Table 8.2.22 Standard-Size Refrigerator-Freezers: Base-Case Efficiency Distributions 

Efficiency Level 

(% less than baseline 

energy use) 

Product Class 3: 

Top-Mount 

Refrigerator-Freezer 

Product Class 5: 

Bottom-Mount 

Refrigerator-Freezer 

Product Class 7: 

Side-by-Side 

Refrigerator-Freezer with 

TTD* 

Market Share 

% 

Market Share 

% 

Market Share 

% 

2007 2014 2007 2014 2007 2014 

Baseline 81 81 12 12 25 25 

1 (10) 6 6 0 0 43 43 

2 (15) 13 0 70 0 30 0 

3 (20)†

 0 13 18 88 2 32 

4 (25) 0 0 0 0 0 0 

5 (30) 0 0 0 0 0 0 

* Through-the-door ice service. 

†

 Meets 2008 ENERGY STAR criteria. 

ENERGY STAR requirements for standard-size freezers and compact products have not 

changed since 2001. Those ENERGY STAR requirements correspond to efficiency level 1 for 

standard-size freezers and level 3 for compact products. DOE assumed that the market shares of 

those ENERGY STAR products would remain the same between 2007 and 2014 (Tables 8.2.24 

and 8.2.25). 

Table 8.2.23 Standard-Size Freezers: Base-Case Efficiency Distributions 

Efficiency Level 

(% less than baseline energy 

Product Class 9: 

Upright Freezer 

Product Class 10: 

Chest Freezer 

Market Share 

% 

Market Share 

% 

use) 2007 2014 2007 2014 

Baseline 82 82 85 85 

1 (10)* 17 17 14 14 

2 (15) 1 1 1 1 

3 (20) 0 0 0 0 

4 (25) 0 0 2 2 

5 (30) 0 0 0 0 

* Meets 2008 ENERGY STAR criteria. 

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Table 8.2.24 Compact Refrigeration Products: Base-Case Efficiency Distributions 

Efficiency Level 

(% less than baseline energy 

use) 

Product Class 11: 

Compact Refrigerator 

Product Class 18: 

Compact Freezer 

Market Share 

% 

Market Share 

% 

2007 2014 2007 2014 

Baseline 97 97 95 95 

1 (10) 0 0 5 5 

2 (15) 0 0 0 0 

3 (20)* 1 1 0 0 

4 (25) 2 2 0 0 

5 (30) 0 0 0 0 

* Meets 2008 ENERGY STAR criteria. 

8.3 INPUTS TO PAYBACK PERIOD ANALYSIS 

The payback period (PBP) refers to the time it takes a consumer to recover, through 

lower operating costs, the assumed higher purchase cost of more energy efficient products. 

Numerically, the PBP is the ratio of the increase in purchase cost (from a less to a more efficient 

design) to the decrease in annual operating cost. This type of calculation is known as a simple 

payback period, because it does not account for changes in operating cost over time or the time 

value of money. That is, the calculation is performed at an effective discount rate of 0 percent. 

The equation for determining PBP is: 

ΔIC

PBP = ΔOC 

Where: 

ΔIC = the difference in total installed cost between the more efficient design based on the new 

standard level and the baseline design, and 

ΔOC = the difference in annual operating cost between the two units. 

Payback periods are expressed in years. Payback periods greater than the life of a product 

mean that the increased total installed cost is not recovered through reduced operating cost. 

The data inputs to calculating PBP are the total installed cost to the consumer for each 

product at each efficiency level and the annual (first-year) operating cost for each efficiency 

level. The inputs to calculating total installed cost are the product and installation costs. The 

inputs to calculating operating cost are the annual energy, repair, and maintenance costs. The 

PBP uses the same inputs as the LCC analysis described in section 8.2, except that no energy 

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EERE-2008-BT-STD-0012 COMMENT 20
price trends, product lifetimes, or discount rates are required. Because the PBP is a simple 

payback, the only energy price required is for the year in which a new standard takes effect—in 

this case, the price projected for 2014. 

8.4 RESULTS OF ANALYSES 

This section presents the results of the life-cycle cost (LCC) and payback period (PBP) 

analyses for the considered efficiency levels for refrigeration products. As discussed in section 

8.1.1, DOE’s approach to the LCC analysis involved developing a sample of consumers who use 

each product. DOE also used probability distributions to characterize the uncertainty in many of 

the analytical inputs. DOE used a Monte Carlo simulation technique to perform the LCC 

calculations on data pertaining to the consumers in each sample. For each set of sample 

consumers who use the appliance in each product class, DOE calculated the average LCC, the 

LCC savings, and the median and average PBP for each standard level. 

DOE calculated LCC savings and PBPs relative to the base-case product that it assigned 

to the sampled consumers. As discussed in section 8.2.6, DOE assigned some consumers a basecase product that is more efficient than the baseline product. For that reason, average LCC 

impacts are not equal to the difference between the LCC of a specific possible standard level and 

the LCC of the baseline product. 

In the following subsections, DOE presents figures showing the distribution of LCCs in 

the base case for each product class. Also presented are figures showing the distribution of LCC 

impacts and the distribution of PBPs for specific possible standard levels. The figures represent 

frequency charts that include probabilities of occurrence for the distributions of LCC impacts 

and PBPs. DOE generated the figures for the distributions from a Monte Carlo simulation that 

utilized 10,000 samples. 

LCC and PBP calculations were performed 10,000 times on the sample of consumers 

developed for each product. Each calculation was performed on a single consumer who was 

selected from the sample. The selection was based on weight, that is on how representative a 

particular consumer was of other consumers in the national distribution. Each LCC and PBP 

calculation also sampled from the probability distributions that DOE developed to characterize 

many of the inputs to the analysis. 

Based on the Monte Carlo simulations that DOE performed, for each efficiency level, 

DOE calculated the percentage of consumers who would experience a net LCC benefit, a net 

LCC cost, or no impact. DOE considered a consumer to receive no impact at a given efficiency 

level if the base-case product DOE assigned to that consumer had the same or higher efficiency 

than that of the new standard being evaluated. Note that the average LCC savings at each 

efficiency level are relative to the base-case efficiency distribution, not the baseline efficiency 

level. 

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EERE-2008-BT-STD-0012 COMMENT 20
 

8.4.1 Standard-Size Refrigerator-Freezers 

The following subsections summarize results of LCC and PBP analyses for standard-size 

refrigerator-freezers and present figures showing the distribution of LCCs in the base case for 

each product class. Also presented are figures showing the distribution of LCC impacts and the 

distribution of PBPs for specific efficiency levels. 

8.4.1.1 Summary of Results 

Tables 8.4.1 through 8.4.3 show the LCC and PBP results for each refrigerator-freezer 

product class considered for new standards. DOE determined the median and average values for 

PBPs at each efficiency level by excluding those households that would not be affected by the 

standard. 

Table 8.4.1 Product Class 3, Top-Mount Refrigerator-Freezers: Analytical Results 

Efficiency 

Level 

(% less than 

Life-Cycle Cost 2008$ Life-Cycle Cost Savings 

Payback Period 

(years) 

Average 

Average 

Annual Average 

% of Households that 

Experience 

Median Average 

baseline 

energy use) 

Product 

Cost 

Operating 

Cost 

Average 

LCC 

Savings 

2008$ 

Net 

Cost 

No 

Impact 

Net 

Benefit 

Baseline 591 77 1,505 

1 (10) 595 71 1,434 71 0.0 18.5 81.5 0.6 0.7 

2 (15) 599 67 1,398 106 0.0 13.0 86.9 0.9 1.1 

3 (20) 609 64 1,368 137 0.1 13.0 86.8 1.5 1.7 

4 (25) 631 60 1,343 162 2.6 0.0 97.4 2.8 3.2 

5 (30) 665 56 1,329 176 7.0 0.0 93.0 4.0 4.6 

6 (35) 716 52 1,333 172 16.5 0.0 83.5 5.6 6.4 

7 (40) 781 48 1,350 155 28.2 0.0 71.8 7.3 8.3 

8 (45) 845 44 1,367 138 36.3 0.0 63.7 8.5 9.7 

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EERE-2008-BT-STD-0012 COMMENT 20
 

 

Table 8.4.2 Product Class 5, Bottom-Mount Refrigerator-Freezers: Analytical 

Results 

Efficiency 

Level 

(% less than 

baseline 

energy use) 

Life-Cycle Cost 2008$ Life-Cycle Cost Savings 

Payback Period 

years 

Average 

Product 

Cost 

Average 

Annual 

Operating 

Cost 

Average 

LCC 

Average 

Savings 

2008$ 

% of Households that 

Experience 

Median Average 

Net 

Cost 

No 

Impact 

Net 

Benefit 

Baseline 1,758 70 2,591 

1 (10) 1,759 69 2,581 11 0.0 88.5 11.5 0.3 0.4 

2 (15) 1,759 69 2,576 16 0.0 88.4 11.6 0.7 0.8 

3 (20) 1,760 68 2,571 20 0.0 88.4 11.6 1.2 1.4 

4 (25) 1,782 64 2,542 49 14.8 0.0 85.2 5.1 6.0 

5 (30) 1,799 60 2,508 83 11.2 0.0 88.8 4.6 5.4 

6 (35) 1,879 55 2,538 54 44.1 0.0 55.9 9.5 11.0 

7 (40) 1,969 51 2,577 14 60.5 0.0 39.5 12.6 14.7 

8 (45) 2,053 47 2,610 -19 67.0 0.0 33.0 14.3 16.6 

Table 8.4.3 Product Class 7, Side-by-Side Refrigerator-Freezers with Through-theDoor Ice Service: Analytical Results 

Efficiency 

Level 

(% less than 

Life-Cycle Cost 2008$ Life-Cycle Cost Savings 

Payback Period 

years 

Average 

Average 

Annual Average 

% of Households that 

Experience 

Median Average 

baseline 

energy use) 

Product 

Cost 

Operating 

Cost 

Average 

LCC 

Savings 

2008$ 

Net 

Cost 

No 

Impact 

Net 

Benefit 

Baseline 1,459 116 2,840 

1 (10) 1,463 113 2,804 35 0.0 74.3 25.7 1.1 1.3 

2 (15) 1,469 108 2,759 80 0.1 31.6 68.4 1.5 1.6 

3 (20) 1,499 104 2,738 102 3.1 31.6 65.4 3.8 4.2 

4 (25) 1,563 97 2,724 116 23.8 0.0 76.2 6.7 7.6 

5 (30) 1,638 91 2,722 117 32.7 0.0 67.3 8.1 9.0 

6 (35) 1,714 84 2,721 119 38.5 0.0 61.5 8.9 9.9 

7 (40) 1,815 78 2,744 95 46.2 0.0 53.9 10.1 11.2 

8.4.1.2 Distributions of Impacts 

Figure 8.4.1 presents a frequency chart that shows the distribution of LCC impacts for the 

case of efficiency level 5 for top-mount refrigerator-freezers. The note, “Certainty is 93.01% 

from $0 to +Infinity,” means that 93.01 percent of households owning top-mount refrigeratorfreezers either will not be affected or will have LCC savings under the standard compared to the 

base case. DOE could generate a similar frequency chart for every efficiency level. 

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EERE-2008-BT-STD-0012 COMMENT 20
 

 

Frequency Chart 

Certainty is 93.01% from $0 to +Infinity 

.000 

.005 

.011 

.016 

.021 

0 

53.5 

107 

160.5 

214 

($76) $109 $294 $479 $663 

10,000 Trials 9,715 Displayed 

Forecast: LCCSav_Level5 

Figure 8.4.1 Product Class 3, Top-Mount Refrigerator-Freezers: Distribution of 

Life-Cycle Cost Impacts for Efficiency Level 5 

Figure 8.4.2 is an example of a frequency chart showing the distribution of payback 

periods for efficiency level 5 for top-mount refrigerator-freezers. DOE could generate a similar 

frequency chart for every efficiency level. 

Frequency Chart 

.000 

.007 

.013 

.020 

.027 

0 

66.5 

133 

199.5 

266 

-1.00 2.46 5.93 9.39 12.86 

10,000 Trials 9,812 Displayed 

Forecast: PP_Level5 

Figure 8.4.2 Product Class 3, Top-Mount Refrigerator-Freezers: Distribution of 

Payback Periods for Efficiency Level 5 

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EERE-2008-BT-STD-0012 COMMENT 20
Figures 8.4.3 through 8.4.5 show the range of LCC savings for all the efficiency levels 

considered for each refrigerator-freezer product class. For each efficiency level, the top and 

bottom of the box in the figure indicate the 75th and 25th percentiles, respectively. The bar at the 

middle of the box indicates the median: 50 percent of households have LCC savings that exceed 

this value. The horizontal lines above and below each box indicate the 95th and 5th percentiles, 

respectively. The small box indicates the average LCC savings for each efficiency level. 

-200 

-100 

0 

100 

200 

300 

400 

500 

600 

700 

800 

1 2 3 4 5 6 7 8 

Efficiency Level 

Life-cycle Cost Savings($) 

Mean Median 

Figure 8.4.3 Product Class 3, Top-Mount Refrigerator-Freezers: Range of Life-Cycle 

Cost Savings by Efficiency Level 

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EERE-2008-BT-STD-0012 COMMENT 20
-300 

-200 

-100 

0 

100 

200 

300 

400 

500 

1 2 3 4 5 6 7 8 

Efficiency Level 

Life-cycle Cost Savings($) 

Mean Median 

Figure 8.4.4 Product Class 5, Bottom-Mount Refrigerator-Freezers: Range of LifeCycle Cost Savings by Efficiency Level Life-cycle Cost Savings($) 

800
 

700
 

600
 

500
 

400
 

300
 

200
 

100
 

0
 

-100
 

Mean
 Median -200 

-300 

1 2 3 4 5 6 7 

Efficiency Level 

Figure 8.4.5 Product Class 7, Side-by-Side Refrigerator-Freezers: Range of Life-Cycle 

Cost Savings by Efficiency Level 

Figures 8.4.6 through 8.4.8 show the range of PBPs for all efficiency levels considered 

for each analyzed refrigerator-freezer product class. For each efficiency level, the top and bottom 

of the box in the figure indicate the 75th and 25th percentiles, respectively. The bar at the middle 

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EERE-2008-BT-STD-0012 COMMENT 20
of the box indicates the median: 50 percent of the households have a PBP above this value. The 

horizontal lines above and below each box indicate the 95th and 5th percentiles, respectively. The 

small box indicates the average PBP for each efficiency level. 

0 

2 

4 

6 

8 

10 

12 

14 

16 

18 

20 

1 2 3 4 5 6 7 8 

Efficiency Level 

Payback Period(yr) 

Mean Median 

Figure 8.4.6 Product Class 3, Top-Mount Refrigerator-Freezers: Range of Payback 

Periods by Efficiency Level 

0 

5 

10 

15 

20 

25 

30 

35 

1 2 3 4 5 6 7 8 

Efficiency Level 

Payback Period(yr) 

Mean Median 

Figure 8.4.7 Product Class 5, Bottom-Mount Refrigerator-Freezers: Range of Payback 

Periods by Efficiency Level 

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EERE-2008-BT-STD-0012 COMMENT 20
 

0 

5 

10 

15 

20 

25 

1 2 3 4 5 6 7 

Efficiency Level 

Payback Period(yr) 

Mean Median 

Figure 8.4.8 Product Class 7, Side-by-Side Refrigerator-Freezers: Range of Payback 

Periods by Efficiency Level 

8.4.2 Standard-Size Freezers 

The following subsections summarize results of LCC and PBP analyses for standard-size 

freezers and present figures showing the distribution of LCCs in the base case for both product 

classes. Also presented are figures showing the distribution of LCC impacts and the distribution 

of PBPs for specific efficiency levels. 

8.4.2.1 Summary of Results 

Tables 8.4.4 and 8.4.5 show the LCC and PBP results for each freezer product class 

considered for new standards. DOE determined the median and average values for PBPs at each 

efficiency level by excluding those households not affected by a standard. 

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EERE-2008-BT-STD-0012 COMMENT 20
 

 

Table 8.4.4 Product Class 9, Upright Freezers: Analytical Results 

Efficiency 

Level 

(% less than 

baseline 

energy use) 

Life-Cycle Cost 2008$ Life-Cycle Cost Savings 

Payback Period 

years 

Average 

Equipment 

Cost 

Average 

Annual 

Operating 

Cost 

Average 

LCC 

Average 

Savings 

2008$ 

% of Households that 

Experience 

Median Average 

Net 

Cost 

No 

Impact 

Net 

Benefit 

Baseline 505 113 2,064 

1 (10) 511 104 1,942 122 0.0 19.4 80.6 0.7 0.8 

2 (15) 517 98 1,870 194 0.0 1.6 98.4 0.9 1.1 

3 (20) 520 92 1,793 271 0.0 0.5 99.5 0.7 0.9 

4 (25) 537 87 1,731 333 0.0 0.4 99.6 1.3 1.5 

5 (30) 561 81 1,675 389 0.3 0.2 99.6 1.8 2.1 

6 (35) 598 75 1,633 432 1.2 0.0 98.8 2.5 3.0 

7 (40) 656 69 1,611 453 4.0 0.0 96.0 3.5 4.3 

8 (45) 745 63 1,621 443 9.9 0.0 90.1 5.0 6.0 

Table 8.4.5 Product Class 10, Chest Freezer: Analytical Results 

Efficiency 

Level 

(% less than 

Life-Cycle Cost 2008$ Life-Cycle Cost Savings 

Payback Period 

years 

Average 

Average 

Annual Average 

% of Households that 

Experience 

Median Average 

baseline 

energy use) 

Equipment 

Cost 

Operating 

Cost 

Average 

LCC 

Savings 

2008$ 

Net 

Cost 

No 

Impact 

Net 

Benefit 

Baseline 325 69 1,275 

1 (10) 328 63 1,196 79 0.0 15.3 84.8 0.4 0.5 

2 (15) 332 60 1,153 122 0.0 1.1 98.9 0.8 1.0 

3 (20) 355 56 1,127 148 1.2 0.2 98.6 2.5 3.0 

4 (25) 361 53 1,085 190 0.7 0.2 99.2 2.4 2.8 

5 (30) 368 49 1,044 231 0.4 0.2 99.4 2.3 2.7 

6 (35) 416 46 1,044 231 5.3 0.0 94.7 4.2 4.9 

7 (40) 420 42 999 276 3.7 0.0 96.3 3.8 4.4 

8 (45) 493 39 1,024 251 13.8 0.0 86.2 6.0 6.9 

8.4.2.2 Distributions of Impacts 

Figure 8.4.9 presents a frequency chart showing the distribution of LCCs for the case of 

standard level 5 for upright freezers. The note, “Certainty is 99.67 % from $0 to +Infinity,” 

means that 99.67 percent of households owning upright freezers either will not be affected or 

will have LCC savings under the standard level compared to the base case. DOE could generate 

a similar frequency chart for every efficiency level. 

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EERE-2008-BT-STD-0012 COMMENT 20
 

 

Frequency Chart 

Certainty is 99.67% from $0 to +Infinity 

.000 

.004 

.009 

.013 

.017 

0 

43.25 

86.5 

129.7 

173 

($28) $261 $550 $840 $1,129 

10,000 Trials 9,739 Displayed 

Forecast: LCCSav_Level5 

Figure 8.4.9 Product Class 9, Upright Freezers: Distribution of Life-Cycle Cost 

Impacts for Efficiency Level 5 

Figure 8.4.10 presents a frequency chart showing the distribution of payback periods for 

standard level 5 for upright freezers. DOE could generate a similar frequency chart for every 

efficiency level within each product class. 

Frequency Chart 

.000 

.012 

.024 

.036 

.048 

0 

118.7 

237.5 

356.2 

475 

-1.09 0.73 2.54 4.36 6.18 

10,000 Trials 9,699 Displayed 

Forecast: PP_Level5 

Figure 8.4.10 Product Class 9, Upright Freezers: Distribution of Payback 

Periods for Efficiency Level 5 

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EERE-2008-BT-STD-0012 COMMENT 20
Figures 8.4.11 and 8.4.12 show the range of LCC savings for the efficiency levels 

considered for each freezer product class. For each standard level, the top and bottom of the box 

indicate the 75th and 25th percentiles, respectively. The bar at the middle of the box indicates the 

median: 50 percent of the households have LCC savings that exceed this value. The horizontal 

lines above and below the box indicate the 95th and 5th percentiles, respectively. The small box 

shows the average LCC savings for each efficiency level. 

-200 

0 

200 

400 

600 

800 

1000 

1200 

1400 

1 2 3 4 5 6 7 8 

Efficiency Level 

Life-cycle Cost Savings($) 

Mean Median 

Figure 8.4.11 Product Class 9, Upright Freezers: Range of Life-Cycle Cost Savings by 

Efficiency Level 

8-48
 

EERE-2008-BT-STD-0012 COMMENT 20
-100 

0 

100 

200 

300 

400 

500 

600 

700 

800 

900 

1 2 3 4 5 6 7 8 

Efficiency Level 

Life-cycle Cost Savings($) 

Mean Median 

Figure 8.4.12 Product Class 10, Chest Freezers: Range of Life-Cycle Cost Savings by 

Efficiency Level 

Figures 8.4.13 and 8.4.14 show the range of PBPs for the efficiency levels considered for 

each freezer product class. For each standard level, the top and bottom of the box indicate the 

75th and 25th percentiles, respectively. The bar at the middle of the box indicates the median: 50 

percent of the households have PBPs that exceed this value. The horizontal lines above and 

below the box indicate the 95th and 5th percentiles, respectively. The small box shows the 

average PBP for each efficiency level. 

8-49 

EERE-2008-BT-STD-0012 COMMENT 20
0 

2 

4 

6 

8 

10 

12 

14 

1 2 3 4 5 6 7 8 

Efficiency Level 

Payback Period(yr) 

Mean Median 

Figure 8.4.13 Product Class 9, Upright Freezers: Range of Payback Periods by 

Efficiency Level 

0 

2 

4 

6 

8 

10 

12 

14 

16 

1 2 3 4 5 6 7 8 

Efficiency Level 

Payback Period(yr) 

Mean Median 

Figure 8.4.14 Product Class 10, Chest Freezers: Range of Payback Periods by 

Efficiency Level 

8.4.3 Compact Refrigeration Products 

The following subsections summarize results of LCC and PBP analyses for compact 

refrigeration products and present figures showing the distribution of LCCs in the base case for 

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EERE-2008-BT-STD-0012 COMMENT 20
 

 

both product classes. Also presented are figures showing the distribution of LCC impacts and the 

distribution of PBPs for specific efficiency levels. 

8.4.3.1 Summary of Results 

Tables 8.4.6 and 8.4.7 show the results of LCC and PBP analyses for compact 

refrigerators and freezers. DOE determined the median and average values of PBPs by excluding 

those households not affected by a standard at each efficiency level. 

Table 8.4.6 Product Class 11, Compact Refrigerators: Analytical Results 

Efficiency 

Level 

(% less than 

baseline 

energy use) 

Life-Cycle Cost $2008 Life-Cycle Cost Savings 

Payback Period 

years 

Average 

Product 

Cost 

Average 

Annual 

Operating 

Cost 

Average 

LCC 

Average 

Savings 

$2008 

% of Consumers who 

Experience 

Median Average 

Net 

Cost 

No 

Impact 

Net 

Benefit 

Baseline 166 33 325 

1 (10) 170 30 314 12 4.2 2.9 92.8 1.4 1.3 

2 (15) 173 28 309 16 8.1 2.6 89.4 1.7 1.7 

3 (20) 176 26 305 21 9.3 2.6 88.1 1.8 1.8 

4 (25) 191 25 311 14 34.1 1.7 64.2 3.4 3.3 

5 (30) 192 23 304 21 26.9 0.0 73.2 2.9 2.8 

6 (35) 198 21 303 23 30.5 0.0 69.6 3.1 3.0 

7 (40) 214 20 310 15 44.6 0.0 55.4 4.0 3.9 

8 (45) 236 18 324 1 62.2 0.0 37.8 5.3 5.1 

Table 8.4.7 Product Class 18, Compact Freezers: Analytical Results 

Efficiency 

Level 

(% less than 

Life-Cycle Cost $2008 Life-Cycle Cost Savings 

Payback Period 

years 

Average 

Average 

Annual Average 

% of Consumers who 

Experience 

Median Average 

baseline 

energy use) 

Product 

Cost 

Operating 

Cost 

Average 

LCC 

Savings 

$2008 

Net 

Cost 

No 

Impact 

Net 

Benefit 

Baseline 207 34 399 

1 (10) 214 30 388 11 9.6 5.0 85.4 2.4 2.3 

2 (15) 218 29 382 16 11.7 0.0 88.3 2.5 2.5 

3 (20) 248 27 403 -4 65.8 0.0 34.2 6.7 6.8 

4 (25) 259 25 404 -5 65.6 0.0 34.4 6.7 6.7 

5 (30) 281 24 416 -17 76.5 0.0 23.5 7.9 7.9 

6 (35) 284 22 410 -11 69.3 0.0 30.7 7.1 7.1 

7 (40) 299 20 415 -16 72.0 0.0 28.0 7.4 7.4 

8 (45) 317 19 423 -24 75.9 0.0 24.1 7.8 7.8 

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EERE-2008-BT-STD-0012 COMMENT 20
 

8.4.3.2 Distributions of Impacts 

Figure 8.4.15 presents a frequency chart showing the distribution of LCCs for the case of 

efficiency level 5 for compact refrigerators. The note, “Certainty is 71.04% from $0 to 

+Infinity,” means that 71.04 percent of households owning compact refrigerators either will not 

be affected or will have LCC savings under the standard level compared to the base case. DOE 

could generate a similar frequency chart for every efficiency level. 

Frequency Chart 

Certainty is 71.04% from $0 to +Infinity 

.000 

.006 

.011 

.017 

.022 

0 

56 

112 

168 

224 

($22) $15 $53 $91 $129 

10,000 Trials 9,759 Displayed 

Forecast: LCCSav5 

Figure 8.4.15 Product Class 11, Compact Refrigerators: Distribution of Life-Cycle Cost 

Impacts for Efficiency Level 5 

Figure 8.4.16 presents a frequency chart showing the distribution of payback periods for 

efficiency level 5 for compact refrigerators. DOE could generate a similar frequency chart for 

every efficiency level within each product class. 

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EERE-2008-BT-STD-0012 COMMENT 20
Frequency Chart 

.000 

.010 

.020 

.030 

.040 

0 

99.5 

199 

298.5 

398 

1.05 2.06 3.07 4.08 5.09 

10,000 Trials 9,812 Displayed 

Forecast: PPLev5 

Figure 8.4.16 Product Class 11, Compact Refrigerators: Distribution of 

Payback Period for Efficiency Level 5 

Figures 8.4.17 and 8.4.18 show the range of LCC savings for the standard levels 

considered for compact refrigerators and freezers. For each standard level, the top and bottom of 

the box indicate the 75th and 25th percentiles, respectively. The bar at the middle of the box 

indicates the median: 50 percent of the households have LCC savings that exceed this value. The 

horizontal lines above and below the box indicate the 95th and 5th percentiles, respectively. The 

small box shows the average LCC savings for each efficiency level. Figures 8.4.19 and 8.4.20 

show the range of PBPs for each efficiency level considered. 

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EERE-2008-BT-STD-0012 COMMENT 20
-60 

-40 

-20 

0 

20 

40 

60 

80 

100 

120 

1 2 3 4 5 6 7 8 

Efficiency Level 

Life-cycle Cost Savings($) 

Mean Median 

Figure 8.4.17 Product Class 11, Compact Refrigerators: Range of Life-Cycle Cost 

Savings by Efficiency Level 

Life-cycle Cost Savings($) 

80
 

60
 

40
 

20
 

0
 

-20
 

-40
 

-60
 

-80
 Mean Median 

-100 

1 2 3 4 5 6 7 8 

Efficiency Level 

Figure 8.4.18 Product Class 18, Compact Freezers: Range of Life-Cycle Cost Savings 

by Efficiency Level 

Figures 8.4.19 and 8.4.20 show the range of PBPs for the standard levels considered for 

compact refrigerators and freezers. For each standard level, the top and bottom of the box 

indicate the 75th and 25th percentiles, respectively. The bar at the middle of the box indicates the 

median: 50 percent of the households have PBPs that exceed this value. The horizontal lines 

8-54 

EERE-2008-BT-STD-0012 COMMENT 20
above and below the box indicate the 95th and 5th percentiles, respectively. The small box shows 

the average PBP for each efficiency level. 

0 

1 

2 

3 

4 

5 

6 

7 

8 

1 2 3 4 5 6 7 8 

Efficiency Level 

Payback Period(yr) 

Mean Median 

Figure 8.4.19 Product Class 11, Compact Refrigerators: Range of Payback Periods by 

Efficiency Level 

0 

2 

4 

6 

8 

10 

12 

1 2 3 4 5 6 7 8 

Efficiency Level 

Payback Period(yr) 

Mean Median 

Figure 8.4.20 Product Class 18, Compact Freezers: Range of Payback Periods by 

Efficiency Level 

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EERE-2008-BT-STD-0012 COMMENT 20
 

8.5 REBUTTABLE PAYBACK PERIOD 

DOE presents rebuttable PBPs to provide information for considering the legally 

established rebuttable presumption that an energy efficiency standard is economically justified if 

the additional product costs attributed to the standard are less than three times the value of the 

first-year savings in energy costs. (42 U.S.C. §6295 (o)(2)(B)(iii)) 

8.5.1 Metric 

The basic equation for rebuttable PBP is the same as that shown in section 8.3, Inputs to 

Analysis of Payback Period. Unlike the analyses described in sections 8.2 and 8.3, however, the 

rebuttable PBP is not based on the use of household samples and probability distributions. 

Rather, it is based on discrete, single-point values. For example, although DOE uses a probability 

distribution of regional energy prices in the analysis of payback period, it uses only the national 

average energy price from the probability distribution to determine the rebuttable PBP. 

Other than the use of single-point values, the most notable difference between the 

distribution PBP and the rebuttable PBP is the latter’s reliance on the DOE test procedure to 

determine a product’s annual energy consumption. 

8.5.2 Inputs to Rebuttable Payback Period Analysis 

The following were the key inputs that DOE used in determining rebuttable PBP. 

•	 Incremental manufacturing costs, markup, baseline retail price, sales tax, and 

installation costs were all based on single-point values derived from, or used as inputs 

to, the distributional LCC and PBP analysis. 

•	 Annual energy consumption was based on results of the proposed DOE test procedure 

for a product with the average adjusted volume from the LCC and PBP analysis. 

•	 Energy prices were based on average values for the year that new standards are 

assumed to take effect (2014). 

An average discount rate or lifetime is not required in calculating the rebuttable PBP. 

8.5.3 Results of Analysis 

DOE calculated rebuttable PBPs for each standard level relative to the purchase and 

operating costs of an average baseline product. 

Table 8.5.1 presents the rebuttable PBPs for each standard-size refrigerator product class 

that DOE analyzed for the LCC and PBP analysis. 

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EERE-2008-BT-STD-0012 COMMENT 20
 

 

Table 8.5.1 Standard-Size Refrigerator-Freezers: Rebuttable Payback Periods 

Product Class 3: 

Top-Mount RefrigeratorFreezer 

Product Class 5: 

Bottom-Mount RefrigeratorFreezer 

Product Class 7: 

Side-by-Side Refrigerator-Freezer 

with TTD* 

Efficiency Level 

(% less than baseline 

energy use) 

PBP 

years 

Efficiency Level 

(% less than baseline 

energy use) 

PBP 

years 

Efficiency Level 

(% less than baseline 

energy use) 

PBP 

years 

Baseline - Baseline - Baseline -

1 (10) 0.7 1 (10) 0.3 1 (10) 1.4 

2 (15) 1.0 2 (15) 0.6 2 (15) 1.6 

3 (20) 1.6 3 (20) 1.0 3 (20) 3.4 

4 (25) 2.6 4 (25) 1.8 4 (25) 5.4 

5 (30) 3.9 5 (30) 2.2 5 (30) 7.1 

6 (35) 5.6 6 (35) 4.6 6 (35) 8.3 

7 (40) 7.4 7 (40) 6.6 7 (40) 9.9 

8 (45) 8.7 8 (45) 8.1 - -

*Through-the-door ice service. 

Table 8.5.2 presents the rebuttable PBPs for each standard-size freezer product class that 

DOE analyzed for the LCC and PBP analysis. 

Table 8.5.2 Standard-Size Freezers: Rebuttable Payback Periods 

Product Class 9: 

Upright Freezer 

Product Class 10: 

Chest Freezer 

Efficiency Level 

(% less than baseline energy use) 

PBP 

years 

Efficiency Level 

(% less than baseline energy use) 

PBP 

years 

Baseline - Baseline -

1 (10) 1.0 1 (10) 0.6 

2 (15) 1.2 2 (15) 1.0 

3 (20) 1.1 3 (20) 3.0 

4 (25) 1.8 4 (25) 3.0 

5 (30) 2.5 5 (30) 2.9 

6 (35) 3.5 6 (35) 5.4 

7 (40) 5.0 7 (40) 4.9 

8 (45) 7.0 8 (45) 7.7 

Table 8.5.3 presents the rebuttable PBPs for each compact refrigeration product class that 

DOE analyzed for the LCC and PBP analysis. 

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EERE-2008-BT-STD-0012 COMMENT 20
Table 8.5.3 Compact Refrigerators and Freezers: Rebuttable Payback Periods 

Product Class 11: 

Compact Refrigerator 

Product Class 18: 

Compact Freezer 

Efficiency Level 

(% less than baseline energy use) 

PBP 

years 

Efficiency Level 

(% less than baseline energy use) 

PBP 

years 

Baseline - Baseline -

1 (10) 1.3 1 (10) 2.1 

2 (15) 1.7 2 (15) 2.3 

3 (20) 1.8 3 (20) 6.0 

4 (25) 3.3 4 (25) 6.0 

5 (30) 2.9 5 (30) 7.0 

6 (35) 3.0 6 (35) 6.3 

7 (40) 3.9 7 (40) 6.6 

8 (45) 5.1 8 (45) 7.0 

8-58 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

 

 

 

 

 

 

 

 

 

 

REFERENCES 

1	 U.S. Department of Energy–Energy Information Administration. Residential Energy 

Consumption Survey, 2005 Public Use Data Files. 2009. U.S. Department of Energy: 

Washington, D.C. <www.eia.doe.gov/emeu/recs/recspubuse05/pubuse05.html> 

2	 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> 

3	 The NPD Group, Inc., The NPD Group/NPD Houseworld – POS, Refrigerators, January – 

December 2007, 2007. Port Washington, NY. 

4	 Association of Home Appliance Manufacturers. Aggregated Refrigerator‐Freezer 

Incremental Cost Data for DOE Refrigerator‐Freezer Energy Efficiency Standards 

Rulemaking, March 2, 2009. Memo to Detlef Westphalen, Navigant Consulting, Inc. 

5	 U.S. Department of Energy–Energy Information Administration. Form EIA-861. 

Washington, D.C. <www.eia.doe.gov/cneaf/electricity/page/eia861.html> 

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

Housing Survey. Washington, DC. 

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

7	 U.S. Department of Energy–Energy Information Administration. Commercial Building 

Energy Consumption Survey, 2003 Public Use Data Files. 2008. U.S. Department of Energy: 

Washington, D.C. 

<http://www.eia.doe.gov/emeu/cbecs/cbecs2003/public_use_2003/cbecs_pudata2003.ht

ml> 

8	 The Federal Reserve Board. Survey of Consumer Finances. 1989, 1992, 1995, 1998, 2001, 

and 2004. <www.federalreserve.gov/pubs/oss/oss2/scfindex.html> 

9	 The Federal Reserve Board. Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: CDs (secondary market), Maturity: 6-month, Frequency: 

Annual, Description: Average rate on 6-month negotiable certificates of deposit (secondary 

market), quoted on an investment basis. 2009. Washington, D.C. 

<www.federalreserve.gov/releases/H15/data.htm> 

10	 The Federal Reserve Board. Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: State and local bonds, Maturity: 20-year, Frequency: Monthly, 

8-59 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

 

 

 

 

 

 

 

 

Description: Bond buyer go 20-bond municipal bond index. 2009. Washington, D.C. 

<www.federalreserve.gov/releases/H15/data.htm> 

11	 The Federal Reserve Board. Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: Corporate bonds/Moody’s Seasoned AAA, Frequency: Annual, 

Description: Moody’s yield on seasoned corporate bonds—all industries, AAA. 2009. 

Washington, D.C. <www.federalreserve.gov/releases/H15/data.htm> 

12	 Mortgage-X, Mortgage Information Service. Cost of Savings Index (COSI), Index History. 

2009. <http://mortgage-x.com/general/indexes/default.asp> (Last accessed, February 25, 

2009.) 

13	 Damodaran Online. The Data Page: Historical Returns on Stocks, Bonds, and Bills—United 

States. 2009. <http://pages.stern.nyu.edu/~adamodar/> 

14	 Ibbotsons Associates. SBBI Valuation Edition 2009 Yearbook. 2009. Chicago, IL. pp. 45–48. 

15	 Damodaran Online. The Data Page: Cost of Capital by Industry Sector. 2001–2008. 

<http://pages.stern.nyu.edu/~adamodar/> 

16	 Damodaran Online. The Data Page: Historical Returns on Stocks, Bonds and Bills—United 

States. <http://pages.stern.nyu.edu/~adamodar/> 

17	 Bureau of Economic Analysis–National Income and Product Accounts Tables. Table 1.1.9. 

Implicit Price Deflators for Gross Domestic Product. 

<www.bea.gov/national/nipaweb/SelectTable.asp?Selected=N> 

18	 U.S. Department of Energy. Implementation Report: Energy Conservation Standards 

Activities. Submitted Pursuant to Section 141 of the Energy Policy Act of 2005. August 2006. 

Washington, D.C. 

<www.eere.energy.gov/buildings/appliance_standards/pdfs/implementation_report_0806.pdf 

> 

19	 Association of Home Appliance Manufacturers. Aggregated Refrigerator‐Freezer 

Shipments and Efficiency Data for DOE Refrigerator‐Freezer Energy Efficiency Standards 

Rulemaking, January 16, 2009. Memo to Detlef Westphalen, Navigant Consulting, Inc. 

8-60 

EERE-2008-BT-STD-0012 COMMENT 20
APPENDIX 8-A. USER INSTRUCTIONS FOR LCC AND PBP SPREADSHEETS 

TABLE OF CONTENTS 

8-A.1 INTRODUCTION .................................................................................................8-A-1 

8-A.1.1 Standard Size Refrigerator-Freezers and Freezers.................................................8-A-1 

8-A.1.2 Compact Refrigerators and Compact Freezers ......................................................8-A-2 

8-A.2 BASIC INSTRUCTIONS......................................................................................8-A-3 

8A-i 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

 

 

APPENDIX 8-A. USER INSTRUCTIONS FOR LCC AND PBP SPREADSHEETS 

8-A.1 INTRODUCTION 

It is possible to examine and reproduce the detailed results of the life-cycle cost (LCC) 

and payback period (PBP) analyses using Microsoft Excel spreadsheets available on the U.S. 

Department of Energy’s website at: http://www.eere.energy.gov/buildings/appliance_standards/. 

To fully execute the spreadsheets requires both Microsoft Excel and Crystal Ball software. Both 

applications are commercially available. Crystal Ball is available at: 

http://www.decisioneering.com. 

The seven spreadsheets posted on the DOE website represent the latest versions and have 

been tested with Microsoft Excel 2003. 

8-A.1.1 Standard Size Refrigerator-Freezers and Freezers 

The Standard Size Refrigerator-Freezers and Freezers LCC and PBP spreadsheets or 

workbooks consist of the following worksheets: 

LCC Summary Contains the input selections and a summary table of energy use, 

operating costs, LCC, and Payback. This worksheet also works as 

an interface between user inputs and the rest of the worksheets — 

do not modify this sheet. 

RECS Households For each RECS household being sampled, contains the equipment 

usage data, along with product characteristics (i.e., size, volume, 

product age) and household characteristics (e.g., Census division, 

income). 

RECS UAF Contains the unit adjustment factor calculation which converts 

tested energy consumption into field energy consumption 

Base Case Eff Dist Contains market efficiency distribution in the year the standard 

takes effect 

Equipment Price Contains manufacturer price data for the considered design 

options. Also includes the manufacturer and retail mark-ups, sales 

tax. 

Energy Use Contains unit energy use data (tested and field) 

Energy Price Contains regional electricity prices for the reference year. 

8-A-1
 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

 

 

 

 

 

 

Energy Price Trend Contains the electricity price trends for the reference, high, and 

low economic growth scenarios based on AEO 2009. 

Discount Rate Contains data from which an average discount rate and a 

distribution of discount rates are determined. 

Lifetime Contains the survival function and average lifetime in years. 

Standards Contains past and existing standards by product class along with 

historical trends of energy consumption prior to first standards. 

AV Equations Contains average relation between volume and adjusted volume by 

product class. 

ESAF Contains efficiency standard adjustment factor. 

UAF Range Contains parameters allowing for the sample selection, and 

minimum and maximum UAF assignments. 

EStarModel Contains Energy Star model assigning energy star to households 

based on income. 

8-A.1.2 Compact Refrigerators and Compact Freezers 

The Compact Refrigerators and Compact Freezers LCC and PBP spreadsheet or 

workbook consists of the following worksheets: 

LCC Summary	 Contains the input selections and a summary table of energy use, 

operating costs, LCC, and Payback. This worksheet also works as 

an interface between user inputs and the rest of the worksheets — 

do not modify this sheet. 

Base Case Eff Dist	 Contains market efficiency distribution in the year the standard 

takes effect. 

Division	 Contains number of customers for residential and commercial 

sector by region. 

Equipment Price	 Contains manufacturer price data for the considered design 

options. Also includes the manufacturer and retail mark-ups, sales 

tax. 

Energy Use	 Contains unit energy use data (tested and field) 

Energy Price	 Contains regional electricity prices for the reference year. 

8-A-2
 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

Energy Price Trend	 Contains the electricity price trends for the reference, high, and 

low economic growth scenarios based on AEO 2009. 

Discount Rate Contains data from which an average discount rate and a 

distribution of discount rates are determined. 

Lifetime	 Contains the survival function and average lifetime in years. 

AV Equations	 Contains average relation between volume and adjusted volume by 

product class. 

8-A.2 BASIC INSTRUCTIONS 

Basic instructions for operating the LCC spreadsheets are as follows: 

1.	 Once you have downloaded the LCC file from the Web, open the file using Excel. At the 

bottom, click on the tab for sheet ‘LCC Summary’. 

2.	 Use Excel’s “View/Zoom” commands at the top menu bar to change the size of the 

display to make it fit your monitor. 

3.	 The user interacts with the spreadsheet by clicking choices or entering data using the 

graphical interface that comes with the spreadsheet. Select choices from the various 

inputs listed under "User Options" heading. 

4.	 Under the "User Options" heading, select choices from the selection buttons and boxes 

for the following: (1) type of calculation (Sample or Crystal Ball®), (2) energy price 

Trend, (3) start year, and (4) efficiency market share scenario. By overwriting the code in 

the LCC summary sheet, a new discount rate or lifetime can be entered if a value other 

than the default value is wanted . The Department does not recommend saving the 

spreadsheet after the code is changed. 

5.	 To change inputs listed under "User Input", select the input you wish to change by either 

clicking on the appropriate button or selecting the appropriate input from the input box. 

6.	 This spreadsheet gives the user two types of calculation methods: 

a.	 If the "Sample Calc" is selected, then all calculations are performed for single input 

values, usually an average. The new results are shown on the same sheet as soon as 

the new values are entered. 

b.	 Alternately, if the "CB Calculation" is selected, the spreadsheet generates results that 

are distributions. Some of the inputs are also distributions. The results from the LCC 

distribution are shown as single values and refer only to the results from the last 

8-A-3 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

Monte Carlo sample and are therefore not meaningful. To run the distribution version 

of the spreadsheet, the Microsoft Excel® add-in software called Crystal Ball® must 

be enabled. 

To produce sensitivity results using Crystal Ball, simply select Run from the Run menu 

(on the menu bar). To make basic changes in the run sequence, including altering the number of 

trials, select Run Preferences from the Run menu. After each simulation run, the user needs to 

select Reset (also from the Run menu) before Run can be selected again. Once Crystal Ball has 

completed its run sequence it will produce a series of distributions. Using the menu bars on the 

distribution results, it is possible to obtain further statistical information. The time taken to 

complete a run sequence can be reduced by minimizing the Crystal Ball window in Microsoft 

Excel. A step-by-step summary of the procedure for running a distribution analysis is outlined 

below: 

1.	 Find the Crystal Ball toolbar (at top of screen) 

2.	 Click on Run from the menu bar 

3.	 Select Run Preferences and choose from the following choices: 

a.	 Monte Carloa 

b.	 Latin Hypercube (recommended) 

c.	 Initial seed choices and whether you want it to be constant between runs 

d.	 Select number of Monte Carlo Trials (DOE suggests 10,000). 

4. To run the simulation, follow the following sequence (on the Crystal Ball toolbar) 

Run 

Reset 

Run 

5.	 Now wait until the program informs you that the simulation is completed. 

The following instructions are provided to view the output generated by Crystal Ball. 

1.	 After the simulation has finished, to see the distribution charts generated, click on the 

Windows tab bar that is labeled Crystal Ball. 

2.	 The life-cycle cost savings and payback periods are defined as Forecast cells. The 

frequency charts display the results of the simulations, or trials, performed by Crystal 

Ball. Click on any chart to bring it into view. The charts show the low and high endpoints 

of the forecasts. The View selection on the Crystal Ball toolbar can be used to specify 

whether you want cumulative or frequency plots shown. 

a

 Because of the nature of the program, there is some variation in results due to random sampling when Monte Carlo 

or Latin Hypercube sampling is used. 

8-A-4
 

EERE-2008-BT-STD-0012 COMMENT 20
3.	 To calculate the probability that a particular value of LCC savings will occur, either type 

0 in the box by the left arrow, or move the arrow key with the cursor to 0 on the scale. 

The value in the Certainty box shows the likelihood that the LCC savings will occur. To 

calculate the certainty of payback period being below a certain number of years, choose 

that value as the high endpoint. 

4.	 To generate a printout report, select Create Report from the Run menu. The toolbar 

choice of Forecast Windows allows you to select the charts and statistics in which you 

are interested. For further information on Crystal Ball outputs, please refer to 

Understanding the Forecast Chart in the Crystal Ball manual. 

8-A-5
 

EERE-2008-BT-STD-0012 COMMENT 20
APPENDIX 8-B. UNCERTAINTY AND VARIABILITY 

TABLE OF CONTENTS 

8-B.1 INTRODUCTION ................................................................................................. 8-B-1 

8-B.2 UNCERTAINTY ................................................................................................... 8-B-1 

8-B.3 VARIABILITY...................................................................................................... 8-B-1 

8-B.4 APPROACHES TO UNCERTAINTY AND VARIABILITY ............................. 8-B-2 

8-B.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL................. 8-B-2 

LIST OF FIGURES 

Figure 8-B.5.1 Normal Probability Distribution ................................................................ 8-B-3 

Figure 8-B.5.2 Triangular Probability Distribution............................................................ 8-B-3 

Figure 8-B.5.3 Uniform Probability Distribution............................................................... 8-B-3 

8B-i 

EERE-2008-BT-STD-0012 COMMENT 20
APPENDIX 8-B. UNCERTAINTY AND VARIABILITY 

8-B.1 INTRODUCTION 

Analysis of an energy-efficiency standard involves calculations of impacts, for example, 

the impact of a standard on consumer life-cycle cost (LCC). In order to perform the calculation, 

the analyst must first: 1) specify the equation or model that will be used; 2) define the quantities 

in the equation; and 3) provide numerical values for each quantity. In the simplest case, the 

equation is unambiguous (contains all relevant quantities and no others), each quantity has a 

single numerical value, and the calculation results in a single value. However, unambiguity and 

precision are rarely the case. In almost all cases, the model and/or the numerical values for each 

quantity in the model are not completely known (i.e., there is uncertainty) or the model and/or 

the numerical values for each quantity in the model depend upon other conditions (i.e., there is 

variability). 

Thorough analysis involves accounting for uncertainty and variability. While the 

simplest analysis involves a single numerical value for each quantity in a calculation, arguments 

can arise about what the appropriate value is for each quantity. Explicit analysis of uncertainty 

and variability is intended to provide more complete information to the decision-making process. 

8-B.2 UNCERTAINTY 

When making observations of past events or speculating about the future, imperfect 

knowledge is the rule rather than the exception. For example, the energy actually consumed by a 

particular appliance type (such as the average U.S. commercial air conditioner or heat pump) is 

not directly recorded, but rather estimated based upon available information. Even direct 

laboratory measurements have some margin of error. When estimating numerical values 

expected for quantities at some future date, the exact outcome is rarely known in advance. 

8-B.3 VARIABILITY 

Variability means that different applications or situations produce different numerical 

values when calculating a quantity. Specifying an exact value for a quantity may be difficult 

because the value depends on something else. For example, the number of hours an air 

conditioner is operated by a household depends upon the specific circumstances and behaviors of 

the occupants (e.g., number of persons, personal habits about how comfortable the person wants 

to be, etc.). Variability makes specifying an appropriate population value more difficult in as 

much as any one value may not be representative of the entire population. Surveys can be 

helpful here, and analysis of surveys can relate the variable of interest (e.g., hours of use) to 

other variables that are better known or easier to forecast (e.g., persons per household). 

8-B-1
 

EERE-2008-BT-STD-0012 COMMENT 20
8-B.4 APPROACHES TO UNCERTAINTY AND VARIABILITY 

This section describes two approaches to uncertainty and variability: 

• scenario analysis, and 

• probability analysis. 

Scenario analysis uses a single numerical value for each quantity in a calculation, then 

changes one (or more) of the numerical values and repeats the calculation. A number of 

calculations are done, which provide some indication of the extent to which the result depends 

upon the assumptions. For example, the life-cycle cost of an appliance could be calculated for 

energy rates of 2, 8, and 14¢ per kWh. 

The advantages of scenario analysis are that each calculation is simple; a range of 

estimates is used; and crossover points can be identified. (An example of a crossover point is the 

energy rate above which the life-cycle cost is reduced, holding all other inputs constant. That is, 

the crossover point is the energy rate at which the consumer achieves savings in operating 

expense that more than compensate for the increased purchase expense.) The disadvantage of 

scenario analysis is that there is no information about the likelihood of each scenario. 

 Probability analysis considers the probabilities within a range of values. For quantities 

with variability (e.g., electricity rates in different households), surveys can be used to generate a 

frequency distribution of numerical values (e.g., the number of households with electricity rates 

at particular levels) to estimate the probability of each value. For quantities with uncertainty, 

statistical or subjective measures can be used to provide probabilities (e.g., manufacturing cost to 

improve energy efficiency to some level may be estimated to be $10 ± $3). 

The major disadvantage of the probability approach is that it requires more information, 

namely information about the shapes and magnitudes of the variability and uncertainty of each 

quantity. The advantage of the probability approach is that it provides greater information about 

the outcome of the calculations, that is, it provides the probability that the outcome will be in a 

particular range. 

Scenario and probability analysis provide some indication of the robustness of the policy 

given the uncertainties and variability. A policy is robust when the impacts are acceptable over a 

wide range of possible conditions. 

8-B.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL 

To quantify the uncertainty and variability that exist in inputs to the engineering, LCC, 

and payback period (PBP) analyses, the Department used Microsoft Excel spreadsheets 

combined with Crystal Ball, a commercially available add-in, to conduct probability analyses. 

The probability analyses used Monte Carlo simulation and probability distributions. 

8-B-2
 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

Simulation refers to any analytical method meant to imitate a real-life system, especially 

when other analyses are too mathematically complex or too difficult to reproduce. Without the 

aid of simulation, a spreadsheet model will only reveal a single outcome, generally the most 

likely or average scenario. Spreadsheet risk analysis uses both a spreadsheet model and 

simulation to automatically analyze the effect of varying inputs on outputs of the modeled 

system. One type of spreadsheet simulation is Monte Carlo simulation, which randomly 

generates values for uncertain variables again and again to simulate a model. Monte Carlo 

simulation was named for Monte Carlo, Monaco, where the primary attractions are casinos 

containing games of chance. Games of chance such as roulette wheels, dice, and slot machines, 

exhibit random behavior. The random behavior in games of chance is similar to how Monte 

Carlo simulation selects variable values at random to simulate a model. When you roll a die, you 

know that either a 1, 2, 3, 4, 5, or 6 will come up, but you do not know which for any particular 

roll. It's the same with the variables that have a known range of values but an uncertain value for 

any particular time or event (e.g., equipment lifetime, discount rate, and installation cost). 

For each uncertain variable (one that has a range of possible values), possible values are 

defined with a probability distribution. The type of distribution selected is based on the 

conditions surrounding that variable. Probability distribution types include: 

NORMAL TRIANGULAR UNIFORM 

Figure 8-B.5.1 Normal Probability Figure 8-B.5.2 Triangular Figure 8-B.5.3 Uniform Probability 

Distribution Probability Distribution 

Distribution 

During a simulation, multiple scenarios of a model are calculated by repeatedly sampling 

values from the probability distributions for the uncertain variables and using those values for the 

cell. Crystal Ball simulations can consist of as many trials (or scenarios) as desired—hundreds or 

even thousands. During a single trial, Crystal Ball randomly selects a value from the defined 

possibilities (the range and shape of the probability distribution) for each uncertain variable and 

then recalculates the spreadsheet. 

For each uncertain variable (one that has a range of possible values), possible values are 

defined with a probability distribution. The type of distribution selected is based on the 

conditions surrounding that variable. Probability distribution types include: 

8-B-3
 

EERE-2008-BT-STD-0012 COMMENT 20
APPENDIX 8-C. CONSUMER RETAIL PRICE DISTRIBUTIONS FOR 

BASELINE REFRIGERATOR-FREEZERS AND FREEZERS 

TABLE OF CONTENTS 

8-C.1 INTRODUCTION ................................................................................................. 8-C-1 

8-C.2 DISTRIBUTION HISTOGRAMS......................................................................... 8-C-2 

LIST OF FIGURES 

Figure 8-C.2.1 Baseline Retail Price Distribution for Product Class 3 (Standard-sized 

Top-mount Refrigerator-Freezers without Through-the-door Service)..... 8-C-2 

Figure 8-C.2.2 Baseline Retail Price Distribution for Product Class 5 (Standard-sized 

Bottom-mount Refrigerator-Freezers without Through-the-door 

Service) ...................................................................................................... 8-C-3 

Figure 8-C.2.3 Baseline Retail Price Distribution for Product Class 7 (Standard-sized 

Side-mount Refrigerator-Freezers with Through-the-door Service) ......... 8-C-4 

Figure 8-C.2.4 Baseline Retail Price Distribution for Product Class 9 (Standard-sized 

Upright Freezers) ....................................................................................... 8-C-5 

Figure 8-C.2.5 Baseline Retail Price Distribution for Product Class 10 (Standard-sized 

Chest Freezers)........................................................................................... 8-C-6 

Figure 8-C.2.6 Baseline Retail Price Distribution for Product Class 11 (Compact 

Refrigerators) ............................................................................................. 8-C-7 

Figure 8-C.2.7 Baseline Retail Price Distribution for Product Class 18 (Compact 

Freezers)..................................................................................................... 8-C-8 

8C-i 

EERE-2008-BT-STD-0012 COMMENT 20
APPENDIX 8-C. CONSUMER RETAIL PRICE DISTRIBUTIONS FOR BASELINE 

REFRIGERATOR-FREEZERS AND FREEZERS 

8-C.1 INTRODUCTION 

DOE’s engineering analysis did not attempt to estimate the manufacturing cost for 

baseline models. Instead, it developed incremental increases in manufacturer selling price 

associated with increases in efficiency levels. This approach required DOE to estimate retail 

prices for the baseline model in each product class. 

DOE drew upon proprietary retail price data collected by The NPD Group.1

 These data 

reflect retail prices and sales at a large number of retail outlets in the United States (including 

over 50 percent of retail sales), and include information regarding model number, refrigerated 

volume, configuration of doors and ice-making, and whether the unit is an ENERGY STAR 

product. The data include enough information to assign each model to the correct product class. 

DOE developed a sales-weighted price distribution for non-ENERGY STAR appliances in each 

product class from this data. DOE grouped models by selling price in bins of varying width 

(generally $25 for compact products and $50 to $100 for standard-sized products) in order to 

balance the accuracy and usability of the distributions. These distributions are shown in the 

following section. 

DOE assumed that prices for non-ENERGY STAR models are a reasonable 

approximation of prices for the baseline models. These models may be “baseline” in efficiency, 

but span a wide range of other features and materials, and therefore have a broad distribution of 

prices. DOE chose not to develop volume-dependent baseline retail prices because the data did 

not show a strong relationship between volume and retail price. The price distributions within 

most volume ranges and product classes are almost as broad as the volume-independent 

distributions DOE chose to use, and regression analysis indicated very weak dependence of 

average price on volume. 

8-C-1
 

EERE-2008-BT-STD-0012 COMMENT 20
8-C.2 DISTRIBUTION HISTOGRAMS 

40%

0%

5%

10%

15%

20%

25%

30%

35%

Percent of Sales 

$150$350$550$750$950

$1,150$1,350$1,550$1,750$1,950$2,150$2,350$2,550$2,750$2,950$4,500

Selling Price 

Figure 8-C.2.1	 Baseline Retail Price Distribution for Product Class 

3 (Standard-sized Top-mount Refrigerator-Freezers 

without Through-the-door Service) 

8-C-2
 

EERE-2008-BT-STD-0012 COMMENT 20
Percent of Sales 

18%

16%

14%

12%

10%

8%

6%

4%

2%

0%

$150$350$550$750$950

$1,150$1,350$1,550$1,750$1,950$2,150$2,350$2,550$2,750$2,950$3,150$3,350$3,550$3,750$3,950$4,150$4,350$4,550$4,750$4,950$5,300$5,700

Selling Price 

Figure 8-C.2.2	 Baseline Retail Price Distribution for Product Class 

5 (Standard-sized Bottom-mount RefrigeratorFreezers without Through-the-door Service) 

8-C-3
 

EERE-2008-BT-STD-0012 COMMENT 20
Percent of Sales 

16%

14%

12%

10%

8%

6%

4%

2%

0%

$150$350$550$750$950

$1,150$1,350$1,550$1,750$1,950$2,150$2,350$2,550$2,750$2,950$3,150$3,350$3,550$3,750$3,950$4,150$4,350$4,550$4,750$4,950

Selling Price 

Figure 8-C.2.3	 Baseline Retail Price Distribution for Product Class 

7 (Standard-sized Side-mount Refrigerator-Freezers 

with Through-the-door Service) 

8-C-4
 

EERE-2008-BT-STD-0012 COMMENT 20
Percent of Sales 

25%

20%

15%

10%

5%

0%

$75

$175$275$375$475$575$675$775$875$975

$1,075$1,175$1,275$1,375$1,475$1,650$1,850$2,050$2,250$2,450$2,650$2,850$3,250$4,250$5,250$6,250

Selling Price 

Figure 8-C.2.4	 Baseline Retail Price Distribution for Product Class 

9 (Standard-sized Upright Freezers) 

8-C-5
 

EERE-2008-BT-STD-0012 COMMENT 20
Percent of Sales 

35%

30%

25%

20%

15%

10%

5%

0%

$75

$125$175$225$275$325$375$425$475$525$575$625$675$725$775$825$875$925$975

$1,050$1,150$1,250$1,350$1,450$1,550$1,650$1,750$1,850$1,950$2,250$2,750$3,500$4,500$5,500

Selling Price 

Figure 8-C.2.5	 Baseline Retail Price Distribution for Product Class 

10 (Standard-sized Chest Freezers) 

8-C-6
 

EERE-2008-BT-STD-0012 COMMENT 20
Percent of Sales 

20%

18%

16%

14%

12%

10%

8%

6%

4%

2%

0%

$25$50$75

$100$125$150$175$200$225$250$275$300$325$350$375$400$425$450$475$500$525$550$575$600$625

Selling Price 

Figure 8-C.2.6	 Baseline Retail Price Distribution for Product Class 

11 (Compact Refrigerators) 

8-C-7
 

EERE-2008-BT-STD-0012 COMMENT 20
Percent of Sales 

70%

60%

50%

40%

30%

20%

10%

0%

$25$50$75

$100$125$150$175$200$225$250$275$300$325$350$375$400$425$450$475$500$750

$1,500$2,500$3,500

Selling Price 

Figure 8-C.2.7	 Baseline Retail Price Distribution for Product Class 

18 (Compact Freezers) 

8-C-8
 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

1 

REFERENCES 

The NPD Group, Inc., The NPD Group/NPD Houseworld – POS, Refrigerators, Freezers 

January – December 2007, 2008. Port Washington, NY. 

8-C-9
 

EERE-2008-BT-STD-0012 COMMENT 20
APPENDIX 8-D. HOUSEHOLD DISCOUNT RATE DISTRIBUTIONS 

TABLE OF CONTENTS 

8-D.1 INTRODUCTION .................................................................................................8-D-1 

8-D.2 DISTRIBUTION OF MORTGAGE INTEREST RATES ....................................8-D-1 

8-D.3 DISTRIBUTION OF RATES FOR TYPES OF DEBT AND EQUITY USED 

TO FINANCE REPLACEMENT FURNACES....................................................8-D-2 

8-D.4 DISTRIBUTION OF RATES FOR TYPES OF EQUITY USED TO 

FINANCE REPLACEMENT FURNACES ..........................................................8-D-5 

LIST OF FIGURES 

Figure 8-D.2.1 Distribution of New Home Mortgage Interest Rates .................................8-D-1 

Figure 8-D.3.1 Distribution of Home Equity Loan Interest Rates .....................................8-D-2 

Figure 8-D.3.2 Distribution of Credit Card Interest Rates .................................................8-D-3 

Figure 8-D.3.3 Distribution of Installment Loan Interest Rates.........................................8-D-3 

Figure 8-D.3.4 Distribution of Other Residence Loan Interest Rates ................................8-D-4 

Figure 8-D.3.5 Distribution of Other Lines of Credit Loan Interest Rates ........................8-D-4 

Figure 8-D.4.1 Distribution of Annual Rate of Return on CD’s ........................................8-D-5 

Figure 8-D.4.2 Distribution of Annual Rate of Return on Savings Bonds.........................8-D-6 

Figure 8-D.4.3 Distribution of Annual Rate of Return on Corporate AAA Bonds............8-D-6 

Figure 8-D.4.4 Distribution of Annual Rate of Savings Accounts.....................................8-D-7 

Figure 8-D.4.5 Distribution of Annual Rate of Return on S&P 500..................................8-D-7 

Figure 8-D.4.6 Distribution of Annual Rate of Return on Mutual Funds ..........................8-D-8 

8-D-i 

EERE-2008-BT-STD-0012 COMMENT 20
 

APPENDIX 8-D. HOUSEHOLD DISCOUNT RATE DISTRIBUTIONS 

8-D.1 INTRODUCTION 

DOE derived discount rates for the LCC analysis using data on interest or return rates for 

various types of debt and equity. To account for variation among households in rates for each of 

the types, DOE sampled a rate for each household from a distribution of rates for each debt and 

equity type. This appendix describes the distributions used. 

8-D.2 DISTRIBUTION OF MORTGAGE INTEREST RATES 

Figure 8-D.2.1 shows the distribution of real interest rates for new home mortgages. 

The data source DOE used for mortgage interest rates is the Federal Reserve Board’s Survey of 

Consumer Finances (SCF) in 1989, 1992, 1995, 1998, 2001, 2004 and 2007.1

 Using the 

appropriate SCF data for each year, DOE adjusted the nominal mortgage interest rate for each 

relevant household in the SCF for mortgage tax deduction and inflation. In cases where the 

effective interest rate is equal to or below the inflation rate (resulting in a negative real interest 

rate), DOE set the real effective interest rate to zero. 

0%

5%

10%

15%

20%

25%

30%

35%

0.5%

1.6%

2.5%3.5%

4.4%

5.4%

6.4%7.4%

8.5%

9.5%

10.5%

11.6%

12.4%13.5%

14.4%

16.7%22.9%

Real Interest Rate 

Probability 

Figure 8-D.2.1 Distribution of New Home Mortgage Interest Rates 

8-D-1
 

EERE-2008-BT-STD-0012 COMMENT 20
 

8-D.3	 DISTRIBUTION OF RATES FOR TYPES OF DEBT AND EQUITY USED 

TO FINANCE REPLACEMENT FURNACES 

Figure 8-D.3.1 through Figure 8-D.3.5 show the distribution of real interest rates for 

different types of debt used to finance replacement furnaces. The data source for the interest rates 

for home equity loans, credit cards, installment loans, other residence loans, and other lines of 

credit is the Federal Reserve Board’s SCF in 1989, 1992, 1995, 1998, 2001, 2004, and 2007.1 

DOE adjusted the nominal rates to real rates using the annual inflation rate in each year. For 

home equity loans, DOE calculated effective interest rates in a similar manner as for mortgage 

rates, since interest on such loans is tax deductible. 

0%

5%

10%

15%

20%

25%

30%

35%

0.4%

1.5%

2.6%

3.5%

4.5%

5.4%

6.4%

7.5%

8.5%

9.5%

10.6%

11.8%

12.7%

13.7%

14.7%

16.7%

17.5%

42.5%

Real Interest Rate 

Probability 

Figure 8-D.3.1 Distribution of Home Equity Loan Interest Rates 

8-D-2
 

EERE-2008-BT-STD-0012 COMMENT 20
0%

2%

4%

6%

8%

10%

12%

0.0%

1.4%2.5%

3.5%4.5%5.6%

6.7%7.5%

8.7%9.6%

10.6%

11.5%

12.6%

13.6%

14.7%

15.6%16.4%

17.6%18.5%

19.4%

20.5%

21.4%

22.5%

23.6%

24.6%

25.5%

26.3%

27.3%

Real Interest Rate 

Probability 

Figure 8-D.3.2 Distribution of Credit Card Interest Rates 

0%

5%

10%

15%

20%

25%

30%

35%

0.0%

2.4%

4.5%

6.7%8.6%

10.6%

12.5%

14.7%

16.5%

18.5%

20.5%

22.3%

24.6%

26.6%28.5%

34.1%

43.6%

47.2%

Real Interest Rate 

Probability 

Figure 8-D.3.3 Distribution of Installment Loan Interest Rates 

8-D-3
 

EERE-2008-BT-STD-0012 COMMENT 20
0%

5%

10%

15%

20%

0.2%

1.5%

2.5%3.5%

4.5%5.5%

6.5%

7.5%

8.6%

9.4%

10.6%

11.6%

12.6%14.7%15.7%

16.6%17.9%

Real Interest Rate 

Probability 

Figure 8-D.3.4 Distribution of Other Residence Loan Interest Rates 

0%

2%

4%

6%

8%

10%

12%

14%

16%

0.1%

1.4%

2.5%

3.5%

4.5%

5.5%

6.7%

7.6%

8.7%

9.5%

10.5%

11.6%

12.5%

13.5%

14.7%

15.6%

16.5%

17.3%

18.5%

19.1%

20.2%

21.5%

22.1%

23.5%

Real Interest Rate 

Probability 

Figure 8-D.3.5	 Distribution of Other Lines of Credit Loan Interest 

Rates 

8-D-4 

EERE-2008-BT-STD-0012 COMMENT 20
 

8-D.4	 DISTRIBUTION OF RATES FOR TYPES OF EQUITY USED TO 

FINANCE REPLACEMENT FURNACES 

Figure 8-D.4.1 through Figure 8-D.4.6 show the distribution of real interest rates for 

different types of equity used to finance replacement furnaces. Data for equity classes are not 

available from the Federal Reserve Board’s SCF, so the Department derived data for these 

classes from national-level historical data. The interest rates associated with certificates of 

deposit (CDs),2

 savings bonds,3 and bonds (AAA corporate bonds)4 are from Federal Reserve 

Board time-series data covering 1977–2007. DOE assumed rates on checking accounts to be 

zero. Rates on savings and money market accounts are from Cost of Savings Index data covering 

1984–2007.5

 The rates for stocks are the annual returns on the Standard and Poor’s (S&P) 500 in 

the 1977–2007 period.6

 The mutual fund rates are a weighted average of the stock rates (twothirds weight) and the bond rates (one-third weight) in each year of the 1977–2007 period. DOE 

adjusted the nominal rates to real rates using the annual inflation rate in each year. 

-2%

0%

2%

4%

6%

8%

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Year 

Annual Rate of Return 

Figure 8-D.4.1 Distribution of Annual Rate of Return on CD’s 

8-D-5
 

EERE-2008-BT-STD-0012 COMMENT 20
-2%

0%

2%

4%

6%

8%

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Year 

Annual Rate of Return 

Figure 8-D.4.2 Distribution of Annual Rate of Return on Savings 

Bonds 

-2%

0%

2%

4%

6%

8%

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Year 

Annual Rate of Return 

Figure 8-D.4.3	 Distribution of Annual Rate of Return on Corporate 

AAA Bonds 

8-D-6 

EERE-2008-BT-STD-0012 COMMENT 20
-2%

0%

2%

4%

6%

8%

1984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008

Year 

Annual Rate of Return 

Figure 8-D.4.4 Distribution of Annual Rate of Savings Accounts 

-30%

-20%

-10%

0%

10%

20%

30%

40%

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Year 

Annual Rate of Return 

Figure 8-D.4.5 Distribution of Annual Rate of Return on S&P 500 

8-D-7
 

EERE-2008-BT-STD-0012 COMMENT 20
-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Year 

Annual Rate of Return 

Figure 8-D.4.6 Distribution of Annual Rate of Return on Mutual 

Funds 

8-D-8
 

EERE-2008-BT-STD-0012 COMMENT 20
REFERENCES 

1. The Federal Reserve Board, Survey of Consumer Finances 1989, 1992, 1995, 1998, 2001, 

2004, 2007. <http://www.federalreserve.gov/pubs/oss/oss2/scfindex.html> 

2. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: CDs (secondary market), Maturity: 6-month, Frequency: 

Annual, Description: Average rate on 6-month negotiable certificates of deposit 

(secondary market), quoted on an investment basis, 2008. 

<http://www.federalreserve.gov/releases/H15/data.htm> 

3. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: State and local bonds, Maturity: 20-year, Frequency: 

Monthly, Description: Bond buyer go 20-bond municipal bond index, 2008. 

<http://www.federalreserve.gov/releases/H15/data.htm> 

4. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: Corporate bonds/Moody's Seasoned AAA, Frequency: 

Annual, Description: Moody's yield on seasoned corporate bonds - all industries, AAA, 

2008. <http://www.federalreserve.gov/releases/H15/data.htm> 

5. Wachovia, Cost of Savings Index (COSI) Index History, 2008. (Last accessed March 10, 

2008.) <http://mortgage-x.com/general/indexes/default.asp> 

6. Damodaran Online, Damodaran Online:The Data Page (Historical Returns on Stocks, 

Bonds, and Bills - United States), 2008. Stern School of Business, New York University. 

(Last accessed March 10, 2008.) <http://pages.stern.nyu.edu/~adamodar/> 

8-D-9
 

EERE-2008-BT-STD-0012 COMMENT 20
CHAPTER 9. SHIPMENTS ANALYSIS 

TABLE OF CONTENTS 

9.1 INTRODUCTION ........................................................................................................... 9-1 

9.2 SHIPMENTS MODEL METHODOLOGY.................................................................... 9-1 

9.3 DATA INPUTS AND MODEL CALIBRATION .......................................................... 9-3 

9.3.1 Standard-size Refrigerator-Freezers ................................................................................ 9-4 

9.3.1.1 Historical Shipments...................................................................................... 9-4 

9.3.1.2 Markets and Model Calibration ..................................................................... 9-5 

9.3.1.3 Disaggregation into Separate Refrigerator-Freezer Product Classes............. 9-9 

9.3.1.4 Base Case Shipments ................................................................................... 9-14 

9.3.2 Standard-size Freezers ................................................................................................... 9-15 

9.3.2.1 Historical Shipments.................................................................................... 9-15 

9.3.2.2 Markets and Model Calibration ................................................................... 9-16 

9.3.2.3 Disaggregation into Separate Freezer Product Classes................................9-18 

9.3.2.4 Base Case Shipments ................................................................................... 9-19 

9.3.3 Compact Refrigerators and Refrigerator-Freezers......................................................... 9-21 

9.3.3.1 Historical Shipments.................................................................................... 9-21 

9.3.3.2 Markets and Model Calibration ................................................................... 9-21 

9.3.3.3 Disaggregation into Separate Compact Refrigerator Product Classes......... 9-24 

9.3.3.4 Base Case Shipments ................................................................................... 9-24 

9.3.4 Compact Freezers........................................................................................................... 9-26 

9.3.4.1 Historical Shipments.................................................................................... 9-26 

9.3.4.2 Markets and Model Calibration ................................................................... 9-26 

9.3.4.3 Disaggregation into Separate Compact Freezer Product Classes ................ 9-28 

9.3.4.4 Base Case Shipments ................................................................................... 9-28 

9.4 PURCHASE PRICE, OPERATING COST, AND HOUSEHOLD INCOME 

IMPACTS ...................................................................................................................... 9-29 

9.5 AFFECTED STOCK ..................................................................................................... 9-31 

9.6 RESULTS ...................................................................................................................... 9-32 

LIST OF TABLES 

Table 9.3.1 Standard-size Refrigerator-Freezers: Historical Shipments................................. 9-5 

Table 9.3.2 Historical and Forecasted Housing Starts (millions) ........................................... 9-8 

Table 9.3.3 Market Shares of Side-by-Side Refrigerator-Freezers....................................... 9-10 

Table 9.3.4 Modeled Homeowner Side-by-Side Market Share............................................ 9-11 

Table 9.3.5 Product Class Market Shares of Standard-size Refrigerator-Freezers............... 9-13 

Table 9.3.6 Standard-size Freezers: Historical Shipments ................................................... 9-16 

Table 9.3.7 Saturation of Freezers in New Housing............................................................. 9-17 

Table 9.3.8 Product Class Market Share of Standard-size Freezers ..................................... 9-19 

9-i 

EERE-2008-BT-STD-0012 COMMENT 20
Table 9.3.9 Compact Refrigerators: Historical Shipments ................................................... 9-21 

Table 9.3.10 Product Class Market Shares of Compact Refrigerators ................................... 9-24 

Table 9.3.11 Compact Freezers: Historical Shipments........................................................... 9-26 

Table 9.3.12 Product Class Market Shares of Compact Freezers........................................... 9-28 

Table 9.4.1 Change in Relative Price Elasticity following a Purchase Price Change .......... 9-31 

LIST OF FIGURES 

Figure 9.3.1 Standard-size Refrigerator-Freezers: Survival and Retirement Functions .......... 9-7 

Figure 9.3.2 Projected Market Share of Side- And Bottom-Freezer Product Classes ........... 9-12 

Figure 9.3.3 Standard-Size Refrigerator-Freezers: Historical and Base Case Shipments 

Forecast by Market Segment ............................................................................. 9-14 

Figure 9.3.4 Standard-size Refrigerator-Freezers: Base Case Shipments Forecast by 

Product Class Group .......................................................................................... 9-15 

Figure 9.3.5 Standard-size Freezers: Survival and Retirement Functions ............................. 9-18 

Figure 9.3.6 Standard-size Freezers: Historical and Base Case Shipments Forecast by
 

Market Segment................................................................................................. 9-20 

Figure 9.3.7 Standard-size Freezers: Base Case Shipments Forecast by Product Class........ 9-20 

Figure 9.3.8 Forecast of Saturation of Compact Refrigerators in New Construction 

Market Segments ............................................................................................... 9-23 

Figure 9.3.9 Compact Refrigerators: Survival and Retirement Functions............................. 9-24 

Figure 9.3.10 Compact Refrigerators: Base Case Shipments Forecast by Market 

Segment.............................................................................................................. 9-25 

Figure 9.3.11 Compact Refrigerators: Base Case Shipments Forecast by Product Class........ 9-25 

Figure 9.3.12 Compact Freezers: Survival and Retirement Functions..................................... 9-27 

Figure 9.3.13 Compact Freezers: Historical and Base Case Shipments Forecast by 

Market Segment................................................................................................. 9-28 

Figure 9.3.14 Compact Freezers: Base Case Shipments Forecast Disaggregated by 

Product Class ..................................................................................................... 9-29 

Figure 9.6.1 Standard-Size Refrigerator-Freezers: Base Case and Standards Case 

Shipments Forecasts........................................................................................... 9-33 

Figure 9.6.2 Standard-size Freezers: Base Case and Standards Case Shipments 

Forecasts ............................................................................................................ 9-34 

Figure 9.6.3 Compact Refrigerators: Base Case and Standards Case Shipments 

Forecasts ............................................................................................................ 9-35 

Figure 9.6.4 Compact Freezers: Base Case and Standards Case Shipments Forecasts.......... 9-36 

9-ii 

EERE-2008-BT-STD-0012 COMMENT 20
CHAPTER 9. SHIPMENTS ANALYSIS 

9.1 INTRODUCTION 

Estimates of future product shipments are a necessary input to calculations of the national 

energy savings (NES) and net present value (NPV), as well as to the manufacturer impact 

analysis (MIA). This chapter describes the data and methods the U.S. Department of Energy 

(DOE) used to forecast annual product shipments and presents results for each of the 

refrigeration product classes being considered in this analysis. 

DOE defined four refrigeration product types, and developed models to estimate 

shipments for each type. The four types are: (1) standard-size refrigerator-freezers, (2) standardsize freezers, (3) compact refrigerators, and (4) compact freezers. DOE calibrated each model 

against historical shipments. To estimate the effects of potential standard levels on shipments of 

each product class, each model was structured to account for the combined effects on consumer 

purchase decisions of changes in product cost, annual operating cost, and household income. 

Each model considers specific market segments to estimate shipments of individual 

product classes within each segment. The analytical results from these segments then are 

aggregated to estimate total shipments for each product class and type. For the product types 

included in this analysis, DOE considered three market segments: (1) new construction or new 

owners; (2) replacements for failed products; and (3) for standard-size refrigerators, the purchase 

of an additional refrigerator. 

The shipments models were developed as Microsoft Excel spreadsheets that are 

accessible on the Internet (www.eere.energy.gov/buildings/appliance_standards/). Appendix 10­

A discusses how to access and utilize the shipments model spreadsheets, which are integrated 

into spreadsheets for the National Impact Analysis. The rest of this chapter explains the 

shipments models in more detail. Section 9.2 presents methodology behind the models; section 

9.3 describes the data inputs and calibration of each model; section 9.4 discusses impacts on 

shipments from changes in product cost, operating cost, and household income; section 9.5 

discusses the affected stock; and section 9.6 presents the shipments forecast for different energy 

conservation standard-level scenarios. 

9.2 SHIPMENTS MODEL METHODOLOGY 

DOE developed a model of the national stock of in-service appliances for estimating 

annual shipments for each of the four product types considered for this standards rulemaking. 

The model considers market segments as distinct inputs to the shipments forecast. As represented 

by the following equation, the two primary market segments for standard-size products are new 

installations and replacements. 

9-1 

EERE-2008-BT-STD-0012 COMMENT 20
_ _ 

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. 

DOE’s shipments models take an accounting approach, tracking market shares of each 

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

than simply extrapolating a current shipments trend, the analysis of the base case for each type 

(i.e., the case without new standards) uses input variables as drivers, including construction 

forecasts and product lifetime distributions, to forecast sales in each market segment. As a result, 

DOE’s shipments models assume that new construction drives shipments. 

To estimate shipments of replacement units, the models utilize shipments data from 

previous years and assumptions about the lifetime of each product. Estimated shipments of 

replacement units in a given year are equal to the total stock of the appliance minus those units 

shipped in previous years that remain in the stock. DOE determined the useful service life of 

each product class to estimate how many years it is likely to remain in stock. The following 

equation shows how DOE estimated shipments of replacement units. 

_ ageMax j 1 

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

age=0 j= N 

Where: 

Stockp (j-1) = total stock of appliances in year j-1, 

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

N = year in which the model begins its stock accounting (start year is specific to each 

product type, and is based on available historical shipments data). 

Stock accounting provides an estimate of the age distribution of product stocks for all 

years, using product shipments, a retirement function, and initial product stock as inputs. The age 

distribution of product stocks is a key input to both the NES and NPV calculations because the 

operating costs for any year depend on the age distribution of the stock. Operating cost is 

dependent on the product age distribution under a standards case scenario 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. 

DOE calculated total stock of each product by integrating historical shipments data 

beginning with a specific year. The start year depended on the historical data available for the 

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

9-2 

EERE-2008-BT-STD-0012 COMMENT 20
estimate future shipments, DOE developed a series of equations that define the dynamics and 

accounting of stocks. For new units, the equation is: 

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

Where: 

Stock(j, age) = number of units of a particular age, 

j = year for which the 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. Slightly more complicated equations, such as the 

following equation, describe how the model accounts for the existing stock of units. 

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

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

age+1. Over time, a fraction of the stock is removed; that fraction is determined by a retirement 

probability function, probRtr(age), which is described in section 9.3. Because the products 

considered in this rulemaking are common appliances that have been used by U.S. consumers for 

a long time, replacements typically constitute the majority of shipments. 

9.3 DATA INPUTS AND MODEL CALIBRATION 

For standard-size products designed for residential application, DOE used two inputs to 

estimate shipments driven by new construction: new housing forecasts and saturation of the 

product in new housing. New housing includes newly constructed single- and multi-family units, 

termed “new housing completions,” and mobile home placements. For new housing completions 

and mobile home placements, DOE used recorded data through 2007, and adopted the 

projections from the DOE Energy Information Administration (EIA)’s Annual Energy Outlook 

2009 (AEO2009) for the period 2008–2030.1

 To determine new construction shipments for 

standard-size products, DOE used forecasts of market saturations combined with forecasts of 

housing starts. For compact products, which are used in both residential and commercial 

applications, DOE used market saturations in combination with forecasts of housing starts and 

new commercial construction. 

DOE estimated replacements using product retirement functions that it developed based 

on product lifetimes. The retirement functions for all product types are described in detail in 

chapter 8. The retirement functions use the following equation to determine the probability of 

retirement at a certain age for the considered products: 

9-3 

EERE-2008-BT-STD-0012 COMMENT 20
_ _ 

_ ageMax j 1 

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

age=0 j= N 

Where: 

Stockp (j-1) = total stock of appliances in year j-1, 

Shipj = shipments in year j, 

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

N = year in which the model begins its stock accounting (start year is specific to each 

product type, and is based on available historical shipments data). 

DOE used historical shipments data to calibrate its shipments models. For standard-size 

refrigerator-freezers and standard-size freezers, modeled new construction shipments and 

replacements were insufficient to account for all product shipments. DOE therefore developed 

two additional market segments to calibrate its model: “additional refrigerator purchase” for 

refrigerators and “existing homes without the appliance” for freezers. The “additional 

refrigerator purchase” segment represents those purchases that are meant to be used as a new 

primary refrigerator while the former primary refrigerator is converted to a second refrigerator, 

as well as a small number of direct purchases of second refrigerators. Chapter 8 describes in 

detail how DOE estimated the probability that a primary refrigerator will be converted to a 

second refrigerator. 

The following sections explain in detail each of the data inputs for each product type. 

9.3.1 Standard-size Refrigerator-Freezers 

DOE considered seven product classes of standard-size refrigerator-freezers for this 

rulemaking. DOE’s shipments model for these product classes used the aggregate shipments, that 

is, the shipments for all seven product classes, as the basis for its forecasts. DOE did not develop 

a separate shipments model for each refrigerator-freezer product class. Instead, DOE 

disaggregated total shipments into product classes using various data and assumptions. 

9.3.1.1 Historical Shipments 

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

and calibrate its shipments model for standard-size refrigerator-freezers. It used the following 

sources to establish historical shipments: Appliance magazine’s Statistical Review,2,3,4 AHAM 

Factbooks,5,6 and an AHAM data submittal related to this rulemaking.7

 Table 9.3.1 summarizes 

the historical shipments data. DOE developed a total stock estimate by integrating historical 

shipments. Over time, some of the units are retired and removed from the stock, triggering the 

purchase of a new unit. Because of the relationship between retirements and total stock, there is a 

strong correlation between past and future shipments, independent of conservation standards. 

9-4 

EERE-2008-BT-STD-0012 COMMENT 20
 

Table 9.3.1 Standard-size Refrigerator-Freezers: Historical Shipments 

Year 

Shipments 

millions Year 

Shipments 

millions 

1951 4.075 1980 5.124 

1952 3.570 1981 4.944 

1953 3.650 1982 4.364 

1954 3.600 1983 5.340 

1955 4.200 1984 5.882 

1956 3.700 1985 6.002 

1957 3.350 1986 6.410 

1958 3.117 1987 6.748 

1959 3.785 1988 6.733 

1960 3.475 1989 6.453 

1961 3.480 1990 6.456 

1962 3.775 1991 6.411 

1963 4.125 1992 6.721 

1964 4.545 1993 7.047 

1965 4.931 1994 7.589 

1966 4.974 1995 7.650 

1967 4.713 1996 7.975 

1968 5.150 1997 7.924 

1969 5.296 1998 8.774 

1970 5.286 1999 9.099 

1971 5.691 2000 9.217 

1972 6.315 2001 9.305 

1973 6.774 2002 9.744 

1974 5.982 2003 10.021 

1975 4.577 2004 10.913 

1976 4.817 2005 11.134 

1977 5.707 2006 11.078 

1978 5.890 2007 10.402 

1979 5.707 2008 9.314 

Sources: 2005–2007: AHAM Data Submittal, 2009; 1995–2004: AHAM Fact Book 2005; 1992–1994: AHAM 

Factbook 2003; 1951–1992: Appliance Magazine, “Statistical Review,” various issues. 

9.3.1.2 Markets and Model Calibration 

The market for standard-size refrigerator-freezers is primarily comprised of units for new 

construction, replacement units for products that have been retired, and purchases driven by the 

9-5 

EERE-2008-BT-STD-0012 COMMENT 20
conversion of an existing unit from first to second refrigerator. Total shipments are represented 

by the following equation: 

ShipSRRF ( j) = RplSRRF ( j) + NH SRRF ( j) + ConvSRRF ( j) 

Where: 

ShipSRRF (j) = total shipments of standard-size refrigerator-freezers in year j, 

RplSRRF (j) = replacement shipments in year j, 

NH SRRF(j) = shipments to new households in year j, and 

ConvSRRF (j)= shipments due to additional refrigerator purchase (conversion of first to second 

refrigerator) in year j. 

The following sections discuss these three markets in further detail. 

 New Construction. To estimate shipments driven by new residential construction, DOE 

multiplied the housing starts forecast for each year by the estimated saturation of standard-size 

refrigerator-freezers in new housing. DOE based saturation for a given year on the calculated 

saturation in the previous year. DOE froze the saturation at the level corresponding to year 2008 

(1.3 per new home), the last year for which shipments data are available. The following equation 

describes the method used for calculating saturation for new construction: 

Sat NC ( j −1) = Stock( j) / HStock( j) 

Sat NC ( j) = Sat NC (2008)∀j > 2008

Where: 

SatNC(j) = market saturation of standard-size refrigerator-freezers in the new housing market 

segment in year j, 

Stock(j) = total stock of standard-size refrigerator-freezers in year j, 

HStock(j) = total number of housing units in year j, and 

SatNC(2008) = market saturation of standard-size refrigerator-freezers in the new housing market 

segment in 2008. 

Table 9.3.2 presents historical and forecasted new housing starts based on EIA’s 

AEO2009 for the period 1990–2030. AEO2009 provides three sets of housing starts forecasts, 

based on economic growth scenarios: a Reference case, a High Economic Growth case, and a 

Low Economic Growth case. DOE used the Reference Case that incorporates the effects from 

the American Recovery and Reinvestment Act.1 DOE used the high and low growth scenarios 

from the March 2009 release of the AEO2009.

8

 DOE used the forecasts from the Reference case 

to estimate its shipments to new housing, which is comprised of single- and multi-family units 

(new housing completions) and mobile home placements. For 2031−2043, DOE froze 

completions at the 2030 level for all three economic scenarios. 

9-6 

EERE-2008-BT-STD-0012 COMMENT 20
% Retiring 

 Replacements. DOE determined refrigerator-freezer shipments to the replacement market 

using an accounting method that tracks the total stock of units by vintage. DOE estimated a stock 

by vintage by integrating historical shipments. Over time, some units are retired and removed 

from the stock, thereby triggering the shipment of replacement units. A certain percentage 

(depending on the age) of units will fail each year and need to be replaced. To determine when a 

unit fails, DOE used a survival function based on a product lifetime distribution with an average 

value of 17.1 years. Chapter 8 provides a more thorough discussion of product lifetimes. Figure 

9.3.1 shows the survival and retirement functions that DOE used to estimate replacement 

shipments for all product classes of standard-size refrigerator-freezers. 

100% % Surviving 

90% 

80% 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

0 5 10 15 20 25 30 35 40 45 50 

Age 

Figure 9.3.1 Standard-size Refrigerator-Freezers: Survival and 

Retirement Functions 

9-7 

EERE-2008-BT-STD-0012 COMMENT 20
 

Table 9.3.2 Historical and Forecasted Housing Starts (millions) 

Year 

Reference 

Case 

High 

Growth 

Low 

Growth Year 

Reference 

Case 

High 

Growth 

Low 

Growth 

1990 1.38 1.38 1.38 2017 1.99 2.40 1.60 

1991 1.18 1.18 1.18 2018 2.00 2.42 1.59 

1992 1.41 1.41 1.41 2019 2.01 2.34 1.53 

1993 1.54 1.54 1.54 2020 1.99 2.16 1.39 

1994 1.76 1.76 1.76 2021 1.93 2.11 1.33 

1995 1.69 1.69 1.69 2022 1.89 2.10 1.30 

1996 1.82 1.82 1.82 2023 1.87 2.11 1.27 

1997 1.49 1.49 1.49 2024 1.87 2.15 1.26 

1998 1.54 1.54 1.54 2025 1.89 2.21 1.27 

1999 1.64 1.64 1.64 2026 1.88 2.27 1.25 

2000 1.70 1.70 1.70 2027 1.84 2.28 1.22 

2001 1.72 1.72 1.72 2028 1.79 2.26 1.19 

2002 1.75 1.75 1.75 2029 1.76 2.27 1.17 

2003 1.79 1.79 1.79 2030 1.77 2.30 1.18 

2004 1.80 1.80 1.80 2031 1.77 2.30 1.18 

2005 2.22 2.22 2.22 2032 1.77 2.30 1.18 

2006 1.92 1.92 1.92 2033 1.77 2.30 1.18 

2007 1.75 1.75 1.75 2034 1.77 2.30 1.18 

2008 1.48 1.61 1.28 2035 1.77 2.30 1.18 

2009 1.30 1.70 1.28 2036 1.77 2.30 1.18 

2010 1.08 1.38 1.02 2037 1.77 2.30 1.18 

2011 1.51 1.82 1.31 2038 1.77 2.30 1.18 

2012 1.73 2.06 1.47 2039 1.77 2.30 1.18 

2013 1.86 2.23 1.56 2040 1.77 2.30 1.18 

2014 1.89 2.30 1.59 2041 1.77 2.30 1.18 

2015 1.93 2.39 1.59 2042 1.77 2.30 1.18 

2016 1.98 2.42 1.60 2043 1.77 2.30 1.18 

Source: EIA, AEO2009. 

Shipments Due to Additional Refrigerator Purchase. To calibrate estimated shipments 

with historical data, DOE introduced into the model a market segment corresponding to 

purchases of additional standard-size refrigerator-freezers that are not intended as replacements. 

Because such purchases involve converting a first unit to a second refrigerator, DOE estimated 

shipments to this market segment by applying the probability of conversion (developed in 

chapter 8) to the stock of surviving refrigerators. To determine when a household converts a first 

refrigerator to a second one, DOE used a conversion function based on the total installed stock of 

refrigerators of a certain age. Chapter 8 provides a detailed discussion of the conversion function. 

The following equation shows how DOE calculated shipments to this market segment. 

9-8 

EERE-2008-BT-STD-0012 COMMENT 20
 

_ ageMax j 1 

SNewSRRF ( j) = Cj × ∑∑Stock(age) j × probconv (age) 

age=0 j=N 

Where: 

SNewSRRF (j )= shipments due to additional refrigerator purchase (conversion of first to second 

refrigerator) in year j, 

Cj = a calibration factor, equal to the ratio of new shipments in year j to the installed 

stock in that year, and 

Probconv(age) = probability that the refrigerator has been converted at a given age 

9.3.1.3 Disaggregation into Separate Refrigerator-Freezer Product Classes 

DOE examined the historical trends in the market shares of various refrigerator-freezer 

configurations to disaggregate the total shipments of refrigerator-freezers into shipments to each 

of the three considered refrigerator-freezer product categories (top-mount, bottom-mount and 

side-by-side configurations). The market share of side-by-side refrigerator-freezers models has 

grown significantly during the past two decades. Bottom-freezer models historically have had a 

small market share, but that share has grown in recent years. To forecast the market share for 

these three configurations throughout the 30-year analysis period (beginning in 2014), DOE built 

a simple model of aggregate consumer behavior, fit its model to the historical growth in side-byside market share, and then used its model to estimate future market shares for all three 

configurations. 

DOE assumed that bottom-freezer models were an insignificant portion of the market 

prior to 2005, and that consumer behavior related to these classes in the future would mirror 

behavior regarding side-by-side models. Therefore, DOE forecast the combined market share for 

side-by-side and bottom-freezer products. DOE assumed that the ratio between the market share 

of bottom-mount and the market share of side-mount products would remain fixed at its 2008 

value. 

DOE based its model on the market share of each product category as reported by 

households in the Energy Information Administration’s Residential Energy Consumption 

Survey9

 (RECS) and as reported by the Association of Home Appliance Manufacturers 

(AHAM).7

 RECS reports whether a household owns a side-by-side refrigerator, but does not 

distinguish between top- and bottom-mount units. For the purpose of this model, DOE assumed 

that all recently-purchased top-or-bottom-mount refrigerators reported in the RECS surveys 

conducted in 1990, 1993, 1997, 2001, and 2005 are top-mount units. 

DOE used RECS data first to estimate the maximum (“limiting”) market share that sideby-side and bottom-mount product classes would attain in the future. RECS reports the income 

of each household in its sample. DOE assumed that households that have annual incomes greater 

than $100,000 and that own their own homes are able to select the appliance that best meets their 

needs and preferences. The market share of side-by-side units in these households is significantly 

9-9 

EERE-2008-BT-STD-0012 COMMENT 20
higher than the current overall market share of side-by-side products. DOE determined market 

shares for recent purchases from RECS by considering only households which reported 

purchasing their appliance within two years prior to the survey. The market share of side-by-side 

refrigerator purchases in the selected household group from each RECS survey is shown in Table 

9.3.3. Over time, DOE assumed that all homeowners will be free to choose the product of their 

choice, as DOE assumed high-income homeowners can today. As a result, DOE assumed that 

market share of side-by-side units among all homeowners will increase until it equals the mean 

market share among high-income home-owning households, 66.4 percent. 

Table 9.3.3 Market Shares of Side-by-Side Refrigerator-Freezers 

RECS Survey Year 

Market Share of Side-by-Side Units for HighIncome Homeowners % 

1990 57.7 

1993 71.7 

1997 73.4 

2001 61.6 

2005 67.5 

Mean 66.4 

Source: EIA, Residential Energy Consumption Survey, for the years listed. 

DOE used RECS to determine what percent of side-by-side units is sold to home-owners 

in order to convert a limiting market share among home-owning households into a market share 

for all shipments. This pecentage was roughly constant in the five most recent RECS surveys, 

and DOE assumed that it would remain equal to their mean, 90.9 percent, throughout the forecast 

period. DOE also assumed that the percent of American households that own their home would 

return to its historical level of roughly 65 percent by 2025. (American home ownership was 

between 63 percent and 66 percent from 1962 to 1997, peaked at 69 percent in 2004, and fell to 

67.8 percent in 2008.) 

DOE combined the three factors (the observed preference of high-income consumers, 

percent of side-by-side refrigerator-freezers purchased by homeowners, and homeownership) to 

predict the limiting market share for the side- and bottom-freezer product classes. This limit is 

MS HO−LIM LIM × HO MS SF −LIM = = 47.45%

SFHO 

Where: 

MSSF-LIM = limiting market share for side-by-side and bottom-mount product classes, 

MSHO-LIM = limiting market share for side-by-side and bottom- mount product classes among 

homeowners (66.4 percent), 

HOLIM = DOE’s assumed value for the eventual percentage of households that will own 

their own home (65 percent), and 

9-10 

EERE-2008-BT-STD-0012 COMMENT 20
SFHO = the percentage of side-by-side and bottom-mount products sold to homeowners 

(90.9 percent). 

DOE modeled the approach to this limiting value as an exponential curve, and fit the 

model parameters to data from RECS and AHAM in order to determine the rate at which the 

market will approach the limit. RECS micro-data enabled DOE to determine the approximate 

market share of side-by-side products among all homeowners in the years preceding each RECS 

survey (1989, 1992, 1996, 2000, and 2004). DOE also calculated the market share among 

homeowners from the AHAM data by multiplying by 90.9 percent (SFHO) and dividing by the 

homeownership (HO) for each year. The resulting estimates for market shares of side-by-side 

products among homeowners are shown in Table 9.3.4. 

Table 9.3.4 Modeled Homeowner Side-by-Side Market Share 

Survey Year 

Homeowner Side-freezer 

Market Share 

AHAM 1998 41.0% 

1999 41.9% 

2000 42.2% 

2001 43.0% 

2002 43.9% 

2003 45.9% 

2004 46.2% 

2005 49.1% 

2006 59.8% 

2007 61.4% 

RECS 1989 32.5% 

1992 31.9% 

1996 41.1% 

2000 48.9% 

2004 53.0% 

DOE fit an exponential curve to the data in Table 9.3.4, approaching the limiting value 

MSSF-LIM: 

α (β − year ) MS = MS − , HO− year HO−LIM e 

Where: 

MSHO-year = market share of side-by-side and bottom-mount refrigerator-freezers among all 

homeowners in a given year, 

α, β = fit parameters, and 

MSHO-LIM = limiting market share for side- and bottom-freezer product classes among 

homeowners (66.4 percent). 

9-11 

EERE-2008-BT-STD-0012 COMMENT 20
 

The best fit parameters are: α = 0.05555 and β = 1971.62. 

The R-squared value for the fit is 0.924, indicating that the model is a relatively good fit. 

DOE multiplied the best-fit estimate of MSHO-year by homeownership in each year, then divided 

by SFHO to account for side- and bottom-mount refrigerators sold to non-homeowners. The 

results are estimated market shares of side-by-side or bottom-mount products throughout the 

analysis period. This estimate, along with the markets shares derived from the RECS and AHAM 

data, is shown in Figure 9.3.2. 

20.00% 

25.00% 

30.00% 

35.00% 

40.00% 

45.00% 

50.00% 

Side- and Bottom-freezer Market Share

Forecast market share 

AHAM Shipments Share 

RECS Side-freezer 

Limiting fraction 

1985 1995 2005 2015 2025 2035 2045 

Year 

Figure 9.3.2 Projected Market Share of Side- And Bottom-Freezer 

Product Classes 

DOE disaggregated the side and bottom-mount freezer classes into their respective 

product classes based on the AHAM data submittal for the years 2005-2008. For future years, 

DOE maintained the market shares of the product classes within each considered category of 

side-by-side and bottom-mount, and top-mount refrigerator-freezer as they existed in 2008. 

Table 9.3.5 presents the resulting market share forecast DOE used to disaggregate total modeled 

shipments. 

9-12 

EERE-2008-BT-STD-0012 COMMENT 20
Table 9.3.5 Product Class Market Shares of Standard-size Refrigerator-Freezers 

Year PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC5A 

2005 0.2% 0.1% 62.4% 1.0% 1.6% 0.1% 34.1% 0.4% 

2006 0.2% 0.1% 54.4% 1.0% 8.4% 0.1% 33.6% 2.2% 

2007 0.1% 0.0% 53.8% 0.8% 10.7% 0.1% 31.6% 2.9% 

2008 0.1% 0.0% 55.8% 0.8% 10.3% 0.1% 30.2% 2.7% 

2009 0.1% 0.0% 57.7% 0.7% 9.8% 0.1% 28.8% 2.6% 

2010 0.1% 0.0% 59.6% 0.7% 9.4% 0.1% 27.6% 2.5% 

2011 0.1% 0.0% 59.3% 0.7% 9.5% 0.1% 27.8% 2.5% 

2012 0.1% 0.0% 59.0% 0.7% 9.5% 0.1% 28.0% 2.5% 

2013 0.1% 0.0% 58.7% 0.7% 9.6% 0.1% 28.2% 2.6% 

2014 0.1% 0.0% 58.4% 0.7% 9.7% 0.1% 28.4% 2.6% 

2015 0.1% 0.0% 58.1% 0.7% 9.7% 0.1% 28.6% 2.6% 

2016 0.1% 0.0% 57.8% 0.7% 9.8% 0.1% 28.8% 2.6% 

2017 0.1% 0.0% 57.6% 0.7% 9.9% 0.1% 28.9% 2.6% 

2018 0.1% 0.0% 57.3% 0.8% 9.9% 0.1% 29.1% 2.6% 

2019 0.1% 0.0% 57.1% 0.8% 10.0% 0.1% 29.3% 2.7% 

2020 0.1% 0.0% 56.8% 0.8% 10.0% 0.1% 29.5% 2.7% 

2021 0.1% 0.0% 56.7% 0.8% 10.1% 0.1% 29.6% 2.7% 

2022 0.1% 0.0% 56.5% 0.8% 10.1% 0.1% 29.7% 2.7% 

2023 0.1% 0.0% 56.3% 0.8% 10.2% 0.1% 29.8% 2.7% 

2024 0.1% 0.0% 56.1% 0.8% 10.2% 0.1% 29.9% 2.7% 

2025 0.1% 0.0% 56.0% 0.8% 10.2% 0.1% 30.0% 2.7% 

2026 0.1% 0.0% 55.8% 0.8% 10.3% 0.1% 30.2% 2.7% 

2027 0.1% 0.0% 55.6% 0.8% 10.3% 0.1% 30.3% 2.7% 

2028 0.1% 0.0% 55.4% 0.8% 10.4% 0.1% 30.4% 2.8% 

2029 0.1% 0.0% 55.3% 0.8% 10.4% 0.1% 30.5% 2.8% 

2030 0.1% 0.0% 55.1% 0.8% 10.4% 0.1% 30.7% 2.8% 

2031 0.1% 0.0% 55.0% 0.8% 10.5% 0.1% 30.7% 2.8% 

2032 0.1% 0.0% 54.8% 0.8% 10.5% 0.1% 30.8% 2.8% 

2033 0.1% 0.0% 54.7% 0.8% 10.5% 0.1% 30.9% 2.8% 

2034 0.1% 0.0% 54.5% 0.8% 10.6% 0.1% 31.0% 2.8% 

2035 0.1% 0.0% 54.4% 0.8% 10.6% 0.1% 31.1% 2.8% 

2036 0.1% 0.0% 54.3% 0.8% 10.6% 0.1% 31.2% 2.8% 

2037 0.1% 0.0% 54.2% 0.8% 10.6% 0.1% 31.3% 2.8% 

2038 0.1% 0.0% 54.1% 0.8% 10.7% 0.1% 31.3% 2.8% 

2039 0.1% 0.0% 54.0% 0.8% 10.7% 0.1% 31.4% 2.8% 

2040 0.1% 0.0% 53.9% 0.8% 10.7% 0.1% 31.5% 2.8% 

2041 0.1% 0.0% 53.8% 0.8% 10.7% 0.1% 31.6% 2.9% 

2042 0.1% 0.0% 53.5% 0.8% 10.8% 0.1% 31.7% 2.9% 

2043 0.1% 0.0% 53.4% 0.8% 10.8% 0.1% 31.8% 2.9% 

9-13 

EERE-2008-BT-STD-0012 COMMENT 20
 

9.3.1.4 Base Case Shipments 

Figure 9.3.3 shows the forecasted shipments in the base case (without new energy 

conservation standards) and the historical shipments DOE used to calibrate the forecast. The 

figure also presents shipments due to retirements, shipments to new housing, and shipments due 

to conversion from first to second unit. Figure 9.3.4 presents forecasted refrigerator-freezer 

shipments disaggregated by product category. 

25.0
 

Conversion
 

New construction
 

0.0

5.0

10.0

15.0

20.0

Millions 

Replacements

Modeled Shipments

Historical Shipments

19901995200020052010201520202025203020352040

Figure 9.3.3 Standard-Size Refrigerator-Freezers: Historical and Base 

Case Shipments Forecast by Market Segment 

9-14 

EERE-2008-BT-STD-0012 COMMENT 20
 

25.0

PC: 5, 5A 

0.0

5.0

10.0

15.0

20.0

Millions 

PC: 4, 7 

PC: 1, 1A, 3, 3A, 6 

Historical Shipments 

Total Shipments 

19901995200020052010201520202025203020352040

Figure 9.3.4 Standard-size Refrigerator-Freezers: Base Case Shipments 

Forecast by Product Class Group 

9.3.2 Standard-size Freezers 

DOE considered four product classes of standard-size freezers in its analysis. DOE’s 

shipments model for these classes uses the aggregate shipments for all four product classes as the 

basis for its forecasts. In other words, DOE did not develop a separate shipments model for each 

freezer product class. Instead, DOE used various data and assumptions to disaggregate total 

shipments into product classes. 

9.3.2.1 Historical Shipments 

DOE used data on historical shipments (i.e., domestic shipments and imports) from 

Appliance magazine’s Statistical Review,10 AHAM Factbook,5 and an AHAM data submittal7 to 

populate and calibrate its shipments model. DOE built up a total stock of freezers by integrating 

historical shipments. Over time, some of the units are retired and removed from the stock, 

triggering the shipment of new units to replace them. Because of the relationship between 

retirements and total stock, there is a strong correlation between past and future shipments, 

independent of conservation standards. Table 9.3.6 summarizes the historical shipments data for 

standard-size freezers. 

9-15 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

Table 9.3.6 Standard-size Freezers: Historical Shipments 

Year Shipments 

millions Year Shipments millions 

1983 1.320 1996 1.548 

1984 1.261 1997 1.490 

1985 1.222 1998 1.628 

1986 1.209 1999 1.988 

1987 1.242 2000 1.963 

1988 1.316 2001 2.215 

1989 1.172 2002 2.535 

1990 1.241 2003 2.523 

1991 1.324 2004 2.516 

1992 1.525 2005 2.214 

1993 1.438 2006 2.148 

1994 1.546 2007 1.992 

1995 1.558 

Sources: 2005–2007: AHAM Data Submittal, 2009; 1997–2004: AHAM Fact Book 2005; 1983–1996: Appliance 

Magazine, “Statistical Review,” various issues 

9.3.2.2 Markets and Model Calibration 

The shipments market for standard-size freezers is primarily comprised of units for new 

construction and replacement units for products that have been retired. DOE’s shipments model 

also assumes that some existing households that do not currently own the appliance (i.e. EHA) 

will enter the market as new owners. Total shipments are represented by the following equation: 

Ship ( j) = Rpl ( j) + NH ( j) + EHA ( j) SF SF SF SF 

Where: 

ShipSF (j) = total shipments of standard-size freezers in year j, 

RplSF (j) = replacement shipments in year j, 

NH SF(j) = shipments to new households in year j, and 

EHASF (j)= shipments to existing households without the appliance in year j. 

The following sections discuss all three of these markets in further detail. 

New Construction. To forecast the shipments of standard-size freezers for new 

construction for any given year, DOE multiplied the forecasted housing starts by the forecasted 

saturation of standard-size freezers for new housing. DOE used saturation in new housing from 

the 1993, 1997, 2001, and 2005 RECS surveys.8

 DOE determined the saturations in new homes 

by using a sample of RECS household records whose home is less than 5 years old, with freezers 

9-16 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

of age less than 5 years. DOE froze the saturation at the estimated level corresponding to year 

2007 (0.119 per new home). Table 9.3.7 presents the freezer saturation data in new housing 

units. 

Table 9.3.7 Saturation of Freezers in New Housing 

Year 

New Housing Units that Contain New Freezers 

% 

1993 7.78 

1994 

1995 

1996 

1997 11.31 

1998 

1999 

2000 

2001 11.22 

2002 

2003 

2004 

2005 11.67 

Source: Source: EIA, Residential Energy Consumption Survey, for the years listed. 

Replacements. DOE determined shipments to the replacement market using an 

accounting method that tracks the total stock of units by vintage. DOE estimated the stock of 

standard-size freezers by vintage by integrating historical shipments. Over time, some units are 

retired and removed from the stock, triggering the shipment of a replacement unit. A certain 

percentage (depending on the age) of units will fail each year and need to be replaced. To 

determine when a unit fails, DOE used a survival function based on a product lifetime 

distribution that had an average value of 22.7 years. Chapter 8 presents a more thorough 

discussion of the lifetimes of standard-size freezers. Figure 9.3.5 shows the survival and 

retirement functions that DOE used to estimate shipments of replacement freezers. 

9-17 

EERE-2008-BT-STD-0012 COMMENT 20
100%
 

90%
 

80%
 

70%
 

60%
 

50%
 

40%
 

30%
 

20%
 

10%
 

0% 

0 5 10 15 20 25 30 35 40 45 50 

% Surviving 

% Retiring 

Age 

Figure 9.3.5 Standard-size Freezers: Survival and Retirement Functions 

Model Calibration—New Owners. To calibrate estimated shipments with the historical 

data, DOE introduced a market segment that consists of households that currently do not own a 

freezer. DOE estimated shipments to this market segment as residual historical shipments after 

shipments for new housing and replacements were subtracted from total shipments. DOE then 

used a 3-year moving average to estimate the percent of households who enter the market as new 

owners for 2008-2043. The following equation illustrates the calculations. 

EHASF ( j) = (1 − StockSat j ) × HStock j × EHAfrac j 

Where: 

EHASF (j) = number of freezers shipped to existing households without the appliance in year j, 

StockSatj = stock saturation of freezers in year j, 

HStockj = housing stock in year j, and 

EHAfracj = fraction of housing units without the appliance and obtain a freezer in year j. 

9.3.2.3 Disaggregation into Separate Freezer Product Classes 

To disaggregate the total shipments of standard-size freezers into shipments to each of 

the freezer product classes, DOE used the market share information submitted by AHAM.7

 The 

data submitted by AHAM provided an aggregated market share of 50.6 percent for product 

9-18 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

classes 8 and 10. DOE then used the shipments data from the 2005 AHAM Fact Book5 and 

Appliance magazine2 to disaggregate market shares for product classes 8 and 10. The AHAM 

Fact Book indicates a 59.5 percent market share for the combination of product classes 10 and 

10A. Since the market share for 10A is near zero, DOE attributed the combined market share of 

product classes 8 and 10 from the AHAM data submittal (50.6 percent) entirely to product class 

10 in order to be as close as possible to the AHAM Fact Book data. Table 9.3.8 presents the 

market share forecast used for disaggregating modeled shipments. DOE used these estimated 

market shares throughout the forecast period. 

Table 9.3.8 Product Class Market Share of Standard-size Freezers 

Year PC8 PC9 PC10 PC10A 

2008 0.0% 49.4% 50.6% 0.0% 

9.3.2.4 Base Case Shipments 

Figure 9.3.6 shows the forecasted shipments of standard-size freezer in the base case 

(without new energy conservation standards) and the historical shipments DOE used to calibrate 

the forecast. The figure also presents forecasted shipments disaggregated into its modeled market 

segments. Figure 9.3.7 presents shipment forecasts of standard-size freezers in the base case, 

disaggregated into product class groups.a 

a

 DOE grouped product classes 10 and 10A into one group. 

9-19 

EERE-2008-BT-STD-0012 COMMENT 20
 

1990

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

19901995200020052010

201520202025203020352040

Millions 

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

EHA Shipments 

NC Shipments 

Retirements 

Historical Shipments 

Modeled Shipments 

Figure 9.3.6	 Standard-size Freezers: Historical and Base Case 

Shipments Forecast by Market Segment 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Millions 

PC: 10, 10A 

PC9 

PC8 

Historical Shipments 

Modeled Shipments 

Figure 9.3.7	 Standard-size Freezers: Base Case Shipments Forecast 

by Product Class 

9-20 

EERE-2008-BT-STD-0012 COMMENT 20
 

9.3.3 Compact Refrigerators and Refrigerator-Freezers 

DOE analyzed five product classes of compact refrigerators for this rulemaking. DOE’s 

shipments model for these products utilizes aggregate shipments for all five product classes as 

the basis for its forecasts. In other words, DOE did not develop a separate shipments model for 

each product class. Instead, DOE used various data and assumptions to disaggregate total 

shipments into product classes. 

9.3.3.1 Historical Shipments 

DOE used data on historical shipments (domestic shipments and imports) to calibrate its 

shipments model. It developed historical shipments data based on data submitted by AHAM7

 and 

various issues of Appliance magazine.2,9 DOE built up a total stock of compact refrigerators by 

integrating historical shipments. Over time, some units are retired and removed from the stock, 

triggering the shipment of new units to replace them. Because of the relationship between 

retirements and total stock, there is a strong correlation between past and future shipments. Table 

9.3.9 summarizes the historical shipments data for compact refrigerators. 

Table 9.3.9 Compact Refrigerators: Historical Shipments 

Year Shipments (millions) Year Shipments (millions) 

1983 0.568 1996 1.070 

1984 0.602 1997 1.110 

1985 0.783 1998 1.186 

1986 0.810 1999 1.498 

1987 0.830 2000 1.530 

1988 1.000 2001 1.355 

1989 0.925 2002 2.038 

1990 0.933 2003 2.844 

1991 0.925 2004 2.567 

1992 0.950 2005 2.792 

1993 1.030 2006 1.641 

1994 0.950 2007 2.194 

1995 1.032 

Sources: 2005–2007: estimates based on AHAM Data Submittal, 2009;and 1983–2004: Appliance Magazine, 

“Statistical Review,” various issues. 

9.3.3.2 Markets and Model Calibration 

The market for compact refrigerators is primarily comprised of units that replace products 

that have been retired from service, and units installed in new housing, new lodging in the 

commercial sector (such as hotel rooms and dormitories), and in other new construction in the 

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EERE-2008-BT-STD-0012 COMMENT 20
 

 

commercial sector. Total compact refrigerator shipments are represented by the following 

equation: 

Ship ( j) = Rpl ( j) + NH ( j) + NLodg ( j) + NOthComm ( j) CRRF CRRF CRRF CRRF CRRF 

Where: 

ShipCRRF (j) = total shipments of compact refrigerators in year j, 

RplCRRF(j) = replacement shipments in year j, 

NHCRRF (j) = shipments to new households in year j, 

NLodgCRRF (j) = shipments to new lodging units in year j, and 

NOthCommCRRF (j) = shipments to other new commercial establishments in year j. 

The following sections discuss these markets. 

 New Housing. To estimate shipments to new housing in each year, DOE multiplied 

forecasted housing starts by the estimated saturation of compact refrigerators in new housing 

units. DOE forecasted market saturation for this segment using the saturation of compact 

refrigerators in newly built homes in RECS 2001 and 2005 (2.7 percent and 3.2 percent, 

respectively). For years beyond 2005, DOE maintained the growth in new housing saturation 

measured between 2001 and 2005. 

New Lodging and Other Commercial New Construction. To estimate shipments to new 

commercial establishments, DOE used forecasts of new construction in lodging and other 

commercial establishments coupled with market saturation data (in units per building). DOE 

obtained the saturation data from the American Lodging Association (ALA)11 and the EIA’s 

Commercial Building Energy Consumption Survey (CBECS).12 For lodging, DOE used 

saturations from ALA for the years 1998, 2003, and 2008. For future years, DOE maintained the 

growth in saturation rates seen between 2003 and 2008. For other commercial applications, DOE 

used saturations from CBECS for the years 1999 and 2003. DOE maintained the growth in 

saturation rates seen between 1999 and 2003 for subsequent years. 

Figure 9.3.8 presents the forecast for saturation of compact refrigerators for the three 

market segments for new construction. 

9-22 

EERE-2008-BT-STD-0012 COMMENT 20
 

120% 

100% 

80% 

60% 

40% 

20% 

0% 

Lodging 

Other Commercial 

Residential 

2003 - 72% 

2003 - 48% 

2005 - 3.2% 

199019931996199920022005200820112014201720202023202620292032203520382041

Figure 9.3.8 Forecast of Saturation of Compact Refrigerators in New 

Construction Market Segments 

 Replacements. DOE determined shipments to the replacement market using an 

accounting method that tracks the total stock of units by vintage. DOE integrated historical 

shipments to estimate each year’s stock of compact refrigerators by vintage. Over time, some of 

the units are retired and removed from the stock, triggering the shipment of a new unit. Because 

of the relationship between retirements and total stock, there is a strong correlation between past 

and future shipments, independent of conservation standards. 

A certain percentage (depending on the age) of units will fail each year and need to be 

replaced. To determine when a compact refrigerator fails, DOE used a product survival function 

based on a lifetime distribution with an average value of 5.6 years. Chapter 8 presents a more 

thorough discussion of product lifetimes for compact refrigerators. Figure 9.3.9 shows the 

survival and retirement functions that DOE used to estimate replacement shipments. 

9-23 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

100% 

0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% % Surviving 

% Retiring 

0 5 10 15 20 25 30 

Age 

Figure 9.3.9 Compact Refrigerators: Survival and Retirement 

Functions 

Model Calibration. To calibrate estimated shipments with the historical data, DOE 

utilized compact refrigerator stock data for 2003. DOE obtained commercial stock data from 

CBECS 2003 and estimated the 2003 residential stock from various years of RECS. Based on 

these sources, DOE estimated that in 2003 the stock was split 30 percent, 18 percent, and 52 

percent between residential, lodging, and other commercial sectors, respectively. 

9.3.3.3 Disaggregation into Separate Compact Refrigerator Product Classes 

DOE based its product class market shares for compact refrigerators on data submitted by 

AHAM7

 and CEC data13 on available compact refrigerator models. Table 9.3.10 presents the 

market share forecast used for disaggregating total modeled shipments. DOE used these 

estimated market shares throughout the forecast period. 

Table 9.3.10 Product Class Market Shares of Compact Refrigerators 

Year PC11 PC12 PC13 PC14 PC15 

2008 84.40% 5.92% 9.03% 0.32% 0.32% 

9.3.3.4 Base Case Shipments 

Figures 9.3.10 shows the forecasted shipments of compact refrigerators in the base case 

(without new energy conservation standards) along with the historical shipments DOE used to 

9-24 

EERE-2008-BT-STD-0012 COMMENT 20
 

1990

1993

1996

1999

2002

2005

2008

2011

2014

2017

2020

2023

2026

2029

2032

2035

2038

2041 

1990

1993

1996

1999

2002

2005

2008

2011

2014

2017

2020

2023

2026

2029

2032

2035

2038

2041

calibrate the forecast. The figure also presents forecasted shipments disaggregated into modeled 

market segments. Figure 9.3.11 presents the base case shipments forecast for compact 

refrigerators disaggregated by product class. (Product classes 14 and 15 are not visible in the 

figure because they account for a very small share of shipments.) 

7.00 

New other commercial 

0.00 

1.00 

2.00 

3.00 

4.00 

5.00 

6.00 

Millions 

New Lodging 

New housing 

Replacements 

Historical Shipments 

Modeled Shipments 

Figure 9.3.10	 Compact Refrigerators: Base Case Shipments Forecast 

by Market Segment 

7.0

PC15 

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Millions 

PC14 

PC: 13, 13A 

PC12 

PC: 11, 11A 

Historical Shipments 

Total Shipments 

Figure 9.3.11	 Compact Refrigerators: Base Case Shipments Forecast 

by Product Class 

9-25 

EERE-2008-BT-STD-0012 COMMENT 20
 

9.3.4 Compact Freezers 

DOE considered three product classes of compact freezers for this analysis. DOE based 

its forecast shipments compact freezers on aggregate shipments, that is, shipments for all three 

product classes. In other words, DOE did not develop a separate shipments model for each 

product class. Instead, DOE used various data and assumptions to disaggregate total estimated 

shipments into product classes. 

9.3.4.1 Historical Shipments 

DOE used data on historical shipments (domestic shipments and imports) to populate and 

calibrate its shipments model for compact freezers. It obtained historical shipments data from 

data submitted by AHAM7

 and various issues of Appliance magazine2,9 ( Table 9.3.11). DOE 

built up a total stock of compact freezers by integrating historical shipments. Over time, some 

units are retired and removed from the stock, triggering the shipment of new units to replace 

them. Because of the relationship between retirements and total stock, there is a strong 

correlation between past and future shipments. 

Table 9.3.11 Compact Freezers: Historical Shipments 

Year Shipments (millions) Year Shipments (millions) 

1983 0.237 1996 0.335 

1984 0.242 1997 0.378 

1985 0.234 1998 0.393 

1986 0.247 1999 0.434 

1987 0.255 2000 0.474 

1988 0.287 2001 0.490 

1989 0.325 2002 0.520 

1990 0.337 2003 0.512 

1991 0.333 2004 0.696 

1992 0.319 2005 0.591 

1993 0.326 2006 0.524 

1994 0.304 2007 0.500 

1995 0.324 

Sources: 2005–2007: estimates based on AHAM Data Submittal, 2009; and 1983–2004: Appliance Magazine, 

“Statistical Review,” various issues. 

9.3.4.2 Markets and Model Calibration 

The market for compact freezers is primarily comprised of replacement units for products 

that have been retired from service and units purchased by new owners (not new construction) in 

both residential and commercial sectors. Total compact freezer shipments are represented by the 

following equation: 

9-26 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

Ship ( j) = Rpl ( j) + NR ( j) + NC ( j) CF CF CF CF 

Where: 

ShipCF (j) = total shipments of compact freezers in year j, 

RplCF (j) = replacement shipments in year j, 

NR CF(j) = shipments to new residential owners in year j, and
 

NCCF (j)= shipments to new commercial owners in year j. 

The following sections discuss these markets in further detail. 

 Replacements. DOE determined shipments to the replacement market using an 

accounting method that tracks the total stock of units by vintage. DOE integrated historical 

shipments to estimate each year’s stock of compact freezers by vintage. Over time, some units 

are retired and removed from the stock, triggering the shipment of a replacement unit. A certain 

percentage (depending on the age) of units will fail each year and need to be replaced. To 

determine when a compact freezer fails, DOE used a product survival function based on a 

lifetime distribution with an average value of 7.46 years. Chapter 8 provides a more thorough 

discussion of product lifetimes for compact freezers. Figure 9.3.12 shows the survival and 

retirement functions that DOE used to estimate shipments of replacement units. 

0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

100% 

% Surviving 

% Retiring 

0 5 10 15 20 25 30 

Age 

Figure 9.3.12 Compact Freezers: Survival and Retirement Functions 

New Owner. In the absence of data on saturation of compact freezers in homes or 

commercial applications, DOE estimated historical shipments to new owners based on the 

9-27 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

1990

1993

1996

1999

2002

2005

2008

2011

2014

2017

2020

2023

2026

2029

2032

2035

2038

2041 

difference between total shipments of compact freezers and estimated replacement shipments. 

DOE forecast new owner shipments using a 3-year moving average method. DOE assumed an 

even split between residential and commercial new owners for this segment. 

9.3.4.3 Disaggregation into Separate Compact Freezer Product Classes 

DOE used California Energy Commission (CEC) data on available freezer models to 

estimate market shares of product classes 16, 17, and 18.14 Table 9.3.11 presents the market 

share forecast used in this analysis for disaggregating total modeled shipments. DOE used the 

2008 market share estimates throughout the forecast period. 

Table 9.3.12 Product Class Market Shares of Compact Freezers 

Year PC16 PC17 PC18 

2008 50.0% 30.0% 20.0% 

9.3.4.4 Base Case Shipments 

Figure 9.3.13 shows the forecasted compact freezer shipments in the base case (without 

new energy efficiency standards), along with the historical shipments DOE used to calibrate the 

forecast. The figure presents compact freezer shipments disaggregated into the modeled market 

segments. Figure 9.3.14 presents forecasted base case compact freezer shipments disaggregated 

into the product classes. 

0.0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

Millions 

New Owner 

Replacements 

Historical Shipments 

Modeled Shipments 

Figure 9.3.13 Compact Freezers: Historical and Base Case Shipments 

Forecast by Market Segment 

9-28 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

1990

1993

1996

1999

2002

2005

2008

2011

20142017

2020

2023

2026

2029

2032

2035

2038

2041

1.2

PC18 

PC17 

1.0 PC16 

Figure 9.3.14	 Compact Freezers: Base Case Shipments Forecast 

Disaggregated by Product Class 

9.4	 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 product purchase price, 

decreases in product operating costs, and changes to household income. Appendix 9-A provides 

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

variables. 

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

this rulemaking analysis. DOE identified no studies that use time-series data of product 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 appliances. 

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

 when 

comparing appliance prices and appliance operating costs. 

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. 

0.0

0.2

0.4

0.6

0.8

Millions 

Historical Shipments 

Total Shipments 

9-29 

EERE-2008-BT-STD-0012 COMMENT 20
DOE found insufficient data on product purchase price and operating cost to perform a 

thorough 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 this 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 3.4 percent decrease in shipments. 

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 importantly, 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 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 elasticity provided by the preceeding 

9-30 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

 

analysis to be a short-run 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.15 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 smaller (more inelastic) until it reaches a terminal 

value around the tenth year after the price change. Table 9.4.1 shows the relative change in the 

price elasticity of demand for automobiles over time. 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 Table 9.4.1, DOE performed a linear interpolation 

to obtain the relative price elasticity. 

Table 9.4.1 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 

this equation, 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 purchases for existing homes for standard-size freezers, and purchase 

of an additional refrigerator for standard-size refrigerators), 

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. 

9.5 AFFECTED STOCK 

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

standards case, the affected stock is a key output of DOE’s shipments models. The affected stock 

9-31 

EERE-2008-BT-STD-0012 COMMENT 20
(stock that is affected by a standards level) 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 in service 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 in service in year j, 

age = age of the units (years), and 

Std_yr = effective date of the standard. 

For the NES and NPV results presented in Chapter 10, DOE assumed that new energy 

efficiency standards will become effective in the year 2014. Thus, all appliances purchased 

starting on the first day of the year 2014 are affected by the standard level. 

9.6 RESULTS 

This section presents the shipments forecasts for the various standard levels that DOE 

considered for each of the four refrigeration product types. 

Figure 9.6.1 shows the standard-size refrigerator-freezer shipment forecasts for the base 

case and for two standard levels for which an impact is evident. The differences between the 

base case and standard level shipments forecasts represent the annual shipments reductions 

attributable to the standard levels. 

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EERE-2008-BT-STD-0012 COMMENT 20
25.0 

5.0 

10.0 

15.0 

20.0 

Millions 

Base 

CSL6 

CSL8 

0.0 

1990 2000 2010 2020 2030 2040 

Figure 9.6.1 Standard-Size Refrigerator-Freezers: Base Case and Standards Case 

Shipments Forecasts 

Figure 9.6.2 shows the standard-size freezer shipment forecasts for the base case and for 

several standard levels for which an impact is evident. The differences between the base case and 

standard level shipments forecasts represent the annual shipments reductions attributable to the 

standard levels. 

9-33 

EERE-2008-BT-STD-0012 COMMENT 20
1990

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

4.5

4.0

3.5

3.0

2.5

ons 

2.0

Milli

1.5 CSL5 

1.0

CSL7 

CSL8 

0.5 Base 

0.0

Figure 9.6.2 Standard-size Freezers: Base Case and Standards Case Shipments 

Forecasts 

Figure 9.6.3 shows the compact refrigerator shipment forecasts for the base case and for 

those standard levels for which an impact is evident. The differences between the base case and 

standard level shipments forecasts represent the annual shipments reductions attributable to the 

standard levels. 

9-34 

EERE-2008-BT-STD-0012 COMMENT 20
1990

1993

1996

1999

2002

2005

2008

2011

2014

2017

2020

2023

2026

2029

2032

2035

2038

2041 

0.0 

1.0 

2.0 

3.0 

4.0 

5.0 

6.0 

7.0 

Miliions 

CSL6 

CSL7 

CSL8 

Base 

Figure 9.6.3 Compact Refrigerators: Base Case and Standards Case Shipments 

Forecasts 

Figure 9.6.4 shows the compact freezer shipment forecasts for the base case and for 

selected standard levels for which an impact is evident. The differences between the base case 

and standard level shipments forecasts represent the annual shipments reductions attributable to 

the standard levels. 

9-35 

EERE-2008-BT-STD-0012 COMMENT 20
1990 

1993 

1996 

1999 

2002 

2005 

2008 

2011 

2014 

2017 

2020 

2023 

2026 

2029 

2032 

2035 

2038 

2041 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

0. 

90
 

80
 

70
 

60
 

50
 

40
 

30
 

20
 

10
 

00
 

Base 

CS L3 

CS L5 

CS L8 

Millions 

Figure 9.6.4 Compact Freezers: Base Case and Standards Case Shipments Forecasts 

9-36
 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

 

 

 

 

 

 

 

 

 

 

 

 

REFERENCES 

1	 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> 

2	 Statistical Review. Appliance Magazine, April, 1993. 

3	 Statistical Review. Appliance Magazine, April, 1990. 

4	 Statistical Review. Appliance Magazine, April, 1982. 

5	 Association of Home Appliance Manufacturers. AHAM 2005 Fact Book, 2005. Washington, 

DC. 

6	 Association of Home Appliance Manufacturers. AHAM 2003 Fact Book, 2003. Washington, 

DC. 

7	 Association of Home Appliance Manufacturers. Aggregated Refrigerator‐Freezer 

Shipments and Efficiency Data for DOE Refrigerator‐Freezer Energy Efficiency Standards 

Rulemaking, January 16, 2009. Memo to Detlef Westphalen, Navigant Consulting, Inc. 

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

2009 with Projections to 2030. March 2009. U.S. DOE–EIA: Washington, D.C. DOE/EIA0383(2009). <http://www.eia.doe.gov/oiaf/aeo/pdf/0383(2009).pdf> 

9	 U.S. Department of Energy–Energy Information Administration. Residential Energy 

Consumption Survey. Washington, D.C. Surveys for 1990, 1993, 1997, 2001, and 2005. 

<www.eia.doe.gov/emeu/recs/contents.html> 

10	 Statistical Review. Appliance Magazine, April, 1997. 

11	 American Hotel & Lodging Association. 2008 Lodging Survey: Lodging Services, Facilities, 

and Trends, 2008. Washington, DC. p. 52, Percentage of Hotels with Refrigerators in 

Room. 

12	 U.S. Department of Energy–Energy Information Administration. Commercial Building 

Energy Consumption Survey. Washington, D.C. Surveys for 1999 and 2003. 

<http://www.eia.doe.gov/emeu/cbecs/> 

13	 California Energy Commission. Appliances Database—Refrigeration. 

<www.energy.ca.gov/appliances/database/excel_based_files/Refrigeration/> (Last accessed 

April 25, 2009.) 

9-37 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

 

14	 California Energy Commission. Appliances Database—Refrigeration. 

<www.energy.ca.gov/appliances/database/excel_based_files/Refrigeration/> (Last accessed 

April 25, 2009.) 

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

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

9-38 

EERE-2008-BT-STD-0012 COMMENT 20
APPENDIX 9-A. RELATIVE PRICE ELASTICITY OF DEMAND FOR 

APPLIANCES 

TABLE OF CONTENTS 

9-A.1 INTRODUCTION .................................................................................................9-A-1 

9-A.2 LITERATURE REVIEW ......................................................................................9-A-1 

9-A.2.1 Price .......................................................................................................................9-A-1 

9-A.2.2 Income....................................................................................................................9-A-1 

9-A.2.3 Appliance Efficiency and Discount Rates .............................................................9-A-2 

9-A.3 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS, 

CLOTHES WASHERS, AND DISHWASHERS .................................................9-A-2 

9-A.3.4 Physical Household/Appliance Variables..............................................................9-A-3 

9-A.3.5 Economic variables................................................................................................9-A-3 

9-A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE 

SHIPMENTS .........................................................................................................9-A-4 

9-A.4.1 Broad Trends..........................................................................................................9-A-5 

9-A.4.2 Model Specification...............................................................................................9-A-7
 

9-A.4.3 Model Discussion...................................................................................................9-A-8 

9-A.4.4 Analysis Results.....................................................................................................9-A-8 

9A.4.4.1 Individual Appliance Model ................................................................9-A-8 

9A.4.4.2 Combined Appliance Model................................................................9-A-9 

9-A.4.5 Additional Regression Specifications and Results ..............................................9-A-10 

9A.4.5.1 Lower Consumer Discount Rate........................................................9-A-10 

9A.4.5.2 Disaggregated Variables ....................................................................9-A-10 

9-A.5 LONG RUN IMPACTS.......................................................................................9-A-11 

9-A.6 SUMMARY.........................................................................................................9-A-12 

9-A.7 DATA USED IN THE ANALYSIS ....................................................................9-A-13 

LIST OF TABLES 

Table 9-A.2.1 Estimates of the Impact of Price, Income and Efficiency on 

Automobile and Appliance Sales...............................................................9-A-2 

Table 9-A.3.1 Physical Household/Appliance Variables..................................................9-A-3 

Table 9-A.3.2 Economic Variables...................................................................................9-A-4 

Table 9-A.4.1 Simple Estimate of Total Price Elasticity of Demand ...............................9-A-6 

Table 9-A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand........9-A-7 

Table 9-A.4.3 Individual Appliance Model Results .........................................................9-A-9 

Table 9-A.4.4 Combined Appliance Model Result...........................................................9-A-9 

Table 9-A.4.5 Combined and Individual Results, 20 percent discount rate....................9-A-10 

Table 9-A.4.6 Disaggregated Regression Results, 37 percent discount rate...................9-A-11 

Table 9-A.5.1 Change in Price Elasticity of Demand for Automobiles following a 

Purchase Price Change.............................................................................9-A-12 

9A-i 

EERE-2008-BT-STD-0012 COMMENT 20
Table 9-A.5.2 Change in Relative Price Elasticity for Home Appliances following a 

Purchase Price Change.............................................................................9-A-12 

LIST OF FIGURE 

Figure 9-A.3.1 Trends in Appliance Shipment, Housing Starts and Replacements...........9-A-4 

Figure 9-A.4.1 Residual Shipments and Appliance Price ..................................................9-A-6 

9A-ii 

EERE-2008-BT-STD-0012 COMMENT 20
APPENDIX 9-A. RELATIVE PRICE ELASTICITY OF DEMAND FOR APPLIANCES 

9-A.1 INTRODUCTION 

This appendix summarizes DOE’s study of the price elasticity of demand for home 

appliances, including refrigerators, clothes washers and dishwashers. DOE chose this particular 

set of appliances because of the availability of data to determine a price elasticity. This appendix 

begins with a review of the existing economics literature describing the impact of economic 

variables on the sale of durable goods in section 9A.2. In section 9A.3, the market for home 

appliances and changes in it over the past 20 years is described. In section 9A.4, DOE 

summarizes the results of its regression analysis and presents estimates of the price elasticity of 

demand for the three appliances. In section 9A.5, DOE presents development of an ‘effective’ 

purchase price elasticity. DOE’s interpretation of its results is presented in section 9A.6. 

Finally, section 9A.7 describes the data used in DOE’s analysis. 

9-A.2 LITERATURE REVIEW 

There are relatively few studies measuring the impact of price, income and efficiency on 

the sale of household appliances. In this section DOE provides a short review of this literature 

which suggests the likely importance of these variables. 

9-A.2.1 Price 

The goal of many of the studies covered in this review is to measure the impact of price 

on sales in a dynamic market. One study of the automobile market prior to 1970 finds the price 

elasticity of demand to decline over time. The author explains this as the result of buyers 

delaying purchases after a price increase but eventually making the purchase (Table 9A.2.1).1 A 

contrasting study of household white goods also prior to 1970, finds the elasticity of demand to 

increase over time as more price-conscious buyers enter the market.2

 A recent analysis of 

refrigerator market survey data finds that consumer purchase probability decreases with survey 

asking price.3

 Estimates of the price elasticity of demand for different brands of the same 

product tend to vary. A review of 41 studies of the impact of price on market share found the 

average price elasticity to be -1.75.4

 The average estimate of price elasticity of demand reported 

in these studies is -0.33 in the appliance market and -0.47 in the combined automobile and 

appliance markets. 

9-A.2.2 Income 

Higher income households are more likely to own household appliances.5

 The impact of 

income on appliance shipments is explored in two econometric studies of the automobile and 

appliance markets. 1,2 The average income elasticity of demand is 0.50 in the appliance study 

cited in the literature review, much larger in the automobile study (Table 9.A.2.1). 

9-A-1
 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

 

 

9-A.2.3	 Appliance Efficiency and Discount Rates 

Many studies estimate the impact of appliance efficiency on consumer appliance choice. 

Typically, this impact is summarized by the implicit discount rate, i.e., the rate consumers use to 

compare future appliance operating cost savings against an appliance purchase price premium. 

One early and much cited study concludes that consumers use a 20 percent implicit discount rate 

when purchasing room air conditioners (Table 9A.2.1).6 A survey of several studies of different 

appliances suggests that the consumer implicit discount rate has a broad range and averages 

about 37 percent.7 

Table 9-A.2.1	 Estimates of the Impact of Price, Income and Efficiency on Automobile 

and Appliance Sales 

Durable Good 

Price 

Elasticity 

Income 

Elasticity 

Brand 

Price 

Elasticity 

Implicit 

Discount 

Rate Model 

Data 

Years 

Time 

Period 

Automobiles1

 -1.07 3.08 - - Linear Regression, stock 

adjustment - Short run 

Automobiles1

 -0.36 1.02 - - Linear Regression, stock 

adjustment - Long run 

Clothes Dryers2

 -0.14 0.26 - - Cobb-Douglas, diffusion 1947-1961 Mixed 

Room Air 

Conditioners2 -0.378

 0.45 - - Cobb-Douglas, diffusion 1946-1962 Mixed 

Dishwashers2

 -0.42 0.79 - - Cobb-Douglas, diffusion 1947-1968 Mixed 

Refrigerators3

 -0.37 - - 39% Logit probability, survey 

data 1997 Short run 

Various4

 - - -1.769 - Multiplicative regression - Mixed 

Room Air 

Conditioners5 - - -1.72 - Non-linear diffusion 1949-1961 Short run 

Clothes Dryers5 - - -1.32 - Non-linear diffusion 1963-1970 Short run 

Room Air 

Conditioners6 - - - 20% Qualitative choice, survey 

data - -

Household 

Appliances7 - - - 37%10 Assorted - -

Sources: 1

 S. Hymens. 1971; 2 P. Golder and G. Tellis, 1998; 3 D. Revelt and K. Train, 1997; 4

 G. Tellis, 1988; 5 D. Jain and R. Rao; 6 J. Hausman; 7 K. Train, 1985. 

Notes:	 8

 Logit probability results are not directly comparable to other elasticity estimates in this table. 9

 Average brand price elasticity across 41 studies. 

10 Averaged across several household appliance studies referenced in this work. 

9-A.3 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS, 

CLOTHES WASHERS, AND DISHWASHERS 

In this section DOE evaluates variables that appear to account for refrigerator, clothes 

washer and dishwasher shipments, including physical household/appliance variables, and 

economic variables. 

9-A-2
 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

9-A.3.4 Physical Household/Appliance Variables 

Several variables influence the sale of refrigerators, clothes washers and dishwashers. 

The most important for explaining appliance sales trends are the annual number of new 

households formed (housing starts) and the number of appliances reaching the end of their 

operating life (replacements). Housing starts influence sales because new homes are often 

provided with, or soon receive, new appliances, including dishwashers and refrigerators. 

Replacements are correlated with sales because new appliances are typically purchased when old 

ones wear out. In principle, if households maintain a fixed number of appliances, shipments 

should equal housing starts plus appliance replacements. 

9-A.3.5 Economic variables 

Appliance price, appliance operating cost and household income are important economic 

variables affecting shipments. Low prices and costs encourage household appliance purchases 

and a rise in income increases householder ability to purchase appliances. In principle, changes 

in economic variables should explain changes in the number of appliances per household. 

During the 1980–2002 study period, annual shipments grew 69 percent for clothes 

washers, 81 percent for refrigerators and 105 percent for dishwashers (Table 9A.3.1). This rising 

shipments trend is explained in part by housing starts, which increased 6 percent and by 

appliance replacements, which rose between 49 percent and 90 percent, depending on the 

appliance, over the period (Table 9A.3.1).a

 For mature markets such as these, replacements 

exceed appliance sales associated with new housing construction. 

Table 9-A.3.1 Physical Household/Appliance Variables 

 Shipments1 (millions) Housing Starts2 (millions) Replacements3 (millions) 

Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change 

Refrigerators 5.124 9.264 81% 1.723 1.822 6% 3.93 5.84 49% 

Clothes Washers 4.426 7.492 69% 1.723 1.822 6% 3.66 5.50 50% 

Dishwashers 2.738 5.605 105% 1.723 1.822 6% 1.99 3.79 90% 1

Shipments: Number of units sold. Sources: AHAM Fact Book and Appliance Magazine. 2

Housing Starts: Annual number of new homes constructed. Source: U.S. Census. 3

Replacements: Average of annual lagged shipments, with lag equal to expected appliance operating life, ± 5 years.
 

Nevertheless, it is apparent that appliance shipments increased somewhat more rapidly 

than housing starts and replacements. This is shown by comparing the beginning and end points 

of lines representing “starts plus replacements” (uppermost solid line in Figure 9A.3.1) and 

“shipments” (diamond linked line in Figure 9A.3.1). In 1980 the “shipment” line begins below 

the “starts plus replacements” line. In 2002, the “shipments” line ends above the “starts plus 

replacements” line. This more rapid increase in shipments, compared to housing starts plus 

replacements, suggests that the appliance per household ratio increased over the study period. 

a

 Appliance replacements are determined from the expected operating life of refrigerators (19 years), clothes 

washers (14 years), and dishwashers (12 years) and from past shipments. Replacements are further discussed in 

section 9A.3. 

9-A-3
 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

 

 

 

Refrigerator Shipments 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

1980 1984 1988 1992 1996 2000 

Millions 

New Housing 

Retirements 

Shipments 

Clothes Washer Shipments 

0 

1 

2 

3 

4 

5 

6 

7 

8 

1980 1984 1988 1992 1996 2000 

Millions 

Dishwasher Shipments 

0 

1 

2 

3 

4 

5 

6 

1980 1984 1988 1992 1996 2000 

Millions 

Figure 9-A.3.1	 Trends in Appliance Shipment, Housing Starts and 

Replacements 

Economic variables, including price, cost and income, may explain this increase in 

appliances per household. Over the period, appliance prices decreased 40 percent to 50 percent, 

operating costs fell between 33 percent and 72 percent, and median household income rose 16 

percent (Table 9A.3.2). 

Table 9-A.3.2 Economic Variables 

Price1 (1999$) Operating Cost2 (1999$) Household Income3 (1999$) 

Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change 

Refrigerators 1208 726 -40% 333 94 -72% 37,447 43,381 16% 

Clothes Washers 779 392 -50% 262 175 -33% 37,447 43,381 16% 

Dishwashers 713 369 -48% 183 95 -48% 37,447 43,381 16% 1

Price: Shipment weighted retail sales price. Sources: AHAM Fact Book and Appliance Magazine. 2

Operating Cost: Annual electricity price times electricity consumption. Source: AHAM Fact Book. 3

Income: Mean Household income. Source: U.S. Census. 

9-A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE 

SHIPMENTS 

Little data is available for estimating the impact of economic variables on the demand for 

appliances. Industry operating cost data is incomplete—appliance energy use data is available 

for only 12 years of the 1980-2002 study period. Industry price data is also incomplete— 

available for only 8 years of the study period for each of the appliances. 

The lack of data suggests that regression analysis can at best evaluate broad data trends, 

utilizing relatively few explanatory variables. This section begins by describing broad trends 

apparent in the economic and physical household data sets and then specifies a simple regression 

model to measure these trends, making assumptions to minimize the number of explanatory 

9-A-4
 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

 

 

variables. Finally, results are presented of the regression analysis and the estimate of the price 

elasticity of demand for appliances. In this section (specifically section 9A.4.5), DOE also 

presents the results of regression analysis performed with more complex models, and used to test 

assumptions made to specify the simple model. These results support the simple model 

specification, and estimates of the price elasticity of appliance demand measured with that 

model. 

9-A.4.1 Broad Trends 

In this section DOE reviews trends in the physical household and economic data sets and 

posit a simple approach for estimating the price elasticity of appliance demand. As noted above, 

the physical household variables (starts and appliance replacements), explain most of the 

variability in appliance shipments over the period.b DOE assumes the rest of the variability in 

shipments (referred to as “residual shipments”) is explained by economic variables, and present a 

tabular method for measuring price elasticities described below. 

To illustrate this tabular approach, DOE defines two new variables—residual shipments 

and total price. Residual shipments are defined as the difference between shipments and 

physical household demand (starts plus replacements). Total price, represented by the following 

equation, is defined as appliance price plus the present value of lifetime appliance operating 

cost:c 

TP = PP + PVOC

where: 

TP = Total price, 

PP = Appliance purchase price, and 

PVOC = Present value of operating cost. 

Over the study period, residual shipments increase 30 percent for refrigerators, 19 percent for 

clothes washers, and 23 percent for dishwashers in proportion to total shipments. At the same 

time, total prices decline 47 percent, 45 percent and 48 percent for refrigerators, clothes washers, 

and dishwashers, respectively. Assuming that total price explains the entire change in per 

household appliance usage, a rough estimate is calculated of the total price elasticity of demand 

equal to -0.48 for refrigerators, -0.32 for clothes washers and -0.37 for dishwashers (Table 

9A.4.1). 

b

 A log regression of the form: Shipments = a + b • Housing Starts + c • Retirements, indicates that these two 

variables explain 89 percent of the variation in refrigerator shipments, 97 percent of the variation in clothes washer 

shipments, and 97 percent of the variation in dishwasher shipments. 

c

 Present value operating cost is calculated assuming a 19 year operating life for refrigerators, 14 year operating life 

for clothes washers, and a 12 year operating life for dishwashers. A 37 percent discount rate is used to sum annual 

operating costs into a present value operating cost. 

9-A-5
 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

 

 

Table 9-A.4.1 Simple Estimate of Total Price Elasticity of Demand 

Residual Shipments (millions) Total Price (199$) 

Appliance 1980 2002 Difference Change 1980 2002 Change Elasticity

Refrigerators -0.5 1.6 2.1 30% 1541 820 -61% -0.48 

Clothes Washers -1.0 0.2 1.1 19% 1042 567 -59% -0.32 

Dishwashers -1.0 -0.01 1.0 23% 896 464 -64% -0.37 

The negative correlation between total price and residual shipments suggested by these 

negative price elasticities is illustrated in a graph of residual shipments on the y-axis and total 

price on the x-axis (Figure 9A.4.1). 

2000 

Refrigerators 

1200 

Clothes Washers 

1000 

Dishwashers 

1600 

ents 

1000 

ments 

800 

ments 

Shipm 1200 

Shipual 

800 

Shipual 

600 

800 

Residual 

Resid

600 

Resid

400 

400 400 200 

-2.0 -1.0 0.0 1.0 2.0 -1.5 -1.0 -0.5 0.0 0.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 

Total Price Total Price Total Price 

Yellow points are observed price data; red points are interpolated price data. 

Figure 9-A.4.1 Residual Shipments and Appliance Price 

Household income rose during the study period, making it easier for households to 

purchase appliances. Assuming that a rise in income has a similar impact on shipments as a 

decline in price, the impact of income is incorporated by defining a third variable, termed 

relative price, calculated as total price divided by household income and represented by the 

following equation:d 

RP = 

Income

TP

where: 

RP = Relative price, 

TP = Total price, and 

Income = Household income. 

d

 Recall that the income elasticity of demand cited in the literature review is 0.50 and the price elasticity of demand 

cited in the review averages -0.35. This suggests that combining the effects of income and price will yield an 

elasticity less negative than price elasticity alone. 

9-A-6
 

EERE-2008-BT-STD-0012 COMMENT 20
 

 

 

 

The percent decline in relative price for the three appliances divided by the percent decline in 

residual shipments suggests a rough estimate of relative price elasticity equal to -0.40 for 

refrigerators, -0.26 for clothes washers and -0.30 for dishwashers (Table 9A.4.2). 

Table 9-A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand 

Residual Shipments (millions) Relative Price (1999$) 

Appliance 1980 2002 Change 1980 2002 Change Elasticity

Refrigerators -0.532 1.597 30% 0.041 0.019 -74% -0.40 

Clothes Washers -0.953 0.174 19% 0.028 0.013 -72% -0.26 

Dishwashers -0.974 -0.005 23% 0.024 0.011 -76% -0.30 

9-A.4.2 Model Specification 

The limited price data suggests using a simple regression model to estimate the impact of 

economic variables on shipments, using few explanatory variables. The following equation 

chosen for this analysis includes one physical household variable (starts plus replacements) and 

one relative price variable (the sum of purchase price plus operating cost, divided by income). 

Ship = a + b× RP + c ×[Starts + Rplc] Eq. 9A.1 

where: 

Ship = Quantity of appliance sold, 

RP = Relative price, 

Starts = Number of new homes, and 

Rplc = Number of appliances at the end of their operating life. 

The natural logs are taken of all variables so that the estimated coefficients for each 

variable in the model may be interpreted as the percent change in shipments associated with the 

percent change in the variable. Thus, the coefficient b in this model is interpreted as the relative 

price elasticity of demand for the three appliances. 

The following combined regression equation is used to estimate an average price 

elasticity of demand across the three appliances, using pooled data in a single regression. A 

combined regression specification is justified, given limited data availability and similarity in 

price and shipment behavior across appliances (see Figure 9A.4.1). Thus, the model represented 

by the combined regression equation is considered the basic model in DOE’s analysis of 

appliance shipments. 

Ship = a + b× RP + c ×[Starts + Rplc]+ d × CW + e × DW Eq. 9A.2 

where: 

CW = Quantity of clothes washers sold, and 

DW = Quantify of dishwashers sold. 

9-A-7
 

EERE-2008-BT-STD-0012 COMMENT 20
 

9-A.4.3 Model Discussion 

The most important assumption used to specify this model is that changes in economic 

variables over the study period—income, price, and operating cost—are responsible for all 

observed growth in residual appliance shipments. In other words, DOE assumes other possible 

explanations, such as changing consumer preferences and increases in the quality of 

appliances—had no impact. This assumption seems unlikely but without additional data, the 

impact of this assumption on the price elasticity of demand cannot be measured. DOE effectively 

assumes that changes in consumer preferences and appliance characteristics, while affecting 

which specific models are purchased, have relatively little impact on the total number of 

appliances purchased in a year. 

Three additional assumptions used to specify this model deserve comment. The relative 

price variable is specified in the model, assuming that (1) the correct implicit discount rate is 

used to combine appliance price and operating cost and that (2) rising income has the same 

impact on shipments as falling total price. The “starts + replacements” variable is specified, 

assuming (3) that starts and replacements have similar impacts on shipments. 

To investigate the first assumption about discount rates, DOE calculated “present value 

operating cost” using a 20 percent implicit discount rate and performed a second regression 

analysis based on the models described in equations. 9A.1 and 9A.2. The results of this analysis, 

presented in section 9A.4.5, indicate that the elasticity of relative price is relatively insensitive to 

changes in the discount rate. 

To investigate the second and third assumptions, DOE specified a regression model 

separating income from total price and replacements from starts, thus adding two additional 

explanatory variables to the basic model as shown in the following equation: 

Ship = a + b×TP + c × Incone + d × Start + e × Rplc + f × CW + g × DW Eq. 9A.3 

The results of the regression analysis of this model are also presented in section 9A.4.5. 

These results suggest that the elasticity of total price (coefficient b) is relatively insensitive to 

changes in the treatment of income and “starts + replacements” in the model. 

9-A.4.4 Analysis Results 

9A.4.4.1 Individual Appliance Model 

The individual appliance regression equations are specified as followed (as shown earlier 

as Eq. 9A.1): 

Ship = a + b× RP + c ×[Starts + Rplc] 

In regression analysis of this model, the elasticity of relative price (b) is estimated to be 

9-A-8
 

EERE-2008-BT-STD-0012 COMMENT 20
-0.40 for refrigerators, -0.31 for clothes washers and -0.32 for dishwashers (Table 9A.4.3), 

averaging -0.35. These elasticities are similar to those reported in the literature survey for 

appliances (Table 9A.2.1). They are remarkably similar to the price elasticity calculated using a 

tabular approach presented above (Table 9A.4.2). 

The estimated coefficient associated with the “starts + replacements” variable is close to 

one. A coefficient equal to one for this variable would imply that shipments increase in direct 

proportion to an increase in “starts + replacements”, holding economic variables constant. The 

high R squared values (above 95) and t statistics (above 5) in the results provide a measure of 

confidence in this analysis, despite the very small data set. 

Table 9-A.4.3 Individual Appliance Model Results 

Refrigerator Clothes Washer Dishwasher 

Variable Coefficient t-stat Coefficient t-stat Coefficient t-stat 

Intercept -1.51 -7.26 -1.47 -8.23 -2.08 -16.78 

Relative Price -0.40 -6.60 -0.31 -5.69 -0.32 -7.03 

Starts + Replacements 1.05 5.90 1.08 6.41 1.35 11.46 

R2

 0.954 0.954 0.975 

Observations 23 23 23 

9A.4.4.2 Combined Appliance Model 

The combined appliance regression equation is specified as follows (as shown earlier as 

Eq. 9A.2): 

Ship = a + b× RP + c ×[Starts + Rplc]+ d × CW + e × DW

This regression analysis indicates that the model fits the existing shipments data well 

(high R squared) and that the variables included in the model are statistically significant (Table 

9A.4.4). The elasticity of relative price estimated with this model is -0.34, close to the average 

value estimated in the individual appliance models (-0.35). It is also similar to elasticity 

estimates reported in the literature survey and calculated using the tabular approach above. 

Table 9-A.4.4 Combined Appliance Model Result 

Variable Coefficient t-stat 

Intercept -1.60 -15.54 

Relative Price -0.34 -10.74 

Starts + Replacements 1.21 13.95 

CW -0.20 -9.04 

DW -0.32 -6.58 

R2

 0.983 

Observations 69 

9-A-9
 

EERE-2008-BT-STD-0012 COMMENT 20
9-A.4.5 Additional Regression Specifications and Results 

As described above in section 9A.4.3, DOE used three assumptions to specify its 

appliance models. The first is that the implicit price variable in the basic regression model is 

specified using a 37 percent implicit discount rate, to aggregate appliance price and operating 

cost. The second states that the implicit price variable is defined assuming that rising income has 

the same impact on shipments as falling total price. The third states that the “starts + 

replacements” variable is defined assuming that housing starts have a similar impact on 

shipments as appliance replacements. 

9A.4.5.1 Lower Consumer Discount Rate 

To investigate the first assumption about discount rates, DOE calculated “present value 

operating cost” using a 20 percent implicit discount rate and performed a second regression 

analysis based on the models described in equations 9A.1 and 9A.2. The estimated coefficient 

associated with the relative price variable in these regressions is almost identical to the 

coefficients estimated for same variable reported above using a 37 percent implicit discount rate. 

The elasticity of relative price calculated using a 20 percent discount rate is -0.33 in the 

combined regression and averages -0.35 for the three appliances (Table 9A.4.5). The elasticity 

of price calculated using a 37 percent discount rate is -0.34 in the combined regression and 

averages -0.35 for the three appliances. DOE concludes from this analysis that the elasticity of 

relative price is relatively insensitive to changes in the discount rate. 

Table 9-A.4.5 Combined and Individual Results, 20 percent discount rate 

Three Appliances 

Variable 

Intercept 

Total Price / Income 

Starts + Retirements 

CW 

DW 

Coefficient 

-1.53 

-0.33 

1.20 

-0.18 

-0.32 

t-Stat 

-14.61 

-10.69 

13.65 

-8.69 

-6.57 

R2 

Observations 

0.982 

69 

Variable 

Intercept 

Total Price / Income 

Starts + Retirements 

Refrigerator 

Coefficient 

-1.36 

-0.38 

1.04 

t-Stat 

-6.26 

-6.50 

5.73 

Clothes Washers 

Coefficient t-Stat 

-1.41 -7.49 

-0.32 -5.29 

1.06 5.83 

Dishwasher 

Coefficient 

-2.04 

-0.33 

1.34 

t-Stat 

-17.23 

-7.30 

11.64 

R2 

Observations 

0.953 

23 

0.950 

23 

0.977 

23 

9A.4.5.2 Disaggregated Variables 

To investigate the second and third assumptions, DOE constructed a regression model 

separating income from total price and replacements from starts, thus adding two additional 

explanatory variables to the basic model (as shown earlier as Eq. 9A.3). 

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Ship = a + b×TP + c × Incone + d × Start + e × Rplc + f × CW + g × DW

The estimated coefficient associated with the total price variable in these regressions is 

almost identical to the coefficients estimated for the relative price variable reported above. The 

elasticity of total price in the above equation is -0.36 in the combined appliance regression and 

averages -0.35 for the three appliances (Table 9A.4.6). The elasticity of relative price based on 

the model described in equation 9A.2 is -0.34 in the combined regression (Table 9A.4.4) and 

averages -0.35 across the individual appliances (Table 9A.4.3). DOE concludes that the price 

elasticity calculated in this analysis is relatively insensitive to the specification of household 

income and “starts + replacements” variables in the model. 

Table 9-A.4.6 Disaggregated Regression Results, 37 percent discount rate 

Three Appliances 

Variable Coefficient t-Stat 

Intercept -2.92 -1.26 

Income 0.58 2.92 

Total Price -0.36 -7.06 

Housing Starts 0.44 10.02 

Retirements 0.62 8.12 

CW -0.24 -9.25 

DW -0.46 -7.68 

R2 0.985 

Observations 69 

Refrigerator Clothes Washers Dishwasher 

Variable Coefficient t-Stat Coefficient t-Stat Coefficient t-Stat 

Intercept -6.19 -2.24 -6.64 -1.63 1.00 0.23 

Income 0.89 3.80 0.87 2.31 0.20 0.52 

Total Price -0.35 -5.48 -0.27 -2.51 -0.43 -5.18 

Housing Starts 0.41 7.38 0.25 3.29 0.62 8.24 

Retirements 0.56 6.06 0.56 2.09 0.65 5.86 

R2 0.984 0.958 0.979 

Observations 23 23 23 

9-A.5 LONG RUN IMPACTS 

As noted above in Table 9A.2.1 in section 9A.2, the literature review provides price 

elasticities over short and long time periods, also referred to as short run and long run price 

elasticities. As noted in the first two rows of Table 9A.2.1, one source (i.e., Hymans) shows that 

the price elasticity of demand is significantly different over the short run and long run for 

automobiles.1

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

over a 30-year time period, consideration must be given as to how the relative price elasticity is 

affected once a new standard takes effect. 

DOE considers the relative price elasticities determined above in section 9A.4 to be short 

run elastcities. DOE 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 the Hymans study pertaining 

to automobiles. Based on the Hymans study, Table 9A.5.1 shows how the automobile price 

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elasticity of demand changes in the years following a purchase price change. With increasing 

years after the price change, the price elasticity becomes more inelastic until it reaches a terminal 

value around the tenth year after the price change. 

Table 9-A.5.1 Change in Price Elasticity of Demand for Automobiles following a Purchase 

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 

Source: Hymans, 1971. 

Based on the relative change in the automobile price elasticity of demand shown in Table 

9A.5.1, DOE developed a time series of relative price elasticities for home appliances. Table 

9A.5.2 presents the time series. 

Table 9-A.5.2 Change in Relative Price Elasticity for Home Appliances 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 

9-A.6 SUMMARY 

This appendix describes the results of a literature search, tabular analysis and regression 

analysis of the impact of price and other variables on appliance shipments. In the literature, 

DOE finds only a few studies of appliance markets that are relevant to this analysis, and no 

studies using time series 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 appliances. Finally, the literature suggests that consumers use relatively 

high implicit discount rates, when comparing appliance prices and appliance operating costs. 

There is not enough price and operating cost data available to perform complex analysis 

of dynamic changes in the appliance market. In this analysis, DOE uses data available for 

refrigerators, clothes washers and dishwashers to evaluate broad market trends and to perform 

simple regression analysis. 

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

appliance price and operating cost over the period. Household income has also risen during this 

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

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variable, termed relative price, and used this variable in a tabular analysis of market trends, and a 

regression analysis. 

DOE’s tabular analysis of trends in the number of appliances per household suggests that 

the price elasticity of demand for the three appliances is inelastic. Our regression analysis of 

these same variables suggests that the relative price elasticity of demand is -0.34. The price 

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

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

measure is based on an assumption that economic variables, including price, income and 

operating costs, 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 they are not accounted for in this analysis. 

9-A.7 DATA USED IN THE ANALYSIS 

•	 Appliance Shipments: Shipments are defined as the annual number of units shipped in 

millions. These data were collected from the Association of Home Appliance Manufacturers 

(AHAM)8

 and Appliance Magazine9 as annual values for each year, 1980–2002. AHAM 

was used for the period 1989–2002 while Appliance Magazine was used for the period 1980– 

1988. 

•	 Appliance Price: Price is defined as the shipments weighted retail sales price of the unit in 

1999 dollars. Price values for 1980, 1985, 1986, 1991, 1993, 1994, 1998, and 2002 were 

collected from AHAM Fact Books.10 Price values for other years were interpolated from 

these eight years of data. 

•	 Housing Starts: Housing starts data were collected from U.S. Census construction statistics 

(C25 reports) as annual values for each year, 1980–2002.11 

•	 Replacements: Retirement-driven replacements are estimated with the assumption that some 

fraction of sales arise from consumers replacing equipment at the end of its useful life. Since 

each appliance has a different expected lifespan (19 years for refrigerators12, 14 years for 

clothes washers13, 12 years for dishwashers14), replacements are calculated differently for 

each appliance type. Replacements are estimated as the average of shipments 14–24 years 

previous for refrigerators, 9–19 years previous for clothes washers, and 7–17 years previous 

for dishwashers. Historical shipments data were collected from AHAM and Appliance 

Magazine. 

•	 Annual Electricity Consumption: Electricity Use (UEC) is defined as the energy 

consumption of the unit in kilowatt-hours. Electricity consumption is dependent on appliance 

capacity and efficiency. These data were provided by AHAM for 1980, 1990–1997 and 

1999–2002.15 Data were interpolated in the years for which data were not available. 

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EERE-2008-BT-STD-0012 COMMENT 20
• Operating Cost: Operating Cost is the present value of the electricity consumption of an 

appliance over its expected lifespan. The lifespans of refrigerators, clothes washers and 

dishwashers are assumed to be 19, 14, and 12 years respectively. Discount rates of 20 

percent6

 and 37 percent16 were used, producing similar estimates of price elasticity. A study 

by Hausman recommended a discount rate of “about 20 percent” in its introduction, and 

presented results ranging from 24.1 percent to 29 percent based on his calculations for room 

air conditioners. A study by Train suggests a range of implicit discount rates averaging 35 

percent for appliances. 

• Income: Median annual household income in 2003 dollars. This data was collected for each 

year, 1980–2002, from Table H-6 of the U.S. Census.17 

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REFERENCES 

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

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

2	 P. Golder and G. Tellis. Beyond Diffusion: An Affordability Model of the Growth of New 

Consumer Durables, Journal of Forecasting, 1998. 

3	 D. Revelt and K. Train. Mixed Logit with Repeated Choices: Household's Choice of 

Appliance Efficiency Level, Review of Economics and Statistics, 1997. 

4	 G. Tellis. The Price Elasticity of Selective Demand: A Meta-Analysis of Econometric 

Models of Sales, Journal of Marketing Research, 1988. 

5	 U.S. Department of Energy-Energy Information Agency. The Effect of Income on 

Appliances in U.S. Households, 2002. Washington, DC. 

<http://www.eia.doe.gov/emeu/recs/appliances/appliances.html> 

6	 J. Hausman. Individual discount rates and the purchase and utilization of energy using 

durables, The Bell Journal of Economics, 1979. 

7	 K. Train. Discount rates in consumer' energy related decisions: a review of the literature, 

Energy, 1985. 

8	 Association of Home Appliance Manufacturers. AHAM Fact Books, 2000 and 2003. 

Washington, DC. 

9	 Statistical Review. Appliance Magazine, April, 1990. 

10	 Association of Home Appliance Manufacturers. AHAM Fact Books, 1987, 1988, 1993, 1995, 

2000, and 2003. Washington, DC. 

11	 U.S. Census Bureau. Current Construction Reports, C25 – New One-Family Houses Sold. 

Washington, DC. <http://www.census.gov/prod/www/abs/c25.html> 

12	 Duemling, Reubin. Product Life of Refrigerators, 1999. University of California, Berkeley, 

Energy and Resources Group masters thesis. 

13	 U.S. Department of Energy-Office of Building Research and Standards. Final Rule Technical 

Support Document (TSD): Energy Efficiency Standards for Consumer Products: Clothes 

Washers, December, 2000. Washington, DC. Chapter 7. 

14	 U.S. Department of Energy-Office of Codes and Standards. Technical Support Document: 

Energy Conservation Standards for Consumer Products: Dishwashers, Clothes Washers, and 

Clothes Dryers, December, 1990. Washington, DC. DOE/CE-02999P. Chapter 4. 

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15	 Association of Home Appliance Manufacturers. AHAM 2003 Fact Book, 2003. Washington, 

DC. 

16	 K.E. Train, and Atherton, T. Rebates, loans, and customers’ choice of appliance efficiency 

level: combining stated- and revealed-preference data, The Energy Journal, 1995. 

17	 U.S. Census Bureau. Income, Historical Income Tables – Households, Table H-6. 

Washington, DC. <http://www.census.gov/hhes/www/income/histinc/inchhtoc.html> 

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