CHAPTER 10. SHIPMENTS ANALYSIS 

TABLE OF CONTENTS 

10.1 INTRODUCTION ......................................................................................................... 10-1 

10.2 SHIPMENTS MODEL METHODOLOGY.................................................................. 10-1 

10.3 DATA INPUTS AND MODEL CALIBRATION ........................................................ 10-4 

10.3.1 Historical Shipments...................................................................................................... 10-5 

10.3.2 Markets and Model Calibration ..................................................................................... 10-7 

10.3.2.1 New Construction ........................................................................................ 10-7 

10.3.2.2 Replacements ............................................................................................. 10-11 

10.3.2.3 Model Calibration—Non-Replacements ...................................................10-12 

10.3.3 Base Case Shipments ................................................................................................... 10-14 

10.4 PURCHASE PRICE, OPERATING COST, AND HOUSEHOLD INCOME
 

IMPACTS .................................................................................................................... 10-15 

10.5 AFFECTED STOCK ................................................................................................... 10-18 

10.6 RESULTS .................................................................................................................... 10-18 

LIST OF TABLES 

Table 10.3.1 Commercial Clothes Washers: Historical Shipments, Domestic plus 

Imports ............................................................................................................... 10-6 

Table 10.3.2 Historical and Forecasted Multi-Family Housing Starts (millions)................... 10-9 

Table 10.3.3 Commercial Clothes Washers: Historical New Multi-Family Building 

Saturations........................................................................................................ 10-10 

Table 10.3.4 Commercial Clothes Washers: Historical New Multi-Family Building 

Saturations........................................................................................................ 10-13 

Table 10.3.5 Multi-Family In-Unit Washer Saturations....................................................... 10-13 

Table 10.4.1 Change in Relative Price Elasticity following a Purchase Price Change ........ 10-17 

Table 10.6.1 Top-Loading Commercial Clothes Washers: Cumulative Shipments 

Impacts Caused by Standards over the Period 2013–2043.............................. 10-19 

Table 10.6.2 Front-Loading Commercial Clothes Washers: Cumulative Shipments 

Impacts Caused by Standards over the Period 2013–2043.............................. 10-19 

LIST OF FIGURE 

Figure 10.3.1 Commercial Clothes Washer Shipments and Trend in Quantity Index............. 10-7 

Figure 10.3.2 Commercial Laundry Saturation in New Multi-Family Housing.................... 10-11 

Figure 10.3.3 Commercial Clothes Washers: Survival and Retirement Functions............... 10-12 

10-i 

EERE-2006-STD-0127 COMMENT 69.1
Figure 10.3.4 Commercial Clothes Washers: Historical and Base Case Shipments 

Forecast............................................................................................................ 10-14 

Figure 10.3.5 Commercial Clothes Washers: Disaggregated Base Case Shipments 

Figure 10.6.1 Top-Loading Commercial Clothes Washers: Base Case and TSL 

Figure 10.6.2 Front-Loading Commercial Clothes Washers: Base Case and TSL 

Forecast............................................................................................................ 10-15 

Shipments Forecasts......................................................................................... 10-19 

Shipments Forecasts......................................................................................... 10-20 

10-ii 

EERE-2006-STD-0127 COMMENT 69.1
 

CHAPTER 10. SHIPMENTS ANALYSIS 

10.1 INTRODUCTION 

Product shipments estimates are a necessary input to the national energy savings (NES) 

and net present value (NPV) calculations. Shipments are also a necessary input to the 

manufacturer impact analysis (MIA), which DOE conducts for its notices of proposed 

rulemaking (NOPRs). This chapter describes DOE’s methodology for projecting annual 

shipments and presents results commercial clothes washers. 

DOE estimated shipments for commercial clothes washers with a Shipments Model. 

DOE calibrated the Shipments Model against historical shipments. For purposes of estimating 

the impacts of prospective trial standard levels (TSL) on product shipments, the Shipments 

Model accounts for the combined effects of changes in purchase price, annual operating cost, and 

household income on the consumer purchase decision. 

The Shipments Model considers specific market segments to estimate shipments, the 

results for which are then aggregated to estimate total product shipments. In the case of 

commercial clothes washers, DOE accounted for three market segments: (1) new construction; 

(2) existing buildings (i.e., replacing failed equipment); and (3) retired units not replaced. 

The Shipments Model is a Microsoft Excel spreadsheet that is accessible on the Internet 

(http://www.eere.energy.gov/buildings/appliance_standards/). Appendix 11A discusses how to 

access the Shipments Model spreadsheet and other related spreadsheets, and provides basic 

instructions for using them. The rest of this chapter explains the Shipments Model in more 

detail. Section 10.2 presents the Shipments Model methodology; section 10.3 describes the data 

inputs and the model calibration; section 10.4 discusses impacts on shipments from changes in 

equipment purchase price, operating cost, and household income; section 10.5 discusses the 

affected stock; and section 10.6 presents the results for different TSL scenarios. 

This chapter presents information and results pertaining solely to CCWs. As described in 

section 1.3 of chapter 1 of this technical support document (TSD), DOE is continuing the 

rulemaking for energy conservation standards for microwave oven standby power, and analyses 

related to these products will be published in a separate TSD. In addition, DOE issued a final 

rule1

 adopting energy conservation standards for conventional cooking products (i.e., cooktops 

and ovens) and microwave oven energy factor (EF), and details of the analyses for these products 

are contained in the corresponding final rule TSD.2 

10.2 SHIPMENTS MODEL METHODOLOGY 

DOE developed a national stock model for estimating annual shipments for commercial 

clothes washers. The model considers market segmentation as a distinct input to the shipments 

10-1 

EERE-2006-STD-0127 COMMENT 69.1
_ _ 

 

forecast. As represented by the following equation, the two primary market segments are new 

installations and replacements. 

Ship ( j ) = Rpl ( j ) + NI ( j ) p p p 

where: 

Shipp(j) = Total shipments of product p in year j, 

Rplp(j) = Units of product p retired and replaced in year j, and 

NIp(j) = Number of new installations of product p in year j. 

As the product-specific sections below discuss, DOE also considered a third market 

segment for most of the products to calibrate its Shipments Models to historical shipments data. 

In principle, each market segment and each product class responds differently to both the 

base case demographic and economic trends and to the implementation of standards. 

Furthermore, retirements, early replacements, and efficiency trendsa

 are dynamic and can vary 

among product classes. Rather than simply extrapolating a current shipments trend, the base 

case shipments analysis (i.e., the case without new standards) uses driver input variables, such as 

construction forecasts and product lifetime distributions, to forecast sales in each market 

segment. Thus, DOE’s Shipments Model assumes that construction, i.e., new housing units, 

drives new installations. In each year, the product shipments from the new construction market 

segment are equal to the number of new housing units built times the purchase rate, which is 

determined by the product class market share and the market saturation of the product under 

consideration. 

DOE’s Shipments Model takes an accounting approach, tracking market shares of each 

product class, the vintage of units in the existing stock, and expected construction trends. The 

models estimate shipments due to replacements using sales in previous years and assumptions 

about the lifetime of the equipment. Therefore, estimated sales due to replacements in a given 

year are equal to the total stock of the appliance minus the sum of the appliances sold in previous 

years that still remain in the stock. DOE determined the useful service life of each appliance to 

estimate how long the appliance is likely to remain in stock. The following equation represents 

how DOE estimated replacement shipments. 

_ ageMax j 1 

Rpl p ( j ) = Stock p ( j 1) ‡” ‡” Ship j × probRtr ( age ) 

age=0 j= N 

where: 

a

 Efficiency trends affect shipments only in the standards case. A change in the efficiency distribution of the stock 

results in a change in the purchase price and operating cost and, therefore, produces a purchase price and operating 

cost impact on the shipments. This is discussed later in the chapter in Section 10.4. 

10-2 

EERE-2006-STD-0127 COMMENT 69.1
Stockp (j-1) = Total stock of in-service appliances in year j-1, 

probRtr (age) = Probability that an appliance of a particular age will be retired, and 

N = Start year for when the model begins its stock accounting (start year is 

specific to each product based on available historical shipments data). 

Stock accounting takes product shipments, a retirement function, and initial in-service 

product stock as inputs and provides an estimate of the age distribution of in-service product 

stocks for all years. The age distribution of in-service product stocks is a key input to both the 

NES and NPV calculations—the operating costs for any year depend on the age distribution of 

the stock. The dependence of operating cost on the equipment age distribution occurs under a 

TSL that produces increasing efficiency over time, where older, less efficient units may have 

higher operating costs, while younger, more-efficient units will have lower operating costs. 

Furthermore, in the case of an early replacement scenario, retirements due to early replacement 

will depend on the age of the units that are subject to early replacement. 

DOE calculated total in-service stock of equipment by integrating historical shipments 

data starting from a specific year. The start year depended on the historical data available for 

the product. As units are added to the in-service stock, some of the older ones retire and exit the 

stock. To estimate future shipments, DOE developed a series of equations that define the 

dynamics and accounting of in-service stocks. For new units, the equation is: 

_ Stock( j,age = 1) = Ship( j 1) 

where: 

Stock(j, age) = The population of in-service units of a particular age, 

j = Year for which the in-service stock is being estimated, and 

Ship (j) = Number of units purchased in year j. 

The above equation states that the number of one-year-old units is simply equal to the 

number of new units purchased the previous year. The slightly more complicated equations 

(e.g., the following equation) are those that describe the accounting of the existing in-service 

stock of units: 

Stock( j +1,age +1) = Stock( j,age )×[1 _ prob Rtr ( age )] 

In the above equation, as the year is incremented from j to j+1, the age is also 

incremented from age to age+1. With time, a fraction of the in-service stock is removed, and 

that fraction is determined by a retirement probability function, probRtr(age), which is described 

in section 10.3. Because commercial clothes washers are considered to be common appliances 

that have been used by U.S. consumers for a long time, replacements typically constitute the 

majority of shipments. Most replacements are made when equipment wears out and fails. 

10-3 

EERE-2006-STD-0127 COMMENT 69.1
10.3 DATA INPUTS AND MODEL CALIBRATION 

As discussed above, shipments are driven primarily by two market segments: new 

construction and replacements. DOE used historical shipments as the basis for calibrating its 

Shipments Model. Because new construction shipments and replacements were not enough to 

account for all product shipments, DOE developed another market segment to calibrate its 

Shipments Model. This additional market segment, retired units not replaced, represented a 

small share of total shipments. Shipments to each of these market segments are discussed below. 

DOE designed its Shipments Model for commercial clothes washers by developing a 

single Shipments Model for all washers destined for the two most predominant product 

applications—multi-family buildings and laundromats—and then disaggregating the shipments 

into its two product classes—top-loading and front-loading washers. As described in Chapter 8, 

section 8.2.6, DOE estimated that 70 percent of the market is comprised of top-loading washers 

and 30 percent is comprised of front-loading washers. DOE estimated that the market share of 

top-loading and front-loading washers would remain fixed at 70 percent and 30 percent, 

respectively, through the forecast period. 

DOE’s approach for estimating shipments relies on using aggregate historical shipments 

data for all commercial clothes washers. Tracking of product stock and vintage relies on a 

retirement function, which describes the likelihood that equipment installed in a certain year will 

still be in service in any subsequent year. Once a product reaches the end of its useful life it is 

removed from service. In most cases, retirement is followed by replacement, which is a key 

driver of further shipments. The difference between replacements and total shipments for the 

following year provides the shipments going into new construction. The output includes units 

going into both laundromats and multi-family apartment buildings. 

DOE based its Shipments Model on the following three assumptions: (1) All product 

shipments for new construction are driven by the new multi-family housing market, (2) the 

relative market shares of the two product applications, laundromats and common-area laundry 

facilities in multi-family housing, are constant over time, and (3) the U.S. Census Bureau’s 

quantity index data can be used to validate the shipments trend observed in the historical data. 

DOE’s first assumption is based on the premise that multi-family occupants who do not 

have their own personal washers and dryers must either wash their laundry in the common area 

laundry facility within their building or in a laundromat. Because the vast majority of people 

that use laundromats live in multi-family buildings, the construction of any new laundromats 

would seem to be driven by new multi-family customers. Thus, DOE believes it is reasonable to 

expect that new multi-family construction starts capture all new product shipments, regardless of 

whether the units are destined for multi-family buildings or laundromats. 

With regard to the second assumption, i.e., constant market shares, DOE did not have 

sufficient data to forecast whether the market shares of laundromats and multi-family housing 

will vary over time. Therefore, DOE assumed that market shares will remain constant at current 

levels. DOE relied on a report from the Consortium for Energy Efficiency (CEE) to estimate 

10-4 

EERE-2006-STD-0127 COMMENT 69.1
 

current market shares. Based on the CEE report, multi-family buildings and laundromats 

comprise 85 percent and 15 percent of the commercial clothes washer market, respectively.3 

Although the CEE report was conducted over 10 years ago, it is the most reliable data source for 

developing a market split for CCW shipments that was available. However, because the assumed 

shares of laundromats and multi-family housing in shipments can have an effect on overall CCW 

shipments and, in turn, the NES and NPV results, DOE conducted a sensitivity analysis in which 

it used data from Alliance Laundry System’s 2008 10-K report, coupled with a number of 

assumptions, to estimate the shares of laundromats and multi-family housing in shipments of 

CCWs in 2008. The analysis, which is described in appendix 11C, yields an estimate of a 66 

percent share for multi-family housing and a 34 percent share for laundromats. 

The third assumption pertains to the use of the U.S Bureau of Economic Analysis’ 

quantity index data to validate historical shipments data.4

 The Census Bureau’s quantity index 

for commercial laundry equipment includes shipments of commercial laundry products beyond 

those covered by this rulemaking. Because disaggregated quantity index data were not 

available, DOE assumed that the market share of commercial clothes washers covered 

under this rulemaking relative to all commercial laundry was constant over the period

1977–2004.  Based on this assumption, DOE derived quantity index data specific to the 

commercial clothes washers of interest to this rulemaking and used the data to validate the 

historical shipments. 

10.3.1 Historical Shipments 

DOE used historical shipments data (i.e., domestic shipments and imports) to calibrate its 

Shipments Model. It used two sources to establish historical shipments: (1) data AHAM 

provided for the period 2002–20055

 and (2) data from Appliance Magazine6 for the period 1988– 

1998. For the period 1999–2001, DOE interpolated between the shipments for 1998 and 2002. 

For the period 1979–1987, DOE used the shipments in1989 as the reference point and the 

corresponding Census Bureau’s quantity index data to estimate shipments. Lastly, DOE used a 

five-year moving average to backcast shipments for the period 1972−1978. Table 10.3.1 

summarizes the historical commercial clothes washer shipments data. 

10-5 

EERE-2006-STD-0127 COMMENT 69.1
 

Table 10.3.1 Commercial Clothes Washers: Historical Shipments, Domestic plus Imports 

Year Shipments (thousands) Year Shipments (thousands) 

1972 167 1989 215 

1973 167 1990 225 

1974 166 1991 193 

1975 167 1992 188 

1976 167 1993 190 

1977 167 1994 205 

1978 166 1995 209 

1979 164 1996 232 

1980 170 1997 241 

1981 171 1998 265 

1982 163 1999 239 

1983 163 2000 215 

1984 167 2001 194 

1985 176 2002 175 

1986 186 2003 191 

1987 194 2004 178 

1988 213 2005 177 

Sources: 2002–2005: AHAM Data Submittal, 2006; 1988–1998: Appliance Magazine, “Statistical Review,” 1998 

and 1999. 1972–1987: Based on Census Bureau’s Quantity Index Data. 1999–2001: DOE interpolated values. 

As indicated in Table 10.3.1, there was a significant drop in commercial clothes washer 

shipments between 1998 and 2002. Because DOE used different data sources to estimate the 

shipments in 1998 and 2002, it validated the shipments data through a review of the U.S. Bureau 

of Economic Analysis’ quantity index data.4

 DOE compared the historical shipments data 

against the quantity index for commercial laundry equipment (represented by North American 

Industry Classification System (NAICS) code 333312). The largest product group in this 

industry is washers and extractors, which accounts for almost 40 percent of sales and about 25 

percent of the shipment value.4

 As shown in Figure 10.3.1 below, the commercial laundry 

quantity index tracks the shipments trend closely. Thus, DOE concluded that the downward 

trend observed in the historical shipments between 1998 and 2002 was real. 

10-6 

EERE-2006-STD-0127 COMMENT 69.1
0 

20 

40 

60 

80 

100 

120 

140 

160 

1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 

Quantity Index (2000 base) 

0.00 

0.05 

0.10 

0.15 

0.20 

0.25 

0.30 

Shipments in millions 

QI Shipments QI-based Shipments 

Pre-1988 shipments 

based on QI 

Extrapolated 

after 2005 

Figure 10.3.1 Commercial Clothes Washer Shipments and Trend in Quantity 

Index 

10.3.2 Markets and Model Calibration 

The market for commercial clothes washers primarily comprises replacement units for 

equipment that has been retired and units for new housing. Total commercial clothes washers 

shipments are represented by the following equation: 

ShipCCW ( j) = RplCCW ( j) + NICCW ( j) 

where: 

ShipCCW (j) = Total shipments of commercial clothes washers in year j, 

RplCCW (j) = Replacement shipments in year j, and
 

NICCW (j) = Shipments to new households in year j. 

The sections below discuss both of these markets in further detail. The model calibration 

is also discussed. 

10.3.2.1 New Construction 

To forecast shipments to the new construction market, DOE assumed shipments are 

driven solely by multi-family construction starts. Implicit in this assumption is the fact that a 

certain percentage of multi-family residents will need to wash their laundry in either a commonarea laundry facility (within the multi-family building) or a laundromat. 

To estimate shipments to new construction, DOE used forecasts of multi-family housing 

starts coupled with commercial clothes washer saturation data. In other words, to forecast the 

10-7 

EERE-2006-STD-0127 COMMENT 69.1
shipments for new construction for any given year, DOE multiplied the multi-housing forecasts 

by the forecasted saturation of commercial clothes washers for new multi-family housing. 

Because DOE used only multi-family housing starts as a driver for new construction shipments, 

the saturation represents washers in both the multi-family housing and laundromat markets. 

Table 10.3.2 presents historical and forecasted new multi-family housing starts based on 

the DOE Energy Information Administration (EIA)’s Annual Energy Outlook 2009 updated to 

include provisions of the American Recovery and Reinvestment Act (AEO2009 ARRA) for the 

period 1990–2030.7 The AEO typically provides three scenarios of multi-family housing starts: 

the Reference case, the High Economic Growth case, and the Low Economic Growth case. 

AEO2009 ARRA only provided housing starts for the Reference Case. As result, to estimate 

housing starts for the high and low economic growth cases, DOE used the AEO2009 March 

release to estimate the high and low growth forecasts.8

 All three housing starts forecasts are 

presented in Table 10.3.2. DOE used only the forecasts from the Reference case to estimate its 

shipments to new construction. For the period 2031−2043, DOE froze completions at the level 

in the year 2030 for all three economic forecasts. 

10-8 

EERE-2006-STD-0127 COMMENT 69.1
 

Table 10.3.2 Historical and Forecasted Multi-Family Housing Starts (millions) 

Year Reference High Low Year Reference High Low 

1990 0.30 0.30 0.30 2017 0.39 0.47 0.32 

1991 0.17 0.17 0.17 2018 0.41 0.47 0.34 

1992 0.17 0.17 0.17 2019 0.42 0.45 0.33 

1993 0.16 0.16 0.16 2020 0.43 0.40 0.29 

1994 0.26 0.26 0.26 2021 0.42 0.38 0.28 

1995 0.28 0.28 0.28 2022 0.41 0.39 0.28 

1996 0.32 0.32 0.32 2023 0.41 0.40 0.29 

1997 0.34 0.34 0.34 2024 0.41 0.41 0.29 

1998 0.35 0.35 0.35 2025 0.41 0.42 0.29 

1999 0.34 0.34 0.34 2026 0.41 0.44 0.29 

2000 0.34 0.34 0.34 2027 0.41 0.46 0.30 

2001 0.33 0.33 0.33 2028 0.40 0.47 0.30 

2002 0.35 0.35 0.35 2029 0.40 0.47 0.30 

2003 0.35 0.35 0.35 2030 0.40 0.47 0.30 

2004 0.34 0.34 0.34 2031 0.40 0.47 0.30 

2005 0.34 0.34 0.34 2032 0.40 0.47 0.30 

2006 0.35 0.34 0.34 2033 0.40 0.47 0.30 

2007 0.38 0.37 0.30 2034 0.40 0.47 0.30 

2008 0.41 0.39 0.20 2035 0.40 0.47 0.30 

2009 0.28 0.32 0.22 2036 0.40 0.47 0.30 

2010 0.18 0.22 0.24 2037 0.40 0.47 0.30 

2011 0.19 0.27 0.26 2038 0.40 0.47 0.30 

2012 0.23 0.33 0.26 2039 0.40 0.47 0.30 

2013 0.29 0.41 0.26 2040 0.40 0.47 0.30 

2014 0.31 0.43 0.26 2041 0.40 0.47 0.30 

2015 0.33 0.45 0.27 2042 0.40 0.47 0.30 

2016 0.36 0.46 0.28 2043 0.40 0.47 0.30 

Source: EIA, AEO2009 ARRA for Reference; EIA, AEO 2009 March release for High and Low. 

DOE deduced historical commercial laundry saturations for the new construction market 

based on the remaining portion of historical shipments that did not serve as replacement units. 

(The determination of replacement shipments is discussed below.) Table 10.3.3 shows the 

historical saturation of commercial clothes washers in new multi-family buildings. 

10-9 

EERE-2006-STD-0127 COMMENT 69.1
 

Table 10.3.3 Commercial Clothes Washers: Historical New Multi-Family Building 

Saturations 

Year Saturation 

1990 19.4% 

1991 14.7% 

1992 12.4% 

1993 14.3% 

1994 13.8% 

1995 12.6% 

1996 15.8% 

1997 14.6% 

1998 18.7% 

Average 15.1% 

DOE assumed that the average saturation over the period 1990−1998 of 15.1 percent 

would remain constant over the period 1999−2043. CEE’s report on the commercial laundry 

industry estimates a 14 percent saturation (i.e., approximately 14 percent of total domestic 

laundry is done in non-residential washers). CEE’s 14 percent figure is close to the 15.1 percent 

saturation value that DOE determined. Thus, DOE has confidence that its calculated saturation 

is representative of the commercial clothes washer market. Figure 10.3.2 shows the historical 

variation in the new construction saturation as well as the forecasted saturation for the years after 

1998. 

10-10 

EERE-2006-STD-0127 COMMENT 69.1
 

 

 

0% 

5% 

10% 

15% 

20% 

25% 

1985 1995 2005 2015 2025 2035 2045 

Year 

Commercial Laundry Saturation in new MultiFamily Housing 

Figure 10.3.2	 Commercial Laundry Saturation in New Multi-Family 

Housing 

10.3.2.2 Replacements 

DOE determined shipments to the replacement market using an accounting method that 

tracks the total stock of units by vintage. DOE estimated a stock of commercial clothes washers 

by vintage by integrating historical shipments starting from the year 1972. Over time, some 

units will be retired and removed from stock, thereby triggering the shipment of a replacement 

unit. Depending on the vintage, a certain percentage of each type of unit will fail and need to be 

replaced. To determine when a unit fails, DOE used a survival function based on the product 

lifetime distributions of the two product applications considered in the analysis—laundromats 

and multi-family buildings. DOE based the retirement function on a Weibull probability 

distribution for the product lifetime. The Shipments Models assume that no units are retired 

below a minimum product lifetime and all units are retired before exceeding a maximum product 

lifetime. The models determine the probability of retirement at a certain age for all products 

using the following equations: 

probRtr ( age ) = 0	 for age < AgeMin 

1

probRtr ( age ) = _ for AgeMin ≤ age ≤ AgeMax AgeMax AgeMin 

probRtr ( age ) = 1	 for age > AgeMax 

10-11 

EERE-2006-STD-0127 COMMENT 69.1
where: 

probRtr(age) = Probability of retirement at the age of the product, 

AgeMin = Minimum retirement age, and 

AgeMax = Maximum retirement age. 

For the commercial clothes washer Shipments Model, DOE combined the average values 

for the two product applications based on the market share of each application (85 percent for 

multi-family housing and 15 percent for laundromats). The survival function that DOE used had 

an average lifetime of 10.6 years with minimum and maximum values of 6.7 years and 14.6 

years, respectively. For a more complete discussion of commercial clothes washer lifetimes, 

refer back to section 8.2.3 of Chapter 8. Figure 10.3.3 shows the survival function that DOE 

used to estimate replacement shipments. 

0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

100% 

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 

Age (years) 

Percent Surviving 

Figure 10.3.3	 Commercial Clothes Washers: Survival and Retirement 

Functions 

10.3.2.3 Model Calibration—Non-Replacements 

To account for the significant drop in shipments after 1998, and to reconcile the historical 

shipments with the accounting model, DOE assumed every retired unit is not replaced 

immediately. The market share of retired units not replaced was relatively large due to the 

significant drop in shipments after 1998 (as discussed above in Section 10.3.1). Staring in 1999 

and extending to 2005, DOE estimated the share of retired units that were not replaced. The 

10-12 

EERE-2006-STD-0127 COMMENT 69.1
 

 

share of retired units not replaced fluctuated between nine and 31 percent. Table 10.3.4 shows 

the share of retired units not replaced between 1999–2005. 

Table 10.3.4	 Commercial Clothes Washers: Historical New Multi-Family Building 

Saturations 

Year Saturation 

1999 9.2% 

2000 12.9% 

2001 19.7% 

2002 25.2% 

2003 12.1% 

2004 30.8% 

2005 18.2% 

Average 18.3% 

To determine the share of retired units not replaced after the year 2005, DOE reviewed 

data from the Census Bureau’s American Housing Survey (AHS) on the saturation of in-unit 

washers (i.e., residential washers used in multi-family units) in the multi-family stock and new 

multi-family construction.9

 DOE utilized the in-unit washer trends in multi-family buildings to 

estimate the associated trends in the use of commercial clothes washers in multi-family common 

area laundry facilities. Table 10.3.5 shows the saturation of in-unit washers in both the multifamily stock and new construction from 1997–2005. 

Table 10.3.5	 Multi-Family In-Unit Washer Saturations 

In-Unit Washer Saturation 

Multi-Family Housing 1997 1999 2001 2003 2005 

New Construction 70.4% 64.4% 68.7% 68.1% 73.4% 

Stock 32.3% 32.7% 35.6% 36.1% 37.3% 

Source: AHS, 1997–2005 

As evident from Table 10.3.5, the saturation of in-unit washers in new construction has 

stayed relatively constant while the saturation in the multi-family building stock has increased by 

approximately 16 percent. Because the saturation of in-unit washers in new construction has 

stayed relatively constant, there must be another driver for the increase of in-unit washers in the 

building stock. DOE estimates that the growth in in-unit washer saturations in the multi-family 

stock over the last ten years was caused by conversions of rental property to condominiums, 

resulting in the gradual phase out or non-replacement of failed commercial clothes washers in 

common-area laundry facilities. Based on this apparent trend, DOE used the average percent of 

non-replacements over the 1999–2005 timeframe—18 percent—and maintained it over the entire 

forecast period of 2006–2043. 

10-13 

EERE-2006-STD-0127 COMMENT 69.1
10.3.3 Base Case Shipments 

Figure 10.3.4 shows the forecasted commercial clothes washer shipments in the base case 

(i.e., the case without new energy efficiency standards) and the historical shipments DOE used to 

calibrate the forecast. In the figure below, note that the replacement shipments for the period 

1998─2043 are lower than the actual retired units. The area between the two lines showing 

retired units and replacement shipments represents the retired units that were not replaced. To 

emphasize, all commercial clothes washers shipments, i.e., top-loading and front-loading units, 

are represented in the shipments shown in Figure 10.3.4. As previously stated, DOE estimated 

that 70 percent of shipments in every year of the forecast are comprised of top-loading washers 

and 30 percent of shipments in every year are comprised of front-loading washers. 

0.00 

0.05 

0.10 

0.15 

0.20 

0.25 

0.30 

1985 1995 2005 2015 2025 2035 2045 

Year 

Shipments (million) 

Historical Shipments 

Total Shipments 

Retired Modeled Units 

Replacement Modeled Shipments 

New Construction Modeled Shipments 

Figure 10.3.4	 Commercial Clothes Washers: Historical and Base Case Shipments 

Forecast 

Figure 10.3.5 presents forecasted base case commercial clothes washer shipments 

disaggregated into shipments due to retirements and shipments for new construction. DOE 

determined that replacements accruing from retirements range from 80 percent to 85 percent of 

total commercial clothes washer shipments over the period 2006–2043. 

10-14 

EERE-2006-STD-0127 COMMENT 69.1
0.00 

0.05 

0.10 

0.15 

0.20 

0.25 

2005 2010 2015 2020 2025 2030 2035 2040 

Year 

Shipments (million) 

New Construction Modeled Shipments 

Replacement Modeled Shipments 

Figure 10.3.5	 Commercial Clothes Washers: Disaggregated Base Case Shipments 

Forecast 

10.4	 PURCHASE PRICE, OPERATING COST, AND HOUSEHOLD INCOME 

IMPACTS 

DOE conducted a literature review and an analysis of appliance price and efficiency data 

to estimate the combined effects on product shipments from increases in equipment purchase 

price, decreases in equipment operating costs, and changes to household income. Appendix 10A 

provides a detailed explanation of the methodology DOE used to quantify the impacts from the 

above variables. 

In the literature, DOE found only a few studies of appliance markets that are relevant to 

this rulemaking analysis, and identified no studies that use time-series data of equipment price 

and shipments data after 1980. The information that can be summarized from the literature 

suggests that the demand for appliances is price inelastic. Other information in the literature 

suggests that appliances are a normal good, such that rising incomes increase the demand for 

10-15 

EERE-2006-STD-0127 COMMENT 69.1
 

 

appliances. Finally, the literature suggests that consumers use relatively high implicit discount 

ratesb

 when comparing appliance prices and appliance operating costs. 

DOE did not find enough equipment purchase price and operating cost data to perform a 

complex analysis of dynamic changes in the appliance market. Rather, it used purchase price 

and efficiency data specific to residential refrigerators, clothes washers, and dishwashers over the 

period 1980–2002 to evaluate broad market trends and conduct simple regression analyses. 

These data indicate that there has been a rise in appliance shipments and a decline in appliance 

purchase price and operating costs over the time period. Household income has also risen during 

this time. To simplify the analysis, DOE combined the available economic information into one 

variable, termed the relative price, and used this variable in an analysis of market trends, as well 

as to conduct a regression analysis. The relative price is defined with the following expression: 

TP PP + PVOC

RP = = 

Income Income 

where: 

RP = Relative price, 

TP = Total price, 

Income = Household income, 

PP = Appliance purchase price, and 

PVOC = Present value of operating cost. 

In the above equation, DOE used an implicit discount rate of 37 percent to determine the 

present value of operating costs. 

DOE’s analysis of market trends suggests that the relative price elasticity of demand for 

the three appliances is relatively inelastic (i.e., under 1.0). DOE’s regression analysis suggests 

that the relative price elasticity of demand, averaged over the three appliances, is -0.34. For 

example, a relative price increase of 10 percent results in a shipments decrease of 3.4 percent. 

Note that, because the relative price elasticity incorporates the impacts from three effects (i.e., 

purchase price, operating cost, and household income), the impact from any single effect is 

mitigated by changes from the other two effects. The relative price elasticity of -0.34 is 

consistent with estimates in the literature. Nevertheless, DOE stresses that the measure is based 

on a small data set, using simple statistical analysis. More important, the measure is based on an 

assumption that economic variables, including purchase price, operating costs, and household 

income, explain most of the trend in appliances per household in the United States since 1980. 

Changes in appliance quality and consumer preferences may have occurred during this period, 

but DOE did not account for them in this analysis. Despite these uncertainties, DOE believes 

b

 A high implicit discount rate with regard to operating costs means that consumers do not put much economic value 

on the operating cost savings realized from more-efficient appliances. In other words, consumers are much more 

concerned with higher purchase prices. 

10-16 

EERE-2006-STD-0127 COMMENT 69.1
 

 

 

that its estimate of the relative price elasticity of demand provides a reasonable assessment of the 

impact that purchase price, operating cost, and household income have on product shipments. 

Because DOE’s forecasts of shipments and national impacts due to standards is over a 

30-year time period, it needed to consider how the relative price elasticity is affected once a new 

standard takes effect. DOE considered the relative price elasticitiy provided above to be a shortrun value. It was unable to identify sources specific to household durable goods, such as 

appliances, to indicate how short-run and long-run price elasticities differ. Therefore, to estimate 

how the relative price elasticity changes over time, DOE relied on a study pertaining to 

automobiles.10 This study shows that the automobile price elasticity of demand changes in the 

years following a purchase price change. With increasing years after the purchase price change, 

the price elasticity becomes more inelastic until it reaches a terminal value around the tenth year 

after the price change. Table 10.4.1 shows the relative change in the price elasticity of demand 

for automobiles over time. As shown in the table, DOE developed a time series of relative price 

elasticities for home appliances based on the relative change in the automobile price elasticity of 

demand. For years not shown in the table below, DOE performed a linear interpolation to obtain 

the relative price elasticity. 

Table 10.4.1 Change in Relative Price Elasticity following a Purchase Price Change 

Years Following Price Change 

1 2 3 5 10 20 

Relative Change in 

Elasticity to 1st year 

1.00 0.78 0.63 0.46 0.35 0.33 

Relative Price Elasticity -0.34 -0.26 -0.21 -0.16 -0.12 -0.11 

Based on the following equation, DOE estimated standards case shipments by 

incorporating the impact of the relative price into the base case shipments forecast. Note that in 

the equation below, the relative price and the relative price elasticity are functions of the year 

because both change with time. 

_ Ship ( j ) = (Rpl ( j ) + NI ( j ) + M ( j ))× (1 e ( j )× ΔRP( j )) STD _ p BASE _ p BASE _ p BASE _ p RP 

where: 

ShipSTD_p(j) = Total shipments under the standards case of product p in year j, 

RplBASE_p(j) = Units of product p under the base case retired and replaced in year j, 

NIBASE_p(j) = Number of new construction installations under the base case of product p 

in year j, 

MBASE_p(j) = Units installed in Market M under the base case of product p in year j (M 

represents early replacements for cooking products and microwave ovens), 

eRP(j)= Relative price elasticity in year j (equals -0.34 for year 1), and 

ΔRP(j)= Change in relative price due to a standard level in year j. 

10-17 

EERE-2006-STD-0127 COMMENT 69.1
10.5 AFFECTED STOCK 

The affected stock is the in-service stock of the appliance or product that is affected by a 

TSL. In addition to the forecast of product shipments under both the base case and the standards 

case, the affected stock (which represents the difference in the appliance stock for the base case 

and the standards case) is a key output of DOE’s Shipments Models. The affected stock 

quantifies the impact that new product shipments have on the appliance stock due to a TSL. 

Therefore, the affected stock consists of those in-service units that are purchased in or after the 

year the standard has taken effect, as described by the following equation: 

j Std _ yr 

Aff Stock ( j ) = Ship ( j ) + 

_ 

‡”Stock ( age ) p p p 

age =1

where: 

Aff Stockp(j) = Affected stock of units of product p of all vintages that are operational in 

year j, 

Shipp(j) = Shipments of product p in year j, 

Stockp(j) = Stock of units of product p of all vintages that are operational in year j, 

age = Age of the units (years), and 

Std_yr = Effective date of the standard . 

As noted in the above equation, to calculate the affected stock, DOE must define the 

effective date of the standard. For the NES and NPV results presented in Chapter 11, DOE 

assumed that new energy efficiency standards will become effective in the year 2013. Thus, all 

appliances purchased starting on the first day of the year 2013 are affected by the standard level. 

10.6 RESULTS 

The following sections show the shipments forecasts for the various TSLs that DOE 

considered for each of the products. The TSLs are identified and described in Chapter 9. 

Tables 10.6.1 and 10.6.2 show the cumulative shipments impacts over the period 2013– 

2043 caused by each of the TSLs considered for top-loading and front-loading commercial 

clothes washers, respectively. For all TSLs, shipments are forecasted to decrease—the effects 

from the increase in product purchase prices offset the effects from decreased operating costs, 

resulting in a net decrease in shipments. Figure 10.6.1 compares the shipments forecasts for the 

base case and each of the two TSLs for top-loading washers. In Figure 10.6.1, the difference 

between the base case and TSL shipments forecasts depicts the annual shipments reductions 

caused by the TSLs. Figure 10.6.2 compares the shipments forecasts for the base case and each 

of the four TSLs for front-loading washers. 

10-18 

EERE-2006-STD-0127 COMMENT 69.1
Table 10.6.1 Top-Loading Commercial Clothes Washers: Cumulative Shipments Impacts 

Caused by Standards over the Period 2013–2043 

TSL 

Modified Energy Factor/ 

Water Factor 

Cumulative Impacts 

(thousands) 

1 1.42/9.5 -143 

2 1.60/8.5 -302 

Table 10.6.2 Front-Loading Commercial Clothes Washers: Cumulative Shipments 

Impacts Caused by Standards over the Period 2013–2043 

TSL 

Modified Energy Factor/ 

Water Factor 

Cumulative Impacts 

(thousands) 

1 1.80/7.5 0 

2 2.00/5.5 0 

3 2.20/5.1 -14 

4 2.34/4.4 -34 

0. 1 1 6 

0. 1 2 0 

0. 1 2 4 

0. 1 2 8 

0. 1 3 2 

0. 1 3 6 

0. 1 4 0 

0. 1 4 4 

0. 1 4 8 

0. 1 5 2 

2009 

2011 

2013 

2015 

2017 

2019 

2021 

2023 

2025 

2027 

2029 

2031 

2033 

2035 

2037 

2039 

2041 

2043 

Year 

Shipments (million) 

B a se C a se 

St d L e v e l 1 

St d L e v e l 2 

Figure 10.6.1 Top-Loading Commercial Clothes Washers: Base Case and TSL Shipments 

Forecasts 

10-19 

EERE-2006-STD-0127 COMMENT 69.1
0. 0 5 4 

0. 0 5 6 

0. 0 5 8 

0. 0 6 0 

0. 0 6 2 

0. 0 6 4 

0. 0 6 6 

0. 0 6 8 

0. 0 7 0 

2009 

2011 

2013 

2015 

2017 

2019 

2021 

2023 

2025 

2027 

2029 

2031 

2033 

2035 

2037 

2039 

2041 

2043 

Year 

Shipments (million) 

B a se C a se 

St d L e v e l 1 

St d L e v e l 2 

St d L e v e l 3 

St d L e v e l 4 

Figure 10.6.2 Front-Loading Commercial Clothes Washers: Base Case and TSL 

Shipments Forecasts 

10-20
 

EERE-2006-STD-0127 COMMENT 69.1
 

 

 

 

 

 

 

 

 

 

 

 

REFERENCES 

1	 U.S. Department of Energy-Office of Energy Efficiency and Renewable Energy, Energy 

Conservation Standards for Certain Consumer Products (Dishwashers, Dehumidifiers, 

Microwave Ovens, and Electric and Gas Kitchen Ranges and Ovens) and for Certain 

Commercial and Industrial Equipment (Commercial Clothes Washers); Final Rule. Federal 

Register, 2009. 74(66): pp. 16040-16096. 

2	 U.S. Department of Energy-Office of Energy Efficiency and Renewable Energy. Technical 

Support Document: Energy Efficiency Program for Consumer Products and Commercial and 

Industrial Equipment: Residential Dishwashers, Dehumidifiers, Cooking Products, and 

Commercial Clothes Washers, March, 2009. Washington, DC. 

<http://www1.eere.energy.gov/buildings/appliance_standards/residential/cooking_products_final_rul 

e_tsd.html> 

3	 Consortium for Energy Efficiency. Commercial Family-Sized Washers: An Initiative 

Description of the Consortium for Energy Efficiency, 1998. Boston, MA. 

4	 U.S. Department of Commerce-Bureau of Economic Analysis. Industry Economic Accounts, 

Gross-Domestic-Product-(GDP)-by-Industry-Data, 1998-2005 NAICS Data: 

GDPbyInd_SHIP_NAICS and SIC Data: GDPbyInd_SHIP_SIC, Commercial Laundry 

Quantity Index Data, NAICS code 333312. Washington, DC. 

<http://preview.bea.gov/industry/gdpbyind_data.htm> 

5	 Association of Home Appliance Manufacturers. AHAM Data on Commercial Clothes 

Washers for DOE Efficiency Standards Rulemaking, October 12, 2006. Letter to Building 

Technologies Program, U.S. Department of Energy. 

6	 Statistical Review. Appliance Magazine, April, 1998, 1999. 

7	 U.S. Department of Energy-Energy Information Administration. An Updated Annual Energy 

Outlook 2009 Reference Case Reflecting Provisions of the American Recovery and 

Reinvestment Act and Recent Changes in the Economic Outlook, April, 2009. Washington, 

DC. SR/OIAF/2009-03. 

<http://www.eia.doe.gov/oiaf/servicerpt/stimulus/pdf/sroiaf(2009)03.pdf> 

8	 U.S. Department of Energy-Energy Information Administration. Annual Energy Outlook 

2009 with Projections to 2030, March, 2009. Washington, DC. DOE/EIA-0383(2009). 

<http://www.eia.doe.gov/oiaf/aeo/pdf/0383(2009).pdf> 

9	 U.S. Census Bureau, Housing and Household Economic Statistics Division. The American 

Housing Survey, 1997—2005. Washington, DC. 

<http://www.census.gov/hhes/www/housing/ahs/ahs.html> 

10-21 

EERE-2006-STD-0127 COMMENT 69.1
 

 10 S. Hymans. Consumer Durable Spending: Explanation and Prediction, Brookings Papers on 

Economic Activity, 1971. Vol. 1971, No. 1, pp. 234-239. 

10-22 

EERE-2006-STD-0127 COMMENT 69.1