2015 AHS Redesign Paper 5

Utility Cost Estimation Model 

Development and Decisions for the 2015 

American Housing Survey and Beyond

George R. Carter III

U.S. Department of Housing and Urban Development

Brian Shaffer

U.S. Census Bureau

August 2017

U.S. Department of Housing and Urban Development | Office of Policy Development and Research
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 1

Utility Cost Estimation Model Development and Decisions 

for the 2015 American Housing Survey and Beyond

Purpose

The purpose of this paper is to summarize the results of research 

on utility cost estimation conducted by the U.S. Census Bureau 

and the U.S. Department of Housing and Urban Development 

(HUD) as part of the redesign of the American Housing Survey 

(AHS) for 2015. Utility costs are an important component of 

housing costs, which also include rent or mortgage payments; 

garbage, trash, water, and sewage costs; real estate taxes; and 

other housing-related fees. Accurate estimation of housing costs 

is crucial to monitoring trends in affordable housing supply 

over time. Housing costs are incorporated into measures of 

housing burden that examine the percentage of a household’s 

income spent on housing. Housing burdens are a component 

of HUD’s Worst Case Needs measure,1 which is used to estimate 

the number of households in the United States with the greatest 

housing needs. Since 1993, utility cost data collected in the 

AHS have been adjusted and imputed using regression models 

developed from the Residential Energy Consumption Survey 

(RECS).

The research discussed in this white paper analyzes the implications of estimating utility costs in the AHS in the absence of 

billing data provided by respondents. The results of the research 

informed the decision to not collect billing data for electricity, 

natural gas, and bottled gas for most households (around 99 

percent of households) and instead to use housing and household 

characteristics and climate data to model utility consumption 

and costs for housing units in the 2015 AHS.

1. The American Housing 

Survey

The AHS is a key source for housing and utility cost data in 

the United States. The AHS is the largest, most comprehensive, 

longitudinal housing survey in the United States and is collected 

by the Census Bureau on the behalf of HUD. The AHS employs 

two types of housing unit samples—a national sample and 

metropolitan area samples, with the metropolitan area samples 

surveyed on a rotating basis. In both types of sample, the same 

housing units are followed over time until a new sample is 

collected. Between 2 and 29 metropolitan area samples have 

been surveyed in the years the metropolitan AHS has been 

fielded, depending on funding availability. The national AHS 

started in 1973 and has surveyed the same housing units since 

1985, drawing additional samples to account for new construction, improve coverage, and oversample subpopulations. From 

1973 and 1981, the national AHS was conducted annually. 

Since 1983, the national AHS has collected data in every oddnumbered year. Since 2007, national and metropolitan area 

samples have been surveyed in the same year. Since 1997, the 

AHS has been collected via in-person and telephone interviews 

using an electronic questionnaire.

1.1 2015 AHS Redesign

In 2015, the AHS retired the national sample it had surveyed 

since 1985 and all metropolitan samples, and it drew a new 

sample from the Census Bureau’s Master Address File. Along 

with the introduction of a new sample, the AHS underwent a 

redesign for 2015, balancing the goals of increasing data quality 

and reducing respondent burden. As part of the 2015 AHS 

redesign, HUD and the Census Bureau reevaluated all the questions in the AHS. Related to the goal of reducing respondent 

burden, HUD and the Census Bureau conducted research on 

the implications of collecting less data on electric and gas utility 

costs in the AHS and relying more on modeling for the estimation of utility costs. This research informed the decision to stop 

collecting most utility billing data on the AHS and to estimate 

utility costs through regression model-based imputation.2

1

 One criterion for classification of Worst Case Needs household is being an unassisted renter household with very low income (income not more than 50 percent of 

area median income) having “severe rent burden,” paying more than one-half of their income on housing costs (Steffen et al., 2015).

2

 Appendix table J1 presents the distribution of 2015 AHS cases by type of electric and natural gas model (modeled with housing and household characteristics, 

modeled with billing data, and estimated using self-reported estimates of average monthly utility costs).
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

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1.2 Utility Cost Data Collection Prior 

to 2015

Before 2015, the AHS asked respondents to refer to their utility 

bills when reporting monthly electric and gas costs. In addition 

to electric and gas costs, the survey asked questions about 

other utility costs, including fuel oil, other fuels (that is, coal, 

kerosene, wood, and solar), garbage and trash, and water and 

sewage. The series of utility questions in the AHS was highly 

burdensome, requiring respondents to locate and report their 

utility bills and to estimate costs when they did not have bills. 

To prepare respondents for the survey, a letter sent in advance 

of the interview contained a worksheet with questions about 

utility costs.

1.3 Electric and Gas Cost Data 

Collection Prior to 2015

Prior to 2015, the electric and gas cost questions in the AHS 

collected billing data for the months of January, April, August, 

and December—months that Gorsak and Schwanz (2004) 

found most predictive of average monthly utility costs. Gorsak 

and Schwanz reported that, historically, one-third of AHS 

respondents provided billing data, and the majority of those 

who did provided data for all 4 months. Using 2007 AHS data, 

Carter (2010) found that 39.5 percent of respondents reported 

at least 1 month and 35.9 percent reported all 4 months of 

electric bills, and 39.9 percent of respondents reported at least 

1 month and 36.5 percent reported all 4 months of gas bills. 

Increasingly, online billing and automatic bill pay options 

present barriers to respondents possessing physical bills. Even 

though respondents are asked to use their bills during the interview, it was not until 2011 that the AHS asked respondents 

explicitly if they used bills or the worksheet in the interview. 

The 2011 AHS recorded 20 percent of respondents as using an 

electric bill, 18 percent as using a gas bill, and 10 percent as 

using the worksheet to answer the survey. Of those using the 

worksheet, it is not known whether respondents filled out the 

utility bill information. 

Respondents without 4 months of billing data were asked 

other questions to allow for the estimation of utility costs. 

If respondents did not have bills, did not know the billing 

amount, refused to answer, or provided fewer than two valid 

monthly amounts, the survey asked: “How much was your 

most recent bill?” and “What month was the bill for?” The AHS 

asked a subset of respondents to estimate their average monthly 

electricity costs. Respondents who used gas were asked similar 

cost questions and whether their gas comes from underground 

pipes or bottled gas.

1.4 Other Utilities

In addition to electric and gas costs, the AHS collected (and 

still collects) information for fuel oil, other fuels, garbage and 

trash collection, and water supply and sewage disposal. The 

level of detail collected for these costs is less than that for 

electric and gas costs. Monthly billing data were not collected 

for these items. However, the AHS collected (and still collects) 

whether the utility was used, whether it was billed separately 

or combined with another bill, and the total costs for the year. 

In addition to reevaluating the estimation of electric and piped 

natural gas costs, HUD and the Census Bureau explored using 

modeling to estimate bottled gas and fuel oil costs for the 2015 

AHS.

2. Utility Data Editing, 

Estimation, and 

Imputation

After data collection, consistency edits are performed to check 

the utility data against relevant use (that is, if electricity is 

used) and equipment (that is, electricity is used as the heating 

equipment fuel) variables and to identify if utilities are billed 

separately. Because the AHS did not ask for billing data for all 

months, and respondents’ recall of utility costs is not always 

accurate, the utility estimation system (UES) was created to 

estimate annual utility costs using regression models developed 

from the RECS. This estimate was divided by 12 to calculate 

average monthly utility costs. The advantage of using the RECS 

to model utility costs is that the RECS collects administrative 

data from suppliers on actual billing amounts. The RECS also 

collects some housing characteristics similar to those the AHS 

collects, which allows for the construction of models that can 

then be applied to the AHS. Table 1 lists the 25 regression 

models that comprise the UES.
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Table 1. Billing Data for Each Model 

Model Month Model Month

1 January April August December 12 January

2 January August December 13 April

3 January April December 14 August

4 January April August 15 December

5 April August December 16 March

6 January April 17 May

7 January August 18 June

8 January December 19 July

9 April August 20 February

10 April December 21 September

11 August December 22 October

23 November

24 Self-reported average monthly costs

25 No monthly data

2.1 The Utility Estimation System

From 1993 to 2013, the UES was used to calculate annual 

estimates for households that use electricity and households 

that use underground piped natural gas. The calculation 

process consisted of three steps: (1) calculating the utility cost 

estimate, (2) comparing the cost estimate with a cutoff value, 

and (3) aligning cost estimates to the RECS.

In the first step, cost estimates were calculated by applying 

one of a set of regression models to data collected from the 

respondents. Utility cost regression models were created with 

the RECS. Because the RECS collects respondent monthly billing 

data directly from suppliers and asks respondents questions 

that are also asked on the AHS, the Census Bureau was able 

to create regression models with the RECS and apply AHS 

responses to the regression parameters to obtain a total utility 

cost estimate for AHS respondents.

In the second step, each cost estimate was compared to a set of 

values to avoid extremely low or high estimates. These cutoff 

values were calculated at the census division level. If a cost 

estimate was out of the range of cutoff values, extreme AHSreported individual bills were dropped and a new cost estimate 

was calculated with the appropriate model. If no extreme 

individual bills were identified for cases exceeding the cutoff, 

the cost estimate was obtained through hot deck imputation.

In the final step, cost estimates were adjusted to align with the 

RECS total utility cost. An adjustment factor was calculated, 

such that the AHS cost estimates that were calculated with 

billing data plus the adjusted sum of the AHS cost estimates 

that were calculated without billing data was equal to the RECS 

average monthly cost multiplied by the AHS sample size. Cost 

estimates calculated with billing data were assumed to be correct; 

therefore, the adjustment factor was applied only to estimates 

calculated without billing data.

2.2 History of Model Development

The electricity and gas models evolved over time. The Census 

Bureau developed the UES in 1993 based on a recommendation 

from the Office of Management and Budget. In the initial version, 15 models were developed for each census division;3

 each 

model contained parameter estimates for the billing months 

of January, April, August, and December, corresponding with 

the amount of billing data obtained from the respondent. If a 

respondent had only 1 month of billing data, electric bills from 

only January and December, or gas bills from only April and 

August, the model added parameter estimates corresponding 

with home heating equipment, water heating equipment, and 

housing and household characteristics. Parameter estimates 

were calculated from the 1990 RECS and inflated to reflect 

1993 utility cost. Separate inflation factors were calculated for 

electric costs and natural gas costs. Both inflation factors were 

calculated at the national level. After 1993, each AHS survey 

updated the inflation factors and average monthly costs.

3

 Alaska was assigned to the New England division. Hawaii was assigned to the West South Central division.
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In 2003, based on research by Gorsak and Schwanz (2004), 

parameter estimates were updated using data from the 1997 

RECS. Also, two regression models were added—one using 

average electric and gas costs estimated by respondents and 

the other assigning zero to all regression parameters, thereby 

forcing the case through the hot deck imputation method.4

In 2007, the parameter estimates were updated with data 

from the 2001 RECS. Four models were added. These models 

used billing data for March, May, June, and July to allow for 

modeling of costs for respondents who provided the past 

month’s bill. 

In 2009, four models corresponding with the remaining billing 

months (February, September, October, and November) were 

added to the system. The 2003 model, which assigned zero to 

all regression parameters, was updated with 2001 RECS-based 

parameters for the housing and household characteristics data. 

This housing and household characteristics (HHC) model was 

applied to cases with no billing data and no estimate of average 

monthly costs. In this update to the system, the Census Bureau 

also split the electric universe into two smaller universes—

housing units that use electric only and those that use both 

electricity and gas—to better estimate the costs of units using 

different combinations of electric and gas. This update also saw 

the introduction of division-level inflation factors. The 2009 

system update was used through 2013, with inflation factors and 

average monthly costs reflecting the current year of enumeration.

2.3 Strengths and Limitations of 

Using the RECS

The RECS was chosen to derive regression parameter estimates 

that could be applied to AHS data to estimate utility costs 

because it collects housing and household variables similar 

to AHS variables and collects high-quality utility data. Rather 

than collecting self-reported utility billing data, the RECS 

collects billing data directly from suppliers, who are mandated 

to provide the data to the U.S. Department of Energy. These 

factors help maintain the high quality and low nonresponse 

rates of RECS data. One limitation of using the RECS to model 

utility costs is that the models are dependent on getting current 

RECS data. Inflation factors are applied to the models to adjust 

costs to the year of the AHS data collection. In the past, the 

RECS released data at only the regional and census division 

levels. The 2009 RECS,5 used to model costs and consumption 

in our research, includes geography at the reportable domain 

level (states and groups of states), which improved the models’ 

level of geographical precision. 

The RECS underwent a redesign and was the subject of a 

National Research Council review panel, which published its 

findings in Effective Tracking of Building Energy Use in 2012. The 

panel made several recommendations to improve the RECS, including improving timeliness, revising edit procedures, releasing prepublication estimates, increasing sample size to make 

more state data available, working more closely with suppliers, 

introducing a multimode approach for data collection (the 

RECS already uses a web survey for suppliers), and conducting 

ongoing evaluations of administrative record use for data and 

imputation (for example, square footage can be used to replace 

self-reported data or to impute missing data from self-reported 

items) (NRC, 2012). In Spring 2017, data from the 2015 RECS 

will be released (https://www.eia.gov/consumption/residential/

data/2015/index.php?view=microdata) that will be used to 

update future AHS utility estimation models.

3. Changes for 2015 

Evaluated in Research

The utility cost questions in the AHS are complicated, and 

many respondents do not have the billing information available 

to answer them. Because the utility questions are burdensome 

to respondents and costly to collect, the Census Bureau and 

HUD conducted simulation studies to evaluate the impact of 

estimating utility costs with less or no billing data. The research 

explored several options for redesigning the 2015 AHS utility 

cost models.

1. Continue with the current AHS approach. Ask respondents for 

4 months of billing information and update the UES as new 

RECS data become available.

2. Collect no utility data for electric and natural gas costs. Model 

utility costs with a revised housing and household characteristics (HHCR) model. 

4

 The hot deck imputation method includes 180 cells for each utility: nine census divisions, electric or natural gas home heating, electric or natural gas water heating, 

and five categories for total rooms—less than four, four, five, six, and seven or more (Gorsak and Schwanz, 2004).

5

 The 2009 RECS had a sample size of 12,083 cases. Sample and methodology documentation for the 2009 RECS is at https://www.eia.gov/consumption/residential/

data/2009/. Information on all RECS surveys is at https://www.eia.gov/consumption/residential/index.php.
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3. Adopt the American Community Survey approach. Collect only 

costs for the past month and model utility costs using the 

UES for the past month’s bill.

4. Collect average monthly costs. Model utility costs using the 

UES for average monthly costs (we did not have the data 

to explore this option, as not all cases provided average 

monthly cost information).

We present evaluations of options 1, 2, and 3 in this paper. For 

option 3, we chose to test with households that provided April 

billing information, because that was the month with the most 

2009 AHS data available. We ran the model only on cases with 

April bills. We did not have enough data to test option 4, as 

many respondents are not asked to estimate average monthly 

costs. In addition, Gorsak and Schwanz (2004) questioned the 

validity and reliability of self-reported average monthly cost 

estimates, because they are highly burdensome for respondents 

to answer. The task of estimating average monthly costs 

requires respondents to quickly remember all their bills from 

the past year and convert them into a monthly value.

3.1 Study Design

We estimated regression models for electric and gas costs and 

consumption with public use data from the 2009 RECS.6 We 

applied the models to data from the 2009 AHS internal use file 

to yield estimates for monthly electric and gas consumption 

and costs. We compared median estimates and frequency 

distributions of monthly electric and gas costs from our models 

and published estimates from the 2009 AHS with estimates 

from the 2009 RECS to assess how closely the models approximated costs at national and regional levels. As the RECS is the 

gold standard for utility costs, we benchmarked our estimates 

to the 2009 RECS estimates.

3.2 Models

The models used in the research are described in table 2, and 

the variables in the initial HHC model and the final HHC 

(HHCFIN) model are in table 3. All models exclude cases in 

Table 2. Models 

UES Approach used in 2009 AHS.

HHC Applies housing and household characteristics cost 

model to all units using electric, gas, or both.

HHCR Revised HHC model. Changed to consumption 

model. Degree days added.

UESR Revised UES model. Updated with monthly billing 

data from 2009 RECS.

HHCR2 Revised HHCR model. Adds presence of steam 

heat to gas model, collapses geographies with small 

sample sizes, and adds household income.

ONE BILL HHCR2 model plus April utility bill.

HHCFIN HHCR2 with degree day normals.

AHS = American Housing Survey. RECS = Residential Energy Consumption 

Survey.

Table 3. Variables in Models

HHC HHCFIN

Response variable Cost ln (Consumption + 1)

Independent variables Electric heat Heating degree days (if electric heat)

Gas heat Heating degree days (if gas heat)

Cooling degree days (if central air)

Cooling degree days (if wall air unit)

Electric hot water Electric hot water

Gas hot water Gas hot water

Year built Year built

Type of housing unit (single-family = 1, multifamily = 2, mobile = 3) Single unit (indicator)

Multiunit (indicator)

Mobile home (indicator)

Total rooms Total rooms

Total bathrooms Total bathrooms

Total major appliances Total major appliances

Number of household members Number of household members

Household income

ln = natural log.

6

 For more on regression techniques and the use of regression in imputation, see Little and Rubin (2002), Myers (1990), and Raghunathan (2015).
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which the given utility costs are included in rent or condominium fees. Both the RECS and the AHS required recoding variables 

so that regression parameters could be applied across surveys. 

Appendix A contains a full list of variables in all models and a 

crosswalk between RECS and AHS variables.7 The UES model 

is the billing model used to produce published estimates for 

the 2009 AHS, which uses data from the 2001 RECS, adjusted 

for inflation to reflect 2009 values. The model uses billing data 

primarily for estimation and incorporates some housing and 

household characteristics for units with less billing data. Because 

the HHC model was developed for the 2009 AHS public use file 

using only housing and household characteristics, we evaluated 

the predictive capability of this model when applied to all 

cases in the 2009 AHS national sample. The HHC model was 

developed with 2001 RECS data, and because 2009 RECS data 

are now available, we created the HHCR model with the 2009 

RECS data. The HHCR model incorporates heating degree days 

(HDD) and cooling degree days (CDD) to account for variability in consumption.8

 The model uses degree days available on 

the 2009 AHS internal use file. HDD affect heating equipment 

usage, and CDD affect air conditioning usage; therefore, we 

apply this information depending on the presence of specific 

types of heating and cooling equipment. 

We attempted several enhancements in HHCR model development to create a model with high predictive capabilities that 

would not require applying cutoffs or aligning to the RECS cost 

totals, as required with pre-2015 models. First, we attempted 

to develop models at a more-refined geographic level than the 

census division. The RECS publishes data at the state (or group 

of states) level, which the U.S. Energy Information Administration (EIA) calls “reportable domain.” Because some census 

divisions cover relative large geographic areas, analysis at the 

reportable domain level can account for some cost variability at 

smaller geographic levels.

Second, cost data are skewed. To address skewed data, we 

explored modeling the natural logarithm of monthly cost as 

a function of the independent variables; more specifically, 

the natural log of cost + 1, to account for units that reported 

0. During the evaluation, we noticed differences in average 

costs from state to state. Therefore, we estimate consumption 

(natural log of consumption + 1, because consumption data are 

also skewed) with independent variables.

After iteratively removing outliers,9 we used stepwise regression 

to determine which variables to include and which variables to 

remove from each reportable domain model.

We applied the new regression parameters to the recoded 2009 

AHS data. After transforming our estimates from the natural log 

scale back to our base consumption, we multiplied our monthly 

consumption estimates by each state’s average price per unit 

of consumption to obtain the unit’s average monthly cost. For 

fuel prices, we obtained price and consumption data from EIA’s 

website for each state.10 We then calculated a weighted average 

across the 12 months prior to the 2009 AHS enumeration, or 

May 2008 through April 2009. Next, we multiplied the average 

by a factor to put the value into a unit of measure equivalent to 

the RECS public use data. For electricity, the factor was 0.01 to 

convert cents to dollars. For gas, the factor was 0.1 to convert 

dollars per 1,000 cubic feet to dollars per 100 cubic feet. Finally, 

we summarized our results and compared them with the RECS. 

We requested 2009 RECS internal use billing data from EIA and 

used the data to reestimate the UES with the same parameters 

as in the 2009 UES. This model is referred to as UESR. The HHCR2 

model is a revision of the HHCR model that incorporates household income to more accurately estimate consumption across 

the income distribution. The HHCR2 model is weighted, uses 

degree days from the 2009 AHS internal file, adds a variable 

to the gas model for the use of steam or hot water radiators or 

other hot water heating systems, and collapses some geographies 

with small samples sizes. The ONE BILL model explores the 

option of collecting one bill and incorporating that one bill into 

a consumption model. Because April was the month with the 

most 2009 AHS cases, we ran the ONE BILL model on cases with 

an April bill. The final model (the model chosen for electric 

and gas estimates in 2015), the HHCFIN model, is a revision of 

the HHCR2 model that uses HDD and CDD data obtained from 

a National Oceanic and Atmospheric Administration (NOAA) 

30-year climate normals file from 1980 to 2010.

7

 Independent variable cumulative distribution functions (CDFs) that compare the distribution of the 2009 RECS variables with distributions of the 2009 HHCFIN 

AHS variables is in appendix M. Appendix N shows the extent of imputation in the RECS variables used in different models. 

8

 A degree day is an index based on the difference between the daily average temperature and 65 degrees Fahrenheit. HDD are the negative differences, and CDD are 

the positive differences.

9

 Outliers were determined based on a negative effect on the regression model. We defined an outlier as a value with a large Cook’s D and a small COVRATIO. See 

appendix K for the extent of outlier removal.

10 See appendix O for information on obtaining price and consumption data for 2009 and 2015.
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NOAA no longer provides the data product that EIA used to 

assign HDD and CDD to the RECS, so we experimented with 

several approaches to assigning HDD and CDD to AHS cases. 

The 30-year climate normal HDD and CDD were closest to the 

2009 RECS and AHS distributions.11

Electric models are split into two categories: electric-only 

models are applied to occupied units that pay for electricity 

and do not use natural gas, and electric-gas mixed models 

are applied to occupied units that pay for electricity and use 

natural gas. Natural gas models are applied to occupied units 

where households pay for gas that is piped into the unit. 

4. Evaluation Methods

The AHS and RECS are independent samples, so we cannot 

compare each individual unit’s estimates of monthly costs 

between the surveys. Therefore, comparisons must be at the 

aggregate level. We evaluated the quality of our estimates 

by how closely AHS distributions matched the medians and 

distributions calculated from the RECS.

We calculated quantiles for all units, medians for selected 

characteristics, and categorical percent distributions for AHS 

models and compared these estimated distributions with 

distributions from the 2009 RECS. We estimated variation 

in quantiles, medians, and categorical percent distributions 

attributed to repeated sampling for each survey using replicate 

weights. We obtained a pooled standard error estimate and 

performed t tests on the null hypothesis of zero difference 

between each distribution and the RECS distribution with a 

two-sided alpha of 0.1. 

4.1 Quantiles for All Units

Tables 4 and 5 show the national electric and natural piped 

gas quantiles of average monthly costs (in dollars).12 The 

UES-derived values were top-coded for publication, and 

therefore we suppress values at or above the 97.5th percentile 

for confidentiality. At the national level, all differences between 

evaluation medians and the RECS median are statistically significant for both electric costs and natural gas costs. However, 

the national distributions calculated with the models, including 

the proposed HHCFIN model, visually follow the RECS. The 

electric medians for the HHCR, HHCR2, and HHCFIN models 

are lower than the 2009 RECS; the UES, HHC, UESR, and ONE 

BILL medians are higher than the RECS median. Gas medians 

for all AHS models are higher than the 2009 RECS gas median, 

but the HHCFIN model comes closest to the RECS median. 

Regional Electric Quantiles and Regional Natural Gas Quantiles 

tables showing average monthly costs are in appendixes B and C, 

respectively.

Table 4. Average Monthly Cost of Electricity ($), National Quantiles

Quantile

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

1% 21 13 14.38 23 26.64 13.92 26.90 17.67

5% 37 42 35.88 39 38.30 38.43 38.80 30.33

10% 48 55 44.89 50 46.08 46.31 46.84 39.58

25% Q1 71 77 62.76 74 63.46 65.51 64.50 62.08

50% median 108 106 89.77 111 90.05 101.27 91.76 96.92

75% Q3 159 147 127.74 167 128.10 158.45 129.53 144.33

90% 224 193 168.91 237 169.51 257.22 171.34 203.50

95% 273 220 198.45 295 199.29 367.69 200.70 245.08

99% * 273 267.96 456 267.67 1,026.39 269.88 356.33

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy Consumption Survey. 

* The value at the 99% quantile is suppressed, because UES data on the public use file are suppressed at the 97.5th percentile.

11 See appendix M for CDFs of HDD and CDD for differences between our original approach (Original Match) and the 30-year climate normals (Norms).

12 See appendix L for consumption and cost estimate CDFs for electric, piped natural gas, and bottled natural gas in our final AHS models and the 2009 RECS. The 

CDFs show the probability that consumption and cost estimates have values less than or equal to the argument of the function. Although each of the CDFs exhibit 

similarly shaped distributions, all Kolmogorov-Smirov tests yielded a statistically significant difference at the maximum distance between each CDF at p < .0001.
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Table 5. Average Monthly Cost of Natural Gas ($), National Quantiles 

Quantile

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

1% 11 3 6.66 12 8.04 0.16 7.47 3.50

5% 22 32 21.78 23 22.70 15.24 20.83 11.33

10% 31 47 31.45 31 32.62 24.96 30.11 20.42

25% Q1 48 68 47.54 46 47.40 42.65 44.67 38.08

50% median 73 90 71.46 70 69.93 67.22 66.60 60.58

75% Q3 111 119 98.24 107 97.44 111.48 94.26 90.25

90% 162 149 129.55 156 130.41 188.22 122.71 125.67

95% 203 165 153.89 200 155.84 275.43 145.10 155.00

99% * 200 209.85 334 218.74 812.71 192.15 225.92

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy Consumption Survey. 

* The value at the 99% quantile is suppressed, because UES data on the public use file are suppressed at the 97.5th percentile.

4.2 Medians for Selected Housing 

and Household Characteristics

Even if the AHS model quantile estimates were to match the 

2009 RECS perfectly at the national level, we would want to know 

if average utility cost medians were similar across different 

types of housing units. For this reason, we compared electric 

and natural gas monthly costs for subsets of the total housing 

population. We calculated median electric and natural gas 

average monthly costs (in dollars) for the AHS models and 

compared them with the 2009 RECS, based on the following 

housing and household characteristics—

• Housing unit type (one unit attached, one unit detached, 

building with two or more units, and manufactured or 

mobile home).

• Tenure (owner or renter).

• Year built.

• Square feet.

• Total number of rooms.

• Bedrooms.

• Complete bathrooms.

• Householder characteristics (Black, White, Hispanic, elderly, 

and married).

• Education level of householder.

• Number of household members.

• Household income.

• Below poverty.

• Subsidized housing (owned by public housing authority or 

receives government subsidy).

Altogether, 75 categories exist across the aforementioned 

characteristics.13

Electric

Table 6 summarizes the number of electric medians for the 75 

categories produced by different models that are not significantly different from the RECS medians. The UES and the HHC 

models overestimate the medians of most characteristics if the 

difference is significant. The HHCR, HHCR2, and HHCFIN 

models underestimate the medians.

Table 6. Electric Medians for Selected Housing and 

Household Characteristics 

Model Categories Not Significantly Different 

From the RECS (out of 75) 

UES 7

HHC 14

HHCR 27

UESR 2

HHCR2 26

ONE BILL 31

HHCFIN 36

RECS = Residential Energy Consumption Survey.

Natural Gas

Table 7 summarizes the number of natural piped gas medians 

for 74 categories produced by different models that are not 

significantly different from the RECS medians. The 0 bedrooms 

characteristic is not evaluated, because the RECS contains only 

two units using natural gas with zero bedrooms. AHS natural 

gas models consistently overestimate the RECS medians.

13 See appendixes H and I for tables with complete information on average monthly cost medians by selected housing and household characteristics for electric and 

piped gas.
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 9

Table 7. Natural Gas Medians for Selected Housing and 

Household Characteristics 

Model Categories Not Significantly Different 

From the RECS (out of 74)

UES 0

HHC 0

HHCR 5

UESR 3

HHCR2 8

ONE BILL 19

HHCFIN 19

RECS = Residential Energy Consumption Survey.

4.3 Categorical Percent Distributions

The AHS national publication provides categorical distributions 

of the number of housing units paying different monthly costs 

for electricity and for gas (HUD and Census Bureau, 2009). Each 

table contains seven groups to show the distribution of housing 

units’ monthly utility expenses, grouped into the following 

categories: less than $25, $25 to $49, $50 to $74, $75 to $99, 

$100 to $149, $150 to $199, and $200 or more.

For our analysis, we converted the counts to percentages to 

account for differences in the number of housing unit estimates 

by RECS and those estimated by AHS. We then performed 

statistical tests on the differences in percentages between our 

AHS models and the RECS. Groups with nonsignificant differences are highlighted in tables 8 and 9. Overall, no one model 

for electric (table 8) outperforms the others in replicating the 

RECS distribution. Although all natural gas models (table 9) 

outperform the HHC model, none consistently outperforms the 

others in matching the RECS distribution. 

4.4 Regional Distribution 

Comparisons—Electric and 

Natural Gas

We performed significance testing at the regional level and 

identified how closely the AHS estimates matched the RECS 

distribution. Table 10 summarizes the electric comparisons, and 

table 11 summarizes the natural gas comparisons. Tables with 

detailed regional electric cost distributions are in appendix D. 

Tables with regional piped natural gas distributions are in 

appendix E.

Table 8. National Electric Cost Distributions (%) 

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

Less than $25 1.6 2.2 1.8 1.2 0.7 1.6 0.7 2.7

$25 to $49 9.4 5.1 11.6 8.7 11.9 10.7 11.3 13.4

$50 to $74 16.7 16.5 22.9 15.8 23.2 19.7 22.7 18.2

$75 to $99 17.6 21.2 21.5 17.2 21.6 17.0 21.7 17.4

$100 to $149 26.6 31.1 26.3 26.1 26.6 23.4 27.3 25.3

$150 to $199 14.3 15.4 11.0 14.6 10.9 11.2 11.2 12.3

$200 or more 13.8 8.6 4.9 16.4 5.0 16.4 5.2 10.7

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.

Note: Highlighted cells indicate nonsignificant differences between model and RECS.

Table 9. National Natural Gas Cost Distributions (%) 

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

Less than $25 6.3 3.2 6.4 5.9 5.9 9.6 7.2 13.1

$25 to $49 20.4 8.1 20.5 22.3 21.7 22.9 23.7 25.1

$50 to $74 24.3 20.5 26.0 25.2 27.0 22.9 26.4 25.0

$75 to $99 17.5 27.3 22.9 17.5 21.7 14.7 21.5 16.9

$100 to $149 19.0 31.1 18.6 17.6 17.8 14.9 16.9 14.2

$150 to $199 7.1 8.7 4.3 6.2 4.3 6.1 3.5 3.9

$200 or more 5.3 1.1 1.4 5.3 1.6 8.9 0.8 1.8

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.

Note: Highlighted cells indicate nonsignificant differences between model and RECS.
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

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Table 10. Regional Electric Categorical Percent Distributions 

Model Categories Not Significantly Different 

From the RECS (out of 28)

UES 8

HHC 7

HHCR 5

UESR 7

HHCR2 5

ONE BILL 4

HHCFIN 5

RECS = Residential Energy Consumption Survey.

Table 11. Regional Natural Gas Categorical Percent 

Distributions 

Model Categories Not Significantly Different 

From the RECS (out of 28)

UES 6

HHC 3

HHCR 8

UESR 4

HHCR2 7

ONE BILL 7

HHCFIN 14

RECS = Residential Energy Consumption Survey.

4.5 Propane Model

After developing consumption and cost models for electric and 

natural piped gas, we explored models to estimate propane 

and oil consumption and costs. The propane model includes 

the following variables: census division; (use of bottled gas for 

heat)*(heating degree days); use of bottled gas for water heating; (use of bottled gas for cooking)*(number of people in the 

household); (use of bottled gas for clothes drying)*(number of 

people in the household); household income; built date of unit; 

unit type; total number of rooms; and housing tenure. Table 12 

presents statistics from the original 2009-published AHS propane 

estimates and propane model estimates compared with the RECS. 

At the national level, average monthly cost medians for propane 

from the 2009 AHS and the model are not significantly different from the RECS. At the census division level, the 2009 AHS 

estimates for four divisions are significantly different from the 

RECS, whereas only one division estimate based on the model 

is statistically different from the RECS. Propane cost quantiles 

by census division are in appendix F.

Propane models are converted from consumption estimates to 

costs using prices estimated from the 2009 RECS. After discussions 

with EIA and comparisons of propane price data with RECS 

Table 12. Average Monthly Cost of Propane, National 

Quantiles ($) 

Quantile 2009 AHS Model 2009 RECS

1% 11 13.23 4.42

5% 17 18.88 9.92

10% 24 22.09 15.17

25% Q1 42 33.11 32.75

50% median 62 69.54 65.25

75% Q3 94 124.17 115.75

90% 142 172.88 167.50

95% 182 214.40 217.00

99% * 323.12 337.92

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

* The value at the 99% quantile is suppressed, because the 2009 AHS estimate 

on the public use file are suppressed at the 97.5th percentile.

price estimates, we decided to use actual price data for the 38 

states that currently have price data and EIA’s Annual Energy 

Outlook price data for the remaining states (EIA, 2009).14 Prices 

from both sources are close to the RECS-derived estimates.

4.6 Fuel Oil Model

Table 13 presents statistics on published 2009 AHS estimates 

and model estimates for fuel oil compared with the 2009 RECS. 

Fuel oil model variables include: use of fuel oil for heating fuel; 

use of fuel oil for water heating fuel; built date; total number of 

rooms; housing tenure; household income; and heating degree 

days. At the national level, medians for annual costs of fuel oil 

from published 2009 AHS estimates are significantly different 

from the RECS, but medians from the model are not. At the 

regional level (we did not examine fuel oil at the census division 

level because of small sample sizes for oil in some divisions), 

2009 AHS published estimates are statistically different from the 

RECS in two out of four regions. Fuel oil model medians are 

not statistically different from the RECS at the regional level.15

Table 13. Annual Cost of Fuel Oil ($), National Quantiles 

Quantile 2009 AHS Model 2009 RECS

1% 80 1.25 191

5% 350 2.71 367

10% 600 260.99 571

25% Q1 1,000 1,083.17 847

50% median 1,600 1,465.05 1,245

75% Q3 2,500 1,712.43 1,871

90% 3,500 1,917.54 2,641

95% 4,162 2,097.44 2,985

99% * 2,879.65 3,955

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

* The value at the 99% quantile is suppressed, because the 2009 AHS estimate 

on the public use file are suppressed at the 97.5th percentile.

14 See appendix O for information on obtaining price data.

15 See appendix G for fuel oil quantiles by region.
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Oil costs are converted from consumption estimates to costs 

using prices estimated from the 2009 RECS. After consulting 

with EIA, we concluded that adequate price data did not exist 

to convert consumption estimates into costs. Additionally, 

although the model does predict median oil costs well, it does 

not follow the RECS distribution in the tails of the distribution 

very well. This result is likely influenced by the small number 

of variables on fuel oil usage available in the AHS. For these 

reasons, we decided not to implement oil models in the 2015 

AHS.

4.7 Effect of Modeled Estimates on 

Housing Burden Estimates (an 

Element of Worst Case Needs)

We adopted the HHCFIN models for electric and natural 

gas and the propane model for bottled gas in the 2015 AHS. 

Housing costs are part of the housing burden calculation used 

in identifying worst case needs households; therefore, we 

wanted to assess the impact of adopting the models on housing 

burden estimates for very low-income renters (in particular, 

those spending more than 50 percent of household income on 

housing costs, who are defined as having “worst case needs”).

Tables 14 and 15 show percentages and counts of renteroccupied housing units with household incomes of not more 

than 50 percent of area median income that spend more than 

half of their household income on housing costs. We see declines 

Table 14. Households With Housing Costs of More Than 

50 Percent of Household Income, Very Low-Income 

Households, 2009 AHS (Weighted Percentages) 

Percentage Standard Error LCL95 UCL95

Old burden 41.5 0.6 40.2 42.7

New burden 39.8 0.7 38.5 41.2

AHS = American Housing Survey.

Table 15. Households With Housing Costs of More Than 

50 Percent of Household Income, Very Low-Income 

Households, 2009 AHS (Weighted N, 1,000s) 

Model Count Standard Error LCL95 UCL95

Original estimate 7,096 129 6,843 7,350

New estimate 6,821 129 6,567 7,074

AHS = American Housing Survey.

in the percentage and count of burdened households at the 

national level using the new models. This finding is based on 

a significance test on the differences between burden estimates 

calculated using published 2009 cost estimates (old burden) 

and those calculated using the HHCFIN models (new burden). 

Looking at the comparison of the published 2009 AHS UES 

estimates with the RECS, evidence suggests that the UES was 

overestimating electric and natural gas costs compared with the 

RECS. This overestimation partially explains higher housing 

burden in the old burden calculations.

5. Decisions for 2015 AHS 

Utility Data Collection 

Based on the research findings, we stopped collecting monthly 

bills for electric, piped natural gas, and bottled gas when those 

utilities are billed separately or when combined (that is, electric 

is billed with gas or gas is billed with electric; but not electric 

or gas billed with oil, other fuels, trash, or water). For 2015, we 

modeled average monthly costs for electric, natural piped gas, 

and bottled gas using final models developed from the RECS. 

We concluded that the fuel oil model does not adequately estimate 

fuel oil consumption and that no price data were adequate to 

convert consumption estimates into costs. For that reason, we 

continued to collect data on annual fuel oil costs in the 2015 AHS.

5.1 Combined Bills

In the 2015 AHS, when utility bills were combined with utilities 

that could not be modeled separately (such as fuel oil, other 

fuels, trash, water, and sewage), we prioritized the bills and 

recorded the cost in the bill of highest priority (highest to lowest 

priority: electricity, gas, oil, other fuel, trash, water). For combined bills that could not be separated and were combined with 

electric or gas, we asked respondents to provide the amounts of 

the most recent electric and gas bills. If they did not have those 

bills, we asked them to estimate the average monthly costs of 

electricity and gas. If respondents provided the most recent bill, 

the cost was estimated with the appropriate individual monthly 

model from the UESR model adjusted for inflation (see table 1).16

If respondents provided average monthly cost estimates, we put 

the estimates through the self-reported average monthly costs 

16 For model assignment for the last bill models, we needed to determine the billing month. For cases billed monthly, we asked respondents for the month of the 

bill. We used the month respondents provided. If respondents did not provide a month, we selected the month prior to their interview. If the billing cycle is every 

2 months, we divided the amount by 2 and assigned the month prior to respondent interview. A 1-month difference does not affect the estimates much. Cases 

reporting multiple payments per month were run through the HHCFIN model for the bill with the highest priority, because we could not tell how many times they 

pay per month and thus cannot calculate the monthly payment.
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 12

model, or model 24 in table 1, which uses only the average 

monthly costs provided by respondents as the estimate of costs. 

If a respondent provided no bill or average monthly estimate, 

we put the case through the HHCFIN model of the utility 

with the highest priority. These procedures are in line with the 

procedures used in the 2013 AHS for handling these types of 

bills. For bills not combined with electric or gas, the utility 

bill’s priority is determined and the cost is recorded with the 

utility of highest priority.

5.2 Post-Model Imputations

Average monthly costs for electric, piped gas, and bottled gas 

are calculated from either HHCFIN models, cost models based 

on the last bill, or self-reported average monthly costs. For 

the other utilities, (fuel oil, other fuels, garbage and trash, and 

water and sewage), we did not propose any changes to the 

pre-2015 hot deck imputation methods. 

6. Conclusions

The HHCFIN electric models underestimate the median electric 

average monthly cost at the national level, but the median value 

using the HHCFIN models is closer to the RECS than the UES 

value.17 Using a natural logarithm transformation on consumption helped us to capture a skewed consumption distribution. 

Adding household income and degree day normals in the 

models helped us more closely mirror the RECS electric 

distribution. Like the UES, the HHCFIN model overestimates 

the median natural gas average monthly cost, but the HHCFIN 

comes closer to the RECS estimate. We developed propane 

and fuel oil models. Both models yield medians not statistically 

different than the RECS. We did not adopt the fuel oil models, 

because we lacked adequate residential fuel oil price data for 

the whole country, and the model did not adequately predict 

costs across the distribution. We adopted the HHCFIN models 

for electric and natural gas and the propane model and did 

not implement the fuel oil model. For 2017, recently released 

RECS data for 2015 will be used to update model parameters, 

and we are exploring adding a random contribution from the 

error term to the utilities consumption estimates.18

7. References

Carter, George. 2010. “The Use of Utility Records as an 

Information Retrieval Aid in the American Housing Survey.” 

Proceedings of the American Statistical Association, Survey 

Research Methods Section. http://ww2.amstat.org/sections/

srms/Proceedings/y2013/files/309486_82770.pdf.

Gorsak, Mark, and Dennis Schwanz. 2004. “Synchronizing 

Survey Edit and Imputation Systems: A Look at the American 

Housing Survey’s Utility System.” Proceedings of the American 

Statistical Association, Survey Research Methods Section. 

http://ww2.amstat.org/sections/srms/Proceedings/y2004/files/

Jsm2004-000272.pdf.

Little, Roderick J.A., and Donald B. Rubin. 2002. Statistical 

Analysis With Missing Data, 2nd ed. New York: John Wiley.

Myers, Raymond H. 1990. Classical and Modern Regression With 

Applications, 2nd ed. Pacific Grove, CA: Duxbury Press.

National Research Council (NRC). 2012. Effective Tracking of 

Building Energy Use: Improving the Commercial Buildings and 

Residential Energy Consumption Survey. Washington, DC: The 

National Academies Press.

Raghunathan, Trivellore. 2015. Missing Data Analysis in Practice. 

Ann Arbor, MI: CRC Press.

Steffen, Barry L., George R. Carter, Marge Martin, Danilo 

Pelletiere, David A. Vandenbroucke, and Yunn-Gann David 

Yao. 2015. Worst Case Needs: 2015 Report to Congress. Report 

prepared for the U.S. Department of Housing and Urban 

Development, Office of Policy Development and Research. 

Washington, DC: U.S. Government Publishing Office. 

U.S. Department of Housing and Urban Development (HUD) 

and U.S. Census Bureau. 2009. American Housing Survey for the 

United States: 2009, Current Housing Reports. Washington, DC: 

U.S. Government Printing Office.

U.S. Energy Information Administration (EIA). 2009. Annual 

Energy Outlook 2009 with Projections to 2030. Washington, DC: 

National Energy Information Center.

17 Aggregate electric costs with the UES were higher than RECS aggregate electric costs, whereas aggregate electric costs using the HHCFIN models were lower but 

closer to the RECS estimates. Aggregate natural gas costs using HHCFIN models were also closer to the aggregate RECS natural gas cost estimates than the aggregate 

UES natural gas estimates to the RECS.

18 A possible way to add a random contribution to the error term is to: (1) produce a regression-based prediction interval variance term, which incorporates the uncertainty in the parameter estimates and the residuals; and (2) using a random number generator, assuming a normal distribution with mean = y-hat and the variance 

previously described, produce the imputed consumption value.
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 13

Appendix A. Variables in Models

Table A1. Variables in Models

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

Input data 2001 RECS 

(inflated to 

2009)

2001 RECS 

(inflated to 

2009)

2009 RECS 2009 

RECS

2009 RECS 2009 RECS 2009 RECS

Response 

variable

Cost Cost ln (consumption+1) Cost ln (consumption+1) ln (consumption+1) ln (consumption+1)

Independent 

variables

Monthly 

billing data 

and some 

housing and 

household 

characteristics 

(similar to 

HHC model) 

for units with 

less billing 

data

Electric heat Heating degree 

days (if electric 

heat)

Same as 

UES

Same as HHCR 

models, used 

degree days 

from 2009 AHS 

file, household 

income, weighted, 

some collapsed 

geographies 

compared with 

HHCR

Same as HHCR2, 

but run only on 

cases with April 

bills and including 

the April bill as a 

predictor.

Same as HHCR2, 

but using heating 

degree days and 

cooling degree 

days from 30-year 

(1980–2010) climate 

normals from NOAA

Gas heat Heating degree 

days (if gas heat)

Cooling degree 

days (if central air)

Cooling degree 

days (if wall air unit)

Electric hot 

water

Electric hot water

Gas hot 

water

Gas hot water

Year built Year built

Type of 

housing unit 

(single = 1, 

multi = 2, 

mobile = 3)

Single unit 

(indicator)

Multiunit (indicator)

Mobile home 

(indicator)

Total rooms Total rooms

Total 

bathrooms

Total bathrooms

Total major 

appliances

Total major 

appliances

Number of 

household 

members

Number of 

household 

members

AHS = American Housing Survey. ln = natural log. NOAA = National Oceanic and Atmospheric Administration. RECS = Residential Energy Consumption Survey.
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 14

Table A2. Electric Final Models—Variable Recoding (1 of 2)

Model Estimating Universe RECS Input 

Variable Definition Using AHS Variables Levels Notes

Electric-only Average monthly 

costs of electricity

status in (‘1’) and 

uselect = ‘1’ and 

buye not (‘1’,’2’,’3’) 

and usegas ne ‘1’

RDCOLL 

(reportable 

domain)

FIPSTATE coded into Reportable 

domain categories

14 ‘BY’ VARIABLE—

Started with 27 

levels, but collapsed 

to 14 to bring sample 

size to 100

EHDEMAND (hfuel=‘1’)*HEATDAY HEATDAY is the 

heating degree days 

variable.

EH20 (wfuel=‘1’)

YRBUILT BUILT 7 RECS 

YEARMADERANGE 

collapsed to link to 

AHS

NU1 nunit2 in (‘1’,’2’) 

NU2 nunit2 = ‘3’

NU3 nunit2 = ‘4’

TOTROOMS min (bedrms + kitch + living + dining 

+ famrm + recrm +dens + othfn,25)

RECS ranges are 

capped at 25

BATHRMS min (baths + (0.5*halfb),7) RECS ranges went to 

9 each, we capped 

at 7

MAJAPPL (refr=‘1’) + (2*(cook+’1’)) + 

(oven=‘1’) + (burner=‘1’) + (dish=‘1’) 

+ (wash=‘1’) + (dry=‘1’)

PEOPLE min(curper,12) RECS ranges went to 

15, we capped them 

at 12

CENAIR (airsys=‘1’) * COOLDAY COOLDAY is the 

cooling degree days 

variable

WALL (airsys=‘2’ and numair ge 1) * 

COOLDAY

COOLDAY is the 

cooling degree days 

variable

MONEY_R ZINC2 RECS ranges 

collapsed to $10,000 

groups

Electric-gas 

mixed

Average monthly 

cost of electricity

status in (‘1’) and 

uselect=‘1’ and buye 

not in (‘1’,’2’,’3’) and 

usegas = ‘1’ 

RDCOLL 

(Reportable 

Domain)

FIPSTATE coded into Reportable 

Domain categories

17 ‘BY’ VARIABLE—

started with 27, 

collapsed to bring 

sample size 100

EHDEMAND (hfuel=‘1’) * HEATDAY HEATDAY is heating 

degree days

GHDEMAND (hfuel=‘2’ and gaspip=‘1’)*HEATDAY HEATDAY is heating 

degree days

EH20 (wfuel=‘1’)

GH20 (wfuel=‘2’ and gaspip=‘1’)

YRBUILT BUILT 7 RECS 

YEARMADERANGE 

collapsed to link to 

AHS

NU1 nunit2 in (‘1’,’2’)
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 15

Table A2. Electric Final Models—Variable Recoding (2 of 2)

Model Estimating Universe RECS Input 

Variable Definition Using AHS Variables Levels Notes

NU2 nunit2=‘3’

NU3 nunit2=‘4’

TOTROOMS min (bedrms + kitch + living + dining 

+ famrm + recrm + dens + othfn,25)

RECS ranges are 

capped at 25

BATHRMS min (baths+ (0.5*halfb),7) RECS ranges, I 

capped at 7

MAJAPPL (refr=‘1’) + (2*(cook+’1’)) + 

(oven=‘1’) + (burner=‘1’) + (dish=‘1’) 

+ (wash=‘1’) + (dry=‘1’)

PEOPLE min(curper,12) RECS ranges went to 

15, I capped at 12

CENAIR (airsys=‘1’) * coolday COOLDAY is cooling 

degree days

WALL (airsys=‘2’ and numair ge 1) * 

coolday

COOLDAY is cooling 

degree days

MONEY_R ZINC2 12 RECS ranges 

collapsed to $10,000 

groups

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.

Table A3. Gas Final Models—Variable Recoding (1 of 2) 

Model Estimating Universe Input Definition Using Levels Notes

Natural gas Average monthly 

cost of piped 

natural gas

status in (‘1’) and usegas=‘1’ and 

buyg not in (‘1’,’2’,’3’) and gaspip 

= ‘1’

RDCOLL FIPSTATE coded 

into reportable domain 

categories

17 ‘BY’ VARIABLE—Started 

with 27 levels, but 

collapsed to 14 to bring 

sample size to 100

EHDEMAND (hfuel = ‘1’) * HEATDAY HEATDAY is heating 

degree days

EH2O (wfuel = ‘1’)

NOSTEAM (hequip ne 2) * (hfuel = 

‘2’ and gaspip = ‘1’)

/*removed degree days 

because distributions 

were adversely affected*/

STEAM (hequip = 2) * (hfuel = 

‘2’ and gaspip = ‘1’)

/*removed degree days 

because distributions 

were adversely affected*/

GH2O (wfuel = ‘2’ and gaspip 

= ‘1’)

YRBUILT BUILT 7 RECS YEARMADERANGE collapsed to 

link to AHS

NU1 nunit2 in (‘1’,’2’)

NU2 nunit2 = ‘3’

NU3 nunit2 = ‘4’

TOTROOMS min (bedrms + kitch + 

living + dining + famrm 

+ recrm + dens + 

othfn,25)

RECS ranges are 

capped at 25
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 16

Table A3. Gas Final Models—Variable Recoding (2 of 2)

Model Estimating Universe Input Definition Using Levels Notes

BATHRMS min (baths + (0.5 * 

halfb),7)

RECS ranges went to 9 

each, I capped at 7

MAJAPPL (refr = ‘1’) + (2 * (cook 

= ‘1’)) + (oven = ‘1’) + 

(burner = ‘1’) + (dish = 

‘1’) + (wash = ‘1’) + (dry 

= ‘1’)

PEOPLE min (curper,12) RECS ranges went to 

15, I capped at 12

CENAIR (airsys = ‘1’) * coolday COOLDAY is cooling 

degree days.

WALL (airsys = ‘2’ and numair 

ge 1) * coolday

COOLDAY is cooling 

degree days.

MONEY_R ZINC2 12 RECS ranges collapsed 

to $10,000 groups

Propane Average monthly 

cost of bottled 

natural gas

status in (‘1’) and usegas=‘1’ and 

(buyg not in (‘1’,’2’,’3’) or (buyg 

in (‘1’,’2’,’3’) and jbuyg = ‘2’)) and 

gaspip=‘2’

DIV DIV 9 ‘BY’ VARIABLE—smallest sample size 56—

opted to use instead of 

REGION

GASHEAT (hfuel = ‘2’ and gaspip 

= ‘2’) * heatday

HEATDAY is heating 

degree days

GASH2O (wfuel = ‘2’ and gaspip 

= ‘2’)

GCOOK (cfuel = ‘2’ and gaspip 

= ‘2’) * min (curper,12)

RECS ranges went to 

15, I capped at 12

GDRY (dfuel = ‘2’ and gaspip 

= ‘2’) * min(curper,12)

RECS ranges went to 

15, I capped at 12

MONEY_R ZINC2 12 RECS ranges collapsed 

to $10,000 groups

YRBUILT BUILT 7 RECS YEARMADERANGE collapsed to 

link to AHS

NU1 nunit2 in (‘1’,’2’)

NU2 nunit2 = ‘3’

NU3 nunit2 = ‘4’

TOTROOMS Min (bedrms + kitch + 

living + dining + famrm 

+ recrm + dens + 

othfn,25)

OWNER (tenure=‘1’)

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 17

Appendix B. Regional Electric Quantiles

Table B1. Average Monthly Cost of Electricity ($), Northeast Quantiles 

Quantile

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

1% 24 44 0.06 26 32.33 25.55 33.15 20

5% 26 55 40.76 41 41.62 37.52 42.83 34

10% 45 57 46.43 50 46.76 43.91 48.57 42

25% Q1 63 77 61.13 70 61.12 58.50 63.26 59

50% median 94 97 87.70 101 87.06 85.65 89.58 93

75% Q3 144 119 122.50 155 124.03 133.74 127.22 143

90% 218 144 161.39 231 166.35 210.69 171.01 204

95% 276 163 194.34 300 197.34 301.57 201.35 243

99% * 207 273.51 484 273.72 728.51 277.73 337

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy Consumption Survey. 

* The value at the 99% quantile is suppressed, because UES data on the public use file are suppressed at the 97.5th percentile.

Table B2. Average Monthly Cost of Electricity ($), Midwest Quantiles 

Quantile

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

1% 20 4 0.10 24 22.73 0.02 23.03 20

5% 34 29 27.56 35 32.50 33.28 32.78 31

10% 44 44 37.99 45 40.71 39.56 41.10 38

25% Q1 64 62 53.73 64 54.98 50.78 55.31 57

50% median 93 83 72.71 92 73.68 68.96 73.94 84

75% Q3 134 112 98.64 132 99.71 108.18 100.08 115

90% 187 145 133.81 189 133.38 174.74 133.38 156

95% 230 165 165.47 236 162.01 269.21 162.03 190

99% * 201 229.08 350 219.36 666.30 220.15 260

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy Consumption Survey. 

* The value at the 99% quantile is suppressed, because UES data on the public use file are suppressed at the 97.5th percentile.

Table B3. Average Monthly Cost of Electricity ($), South Quantiles

Quantile

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

1% 38 42 27.34 35 35.94 15.75 36.45 29

5% 57 70 52.72 57 55.86 48.76 56.47 48

10% 70 86 65.51 71 67.11 62.45 67.69 59

25% Q1 98 115 88.02 100 87.78 88.78 88.54 85

50% median 136 151 119.95 141 118.58 129.67 119.30 124

75% Q3 189 192 155.06 197 154.85 200.41 155.48 174

90% 248 228 194.73 262 195.77 316.83 196.12 229

95% 298 250 223.17 315 224.98 446.33 225.97 271

99% * 303 286.57 460 289.28 1,491.22 291.52 374

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy Consumption Survey. 

* The value at the 99% quantile is suppressed, because UES data on the public use file are suppressed at the 97.5th percentile.
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Table B4. Average Monthly Cost of Electricity ($), West Quantiles

Quantile

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

1% 14 11 0.05 16 22.84 23.69 23.07 11

5% 26 24 31.96 29 32.58 40.67 32.77 21

10% 36 41 38.33 38 38.50 50.39 39.19 27

25% Q1 55 66 51.59 58 51.74 73.38 52.50 43

50% median 86 85 70.79 90 71.29 107.97 73.38 72

75% Q3 134 110 95.41 146 97.06 157.99 101.01 118

90% 199 135 124.34 222 126.82 252.08 131.52 177

95% 253 150 146.75 291 150.71 373.72 155.31 220

99% * 192 210.18 500 213.54 994.11 218.42 348

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy Consumption Survey. 

* The value at the 99% quantile is suppressed, because UES data on the public use file are suppressed at the 97.5th percentile.
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Appendix C. Regional Natural Gas Quantiles

Table C1. Average Monthly Cost of Natural Gas ($), Northeast Quantiles 

Quantile

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

1% 10 – 31 5.50 10 5.85 1.22 5.69 4

5% 22 3 7.37 22 7.58 11.73 7.34 8

10% 36 42 22.33 34 17.63 17.44 13.53 17

25% Q1 68 103 79.41 75 78.19 46.15 74.19 64

50% median 106 135 117.74 113 120.17 84.40 107.59 99

75% Q3 153 162 151.19 149 154.38 146.97 137.47 141

90% 212 180 185.25 207 191.00 271.58 166.47 186

95% 263 198 209.98 269 220.89 425.82 185.49 218

99% * 238 283.91 428 298.88 1,152.18 249.78 311

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy Consumption Survey. 

* The value at the 99% quantile is suppressed, because UES data on the public use file are suppressed at the 97.5th percentile.

Table C2. Average Monthly Cost of Natural Gas ($), Midwest Quantiles

Quantile

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

1% 19 34 13.65 21 29.24 0.00 27.84 5

5% 35 59 43.31 39 42.76 11.59 42.29 31

10% 43 69 53.08 49 51.52 32.10 51.23 41

25% Q1 60 85 68.25 66 67.16 55.36 66.12 56

50% median 89 103 83.72 85 82.31 74.84 81.47 75

75% Q3 126 124 100.90 116 99.67 106.08 98.48 99

90% 173 146 118.02 164 116.71 152.49 116.18 127

95% 212 160 130.04 205 128.42 195.97 130.94 149

99% * 188 164.00 326 160.86 433.94 173.59 196

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy Consumption Survey. 

* The value at the 99% quantile is suppressed, because UES data on the public use file are suppressed at the 97.5th percentile.

Table C3. Average Monthly Cost of Natural Gas ($), South Quantiles

Quantile

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

1% 11 28 11.86 14 21.91 0.35 19.27 3

5% 23 45 25.73 25 30.83 23.81 28.94 11

10% 30 56 33.75 32 36.87 30.82 35.81 22

25% Q1 46 70 46.55 46 47.95 45.04 47.42 37

50% median 70 86 63.89 64 62.84 74.85 60.57 54

75% Q3 101 106 86.32 94 86.25 123.84 82.30 80

90% 145 126 107.91 144 111.82 215.03 103.02 105

95% 189 138 121.10 189 126.40 319.91 116.66 120

99% * 167 155.13 305 153.45 839.65 153.07 168

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy Consumption Survey. 

* The value at the 99% quantile is suppressed, because UES data on the public use file are suppressed at the 97.5th percentile.
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Table C4. Average Monthly Cost of Natural Gas ($), West Quantiles

Quantile

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

1% 8 7 16.12 10 16.11 0.21 15.63 4

5% 16 23 20.32 18 20.16 14.51 19.16 9

10% 23 32 23.42 23 23.50 21.60 22.02 15

25% Q1 36 50 37.17 35 37.28 32.44 34.44 25

50% median 54 68 48.58 48 48.22 45.08 44.06 40

75% Q3 73 83 66.13 66 64.25 68.60 58.09 58

90% 104 102 87.14 100 84.59 145.78 81.38 82

95% 136 117 103.69 131 95.74 226.31 99.42 101

99% * 160 141.10 237 132.40 621.03 147.94 160

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy Consumption Survey. 

* The value at the 99% quantile is suppressed, because UES data on the public use file are suppressed at the 97.5th percentile.
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Appendix D. Regional Average Monthly Cost for Electricity 

Distributions

Table D1. Regional Electric Cost Distributions (%), Northeast 

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

Less than $25 1.0 — 1.1 0.9 — 1.0 — 2.1

$25 to $49 12.3 3.7 12.1 8.7 12.8 14.2 11.0 14.5

$50 to $74 21.7 20.5 24.4 18.8 25.6 25.3 25.1 20.4

$75 to $99 18.9 28.3 22.4 19.3 21.4 19.4 22.1 17.4

$100 to $149 22.8 38.9 26.7 25.0 25.8 20.6 26.0 23.0

$150 to $199 10.8 7.1 8.9 12.0 9.6 8.4 10.6 11.7

$200 or more 12.5 1.4 4.5 15.3 4.7 11.2 5.2 10.8

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.

— Less than 5 cases in cell. 

Table D2. Regional Electric Cost Distributions (%), Midwest

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

Less than $25 2.2 4.0 3.7 1.0 1.6 2.6 1.5 2.1

$25 to $49 11.6 9.2 16.2 12.4 17.5 20.5 17.1 16.8

$50 to $74 20.5 28.4 32.2 21.4 32.0 32.1 32.1 22.6

$75 to $99 20.7 24.6 23.4 22.1 23.8 16.4 23.9 22.7

$100 to $149 26.6 25.1 17.2 24.3 18.4 14.7 18.6 24.2

$150 to $199 10.7 7.6 5.2 10.2 5.0 6.2 4.9 7.6

$200 or more 7.7 1.1 2.1 8.5 1.8 7.6 1.9 3.9

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.

Table D3. Regional Electric Cost Distributions (%), South

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

Less than $25 0.2 0.5 0.9 0.4 0.2 1.4 0.2 0.6

$25 to $49 2.5 1.0 3.5 2.7 3.0 3.8 2.8 5.0

$50 to $74 9.5 4.5 11.1 8,7 11.7 10.8 11.4 12.3

$75 to $99 14.8 10.1 18.9 13.0 19.6 16.0 19.6 15.8

$100 to $149 31.9 32.6 36.9 30.4 37.4 27.6 37.4 31.4

$150 to $199 20.4 30.3 19.7 20.9 19.2 15.3 19.2 17.9

$200 or more 20.7 21.1 9.1 24.0 9.2 25.1 9.4 16.9

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.

Table D4. Regional Electric Cost Distributions (%), West

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

Less than $25 4.0 5.0 2.0 3.2 1.3 1.2 1.3 7.5

$25 to $49 16.7 9.0 20.4 15.2 20.9 8.4 20.0 23.2

$50 to $74 21.3 21.7 32.3 19.9 31.7 16.2 30.2 21.6

$75 to $99 18.1 30.9 23.5 17.5 23.0 17.3 22.4 14.7

$100 to $149 20.4 28.2 16.9 21.2 17.7 28.8 20.1 17.9

$150 to $199 10.3 4.4 3.6 10.5 3.9 12.3 4.5 8.2

$200 or more 9.3 0.7 1.2 12.5 1.4 15.9 1.5 6.8

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.
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Appendix E. Regional Average Monthly Cost for Piped Gas 

Distributions

Table E1. Regional Natural Gas Cost Distributions (%), Northeast 

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

Less than $25 5.9 7.9 10.3 5.9 11.9 12.8 14.0 12.4

$25 to $49 9.4 3.1 5.3 10.2 6.3 14.3 5.1 7.0

$50 to $74 12.3 4.0 6.2 8.8 5.0 16.6 6.1 13.3

$75 to $99 16.3 8.7 14.7 14.7 12.7 14.1 18.1 17.7

$100 to $149 29.8 40.5 37.3 35.7 36.0 18.0 39.4 28.9

$150 to $199 14.2 31.0 19.0 12.8 19.9 8.3 14.0 13.2

$200 or more 11.8 4.7 7.0 11.9 8.3 15.9 3.4 7.6

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.

Table E2. Regional Natural Gas Cost Distributions (%), Midwest 

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

Less than $25 1.7 0.5 1.3 1.4 0.5 8.2 0.8 3.4

$25 to $49 13.4 2.5 6.7 8.8 8.1 10.5 8.0 14.1

$50 to $74 22.4 10.3 26.3 26.1 27.9 30.8 29.5 31.7

$75 to $99 20.5 31.2 39.0 29.2 38.3 21.9 37.9 26.4

$100 to $149 26.4 47.0 24.8 21.2 23.4 17.7 21.4 19.6

$150 to $199 9.5 8.0 1.6 7.6 1.5 6.3 2.0 3.9

$200 or more 6.1 0.5 0.3 5.7 0.2 4.7 0.5 0.9

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.

Table E3. Regional Natural Gas Cost Distributions (%), South 

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

Less than $25 5.9 0.8 4.1 4.5 1.7 5.4 2.5 12.5

$25 to $49 22.7 5.7 23.8 25.5 25.3 24.0 25.6 30.9

$50 to $74 25.7 24.7 34.6 31.1 36.8 20.4 39.4 28.0

$75 to $99 20.4 35.8 22.3 16.4 20.0 14.6 20.6 16.6

$100 to $149 16.2 30.4 13.8 13.5 14.9 17.4 10.6 10.2

$150 to $199 5.2 2.4 1.1 4.8 1.1 7.1 1.0 1.3

$200 or more 4.0 0.2 0.2 4.1 0.2 11.2 0.2 0.6

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.

Table E4. Regional Natural Gas Cost Distributions (%), West

2009 AHS

2009 RECS

UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN

Less than $25 11.7 5.1 11.3 11.7 11.4 13.8 13.6 23.8

$25 to $49 32.1 19.1 40.5 40.4 41.5 43.6 49.3 41.3

$50 to $74 32.3 37.2 30.0 29.0 30.8 20.6 24.2 22.4

$75 to $99 12.6 27.2 12.4 8.7 12.2 6.3 8.0 7.1

$100 to $149 7.5 10.1 5.0 6.5 3.6 6.1 4.1 4.1

$150 to $199 2.1 1.2 0.6 1.9 0.4 3.0 0.7 0.9

$200 or more 1.6 0.2 0.2 1.7 0.1 6.6 0.2 0.4

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.
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Appendix F. Census Division Propane Quantiles

Table F1. Average Monthly Cost of Propane ($), 

New England Quantiles

Quantile 2009 AHS Model 2009 RECS

1% 9 — 6.25

5% 18 — 8.50

10% — 20.31 13.00

25% Q1 28 25.72 21.25

50% median 48 34.31 39.25

75% Q3 59 104.31 107.42

90% 79 205.43 182.67

95% 79 250.03 201.17

99% * — 243.58

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

— Less than 10 cases. * The value at the 99% quantile is suppressed, because 

the 2009 AHS estimate on the public use file are suppressed at the 97.5th 

percentile.

Table F2. Average Monthly Cost of Propane ($), 

Middle Atlantic Quantiles

Quantile 2009 AHS Model 2009 RECS

1% 17 — 4.92

5% 21 15.19 8.83

10% 31 16.76 11.75

25% Q1 43 21.09 27.25

50% median 72 28.04 67.75

75% Q3 128 132.32 135.00

90% 156 199.22 227.33

95% 182 229.21 280.83

99% * 295.95 398.25

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

— Less than 10 cases. * The value at the 99% quantile is suppressed, because 

the 2009 AHS estimate on the public use file are suppressed at the 97.5th 

percentile.

Table F3. Average Monthly Cost of Propane ($), 

East North Central Quantiles

Quantile 2009 AHS Model 2009 RECS

1% 19 — 4.33

5% 42 53.91 8.50

10% 48 58.57 15.67

25% Q1 64 96.01 69.17

50% median 91 123.81 115.33

75% Q3 132 147.31 154.17

90% 440 194.87 217.00

95% 440 223.60 271.17

99% * 308.22 396.58

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

— Less than 10 cases. * The value at the 99% quantile is suppressed, because 

the 2009 AHS estimate on the public use file are suppressed at the 97.5th 

percentile.

Table F4. Average Monthly Cost of Propane ($), 

West North Central Quantiles

Quantile 2009 AHS Model 2009 RECS

1% 20 — 5.00

5% — 41.99 28.25

10% 28 52.44 50.08

25% Q1 51 73.46 68.33

50% median 62 104.35 102.33

75% Q3 96 134.40 132.67

90% 185 164.15 189.00

95% 185 179.86 244.83

99% * 212.13 448.17

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

— Less than 10 cases. * The value at the 99% quantile is suppressed, because 

the 2009 AHS estimate on the public use file are suppressed at the 97.5th 

percentile.

Table F5. Average Monthly Cost of Propane ($), 

South Atlantic Quantiles

Quantile 2009 AHS Model 2009 RECS

1% 14 — 6.17

5% 17 17.16 10.75

10% 34 19.95 14.08

25% Q1 43 26.49 25.67

50% median 63 41.25 40.42

75% Q3 103 83.82 74.75

90% 122 141.67 121.00

95% 196 180.15 155.92

99% * 323.12 337.17

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

— Less than 10 cases. * The value at the 99% quantile is suppressed, because 

the 2009 AHS estimate on the public use file are suppressed at the 97.5th 

percentile.

Table F6. Average Monthly Cost of Propane ($), 

East South Central Quantiles

Quantile 2009 AHS Model 2009 RECS

1% 9 — 2.25

5% 13 33.64 8.58

10% 14 33.64 28.83

25% Q1 31 47.66 38.50

50% median 60 63.30 53.67

75% Q3 88 76.41 83.33

90% 133 96.62 133.92

95% 157 117.43 151.83

99% * 156.33 191.50

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

— Less than 10 cases. * The value at the 99% quantile is suppressed, because 

the 2009 AHS estimate on the public use file are suppressed at the 97.5th 

percentile.
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Table F7. Average Monthly Cost of Propane ($), 

West South Central Quantiles

Quantile 2009 AHS Model 2009 RECS

1% 16 — 8.17

5% 18 20.21 9.58

10% 23 21.03 13.50

25% Q1 31 25.63 22.92

50% median 49 48.51 53.42

75% Q3 73 75.67 87.00

90% 96 118.60 136.58

95% — 139.22 172.00

99% * 162.55 273.33

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

— Less than 10 cases. * The value at the 99% quantile is suppressed, because 

the 2009 AHS estimate on the public use file are suppressed at the 97.5th 

percentile.

Table F8. Average Monthly Cost of Propane ($), 

Mountain Quantiles

Quantile 2009 AHS Model 2009 RECS

1% 11 — 9.917

5% — 20.53 9.917

10% 24 — 15.167

25% Q1 32 39.92 35.250

50% median 46 112.07 88.917

75% Q3 60 191.65 121.917

90% 66 279.57 168.167

95% — 396.09 242.500

99% * 699.33 303.833

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

— Less than 10 cases. * The value at the 99% quantile is suppressed, because 

the 2009 AHS estimate on the public use file are suppressed at the 97.5th 

percentile.

Table F7. Average Monthly Cost of Propane ($), 

Pacific Quantiles

Quantile 2009 AHS Model 2009 RECS

1% 8 — 1.583

5% — — 13.833

10% — — 14.917

25% Q1 36 40.07 33.333

50% median 60 78.11 66.917

75% Q3 89 118.51 100.917

90% 128 164.92 137.500

95% — — 150.917

99% * — 198.917

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

— Less than 10 cases. * The value at the 99% quantile is suppressed, because 

the 2009 AHS estimate on the public use file are suppressed at the 97.5th 

percentile.
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Appendix G. Regional Oil Quantiles

Table G1. Annual Cost of Fuel Oil ($), Northeast 

Quantiles

Quantile 2009 AHS Model 2009 RECS

1% 175.00 1.09 256.00

5% 500.00 2.16 381.00

10% 700.00 967.60 651.00

25% Q1 1,199.00 1,302.36 959.00

50% median 1,876.00 1,534.51 1,364.00

75% Q3 2,765.00 1,740.68 2,002.00

90% 3,800.00 1,891.55 2,783.00

95% 4,566.00 2,029.07 3,241.00

99% * 2,373.91 4,085.00

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

* The value at the 99% quantile is suppressed, because the 2009 AHS estimate 

on the public use file are suppressed at the 97.5th percentile.

Table G2. Annual Cost of Fuel Oil ($), Midwest 

Quantiles

Quantile 2009 AHS Model 2009 RECS

1% 50.00 — 262.00

5% 65.00 — 319.00

10% 220.00 112.32 618.00

25% Q1 650.00 317.33 730.00

50% median 1,200.00 648.70 905.00

75% Q3 1,663.00 1,244.96 1,205.00

90% 2,500.00 2,034.83 1,674.00

95% 3,000.00 2,939.96 1,811.00

99% * 5,889.82 2,760.00

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

— Less than 10 cases. * The value at the 99% quantile is suppressed, because 

the 2009 AHS estimate on the public use file are suppressed at the 97.5th 

percentile.

Table G3. Annual Cost of Fuel Oil ($), South Quantiles

Quantile 2009 AHS Model 2009 RECS

1% 96.00 — 133.00

5% 225.00 108.44 268.00

10% 400.00 249.83 392.00

25% Q1 770.00 445.16 636.00

50% median 1,200.00 973.87 841.00

75% Q3 1,700.00 1,495.01 1,473.00

90% 2,400.00 2,109.86 1,822.00

95% 3,360.00 — 2,323.00

99% * 3,168.23 2,907.00

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

— Less than 10 cases. * The value at the 99% quantile is suppressed, because 

the 2009 AHS estimate on the public use file are suppressed at the 97.5th 

percentile.

Table G4. Annual Cost of Fuel Oil ($), West Quantiles

Quantile 2009 AHS Model 2009 RECS

1% 60.00 — 298.00

5% 190.00 — 298.00

10% 400.00 — 544.00

25% Q1 800.00 — 708.00

50% median 1,200.00 621.30 859.00

75% Q3 1,637.00 1,166.06 1,087.00

90% 2,040.00 — 1,236.00

95% 3,300.00 — 1,258.00

99% * — 1,533.00

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy 

Consumption Survey. 

— Less than 10 cases. * The value at the 99% quantile is suppressed, because 

the 2009 AHS estimate on the public use file are suppressed at the 97.5th 

percentile.
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Appendix H. Medians for Average Monthly Cost of 

Electricity by Selected Housing and 

Household Characteristics

Table H1. Electric Medians by Selected Housing and Household Characteristics (1 of 2)

Median Monthly Electric Costs UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN 2009 RECS

Housing unit type

One unit building detached 120 112 100 124 101 114 103 113

One unit building attached 98 104 87 101 86 95 90 82

Building with two or more apartments 71 73 60 74 59 67 60 59

Manufactured/mobile home 122 133 106 128 110 111 109 110

Tenure

Owned 118 113 99 122 100 111 102 110

Rented 84 84 68 87 68 76 68 70

Year built

2005 to 2009 124 135 115 129 114 127 115 112

2000 to 2004 125 129 110 129 110 124 111 118

1995 to 1999 124 127 106 127 107 116 108 117

1990 to 1994 122 123 103 125 104 119 106 113

1984 to 1989 114 119 99 117 101 109 103 106

1980 to 1984 115 120 102 120 103 115 104 98

1975 to 1979 113 110 94 116 94 107 96 101

1970 to 1974 106 105 88 109 89 97 90 89

1960 to 1969 102 101 85 106 85 97 86 87

1950 to 1959 98 92 80 101 80 93 81 84

1940 to 1949 96 89 75 100 76 88 77 82

1930 to 1939 93 88 72 97 72 84 74 83

1929 or earlier 90 88 70 93 71 74 73 81

Square feet

Less than 500 82 86 70 89 69 72 71 45

500 to 749 68 69 58 71 58 64 58 55

750 to 999 82 80 67 84 67 74 68 71

1,000 to 1,499 102 101 85 105 84 94 85 88

1,500 to 1,999 116 111 97 119 98 113 100 102

2,000 to 2,499 127 121 106 131 106 122 109 108

2,500 to 2,999 140 132 115 143 116 134 118 109

3,000 to 3,999 145 135 119 150 121 138 123 117

4,000 or more 150 136 120 159 122 146 124 146

Total number of rooms

1 to 2 47 39 41 49 42 50 42 44

3 to 4 73 71 61 76 60 70 61 64

5 to 6 109 103 89 112 89 102 91 96

7 or more 139 130 117 143 118 133 121 127

Bedrooms

0 43 39 40 45 39 46 40 36

1 58 57 51 60 51 57 52 52

2 87 85 70 89 70 79 71 75

3 117 111 97 121 98 112 100 107

4 or more 146 135 122 150 123 144 125 134
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 27

Table H1. Electric Medians by Selected Housing and Household Characteristics (2 of 2)

Median Monthly Electric Costs UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN 2009 RECS

Complete bathrooms

None 69 67 60 77 60 58 60 24

1 80 76 64 82 64 71 65 67

1 1⁄2 103 95 83 105 83 89 85 91

2 or more 129 129 112 134 112 127 114 122

Householder characteristics

Black 113 108 85 115 84 99 85 95

White 108 106 91 111 92 102 93 99

Hispanic 98 98 88 103 89 109 91 84

Elderly (65+) 96 96 82 99 80 88 81 84

Married 126 119 106 130 108 122 110 116

Education

Up to 12th grade, no diploma 102 100 83 105 82 95 83 82

High school graduate 107 103 87 110 87 95 88 95

Some college, no degree 109 102 88 112 88 104 89 98

Associate’s degree 108 109 93 110 93 101 95 101

Bachelor’s degree 110 110 95 113 96 107 98 101

Graduate or professional degree 113 112 97 116 100 110 102 103

Number of household members

1 77 80 67 78 66 72 67 65

2 107 102 86 109 86 100 88 97

3 123 115 99 128 100 119 102 112

4 134 126 115 140 115 137 118 124

5 143 135 129 150 131 152 134 128

6 154 143 136 156 138 171 141 139

7 or more 147 153 165 158 162 183 168 134

Household income

Less than $5,000 85 89 72 89 67 77 69 75

$5,000 to $9,999 85 85 70 87 66 73 66 69

$10,000 to $14,999 85 86 70 88 68 78 68 73

$15,000 to $19,999 93 93 75 94 74 76 74 78

$20,000 to $24,999 94 95 78 95 75 83 76 79

$25,000 to $29,999 100 101 84 103 81 89 82 85

$30,000 to $39,999 99 102 82 102 81 90 82 84

$40,000 to $49,999 104 102 86 107 84 92 85 99

$50,000 to $59,999 107 105 91 111 90 99 92 97

$60,000 to $79,999 114 108 95 117 96 108 97 104

$80,000 to $99,999 119 112 99 122 104 119 105 115

$100,000 to $119,999 129 118 109 134 115 133 118 120

$120,000 or more 140 126 113 146 120 144 124 144

Poverty

Income below 100% poverty line 94 95 76 97 72 84 74 83

Subsidized housing

Owned by a public housing authority 70 71 58 73 55 59 56 62

Government subsidy 79 79 63 78 61 65 61 56

RECS = Residential Energy Consumption Survey.
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 28

Appendix I. Median Average Monthly Costs for Piped 

Gas by Selected Housing and Household 

Characteristics

Table I1. Natural Gas Medians by Selected Housing and Household Characteristics (1 of 2) 

Median Monthly Electric Costs UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN 2009 RECS

Housing unit type

One unit building detached 80 95 76 75 75 72 72 66

One unit building attached 74 90 77 74 77 61 73 56

Building with two or more apartments 46 59 42 45 41 46 39 34

Manufactured/mobile home 58 67 50 60 48 0 45 47

Tenure

Owned 80 96 77 76 75 71 72 66

Rented 55 70 53 54 52 55 49 44

Year built

2005 to 2009 70 84 67 67 67 59 64 60

2000 to 2004 69 84 66 68 66 65 64 57

1995 to 1999 68 81 70 67 70 65 66 58

1990 to 1994 72 82 69 69 68 64 66 60

1984 to 1989 69 84 64 65 63 60 60 59

1980 to 1984 63 77 60 60 59 61 57 48

1975 to 1979 66 81 64 64 61 60 58 51

1970 to 1974 67 82 65 64 62 63 60 57

1960 to 1969 74 91 71 70 68 68 65 61

1950 to 1959 75 94 75 72 72 68 69 66

1940 to 1949 71 95 72 71 71 66 68 61

1930 to 1939 85 106 81 82 77 77 76 70

1929 or earlier 95 120 93 91 90 88 89 78

Square feet

Less than 500 70 93 77 75 74 63 71 21

500 to 749 43 56 41 43 40 35 38 23

750 to 999 53 66 51 53 50 52 47 38

1,000 to 1,499 65 79 64 63 62 59 59 49

1,500 to 1,999 73 87 70 68 68 65 65 58

2,000 to 2,499 83 99 79 79 78 77 75 61

2,500 to 2,999 89 105 84 84 84 85 81 71

3,000 to 3,999 94 113 90 88 89 84 86 78

4,000 or more 107 125 100 100 99 111 96 90

Total number of rooms

1 to 2 22 31 25 27 25 32 23 15

3 to 4 46 56 45 47 45 43 42 36

5 to 6 70 82 68 68 67 63 63 57

7 or more 92 112 88 86 87 85 84 76

Bedrooms

0 22 30 25 27 23 32 20 —

1 36 43 37 38 36 32 33 26

2 60 73 59 59 57 57 53 48

3 76 90 74 72 72 67 68 62

4 or more 92 113 88 86 88 88 84 77
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 29

Table I1. Natural Gas Medians by Selected Housing and Household Characteristics (2 of 2)

Median Monthly Electric Costs UES HHC HHCR UESR HHCR2 ONE BILL HHCFIN 2009 RECS

Complete bathrooms

None 65 83 49 66 45 76 51 15

1 62 78 61 61 59 59 56 50

1 1⁄2 84 99 80 82 77 73 75 68

2 or more 77 94 74 72 73 71 69 64

Householder characteristics

Black 79 90 70 79 69 80 67 66

White 73 90 73 70 71 66 68 61

Hispanic 54 70 48 50 48 45 45 41

Elderly (65+) 75 89 72 73 69 67 66 65

Married 80 98 77 76 76 73 73 65

Education

Up to 12th grade, no diploma 64 78 58 61 56 55 55 49

High school graduate 74 89 72 72 70 67 68 59

Some college, no degree 71 86 68 67 67 65 65 60

Associate’s degree 74 92 74 71 72 67 68 61

Bachelor’s degree 75 94 75 71 73 72 70 65

Graduate or professional degree 83 101 79 80 78 77 75 69

Number of household members

1 62 78 64 61 61 59 57 52

2 73 88 72 70 70 66 66 61

3 76 94 73 73 72 69 68 63

4 83 105 80 80 78 83 76 67

5 82 108 78 78 77 80 75 68

6 81 104 76 80 77 81 73 69

7 or more 80 106 73 77 75 81 73 56

Household income

Less than $5,000 59 80 57 60 54 60 55 48

$5,000 to $9,999 65 75 62 62 58 57 55 47

$10,000 to $14,999 64 78 62 63 59 61 56 50

$15,000 to $19,999 66 79 64 66 61 67 57 51

$20,000 to $24,999 67 81 66 66 63 59 60 57

$25,000 to $29,999 71 86 69 70 66 63 63 60

$30,000 to $39,999 70 82 67 68 65 64 62 57

$40,000 to $49,999 70 86 69 66 67 66 64 59

$50,000 to $59,999 71 89 71 69 70 66 67 59

$60,000 to $79,999 73 90 73 69 73 66 70 60

$80,000 to $99,999 78 97 77 75 76 74 73 66

$100,000 to $119,999 77 96 77 75 76 70 72 71

$120,000 or more 87 107 82 82 82 81 78 79

Poverty

Income below 100 percent poverty line 63 78 59 62 57 60 55 50

Subsidized housing

Owned by a public housing authority 48 54 45 46 44 53 41 34

Government subsidy 55 64 49 54 49 51 47 36

— Too few cases to calculate. RECS = Residential Energy Consumption Survey.
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 30

Appendix J. Number of 2015 AHS Cases in Different Models 

Model Number of Cases

Electric only

Consumption (no bills) 18,095

Cost (combined bills) 153

AMTE (self-reported average monthly costs) 4

Electric mixed

Consumption (no bills) 38,376

Cost (combined bills) 563

AMTG (self-reported average monthly costs) 6

Natural gas

Consumption (no bills) 33,515

Cost (combined bills) 123

AMTG (self-reported average monthly costs) 1

Propane

Consumption (no bills) 3,364

AMTG (self-reported average monthly costs) 10
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 31

Appendix K. Count of RECS and AHS Cases in 2009 Models

Table K1. Count of RECS and AHS Cases in 2009 Electric-Only Models

Collapsed Reportable Domains RECS Cases Used Outliers AHS Cases

1 403 369 34 782

2 315 296 19 1,456

3 207 197 10 887

4 234 227 7 267

5 263 242 21 160

6 257 245 12 889

7 193 180 13 410

8 226 210 16 916

9 783 745 38 2,099

10 353 337 16 1,033

11 489 459 30 1,491

12 186 175 11 465

13 170 165 5 223

14 258 243 15 761

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.

Table K2. Count of RECS and AHS Cases in 2009 Electric and Gas Mixed Models

Collapsed Reportable Domains RECS Cases Used Outliers AHS Cases

1 451 427 24 1,091

2 632 595 37 2,239

3 304 286 18 3,974

4 490 462 28 4,238

5 211 202 9 2,515

6 191 188 3 671

7 456 435 21 1,216

8 248 239 9 499

9 403 391 12 638

10 219 210 9 1,260

11 484 464 20 1,908

12 249 240 9 1,466

13 675 637 38 2,543

14 235 224 11 760

15 356 340 16 1,375

16 1398 1355 43 3,839

17 181 170 11 769

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 32

Table K3. Counts of RECS and AHS Cases in 2009 Natural Gas Models

Collapsed Reportable Domains RECS Cases Used Outliers AHS Cases

1 380 317 63 741

2 459 398 61 1,666

3 268 242 26 3,376

4 454 430 24 3,602

5 183 171 12 2,162

6 184 170 14 520

7 414 399 15 923

8 240 235 5 442

9 393 355 38 537

10 201 178 23 1,005

11 469 417 52 1,367

12 240 218 22 1,096

13 644 602 42 2,198

14 222 206 16 683

15 350 335 15 1,186

16 1325 1235 90 3,620

17 171 149 22 652

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.

Table K4. Count of RECS and AHS Cases in 2009 Propane Models

Census Division RECS Cases Used Outliers AHS Cases

1 86 86 0 257

2 82 81 1 531

3 78 72 6 662

4 177 162 15 348

5 187 178 9 694

6 81 77 4 337

7 56 48 8 281

8 59 57 2 216

9 96 89 7 170

AHS = American Housing Survey. RECS = Residential Energy Consumption Survey.
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 33

Appendix L. Consumption and Cost Estimate Cumulative 

Distribution Functions (CDFs)

Consumption

recs_cdf

0

10

20

30

40

50

60

70

80

90

100

Total Site Electricity usage, in kilowatt-hours, 2009

0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000 110000 120000 130000 140000 150000 160000

2009 ahs vs. 2009 recs electric consumption cdfs

PLOT RECS AHS

The NPAR1WAY Procedure

Kolmogorov-Smirnov Test for Variable cons

Classified by Variable survey

EDF at Deviation from Mean

survey N Maximum at Maximum

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ

RECS 107852972 0.837276 -326.25750

AHSN 106849315 0.900402 327.78622

Total 214702287 0.868692

Maximum Deviation Occurred at Observation 22446

Value of cons at Maximum = 18118.0

Kolmogorov-Smirnov Two-Sample Test (Asymptotic)

KS 0.031563 D 0.063126

KSa 462.480012 Pr > KSa <.0001
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 34

recs_cdf

0

10

20

30

40

50

60

70

80

90

100

Total Natural Gas usage, in hundred cubic feet, 2009

0 1000 2000 3000 4000 5000

2009 ahs vs. 2009 recs natural gas consumption cdfs

PLOT RECS AHS

The NPAR1WAY Procedure

Kolmogorov-Smirnov Test for Variable cons

Classified by Variable survey

EDF at Deviation from Mean

survey N Maximum at Maximum

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ

RECS 60006744 0.242840 341.55893

AHSN 61250166 0.155550 -338.07421

Total ******** 0.198747

Maximum Deviation Occurred at Observation 1986

Value of cons at Maximum = 353.0

Kolmogorov-Smirnov Two-Sample Test (Asymptotic)

KS 0.043643 D 0.087290

KSa 480.579518 Pr > KSa <.0001
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 35

recs_cdf

0

10

20

30

40

50

60

70

80

90

100

Total LPG/Propane usage, in gallons, 2009

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000

2009 ahs vs. 2009 recs propane consumption cdfs

PLOT RECS AHS

The NPAR1WAY Procedure

Kolmogorov-Smirnov Test for Variable cons

Classified by Variable survey

EDF at Deviation from Mean

survey N Maximum at Maximum

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ

RECS 9883427 0.127115 148.94200

AHSN 9472776 0.030308 -152.13612

Total 19356203 0.079739

Maximum Deviation Occurred at Observation 600

Value of cons at Maximum = 82.0

Kolmogorov-Smirnov Two-Sample Test (Asymptotic)

KS 0.048393 D 0.096807

KSa 212.906357 Pr > KSa <.0001
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 36

Cost

recs_cdf

0

10

20

30

40

50

60

70

80

90

100

Total Electricity cost, in whole dollars, 2009

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000

2009 ahs vs. 2009 recs electric cost cdfs

PLOT RECS AHS

The NPAR1WAY Procedure

Kolmogorov-Smirnov Test for Variable cons

Classified by Variable survey

EDF at Deviation from Mean

survey N Maximum at Maximum

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ

RECS 107855836 0.743557 -353.04318

AHSN 106840450 0.811869 354.71684

Total 214696286 0.777551

Maximum Deviation Occurred at Observation 30340

Value of cons at Maximum = 1715.960

Kolmogorov-Smirnov Two-Sample Test (Asymptotic)

KS 0.034155 D 0.068312

KSa 500.463310 Pr > KSa <.0001
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 37

recs_cdf

0

10

20

30

40

50

60

70

80

90

100

Total Natural Gas cost, in whole dollars, 2009

0 1000 2000 3000 4000 5000 6000 7000

2009 ahs vs. 2009 recs natural gas cost cdfs

PLOT RECS AHS

The NPAR1WAY Procedure

Kolmogorov-Smirnov Test for Variable cons

Classified by Variable survey

EDF at Deviation from Mean

survey N Maximum at Maximum

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ

RECS 60006606 0.246408 336.69502

AHSN 61244700 0.160357 -333.27441

Total ******** 0.202943

Maximum Deviation Occurred at Observation 403

Value of cons at Maximum = 450.0

Kolmogorov-Smirnov Two-Sample Test (Asymptotic)

KS 0.043023 D 0.086051

KSa 473.746105 Pr > KSa <.0001
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 38

recs_cdf

0

10

20

30

40

50

60

70

80

90

100

Total cost of LPG/Propane, in whole dollars, 2009

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000

2009 ahs vs. 2009 recs propane cost cdfs

PLOT RECS AHS

The NPAR1WAY Procedure

Kolmogorov-Smirnov Test for Variable cons

Classified by Variable survey

EDF at Deviation from Mean

survey N Maximum at Maximum

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ

RECS 9883356 0.118041 139.59620

AHSN 9472310 0.027307 -142.59289

Total 19355666 0.073637

Maximum Deviation Occurred at Observation 69

Value of cons at Maximum = 198.0

Kolmogorov-Smirnov Two-Sample Test (Asymptotic)

KS 0.045357 D 0.090735

KSa 199.549066 Pr > KSa <.0001
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 39

Appendix M. Independent Variable Cumulative Distribution 

Functions

Independent Variable Definitions—

• EHDEMAND = (hfuel=‘1’)*HEATDAY

• GHDEMAND = (hfuel=‘2’ and gaspip=‘1’)*HEATDAY

• CENAIR = (airsys=‘1’) * coolday

• WALL = (airsys = ‘2’ and numair ge 1) * coolday

• EH20 = (wfuel=‘1’)

• GH20 = (wfuel=‘2’ and gaspip=‘1’)

• GSTEAM = (hequip = 2) * (hfuel = ‘2’ and gaspip = ‘1’)

• GNOSTEAM = (hequip ne 2) * (hfuel = ‘2’ and gaspip = ‘1’)

• YRBUILT = BUILT

• NU1 = nunit2 in (‘1’,’2’)

• NU2 = nunit2 = ‘3’

• NU3 = nunit2 = ‘4’

• TOTROOMS = min (bedrms + kitch + living + dining + 

famrm + recrm + dens + othfn,25)

• BATHS = min (baths + (0.5 * halfb),7)

• MAJAPPL = (refr = ‘1’) + (2 * (cook = ‘1’)) + (oven = ‘1’) + 

(burner = ‘1’) + (dish = ‘1’) + (wash = ‘1’) + (dry = ‘1’)

• PEOPLE = min (curper,12)

• MONEY_R = ZINC2 in Groups of $10,000

• PH20 = (wfuel = ‘2’ and gaspip = ‘2’)

• PCOOK= (cfuel = ‘2’ and gaspip = ‘2’) * min (curper,12)

• PDRY = (dfuel = ‘2’ and gaspip = ‘2’) * min(curper,12)

• HEATDAY = heating degree days

• COOLDAY = cooling degree days

recs_cdf

60

70

80

90

100

ehdemand

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000

2009 ahs vs. 2009 recs ehdemand cdfs

PLOT RECS AHS
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 40

recs_cdf

40

50

60

70

80

90

100

ghdemand

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000

2009 ahs vs. 2009 recs ghdemand cdfs

PLOT RECS AHS

recs_cdf

30

40

50

60

70

80

90

100

cenair

0 1000 2000 3000 4000 5000 6000

2009 ahs vs. 2009 recs cenair cdfs

PLOT RECS AHS
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 41

recs_cdf

70

80

90

100

wall

0 1000 2000 3000 4000 5000 6000

2009 ahs vs. 2009 recs wall cdfs

PLOT RECS AHS

recs_cdf

50

60

70

80

90

100

eh2o

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

2009 ahs vs. 2009 recs eh2o cdfs

PLOT RECS AHS
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 42

recs_cdf

40

50

60

70

80

90

100

gh2o

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

2009 ahs vs. 2009 recs gh2o cdfs

PLOT RECS AHS

recs_cdf

93

94

95

96

97

98

99

100

gsteam

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

2009 ahs vs. 2009 recs gsteam cdfs

PLOT RECS AHS
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 43

recs_cdf

50

60

70

80

90

100

gnosteam

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

2009 ahs vs. 2009 recs gnosteam cdfs

PLOT RECS AHS

recs_cdf

10

20

30

40

50

60

70

80

90

100

yrbuilt

1 2 3 4 5 6 7

2009 ahs vs. 2009 recs yrbuilt cdfs

PLOT RECS AHS
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 44

recs_cdf

20

30

40

50

60

70

80

90

100

nu1

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

2009 ahs vs. 2009 recs nu1 cdfs

PLOT RECS AHS

recs_cdf

70

80

90

100

nu2

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

2009 ahs vs. 2009 recs nu2 cdfs

PLOT RECS AHS
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 45

recs_cdf

93

94

95

96

97

98

99

100

nu3

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

2009 ahs vs. 2009 recs nu3 cdfs

PLOT RECS AHS

recs_cdf

0

10

20

30

40

50

60

70

80

90

100

Total number of rooms in the housing unit

0 10 20 30

2009 ahs vs. 2009 recs totrooms cdfs

PLOT RECS AHS
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 46

recs_cdf

0

10

20

30

40

50

60

70

80

90

100

baths

0 1 2 3 4 5 6 7 8 9 10

2009 ahs vs. 2009 recs baths cdfs

PLOT RECS AHS

recs_cdf

0

10

20

30

40

50

60

70

80

90

100

majappl

0 1 2 3 4 5 6 7 8 9 10 11 12

2009 ahs vs. 2009 recs majappl cdfs

PLOT RECS AHS
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 47

recs_cdf

20

30

40

50

60

70

80

90

100

people

1 2 3 4 5 6 7 8 9 10 11 12 13 14

2009 ahs vs. 2009 recs people cdfs

PLOT RECS AHS

recs_cdf

0

10

20

30

40

50

60

70

80

90

100

money_r

1 2 3 4 5 6 7 8 9 10 11 12

2009 ahs vs. 2009 recs money_r cdfs

PLOT RECS AHS
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 48

recs_cdf

96

97

98

99

100

ph2o

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

2009 ahs vs. 2009 recs ph2o cdfs

PLOT RECS AHS

recs_cdf

95

96

97

98

99

100

pcook

0 1 2 3 4 5 6 7 8 9 10

2009 ahs vs. 2009 recs pcook cdfs

PLOT RECS AHS
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 49

recs_cdf

98.8

98.9

99.0

99.1

99.2

99.3

99.4

99.5

99.6

99.7

99.8

99.9

100.0

pdry

0 1 2 3 4 5 6 7 8 9 10 11 12

2009 ahs vs. 2009 recs pdry cdfs

PLOT RECS AHS

recs_cdf

0

10

20

30

40

50

60

70

80

90

100

heatday

0 10000 20000 30000

2009 recs vs. 2009 match vs. 2009 norms heatday cdfs

PLOT RECS Orig. Match Norms
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 50

recs_cdf

0

10

20

30

40

50

60

70

80

90

100

coolday

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000

2009 recs vs. 2009 match vs. 2009 norms coolday cdfs

PLOT RECS Orig. Match Norms
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 51

Appendix N. Imputation Rates in Different RECS Models

Table N1. 2009 RECS Electric-Only Model Input 

Variables, Imputation Rates

Variable Name Imputation Rate (%)

FUELHEAT 0.82

FUELH20 3.76

YEARMADERANGE 2.45

ZNHSLDMEM 0.16

COOLTYPE 0.18

MONEYPY 10.24

STOVEN 0.02

STOVE 0.00

OVEN 0.00

NUMFRIG 0.02

DISHWASH 0.00

CWASHER 0.00

DRYER 0.00

NCOMBATH 0.10

NHAFBATH 0.09

RECS = Residential Energy Consumption Survey. 

Table N2. RECS Electric-Gas Mixed Model Input 

Variables, Imputation Rates 

Variable Name Imputation Rate (%)

FUELHEAT 1.47

FUELH20 7.49

YEARMADERANGE 2.88

ZNHSLDMEM 0.44

COOLTYPE 0.09

MONEYPY 14.16

STOVEN 0.00

STOVE 0.00

OVEN 0.00

NUMFRIG 0.00

DISHWASH 0.00

CWASHER 0.00

DRYER 0.00

NCOMBATH 0.01

NHAFBATH 0.01

RECS = Residential Energy Consumption Survey. 

Table N3. 2009 RECS Natural Gas Model Input 

Variables, Imputation Rates

Variable Name Imputation Rate (%)

FUELHEAT 0.76

FUELH20 5.06

YEARMADERANGE 2.72

ZNHSLDMEM 0.45

COOLTYPE 0.10

MONEYPY 13.92

STOVEN 0.00

STOVE 0.00

OVEN 0.00

NUMFRIG 0.00

DISHWASH 0.00

CWASHER 0.00

DRYER 0.00

NCOMBATH 0.01

NHAFBATH 0.01

RECS = Residential Energy Consumption Survey. 

Table N4. 2009 RECS Propane Model Input Variables, 

Imputation Rates 

Variable Name Imputation Rate (%)

FUELHEAT 1.05

FUELH20 2.70

DRYERFUEL 0.51

FUELFOOD 0.37

KOWNRENT 0.72

YEARMADERANGE 0.88

ZNHSLDMEM 0.07

MONEYPY 10.19

RECS = Residential Energy Consumption Survey. 
Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond

2015 AHS Redesign Series 52

Appendix O. Utility Price Data

Electric

1. Navigate to http://www.eia.gov/electricity/monthly.

2. Locate tables 5.4.A (Retail Sales of Electricity to Ultimate 

Customers by End-Use Sector) and 5.6.A (Average Retail Price 

of Electricity to Ultimate Customers by End-Use Sector) for 

the months of May 2014 through April 2015.

• Note that the reports are 2 months behind the reporting 

month; for example, the June 2014 report contains the 

data applicable for April 2014.

3. Obtain the residential sales and average price from each 

month for each state.

Natural Gas

1. Navigate to http://www.eia.gov/naturalgas/monthly.

2. Locate tables 2 (Natural Gas Consumption by End Use) and 

3 (Natural Gas Price).

3. Obtain the monthly residential consumption and price for 

each state for the months of May 2014 through April 2015.

• If a value is missing, use the previous year’s value for that 

month. Consumption and price overall are fairly cyclical.

Propane

We calculated an average price from the Residential Energy 

Consumption Survey cost and consumption data using the 

propane universe we defined with the cases in RECS2009_

PUBLIC_V3.sas7bdat.

For 2015 American Housing Survey production, we used the 

following.

1. Navigate to http://www.eia.gov/dnav/pet/pet_pri_wfr_a_

EPLLPA_PRS_dpgal_w.htm.

2. Obtain each state’s weekly propane residential price data 

from October 13, 2014, through March 30, 2015.

• Note that price data are collected only through the winter 

months.

3. Calculate the division’s average price with the price data.

• Use the available states, even if the division is incomplete.

4. If a division has no states, navigate to http://www.eia.gov/

forecasts/aeo/data.cfm#enprisec.

5. Locate the appropriate census division in table 3.

6. Locate the 2015 projection for propane, in 2013 $/million 

British thermal units (BTUs).

7. Divide this number by 10.948945, which is the conversion 

factor to convert BTUs to gallons.

• Refer to http://www.eia.gov/Energyexplained/?page=about_

energy_units for more details.

8. Inflation-adjust the 2013 dollars to 2015 dollars.

9. Use the calculator in http://data.bls.gov/cgi-bin/cpicalc.pl.