CHAPTER 9

. SHIPMENTS ANALYSIS

TABLE OF CO NTENTS

9.1 INTRODUCTION

................................ ................................ ................................ ........... 9

- 1
9.2 SHIPMENTS MODEL METH ODOLOGY

................................ ................................ .... 9

- 1
9.3 DATA INPUTS AND MODE L CALIBRATION

................................ .......................... 9

- 3
9.3.1 Historical Shipments

................................ ................................ ................................ ........ 9

- 5
9.3.2 Markets and Model Calibration

................................ ................................ ....................... 9

- 7
9.3.2.1 New Construction

................................ ................................ .......................... 9

- 7
9.3.2.2 Replacements

................................ ................................ ............................... 9

- 10
9.3.2.3 Model Calibration

— Non

- Replacements

................................ ..................... 9

- 11
9.3.2.4 Base Case Shipments

................................ ................................ ................... 9

- 12
9.3.3 Market Share Forecast

................................ ................................ ................................ ... 9

- 14
9.4 PURCHASE PRICE AND O PERATING COST IMPACT S

................................ ....... 9

- 15
9.5 AFFECTED STOCK

................................ ................................ ................................ ..... 9

- 17

9.6 RESU LTS

................................ ................................ ................................ ...................... 9

- 18

LIST OF TABLES

Table 9.3.1 Commercial Clothes Washers: Historical Shipments (1972

– 2013)

.................... 9

- 5
Table 9.3.2 Commercial Clothes Washers: Historical New Multi

- Family Building

................ 9

- 8
Table 9.3.3 Multi

- Family In

- Unit Washer Saturations

................................ .............................. 9

- 9
Table 9.3.4 Commercial Clothes Washers: Historical New Multi

- Family Building

.............. 9

- 12
Table 9.3.4 Commercial Clothes Washers: Histori cal Market Share of Top

- loading
Units

................................ ................................ ................................ ...................... 9

- 14 Table 9.4.1 Price Elasticity Decrease after CCW Price Change

................................ .............. 9

- 16 Table 9.6.1 Top

- Loading Commercial Clothes Washers: Cumulative Shipments by
Standards over the Period 2018

– 2047

................................ ................................ .. 9

- 18
Table 9.6.2 Front

- Loading Commercial Clothes Washers: Cumulative Shipments

............... 9

- 18

LIST OF FIGURE Figure 9.3.1 Commercial Clothes Washer Shipments and Trend in Quantity Index

............... 9

- 6 Figure 9.3.2 Historical and Projected Multi

- Family Housing Starts (millions)

....................... 9

- 8 Figure 9.3.3 Commercial Laundry Saturation in New Multi

- Family Housing

....................... 9

- 10 Figure 9.3.4 Commercial Clothes Washers: Survival and Retirement Functions

................. 9

- 11 Figure 9.3.5 Commercial Clothes Washers: Historical and Base Case Shipments

............... 9

- 13 Figure 9.3.6 Commercial Clothes Washers: Disaggregated Base Case

................................ . 9

- 13 Figure 9.3.8 Forecast of Market Share in Base Case and Standards Case TSL 2 for

............. 9

- 15 Figure 9.6.1 Top

- Loading Commercial Clothes Washers: Base Case and TSL Shipments Projection

................................ ................................ ......................... 9

- 19 Figure 9.6.2 Front

- Loading Commercial Clothes Washers: Base Case and TSL Shipments Projection

................................ ................................ ......................... 9

- 19

9

- iCHAPTER 9. SHIPMENTS ANALYSIS

9.1 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 the U.S. Department of Energy ( DOE ) conducts for

its notic e of proposed rulemaking (NOPR). This chapter describes DOE’s methodology for projecting annual shipments and presents results for 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 s ) on product shipments, the shipments model accounts for the combined effects of changes in purchase price and annual opera ting cost on the product purchase decision.

The shipments model considers specific market segments to estimate shipments, the

results of which are then aggregated to estimate total product shipments. In the case of commercial clothes washers, DOE accoun ted for two market segments: (1) shipments due to

new construction

, and (2) replacements of retired units from existing buildings

.

The shipments model is a Microsoft Excel spreadsheet that is accessible on the Internet ( http://www.eere.energy.gov/buildings/appliance_standards/ ). Appendix 10

- B discusses how to

access the shipments model contained in the NIA spreadsheet, and provides basic instructions for using it . The rest of this chapt er explains the shipments model in more detail. Section 9 .2 presents the shipments model methodology ; section 9 .3 describes the data inputs and the model calibration; section 9 .4 discusses impacts on shipments from cha nges in equipment purchase price and operating cost; section 9 .5 discusses the affected stock; and section 9 .6 presents the

results for different TSL scenarios.

9.2 SHIPMENTS MODEL METH ODOLOGY

DOE developed a national stock model for estimating annual shipmen ts for commercial
clothes washers

. The model considers market segmentation as a distinct input to the shipments projection . As represented by the following equation, the two primary market segments are new installations and replacements.

) ( ) ( ) ( j NI j Rpl j Ship

p p p + =

W here:

9-1Ship p (j) = t otal shipments of product p in year j

, Rpl p (j) = u nits of product p retired and replaced in year j

, and

NI p (j) = n umber of new installations of product p in year j

.

As section 9.3.2.3 discusses , DOE also considered non

- replacements as a factor for most of the products to calibrate its shipments model to historical shipments data.

In principle, each market segment and eac h product class responds differently to both the base case demographic and economic trends and to the implementation of standards. Furthermore, retirement s , early replacement s, and efficiency trends a 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 project sales in each mar ket segment

. Thus, DOE’s shipments model assume s that construction

-- i.e., new multi

- family housing units

-- drives new installations. In each year, the product shipments from the new construction market segment are equal to the number of new multi

- family h ousing units built times the purchase rate, which is determined by the product class market share and the market saturation of the product

under consideration b

.

DOE’s shipments model take s an accounting approach, tracking market shares of each product class, the vintage of units in the existing stock, and expected construction trends. The

model estimate s shipments due to replacements using sales in previous years and assumptions about the lifetime of the equipment. Therefore, estimated sales due to re placements 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.

RRRRRR pp ( jj ) = SSSSSSSSSS pp ( jj

− 1 )

− � � SS h ii jj jj

− 1

jj = NN aaaaaaaaaaaa

aaaaaa = 0 × pp pp RRRRRR ( aaaaaa )

W here:

Stock p (j

- 1) = t otal stock of in

- service appliances in year j

- 1

, prob Rtr (age) = p robability that an appliance of a particular age will be retired, and

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 sh ipments. This is discussed later in the chapter in section 9.4.

b In this case, market saturation of the product is related to saturation of in

- home clothes washer units in multi

family households.

9-2N = s tart year for when the model begins its stock accounting (start year is specific to each product based on availabl e 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 distributi on 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 pr oduces increasing efficiency over time, where older, less efficient units may have higher operating costs, while younger, more

- efficient units will have lower operating costs.

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 dev eloped a series of equations that define the dynamics and accounting of in

- service stocks. For new units, the equation is:

) 1 ( ) 1

, (

_ j Ship age j Stock = =

W here:

Stock(j, age) = t he population of in

- service units of a particular age, j = y ear for which the in

- service stock is being estimated, and Ship ( j ) = n umber of units purchased in year j

.

The previous 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 m ore complicated equations ( e.g., the following equation) are those that describe the accounting of the existing in

- service
stock of units:

SSSSSSSSSS ( jj + 1

, aaaaaa + 1 ) = SSSSSSSSSS ( jj

, aaaaaa ) × [ 1

− pp pp RRRRRR ( aaaaaa ) ]

In th is equation, as the year is incremen ted 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, p rob Rtr (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.

9.3 DATA INP UTS AND MODEL CALIBR ATION

As discussed previously , shipments are driven primarily by two market segments: new construction and replacements. DOE used historical shipments as the basis for calibrating its

9-3shipments model

. Because new construction shi pments and replacements were not enough to account for all product shipments, DOE developed another factor t o calibrate its shipments

model . This factor , retired units not replaced, represent s a small share of total shipments. Shipments to the different 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

- l oading and front

- loading washers

. DOE developed a cross

price elasticity to estimate the market shares of the considered product classes through the forecast period. The Cross

- Price e lasticity is covered in section 9.3.3

.

DOE ’s approach for estimating shipments relies on using aggr egate historical shipments

data for all commercial clothes was hers. 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 i s 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) a ll product shipments for new construction are driven by the new multi

- family housing mar ket, (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 project 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

cu rrent 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. 1
Although the CEE report was published more than 10 years ago, it continues to be the most reliable data source for developing a market split for commercial clothes washer shipments that
was available.

9-4The third assumption pertains to the use of the U.S Bureau of Economic Analysis’ quantity index data to validate historical shipme nts data. 2 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 ma rket share of commercial clothes washers covered under this rulemaking relative to all commercial laundry equipment was constant over the period 1977

– 20 11 . Based on this assumption, DOE derived quantity index data specific to the commercial
clothes washer s of interest to this rulemaking and used the se data to validate the historical shipments.

9.3.1 Historical Shipments

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

model . It used two sources to establish historical sh ipments: (1) data the Association of Home Appliance Manufacturers ( AHAM ) provided for the period 2010

– 201 3 3 and (2) data for the period 197 2

– 2005 4 f rom the last rulemaking . DOE used a five

- year moving average to estimate shipments for the period 2006

– 20 09

. Table 9.3

. 1 summarizes the historical commercial clothes washer shipments data.

Table 9.3

. 1 Commercial Cloth es Washers: Historical Shipments (1972

– 201 3 )

Year Shipments (thousands) Year Shipments (thousands)

1972 167 1993 190

1973 167 1994 205

1974 166 1995 209

1975 167 1996 232

1976 167 1997 241

1977 167 1998 265

1978 166 1999 239

1979 164 2000 215

1980 170 2001 194

1981 171 2002 175

1982 163 2003 191

1983 163 2004 178

1984 167 2005 177

1985 176 2006 175

1986 186 2007 172

1987 194 2008 169

1988 213 2009 167

9-51989 215 2010 164

1990 225 2011 161

1991 193 2012 173

1992 188 2013 206

Sources: 2010

– 2013: AHAM Data Submittal, 2014; 2006

– 2009, DOE interpolated values; 1972

– 2005: DOE Standards for Commercial Clothes Washers, Technical Support Document, 2010.

As indicated in Table 9.3

. 1 , there was a significant drop in commercial clothes wa sher shipments between 1998 and 2011

. Therefore, DOE validated the shipments data through a review of the U.S. Bureau of Economic Analysis’ quantity index data. 2 DOE compared the

historical shipments data against the quantity index for commercial laundry equipment for the period between 1977 and 2011 (represented by North American Industry Classification System (NAICS) code 333312) c

. As shown in Figure 9.3

. 1 below , the commerci al laundry quantity

index tracks the shipments trend closely . Thus, DOE concluded that the downward trend observed in the historical shipments betwee n 1998 and 2011 w as real.

Figure 9.3

. 1 Commercial Clothes Washer Shipments and Trend in Quantity Index

c DOE was not able to compare the historical shipments da ta with the quantity index for commercial laundry equipment for the period between 1977 and 2012, as the industry codes were changed in the Bureau of Economic Analysis’s 2014 publication. 0.00 0.05 0.10 0.15 0.20 0.25 0.30

0 20 40 60 80 100 120 140 160Shipments (millions)Quantity Index (2000 =100)

Quantity Index Historical Shipments QI

- based Shipments Pre

- 1988 shipments based on QI Decling
trend since
1999

9-69.3.2 Markets and Model Calibration

The market for commercial clothes washers primarily consists of replacement units for equipment that has been retired and units for new housing. Total commercial clothes washers shipments are represented by the following equation:

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

W here :

Ship CCW (j) = t otal shipments of commercia l clothes washers in year j

, Rpl CCW (j) = r eplacement shipments in year j , and

NI CC W (j) = s hipments due to new construction in year j

.

The following sections discuss both of these markets in further detail. The model
calibration is also discussed.

9.3.2.1 New Con struction

To project 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 common

- area laundry facility (within the multi

- family building) or a laundromat.

To estimate shipments to new construction, DOE used projections of multi

- family housing starts coupled with commercial clothes washer satura tion data. In other words, to project the shipments for new construction for any given year, DOE multiplied the multi

- housing projections by the projected saturation of commercial clothes washers for new multi

- family housing. Because DOE used only multi

- f amily housing starts as a driver for new construction shipments, the saturation represents the fulfilled washer demand in both the multi

- family housing
and laundromat markets.

Figure 9.3

. 2 presents historical and projected new multi

- family housing starts based on the DOE Energy Information Administration (EIA)’s Annual Energy Outlook 2014 for the period

2014

– 20 40

. 5 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. All three housing starts projections are presented in Figure 9.3

. 2 . DOE used only the projections from the Reference case to estimate its shipments to new construction. For the period 20 41

− 204 7 , DOE froze completions at the level in the year 20 40 for all three economic projections

.

9-7Figure 9.3

. 2 Historical and Projected Multi

- Family Housing Starts (millions)

DOE deduced historical commercial laundry saturations for the new constru ction market based on the remaining portion of historical shipments that did not serve as replacement units. (The determination of replacement shipments is discussed in section 9.3.2.2 .) Table 9.3

. 2 3
shows the historical saturation of commercial clothes washers in new multi

- family buildings.

Table 9.3

. 2 Commercial Clothes Washers: Historical New Multi

- Family Building
Saturations

Year Saturation

1990 18.6%

1991 10.8%

1992 9.8%

1993 11.6%

1994 15.6%

1995 11.9%

1996 17.0%

1997 19.0%

1998 23.7%
Average 15.3 % 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90Housing Starts (millions)

Year High

Low

Reference

9-8DOE assumed that the average saturation over the period 1990 −1998 of 15.3 percent
would like ly change during the period 1999

– 2011

, due to the increasing saturation of in

- unit
washers in multi

- family units as shown by historical data from the Census Bureau’s American Housing Survey (AHS)

. 6 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 facilit ies.

Table 9.3

. 3 4 shows the saturation of in

- unit washers in both the multi

- family stock and
new construction from 1997 to 20 11

. Between 1997 and 20 11 , t he annual growth rate of

saturation of in

- unit washers i n new multi

- family construction ( i.e., residential washers used in

new ly constructed multi

- family units) is 0. 68 percent

. Therefore, DOE assume d that the annual growth rate of saturation of commercial clothes washers is

- 0. 68 percent during the period 2000

– 20 11 . DOE also assumed that the average saturation over the period 1999

– 2013 of 14. 2 percent would remain constant over the period 2014

– 2047.

Table 9.3

. 3 M ulti

- Family In

- Unit Washer Saturations

In

- Unit Washer Saturation

Multi

- Family Housing 1997 1999 2001 2003 2005 2007 2009 2011

New Construction 70.4% 64.4% 68.7% 68.1% 73.4% 73.5% 78.1% 77.41%

Stock 32 .3 % 32.7 % 35.6 % 36 .1 % 37 .3 % 37.5% 39.1% 39.56%

Source: AHS, 1997

– 2011

.

Figure 9.3.3 sh ows the historical variation in the new construction saturation as well the project ed saturation for the years after 1998.

9-9Figure 9.3

. 3 Commercial Laundry Saturation in New Multi

- Family Housing

9.3.2.2 Replacements

D OE 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 applicati ons considered in the analysis

— laundromats

and multi

- family buildings. DOE based the retirement function on a Weibull probability distribution for the product lifetime.

F or the commercial clothes washer shipments model , DOE combined the maximum and

mi nimum age 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 an d maximum values of 6.7 years and 14.6 years, respectively. For a more complete discussion of commercial clothes washer lifetimes, ref er back to section 8.2.3 of Chapter 8

.

Figure 9.3.4 shows the survival function that DOE used to estimate replacement shipments. 0.0% 5.0% 10.0% 15.0% 20.0% 25.0%Commercial Laundry Saturation in NewMulti-Family Housing (%)

9-10Figure 9.3

. 4 C ommercial Clothes Washers: Survival and Retirement Functions

9.3.2.3 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 in section 9 .3. 1). Star t ing in 1999 and extending to 20 13 , DOE estimated the share of retired units that were not replaced. The share of retired units not replaced fluctuated between 16 and 41 percent. Table 9.3

. 4 5 shows the share of retired units not replaced between 1999

– 2013

.

9-11 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 % Surviving by AgeTable 9.3

. 4 Commercial Clothes Washers: Historical New Multi

- Family Buildin g Saturations

Year % of Retired units not replaced

1999 0.0%

2000 15.9%

2001 27.1%

2002 38.8%

2003 32.3%

2004 38.8%

2005 40.4%

2006 40.9%

2007 40.5%

2008 39.9%

2009 29.2%

2010 29.9%

2011 34.2%

2012 31.0%

2013 16.7% Average 30.4%

To determine the share of retired units not replaced after the year 20 11 , DOE reviewed

data from the American Housing Survey.

As evident from Table 9.3

. 3 4 , the saturation of in

- unit washers in new construction has increased by approximately 10 percent, while the saturation in the multi

- family building stock has increased by approximately 23 per cent. Because the saturation of in

- unit washers in new

construction has increased relatively slow ly , there must be another driver for the increase of in

unit washers in the building stock. DOE postu lates that the growth in in

- unit washer saturations

in t he 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 apparen t trend, DOE used the average percent of non

- replacements over the 1999

– 20 13 timeframe

— 30.4 percent

— and

maintained it over the entire projection period of 20 14

– 2047

.

9.3.2.4 Base Case Shipments

Figure 9.3.5 shows the projected commercial clothes washer shipments in th e base case ( i.e., the case without new energy efficiency standards) and the historical shipments DOE used to

calibrate the projection

. To be clear , all commercial clothes washer shipments, including both
top

- loading and front

- loading units, are represented in the s hipments shown in Figure 9.3

. 5

.

9-12Figure 9.3

. 5 Commercial Clothes Washers: Historical and Base Case Shipments Projection

Figure 9.3. 6 presents projected base case commercial clothes washer shipments disaggregated into shipments due to retirements and shipments for new construction. The unusual jump in shipments going from 2012 to 2013 is driven primarily by the new construction starts in multifamily housi ng.

Figure 9.3

. 6 Commercial Clothes Washers: Disaggregated Base Case Shipments Projection 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28Shipments (million)

Year Historical Shipments

Projected Total Shipments

0.00 0.05 0.10 0.15 0.20 0.25Shipments (millions)

Year Replacement Modeled Shipments

New Construction Modeled Shipments

9-139.3.3 Market Share Forecast

DOE project ed the change in market share between the front

- loading and top

- loading
units in the base case on the basis of available market share data

. For the base case projection

, DOE extrapolated compounded annual growth rate (CAGR) of the to p

- loading market share using data submitted by AHAM for the period between 2005 7 and 2013 3 , as shown in Figure
9.3. 7

. DOE assumed the market share of top

- loading units would decrease by 1.1 percent annually for the period between 2013 and 2047.

Table 9.3

. 5 Commercial Clothes Washers: Historical Market Share of Top

- loading Units

Market Share (%) 2005 2013 CAGR (05

- 13)

Top

- loading 79.7% 73.19%

- 1.1%

For the standards case, both available data and cross

- price elasticities were used to forecast market share changes. Due to insufficient data, DOE was not able to estimate cross

- price impacts on the market share of top

- loading and front

- loading commercial clothes washers and instead relies on its recent analysis performed for residential clothes washers

. 8

In order to estimate the impact of price changes on market share, DOE applied a logistic regression method to regress market share on price variables and other attributes. The basic form of a logistic regression is:

ii = � 1

1 + − � ββ 0 + ββ 1 1

, ii +

⋯ + ββ kk kk

, ii � �

With some simple operations, the model can be transformed to an equivalent log

- linear form:

ll � ii
1

− ii � ββ 0 + ββ 1 xx 1

, ii +

⋯ + ββ kk xx kk

, ii

In this case, is the market share of either top

- or front

- load ing clothes washers, and is the ratio of clothes washer market shares, i.e., the relative probability of the market share of top

- and

front

- load ing clothes washers.

These regression results were used to derive the cross

- price impact of a change in the

front

- loading washer price on the top

- loading market share (and vice versa). For example, these results suggest that a 10% increase in the price of front

- load ing washers would lead to a 10.7% decrease in top

- loading washers’ market share, holding other variables constant and measured as changes from the reference case using average values for each variable. In this case, the front

loading cross

- price impact (pe rcent change in top

- loading market share over percent change in

front

- loading price) is 1.07. d Similarly, these results indicate that a 20% price increase for top

d Price elasticity varies according to the reference price and mark et share

– typically measured as changes from mean values for these variables in the data set. In DOE’s data set, the reference top

- loading washer price is

9-14loading washers would yield a 21.49 % increase in front

- loading market share, measured as

cha nges from the variable means. Thus, in this example

, the top

- load ing washer cross

- price impact is also 1.07

. See the residential clothes washers 2012 Direct Final Rule Technical Support Document for additional details. 7

Figure 9.3

. 8 show s forecasted base and standards case market share of commercial clothes washers disaggregated by the two product types. The figures show the standards case for
top

- loading and front

- loading clothes washers, TSL 2 . Given the direct correlation between price and market share, as long as the price changes the market share will also continue to change.

Figure 9.3

. 7 Forecast of Market Share in Base Case and Standards Case TSL 2 for

Front

- Loading ( E L 2 ) and for Top

- Loading ( E L 1 )

9.4 PURCHASE PRICE AND OPERATING COST IMPAC TS

DOE conducted a literature review to establish the likely r ange of values for the price elasticity of demand for commercial clothes washers. The price elasticity of demand describes the responsiveness of the quantity purchased of a product to a change in its price and can be generically defined as the ceteris paribus percent change in shipments given a 1% change in price, or:

dd = PP 0 QQ 0 ∗ ∂∂

∂∂

$403.78, the reference front

- loading washer price is $823.83, the corresponding reference top

- loadin g washer market share is 71%, and the corresponding reference front washer market share is 29%.

9-15where P and Q represent price and shipments quantity, respectively

. 9

DOE believes commercial clothes washers to be highly price inelastic an d applies a

default price elasticity of demand of zero for this product , meaning that any cost and price increases resulting from efficiency standards are unlikely to substantially affect the quantity of
washers demanded . As these are somewhat niche produ ct s , estimates of commercial clothes
washer price elasticity were not found in the literature, and DOE does not have the necessary data to perform an accurate independent calculation of price elasticity. Instead, the value of zero
is based on DOE’s unders tanding of the commercial clothes washer market and distribution channels, particularly in relation to the demand for laundry services

.

To validate the use of a price elasticity of zero, DOE estimate d an implied commercial clothes washer price elasticity based on known variables describing the market for laundry services. The implied price elasticity is approximated as follows:

IIII II dd

, CCCCCC = dd

, LL LLLL LLLLLLLL ∗ � PP CCCCCC
PP LL LLLL LLLLLLLL �

W here :

= the price elasticity of demand for commercial clothes washers,

= the average price elasticity of demand for laundry service,

= the commercial clothes washers wholesale price (or cost), and

= the price (or cost) of providing laundry service.

DOE estimate d an implied price elasticity of demand for commercial clothes washers of approximately

- 0.03 to

- 0.12 based on laundry equipment distribution channels and the market for laundry services. These values are provided as alternatives to the default price elasticity of

zero.

Additionally, price elasticity for a given product is not constant over time; numerous studies have demonstrated decreases in price elasticity for durable goods as the period of time since a price chang e increases. 10

, 11

, 12 Similarly, decreases in price elasticity have been demonstrated through the stages of a product’s life cycle

. 13 DOE projects that any non

- zero commercial clothes washer price elasticity will decrease over time, following the proportional

decreases demonstrated for automobiles ( Table 9.4

. 1 ).

Table 9.4

. 1 Price Elasticity Decrease after CCW Price Change Years Following Price Change

1 2 3 5 10 20 Price Elasticity of Demand

- 1.20

- 0.93

- 0.75

- 0.55

- 0.42

- 0.40 Relative Change in Elasticity to 1st

year 1.00 0.78 0.63 0.46 0.35 0.33

Implied Price Elasticity (low)

- 0.031

- 0.024

- 0.015

- 0.007

- 0.002

- 0.001 Implied Price Elasticity (high)

- 0.123

- 0.096

- 0.060

- 0.028

- 0.010

- 0.003 Source: Hymans 1979 and DOE calculations

.

9-16Based on the following equation, DOE estimated standards case shipments by incorporating the impact of the price into the base case shipments projection . Note that in the equation below, the price and the price elasticity are functions of the year

, because both change
with time. 

# ( ) 

# ( ) ) ( ) ( 1 ) ( ) ( ) ( ) (

_

_ _ _ _ 

### j RP j e j M j NI j Rpl j Ship RP p BASE p BASE p BASE p STD 

### D × × + + =

W here:

Ship STD_p (j) = t otal shipments under the standards case of product p in year j

, Rpl BASE_p (j) = u nits of product p under the base case retired and replaced in year j

, NI BASE_p (j) = n umber of new construction installations under the base case of product p in year j

, M BASE_p (j) = u nits installed in Market M under the base case of product p in year j ( M represents early replacements for commercial clothes washers ),

e d (j)= p rice elasticity in year j (equals

- 0.031 or

- 0.123 for year 1), and

Δ P(j)= c hange in price due to a standard level in year j

.

9.5 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 projection 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 model s. 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:



## ∑ _

1 _ ) ( ) ( ) ( yr Std j

age p p p age Stock j Ship j Stock Aff

= + =

W here:

Aff Stock p (j) = a ffected stock of units of product p of all vintages that are operational in

year j

, Ship p (j) = s hipments of product p in year j

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

, age = a ge of the units (years), and Std_yr = e ffective date of the standard.

9-17As noted in this 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 standard s will b ecome effective in the year 2018 . Thus, all appliances purchased starting o n the first day of the year 2018 are affected by the standard level.

9.6 RESULTS

The following sections show the shipments projections for the various TSL s that DOE

considere d for each of the products. The TSLs are identified and described in chapter 10.

Table 9.6

. 1 and Table 9.6

. 2 show the cumulative shipments over t he pe riod 2018

– 204 7 caused by eac h of the TSLs considered for top

- loading and front

- loading commercial clothes
washers , respectively

. Figure 9.6.1 compares the shipments projections for the base case and

each of the four TSL s for top

- loading washers . In Figure 9.6.1 , the difference between the base

case and TSL shipments projectio ns depicts the annual shipments reductions caused by cross

price elasticities

. Figure 9.6.2 compares the shipments projections for the base case and ea ch of

the three TSLs for front

- loading washers.

Table 9.6

. 1 Top

- Loading Commercial Clothes Washers: Cumulative Shipments b y Standards over the Period 201 8

– 204 7

TSL MEF /IWF (cu.ft/kWh/cycle gal/cu.ft) Cumulative Shipments ( millions )

1 1. 3 5 / 8. 8 0 3.74

2 1. 35 / 8.80 3.74

3 1. 55 / 6.90 3.61

4 1. 55 / 6.90 3.68

Table 9.6

. 2 Front

- Loading Commercial Clothes Washers: Cumulative Shipments
Caused b y Standards over the Period 201 8

– 204 7

TSL MEF /IWF (cu.ft/kWh/cycle gal/cu.ft) Cumulative Shipments ( millions )

1 1. 80 / 4.50 2.55

2 2.00 / 4.10 2.55

3 2.00 / 4.10 2.68

4 2.20/3.9 0 2.61

9-18Figure 9.6

. 1 Top

- Loading Commercial Clothes Washers: Base Case and TS L Shipments Projection

Figure 9.6

. 2 Front

- Loading Commercial Clothes Washers: Base Case and TSL Shipments Projection 0.10 0.11 0.12 0.13 0.14 0.15 0.16Shipments (million)

Year Top

- loading

TSL 3 Base Case

TSL 2 TSL 1

TSL 4

0.05 0.06 0.07 0.08 0.09 0.10 0.11 0.12Shipments (million)

Year Front

- loading

TSL 3 Base Case

TSL 2 TSL 1

TSL 4

9-19REFERENCES

1 C onsortium for Energy Efficiency. Commercial Family

- Sized Washers: An Initiative Description of the Consortium for Energy Efficiency , 1998. Boston, MA.

2 U.S. Department of Commerce

- Bureau of Economic Analysis. Industry Economic Accounts, Gross

- Domestic

- Product

- (GDP)

- by

- Industry

- Data, 1998

- 2011 NAICS Data: GDPbyInd_SHIP_NAICS and SIC Data: GDPbyInd_SHIP_SIC , Commercial Laundry Quantity Index Data, NAICS code 333312 . Washington, DC.

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

3 Association of Home Appliance Manufacturers (AHAM)

. AHAM Data on Commercial
Clothes Washers for DOE Efficiency Standards Rulemaking, August 11, 2014. Letter to Building Technologies Program, U.S. Department of Energy.

4 U.S. Department of Energy, Commercial Clothes Washers Final Rule Technical Su pport Document, Chapter 10. Shipment Analysis

, EERE

- 2006

- STD

- 0127 COMMENT 69.1, 2010

- 01

- 19.

5 U.S. Department of Energy

- Energy Information Administration. Annual Energy Outlook

201 4 with Projections to 2040 , May 2014 . Washington, DC. DOE/EIA

- 0383(2014)

. < ht tp://www.eia.gov/forecasts/aeo/pdf/0383( 2014 ).pdf >

6 U.S. Census Bureau, Housing and Household Economic Statistics Division. The American Housing Survey, 1997

— 2011. Washington, DC.

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

7 AHAM, AHAM Data on Commercial Clothes Washers for DOE Efficiency Standards Rulemaking, 2010 . Letter to Building Technologies Program, U.S. Department of Energy.

8 U.S. Department of Energy, Technical Support Document: Energy Efficiency Program for Consumer Products and Commercial and Industrial Equipment: Residential Clothes Washers (Direct Final Rule), Building Technologies Program, Editor. 2012: Wash inton, D.C.

9 Varian, H. R., Intermediate Microeconomics: A Modern Approach. 1999. Vol. 5th Edition. W.W. Norton & Company: New York, London.

10 Hymans, S., Consumer Durable Spending: Explanation and Prediction. Brookings Papers on Economic Activity, 1971. 1971(1): pp. 234

- 239.

11 Fibrich, G., A. Gavious, and O. Lowngart, The Dynamics of Price Elasticity of Demand in the Presence of Reference Price Effects. Journal of the Academy of Marketing Science, 2005. 33(1): pp. 66

- 78.

12 Gowrisankaran, G. and M. Rysman, Dynamics of Consumer Demand for New Durable

9-20Goods. 2009, National Bureau of Economic Research Working Paper: Cambridge, MA.

13 Lilien, G. L. and E. Yoon, An Exploratory Analysis of the Dynamic Behavior of Price Elasticity over the Product Life Cycle: An Empirical Analysis of Industrial Chemical

Products

. Issues in Pricing , 1988: pp. 261

- 408

.

9-21