8

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- i APPENDIX 8

- J. ESTIMATION OF EQUIPM ENT PRICE TRENDS FOR RESIDENTIAL

CLOTHES DRYERS AND ROOM AIR CONDITIONERS

TABLE OF CO NTENTS

8-J.1 INTRODUCTION

.................................................................................................. 8-J-1

8-J.2 PRICE, COST AND MARK ET STRUCTURE

..................................................... 8-J-2

8-J.3 DATA EVALUATION AND ANALYSIS

............................................................ 8-J-3

8-J.4 PRICE TRENDS FOR SEN SITIVITY ANALYSES

............................................ 8-J-8

LIST OF TABLES

Table 8 -J.4.1 Learning parameter as a function of time period ........................................ 8-J-8

LIST OF FIGURES

Figure 8 -J.3.1 Historical Normalized Prices of Household Laundry Equipment .............. 8-J-3 Figure 8 -J.3.2 Historical Normalized Prices of Room Air Conditioners ........................... 8-J-4 Figure 8 -J.3.3 Historical and Projected Total Shipments of Household Laundry Equipment ................................................................................................... 8-J-5 Figure 8 -J.3.4 Historical and Projected Total Shipments of Room Air Conditioners ....... 8-J-5 Figure 8 -J.3.5 Relative Price versus Cumulative Shipments of Household Laundry Equipment, with Power Law Fit ................................................................. 8-J-6 Figure 8 -J.3.6 Relative Price versus Cumulative Shipments of Room Air Conditioners, with Power Law Fit .............................................................. 8-J-78

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- 1 APPENDIX 8

- J. ESTIMATION OF EQUIPM ENT PRICE TRENDS FOR RESIDENTIAL

CLOTHES DRYERS AND ROOM AIR CONDITIONERS

8- J.1 INTRODUCTION

In the standards established in today’s direct final rule, DOE assumes that the manufacturer costs and retail prices of products meeting various efficiency levels may not remain fixed, in real terms, after 2010 (the year for which the engineering analysis estimated costs) and throughout the period of the analys is. In its Notice of Data Availability (NODA) published on February 22, 2011 (76 FR 9696) , DOE stated that it may consider improving regulatory analysis by addressing equipment price trends. Consistent with the NODA, DOE examined historical producer price indices (PPI) for household laundry equipment and room air conditioners. For this equipment, DOE found consistent negative real price trends. Therefore,

DOE concluded that the real prices of residential clothes dryers and room air conditioners have a different long term tr end than prices in the economy as a whole. DOE maintained the constant real price trend as a sensitivity analysis to evaluate how the impact of potential standards might change under this scenario.

DOE stated in the NODA that examination of historical p rice data for certain appliances and equipment that have been subject to energy conservation standards indicates that the assumption of constant real prices and costs may, in many cases, over- estimate long

- term appliance and equipment price trends. Economi c literature and historical data suggest that the

real costs of these products may in fact trend downward over time according to “learning” or “experience” curves , or alternatively that the price trends for certain sectors of the US economy may be differen t than the price trends for the economy as a whole. A draft paper

, “Using the Experience Curve Approach for Appliance Price Forecasting ,” posted on the DOE web site at http://www1.eere.energy.gov/buildings/appliance_standards/supplemental_info_equipment_pric e_forecasting.html , provides a summary of the data and literature currently available to DOE that is relevant to price forecasts fo r selected appliances and equipment.

The extensive literature on the “learning” or “experience” curve phenomenon is typically based on observations in the manufacturing sector. a In the experience curve method, the real cost of production is related to the cumulative production or “experience” with a manufactured product. This experience is usually measured in terms of cumulative production. A common functional relationship used to model the evolution of production costs in this case is:

Y = aX -b

a In addition to the draft paper mentioned above, see Weiss, M., Junginger, H.M., Patel, M.K., Blok, K., (2010a). A

Review of Experience C urve Analyses for Energy Demand T echnologies. Technological Forecasting & Social Change . 77:411

- 428

.8

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- 2 where a is an initial price (or cost), b is a positive constant known as the learning rate parameter,

X

is cumulative production, and Y is the price as a function of cumulative production. Thus, as experience (production) accumulates, the cost of prod ucing the next unit decreases. The percentage reduction in cost that occurs with each doubling of cumulative production is known as the learning rate (LR), given by:

LR = 1 – 2 -b

In typical learning curve formulations, the learning rate parameter is deri ved using two historical data series: cumulative production and price (or cost).

8- J.2 PRICE, COST AND MARK ET STRUCTURE

DOE uses a cost

- based analysis in estimating equipment prices. To estimate equipment prices in both the standards and the baseline or no-standards case, DOE develops engineering cost estimates that DOE then uses to estimate manufacturer selling price. The manufacturer selling price includes direct manufacturing production costs (labor, material, and overhead

estimated in DOE’s manufacturer production cost s) and all non-production costs (SG&A, R&D,

and interest), along with profit . The process of the cost-based method for developing the manufacturer selling prices is described in the engineering analysis described in Chapter 5 of this

TSD. To convert the manufacturer selling price to an equipment price for the consumer, DOE performs an analysis of distribution chain markups and estimates markups on both the baseline and incremental manufacture selling prices to determine equipment prices after distribution to

the consumer.

In analyzing experience curves to estimate price trends, DOE uses producer price indices as a key data input and analyzes this data to estimate the experience curve exponent. This approach has only one model parameter to describe the price trend and assumes a simple relationship between producer price and retail equipment price. Specifically, the approach assumes that producer prices, distribution chain markups and equipment prices all scale proportionally over time for t he same product.

DOE could have developed a more complex price trend forecasting model with more parameters that could explain different trends in different equipment price and cost components over time. But the relatively few available data points presen ts a risk that a fit with multiple parameters would “overfit” the data. Overfitting occurs when there are too many degrees of freedom in a statistical model compared to the data and the fits are sensitive to random noise unrelated to long term trends. Du e to the risk of overfitting the available data, DOE has decided to not develop a more complex multi- parameter price trend estimation model at this time.

Due to the simple nature of the price trend estimation model, there are several well known economic and market phenomenon that will not be captured in detail by the price trend forecast. Some effects might lead to an overestimate of the long term price trend and other effects may lead to an underestimate.8

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- 3 For example, if there has been increasing m arket concentration historically on the part of manufacturers, this may have resulted in increasing manufacturer and wholesale markups over time. This would result in an observed historical producer price trend that did not decrease as fast as the underly ing industrial learning rate. Depending on if market concentration accelerated or decelerated into the future this could lead to an over- or under-estimation of future price

trends.

Similarly, if there are cost components that have relatively slow long term price trends that have an increasing impact on price over time, the decreasing share of costs that are declining rapidly can result in a change in the empirically estimated experience curve exponent over time.

8- J.3 DATA EVALUATION AND ANALYSIS

To deriv e a learning rate parameter for residential clothes dryers and room air conditioners, DOE obtained historical Producer Price Index (PPI) data for household laundry equipment and room air conditioners from the Bureau of Labor Statistics’ (BLS). Because PPI data specific to residential clothes dryers were not available, DOE used PPI data for household laundry equipment as representative of residential clothes dryers . For household laundry equipment, DOE used PPI data spanning the time period 1980-2010, while for room air conditioners, DOE use data f rom 1990-20 09. Inflation -adjusted price indices for household laundry equipment and room air conditioners were calculated by dividing the PPI series by the

Consumer Price Index ( CPI ) “all items” index for the same years. These inflation

- adjusted price indices (shown in Figure 8-J.3.1 and Figure 8 -J.3.2) were used in subsequent analysis steps.

Figure 8 -J.3.1 H istorical Normalized Prices of Household Laundry Equipment8

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- 4 Figure 8 -J.3.2 H istorical Normalized Prices of Room Air

Conditioners

DOE assembled a time -series of annual shipments for 1946-2009 for clothes dryers, 1972-2008 for clothes washers, and for 1946-2009 for room air conditioners from data submittals from AHAM, AHAM Fact Books, and Appliance Magazine. Decadal shipments up to 1979 for clothes washers were obtained from the 2005 AHAM Fact Book. Household laundry equipment is assumed to consist of only residential clothes washers and dryers. The annual and decadal shipments data were used to estimate cumulative shipments (production). Projected shipments

after 2008/2009 were obtained from the base case projections made for the NIA (see chapter 9 of this TSD)

. Figure 8-J.3.3 and Figure 8 -J.3. 4 show the shipments time series used in the analysis.8

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- 5 Figure 8 -J.3.3 Historical and Projected Total Shipments of Household Laundry Equipment

Figure 8 -J.3.4 Historical and Projected Total Shipments of Room

Air Conditioners8

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- 6 To estimate a learning rate parameter, a least -squares power- law fit was performed on the unified price index vers us cumulative shipments.

See Figure 8-J.3.5 and Figure 8 -J.3.6.

Figure 8 -J.3.5 R elative Price versus Cumulative Shipments of Household Laundry Equipment , with Power Law

Fit8

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- 7 Figure 8 -J.3.6 R elative Price versus Cumulative Shipments of

Room Air Conditioners, with Power Law Fit

The form of the fitting equation is:

P(X) = P

o X

-b

,

where the two parameters, b (the learning rate parameter) and P

o (the price or cost of the first unit of production), are obtained by fitting the model to the data. DOE notes that the cumulative shipments on the right hand side of the equation can have a dependence on price, so there is an issue with simultaneity where the independent variable is not truly independent. DOE’s use of a simple least squares fit is equivalent to an assumption of no s ignificant first price elasticity effects in the cumulative shipments variable.

The parameter values obtained are:

P

o = 128 .0

− 22

. 5 + 27

. 4 (95% confidence) for household laundry equipment and 43 .7

− 11

. 7 + 15

. 9 (95% confidence) for room air conditioners, and

b = 0.775±0.034 (95% confidence) for household laundry equipment and 0.710±0.062 (95% confidence) for room air conditioners.

For household laundry equipment , the estimated learning rate (defined as the fractional reduction in price expected from each doubling of cumulative production) is 41 .6 −1

. 4 +1

. 3 % (95% confidence) . For room air conditioners, the estimated learning rate is 38 .9 −2

. 7 +2

. 6 % (95% confidence).8

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- 8 DOE then derived a price factor index, with 2010 equal to 1, to forecast prices in each future year in the analysis period. The index value in a given year is a function of the LR and the cumulative production forecast through that year. Table 1 shows the price factors used in the analysis. DOE applied the same value to forecast prices for each residenti al clothes dryer and room air conditioner product class at each considered efficiency level.

8- J.4 PRICE TRENDS FOR SEN SITIVITY ANALYSES

DOE recognizes that there is uncertainty in its estimates of equipment price trends. Uncertainty arises from potentially sy stematic long term changes in the trend. To provide a potential indication of long term changes in the trend, DOE performed price trend fits to two component periods in the historical data. DOE analyzed years in which the historical efficiency

standards for the product changed that were approximately in the middle of the time series of the available data. For household laundry equipment, the selected year was 1994, and for room air conditioners, the selected year was 2000.

Table 8 -J.4.1 L earning parameter as a function of time period

Product Period Learning Parameter (%) Household Laundry Equipment 19 80

- 2010 41.6 Household Laundry Equipment 19 80

- 199 3 33.9 Household Laundry Equipment 1994

- 2010 42.0

Room Air Conditioners 19 90

- 20 09 38.9

Room Air Conditioners 19 90

- 199 9 34.7

Room Air Conditioners 2000

- 20 09 31.0

DOE examined the impacts of a range of learning parameters on the range of net benefit impacts from potential standards using high, medium and low values of the learning rate parameter. DOE examined the range of estimates as sensitivities to the national impact analysis. The low learning rate is from the time period with the lowest value, or the low end of the 95% confidence range of the full period learning estimate. The medium value is the estimate for the full time period. The high value is the higher of the high time period estimate or upper range of the 95% confidence interval for the full time period estimate. In addition, DOE examined a constant real price scenario as a sensitivity calculation f or its national impact analysis.

DOE considered another sensitivity calculation for residential clothes dryers. An effective PPI series for clothes dryers only was determined from the household laundry equipment PPI series, by factoring out the contribution from clothes washers. DOE used average selling prices from two sources: NPD (2004-2009 for clothes washers, 2007-2008 for clothes dryers), and Consumer Reports (1992, 1993, 1995, 1997, 1999 for clothes washers). Clothes washer prices were separated in to top-loading and front-loading models. Market shares for toploading clothes washers, front- loading clothes washers, and clothes dryers were available for all years. Using the 2007-2008 washer and dryer price data to normalize the relative household

laun dry PPI index to nominal dollars, DOE created an effective PPI series for clothes washers only for years 1992, 1993, 1995, 1997, 1999, and 2004-2009. Assuming the household laundry PPI series is dominated by clothes washers and clothes dryers only, and using the known market8

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- 9 shares between clothes washers and dryers, DOE derived an effective clothes dryer PPI series from the household laundry PPI and the effective clothes washer PPI series. Learning parameters were calculated from the effective clothes drye r PPI series (using clothes dryer cumulative production) and used as another sensitivity estimate.