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 Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Purpose

longitudinal housing survey in the United States and is collected by the Census Bureau on the behalf of HUD. The AHS employs The purpose of this paper is to summarize the results of research two types of housing unit samples—a national sample and on utility cost estimation conducted by the U.S. Census Bureau metropolitan area samples, with the metropolitan area samples and the U.S. Department of Housing and Urban Development surveyed on a rotating basis. In both types of sample, the same (HUD) as part of the redesign of the American Housing Survey housing units are followed over time until a new sample is (AHS) for 2015. Utility costs are an important component of collected. Between 2 and 29 metropolitan area samples have housing costs, which also include rent or mortgage payments; been surveyed in the years the metropolitan AHS has been garbage, trash, water, and sewage costs; real estate taxes; and fielded, depending on funding availability. The national AHS

other housing-related fees. Accurate estimation of housing costs started in 1973 and has surveyed the same housing units since is crucial to monitoring trends in affordable housing supply 1985, drawing additional samples to account for new construc-over time. Housing costs are incorporated into measures of tion, improve coverage, and oversample subpopulations. From housing burden that examine the percentage of a household’s 1973 and 1981, the national AHS was conducted annually.

income spent on housing. Housing burdens are a component Since 1983, the national AHS has collected data in every odd-of HUD’s  Worst Case Needs measure,1 which is used to estimate numbered year. Since 2007, national and metropolitan area the number of households in the United States with the greatest samples have been surveyed in the same year. Since 1997, the housing needs. Since 1993, utility cost data collected in the AHS has been collected via in-person and telephone interviews AHS have been adjusted and imputed using regression models using an electronic questionnaire.

developed from the Residential Energy Consumption Survey (RECS).

1.1  2015 AHS Redesign

The research discussed in this white paper analyzes the implications of estimating utility costs in the AHS in the absence of In 2015, the AHS retired the national sample it had surveyed billing data provided by respondents. The results of the research since 1985 and all metropolitan samples, and it drew a new informed the decision to not collect billing data for electricity, sample from the Census Bureau’s Master Address File. Along natural gas, and bottled gas for most households (around 99

with the introduction of a new sample, the AHS underwent a percent of households) and instead to use housing and house hold redesign for 2015, balancing the goals of increasing data quality characteristics and climate data to model utility consumption and reducing respondent burden. As part of the 2015 AHS

and costs for housing units in 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 1.  The American Housing

the implications of collecting less data on electric and gas utility Survey

costs in the AHS and relying more on modeling for the estimation of utility costs. This research informed the decision to stop The AHS is a key source for housing and utility cost data in collecting most utility billing data on the AHS and to estimate the United States. The AHS is the largest, most comprehensive, 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).

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Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 1.2  Utility Cost Data Collection Prior  monthly amounts, the survey asked: “How much was your to 2015

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

asked a subset of respondents to estimate their average monthly Before 2015, the AHS asked respondents to refer to their utility electricity costs. Respondents who used gas were asked similar bills when reporting monthly electric and gas costs. In addition cost questions and whether their gas comes from underground to electric and gas costs, the survey asked questions about pipes or bottled gas.

other utility costs, including fuel oil, other fuels (that is, coal, kerosene, wood, and solar), garbage and trash, and water and 1.4  Other Utilities

sewage. The series of utility questions in the AHS was highly burdensome, requiring respondents to locate and report their In addition to electric and gas costs, the AHS collected (and utility bills and to estimate costs when they did not have bills.

still collects) information for fuel oil, other fuels, garbage and To prepare respondents for the survey, a letter sent in advance trash collection, and water supply and sewage disposal. The of the interview contained a worksheet with questions about level of detail collected for these costs is less than that for utility costs.

electric and gas costs. Monthly billing data were not collected for these items. However, the AHS collected (and still collects) 1.3  Electric and Gas Cost Data

whether the utility was used, whether it was billed separately Collection Prior to 2015

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

In addition to reevaluating the estimation of electric and piped Prior to 2015, the electric and gas cost questions in the AHS

natural gas costs, HUD and the Census Bureau explored using collected billing data for the months of January, April, August, modeling to estimate bottled gas and fuel oil costs for the 2015

and December—months that Gorsak and Schwanz (2004) AHS.

found most predictive of average monthly utility costs. Gorsak and Schwanz reported that, historically, one-third of AHS

2.  Utility Data Editing,

respondents provided billing data, and the majority of those who did provided data for all 4 months. Using 2007 AHS data, Estimation, and

Carter (2010) found that 39.5 percent of respondents reported Imputation

at least 1 month and 35.9 percent reported all 4 months of electric bills, and 39.9 percent of respondents reported at least After data collection, consistency edits are performed to check 1 month and 36.5 percent reported all 4 months of gas bills.

the utility data against relevant use (that is, if electricity is Increasingly, online billing and automatic bill pay options used) and equipment (that is, electricity is used as the heating present barriers to respondents possessing physical bills. Even equipment fuel) variables and to identify if utilities are billed though respondents are asked to use their bills during the in-separately. Because the AHS did not ask for billing data for all terview, it was not until 2011 that the AHS asked respondents months, and respondents’ recall of utility costs is not always explicitly if they used bills or the worksheet in the interview.

accurate, the utility estimation system (UES) was created to The 2011 AHS recorded 20 percent of respondents as using an estimate annual utility costs using regression models developed electric bill, 18 percent as using a gas bill, and 10 percent as from the RECS. This estimate was divided by 12 to calculate using the worksheet to answer the survey. Of those using the average monthly utility costs. The advantage of using the RECS

worksheet, it is not known whether respondents filled out the to model utility costs is that the RECS collects administrative utility bill information.

data from suppliers on actual billing amounts. The RECS also Respondents without 4 months of billing data were asked collects some housing characteristics similar to those the AHS

other questions to allow for the estimation of utility costs.

collects, which allows for the construction of models that can If respondents did not have bills, did not know the billing then be applied to the AHS. Table 1 lists the 25 regression amount, refused to answer, or provided fewer than two valid models that comprise the UES.

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

such that the AHS cost estimates that were calculated with billing data plus the adjusted sum of the AHS cost estimates From 1993 to 2013, the UES was used to calculate annual that were calculated without billing data was equal to the RECS

estimates for households that use electricity and households average monthly cost multiplied by the AHS sample size. Cost that use underground piped natural gas. The calculation estimates calculated with billing data were assumed to be correct; process consisted of three steps: (1) calculating the utility cost therefore, the adjustment factor was applied only to estimates estimate, (2) comparing the cost estimate with a cutoff value, calculated without billing data.

and (3) aligning cost estimates to the RECS.

In the first step, cost estimates were calculated by applying 2.2  History of Model Development

one of a set of regression models to data collected from the respondents. Utility cost regression models were created with The electricity and gas models evolved over time. The Census the RECS. Because the RECS collects respondent monthly billing Bureau developed the UES in 1993 based on a recommendation data directly from suppliers and asks respondents questions from the Office of Management and Budget. In the initial ver-that are also asked on the AHS, the Census Bureau was able sion, 15 models were developed for each census division;3 each to create regression models with the RECS and apply AHS

model contained parameter estimates for the billing months responses to the regression parameters to obtain a total utility of January, April, August, and December, corresponding with cost estimate for AHS respondents.

the amount of billing data obtained from the respondent. If a respondent had only 1 month of billing data, electric bills from In the second step, each cost estimate was compared to a set of only January and December, or gas bills from only April and values to avoid extremely low or high estimates. These cutoff August, the model added parameter estimates corresponding values were calculated at the census division level. If a cost with home heating equipment, water heating equipment, and estimate was out of the range of cutoff values, extreme AHS-housing and household characteristics. Parameter estimates reported individual bills were dropped and a new cost estimate were calculated from the 1990 RECS and inflated to reflect was calculated with the appropriate model. If no extreme 1993 utility cost. Separate inflation factors were calculated for individual bills were identified for cases exceeding the cutoff, electric costs and natural gas costs. Both inflation factors were the cost estimate was obtained through hot deck imputation.

calculated at the national level. After 1993, each AHS survey In the final step, cost estimates were adjusted to align with the updated the inflation factors and average monthly costs.

RECS total utility cost. An adjustment factor was calculated, 3 Alaska was assigned to the New England division. Hawaii was assigned to the West South Central division.

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Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond In 2003, based on research by Gorsak and Schwanz (2004), costs to the year of the AHS data collection. In the past, the parameter estimates were updated using data from the 1997

RECS released data at only the regional and census division RECS. Also, two regression models were added—one using levels. The 2009 RECS,5 used to model costs and consumption average electric and gas costs estimated by respondents and in our research, includes geography at the reportable domain the other assigning zero to all regression parameters, thereby level (states and groups of states), which improved the models’

forcing the case through the hot deck imputation method.4

level of geographical precision.

In 2007, the parameter estimates were updated with data The RECS underwent a redesign and was the subject of a from the 2001 RECS. Four models were added. These models National Research Council review panel, which published its used billing data for March, May, June, and July to allow for findings in  Effective Tracking of Building Energy Use in 2012. The modeling of costs for respondents who provided the past panel made several recommendations to improve the RECS, in-month’s bill.

cluding improving timeliness, revising edit procedures, releas-ing prepublication estimates, increasing sample size to make In 2009, four models corresponding with the remaining billing more state data available, working more closely with suppliers, months (February, September, October, and November) were introducing a multimode approach for data collection (the added to the system. The 2003 model, which assigned zero to RECS already uses a web survey for suppliers), and conducting all regression parameters, was updated with 2001 RECS-based ongoing evaluations of administrative record use for data and parameters for the housing and household characteristics data.

imputation (for example, square footage can be used to replace This housing and household characteristics (HHC) model was self-reported data or to impute missing data from self-reported applied to cases with no billing data and no estimate of average items) (NRC, 2012). In Spring 2017, data from the 2015 RECS

monthly costs. In this update to the system, the Census Bureau will be released (https://www.eia.gov/consumption/residential/

also split the electric universe into two smaller universes—

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

housing units that use electric only and those that use both update future AHS utility estimation models.

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

3.  Changes for 2015

system update was used through 2013, with inflation factors and average monthly costs reflecting the current year of enumeration.

Evaluated in Research

The utility cost questions in the AHS are complicated, and 2.3  Strengths and Limitations of

many respondents do not have the billing information available Using the RECS

to answer them. Because the utility questions are burdensome to respondents and costly to collect, the Census Bureau and The RECS was chosen to derive regression parameter estimates HUD conducted simulation studies to evaluate the impact of that could be applied to AHS data to estimate utility costs estimating utility costs with less or no billing data. The research because it collects housing and household variables similar explored several options for redesigning the 2015 AHS utility to AHS variables and collects high-quality utility data. Rather cost models.

than collecting self-reported utility billing data, the RECS

collects billing data directly from suppliers, who are mandated 1.  Continue with the current AHS approach. Ask respondents for to provide the data to the U.S. Department of Energy. These 4 months of billing information and update the UES as new factors help maintain the high quality and low nonresponse RECS data become available.

rates of RECS data. One limitation of using the RECS to model 2.  Collect no utility data for electric and natural gas costs. Model utility costs is that the models are dependent on getting current utility costs with a revised housing and household character-RECS data. Inflation factors are applied to the models to adjust istics (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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Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 3.  Adopt the American Community Survey approach.  Collect only and costs. We compared median estimates and frequency costs for the past month and model utility costs using the distributions of monthly electric and gas costs from our models UES for the past month’s bill.

and published estimates from the 2009 AHS with estimates from the 2009 RECS to assess how closely the models approxi-4.  Collect average monthly costs. Model utility costs using the mated costs at national and regional levels. As the RECS is the UES for average monthly costs (we did not have the data gold standard for utility costs, we benchmarked our estimates to explore this option, as not all cases provided average to the 2009 RECS estimates.

monthly cost information).

We present evaluations of options 1, 2, and 3 in this paper. For 3.2 Models

option 3, we chose to test with households that provided April billing information, because that was the month with the most The models used in the research are described in table 2, and 2009 AHS data available. We ran the model only on cases with the variables in the initial HHC model and the final HHC

April bills. We did not have enough data to test option 4, as (HHCFIN) model are in table 3. All models exclude cases in many respondents are not asked to estimate average monthly costs. In addition, Gorsak and Schwanz (2004) questioned the Table 2. Models

validity and reliability of self-reported average monthly cost UES

Approach used in 2009 AHS.

estimates, because they are highly burdensome for respondents HHC

Applies housing and household characteristics cost to answer. The task of estimating average monthly costs model to all units using electric, gas, or both.

requires respondents to quickly remember all their bills from HHCR

Revised HHC model. Changed to consumption

model. Degree days added.

the past year and convert them into a monthly value.

UESR

Revised UES model. Updated with monthly billing data from 2009 RECS.

3.1  Study Design

HHCR2

Revised HHCR model. Adds presence of steam

heat to gas model, collapses geographies with small We estimated regression models for electric and gas costs and sample sizes, and adds household income.

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

HHCR2 model plus April utility bill.

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

HHCR2 with degree day normals.

to yield estimates for monthly electric and gas consumption 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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Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond which the given utility costs are included in rent or condomin-costs from state to state. Therefore, we estimate consumption ium fees. Both the RECS and the AHS required recoding variables (natural log of consumption + 1, because consumption data are so that regression parameters could be applied across surveys.

also skewed) with independent variables.

Appendix A contains a full list of variables in all models and a After iteratively removing outliers,9 we used stepwise regression crosswalk between RECS and AHS variables.7 The UES model to determine which variables to include and which variables to is the billing model used to produce published estimates for remove from each reportable domain model.

the 2009 AHS, which uses data from the 2001 RECS, adjusted for inflation to reflect 2009 values. The model uses billing data We applied the new regression parameters to the recoded 2009

primarily for estimation and incorporates some housing and AHS data. After transforming our estimates from the natural log household characteristics for units with less billing data. Because scale back to our base consumption, we multiplied our monthly the HHC model was developed for the 2009 AHS public use file consumption estimates by each state’s average price per unit using only housing and household characteristics, we evaluated of consumption to obtain the unit’s average monthly cost. For the predictive capability of this model when applied to all fuel prices, we obtained price and consumption data from EIA’s cases in the 2009 AHS national sample. The HHC model was website for each state.10 We then calculated a weighted average developed with 2001 RECS data, and because 2009 RECS data across the 12 months prior to the 2009 AHS enumeration, or are now available, we created the HHCR model with the 2009

May 2008 through April 2009. Next, we multiplied the average RECS data. The HHCR model incorporates heating degree days by a factor to put the value into a unit of measure equivalent to (HDD) and cooling degree days (CDD) to account for variabil-the RECS public use data. For electricity, the factor was 0.01 to ity in consumption.8 The model uses degree days available on convert cents to dollars. For gas, the factor was 0.1 to convert the 2009 AHS internal use file. HDD affect heating equipment dollars per 1,000 cubic feet to dollars per 100 cubic feet. Finally, usage, and CDD affect air conditioning usage; therefore, we we summarized our results and compared them with the RECS.

apply this information depending on the presence of specific types of heating and cooling equipment.

We requested 2009 RECS internal use billing data from EIA and used the data to reestimate the UES with the same parameters We attempted several enhancements in HHCR model develop-as in the 2009 UES. This model is referred to as UESR. The HHCR2

ment to create a model with high predictive capabilities that model is a revision of the HHCR model that incorporates house-would not require applying cutoffs or aligning to the RECS cost hold income to more accurately estimate consumption across totals, as required with pre-2015 models. First, we attempted the income distribution. The HHCR2 model is weighted, uses to develop models at a more-refined geographic level than the degree days from the 2009 AHS internal file, adds a variable census division. The RECS publishes data at the state (or group to the gas model for the use of steam or hot water radiators or of states) level, which the U.S. Energy Information Administra-other hot water heating systems, and collapses some geo graphies tion (EIA) calls “reportable domain.” Because some census with small samples sizes. The ONE BILL model explores the divisions cover relative large geographic areas, analysis at the option of collecting one bill and incorporating that one bill into reportable domain level can account for some cost variability at a consumption model. Because April was the month with the smaller geographic levels.

most 2009 AHS cases, we ran the ONE BILL model on cases with an April bill. The final model (the model chosen for electric Second, cost data are skewed. To address skewed data, we and gas estimates in 2015), the HHCFIN model, is a revision of explored modeling the natural logarithm of monthly cost as the HHCR2 model that uses HDD and CDD data obtained from a function of the independent variables; more specifically, a National Oceanic and Atmospheric Administration (NOAA) the natural log of cost + 1, to account for units that reported 30-year climate normals file from 1980 to 2010.

0. During the evaluation, we noticed differences in average 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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Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond NOAA no longer provides the data product that EIA used to in quantiles, medians, and categorical percent distributions assign HDD and CDD to the RECS, so we experimented with attributed to repeated sampling for each survey using replicate several approaches to assigning HDD and CDD to AHS cases.

weights. We obtained a pooled standard error estimate and The 30-year climate normal HDD and CDD were closest to the performed t tests on the null hypothesis of zero difference 2009 RECS and AHS distributions.11

between each distribution and the RECS distribution with a two-sided alpha of 0.1.

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 4.1  Quantiles for All Units

are applied to occupied units that pay for electricity and use natural gas. Natural gas models are applied to occupied units Tables 4 and 5 show the national electric and natural piped where households pay for gas that is piped into the unit.

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 4. Evaluation Methods

for confidentiality. At the national level, all differences between evaluation medians and the RECS median are statistically sig-The AHS and RECS are independent samples, so we cannot nificant for both electric costs and natural gas costs. However, compare each individual unit’s estimates of monthly costs the national distributions calculated with the models, including between the surveys.  Therefore, comparisons must be at the the proposed HHCFIN model, visually follow the RECS. The aggregate level. We evaluated the quality of our estimates electric medians for the HHCR, HHCR2, and HHCFIN models by how closely AHS distributions matched the medians and are lower than the 2009 RECS; the UES, HHC, UESR, and ONE

distributions calculated from the RECS.

BILL medians are higher than the RECS median. Gas medians We calculated quantiles for all units, medians for selected for all AHS models are higher than the 2009 RECS gas median, characteristics, and categorical percent distributions for AHS

but the HHCFIN model comes closest to the RECS median.

models and compared these estimated distributions with Regional Electric Quantiles and Regional Natural Gas Quantiles distributions from the 2009 RECS. We estimated variation tables showing average monthly costs are in appendixes B and C, respectively.

Table 4. Average Monthly Cost of Electricity ($), National Quantiles 2009 AHS

Quantile

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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Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Table 5. Average Monthly Cost of Natural Gas ($), National Quantiles 2009 AHS

Quantile

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

Altogether, 75 categories exist across the aforementioned and Household Characteristics

characteristics.13

Even if the AHS model quantile estimates were to match the Electric

2009 RECS perfectly at the national level, we would want to know Table 6 summarizes the number of electric medians for the 75

if average utility cost medians were similar across different categories produced by different models that are not signifi-types of housing units. For this reason, we compared electric cantly different from the RECS medians. The UES and the HHC

and natural gas monthly costs for subsets of the total housing models overestimate the medians of most characteristics if the population. We calculated median electric and natural gas difference is significant. The HHCR, HHCR2, and HHCFIN

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

compared them with the 2009 RECS, based on the following housing and household characteristics—

Table 6. Electric Medians for Selected Housing and

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

building with two or more units, and manufactured or Categories Not Significantly Different

Model

mobile home).

From the RECS (out of 75)

UES

7

•  Tenure (owner or renter).

HHC

14

•  Year built.

HHCR

27

UESR

2

•  Square feet.

HHCR2

26

ONE BILL

31

•  Total number of rooms.

HHCFIN

36

•  Bedrooms.

RECS = Residential Energy Consumption Survey.

•  Complete bathrooms.

Natural Gas

•  Householder characteristics (Black, White, Hispanic, elderly, and married).

Table 7 summarizes the number of natural piped gas medians for 74 categories produced by different models that are not

•  Education level of householder.

significantly different from the RECS medians. The 0 bedrooms

•  Number of household members.

characteristic is not evaluated, because the RECS contains only

•  Household income.

two units using natural gas with zero bedrooms. AHS natural

•  Below poverty.

gas models consistently overestimate the RECS medians.

•  Subsidized housing (owned by public housing authority or receives government subsidy).

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.

2015 AHS Redesign Series

8

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Table 7. Natural Gas Medians for Selected Housing and For our analysis, we converted the counts to percentages to Household Characteristics

account for differences in the number of housing unit estimates Categories Not Significantly Different

by RECS and those estimated by AHS. We then performed Model

From the RECS (out of 74)

statistical tests on the differences in percentages between our UES

0

AHS models and the RECS. Groups with nonsignificant differ-HHC

0

HHCR

5

ences are highlighted in tables 8 and 9. Overall, no one model UESR

3

for electric (table 8) outperforms the others in replicating the HHCR2

8

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

19

outperform the HHC model, none consistently outperforms the HHCFIN

19

others in matching the RECS distribution.

RECS = Residential Energy Consumption Survey.

4.3  Categorical Percent Distributions

4.4  Regional Distribution

Comparisons—Electric and

The AHS national publication provides categorical distributions of the number of housing units paying different monthly costs Natural Gas

for electricity and for gas (HUD and Census Bureau, 2009). Each We performed significance testing at the regional level and table contains seven groups to show the distribution of housing identified how closely the AHS estimates matched the RECS

units’ monthly utility expenses, grouped into the following distribution. Table 10 summarizes the electric comparisons, and categories: less than $25, $25 to $49, $50 to $74, $75 to $99, table 11 summarizes the natural gas comparisons. Tables with $100 to $149, $150 to $199, and $200 or more.

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.

2015 AHS Redesign Series

9

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Table 10. Regional Electric Categorical Percent Distribu-Table 12. Average Monthly Cost of Propane, National tions

Quantiles ($)

Categories Not Significantly Different

Quantile

2009 AHS

Model

2009 RECS

Model

From the RECS (out of 28)

1%

11

13.23

4.42

UES

8

5%

17

18.88

9.92

HHC

7

10%

24

22.09

15.17

HHCR

5

25% Q1

42

33.11

32.75

UESR

7

50% median

62

69.54

65.25

HHCR2

5

75% Q3

94

124.17

115.75

ONE BILL

4

90%

142

172.88

167.50

HHCFIN

5

95%

182

214.40

217.00

RECS = Residential Energy Consumption Survey.

99%

*

323.12

337.92

AHS = American Housing Survey. Q = quantile. RECS = Residential Energy Table 11. Regional Natural Gas Categorical Percent Consumption Survey.

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

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

Categories Not Significantly Different

Model

From the RECS (out of 28)

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

UES

6

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

3

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

8

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

UESR

4

HHCR2

7

ONE BILL

7

4.6  Fuel Oil Model

HHCFIN

14

RECS = Residential Energy Consumption Survey.

Table 13 presents statistics on published 2009 AHS estimates 4.5  Propane Model

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

Fuel oil model variables include: use of fuel oil for heating fuel; After developing consumption and cost models for electric and use of fuel oil for water heating fuel; built date; total number of natural piped gas, we explored models to estimate propane rooms; housing tenure; household income; and heating degree and oil consumption and costs. The propane model includes days. At the national level, medians for annual costs of fuel oil the following variables: census division; (use of bottled gas for from published 2009 AHS estimates are significantly different heat)*(heating degree days); use of bottled gas for water heat-from the RECS, but medians from the model are not. At the ing; (use of bottled gas for cooking)*(number of people in the regional level (we did not examine fuel oil at the census division household); (use of bottled gas for clothes drying)*(number of level because of small sample sizes for oil in some divisions), people in the household); household income; built date of unit; 2009 AHS published estimates are statistically different from the unit type; total number of rooms; and housing tenure. Table 12

RECS in two out of four regions. Fuel oil model medians are presents statistics from the original 2009-published AHS propane not statistically different from the RECS at the regional level.15

estimates and propane model estimates compared with the RECS.

Table 13. Annual Cost of Fuel Oil ($), National Quantiles At the national level, average monthly cost medians for propane Quantile

2009 AHS

Model

2009 RECS

from the 2009 AHS and the model are not significantly differ-1%

80

1.25

191

ent from the RECS. At the census division level, the 2009 AHS

5%

350

2.71

367

estimates for four divisions are significantly different from the 10%

600

260.99

571

RECS, whereas only one division estimate based on the model 25% Q1

1,000

1,083.17

847

is statistically different from the RECS. Propane cost quantiles 50% median

1,600

1,465.05

1,245

75% Q3

2,500

1,712.43

1,871

by census division are in appendix F.

90%

3,500

1,917.54

2,641

Propane models are converted from consumption estimates to 95%

4,162

2,097.44

2,985

99%

*

2,879.65

3,955

costs using prices estimated from the 2009 RECS. After discus sions AHS = American Housing Survey. Q = quantile. RECS = Residential Energy with EIA and comparisons of propane price data with RECS

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.

2015 AHS Redesign Series

10

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Oil costs are converted from consumption estimates to costs in the percentage and count of burdened households at the using prices estimated from the 2009 RECS. After consulting national level using the new models. This finding is based on with EIA, we concluded that adequate price data did not exist a significance test on the differences between burden estimates to convert consumption estimates into costs. Additionally, calculated using published 2009 cost estimates (old burden) although the model does predict median oil costs well, it does and those calculated using the HHCFIN models (new burden).

not follow the RECS distribution in the tails of the distribution Looking at the comparison of the published 2009 AHS UES

very well. This result is likely influenced by the small number estimates with the RECS, evidence suggests that the UES was of variables on fuel oil usage available in the AHS. For these overestimating electric and natural gas costs compared with the reasons, we decided not to implement oil models in the 2015

RECS. This overestimation partially explains higher housing AHS.

burden in the old burden calculations.

4.7  Effect of Modeled Estimates on

5.  Decisions for 2015 AHS

Housing Burden Estimates (an

Utility Data Collection

Element of Worst Case Needs)

Based on the research findings, we stopped collecting monthly We adopted the HHCFIN models for electric and natural bills for electric, piped natural gas, and bottled gas when those gas and the propane model for bottled gas in the 2015 AHS.

utilities are billed separately or when combined (that is, electric Housing costs are part of the housing burden calculation used is billed with gas or gas is billed with electric; but not electric in identifying worst case needs households; therefore, we or gas billed with oil, other fuels, trash, or water). For 2015, we wanted to assess the impact of adopting the models on housing modeled average monthly costs for electric, natural piped gas, burden estimates for very low-income renters (in particular, and bottled gas using final models developed from the RECS.

those spending more than 50 percent of household income on We concluded that the fuel oil model does not adequately estimate housing costs, who are defined as having “worst case needs”).

fuel oil consumption and that no price data were adequate to Tables 14 and 15 show percentages and counts of renter-

convert consumption estimates into costs. For that reason, we occupied housing units with household incomes of not more continued to collect data on annual fuel oil costs in the 2015 AHS.

than 50 percent of area median income that spend more than half of their household income on housing costs. We see declines 5.1  Combined Bills

Table 14. Households With Housing Costs of More Than In the 2015 AHS, when utility bills were combined with utilities 50 Percent of Household Income, Very Low-Income that could not be modeled separately (such as fuel oil, other Households, 2009 AHS (Weighted Percentages)

fuels, trash, water, and sewage), we prioritized the bills and Percentage Standard Error

LCL95

UCL95

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

41.5

0.6

40.2

42.7

priority: electricity, gas, oil, other fuel, trash, water). For com-New burden

39.8

0.7

38.5

41.2

bined bills that could not be separated and were combined with AHS = American Housing Survey.

electric or gas, we asked respondents to provide the amounts of the most recent electric and gas bills. If they did not have those Table 15. Households With Housing Costs of More Than bills, we asked them to estimate the average monthly costs of 50 Percent of Household Income, Very Low-Income Households, 2009 AHS (Weighted N, 1,000s)

electricity and gas. If respondents provided the most recent bill, the cost was estimated with the appropriate individual monthly Model

Count

Standard Error

LCL95

UCL95

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

Original estimate

7,096

129

6,843

7,350

New estimate

6,821

129

6,567

7,074

If respondents provided average monthly cost estimates, we put AHS = American Housing Survey.

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.

2015 AHS Redesign Series

11

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond model, or model 24 in table 1, which uses only the average 7. References

monthly costs provided by respondents as the estimate of costs.

If a respondent provided no bill or average monthly estimate, Carter, George. 2010. “The Use of Utility Records as an we put the case through the HHCFIN model of the utility Information Retrieval Aid in the American Housing Survey.”

with the highest priority. These procedures are in line with the Proceedings of the American Statistical Association, Survey pro cedures used in the 2013 AHS for handling these types of Research Methods Section. http://ww2.amstat.org/sections/

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

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

bill’s priority is determined and the cost is recorded with the utility of highest priority.

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 5.2  Post-Model Imputations

Statistical Association, Survey Research Methods Section.

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

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

Jsm2004-000272.pdf.

are calculated from either HHCFIN models, cost models based on the last bill, or self-reported average monthly costs. For Little, Roderick J.A., and Donald B. Rubin. 2002.  Statistical the other utilities, (fuel oil, other fuels, garbage and trash, and Analysis With Missing Data, 2nd ed. New York: John Wiley.

water and sewage), we did not propose any changes to the Myers, Raymond H. 1990.  Classical and Modern Regression With pre-2015 hot deck imputation methods.

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

6. Conclusions

National Research Council (NRC). 2012.  Effective Tracking of Building Energy Use: Improving the Commercial Buildings and The HHCFIN electric models underestimate the median electric Residential Energy Consumption Survey. Washington, DC: The average monthly cost at the national level, but the median value National Academies Press.

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

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

value.17 Using a natural logarithm transformation on consump-Ann Arbor, MI: CRC Press.

tion helped us to capture a skewed consumption distribution.

Adding household income and degree day normals in the Steffen, Barry L., George R. Carter, Marge Martin, Danilo models helped us more closely mirror the RECS electric Pelletiere, David A. Vandenbroucke, and Yunn-Gann David distribution. Like the UES, the HHCFIN model overestimates Yao. 2015.  Worst Case Needs: 2015 Report to Congress. Report the median natural gas average monthly cost, but the HHCFIN

prepared for the U.S. Department of Housing and Urban comes closer to the RECS estimate. We developed propane Development, Office of Policy Development and Research.

and fuel oil models. Both models yield medians not statistically Washington, DC: U.S. Government Publishing Office.

different than the RECS. We did not adopt the fuel oil models, U.S. Department of Housing and Urban Development (HUD) because we lacked adequate residential fuel oil price data for and U.S. Census Bureau. 2009.  American Housing Survey for the the whole country, and the model did not adequately predict United States: 2009, Current Housing Reports. Washington, DC: costs across the distribution. We adopted the HHCFIN models U.S. Government Printing Office.

for electric and natural gas and the propane model and did not implement the fuel oil model. For 2017, recently released U.S. Energy Information Administration (EIA). 2009.  Annual RECS data for 2015 will be used to update model parameters, Energy Outlook 2009 with Projections to 2030.  Washington, DC: and we are exploring adding a random contribution from the National Energy Information Center.

error term to the utilities consumption estimates.18

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 un-certainty 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.

2015 AHS Redesign Series

12

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Appendix A. Variables in Models

Table A1. Variables in Models

UES

HHC

HHCR

UESR

HHCR2

ONE BILL

HHCFIN

Input data

2001 RECS

2001 RECS  2009 RECS

2009

2009 RECS

2009 RECS

2009 RECS

(inflated to

(inflated to

RECS

2009)

2009)

Response

Cost

Cost

ln (consumption+1) Cost

ln (consumption+1) ln (consumption+1) ln (consumption+1) variable

Independent  Monthly

Electric heat Heating degree

Same as

Same as HHCR

Same as HHCR2,

Same as HHCR2,

variables

billing data

days (if electric

UES

models, used

but run only on

but using heating

and some

heat)

degree days

cases with April

degree days and

housing and

from 2009 AHS

bills and including  cooling degree

household

file, household

the April bill as a

days from 30-year

characteristics

income, weighted,  predictor.

(1980–2010) climate

(similar to

some collapsed

normals from NOAA

HHC model)

geographies

for units with

compared with

less billing

HHCR

data

Gas heat

Heating degree

days (if gas heat)

Cooling degree

days (if central air)

Cooling degree

days (if wall air unit)

Electric hot  Electric hot water

water

Gas hot

Gas hot water

water

Year built

Year built

Type of

Single unit

housing unit  (indicator)

(single = 1,

multi = 2,

mobile = 3)

Multiunit (indicator)

Mobile home

(indicator)

Total rooms Total rooms

Total

Total bathrooms

bathrooms

Total major  Total major

appliances

appliances

Number of  Number of

household  household

members

members

AHS = American Housing Survey. ln = natural log. NOAA = National Oceanic and Atmospheric Administration. RECS = Residential Energy Consumption Survey.

2015 AHS Redesign Series

13

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Table A2. Electric Final Models—Variable Recoding (1 of 2) RECS Input

Model

Estimating

Universe

Definition Using AHS Variables

Levels

Notes

Variable

Electric-only Average monthly

status in (‘1’) and

RDCOLL

FIPSTATE coded into Reportable

14

‘BY’ VARIABLE—

costs of electricity uselect = ‘1’ and

(reportable

domain categories

Started with 27

buye not (‘1’,’2’,’3’)  domain)

levels, but collapsed

and usegas ne ‘1’

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

RECS ranges are

+ famrm + recrm +dens + othfn,25)

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 is the

COOLDAY

cooling degree days

variable

MONEY_R

ZINC2

RECS ranges

collapsed to $10,000

groups

Electric-gas  Average monthly

status in (‘1’) and

RDCOLL

FIPSTATE coded into Reportable

17

‘BY’ VARIABLE—

mixed

cost of electricity

uselect=‘1’ and buye  (Reportable

Domain categories

started with 27,

not in (‘1’,’2’,’3’) and  Domain)

collapsed to bring

usegas = ‘1’

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’)

2015 AHS Redesign Series

14

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Table A2. Electric Final Models—Variable Recoding (2 of 2) RECS Input

Model

Estimating

Universe

Definition Using AHS Variables

Levels

Notes

Variable

NU2

nunit2=‘3’

NU3

nunit2=‘4’

TOTROOMS min (bedrms + kitch + living + dining

RECS ranges are

+ famrm + recrm + dens + othfn,25)

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 is cooling

coolday

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  status in (‘1’) and usegas=‘1’ and  RDCOLL

FIPSTATE coded

17

‘BY’ VARIABLE—Started

cost of piped

buyg not in (‘1’,’2’,’3’) and gaspip

into reportable domain

with 27 levels, but

natural gas

= ‘1’

categories

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 =

/*removed degree days

‘2’ and gaspip = ‘1’)

because distributions

were adversely af-

fected*/

STEAM

(hequip = 2) * (hfuel =

/*removed degree days

‘2’ and gaspip = ‘1’)

because distributions

were adversely af-

fected*/

GH2O

(wfuel = ‘2’ and gaspip

= ‘1’)

YRBUILT

BUILT

7

RECS YEARMAD-

ERANGE collapsed to

link to AHS

NU1

nunit2 in (‘1’,’2’)

NU2

nunit2 = ‘3’

NU3

nunit2 = ‘4’

TOTROOMS min (bedrms + kitch +

RECS ranges are

living + dining + famrm

capped at 25

+ recrm + dens +

othfn,25)

2015 AHS Redesign Series

15

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Table A3. Gas Final Models—Variable Recoding (2 of 2) Model

Estimating

Universe

Input

Definition Using

Levels

Notes

BATHRMS

min (baths + (0.5 *

RECS ranges went to 9

halfb),7)

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

COOLDAY is cooling

ge 1) * coolday

degree days.

MONEY_R

ZINC2

12

RECS ranges collapsed

to $10,000 groups

Propane

Average monthly  status in (‘1’) and usegas=‘1’ and  DIV

DIV

9

‘BY’ VARIABLE—small-

cost of bottled

(buyg not in (‘1’,’2’,’3’) or (buyg

est sample size 56—

natural gas

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

opted to use instead of

gaspip=‘2’

REGION

GASHEAT

(hfuel = ‘2’ and gaspip

HEATDAY is heating

= ‘2’) * heatday

degree days

GASH2O

(wfuel = ‘2’ and gaspip

= ‘2’)

GCOOK

(cfuel = ‘2’ and gaspip

RECS ranges went to

= ‘2’) * min (curper,12)

15, I capped at 12

GDRY

(dfuel = ‘2’ and gaspip

RECS ranges went to

= ‘2’) * min(curper,12)

15, I capped at 12

MONEY_R

ZINC2

12

RECS ranges collapsed

to $10,000 groups

YRBUILT

BUILT

7

RECS YEARMAD-

ERANGE 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.

2015 AHS Redesign Series

16

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Appendix B. Regional Electric Quantiles

Table B1. Average Monthly Cost of Electricity ($), Northeast Quantiles 2009 AHS

Quantile

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 2009 AHS

Quantile

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 2009 AHS

Quantile

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.

2015 AHS Redesign Series

17

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Table B4. Average Monthly Cost of Electricity ($), West Quantiles 2009 AHS

Quantile

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.

2015 AHS Redesign Series

18

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Appendix C. Regional Natural Gas Quantiles

Table C1. Average Monthly Cost of Natural Gas ($), Northeast Quantiles 2009 AHS

Quantile

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 2009 AHS

Quantile

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 2009 AHS

Quantile

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.

2015 AHS Redesign Series

19

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Table C4. Average Monthly Cost of Natural Gas ($), West Quantiles 2009 AHS

Quantile

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.

2015 AHS Redesign Series

20

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

2015 AHS Redesign Series

21

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

2015 AHS Redesign Series

22

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Appendix F. Census Division Propane Quantiles Table F1. Average Monthly Cost of Propane ($), Table F2. Average Monthly Cost of Propane ($), New England Quantiles

Middle Atlantic Quantiles

Quantile

2009 AHS

Model

2009 RECS

Quantile

2009 AHS

Model

2009 RECS

1%

9

—

6.25

1%

17

—

4.92

5%

18

—

8.50

5%

21

15.19

8.83

10%

—

20.31

13.00

10%

31

16.76

11.75

25% Q1

28

25.72

21.25

25% Q1

43

21.09

27.25

50% median

48

34.31

39.25

50% median

72

28.04

67.75

75% Q3

59

104.31

107.42

75% Q3

128

132.32

135.00

90%

79

205.43

182.67

90%

156

199.22

227.33

95%

79

250.03

201.17

95%

182

229.21

280.83

99%

*

—

243.58

99%

*

295.95

398.25

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

Consumption Survey.

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

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

percentile.

Table F3. Average Monthly Cost of Propane ($), Table F4. Average Monthly Cost of Propane ($), East North Central Quantiles

West North Central Quantiles

Quantile

2009 AHS

Model

2009 RECS

Quantile

2009 AHS

Model

2009 RECS

1%

19

—

4.33

1%

20

—

5.00

5%

42

53.91

8.50

5%

—

41.99

28.25

10%

48

58.57

15.67

10%

28

52.44

50.08

25% Q1

64

96.01

69.17

25% Q1

51

73.46

68.33

50% median

91

123.81

115.33

50% median

62

104.35

102.33

75% Q3

132

147.31

154.17

75% Q3

96

134.40

132.67

90%

440

194.87

217.00

90%

185

164.15

189.00

95%

440

223.60

271.17

95%

185

179.86

244.83

99%

*

308.22

396.58

99%

*

212.13

448.17

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

Consumption Survey.

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

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

percentile.

Table F5. Average Monthly Cost of Propane ($), Table F6. Average Monthly Cost of Propane ($), South Atlantic Quantiles

East South Central Quantiles

Quantile

2009 AHS

Model

2009 RECS

Quantile

2009 AHS

Model

2009 RECS

1%

14

—

6.17

1%

9

—

2.25

5%

17

17.16

10.75

5%

13

33.64

8.58

10%

34

19.95

14.08

10%

14

33.64

28.83

25% Q1

43

26.49

25.67

25% Q1

31

47.66

38.50

50% median

63

41.25

40.42

50% median

60

63.30

53.67

75% Q3

103

83.82

74.75

75% Q3

88

76.41

83.33

90%

122

141.67

121.00

90%

133

96.62

133.92

95%

196

180.15

155.92

95%

157

117.43

151.83

99%

*

323.12

337.17

99%

*

156.33

191.50

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

Consumption Survey.

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

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

percentile.

2015 AHS Redesign Series

23

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Table F7. Average Monthly Cost of Propane ($), Table F8. Average Monthly Cost of Propane ($), West South Central Quantiles

Mountain Quantiles

Quantile

2009 AHS

Model

2009 RECS

Quantile

2009 AHS

Model

2009 RECS

1%

16

—

8.17

1%

11

—

9.917

5%

18

20.21

9.58

5%

—

20.53

9.917

10%

23

21.03

13.50

10%

24

—

15.167

25% Q1

31

25.63

22.92

25% Q1

32

39.92

35.250

50% median

49

48.51

53.42

50% median

46

112.07

88.917

75% Q3

73

75.67

87.00

75% Q3

60

191.65

121.917

90%

96

118.60

136.58

90%

66

279.57

168.167

95%

—

139.22

172.00

95%

—

396.09

242.500

99%

*

162.55

273.33

99%

*

699.33

303.833

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

Consumption Survey.

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

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

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.

2015 AHS Redesign Series

24

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Appendix G. Regional Oil Quantiles

Table G1. Annual Cost of Fuel Oil ($), Northeast Table G2. Annual Cost of Fuel Oil ($), Midwest Quantiles

Quantiles

Quantile

2009 AHS

Model

2009 RECS

Quantile

2009 AHS

Model

2009 RECS

1%

175.00

1.09

256.00

1%

50.00

—

262.00

5%

500.00

2.16

381.00

5%

65.00

—

319.00

10%

700.00

967.60

651.00

10%

220.00

112.32

618.00

25% Q1

1,199.00

1,302.36

959.00

25% Q1

650.00

317.33

730.00

50% median

1,876.00

1,534.51

1,364.00

50% median

1,200.00

648.70

905.00

75% Q3

2,765.00

1,740.68

2,002.00

75% Q3

1,663.00

1,244.96

1,205.00

90%

3,800.00

1,891.55

2,783.00

90%

2,500.00

2,034.83

1,674.00

95%

4,566.00

2,029.07

3,241.00

95%

3,000.00

2,939.96

1,811.00

99%

*

2,373.91

4,085.00

99%

*

5,889.82

2,760.00

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

Consumption Survey.

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

— Less than 10 cases. * The value at the 99% quantile is suppressed, because on the public use file are suppressed at the 97.5th percentile.

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

Table G3. Annual Cost of Fuel Oil ($), South  Quantiles Table G4. Annual Cost of Fuel Oil ($), West Quantiles Quantile

2009 AHS

Model

2009 RECS

Quantile

2009 AHS

Model

2009 RECS

1%

96.00

—

133.00

1%

60.00

—

298.00

5%

225.00

108.44

268.00

5%

190.00

—

298.00

10%

400.00

249.83

392.00

10%

400.00

—

544.00

25% Q1

770.00

445.16

636.00

25% Q1

800.00

—

708.00

50% median

1,200.00

973.87

841.00

50% median

1,200.00

621.30

859.00

75% Q3

1,700.00

1,495.01

1,473.00

75% Q3

1,637.00

1,166.06

1,087.00

90%

2,400.00

2,109.86

1,822.00

90%

2,040.00

—

1,236.00

95%

3,360.00

—

2,323.00

95%

3,300.00

—

1,258.00

99%

*

3,168.23

2,907.00

99%

*

—

1,533.00

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

Consumption Survey.

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

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

percentile.

2015 AHS Redesign Series

25

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

2015 AHS Redesign Series

26

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 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 ½

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.

2015 AHS Redesign Series

27

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

2015 AHS Redesign Series

28

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 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 ½

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.

2015 AHS Redesign Series

29

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

2015 AHS Redesign Series

30

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

2015 AHS Redesign Series

31

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

2015 AHS Redesign Series

32

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Appendix L.  Consumption and Cost Estimate Cumulative Distribution Functions (CDFs)

Consumption

2009 ahs vs. 2009 recs electric consumption cdfs recs_cdf

100

90

80

70

60

50

40

30

20

10

0

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

110000

120000

130000

140000

150000

160000

Total Site Electricity usage, in kilowatt-hours, 2009

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

2015 AHS Redesign Series

33

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs natural gas consumption cdfs recs_cdf

100

90

80

70

60

50

40

30

20

10

0

0

1000

2000

3000

4000

5000

Total Natural Gas usage, in hundred cubic feet, 2009

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

2015 AHS Redesign Series

34

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs propane consumption cdfs recs_cdf

100

90

80

70

60

50

40

30

20

10

0

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

Total LPG/Propane usage, in gallons, 2009

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

2015 AHS Redesign Series

35

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

2009 ahs vs. 2009 recs electric cost cdfs

recs_cdf

100

90

80

70

60

50

40

30

20

10

0

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

13000

14000

15000

16000

17000

18000

19000

20000

Total Electricity cost, in whole dollars, 2009

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

2015 AHS Redesign Series

36

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs natural gas cost cdfs recs_cdf

100

90

80

70

60

50

40

30

20

10

0

0

1000

2000

3000

4000

5000

6000

7000

Total Natural Gas cost, in whole dollars, 2009

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

2015 AHS Redesign Series

37

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs propane cost cdfs

recs_cdf

100

90

80

70

60

50

40

30

20

10

0

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

13000

14000

15000

16000

17000

18000

19000

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

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

2015 AHS Redesign Series

38

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Appendix M. Independent Variable Cumulative Distribution Functions

Independent Variable Definitions—

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

•  EHDEMAND = (hfuel=‘1’)*HEATDAY

famrm + recrm + dens + othfn,25)

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

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

•  CENAIR = (airsys=‘1’) * coolday

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

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

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

•  PEOPLE = min (curper,12)

•  EH20 = (wfuel=‘1’)

•  MONEY_R = ZINC2 in Groups of $10,000

•  GH20 = (wfuel=‘2’ and gaspip=‘1’)

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

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

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

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

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

•  YRBUILT = BUILT

•  HEATDAY = heating degree days

•  NU1 = nunit2 in (‘1’,’2’)

•  COOLDAY = cooling degree days

•  NU2 = nunit2 = ‘3’

•  NU3 = nunit2 = ‘4’

2009 ahs vs. 2009 recs ehdemand cdfs

recs_cdf

100

90

80

70

60

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

ehdemand

PLOT

RECS

AHS

2015 AHS Redesign Series

39

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs ghdemand cdfs

recs_cdf

100

90

80

70

60

50

40

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

13000

ghdemand

PLOT

RECS

AHS

2009 ahs vs. 2009 recs cenair cdfs

recs_cdf

100

90

80

70

60

50

40

30

0

1000

2000

3000

4000

5000

6000

cenair

PLOT

RECS

AHS

2015 AHS Redesign Series

40

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs wall cdfs

recs_cdf

100

90

80

70

0

1000

2000

3000

4000

5000

6000

wal

PLOT

RECS

AHS

2009 ahs vs. 2009 recs eh2o cdfs

recs_cdf

100

90

80

70

60

50

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

eh2o

PLOT

RECS

AHS

2015 AHS Redesign Series

41

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs gh2o cdfs

recs_cdf

100

90

80

70

60

50

40

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

gh2o

PLOT

RECS

AHS

2009 ahs vs. 2009 recs gsteam cdfs

recs_cdf

100

99

98

97

96

95

94

93

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

gsteam

PLOT

RECS

AHS

2015 AHS Redesign Series

42

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs gnosteam cdfs

recs_cdf

100

90

80

70

60

50

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

gnosteam

PLOT

RECS

AHS

2009 ahs vs. 2009 recs yrbuilt cdfs

recs_cdf

100

90

80

70

60

50

40

30

20

10

1

2

3

4

5

6

7

yrbuilt

PLOT

RECS

AHS

2015 AHS Redesign Series

43

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs nu1 cdfs

recs_cdf

100

90

80

70

60

50

40

30

20

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

nu1

PLOT

RECS

AHS

2009 ahs vs. 2009 recs nu2 cdfs

recs_cdf

100

90

80

70

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

nu2

PLOT

RECS

AHS

2015 AHS Redesign Series

44

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs nu3 cdfs

recs_cdf

100

99

98

97

96

95

94

93

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

nu3

PLOT

RECS

AHS

2009 ahs vs. 2009 recs totrooms cdfs

recs_cdf

100

90

80

70

60

50

40

30

20

10

0

0

10

20

30

Total number of rooms in the housing unit

PLOT

RECS

AHS

2015 AHS Redesign Series

45

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs baths cdfs

recs_cdf

100

90

80

70

60

50

40

30

20

10

0

0

1

2

3

4

5

6

7

8

9

10

baths

PLOT

RECS

AHS

2009 ahs vs. 2009 recs majappl cdfs

recs_cdf

100

90

80

70

60

50

40

30

20

10

0

0

1

2

3

4

5

6

7

8

9

10

11

12

majappl

PLOT

RECS

AHS

2015 AHS Redesign Series

46

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs people cdfs

recs_cdf

100

90

80

70

60

50

40

30

20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

people

PLOT

RECS

AHS

2009 ahs vs. 2009 recs money_r cdfs

recs_cdf

100

90

80

70

60

50

40

30

20

10

0

1

2

3

4

5

6

7

8

9

10

11

12

money_r

PLOT

RECS

AHS

2015 AHS Redesign Series

47

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs ph2o cdfs

recs_cdf

100

99

98

97

96

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ph2o

PLOT

RECS

AHS

2009 ahs vs. 2009 recs pcook cdfs

recs_cdf

100

99

98

97

96

95

0

1

2

3

4

5

6

7

8

9

10

pcook

PLOT

RECS

AHS

2015 AHS Redesign Series

48

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 ahs vs. 2009 recs pdry cdfs

recs_cdf

100.0

99.9

99.8

99.7

99.6

99.5

99.4

99.3

99.2

99.1

99.0

98.9

98.8

0

1

2

3

4

5

6

7

8

9

10

11

12

pdry

PLOT

RECS

AHS

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

100

90

80

70

60

50

40

30

20

10

0

0

10000

20000

30000

heatday

PLOT

RECS

Orig. Match

Norms

2015 AHS Redesign Series

49

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond 2009 recs vs. 2009 match vs. 2009 norms coolday cdfs recs_cdf

100

90

80

70

60

50

40

30

20

10

0

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

13000

14000

coolday

PLOT

RECS

Orig. Match

Norms

2015 AHS Redesign Series

50

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Appendix N. Imputation Rates in Different RECS Models Table N1. 2009 RECS Electric-Only Model Input Table N2. RECS Electric-Gas Mixed Model Input Variables, Imputation Rates

Variables, Imputation Rates

Variable Name

Imputation Rate (%)

Variable Name

Imputation Rate (%)

FUELHEAT

0.82

FUELHEAT

1.47

FUELH20

3.76

FUELH20

7.49

YEARMADERANGE

2.45

YEARMADERANGE

2.88

ZNHSLDMEM

0.16

ZNHSLDMEM

0.44

COOLTYPE

0.18

COOLTYPE

0.09

MONEYPY

10.24

MONEYPY

14.16

STOVEN

0.02

STOVEN

0.00

STOVE

0.00

STOVE

0.00

OVEN

0.00

OVEN

0.00

NUMFRIG

0.02

NUMFRIG

0.00

DISHWASH

0.00

DISHWASH

0.00

CWASHER

0.00

CWASHER

0.00

DRYER

0.00

DRYER

0.00

NCOMBATH

0.10

NCOMBATH

0.01

NHAFBATH

0.09

NHAFBATH

0.01

RECS = Residential Energy Consumption Survey.

RECS = Residential Energy Consumption Survey.

Table N3. 2009 RECS Natural Gas Model Input

Table N4. 2009 RECS Propane Model Input Variables, Variables, Imputation Rates

Imputation Rates

Variable Name

Imputation Rate (%)

Variable Name

Imputation Rate (%)

FUELHEAT

0.76

FUELHEAT

1.05

FUELH20

5.06

FUELH20

2.70

YEARMADERANGE

2.72

DRYERFUEL

0.51

ZNHSLDMEM

0.45

FUELFOOD

0.37

COOLTYPE

0.10

KOWNRENT

0.72

MONEYPY

13.92

YEARMADERANGE

0.88

STOVEN

0.00

ZNHSLDMEM

0.07

STOVE

0.00

MONEYPY

10.19

OVEN

0.00

RECS = Residential Energy Consumption Survey.

NUMFRIG

0.00

DISHWASH

0.00

CWASHER

0.00

DRYER

0.00

NCOMBATH

0.01

NHAFBATH

0.01

RECS = Residential Energy Consumption Survey.

2015 AHS Redesign Series

51

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Beyond Appendix O. Utility Price Data

Electric

For 2015 American Housing Survey production, we used the 1. Navigate to http://www.eia.gov/electricity/monthly.

following.

2. Locate tables 5.4.A (Retail Sales of Electricity to Ultimate 1. Navigate to http://www.eia.gov/dnav/pet/pet_pri_wfr_a_

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

EPLLPA_PRS_dpgal_w.htm.

of Electricity to Ultimate Customers by End-Use Sector) for 2. Obtain each state’s weekly propane residential price data the months of May 2014 through April 2015.

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

•  Note that the reports are 2 months behind the reporting

•  Note that price data are collected only through the winter month; for example, the June 2014 report contains the months.

data applicable for April 2014.

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

3. Obtain the residential sales and average price from each

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

month for each state.

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

Natural Gas

forecasts/aeo/data.cfm#enprisec.

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

5. Locate the appropriate census division in table 3.

2. Locate tables 2 (Natural Gas Consumption by End Use) and 6. Locate the 2015 projection for propane, in 2013 $/million 3 (Natural Gas Price).

British thermal units (BTUs).

3. Obtain the monthly residential consumption and price for 7. Divide this number by 10.948945, which is the conversion each state for the months of May 2014 through April 2015.

factor to convert BTUs to gallons.

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

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

month. Consumption and price overall are fairly cyclical.

energy_units for more details.

Propane

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

We calculated an average price from the Residential Energy 9. Use the calculator in http://data.bls.gov/cgi-bin/cpicalc.pl.

Consumption Survey cost and consumption data using the propane universe we defined with the cases in RECS2009_

PUBLIC_V3.sas7bdat.

2015 AHS Redesign Series

52

Document Outline

- Title Page

- Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Bey

- Purpose

- 1. The American Housing Survey

- 1.1 2015 AHS Redesign

- 1.2 Utility Cost Data Collection Prior to 2015

- 1.3 Electric and Gas Cost Data Collection Prior to 2015

- 1.4 Other Utilities

- 2. Utility Data Editing, Estimation, and Imputation

- 2.1 The Utility Estimation System

- 2.2 History of Model Development

- 2.3 Strengths and Limitations of Using the RECS

- 3. Changes for 2015 Evaluated in Research

- 3.1 Study Design

- 3.2 Models

- 4. Evaluation Methods

- 4.1 Quantiles for All Units

- 4.2 Medians for Selected Housing and Household Characteristics

- 4.3 Categorical Percent Distributions

- 4.4 Regional Distribution Comparisons-Electric and Natural Gas

- 4.5 Propane Model

- 4.6 Fuel Oil Model

- 4.7 Effect of Modeled Estimates on Housing Burden Estimates (an Element of Worst Case Needs)

- 5. Decisions for 2015 AHS Utility Data Collection

- 5.1 Combined Bills

- 5.2 Post-Model Imputations

- 6. Conclusions

- 7. References

- Appendix A. Variables in Models

- Appendix B. Regional Electric Quantiles

- Appendix C. Regional Natural Gas Quantiles

- Appendix D. Regional Average Monthly Cost for Electricity Distributions

- Appendix E. Regional Average Monthly Cost for Piped Gas Distributions

- Appendix F. Census Division Propane Quantiles

- Appendix G. Regional Oil Quantiles

- Appendix H. Medians for Average Monthly Cost of Electricity by Selected Housing and Household Charac

- Appendix I. Median Average Monthly Costs for Piped Gas by Selected Housing and Household Characteris

- Appendix J. Number of 2015 AHS Cases in Different Models

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

- Appendix L. Consumption and Cost Estimate Cumulative Distribution Functions (CDFs)

- Appendix M. Independent Variable Cumulative Distribution Functions

- Appendix N. Imputation Rates in Different RECS Models

- Appendix O. Utility Price Data

Table of Contents

Title Page

Utility Cost Estimation Model Development and Decisions for the 2015 American Housing Survey and Bey

Purpose

1. The American Housing Survey

1.1 2015 AHS Redesign

1.2 Utility Cost Data Collection Prior to 2015

1.3 Electric and Gas Cost Data Collection Prior to 2015

1.4 Other Utilities

2. Utility Data Editing, Estimation, and Imputation

2.1 The Utility Estimation System

2.2 History of Model Development

2.3 Strengths and Limitations of Using the RECS

3. Changes for 2015 Evaluated in Research

3.1 Study Design

3.2 Models

4. Evaluation Methods

4.1 Quantiles for All Units

4.2 Medians for Selected Housing and Household Characteristics

4.3 Categorical Percent Distributions

4.4 Regional Distribution Comparisons-Electric and Natural Gas

4.5 Propane Model

4.6 Fuel Oil Model

4.7 Effect of Modeled Estimates on Housing Burden Estimates (an Element of Worst Case Needs)

5. Decisions for 2015 AHS Utility Data Collection

5.1 Combined Bills

5.2 Post-Model Imputations

6. Conclusions

7. References

Appendix A. Variables in Models

Appendix B. Regional Electric Quantiles

Appendix C. Regional Natural Gas Quantiles

Appendix D. Regional Average Monthly Cost for Electricity Distributions

Appendix E. Regional Average Monthly Cost for Piped Gas Distributions

Appendix F. Census Division Propane Quantiles

Appendix G. Regional Oil Quantiles

Appendix H. Medians for Average Monthly Cost of Electricity by Selected Housing and Household Charac

Appendix I. Median Average Monthly Costs for Piped Gas by Selected Housing and Household Characteris

Appendix J. Number of 2015 AHS Cases in Different Models

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

Appendix L. Consumption and Cost Estimate Cumulative Distribution Functions (CDFs)

Appendix M. Independent Variable Cumulative Distribution Functions

Appendix N. Imputation Rates in Different RECS Models

Appendix O. Utility Price Data