPTS), which may differ from the 

building size on the frame or reported by the RECS respondent.

The most prevalent types of common areas reported by respondents included a laundry room (61.4%), 

leasing or management office (59.6%), lobby (59.2%), conference or party room (40.3%), pool or hot tub 

(38.8%), and gym or fitness center (37.7%). Laundry rooms were found in at least 50.0% of buildings 

regardless of size (Table 7). 
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Table 7. Prevalence of common areas by number of residential units

Common area

Combined

Number of residential units: MBS

< 5 units 5–20 units 21–55 units 56–140 units ≥ 141 units

N % N % N % N % N % N %

Gym or fitness 

center 98 37.7% 1 20.0% 12 18.2% 4 10.8% 20 31.7% 61 73.5%

Indoor sports 

area 7 2.8% 0 0.0% 1 1.5% 0 0.0% 1 1.7% 5 6.4%

Conference 

room or party 

room 106 40.3% 0 0.0% 13 19.4% 9 24.3% 27 41.5% 57 68.7%

Lobby 155 59.2% 1 20.0% 12 18.2% 22 57.9% 51 78.5% 67 81.7%

Leasing or 

management 

office 155 59.6% 1 20.0% 24 35.8% 13 35.1% 45 69.2% 72 88.9%

Indoor parking 61 23.9% 1 20.0% 8 12.1% 4 10.5% 16 26.2% 32 40.5%

Pool or hot tub 100 38.8% 2 40.0% 20 29.9% 5 13.5% 18 29.0% 55 67.9%

Sauna or 

steam room 16 6.3% 0 0.0% 3 4.5% 0 0.0% 3 4.8% 10 12.7%

Laundry room 159 61.4% 3 60.0% 34 51.5% 24 64.9% 51 81.0% 47 57.3%

Community 

fireplace or fire 

pit 41 15.9% 0 0.0% 3 4.5% 2 5.4% 6 9.7% 30 36.6%

Other 34 20.0% 1 33.3% 5 11.6% 3 11.5% 9 20.5% 16 31.4%

Data source: U.S. Energy Information Administration, Multifamily Building Study (MBS)

Note: Prevalence estimates exclude respondents with missing values.

Among all MBS respondents, about 12.7% reported commercial space in the building, and the rate was 

much higher in New York (25.3%) than the other three areas (Table 8).
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Table 8. Prevalence of commercial space by geography

Characteristic

Geography

Combined New York California Harris County, Texas

Denver 

Metropolitan 

Statistical Area

N % N % N % N % N %

Commercial space

Yes 34 12.7% 22 25.3% 6 6.5% 2 4.8% 4 8.7%

No 234 87.3% 65 74.7% 87 93.5% 40 95.2% 42 91.3%

Percentage commercial

≤ 5% 11 36.7% 10 52.6% 1 20.0% 0 0.0% 0 0.0%

6%–10% 7 23.3% 2 10.5% 3 60.0% 1 50.0% 1 25.0%

> 10% 12 40.0% 7 36.8% 1 20.0% 1 50.0% 3 75.0%

Data source: U.S. Energy Information Administration, Multifamily Building Study 

Residential units were individually metered for electricity in more than 90.0% of buildings in California; 

Harris County, Texas; and the Denver MSA. In New York, only 74.7% of buildings had individual metering 

(Table 9). Across all geographies, residents were directly responsible for paying for the electricity used in 

residential units in 86.6% of buildings. Less than half of buildings that used natural gas as an energy 

source had individual metering for residential units (42.7%), although individual metering for natural gas 

was more common in California and the Denver MSA (Table 9). In 43.3% of buildings, residents were 

directly responsible for the natural gas used in residential units, and in 37.8% of buildings, it was 

included in the rent, a condo fee, or utility fee.
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Table 9. Respondent distribution of metering and billing characteristics by geography

Metering or 

billing 

characteristic

Geography

Combined New York California Harris County, Texas

Denver 

Metropolitan 

Statistical Area

N % N % N % N % N %

Units individually metered for electricity

Yes 233 86.6% 65 74.7% 85 91.4% 40 95.2% 43 91.5%

No 36 13.4% 22 25.3% 8 8.6% 2 4.8% 4 8.5%

Units individually metered for natural gas

Yes 93 42.7% 27 35.1% 42 52.5% 3 13.6% 21 53.8%

No 125 57.3% 50 64.9% 38 47.5% 19 86.4% 18 46.2%

Pay for electricity in residential units

Residents 221 82.5% 61 70.1% 83 90.2% 39 92.9% 38 80.9%

Included in 

rent, condo 

fee, or 

utility fee 24 9.0% 13 14.9% 5 5.4% 1 2.4% 5 10.6%

Split 

between 

residents 

and rent or 

fees 7 2.6% 1 1.1% 2 2.2% 1 2.4% 3 6.4%

Some other 

arrangement 16 6.0% 12 13.8% 2 2.2% 1 2.4% 1 2.1%

Pay for natural gas in residential units

Residents 94 43.3% 22 28.6% 47 58.8% 4 19.0% 21 53.8%

Included in 

rent, condo 

fee, or utility 

fee

82 37.8% 38 49.4% 23 28.8% 12 57.1% 9 23.1% 

Split 

between 

residents 

and rent or 

fees

10 4.6% 3 3.9% 2 2.5% 1 4.8% 4 10.3% 

Some other 

arrangement

31 14.3% 14 18.2% 8 10.0% 4 19.0% 5 12.8% 

Data source: U.S. Energy Information Administration, Multifamily Building Study 

Note: Distribution is based on nonmissing values for each characteristic.
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Among building ownership types (Table 10), residential units in condominiums (100.0%) were more 

likely to be individually metered for electricity than apartment rentals (86.3%) or buildings that were 

owned as cooperatives (72.7%). With apartment rentals and condominiums, more than 80.0% of 

respondents reported that residents were directly responsible for the electricity used in residential 

units. In buildings that were owned as cooperatives, residents were typically responsible for electricity 

use in residential units (63.6%), but including the cost in the rent, a condo fee, or utility fee was also 

common (36.4%). Individual metering for natural gas in residential units was less common than 

individual metering for electricity across all building types (Table 4.15). Natural gas used in residential 

units was also more likely to be included in the rent, a condo fee, or utility fee compared with electricity 

used.
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Table 10. Respondent distribution of metering and billing characteristics by building type

Metering or billing 

characteristic

Building type

Combined1 Apartment rentals Condominiums Cooperatives

N % N % N % N %

Units individually metered for electricity

Yes 233 86.6% 189 86.3% 27 100% 16 72.7%

No 36 13.4% 30 13.7% 0 0.0% 6 27.3%

Units individually metered for natural gas

Yes 93 42.7% 75 42.1% 7 38.9% 11 50.0%

No 125 57.3% 103 57.9% 11 61.1% 11 50.0%

Pay for electricity in residential units

Residents 221 82.5% 184 84.4% 22 81.5% 14 63.6%

Included in rent, 

condo fee, or 

utility fee 24 9.0% 14 6.4% 2 7.4% 8 36.4%

Split between 

residents and 

rent or fees 7 2.6% 4 1.8% 3 11.1% 0 0.0%

Some other 

arrangement 16 6.0% 16 7.3% 0 0.0% 0 0.0%

Pay for natural gas in residential units

Residents 94 43.3% 78 44.1% 8 44.4% 8 36.4%

Included in rent, 

condo fee, or 

utility fee 82 37.8% 61 34.5% 8 44.4% 13 59.1%

Split between 

residents and rent 

or fees 10 4.6% 9 5.1% 0 0.0% 1 4.5%

Some other 

arrangement 31 14.3% 29 16.4% 2 11.1% 0 0.0%

Data source: U.S. Energy Information Administration, Multifamily Building Study

Note: Distribution is based on nonmissing values for each characteristic 1 The Combined column includes respondents with a missing value for number of residential units. 

Across building sizes (Table 11), individual metering of residential units for electricity ranged from 73.5% 

in buildings with 56 to 140 units to greater than 97.0% in buildings with fewer than 20 units. In buildings 

with 56 or more units, it was more common for electricity costs to be included in the rent, a condo fee, 

or utility fee or through some other arrangement. However, the predominant method of payment was 

for residents to be directly responsible for electricity used in residential units across all building sizes.

Individual metering for natural gas tended to be more common in smaller buildings (that is, 55 or fewer 

units) than larger buildings. For these smaller buildings, more than 50.0% of respondents reported that 
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

residents were directly responsible for natural gas used in residential units, but in buildings with 56 or 

more units, it was more common for other payment arrangements to be made.

Table 11. Respondent distribution of metering and billing characteristics by building size

Metering or 

billing 

characteristic 

Combined2

Number of residential units1

< 5 units 5–20 units 21–55 units 56–140 units ≥ 141 units

N % N % N % N % N % N %

Units individually metered for electricity

Yes 233 86.6% 5 100.0% 66 97.1% 33 86.8% 50 73.5% 72 86.7%

No 36 13.4% 0 0.0% 2 2.9% 5 13.2% 18 26.5% 11 13.3%

Units individually metered for natural gas

Yes 93 42.7% 3 60.0% 33 58.9% 16 51.6% 19 31.7% 22 34.9%

No 125 57.3% 2 40.0% 23 41.1% 15 48.4% 41 68.3% 41 65.1%

Pay for electricity in residential units

Residents 221 82.5% 5 100.0% 64 94.1% 31 81.6% 46 68.7% 68 81.9%

Included in 

rent, condo 

fee, or utility 

fee

24 9.0% 0 0.0% 2 2.9% 1 2.6% 9 13.4% 12 14.5% 

Split 

between 

residents 

and rent or 

fees

7 2.6% 0 0.0% 2 2.9% 3 7.9% 1 1.5% 1 1.2% 

Some other 

arrangement

16 6.0% 0 0.0% 0 0.0% 3 7.9% 11 16.4% 2 2.4% 

Pay for natural gas in residential units

Residents 94 43.3% 3 60.0% 35 60.3% 16 51.6% 16 27.1% 24 38.7%

Included in 

rent, condo 

fee, or utility 

fee

82 37.8% 1 20.0% 17 29.3% 10 32.3% 24 40.7% 29 46.8% 

Split 

between 

residents 

and rent or 

fees

10 4.6% 0 0.0% 3 5.2% 1 3.2% 3 5.1% 2 3.2% 

Some other 

arrangement

31 14.3% 1 20.0% 3 5.2% 4 12.9% 16 27.1% 7 11.3% 

Data source: U.S. Energy Information Administration, Multifamily Building Study

Note: Distribution is based on nonmissing values for each characteristic.

1 Number of residential units is based on MBS responses to NUMAPTS, which may differ from the building size on the frame or 

reported by the RECS respondent.

2 The Combined column includes respondents with a missing value for number of residential units.
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Lessons Learned

The MBS pilot achieved all three pilot objectives. We produced research about the frame design, 

developed a contact protocol, and developed a questionnaire that collected information to enable us to 

develop multifamily building-level end-use models to produce estimates in future data collections. The 

following items are lessons learned and could be tested or built on for any future MBS studies. 

Frame and sampling 

We derived the sample frame for the MBS pilot from the 2020 RECS Household Survey sample. The MBS 

sample was stratified by building size, which was derived using the address fields on the U.S. Postal 

Service’s Computerized Delivery Sequence to estimate how many addresses were from the same 

building. This derived estimate for building size matched information provided by MBS respondents 

approximately 70% of the time but did result in some stratum misclassification and could be a source of 

bias in estimates in a national MBS. 

We excluded drop points from the MBS pilot because they were deliberately not fielded in the 2020 

RECS Household Survey. Given the higher proportion of drop points in certain geographic areas, we 

should do more research on how to incorporate these buildings into future data collections to reduce 

bias. 

At this time, many potential data sources could be useful in constructing a frame for a future MBS. 

However, the accessibility and content of many of these sources will likely change over time, and we 

can’t adequately determine the relative value of a potential source based solely on publicly available 

information. We should also consider combinations of data sources to fill in area coverage gaps or to 

provide additional information.

Contact information

When selecting the sample for the MBS, we gave priority to RECS respondents who provided contact 

information for someone who could answer questions about energy use for the building. Buildings 

associated with RECS respondents who provided contact information had higher response rates than 

other buildings.

We should consider the availability and quality of contact information for buildings when evaluating 

data sources for frame construction.

MBS questionnaire

The MBS survey performed well: 44% of items had 0% missingness, and another 28% had missingness 

rates of less than 5%. One item of concern with high missingness was SQFT (square footage of building); 

however, the missing rate was greatly reduced by the addition of SQFTC, a follow-up question that 

asked the respondent to select the most likely option among several square footage ranges.

For the MBS pilot, we minimized survey mode differences, to the extent possible. Both CATI and web 

instruments were programmed in the same survey software package with minor differences in versions. 

Fewer differences will streamline both programming and testing and reduce the likelihood of errors. 
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

The MBS questionnaire had an estimated burden of 20 minutes to complete. We found that web 

respondents on average completed the survey in 17–18 minutes, and CATI respondents in 28–29 

minutes. Sample members often said they did “not have time” when they declined to take the survey, so 

we should lower the web timeframe estimate to less than 20 minutes to better represent the response 

burden. We should then include a note that interviews may take more time if completed over the 

phone. Any survey items found not to be useful from the pilot should be removed from future 

questionnaire versions to reduce the timing estimate further. 

Of the 78 respondents who started the survey but did not finish it, 24 respondents (31%) broke off after 

consenting but before answering the first question. 

Contact tracing

We needed to search online to identify phone numbers for most locations, and we needed to use those 

phone numbers to obtain email addresses for points of contact. Nearly 64% of phone numbers were 

recorded during online tracing, which raised the proportion of the sample with at least one phone 

number from 23% to 87%. Later, we obtained email address for nearly 71% of the sample during data 

collection. 

A small portion of cases (24 of 112) where we could not find phone or email information after online 

tracing were sent to field tracing, where in-person representatives attempted to obtain contact 

information. We obtained information for about 75% of these buildings, but the end response rate for 

this small group of locations was just 13%, only a few points higher than the 8.4% for other cases that 

did not have a phone number or email address on file. 

For future MBS pilots, we should continue online tracing for all cases. This method was quick (8.5 

minutes per search) and effective in the pilot. Future larger-scale efforts should take steps to further 

standardize a system for tracers to add their inputs, including recording additional information such as 

visible commercial space, size of building, single or multiple buildings at location, renters or owners, and 

if the building address is actually for a townhome community (for possible exclusion). Comparing this 

additional information to sample performance may identify useful trends for data collection. 

We should reserve field tracing for high-priority cases given the expensive cost and lower gains. We 

should define high-priority cases based on the goals of future MBS collections; some examples might 

include buildings with higher base weights, buildings in certain geographic areas, or buildings with 

certain characteristics of interest. To improve gains, we should provide the tracer with a packet to leave 

onsite that includes survey materials. Tracers should also verify contact information in the field prior to 

reporting back to the program office.

We will continue to evaluate other possible contact information sources. The expert panel suggested 

partnering with housing organizations to match to their contact lists; however, we would have to think 

through possible Confidential Information Protection and Statistical Efficiency Act (CIPSEA) concerns. 
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

Contact protocols and response rates

After online tracing, 13% of the sample had a mailing address but no phone number or email address. 

These cases were assigned to a mail-only contact protocol. This mail-only contact protocol yielded an 

8.4% response rate. 

The other 87% of the sample had at least one contact number. Cases with a phone number were 

assigned to a mixed-mode contact protocol, which used mail, phone, and email (when an email address 

was available). Each email or mail blast led to a substantial increase in responses. We achieved a

response rate of 35.6% for cases in the mixed-mode contact protocol. For cases where an email was 

available, the response rate was 39%. 

In the MBS pilot, the sample size was not sufficient to include experiments on the contact protocol. It is 

clear that email and mail follow-up to all pending cases led to an increase in response rates. However, 

we still have more to learn about the optimal number and sequence of contacts. A phased approach to 

data collection with experiments in the first phase would be useful in identifying an optimal contact 

protocol to use with the majority of the sample.

The unweighted MBS response rate ranged, by area, from 29.2% in New York to 36.7% in the Denver 

MSA. Across all geographies combined, larger buildings (that is, buildings with 56 or more units) also 

tended to have higher response rates than smaller buildings (that is, those with 55 or fewer units), 

which is likely related to the availability and quality of contact information.

Survey mode

Phone contacts were essential for establishing a point of contact for the building. Buildings with 1 to 10 

CATI contact attempts produced more respondents than nonrespondents. However, despite a 

considerable level of effort for phone contacts, most participants opted to complete the survey via web. 

Although 65% of surveys were completed by a single individual, many respondents indicated that more 

than one person was needed to compile the information for the survey. Almost half of web respondents 

also needed more than one session to complete the survey. 

Collectively, these findings, along with longer interview times among CATI respondents, suggest that 

web, or even paper, modes may be preferable to telephone interviews for many buildings. Investing 

resources in a paper survey may be more useful for response rates than continued investment in 

offering the CATI survey.

Regardless of survey mode offered, phone contacting is essential to the success of the MBS for both 

establishing and confirming a willing and knowledgeable point of contact and for nonresponse 

prompting.

Multi-building responders

Of the buildings selected, 17% were found to share a property management company with one or more 

other buildings in the sample, including 53 separate property management companies with an average 

of 2.8 and median of 2 buildings per company. Near the end of data collection, after multiple email 

blasts and mailings, MBS data collection managers established contact with leadership at two of the 
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U.S. Energy Information Administration | Methods, Findings, and Lessons Learned from the Multifamily Building Study Pilot

largest companies in the MBS sample and attempted to negotiate participation. As a result of these 

negotiations, one of the representatives agreed to encourage their individual building managers to 

participate, and the other representative agreed to provide data on all the selected buildings and 

requested a customized Excel template to use in providing these responses.

For companies that have only a few buildings in the sample, contacting corporate offices proved less 

efficient. For smaller companies, it may be more productive to contact the buildings directly and then 

approach the corporate entity only on request rather than as a starting point. Although contacting 

corporate first in the case of smaller companies did not yield higher response rates, it is likely still 

important to manage possible linkages across the sample. For example, several interviewers contacting 

a single company about multiple addresses on different phone calls could increase confusion and 

refusals. Interviewers need to be aware of possible linkages so they can capitalize on them should a 

common reporter be willing to report for multiple addresses.

Future MBS data collections should continue to test possible corporate engagement strategies, 

particularly for companies associated with the largest number of buildings. Direct outreach from EIA 

staff or a government (“.gov”) email address could add legitimacy, for example.

Custom corporate outreach that did not rely on the set CATI script or invitation email language was 

useful for the larger corporations. These contacts are best made by interviewers with advanced training 

and skills or by managers.