9

- i CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION

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

- 1
9.2 DEFINITION OF MARKET SEGMENTS FOR THE SHI PMENTS ANALYSIS

....... 9

- 1
9.2.1 Fundamental Model Equations

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

- 2
9.2.1.1 Replacement Shipments

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

- 3
9.2.2 Shipments to New Construction

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

- 4
9.2.3 Shipments to New Owners

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

- 4
9.3 DATA INPUTS AND SUPP ORTING CALCULATIONS

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

- 4
9.3.1 Historical Shipments and Calculation of Replacement Shipments

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

- 4
9.3.1.1 Historical Shipments

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

- 4
9.3.2 Shipments to New Construction

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

- 6
9.3.3 Shipments to New Owners

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

- 6
9.3.3.1 Product Switching

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

- 7
9.4 IMPACT OF STANDARDS ON SHIPMENTS

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

- 8
9.4.1 Purchase Price Elasticity

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

- 8
9.4.2 Impact from Increase in Relative Price

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

- 10
9.5 RESULTS

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

- 11

LIST OF TABLES

Table 9.3.1 Saturations of Residential Boilers in New Construction in 2020

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

- 6
Table 9.3.2 Switch of Replacement Units During the 2020

- 2049 Period

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

- 7
Table 9.4.1 Change in Relative Price Elasticity Following a Purchase Price Change

......... 9

- 10

LIST OF FIGURES

Figure 9.3.1 Historical Shipments of Residential Boilers

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

- 5 Figure 9.3.2 Product Switching Among Residential Boiler Product Classes

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

- 7 Figure 9.5.1 Historic and Projected Base Case Shipments Residential Boilers by
Product Class

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

- 11 Figure 9.5.2 Total Projected Shipments of Residential Gas

- fired Hot Water Boiler s in
the Base Case and Each Standards Case

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

- 129-1 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 project annual product shipments and presents results for residential boiler product classes considered in this analysis.

The shipments model divides the shipments of residential boiler s into specific market segments. The model starts from a historical base year and calculates retirements and shipments by market segment for each year of the analysis period

. Th is approach produces an estimate of the total equipment stock, broken down by ag e or vintage , in each year of the analysis period. In addition, t he product stock efficiency distribution is calculated for the base case and for each
standards case for each product class. The stock distribution is used in the n ational i mpact

a nalysis (NI A) to estimate the total costs and benefits associated with each efficiency level

.

The shipments m odel was developed as a Microsoft Excel spreadsheet that is accessible

on DOE’s Appliance and Commercial Equipment Standards website ( http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=62 ). Appendix 10

- A discusses how to access and utilize the shipments model spreadsheet , which is integrated into the spreadsheet for the NIA . This chapter explains how the sh ipments model is constructed and provides some summary output. The rest of section 9.1 describes the methodological approach.

9.2 DEFINITION OF MARKET SEGMENTS FOR THE SHIPMENTS ANALYSIS

The residential boiler shipments model considers t hree product placement channels (hereafter referred to as “channels”) as follows:

1. New housing : a certain fraction of new buildings acquire boiler s in each future year. This fraction is defined as the new construction saturation, which varies by year, by region

, and by product class

.

2. Existing owners (replacements) : these are defined as existing buildings with boiler s installed. This category receives new shipments when existing equipment is replaced.

3. N ew owners : these are defined as existing buildings that acquire boiler s for the first time during the analysis period

. The new owners primarily consist of households that during a major remodel add hydronic heating us ing a gas

- fired hot water boiler

. Additionally

, some households could decid e to switch from a gas furnace to a boiler with hydronic air
handler. For this analysis, new owners also include households switching between different boiler product classes.9-2 9.2.1 Fund amental Model Equations

The fundamental dependent variable in the shipments model is the equipment stock

, which is represented as a function of analysis year ( indexed by j )

, and equipment vintage or age (the equipment age is noted as a
, and is equal to the analysis year minus the vintage). The stock function is adjusted in each year of the analysis period by new shipments coming in and broken or demolished equipment being taken out.

For existing stock:



## ( ) ( ) ( ) ( ) 1

, 1

, 1

, 1

, − − +

− − − = a j Ship a j Rem a j Stock a j Stock

p p p p Eq. 9

. 1

and for new shipments :


## ( ) ( )

. 1 1

, − = = j Ship a j Stock

p p Eq. 9

. 2

Where:

Stock p (j, a ) = number of units of product class p and age a in analysis year j

, Rem p (j, a ) = number of units of product class p and age a removed in analysis year j

, and Ship p (j) = number of units of product class p shipped in year j

.

Removals due to equipment failure contain a survival function f p (a) that is used to represent the probability that a unit of age a will survive in a given year; equivalently, the probability that this unit will fail is 1

- f p (a)

.

Total removals in the base case are then:



## ( ) ( ) 

# [ ] ( ) a j Stock a f a j Rem

p p p , 1

, ×

− =

. Eq. 9

. 3

In the standards case, there is also a second term that represents the extended repair stock that has been in use for six years following the repair date (see section 9.4.2 for discussion of extended repair).

S hipments are directed to one of the three channels:



## ( ) ( ) ( ) ( ) j NO j NC j Rpl j Ship

p p p p + + = Eq. 9

. 4

W here:9-3 Rpl p (j) = number of u nits of product p replaced in year j

, which depends on removed units and units in demolished buildings

, N

C

p (j) = n umber of units installed in new construction of product p in year j , and

N

O

p (j) = number of units shipped to “ new owners ” of product p in year j

.

9.2.1.1 Replacement Shipments

The shipments model assumes that units that are taken from demolished buildings, Dem(j ), are included in the mix of broken units Rem p (j). As the demolished units do not need to be replaced, they are deducted from Rem p (j) when calculating the required replacements, as represented by the following expression :


## ( ) ( ) ( ) j Dem j Rem j Rpl

p p − = Eq. 9

. 5

When a boiler fails , it is removed from the stock or is repaired for extended use . The following retirement function r p (a) is used to represent the probability that a unit will fail at age

a

. 

## ( ) ( ) ( ) a j Stock a r j Rem

p a a p , × = 

## ∑

Eq. 9

. 6

Retirement functions and product lifetimes are discussed in more detail in chapter 8.

In each year, equipment is removed from demolished buildings. As represented by the following expression, the shipments model assumes th at the saturation of the equipment in the demolished buildings is the same as that of the overall building population.



## ( ) ( ) ( ) 1

, − × = j p sat j D j Dem
Eq. 9

. 7

T he number of demolished buildings is calculated by:

Eq. 9

. 8 

## ( ) ( ) ( ) ( ) j Stock H j Starts H j Stock H j D

_ _ 1

_ − +

− = W here:

H_Stock (j) = number of housing units in analysis year j

, H_Starts(j) = number of new housing units in year j

, D(j) = number of demolished buildings

, Dem (j) = number of equ ipment units demolished in analysis year j

, and sat(p,j) = saturation of equipment of product class p for all buildings in year j

.9-4 9.2.2 Shipments to New Construction

DOE multiplied new construction market saturations by projections of new housing units

to estimate shipments to the new construction channel. On a product class basis, the determination of shipments to new construction is represented by the following expression:



## ( ) ( ) ( ) ( ) ( ) j com Sat NC j com Starts NC j res Sat NC j res Starts NC j NC

p p p

_ _ _ _ _ _ _ _ × + × = Eq. 9

. 9

W her e:

NC_Starts_res (j) = number of new residential housing starts in year j

, NC

_

Sat_res p (j) = new residential housing saturation for product class p and year j

, NC_Starts_com (j) = number of new commercial building starts in year j

, and

NC

_

Sat_com p (j) = new commercial housing saturation for product class p and year j

.

9.2.3 Shipments to New Owners

The third market segment consists of new owners of products in a given product class, and also includes an adjustment for switching to a different product class. Be cause there are no data on the extent of these phenomena, DOE estimated historical shipments to this market
segment as a residual, using the following equation:



## ( ) ( ) ( ) ( ) ) ( j NU j RU j Shipment j NO +

− = Eq. 9

. 10

W here:

j = year where historical shipment data is available

NO (j) = new owners (if positive) or adjustment for switching (if negative) for year j

, Shipment (j) = historical shipment in year j

, RU (j) = estimated replacement units in year j

, and

NU (j) = new units for n ew homes in year j

.

9.3 DATA INPUTS AND SUPP ORTING CALCULAT ION S

9.3.1 Historical Shipments and Calculation of Replacement Shipments

9.3.1.1 Historical Shipments

DOE used historical shipments data (i.e., domestic shipments and imports) to populate its shipments model for residential boiler s

. The historical shipments data for all gas and oil boilers
were collected from Appliance Magazine (AM)

, Gas Appliance Manufact urers Association9-5 (GAMA) a and the Energy Star program

. 2

, 3

, 4 DOE adjusted the historical shipment data reported by AM, GAMA, and the Energy Star and disaggregated residential gas and oil boilers into hot
water boilers and steam boilers by using AHRI’s recent shipment information provided to DOE.

DOE also estimated that the fraction of electric hot water boiler s is 3.4% and the fraction
of electric st eam boilers is the same with the fraction of gas

- fired and oil

- fired steam boiler based
on the boiler units information derived from Energy Information Administration (EIA) ’s 2009 Residential Energy Consumption Survey (RECS 2009) 8 and EIA ’s 2003 Commercial Buildings Energy Consumption Survey (CBECS 2003)

. 9 Further details about historical shipments are
described in a ppendix 9

- B. Figure 9.3.1 summarizes the historical shipments data that DOE
assembled.

Figure 9.3.1 Historical Shipments of Residential Boilers

A fraction of residential boilers are shipped to commercial buildings ; therefore

, DOE considered the future shipments of residential boilers to commercial buildings in this analysis.
DOE used RECS 2009 and CBECS 2003 to determine the number of residential boilers in residential and commercial applications. DOE estimated that residenti al boilers shipped to commercial buildings accounted for 7 % of the total historical shipments of residential boilers

. Details of the methodology to determine the fraction of residential boilers shipped to commercial buildings are described in a ppendix 9

- B.

a Total shipments reported by AM and GAMA included commercial boilers with input between 300 kBtu/h and 450 kBtu/h. Note that DOE adjust ed for shipments from manufacturers that were not part of GAMA or currently part of

AHRI. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010Historical shipments (million) Steam Electric Boiler Hot Water Electric Boiler Oil-fired Steam Boiler Oil-fired Hot Water Boiler Gas-fired Steam Boiler Gas-fired Hot Water Boiler9-6 9.3.2 Shipments to New Construction

DOE determined new residential housing and commercial building construction starts by using recorded data through 2012 10, 11 and projections from EIA’s Annual Energy Outlook 20 13 ( AEO 20 13 ). 12

DOE developed new housing boiler type market saturations from Characteristics of New Housing data from the U.S. Census Bureau

, 13 RECS 2009

, and CBECS 2003

. DOE used
historical new housing saturations for each boiler type to project future saturations for each class

. DOE used the U.S. Census data to estimate nation al saturations for gas boilers, oil boilers and
electric boilers in new single

- family house s in the 1999

- 2013 time period. DOE estimated future national saturations in single family house s using the average saturation in the 2010

− 2013 by

C ensus division . I t derived national saturation values over time using weights based on the AEO

20 13 housing forecast o f new single family (SF) start s by Census division. T o estimate future
saturations in multifamily (MF) and manufactured home (MH) for gas boilers, oil boilers and electric boilers, DOE used the average saturations in homes built in 1990

- 2009 ( from RECS
2009 data ) that use each type of heating equipment

.

Table 9.2.1 shows the projected market saturation s of residential boilers by housi ng types in year 2020

. DOE assumed that steam boilers are primarily a replacement product, so the new
construction saturations for steam boilers w ere set equal to 0 percent.

Table 9.3.1 Saturations of Residential Boilers in New Construction in 2020

Product Class Single Family Multi

Family Manufacture d

Home Commercial Buildings

Gas

- fired Hot Water Boiler 1.09% 1.15% 0.00% 0.72%

Gas

- fired Steam Boiler 0.00% 0.26% 0.00% 0.17%

Oil

- fired Hot Water Boiler 0.19% 0.11% 0.00% 0.28%

Oil

- fired Steam Boiler 0.00% 0.02% 0.00% 0.05%

Hot Water Electric Boiler 0.19% 0.08% 0.00% 0.08%

Steam Electric Boiler 0.00% 0.10% 0.00% 0.02%

9.3.3 Shipments to New Owners

The new owners primarily consist of households that during a major remodel add hydronic heating using a gas

- fired hot water boiler and households that choose to install a boiler
for a hydronic air handler to replace a gas furnace. For this analysis, new o wners also include households switching between different boiler product classes.

To estimate future shipments to households that choose to install a boiler for a hydronic air handler to replace a gas furnace

, DOE used information from the 2008 American Home9-7 Comfort Survey, which indicated that about 9% of new central boilers went to new owners

. 14
Because the potential number of new owners is limited, DOE assumed that in the future this
fraction would decrease to 3%.

9.3.3.1 Product Switching

For replacement units, DOE estimated product switching between different boiler product classes (steam to hot water and oil to gas ) based on stakeholder input on market trends

. DOE estimated that 1 % of steam oil boiler replacement units were re placed by steam gas boilers,
1% of steam oil boiler replacement units were replaced by hot water oil boilers, and 1% of steam oil boiler replacement units were replaced by hot water gas boilers. Similarly, DOE estimated
that 1% of steam gas boiler replacement units were replaced by hot water gas boilers and 1% of
hot water oil boiler replacement units were replaced by hot water gas boilers. Figure 9.2

. 2
illustrates DOE’s product switching estimates between the residential boiler product classes.

Figure 9.3.2 Product Switching Among Residential Boiler Product Classes

The results from the shipments model for the switched units during the 2020

- 2049 period
are summarized in Table 9.2

. 2 (negative value represents number of boiler units replaced by other product classes and positive value indicates number of boiler units used as replace ments in other product classes):

Table 9.3.2 Switch of Replacement Units During the 2020

- 2049 Period

Product classes Net switch
unit s (thousand)

Gas

- F ired hot water boiler 42.611

Gas

- F ired steam boiler

- 4.118

Oil

- F ired hot water boiler

- 27.154

Oil

- F ired steam boiler

- 11.340 Gas

- Fired Hot
Water Boilers

Oil

- Fired Hot
Water Boilers Gas

- Fired
Steam Boilers

Oil

- Fired
Steam Boilers 1% of repl. units

1% of repl. units 1% of repl. units 1% of repl. units 1% of repl. units9-8 9.4 IMPACT OF STANDARDS ON SHIPMENTS

For replacements

, consumer purchase decisions are influenced by the purchase price and operating cost of equipment, and therefore may be different in the base case and under standards

cases at different efficiency levels ( EL s )

. b These decisions are modeled by estimating the purchase price elasticity for boiler s . The purchase price elasticity is defined as the change in the percentage of consumers acquiring a boiler divided by a change in the relative price (defined below) for tha t equipment. This elasticity , along with information obtained from the life

- cycle cost (LCC) and payback period (PBP) analysis on the change in purchase price and operating

costs at different E L s , are used in the shipments model to estimate the change in s hip ments under
standards at different ELs

.

9.4.1 Purchase Price Elasticity

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 the se variables.

Existing studies of appliance markets suggest that the demand for appliances is price

inelastic. Other information in the literature suggests that appliances are a normal good, s o that rising incomes incre ase the demand for appliances, and that consumer behavior reflects relatively high implicit discount rates c when comparing appliance prices and appliance operating costs.

DOE used the available data for the period 1980

- 2002 on large appliance purchase s t o evaluate broad market trends and conduct simple regression analy se s. These data indicate that there has been a rise in appliance shipments and a decline in appliance purchase price and operating cost s over the time period. Household income has also risen during this time

. Because purchase decisions are sensitive to inc ome, as well as to potential savings in the operating cost of the appliance, DOE combined the available economic information into one variable, termed the relative price

. This variable was used in a regression analysis to parameterize historical market

tre nds

. The relative price is defined with the following expression:

Income PVOC PP

Income TP RP +

= =

Eq. 9

. 11

b Because the percentage change in the cost of boilers due to amended residential boiler standards is re latively small, DOE assumed that the new construction market is unaffected by changes in either the total installed cost or operating costs of the equipment. That is, home builders are not likely to choose to not install a boiler if the installed

cost rise s by a small amount.

c A high implicit discount rate with regard to operating costs suggests that consumers put a low economic value on the operating cost savings realized from more

- efficient appliances. In other words, consumers are much more

concerned wi th higher purchase prices.9-9 W here:

RP = r elative price,

TP = t otal price,

Income = h ousehold income,

PP = a ppliance purchase price, and

PVOC = p resent value of operating cost.

In the above equation, DOE used real prices, as opposed to nominal, and an implicit discount rate of 37 percent to estimate the present value of operating costs. The rate of 37 percent
is based on a survey of several studies of different appliances suggests that the consumer implicit discount rate has a broad range and averages about 37 percent

. 15

DOE’s regression analysis suggests that the r elative price elasticity of demand

, averaged over the three appliances, is

- 0.34

. Th is implies that a relative price increase of 10 percent results
in a 3 .4 percent decrease in shipments

. Note that the relative price elasticity incorporates the impacts fro m purchase price, operating cost, and household income, so the impact from any single effect can be mitigated by changes in the other two effects

.

The relative price elasticity of

- 0.34 is consistent with estimates in the literature

. Nevertheless, DOE s tress es that the measure is based on a small data set, using simple statistical analysis. More important ly , 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 projection s of shipments and national impacts attributabl e to standards are calculated for a lengthy time period, DOE needed to consider how the relative price elasticity is
affected after a new standard takes effect. DOE considered the relative price elasticity, described above, to be a short

- term 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 pertain ing to automobiles. 16 This study shows that the automobile price elasticity of demand changes in the years fo llowing a purchase price change, becom ing smaller (more inelastic) until
it reaches a terminal value around the tenth year after the price change. Table 9.3.1 shows the relative change in the price elasticity of demand for automobiles over time. DOE developed a time series of relative price elasticities based on the relative change in the automobile price elasticity of demand. For years not shown in Table 9.3.1 , DOE performed a linear interpolation
to obtain the relative price elasticity.9-10 Table 9.4.1 Change in Relative Price Elasticity F ollowing a Purchase Price Change Years Following Price Change

1 2 3 5 10 20 Relative Change in Elasticity to 1 st 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

9.4.2 Impact from Increase in Relative Price

Using the r elative price elasticity, DOE was able to estimate the impact of the increase in relative price from a particular standard level . The impact , as shown in the equation below, is expressed as a percentage drop in market share for each year, dMS p j

. 

## ( ) ( ) ( ) j e j base

_ RP j std

_ RP dMS RP

p p p j ×    

           

− = 1

Eq. 9

. 12

Where:

d

MS p j = percentage market share drop for class p
, year j

, RP_std p (j) = relative price in the standards case for product class p
, year j

, RP p (j ) = relative price in the base case for product class p
, year j

, and

e

RP (j) = relative price elasticity in year j

.

To model the impact of the increase in relative price from a particular standard level on boiler shipments

, DOE assumed that the affected consumers would repair their equipment rather
than repl ace it, extending the life of the product by 6 years. When the extended repaired units fail
after 6 more years, they will be replaced with new ones.

The model calculates the relative percentage market drop, dMS p j , due to the equipment price increase from a particular standard level . The extended repair is only applicable to failed equipment that was purchased before 2020

.

The number of failed boiler s that will be repaired instead of being replaced is calculated
as follows:



## ( ) 

## ∑ <

− × =

a p
i i a j for dMS a j m XR 2020 ) (

, Re


## ( ) ( ) 

## ∑

− +

− =

−

a j i j Dem XR XR ) a
, j ( m Re j Rpl 6

Eq. 9

. 13
Where :9-11 d

MS p j = percentage market share drop for class p
, year j

,

a = age of equipment

, j = year , Rem(j,a) = retiring units in year j of age a , XR j = extended repair units , year j

, R

p l(j) = replacement units in year j

, and Dem (j) = number of units gone with demolished buildings in analysis year j

.

9.5 R ESULTS

Figure 9.5.1 shows the his toric and projected shipments of residential boiler s by p roduct

c lass

. The projected shipments of residential boilers show a slightly downtrend due to the flat housing start projection by AEO 2013 and decreasing saturation of residential boilers in single family homes

. The shipments of gas

- fired steam boiler s , oil

- fired hot water boiler s, and oil

- fired
steam boiler s slightly decrease over time due to the product switching from steam to hot water and from oil to gas. T he shipment of gas

- fired hot water boiler s slightly increases due to the product switching and the associated shipments to new owners

.

Figure 9.5.1 Historic and Projected Base Case Shipments Residential Boilers by Product Class

Figure 9.5.2 shows total projected shipments of gas

- fired hot water boilers in the base
case and under each standards case. Because the elasticity is modeled as a delayed replacement of boiler products, t he projection for the ELs shows a decline in the early years, but an increase in later years once the delayed replacements are finally made. As explained above, the elasticity 

### 0.0 0.1 0.2 0.3 0.4 0.5 0.6

1960 1970 1980 1990 2000 2010 2020 2030 2040Shipments (million) 

### Oil-fired Steam Boiler Oil-fired Hot Water Boiler

Gas-fired Steam Boiler Gas-fired Hot Water Boiler9-12 parameter decreases over time, so the impact of the standards on shipments dimin ishes. See appendix 9

- B for total shipment project ion s for other boiler product classes.

Figure 9.5.2 Total Projected Shipments of Residential Gas

- fired
Hot Water Boilers in the Base Case and Each
Standards Case 0.170 0.175 0.180 0.185 0.190 0.195 0.200 0.205 0.210

2015 2020 2025 2030 2035 2040 2045 2050 2055Shipments (million)

Base EL 1 EL 2 EL 3

EL 4 EL 5 EL 69-13 REFERENCES

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< http://www.decisionanalyst.com/Syndicated/Ho meComfort.dai >

2. Gas Appliance Manufacturers Association (GAMA), Updated Shipment Data for Residential Furnaces and Boilers, personal communication. April 25, 2005.

3. U.S. Appliance Industry Statistical Review

. Appliance Magazine , Multiple year.

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< http://www.energystar.gov/index.cfm?c=partners.unit_shipment_data_archives >

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Northw est Laboratory. Richland, WA.

6. Gas Appliance Manufacturers Association, "GAMA Shipment Data," DOE Docket No.

EE

- RM/STD

- 01

- 350 . 2002.

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< http://www.climateactionreserve.org/wp

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- protocol

development_boiler

- efficiency.pdf >

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< http://www.eia.gov/consumption/residential/data/2009/ >

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< http://www.eia.doe.gov/emeu/cbecs/ >

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- Bureau of the Census, New Privately Owned Housing
Units Started: Annual Data 1959

- 2012, 2013. (Last accessed March 15, 2013.)

< http://www.census.gov/construction/mhs/mhsindex.html >

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- Bureau of the Census, Placements of New Manufactured Homes by Region and Size of Home: 1980

- 2011, 201 3. (Last accessed March 15, 2013.)

< http://www.census.gov/construction/mhs/pdf/placnsa_all.pdf >

12. Energy Information Administration, Annual Energy Outlook 2013 with Projections t o 2040 , 2013. Washington, DC. < http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf >9-14 13. U. S. Department of Commerce

- Bureau of the Census, Characteristics of New Housing,

2013. U.S. Censu s. (Posted June 2, 2010) (Last accessed March 15, 2013.)

< http://www.census.gov/construction/chars/ >

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- Related Decisions

. Energy: The
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- 239