2018 CBECS Data Center Pilot 

Results

May 2021

Independent Statistics & Analysis

www.eia.gov

U.S. Department of Energy

Washington, DC 20585
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U.S. Energy Information Administration | 2018 CBECS Data Center Pilot Results 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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2018 CBECS Data Center Pilot Results

Executive summary

• Data centers are currently not published as a separate building type for Commercial Buildings 

Energy Consumption Survey (CBECS); they are included in the Other building type category.

• The U.S. Energy Information Administration (EIA) cannot publish data centers as a separate 

building type for a few reasons, including lack of frame, small sample size with current sampling, 

and low cooperation rates.

• EIA conducted a pilot study of 50 data centers as part of the main 2018 CBECS data collection to 

assess the feasibility of publishing data center estimates in future CBECS. 

• CoStar was used as the pilot frame.

• The pilot response rate was 26%, less than half that of the main sample.

• The pilot cases required two times more contacts per completed case compared with the main 

sample.

• Item nonresponse was very high for items that are important to data centers.

• Interviewers noted that security, finding a respondent, and the voluntary nature of CBECS were 

obstacles to receiving responses.

• Lack of quality frame source continues to be an obstacle to producing data center estimates.

• Data center estimates are likely not feasible with current methods. EIA would need a quality 

frame and cooperation from the industry in order to consider data centers as a separate 

building type.

Background and motivation

Data centers are one of the most energy-intensive building types, using 10–40 times more energy per 

square foot than the typical office building and accounting for almost 2% of the electricity use in the 

United States (Office of Energy Efficiency and Renewable Energy, n.d.). Data center computing capacity 

has rapidly grown in the past decade, but consumption has grown more slowly because of efficiency 

measures (Shehabi, et al., 2016). Therefore, data center buildings are of great interest to the building 

energy data community. 

The Commercial Buildings Energy Consumption Survey (CBECS), the only national-level source of energy 

consumption data and energy-related building characteristics data for commercial buildings, includes 

data centers in the survey. However, because of the small number of data center buildings in the 

respondent dataset,1 EIA does not publish data centers as a separate building type.2 Instead they are 

grouped into the Other building type. This Other category includes buildings that don’t fit into the list of 

CBECS principal building activity categories, such as airplane hangars and public restrooms. 

 1 The 2012, CBECS had six responding data center buildings in the final respondent dataset. 

2 EIA collects data on data center space that is part of a building type such as an office or education building in CBECS and 

publishes this data because this space is much more common than separate data center buildings. 
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CBECS data users inquire frequently about data center estimates. In response to the data user 

community, EIA conducted a Data Center Pilot along with the 2018 CBECS buildings survey data 

collection. EIA aimed to gather data that would assess the feasibility of collecting data and publishing 

estimates for data center buildings in future CBECS. The pilot was a way to test the waters on a small 

number of buildings for a limited cost, instead of investing in a large data collection operation that 

potentially would not give useful results. 

EIA considered many known or suspected obstacles to getting enough data center completed cases to 

publish estimates, including not having a frame of data centers; not being able to identify data centers 

on the current frames to sample enough of them; and high unit nonresponse rate, likely because of 

company privacy concerns. Although item nonresponse is corrected with imputation and does not 

directly prevent publishing estimates for a building type, a very high item nonresponse rate for 

important questionnaire items is a data quality concern. The main goals of the pilot were to test a 

potential frame, measure unit nonresponse, and measure item nonresponse for important 

questionnaire items.

Methods

Frame

To select a sample of data centers for the pilot, EIA needed a frame (a list of data centers with 

information needed for sampling and contacting the buildings). Currently, no known comprehensive list 

of data centers is accessible to EIA.3 EIA used CoStar, a commercial real estate database to which EIA 

purchased a one-year license for experimental purposes, as the frame because of the database’s easy 

file creation process and the completeness of its square footage and address information. CoStar’s 

coverage of data centers is unknown but sufficient for a pilot study in which estimates are not needed. 

To create the frame, all buildings larger than 50,000 square feet4 identified as data centers were 

downloaded from CoStar. EIA and Westat, EIA’s data collection contractor for the 2018 CBECS, identified 

which of those buildings were in the 2018 CBECS primary sampling units (PSUs) and did further file 

cleaning to prepare for sampling. The final frame used for the sample consisted of 115 buildings from 

CoStar that were identified as data centers.

As part of the preparations for sampling, EIA categorized all 115 data centers on the frame as either a 

colocation data center or a private data center. A colocation data center is a data center that hosts and 

operates multiple organizations’ servers. A private data center is a data center building used by just one 

organization, for example, Google or Facebook. The data center type was used as a sampling 

stratification variable because EIA suspected that type would have an effect on both the response rate 

and energy use patterns.

 3 The CBECS area frame does cover data centers; however, because there are so few of them in the population and because 

they are difficult to identify in the area frame listing process and are more geographically dispersed than other buidings, the 

area frame does not provide enough data centers to sample to provide separate estimates.

4 EIA chose a minimum size of 50,000 square feet because smaller buildings are less likely to be whole building data centers. 
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Sample

EIA chose a sample size of 50 data centers in considering costs and desired accuracy (20 private data 

centers and 30 colocation data centers). Essentially, EIA selected all private data centers from the 115 

on the frame because out of all 115 data centers, 22 were private and 3 were on the same campus, so 

one of the buildings on that campus was selected, along with the remaining 19 private data centers. EIA 

grouped the colocation data centers into two building size categories, small (50,000 square feet–

200,000 square feet) and large (more than 200,000 square feet). We selected the colocation data 

centers with proportional allocation in size and and census region strata to ensure geographic and size 

distribution. 

Data collection

The data collection protocol for the pilot cases was the same as the main CBECS sample. The 

interviewers and respondents were unaware that the buildings were part of a pilot. 

Areas of assessment

In evaluating the success of the pilot, we selected five criteria for assessment, calculating metrics for the 

first four and performing a qualitative review of the fifth: 

• Unit nonresponse, compared with the main CBECS sample

• Misclassifications from frame

• Measure of effort for completed cases (number of interviewer contacts per completed case)

• Item nonresponse rates for important questionnaire items

• Qualitative assessment of interviewer notes from contact history file, looking for patterns in 

contacts and attempted contacts 

Results

Unit nonresponse

Of the 50 sampled pilot buildings, 12 completed the survey, 3 were out of scope, and 35 did not 

complete the survey. These results indicate a response rate5 of 

RRRRRRRRRRnndd dd

RRRRRRRRRR RR RR + NN NN = 12

12 + 35 = 25.5%

In comparison, the main CBECS sample response rate was 54.7%.

Only one data center identified on the frame as private responded out of the 20 private data centers 

sampled. 

Frame misclassification

 5 The actual response rate calculation of the main CBECS sample is more complicated, and it is documented in the 2018 CBECS 

Final Report. For this report, EIA used a simplistic calculation.
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EIA discovered that 6 out of the 50 sampled data centers were not true data centers, or were not 

eligible for CBECS. Two of the respondent buildings were offices, and one was an education building. 

Three of the sampled buildings were not eligible for CBECS: one building was under construction, one 

was demolished, and one was not found by the interviewer. 

Interviewer effort for completed cases

The average number of interviewer contacts per completed case from the pilot was 12; in comparison, 

the average number of contacts per completed case from the main sample was 6.

Item nonresponse

EIA calculates item nonresponse rates6 for select questionnaire items by using the 22 responding pilot 

and main sample data centers (9 confirmed as data centers from the pilot7 and 13 from the main 

sample). These rates were compared with office buildings in the main sample. 

Square footage and number of workers, two key items for all building types in CBECS, were compared in 

terms of item nonresponse. In addition, item nonresponse for servers, a key item for data centers, was 

compared with the main CBECS sample. Servers can be reported as a number or a category; if the 

respondent does not give a number, it receives a response set with ranges of numbers and an 

opportunity to report the range in which the number of servers belongs. We calculated item 

nonresponse for power usage effectiveness (PUE),8 a key item for data centers, but we did not compare 

it with offices because this question was only asked of data center buildings.

Table 1 shows the item nonresponse rates for data centers and offices for these five questionnaire 

items. Square footage and number of workers item nonresponse was 3% lower for data centers 

compared with offices. However, item nonresponse for the number of servers was much higher for data 

centers. Of the data center building respondents, 86% answered don’t know or refuse or skipped both 

the numeric value and categorical server question; 90% answered don’t know or refuse or skipped the 

numeric value. The server questions were some of the only questions in the entire questionnaire that 

allowed a refuse option. Because 21% of offices and 68% of data centers selected the refuse option, EIA 

infers that number of servers is a sensitive building characteristic that respondents do not want to 

share, even if they know the answer. 

Item nonresponse was 80% for PUE. We did not ask this question for offices, so there is no basis for 

comparison.

 6 Item nonresponse rate is calculated as the number of responding buildings that did not answer the question, divided by the 

total number of responding buildings for which the question was asked.

7 Though 12 sampled buildings from the pilot responded, three were not actually data centers, as described in the Frame 

misclassification section.

8 Power usage effectiveness (PUE), an energy efficiency measure for data centers, is the ratio of the total energy use for the 

data center divided by the energy use for the IT equipment. The ideal ratio is 1.
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Table 1. Item nonresponse rates for key items

Questionnaire item Data centers Offices

Square footage 18% 22%

Number of workers 23% 26%

Number of servers (numeric) 90% 35%

Number of servers (numeric or categorical) 86% 33%

Power usage effectiveness 80% N/A

Qualitative assessment of interviewer notes

EIA reviewed the interviewer notes available on the contact history paradata file for the pilot cases. In 

reviewing the notes, EIA determined that finding a respondent was a challenge—a respondent was 

identified in only 18 of the 50 cases. Interviewers faced obstacles in either finding a respondent or 

getting a completed interview, including

• High security: interviewers were often denied access to the building.

• Appointment passive refusal: if interviewers gained access to the building, the front desk would 

often say a potential respondent is available by appointment only. This approach often led to a 

passive refusal.

• Voluntary nature of survey: large companies with many data centers saw that the survey was 

voluntary and refused. 

• Lack of activity at the data center: several data centers in the frame appeared to have few or no 

employees present. 

Strategies and interviewer qualities that seemed more effective in gaining cooperation included

• Talking to security: even if security denied access to the data center, they often could provide 

phone numbers, email addresses, or addresses of a corporate office to the interviewer. 

• Talking to corporate office: a respondent with suitable knowledge to answer the survey was not 

always located at the data center. Interviewers were often referred to an employee at a 

corporate office. 

• Corporate response: some companies had multiple data centers in the sample. One corporate 

respondent was able to provide 3 of the 12 completed cases. 

• Persistence: after identifying a respondent, some interviewers were effective at ensuring the 

respondent completed the interview.

Conclusion and recommendations

The 2018 CBECS Data Center Pilot confirmed that the suspected obstacles to data center building

estimates are, in fact, present:

• Lack of a quality, comprehensive frame

• Low cooperation rates for data center buildings sampled

• High item nonresponse rate for important questionnaire items
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EIA used CoStar as the pilot frame; however, CoStar’s coverage of the population of data center 

buildings is unknown. EIA could look into other potential frame sources, such as Data Center Map and 

Wired Real Estate Group, for potential use. However, without external verification of their completeness

and potential bias, the quality of those resources is unknown. EIA would need to use a web scraping 

operation to pull a file of them for sampling, which could potentially present complications with website 

terms of use and conformity of available information into a standard file.

The cost and burden for completed data center building interviews using current methods is high. Pilot 

buildings responded at less than half the rate of buildings in the main CBECS sample, and they required 

two times as many interviewer contacts as the main sample. Difficulty finding respondents, high 

security, the voluntary nature of the survey, and the lack of employees at some buildings were common 

barriers to getting completed interviews. 

For questionnaire items that are important to data centers, the rate of nonresponse was high. More 

than 80% of data center buildings did not answer the questions about the number of servers and the 

PUE, indicating a sensitivity to these questions.

If getting data center building estimates is of high importance to future CBECS, EIA would need more 

cooperation from industry to get access to a frame (if one exists) and to help encourage buildings to 

respond. A different contact strategy may be required for these buildings. Cognitive research and pretests could be helpful in identifying response barriers and ways to get around them. 

Although separate data center building estimates will not be available from the 2018 CBECS, the main 

questionnaire included questions about small data center spaces within other buildings, such as offices, 

and these data will be published similar to how they were in the 2012 CBECS (Lewis, 2016). Small room 

data centers receive much less attention than large data centers, but they account for a significant 

fraction of the total number of servers in the United States and consume 13 billion kilowatthours of 

energy each year (Ganeshalingham, Shehabi, & Desroches, 2017).
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References

Ganeshalingham, M., Shehabi, A., & Desroches, L.-B. (2017, June). Shining a Light on Small Data Centers 

in the U.S. Retrieved from Lawrence Berkeley National Laboratory: 

https://eta.lbl.gov/sites/default/files/publications/lbnl-2001025.pdf

Lewis, K. (2016). Today in Energy. Retrieved from eia.gov: 

https://www.eia.gov/todayinenergy/detail.php?id=28232

Office of Energy Efficiency and Renewable Energy. (n.d.). Retrieved from energy.gov: 

https://www.energy.gov/eere/buildings/data-centers-and-servers

Shehabi, A., Smith, S. J., Sartor, D. A., Brown, R. E., Herrlin, M., Koomey, J. G., . . . Lintner, W. (2016). 

United States Data Center Energy Usage Report. Lawrence Berkeley National Lab.