1 

Using the Experience Curve Approach for Appliance Price Forecasting

February 2011 

Introduction

This report describes a method to account for historic changes in product prices and energy 

efficiency in product price forecasting. Figure 1 illustrates the inflation-adjusted price histories 

of various products over the past four decades. All prices are normalized to their 2008 value, 

with the prices for computers and compact fluorescent bulbs presented on a separate axis to 

accommodate scale differences. Specifically, this document describes how experience curve (aka 

learning curve) analysis can be used can be used in price forecasting and how energy efficiency 

changes over time can be incorporated into that analysis. Simultaneous price declines and 

efficiency improvements of appliances have been noted in the literature (Ellis et al, 2007) and 

(Dale et al, 2009). 

Figure 1. Product price histories. Data Sources: Producer Price Index (PPI) data from Bureau of 

Labor Statistics, except Compact Fluorescent Lamp (CFL) Bulbs (Pulliam, R., 2008). 

The experience curve is an empirical model based on historical fits of price and/or cost data (P) 

to cumulative production (X), which has been applied to a broad range of products from 

automobiles, to power plants, and in recent decades to appliances and consumer products. See as

0

5

10

15

20

25

0

0.5

1

1.5

2

2.5

3

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Electronics & CFLs

All except electronics & CFLs

Appliance Price Histories (Normalized to $2008 )

Unitary AC

Electric lighting 

Electric lamp 

bulbs and tubes 

Household 

refrigerators

Household 

freezers

Room AC and 

dehumidifiers

Household 

laundry 

equipment 

Household 

water heaters 

non- electric

Household 

water heaters, 

electric

Computers

Compact 

Fluorescent 

Bulbs
2 

examples, Bass (1980), Newell (2000), Junginger et al. (2005), and Weiss et al. (2010a and 

2010b). Causes of the experience curve phenomenon have been extensively explored, as have its 

utility and limitations in energy technology policy and price forecasting. See as examples: 

OECD/IEA (2000), McDonald and Schrattenholzer, (2001), Graeker and Sagan (2008), Van 

Bentham et al. (2008), and Neij (2008). 

The experience curve has the following form:

(1) P(X) = PoX-b, 

where the two parameters, b (the learning rate parameter) and Po (the price or cost of the first 

unit of production), are obtained by fitting the model to the data. The specific case of constant 

real prices corresponds to a special case of an experience curve where the learning rate parameter 

is zero.

The price learning rate parameter (LR) describes the fractional reduction in price expected from 

each doubling of cumulative production. 

(2) LR = 1 – 2-b 

The price learning rate indicates the fractional drop in price for a doubling of cumulative 

production. For example, an LR of 0.2 indicates a 20% drop in price for a doubling in cumulative 

production. 

Experience curves and learning curves have identical mathematical form, but reflect observations 

at different scales. The term “learning” is used when the focus is on relatively wellcharacterized factors of production that result in production cost declines of a single standardized 

product (e.g., the Model-T Ford) by a single manufacturer. It captures issues like ‘learning’ by 

workers and management that reduces labor hours needed for production and economies of scale. 

Experience tends to focus on broader classes of products (e.g., all refrigerators) that may have 

many models built by many producers. It may model prices as well as costs, thereby including a 

broader suite of causal factors including increasing efficiencies in distribution channels, price 

mark-ups and other downstream market forces. 

Optimally price (or cost) data are normalized to account for changes in (and variations in) 

product capacity over time (and among models). For example, refrigerator price data can be 

normalized by volume, clothes washers by load capacity, and computer hard drives per 

megabytes of disk space. Ideally, energy efficiency changes over time (and differences among 

models) should be accounted for as well, to facilitate consistent comparisons. Because both 

product capacity and efficiency can change very significantly over time, accounting for these 

effects (or not) can result in large differences in the calculated learning rate. The degree to which 

one can pursue optimal practice is limited by data availability.

The following section describes the basic method for calculating learning rates. 
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Method for calculating learning rates

To obtain the learning rate parameter, Eq. 1 is linearized by taking the natural logarithm of both 

sides of the equation. 

(3) ( )( ) ( ) ( ) Po ln bXP ln X +−= ln . 

This transforms the equation to a straight line of the standard form 

(4) y = mx + c, 

where the variables x and y are:

(5) y = ln(P(X)) and x = ln(X), 

and the b and Po are constants obtained by linear regression on y and x: 

(6) b = -m and Po = ec. 

Cumulative production is estimated from historic shipments as described in Appendix A. 

To apply the method to forecasting, shipments are projected. These are then used to estimate 

cumulative production at future dates (X(t)). Eq. 1 is then used to forecast future prices at desired 

X(t). 

Preparing Price Input Data for Learning Rate Analysis 

The best available data should be used in the analysis. This section addresses how price data can 

be used depending on the nature of available data. Options are ranked from most accurate 

(lowest numbers) to least accurate (highest numbers). 

1. If technology-specific price (or cost) and production data are available and adequate

(optimally at constant efficiency), use those data for P(X) and X, respectively, in Eq. 3. 

2. If historic price and production data are not available (or are inadequate) for the technology 

under consideration, but average product data are available for price (or cost) and production, 

use the average product price data for P(X) and the average product production data for X in 

Eq. 3. 

3. If existing data are inadequate to perform the analysis and the product is similar to existing 

products for which there are well documented learning rates, the average learning rates for 

those products should be used for the learning rate of the product under consideration. 
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Price Learning Rates Results

This section documents the application of the experience curve method using average product 

price and shipments data.1 In all cases except for compact fluorescent light bulbs (CFLs), the 

producer price indexes (PPI) were used for P(X). PPI data are available from the Bureau of 

Labor Statistics online. PPI data were inflation-adjusted using the consumer price index (All City 

Average, All Items). The shipments data used to calculate cumulative shipments (X) came from 

a combination of American Home Appliance Manufactures (AHAM) data sets, Appliance 

Magazine (produced by AHAM), and Energy Information Administration, except for the CFL 

data. CFL data were obtained from Pulliam (2008). 

Figure 2 summarizes the results of the analyses, detailed in Table 1. For the majority of products 

studied the model gives a good fit to the data (see Appendix B). It fails for gas water heaters in 

recent years (2003 – 2008) during which average producer prices increased. This can be seen by 

comparing Figures B-5 and B-6 in Appendix B. While examination of causal factors is beyond 

the scope of this work, a number of factors unrelated to production costs appear to have been 

involved. 

Figure 2. Learning rates for various appliances.

 

1 Price and shipments data used in product-specific energy efficiency standards rulemakings may differ from the 

data used in this report.

0 0.1 0.2 0.3 0.4 0.5 0.6

CFLs

Refrigerators

Computers

Cloths washers

Room AC

Freezers

Unitary AC

Electric Water Heaters

Gas Water Heaters
5 

Table 1. Wholesale price learning rates for clothes washers, refrigerators, freezers, room air 

conditioners, unitary air conditioners, gas water heaters, electric water heaters, 

computers, and compact fluorescent lamps. ‘Price learning’ in column 1 indicates that data 

was prepared and used according to option 3 above. Average product producer price indexes 

were used. 

Trend b 

Parameter Learning Rate Years Included n Notes on Shipments Data

Notes on 

Price/Efficiency Data

CLOTHES WASHERS

Price learning 0.7948 0.42 1974 - 2009 35

Cumulative shipments 

of clothes washers 

sold to US vendors by 

manufacturers

Inflation-adjusted PPI 

($2008) for ‘Household 

laundry equipment 

manufacturing’

REFRIGERATORS

Price learning 1.0735 0.52 1976-2009 33

Cumulative shipments 

of refrigerators sold to 

US vendors by 

manufacturers

Inflation-adjusted ($2008) 

PPI for ‘Household 

refrigerators, including 

combination refrigeratorfreezer’

FREEZERS

Price learning 0.7002 0.38 1989-2009 20

Cumulative shipments 

of freezers (chest and 

upright) sold to US 

vendors by 

manufacturers

Inflation-adjusted ($2008) 

PPI for ‘Household food 

freezers, complete units’

ROOM AC

Price learning 0.7252 0.40 1990-2009 19

Cumulative 

Shipments of room 

AC sold to US 

vendors by 

manufacturers

Inflation-adjusted ($2008) 

PPI for ‘Room airconditioners and 

dehumidifiers, except 

portable dehumidifiers’

UNITARY AC

Price learning 0.2823 0.18 1978-2008 30

Cumulative shipments 

of unitary AC sold to 

US vendors by 

manufacturers

Inflation-adjusted ($2008) 

PPI for ‘Unitary airconditioners, except air 

source heat pumps’

GAS WATER HEATERS

Price learning 0.1935 0.13 1967-2002 35

Cumulative shipments 

of gas water heaters 

sold to US vendors by 

manufacturers

Inflation-adjusted ($2008) 

PPI for ‘Household water 

heaters, except electric’

ELECTRIC WATER HEATERS

Price learning 0.2744 0.17 1950-2002 52

Cumulative shipments 

of electric water 

heaters sold to US 

vendors by 

manufacturers

Inflation-adjusted ($2008) 

PPI for ‘Household water 

heaters, electric, for 

permanent installation’

COMPUTERS

Price learning 1.0366 0.51 1991-2008 17

Cumulative shipments 

of computers sold to 

US vendors by 

manufacturers

Inflation-adjusted ($2008) 

PPI for “Electronic 

computer manufacturing”

COMPACT FLUORESCENT LIGHT BULBS

Price learning 1.095 0.53 1999-2007 8 

Cumulative point of 

sale (POS) of 

compact fluorescent 

bulbs sold by US 

vendors to consumers.

Inflation-adjusted ($2007) 

ITRON prices using CPI 

for medium screw-based 

CFLs.
6 

References

Bass, F. M. (1980). The Relationship between Diffusion Rates, Experience Curves, and Demand 

Elasticities for Consumer Durable Technological Innovations. Journal of Business 53:S51-67 

Dale, L., C. Antinori, M. McNeil, J.E. McMahon, and K.S. Fujita (2009). Retrospective 

evaluation of appliance price trends, Energy Policy. 37:597–605. 

Ellis, M., Jollands, N., Harrington, and L., and Meier, A., (2007). Do energy efficient appliances 

cost more?, ECEEE 2007 Summer Study. 

Junginger, M., A. Faaij, and W.C. Turkenburg (2005). Global experience curves for wind farms, 

Energy Policy: 33:133–150. 

Greaker, M. and E.L. Sagen (2008). Explaining experience curves for new energy technologies: 

A case study of liquefied natural gas, Energy Economics. 30:2899–2911 

McDonald, A and L. Schrattenholzer, (2001). Learning rates for energy technologies, Energy 

Policy. 29:255–261. 

Neij, L. (2008) Cost development of future technologies for power generation—A study based 

on experience curves and complementary bottom-up assessments. Energy Policy 36: 2200– 

2211. 

Newell, R.G. (2000). Incorporation of Technological Learning into NEMS Buildings Modules, 

Energy Information Administration.

OECD/IEA (2000). Experience Curves for Energy Technology Policy, Organization for 

Economic Cooperation and Development and the International Energy Agency. 

Pulliam, R., 2008. California Residential Efficiency Market Share Tracking: Lamps 2007.

Prepared for Southern California Edison by Itron, Inc., San Diego, CA. 

Van Benthem, A., K. Gillingham, and J. Sweeney (2008). Learning-by-Doing and the Optimal 

Solar Policy in California, The Energy Journal. 29:131–151. 

Weiss, M, K. Martin, M.K. Patel, M. Junginger, and K. Blok (2010b). Analyzing price and 

efficiency dynamics of large appliances with the experience curve approach. Energy Policy.

38:770–783. 

Weiss, M., H.M Junginger, M.K. Patel, and K Blok, (2010a). A review of experience curve 

analyses for energy demand technologies. Technological Forecasting & Social Change. 77:411-

428 
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Appendix A

This appendix illustrates how cumulative production (going into US markets) was estimated 

from domestic shipments histories. The majority of shipments data were obtained from 

published DOE Technical Support Documents published in support of energy conservation 

standards. These data rely heavily on Appliance Magazine yearly summaries for residential 

appliances, and on Association of Home Appliance Manufacturers (AHAM) “Annual Shipment 

Trends” reports. Data from the Air-Conditioning, Heating and Refrigeration Institute (AHRI) 

were also used when available and needed. For natural gas appliances, we also use some 

historical data from the Gas Appliance Manufacturers Association (GAMA). For CFLs 

shipments data are from a recent market characterization report (Pulliam, 2008). 

Shipments can typically be linearly extrapolated backwards in time to extend the history back to 

the theoretical first unit of production. Cumulative production at any given year is then estimated 

by summing shipments in all prior years, using the linear extrapolation in years before actual 

data are available. In some cases, if data are shipments data are highly non-linear, this cold give 

an illogical result, and the plot should be forced to cross the x axis at a logical date for initial 

production. In the cases shown below, this was necessary only for the freezer data. Figure A-1 

plots the results for this analysis for clothes washers, refrigerators, freezers, room and unitary 

AC, and gas and electric water heaters. 
8 

Figure A-1. Shipments history for appliances considered in this report. The shipments history has 

been extrapolated backward in time to obtain a complete cumulative history. In the case of 

freezers, only the shipments from 1960-1972 were used in the extrapolation (denoted with red 

data points).

0

2

4

6

8

0

2

4

6

8

0

4

8

12

0

4

8

12

0

4

8

12

0

2

4

6

8

0

2

4

6

8

0

20

40

60

80

0

20

40

60

1930 1940 1950 1960 1970 1980 1990 2000 2010

Shipments (in millions)

Refrigerator

Room AC

Unitary 

Freeze

Electric Water Heater

Computers

Gas Water 

Clothes Washer

Compact Fluorescent Bulbs
9 

Appendix B

The following pages document, in turn, the regression analyses for products for which both 

average product price and average product efficiency were available.

• Clothes washers

• Refrigerators

• Freezers

• Room AC 

• Gas Water Heaters

• Electric Water Heaters
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Figure B-1. Cloths washers, all data included.

y = -0.7948x + 4.0983

R2 = 0.97507

2.00

2.10

2.20

2.30

2.40

2.50

2.60

1.80 2.00 2.20 2.40 2.60

Cloths Washes: Log(P) vs. Log(X)
11 

Figure B-2. Refrigerators: all data included.

y = -1.0735x + 4.7432

R2 = 0.97417

1.80

1.90

2.00

2.10

2.20

2.30

2.40

2.50

2.00 2.20 2.40 2.60 2.80

Refrigerators: Log(P) vs. Log(X)
12 

Figure B-3. Freezers: all data included. 

y = -0.7002x + 3.4814

R2 = 0.8846

2.08

2.10

2.12

2.14

2.16

2.18

2.20

2.22

2.24

2.26

2.28

2.30

1.70 1.75 1.80 1.85 1.90 1.95 2.00

Freezers: Log(P) vs. Log(X)
13 

Figure B-4. Room Air Conditioners: all data included.

y = -0.7252x + 3.7605

R2 = 0.96937

2.00

2.05

2.10

2.15

2.20

2.25

2.30

2.35

2.00 2.10 2.20 2.30 2.40

Room AC: Log(P) vs. Log(X)
14 

Figure B-5. Gas Water Heaters: Excludes 2003 – 2008 data.

y = -0.1935x + 2.7263

R2 = 0.8044

2.26

2.28

2.30

2.32

2.34

2.36

2.38

2.40

2.42

1.50 1.70 1.90 2.10 2.30

Gas Water Heaters: Log(P) vs. Log(X)
15 

Figure B-6. Gas Water Heaters: all data included.

y = -0.0835x + 2.5178

R2 = 0.15636

2.26

2.28

2.30

2.32

2.34

2.36

2.38

2.40

2.42

2.44

2.46

1.50 1.70 1.90 2.10 2.30 2.50

Gas Water Heaters: Log(P) vs. Log(X)
16 

Figure B-7. Electric Water Heaters: excludes 2003 – 2008. 

y = -0.2744x + 2.8273

R2 = 0.98743

2.00

2.10

2.20

2.30

2.40

2.50

2.60

2.70

2.80

2.90

0.00 0.50 1.00 1.50 2.00 2.50

Electric Water Heaters: Log(P) vs. Log(X)