HomeMy WebLinkAbout851240.tiff state population are lower than the Colorado Division of Local Govern-
ment and OBERS forecasts. This demonstrates that the differences in the
Denver metropolitan area forecasts are in large part explained by dif-
fering Colorado projections.
The Series 1 population forecast for 2010 is 2.3 percent higher
than the Colorado Division of Local Government projections. The Series
3 projection is 3.4 percent greater than the state's forecast. The
Series 2030 projection is 11.3 percent greater than the OBERS high sce-
nario forecast for that year.
The Series 1 , 2 and 3 forecasts are adopted for two study areas and
are in close agreement with the Colorado Division of Local Government's
numbers. However, the local projections are higher than the OBERS fore-
casts. The next section discusses, in detail, the OBERS methods and
assumptions.
Discussion of OEMS. Projections from the U.S. Bureau of Economic
Analysis were developed from historical data from 1969-1978 and do not
reflect the Census Bureau's 1980 population count. OBERS represents a
national analysis that essentially disaggregates national projections to
local levels. OBERS projections utilized an interregional version of
the export base methodology. National projections are first prepared,
then projections for every state are generated and Standard Metropolitan
Statistical Area (SMSA) projections are developed from state projec-
tions. For Colorado, industries were disaggregated into "basic" and
"service" industries. "Basic" industry earnings were projected based on
the Colorado's share of corresponding industry earnings in the nation.
"Service" industry earnings were then projected based on assumptions
about the basic/service earnings ratio. Employment projections are sub-
sequently developed from industry earnings based on historical earnings
Appendix 2
222
851240
per employee. Population projections are developed from assumptions
about the labor force participation rate and the percent of the popula-
tion in the labor pool.
The three OBERS scenarios of future Denver population levels differ
in assumptions about the Denver Metropolitan Area's share of Colorado
employment. Key assumptions of the OBERS forecast include:
. The unemployment rate will be 4.8 percent in 1990 and 4.4 per-
cent in the year 2000.
. Productivity growth to 1985 will be 2.0 percent, after 1995 it
will approximate 2.4 percent.
. Regional migration differences will narrow (migration satu-
ration).
The importance of the regional migration assumption is the prime point
of differentiation between OBERS and DRCOG or State of Colorado fore-
casts. Basically, OBERS assumed a very rapid decrease in regional
attractiveness for prospective employers and residents after 1990.
Relative attractiveness of Colorado and Denver is not differentiated
from other areas of the nation after ten to 20 years. The State of
Colorado and DRCOG projections postulate that some regional attractive-
ness will persist throughout the projection period.
The OBERS population forecasts for Denver suffer from a number of
possible flaws:
. The lack of 1980 Census data for callibration of the OBERS model
is a major short-coming. Dozens of assumptions such as labor force par-
Appendix 2
223
ticipation rates, age distributions, and employment distributions begin
at an erroneous point. Extrapolation of trends therefore compounds the
initial error.
. The export-base methodology does not reflect local economic pat-
terns in Denver, now or in the future. Assumptions about which economic
sectors are basic and which are local service are not representative of
the Denver area or Colorado.
. A constant relationship between basic and local service sectors
is unrealistic. In a dynamic economic environment, these economic rela-
tionships will shift continuously, and current relationships are likely
to be quite different in distant time periods.
. The three scenarios of Denver area population growth are based
upon a strict mathematical extrapolation of Denver and Colorado employ-
ment shares. This masks the more complex dynamics of the local economy.
. The rapid erosion of Colorado's and Denver's competitive advan-
tage and attractiveness is questionable. These forces are not entirely
based on the size of a population center. Colorado and Denver have been
attractive relocation alternatives for such reasons as climate and geo-
graphic location.
In sum, the OBERS forecasts were deemed inferior to the DRC00 fore-
casts.
CONCLUSIONS ABOUT THE ECONOMIC
AND DEMOGRAPHIC FORECASTS
The DRCOG Policy forecasts of population, households and employment
through 2000 are acceptable forecasts for the EIS. Beyond 2000, Series
Appendix 2
224
1 is the most reasonable and defensible set of economic and demographic
forecasts. These findings are based on the following conclusions:
. DRCOG's forecasting methodologies and models address specific
economic and demographic influences on the Denver region. These projec-
tions. are employment-driven and yet they are adjusted for reasonableness
through the migration cap.
. DRCOG projections of employment, population and household growth
rates generally follow past trends since 1950 and recent estimates
through 1983.
. The underlying employment forecasts and assumptions about the
future appear sound. The top-down forecasting approach from Chase Eco-
nometrics (national) to the Governor's Blue Ribbon Panel (state) to the
Denver Region is logical. Based upon a methodological review and com-
parison with other forecasts, these national and state projections are
acceptable as a basis for Denver metropolitan area forecasts through the
year 2000.
. The assumptions of the DRCOG forecasting model, including mor-
tality rates, labor force participation and household size are sensible,
considering Denver's unusual age and demographic composition and the
continued influence of in-migrants.
. The alternative series of projections tend to corroborate the
DRCOG projections through 2000. Alternative projections for Denver are
within 11 percent of each other. The Division of Local Government and
DRCOG are in very close agreement. OBERS forecasts diverge considerably
in future years. However, the OBERS forecasts exhibit several possible
flaws.
Appendix 2
225
. While certain economic or demographic assumptions such as fer-
tility rates differ from other projections, they have only a minor
effect on the overall forecasts. The pace of oil shale development is
an example of an assumption with little affect on the Denver metropoli-
tan area.
. Series 1 beyond 2000 is the most reasonable since the employment
and population growth rates are most consistent with the dominant views
concerning the national growth perspective. Convergence of local growth
with national trends is achieved gradually over the forecasting time
frame beyond 2000. Series 3 clearly fails this test while Series 2
exhibits the same drawbacks to a lesser degree.
. After this exhaustive study of economic and demographic fore-
casts, there remains a number of key uncertainties which cannot be
resolved definitively. The quality of life in the Denver metropolitan
area might exhibit an evolution in the future unlike the historical
experience. A related uncertainty lies with the relative competitive
economic advantage of the Denver metropolitan area vis a vis other Front
Range communities and other urban regions of the U.S. The EIS assumes
that quality of life perceptions about this area will not change so
drastically as to cause a significant deviation in the forecasts pre-
sented.
The inherent uncertainties of long term economic and demographic
forecasting should be well understood. The farther out in the future
forecasts are postulated, the greater the chance for shifts in assump-
tions and fundamental demographic relationships which form the basis of
the final forecasts.
Appendix 2
226
^ CHAPTER 7
SOCIOECONOMIC VARIABLE FORECASTS
N`
x
CHAPTER 7
SOCIOECONOMIC VARIABLE FORECASTS
INTRODUCTION
This chapter of the technical appendix presents the forecasts for
variables necessary to develop the water demand forecasts. The deriva-
tion and evaluation of the projections for each variable is also pro-
vided. The economic and demographic variables discussed are:
. Median household income
. SF lot size
. SF and MF households
. Service and nonservice sector employment
. Number of days of measurable precipitation
Appendix 2
227
. Marginal price of water
. Unmetered SF dwellings
. Watering restrictions
In addition, the final section of this chapter examines the internal
consistency of the individual economic and demographic variable assump-
tions.
FORECASTS OF MEDIAN HOUSEHOLD INCOME
Personal income levels are a major determinant of water demand
patterns in the EIS demand area. Median household income is a variable
in the EIS water demand model which, with other socioeconomic variables,
must be projected to predict total water demand. Forecasts of median
household income for the EIS demand area and the methodology for render-
ing these forecasts applicable to individual water suppliers is
described.
HISTORIC TRENDS
NATIONAL TRENDS
Historic trends in median household incomes throughout the United
States are instructive for developing EIS demand area forecasts. Real
or constant dollar household income levels increased approximately 3.1
percent annually between 1950 and 1970. The 1970s had fluctuating
median household incomes, as depicted in table 90. Although the average
annual growth rate between 1967 and 1982 was essentially stable, annual
Appendix 2
228
Table 90
U.S. Median Household Income in Constant Dollars,
1967 Through 1982
Median Household
Income in Annual
Constant Percentage
Year (1979) Dollars Change
1967 $19,458
1970 20,457 1.5%
1971 20,274 -0.9
1972 21,081 4.0
1973 21,514 2.1
1974 20,650 -4.0
1975 19,940 -3.4
1976 20,268 1.6
1977 20,369 0.5
1978 21,000 3.1
1979 20,716 -1.4
1980 19,547 -5.6
1981 19,074 -2.4
1982 19,029 -0.2
Average annual growth
rate 1967-1982 -0.1%
Source: U.S. Department of Commerce, Statistical Abstract of the
United States 1981, 1981.
Appendix 2
229
changes in median household income fluctuated from -5.6 percent in 1980
to 4.0 percent in 1972. Much of the lack of growth can be attributed to
concurrent changes in the household structure. From the long term per-
spective, household incomes in the United States increased 1.9 percent
annually between 1950-1982.
Incomes have increased from 1982 to 1984. Real per capita income
growth from second quarter 1982 to second quarter 1984 averaged 3.0 per-
cent per year. Five percent growth was experienced from 1983 to 1984.
Table 91 shows these trends.
LOCAL TRENDS
Trends in median household income for Colorado and the Denver
metropolitan area are similar to the national trends. Average personal
income per household in Colorado increased 2.8 percent annually from
1960 to 1970 and decreased 0.1 percent per year during the 1970s
(Colorado Blue Ribbon Panel, 1981). Median household income in the
Denver metropolitan area increased 0.4 percent per year during the 1970s
(DRCOG, 1982).
INCOME PROJECTIONS
OTHER FORECASTS
Income forecasts for the U.S., Colorado, and the Denver region,
shown in table 92, were reviewed to develop projections of real median
household income for the EIS demand area.
Appendix 2
230
Table 91
Real Per Capita Personal Income
For The United States, 1982-1984'
Per Capita
Personal Annualized
Income in Growth
Year Quarter 1979 Dollars!/ Rate
1982 I $5,376 --
II 5,403 2.0%
III ' 5,374 (2.1)
IV 5,365 (0.7)
1983 I 5,400 2.6
II 5,460 4.5
III 5,487 2.0
IV 5,579 6.9
1984 I 5,694 8.5
II 5,731 2.6
1/ Adjusted by implicit
p price deflator for personal consumption expen-
ditures.
Source: U.S. Bureau of Economic Analysis, Survey of Current Business,
selected issues.
Appendix 2
231
Table 92
Alternative Real Household Income
Growth Forecasts For Selected Areas, 1980-2035
(percent)
Public
Blue Service
United Ribbon Company of
Period Bank Panel Colorado OBERS
(Colorado) (Colorado) (Service Area) (Denver
Metropolitan
Area)
1980-1990 1/ 0.2 0.4 0.6 1.9
1990-2000 -- 1.0 1.2 1.9
2000-2035 Z/ -- -- 1.6 1.8
1/ 1978-1990 for OBERS forecasts, 1982-1990 for United Bank and
Public Service Company forecasts. The later includes 29 of
63 Colorado Counties.
?/ 2000-2030 for OBERS forecasts, 2000-2008 for Public Service
Company forecasts.
Source: State of Colorado, the Governor's Blue Ribbon Panel, Colorado
Investing in the Future 1981-2001 , Volume Two: Forecasts,
July 1981. United Bank of Colorado, The Economy Over the
Next 15 Years, November 1983. U.S. Department of Commerce,
Bureau of Economic Analysis, BEA Regional Projections, Volume
3 Standard Metropolitan Statistical Area, July 1981. Public
Service Company of Colorado Long Term Personal Income and
Population Forecasts for the 29 County Service Area, March
1983 (based upon Data Resources, Inc. national forecasts).
Professional judgment has been used to adjust and interpret the
various data sources of income projections for applicability to the EIS.
United Bank of Colorado projects 1982 to 1990 growth to be 0.2 percent
per year. The adjusted Blue Ribbon Panel projections show 0.4 percent
annual growth in personal income per household in the 1980's and 1 .0
Appendix 2
232
percent annual growth from 1990 to 2000. These forecasts are based upon
Chase Econometrics national economic forecasts. Public Service Company
of Colorado (PSCo) income forecasts for the PSCo service area exhibit
the following growth rates: from 1982 to 1990, 1.2 percent from 1990 to
2000, and 1.6 percent from 2000 to 2008. The PSCo forecasts are
developed from Data Resources, Inc. national projections of average
earnings per employee. O.S. Bureau of Economic Analysis OBERS
forecasts exhibit the most rapid increases in household incomes. Almost
two percent annual income growth is forecast. These adjusted forecasts
give a reasonably consistent range of alternative projections.
RELATED ECONOMIC AND DEMOGRAPHIC TRENDS
Employment growth, household size and aging of the population are
trends which affect the incomes of EIS demand area households. A key
index reflecting these changes is employment per household. Table 93
examines this ratio for 1980 to 2035.
Table 93
Employment per Household for the
Denver Metropolitan Area, 1980-2035
Employment/Households
Year Series 1 Series 2 Series 3
1980 1.43 1.43 1.43
1990 1.44 1.44 1.44
2000 1.42 1.42 1.42
2010 1.31 .131 .131
2035 1.17 1.18 1.20
Appendix 2
233
Employment growth is strong from 1980 to 2000, declining from 2000 to
2035. Household size decreases substantially between 1980 and 2000 and
is relatively stable from 2000 to 2035. As shown in table 45, the
effects of these trends are largely offset from 1980 to 2000. Employ-
ment per household declines from 1.43 to 1.42 during this period. Aging
of the population into nonworking years is expected to reduce this ratio
to 1.31 employees per household in 2010 and as low as 1.17 employees per
household for Series 1 in 2035.
Household income tends to increase with age of householder until 55
years of age. Median household income for the 55 to 64 age group is
lower because of decreased participation in the labor force. Since most
persons 65 years of age and over are not employed, incomes are much
lower. As the Denver metropolitan area population ages into the 55 to
64 and 65 years and over age groups, income growth can be expected to
moderate.
EIS DEMAND AREA FORECAST
The EIS median household income forecasts reflect the growth pro-
jections reviewed and the influences of long term economic and demo-
graphic changes in the Denver metropolitan area. The historical growth
evident since 1950 is unlikely to be sustained. However, the slow
growth experienced in the 1970s is expected to accelerate. Based upon
recent O.S. per capita income growth, EIS demand area real median house-
hold income is projected to increase 0.6 percent per year from 1982 to
1990.
Annual growth from 1990 to 2000 is forecast to be 1.0 percent
reflecting the increasing growth trend exhibited by the available fore-
casts. Long-term growth is projected to stabilize at 1.0 percent per
year from 2000 to 2010.
Appendix 2
234
By 2035, 25.2 percent of the population would be 65 years of age or
older under Series 1, compared with 22.9 percent for Series 3. The
forecasts for each series reflect the effect of the age distributions on
income levels. Annual household income growth for 2010 to 2035 is pro-
jected to be 0.6 percent for Series 1, 0.7 percent for Series 2 and 0.8
percent for Series 3. These projections are far below the 1.8 percent
annual growth rate projected in the OBERS forecasts but better reflect
the local economic and demographic trends. Table 94 details the 1982 to
2035 projections for each series.
Table 94
Median Household Income Projections
for the EIS Demand Area
Median Household Income Annual Growth Rate
(in 1979 dollars) (percent)
Year Series 1 Series 2 Series 3 Series 1 Series 2 Series 3
1982 20,000 20,000 20,000 -- -- _-
1990 21,000 21,000 21,000 0.6 0.6 0.6
2000 23,400 23,400 23,400 1.0 1.0 1.0
2010 25,900 25,900 25,900 1.0 1.0 1 .0
2035 30,000 30,800 31,600 0.6 0.7 0.8
Constant dollar median household incomes are expected to grow from
approximately $20,000 in 1982 to $23,400 in 2000 for the EIS demand
area. Incomes in 2035 are projected to be $30,000 for Series 1 , $30,800
for Series 2 and $31,800 for Series 3.
Median household incomes were forecast for individual water sup-
pliers the study area for application of the water demand models. The
central assumption was that the current median household incomes of each
water district would maintain a constant relationship relative to other
Appendix 2
235
water suppliers in the EIS demand area. In other words, incomes in
affluent areas will remain high while lower income areas, although
experiencing gains, will remain low relative to other suppliers.
SF LOT SIZE
The lot size variable, or acreage per SF unit for each water
supplier, was projected through 2035 for each water supplier in the EIS
demand area. Sources for the lot size projection were:
. Interviews with municipal and county planning officials and —.
assessor's offices throughout the EIS demand area. Over 40 interviews
were conducted to ascertain the viewpoints of local officials knowlege-
able about residential development patterns and builder plans.
. Review of land use plans and policies of various local govern-
ments throughout the EIS demand area. A number of incorporated juris-
dictions and counties follow land use policies which specify or limit
the density of residential development. From these, the average number
of SF units per acre can be derived.
. Interviews with land developers for areas projected to grow
rapidly, particularly in those water districts encompassing large
planned communities.
Appendix 2
236
. Information from water suppliers on projected SF densities for
their service areas. Several recently formed districts have developed
these forecasts.
. An analysis of the DWD forecast of land use activities as part
of their water forecasting system. These data, which extend to 2000,
were evaluated or verified with local officials.
. Examination of aerial photos of the EIS demand area to evaluate
the potential buildout of areas and the reasonableness of the projec-
tions.
. Final review of new SF unit lot size estimates for
reasonableness. For example, the average lot size for the incremental
growth of SF dwellings was evaluated in light of past patterns and
plans. Average lot sizes were not forecast to change significantly for
slow growing areas.
The average lot size of new SF development is forecast to be less
than lot sizes associated with recent growth in the EIS demand area.
Lot size projections must account for several, somewhat contradictory
trends including: (1) suburbanization, which tends to increase average
lot size; (2) higher densities in areas near the city center,
attributable to high land costs; and (3) urban revitalization, where
larger lots may be replaced with higher density units. As detailed in
table 95, average lot size of new units is projected to decline from .24
acres per unit (about quarter acre lots on average) for units built
between 1980 and 1990 to .20 (8,700 ft2 lots) for development between
2010 and 2035.
•
Appendix 2
237
Table 95
Average Lot Size of New
SF Units Within
the EIS Demand Area
Average Lot Size for
Future SF Dwellings
Square Acres
Time Period Feet per Unit
1980-1990 10,500 .24
1990-2000 10,000 .23
2000-2010 9,600 .22
2010-2035 8,700 .20
The current average SF lot size in the demand area is estimated to
be .223 acres per unit (9,700 ft2), although new units built during the
1960s and 1970s were believed to be substantially larger, up to 12,000
square feet or more. Although new SF lot sizes are believed to be in a
declining period, new lots in the 1980 to 1990 period are expected to be
slightly higher than current averages. This will cause a slight
increase in overall average lot size until 1990. This .223 average is
projected to be relatively stable over the next 50 years, increasing
slightly between 1980 and 1990 and decreasing after 1990. Differences
among projection series are very slight so no differentiations are made
among series. These forecasts are detailed in Table 96.
Table 96
Average Lot Size of New and Existing
SF Units Within the
EIS Demand Area
Average Lot Size
Square Acres
Year Feet per Unit
1980 9,700 .223
1990 9,900 .227
2000 9,900 .227
2010 9,900 .227
2035 9,700 .223
Appendix 2
238
The above data sources were utilized in preparing SF lot size
projections for individual suppliers. Water suppliers which currently
have small average lot sizes will experience a slight increase in the
future as new growth on the periphery of these districts is more
characteristic of suburban type development densities. Conversely, dis-
tricts with currently large average lot sizes will decline as new
developments result in higher densities consistent with maturing,
urbanized areas.
Average SF lot size is not necessarily indicative of average
overall housing density, however, if the average SF lot size remains
stable while the proportion of MY units increases over time, overall
housing density will increase.
SF AND HF HOUSEHOLDS
The number of SF and HF households must be projected for the water
demand models. The data sources for projecting the proportion of
dwelling units which are SF for each water supplier in the EIS demand
area are similar to those for lot size, namely interviews with planning
officials, land developers, reviews of land use plans and development
projections and information from water districts, including DWD land use
forecasts.
Appendix 2
239
EIS DEMAND AREA FORECASTS
New housing construction is projected to become predominantly MF by
2035. This is likely to occur because of increasing land values,
continued difficulties in qualifying for SF home mortgages, a continued
high proportion of young, single adults as in-migrants and the
increasing acceptance of common-wall dwelling units. As housing costs
rise and interest rates remain high, prospective homeowners will
experience increasing difficulties qualifying for a mortage. A chief
remedy for this dilemma will likely be lower cost MF dwellings.
MF and mobile home units, including townhouse development, are
forecast to comprise 43 percent of new units built between 1980 and
1990, increasing gradually to 55 percent during the 2010 and 2035
period. These projections are examined in table 97.
Table 97
Average Unit Mix of New Housing
Units Constructed, 1980-2035,
for the EIS Demand Area
(percent)
SF MF and Mobile
Period Units Home Units
1980-1990 57% 43%
1990-2000 51 49
2000-2010 51 49
2010-2035 45 55
SF housing units currently comprise almost two-thirds of the total
housing stock in the EIS demand area. As illustrated in table 98, this
proportion is expected to decrease to 56 percent by 2035.
Appendix 2
240
Table 98
Distribution Mix of Housing Units
by Unit Type for EIS Demand Area
SF MF and Mobile Home
Year Series 1 Series 2 Series 3 Series 1 Series 2 Series 3
1980 65.5 65.5 65.5 34.5 34.5 34.5
1990 63.2 63.2 63.2 36.8 36.8 36.8
2000 60.6 60.6 60.6 39.4 39.4 39.4
2010 59.2 59.1 59.1 40.8 40.9 40.9
2035 57.1 56.5 56.0 42.9 43.5 44.0
Projections of EIS demand area SF and MF households are presented in
table 51.
Table 99
Households Residing in SF
and MF Dwellings for the EIS Demand Area
SF MF and Mobile Home
Year Series 1 Series 2 Series 3 Series 1 Series 2 Series 3
1980 354,900 354,900 354,900 187,400 187,400 187,400
1990 470,500 470,500 470,500 274, 100 274, 100 274, 100
2000 573,200 573,200 573,200 372,800 372,800 372,800
2010 658,000 660,200 661,500 454,300 456,500 457,600
2035 740,600 774,000 803,800 555,200 595,700 631 ,400
COMPARISONS WITH NATIONAL FORECASTS
SF housing units accounted for 69 percent of the United States
housing stock in 1975. National projections developed for the U.S.
Bureau of the Census for new construction, based on housing occupancy
trends by age groups, are shown in table 100.
Appendix 2
241
Table 100
Average Unit Mix of New Housing Units Constructed,
1980-2000 for the U.S.
(percent)
Series A-2 Series B-2 (Moderate
(High Housing Consumption) Housing Consumption)
New MF New MF
New SF and Mobile New SF and Mobile
Period Units Home Units Units Home Units
1980-1990 57 43 50 50
1990-2000 64 36 58 42
Source: Pitkin, J. and G. Masnick, Projections of Housing Consumption
in the U.S. , 1980 to 2000, by a Cohort Method. Annual Housing
Survey Studies NO. 9 U.S. Department of Housing and Urban
Development, June 1980.
SF units are expected to account for 57 percent of new construction
from 1980 to 1990 under a high forecast and 50 percent under the
moderate forecast, similar to the EIS demand area projection. SF units
are projected to increase as a proportion of new construction for 1990
to 2000. The 1990 to 2000 Denver metropolitan area forecasts differ
from national forecasts because of the relatively young age of household
heads, the high proportion of single persons, a large in-migration
component, and small household sizes which translate into greater demand
for new MF units.
Appendix 2
242
SERVICE SECTOR AND NONSERVICE SECTOR EMPLOYERS
The projections of service sector and nonservice sector employees
for the EIS demand area were derived largely from DRCOG forecasts
through the year 2000, the last year which this regional planning agency
projected employment by sector. Non-service employee forecasts were
developed as the difference betwen total employment forecasts and ser-
vice sector employment forecasts.
For the six-county Denver metropolitan area, DRCOG projected that
service sector employment would increase from 19.3 percent of total
employment in 1980 to 20.5 percent of the total in the year 2000 (DRCOG,
1982). These proportions follow Governor's Blue Ribbon Panel forecasts
for the State of Colorado and Chase Econometrics national forecasts.
This gradual growth in proportional service sector employment was extra-
polated through 2035, when 22 percent of total Denver metropolitan area
employment is assumed to be in the service sector.
As a proportion of total employment by place of work, service
employment is currently about one percent less in the EIS demand area
than in the Denver metropolitan area. Demand area employment by sector
is developed from census tract information. EIS demand area forecasts
of service employment mirror the trend in the proportion of service to
total employment evident in the Denver metropolitan area as presented in
table 101.
Appendix 2
243
Table 101
Projections by Place of Work
for the EIS Demand Area
Percent of
Service Employment Total Employment
Year Series 1 Series 2 Series 3 Series 1 Series 2 Series 3
1980 144, 100 144,100 144,100 18.4 18.4 18.4
1990 206,300 206,300 206,300 18.8 18.8 18.8
2000 272,200 272,200 272,200 19.5 19.5 19.5
2010 304,100 306,000 307,900 20.0 20.0 20.0
2035 334,800 357,300 382,200 21.0 21.0 21.0
NUMBER OF DAYS OF MEASURABLE PRECIPITATION
The weather conditions assumed for the 1980 to 2035 water forecasts
are representative of average conditions since 1947. The number of days
of measurable precipitation from May through September has averaged 44
days during the past 47 years, which is the extent of historical record
for this summer rainfall measure at Stapleton International Airport.
Measurable precipitation is defined as .01 inch or more in a 24 hour
period.
l-.
Appendix 2
244
MARGINAL PRICE
Because of the unconstrained nature of the water demand forecasts,
the values of marginal price variable are held constant at the 1982
price for each water supplier in the EIS demand area. Water rates are
assumed to increase only due to inflation. The only exceptions to this
are those partially or entirely unmetered districts which currently have
policies in place which will increase the proportion of units that are
metered in the future.
UNMETERED SINGLE FAMILY DWELLING
Future metering status was determined based upon responses to the
DRCOG Regional Water Study Questionnaire. Water suppliers which are
expected to remain unmetered include Brook Forest, East Valley,
Hazeltine Heights, Holly Mutual, Idledale, Maple Grove, Morrison and
Silver Heights. Parker is currently metered but bills on a flat-rate
basis. By 1990, all SF households in Parker will be billed for metered
usage.
No additional metering programs are assumed for Denver or Englewood
for the unconstrained water demand forecasts. Historically, a small
number of unmetered dwellings are metered or are lost from the SF hous-
ing stock each year. The trends from 1974 through 1982 are expected to
continue into the future as shown in table 102.
Appendix 2
245
Table 102
Unmetered SF Dwellings
in the EIS Demand Area
DWD City
and County Englewood Parker
1980 87,625 8,461 170
1990 85,200 8,380 0
2000 82,700 8,300 0
2010 80,200 8,220 0
2035 73,000 8,000 0
PRESENCE OF THIRD DAY, THREE HOUR OUTDOOR WATERING RESTRICTIONS
Third day, three hour outdoor watering restrictions are assumed to
not be implemented on a permanent basis for any water distributors in
the EIS demand area. The third day, three hour restriction variable in
the pooled water demand model is set at zero throughout the forecast
period to reflect the unconstrained forecasting assumption. The third
day, three hour restrictions only occurred in 1977.
CONSISTENCY OF ECONOMIC AND
DEMOGRAPHIC FORECASTS
In addition to being reasonable on their own merit, economic and
demographic projections, must be consistent with each other. Table 103
Appendix 2
246
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Appendix 2
247
summarizes each key component of the EIS demand area forecasts. Many of
these are forthcoming from DRCOG.
DRCOG PROJECTIONS AND ASSUMPTIONS
DRCOG developed projections of fertility, mortality and labor force
participation rates largely based upon, but not coincident with, U.S.
Census Bureau and U.S. Bureau of Labor Statistics projections. DRCOG
then makes adjustments for Denver's special conditions.
Fertility Rates. The apparent contradiction between increasing
fertility rates and the other demographic assumptions stem from the
traditional demographer practice of utilizing total fertility rates as
compared with other fertility rate measures. In fact, DRCOG assumptions
about increasing total fertility rates result in a future downward trend
in children per household and children per employee as indicated in
table 104.
Table 104
Children Per Household and Children
Per Employee, 1980 and 2000
1980 2000
Persons under 10 years of age 234,500 351,700
Persons under 20 505,900 679,400
Households 608,400 1,074,300
Persons Under 10 per
household .39 .33
Persons under 20 per
household .83 .63
Employees 869,500 1,524,700
Persons under 20 per
household .58 .45
Source: Based on DRCOG data, 1983.
Appendix 2
248
r
The above data are based on DRCOG age distributions. Hence, these
trends are consistent with declining household size and increasing
employees per household.
Labor Force Participation Rates. According to DRCOG, these rates
will follow, but not converge upon the national trends through 2000.
This disparity is attributable to high levels of employment-related in-
migration and higher participation rates for older persons. Increasing
employment per household and growing proportions of age-cohorts with
high employment tendencies are consistent with the expanding labor force
participation rates. Rates for 2010 and 2035 are based upon U.S. Bureau
of Labor Statistics participation rates by age cohort.
Migration. DRCOG migration forecasts are in part established
external to the employment model, and this is an inconsistency in the
DRCOG projections. Because of this, the employment and population
models are adjusted downward to conform with the migration assumption.
Migration is fully discussed in the previous chapter.
Household Size. DRCOG projects household size based in part upon
census forecasts of household formation rates by age group. Household
formation rates account for increasing fertility rates and decreasing
mortality rates. Household size is also dependent upon migration levels
since in-migrants tend to be concentrated in high household forming age
groups (young, single adults). Each of the U.S., Colorado and Denver
forecasting series identified for comparative evaluation indicate
declining household size, increasing total fertility rates and decreas-
ing mortality rates.
Appendix 2
249
OTHER PROJECTIONS
Median household income, SF lot size, and housing mix forecasts are
consistent with DRCOG projections. Median household income is forecast
to exhibit slow growth in the 1980's, increasing through 2010, and
declining as the population ages into retirement years. The early
increases are consistent with smaller household size, expanding
employment opportunities on a per household basis and higher labor force
participation. Fertility rates also increase with rising economic
expectations.
SF lot size is forecast to decrease for new units based upon the
increasing absorption of land in proximity to the city center and
available land use plans. As a finite resource, urban area land is
likely to rise in real price faster than the moderate levels of real
personal income growth projected. Hence, homeowners will be encouraged
to accept smaller lots. Smaller household size will also be consistent
with smaller lots and more MF unit living. The average lot size for all
SF dwellings initially increases because of the location of growth.
The proportion of dwelling units which are SF is projected to
decrease. This is attributable to the same basic justification for the
trends in lot size. Again, this is consistent with the forecast age
distribution, income growth, and household size.
Appendix 2
250
CHAPTER 8
EATER DEMAND FORECASTS
FOR THE EIS DEMAND AREA
j
i
i �
CHAPTER
WATER DEMAND FORECASTS
FOR THE EIS DEMAND ►RBA
CHAPTER 8
WATER DEMAND FORECASTS
FOR THE EIS DEMAND AREA
INTRODUCTION
This chapter presents water demand forecasts for the EIS demand
area. These forecasts are based on two water demand models detailed in
Chapters 4 and 5 combined with forecasts of the demographic variables in
Chapter 6 and the socioeconomic measures in Chapter 7. Figure 13 illus-
trates the respective water demand forecasts for the EIS demand area.
These forecasts have not been adjusted for natural retrofit, which is
discussed in this chapter.
Appendix 2
251
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Appendix 2
252
Because of their importance, certain qualifications about the water
demand forecasts are reiterated. A careful understanding of precisely
what the EIS water demand forecasts represent is essential to interpre-
tation or application of the results. First of all, water demand fore-
casts are uniformly expressed as the amount of water delivered to the
end user. Distribution system losses or other losses from the point of
diversion are not accounted for. Unconstrained demand is defined as
extrapolation of conditions which currently exist. Prices for water are
assumed to remain constant in real terms. Weather conditions are
normalized so that forecasts represent average weather conditions. Con-
servation programs and practices currently in existence, such as the ET
Program, are assumed to have the same effect on future consumers as they
have on present consumers. These qualifications are fully discussed in
Chapter 1 of this Appendix.
POOLED MODEL WATER DEMAND FORECASTS
FORECASTING PROCEDURES
The pooled model forecasts utilized the following information:
. Pooled, aggregate water demand model derived in Chapter 4.
. Projection of independent variables specified in the model for
each supplier.
Appendix 2
253
r
. The 1981 and 1982 residuals, weather adjusted, for each supplier
to adjust for existing conservation activities and any other unique
individual water supplier characteristics (a residual is the difference
between actual demand and demand estimated by the model).
. Water savings for natural metering of existing unmetered dwell-
ings (not related to additional conservation programs).
The pooled model forecasts were developed for each year on a
supplier-by-supplier basis. The first step in the forecasting procedure
was to project g.h.d. for each water supplier for 1990, 2000, 2010 and
2035. G.h.d. was calculated by multiplying each coefficient for each
independent variable by the projected values of each independent
variable: household income, average SF lot size, percent SF household
size, service and nonservice employees per household, weather
(normalized), presence of watering restrictions and marginal price. The
variable coefficients as well as the constant term were determined
through the historical water demand modeling exercise discussed in
Chapter 4. Derived g.h.d. is adjusted by the 1981-1982 g.h.d.
residuals for each water supplier and for natural metering.
Once g.h.d. has been projected for each water supplier, demand is
forecast by multiplying g.h.d. by the number of days in the year and by
the number of households projected for each individual supplier. These
annual water supplier water demands are then summed to calculate total
EIS area water demand.
r Appendix 2
254
SUMMARY OF FORECASTS FROM THE POOLED WATER DEMAND MODEL
TOTAL WATER DEMAND
Water demand forecasts have been prepared for population projection
series 1, 2 and 3 applying the pooled water demand model. Under all
series, water demand supplied by public water systems is forecast to
grow about 235,000 acre-feet by the year 2000 reaching 548,000 acre-feet
in that year. Publicly supplied demand is expected to increase to
789,400 acre-feet by the year 2035 under Series 1 . As shown in table
105 and subsequently explained, the annual rate of water demand growth
is projected to decline from 1980 to 2035.
Table 105
Publicly Supplied Water Demand in the EIS Demand Area,
1980 to 2035, Based upon the Pooled Model
Year
(percent)
19801/ 1990 2000 2010 2035
Series 1
Acre-feet (000's) 308.1 420.3 547.5 654.1 789.4
Average Annual
Growth Rate -- 3.2 2.7 1.8 0.8
g.h.d. 507 504 516 525 543
g.c.d. 191 210 221 226 234
Series 2
Acre-feet (000's) 308.1 420.3 547.5 656.9 833.6
Average Annual
Growth Rate -- 3.2 2.7 1.8 1.0
g.h.d. 507 504 516 525 543
g.c.d. 191 210 221 226 234
Series
Acre-feet (000,$) 308.1 420.3 547.5 658.9 875.2
Average Annual
Growth Rate -- 3.2 2.7 1.9 1.1
g.h.d. 507 504 516 525 545
g.c.d. 191 210 221 226 235
1/ This column is not directly comparable to actual 1980
consumption, which was 316,000 acre-feet. The above figures
assume average weather conditions, conservation programs
existing in 1981 and 1982, and 1982 marginal price for water.
Appendix 2
255
Series 1, 2 and 3 forecasts diverge from 2000 to 2035 because of
the differing underlying economic and demographic projections. Water
demand forecasts for 2010 vary by less than one percent among the
series. By 2035, however, Series 3 forecasts are 86,000 acre-feet, or
11 percent higher than the Series 1 forecasts.
G.h.d. remains fairly steady between 1980 and 2000. G.h.d.
increases slightly after 2000. A number of different trends in the
Denver metropolitan area bring about changes in g.h.d.:
. Increasing income levels in the Denver metropolitan area tend to
increase future water demand. Incomes are projected to increase more
rapidly from 1990 to 2010; income growth has the greatest effect on
water demand during this period.
. Growth in the employment base and business activity will tend to
increase area water demand expressed in g.h.d., particularly for those
water suppliers projected to be centers of future economic growth.
. The projected shift of new residential construction from SF
units to MF units will tend to reduce water demand in the EIS demand
area.
. Although lot size for new SF units is expected to decrease,
overall projected average lot size of SF units is representative of the
existing development. This is attributable to the large base of
existing SF lots which are assumed to remain at their current size under
the unconstrained forecasting assumption. Trends vary, however, among
individual suppliers as discussed in Chapter 6.
r
Appendix 2
256
. Average household size declines from 1980 to 2000, stabilizing
after 201O. This trend reduces consumption per household through 2000.
. Several factors do not change over time for the unconstrained
forecast. Constant average weather conditions are forecast for each
year and no imposition of three day, three hour maximum outdoor watering
restrictions is assumed. Water conservation programs existing in 1981
and 1982 are assumed to continue into the future. Marginal price for
water is also assumed to remain constant, after adjusting for inflation.
When considered simultaneously, these influences tend to slightly
reduce demand per household from 1980 to 1990 and moderately increase
demand per household from 1990 to 2035.
FORECASTS BY SUPPLIER
The primary purpose of the demand forecasting task is to project
aggregate water demand for the EIS demand area as opposed to detailed
forecasts by water supplier. Even so, the approach adopted for the EIS
forecasts requires individual water supplier projections, recognizing
the characteristics of these local areas, and an accumulation of these
to achieve demand area totals. Exhibit 2-A appended to this report pro-
vides water demand forecasts for individual suppliers by population
series and demand model. Analysis of future water demand for groups of
suppliers is useful in demonstrating the changing geographic pattern of
water use likely over the next 50 years.
As table 106 indicates, water demand (unadjusted for natural retro-
fit) is projected to increase slowly in the DSA from 1980 to 2035.
Demand increases to 283,000 acre-feet by 2000 and slows thereafter;
demand in 2035 is forecast to be 369,400 acre-feet. The DSA's share of
total publicly supplied water decreases from 61 percent to 42 percent
Appendix 2
257
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Appendix 2
258
over this period. These DSA forecasts include the City and County of
Denver, total service, read and bill, and those master metered suppliers
relying solely upon the DWD for treated water supplies, as previously
defined. The DSA forecasts do not include additional DWD water sales to
other area water suppliers which are accounted for as individual enti-
ties.
Water demand for the other major water suppliers within the demand
area is forecast to increase to almost 168,000 acre-feet by 2000, a 78
percent gain over 1980. Year 2035 water demand increases to 280,000
acre-feet. These suppliers' share of total water demand is relatively
stable from 1980 to 2035.
Water demand in currently sparsely populated water suppliers in the
demand area is projected to grow considerably in future years. Water
demand will increase by 72,000 acre-feet by the year 2000 to 97,500
acre-feet. While these suppliers represent only eight percent of
current water demand, their share will expand to one-quarter of total
publicly supplied use by 2035.
0.4E FACTOR WATER DEMAND W)DEI. FORECASTS
FORECASTING PROCEDURES
The use factor model forecasts applied the following information:
. The use factor model derived in Chapter 5
Appendix 2
259
. Projections of demographic parameters for each supplier
. Projections of independent socioeconomic variables related to
SFM for each supplier
The use factor model forecasts were developed on a supplier-by-
supplier basis. SFM water demand by supplier was calculated by
multiplying each socioeconomic variable coefficient by each supplier-
specific projections of that variable. The result served to adjust the
overall 478 g.h.d. SFM use factor. Other sectoral use factors were
multiplied by supplier-specific forecasts of those demographic
parameters. Finally, water demand from each sector was accumulated to
derive total demand for the supplier for that year.
SUMMARY OF FORECASTS FROM THE USE FACTOR MODEL
The use factor model forecasts water demand for water suppliers in
the EIS demand area using a model disaggregated by consuming sector.
Results of applying the model to the projected independent variable data
base are summarized in figure 13. As shown in the application of the
model to historical data, predictions for the overall EIS demand area
are expected to be more accurate than predictions for individual
supplier's demand.
Table 107 presents a summary of the use faactor model forecasts and
the per unit trends for g.h.d. and g.c.d.
Appendix 2
260
Table 107
Publicly Supplied Water Demand in the EIS Demand Area,
1980 to 2035, Based upon the Use Factor Model
Year
(percent)
1980 1.9M 2000 2010 2035
Series 1
Acre-feet (000's) 313.7 413.6 514.6 597.6 703.0
Average Annual
Growth Rate -- 2.8 2.2 1.5 0.7
g.h.d. 516 496 486 480 484
g.c.d. 195 207 208 207 209
Series 2
Acre-feet (000's) 313.7 413.6 514.6 599.9 739.8
Average Annual
Growth Rate -- 2.8 2.2 1.5 0.8
g.h.d. 516 496 486 480 482
g.c.d. 195 207 208 207 208
Series
Acre-feet (000's) 313.7 413.6 514.6 601.5 773.6
Average Annual
Growth Rate -- 2.8 2.2 1.6 1.0
g.h.d. 516 496 486 480 481
g.c.d. 195 207 208 207 208
These forecasts exclude the effects of natural retrofit.
Little variation in demand is noted between the three series for
the projected years. The average annual growth rate does show a vari-
ation. The average annual growth rate for all three series for the cur-
rent 20-year period (1980-2000) is projected at 2.2 percent. A slower
growth rate is projected for the subsequent 35 years (2000-2035) .
Series 1 projects an average annual growth rate of .89 percent; series
2-1.04 percent; and series 3-1.17 percent.
Appendix 2
261
The distribution of future water demand by user class is shown in
table 108. Proportionate demand by SFM households and MF households
will increase. Demand by SFF households will decrease since there is no
projected growth in the number of unmetered households.
Commercial/industrial and public demand will remain essentially
unchanged. The percent of total water demand by SFM and SFF households
will remain constant at 62-65 percent for the entire period.
Table 108
Distribution of Future Water Demand
by User Class (Percent)1/
Projected
Consuming Sector Current 1990 2000 2010 2035
SF (metered) 42 48 49 51 55
SF (flat) 23 16 13 11 8
MF 16 16 17 18 19
Commercial and industrial
(employment) 12 13 14 13 11
Public (population) 7 7 7 7 7
Total 100 100 100 100 100
1/ Series 1 data. Series 2 and Series 3 yield similar results.
FORECASTS BY SUPPLIER
Individual water supplier forecasts are presented in exhibits 2-A.
Water demand for the City and County Denver, the largest district, is
projected to remain essentially unchanged for the 35 year period, 2000-
2035. In contrast, Aurora's water demand is projected to grow at an
average annual rate of .73 percent during the same time period.
Appendix 2
262
CRITICAL ASPECTS OF OSE FACTOR FORECASTS
Three sensitivity analyses were performed using the series one pro-
jection data. They pertained to the following:
. Marginal price.
. Income
. Lot size model coefficient.
. Weather.
The marginal price analysis examined the effects of changes in mar-
ginal price on water demand in the EIS area. Three alternatives were
tested with the results shown in table 109. In the first alternative,
marginal price is reduced for every supplier by 20 percent. This
resulted in an overall increase in demand of 3.5 percent. Conversely, a
20 percent increase in marginal price resulted in a decrease in demand
of 3.4 percent. A 40 percent increase in marginal price resulted in a
6.9 percent decrease in demand.
Total water demand is also moderately sensitive to changes in the
income forecast. A ten percent increase or decrease in household income
would result in a 1 .9 percent change in demand.
Appendix 2
263
Table 109
Sensitivity Analyses of Series 1 Data
Percent
Difference
EIS Demand From
Variable Sensitivity Area in 2035 Base Case
(thousand
acre-feet)
Base case 703.0 --
Marginal price
Reduce marginal price by 20% 727.8 3.5
Increase marginal price by 20% 678.8 -3.4
Increase marginal price by 40% 654.9 -6.9
Income
Increase income by 10% 716.3 1.9
Decrease income by 10% 689.7 -1.9
Lot Size
Double lot size coefficient
to 854 gallons/acre/day 701.9 -0.2
Weather
High irrigation requirements 773 +10.0
Low irrigation requirements 633 -10.0
The lot size coefficient sensitivity analysis was prompted by con-
cern that the implied elasticity of water demand for lot size was lower
than intuition might suggest. However, table 109 shows that even doubl-
ing the coefficient results in little change in overall demand, in this
case a drop of 0.2 percent.
Finally, the sensitivity of the forecasts to certain weather
variations was analyzed. The model was established to forecast demand
for a typical weather year or expected average demand over a series of
years which together represent average weather conditions. However,
should conditions be significantly different from average for several
consecutive years, it is important to estimate the resulting effect on
demand.
Appendix 2
264
Water demand estimates were made for a year with high irrigation
requirements (drier than normal) and a year with low irrigation require-
ments (wetter than normal). The definition of the high and low year are
such that 95 percent of historical weather data would fall in between
the two cases. Using the results of the weather analysis discussed in
Chapter 3, it is expected that these weather conditions would increase
water demand up to 10 percent in dry years and decrease it as much as 10
percent in wet years. Since this assumes that MF, E and P demand are
affected to the same extent as SF households, actual variation would
probably be less.
SELF-SUPPLIED WATER DEMAND
PROJECTIONS OF SE<.F-SUPPLIED WATER DEMAND
Self-supplied water demand from industrial companies is forecast to
increase from 32,610 m.g. or 100,100 acre-feet in 1980 to 38,590 m.g. or
118,400 acre-feet in the year 2000. As presented in table 110, demand
is expected to remain essentially unchanged from the year 2000 through
the year 2035. The major future self supplied water users are Public
Service Company of Colorado, Adolph Coors Company, Gates Rubber Company,
sand and gravel companies, and agricultural users. This use is in
addition to water these companies might consume which is supplied by
public water districts.
Appendix 2
265
Table 110
Forecast of Self Supplied
Water Demand of Major Industrial Consumers (m.g.)
Public
Service Adolph Gates Sand and Total in
Company of Coors Rubber Gravel Acre-feet
Year Colorado Company Company Companies Total (000's)
1980 18,000 14,200 300 120 32,620 100.1
1990 15,000 20,000 150 130 35,280 108.3
2000 12,300 26,000 150 140 38,590 118.4
2010 12,300 26,000 150 150 38,600 118.4
2035 12,300 26,000 150 170 38,620 118.5
Demand for the presently self-supplied households in the demand area,
numbering approximately 10,100, was assumed to remain at 1982 demand
levels throughout the forecasting period. These household are assumed
to continue relying upon groundwater supplies.
NATURALLY OCCURRING RETROFIT
Both publicly supplied water demand forecasts presented in this
chapter have not been adjusted downward for naturally occurring
retrofit. Based upon the 1983 Household Users Survey, an estimated
434,000 housing units currently lack water-conserving plumbing fixtures.
However, such fixtures are standard for replacement of old toilets and
shower heads. Since the expected life of existing high water using
shower heads is about 15 years (U.S. Department of Housing and Urban
Development, 1982); all inefficient fixtures are assumed to be replaced
by low use fixtures by 1995.
r Appendix 2
266
The life of a toilet is about 40 years (U.S. Department of Housing
and Urban Development, 1982) . By 2020 all existing high water-using
toilets will be replaced by low-use facilities. Water savings for
shower head retrofitting is 13.5 g.c.d., and toilet retrofitting saves
9.0 g.c.d., according to the preliminary conservation analysis of this
EIS.
Total savings are calculated by multiplying daily per capita
savings by average household size and by the number of naturally
retrofit households. These data are provided in table 111.
Table 111
Assumptions for Calculating Water Savings
from Natural Retrofit
Additionally
Per Capita Average Naturally
Daily Water Persons Retrofit
Savings per Household Households
Shower Heads E.
1980 13.5 2.65 0
1990 13.5 2.39 289,000
2000 13.5 2.34 434,000
2010 13.5 2.32 434,000
2035 13.5 2.32 434,000
Toilets ?/
1980 9.0 2.65 0
1990 9.0 2.39 108,500
2000 9.0 2.34 217,000
2010 9.0 2.32 325,500
2035 9.0 2.32 434,000
1/ Assumes conversion from 6.0 gpm to 3.0 gpm shower head, 6
minute showers and 0.75 showers per person per day.
?V Assumes conversion from 5.5 gallons per flush to 3.5
gallons per flush, 4.5 flushes per day.
Source: EIS Conservation Analysis.
Appendix 2
267
As shown in table 112, adjustments for natural retrofit increase
from 13,100 acre-feet in 1990 to 25,400 acre-feet by 2035.
Table 112
Acre-Feet Adjustments for Natural Retrofit
Annual
Year Water Savings
1980 0
1990 13,100
2000 20,500
2010 22,800
2035 25,400
Water demand forecasts can be adjusted for natural retrofit for
consistency with the unconstrained water demand forecast definitions.
COMPARISON OF POOLED & USE FACTOR MODELS
The pooled model predicts g.h.d. for each water supplier based upon
the following independent variables:
. lot size
. family income
. household size
. marginal price
. service employees/household
. nonservice employees/household
Appendix 2
268
. percent SF households
. number of days measurable precipitation
. presence of three-hour/three-day restrictions (0 or 1)
The pooled model is based on ordinary least square regression using
water demand data at the indiviudal supplier level and DECOG, Census,
and other local data for the independent variables.
The use factor model predicts total gallons for each water supplier
based upon the following data for each supplier:
. number of metered SF homes
. number of unmetered SF homes
. number of MF homes
. number of employees
. population
Metered SF demand is modified to reflect the following socio-
economic data:
. marginal price
. lot size
. household size
. family income
The use factor model is based on analysis of nonresidential use factors,
household water demand, and lawn irrigation requirements. The use
factor model is also based on ordinary least squares regression using
household survey data to incorporate the socioeconomic data into the
projections.
Appendix 2
269
BAC[CASTING CAPABILITY
The performance of the two models on historic data is presented in
table 113.
Table 113
Performance Comparison of Use Factor and
Pooled Model on Historical Data ?/
Use Factor
Parameter Model Pooled Model
Percent variation of overall
district demands explained 99.8 99.5Z/
EIS demand area accuracy
(percent) -1.4 2.0
DWD demand (excluding
master meters) accuracy (percent) 6.9 3.9
Average absolute error all
providers (percent) +23.5 +8.9
?/ Both models were applied to the historical data base
from which they were derived. The use factor model
was analyzed on 48 observations while the pooled model
was analyzed on 254 observations.
?1 The percent variation previously reported in Chapter 4
was for the equation unit demand (g.h.d.).
This table shows comparisons for:
. percent of explained variation--This is calculated in a manner
similar to the coefficient of correlation.
. aggregated demand accuracy--This is the total actual demand for
all observations minus the total calculated demand, expressed as a per-
cent of actual demand.
Appendix 2
270
. DWD demand accuracy (except master meters)--This is calculated
similarly to aggregated demand.
. average absolute error for predicting individual suppliers (from
the largest to the smallest)--This shows the absolute value of the
residuals for all observations.
The two models perform similarly in most respects on the historical
data. The pooled model performs somewhat better for individual dis-
tricts, due largely to its residual adjustment feature.
A comparison of the elasticities for the two models is shown in
table 114.
Table 114
Comparison of Elasticities for Use Factor
Model and Pooled Model
Use Factor Pooled
Model 1/ Model 1/
Marginal price -0.34 -0.15
Income 0.26 0.41
Lot size 0.22 0.30
Household size 0.22 0.21
Percent SF 2/ 0.06
Service sector employment per
household 2/ 0.08
Nonservice sector employment
per household 2/ 0.09
Days of measurable precipitation 2/ 0.32
Presence of 3 day/3 hour
restrictions 2/ 0.01
1/ Elasticities for the use factor model apply to SF
metered demand while those in the pooled model apply
to overall g.h.d.
2/ Not in use factor model.
Appendix 2
271
It is difficult to compare the elasticities directly because the use
factor model elasticities apply to SF use while the pooled model elas-
ticities apply to all use. Both models exhibit elasticities for
marginal price that are consistent with those found in the literature.
Given this difference, the use factor model elasticities for income, lot
size, and household size are less than the pooled model elasticities if
they were both on a total water demand basis. A primary difference in
the two models, and a major reason for the lower demand forecasts from
the use factor model, lies in its much lower income elasticity as
compared wtih the pooled model. The other five variables shown in the
table are not part of the use factor model so no comparison was
possible. Except for days of measurable precipitation, the elasticities
for these variables were small.
r-.
SELECTION OF USE FACTOR MODEL
The use factor and pooled models represent two alternative tech-
nical approaches to forecasting water demand. As shown earlier in this
chapter, the two models also result in different forecasts for the EIS
demand area through the year 2035. Use factor model and its forecasts
are selected as the preferred approach for this EIS for two major
reasons.
The first reason for this is that the use factor model is dis-
aggregated by consumer sector: SFM, SFF, MF, E and P. This is essen-
tial for performing the wide range of conservation analyses conducted in
Task 4. Many of these analyses can be performed only with the use
factor model. For example, the demand reductions associated with meter-
ing unmetered homes must be completed using the use factor model because
it makes a distinction between the metered and unmetered demand sectors.
The affect of price on SFM households can only be examined by the use
factor model.
Appendix 2
272
The second major reason for the selection of the use factor model
is that it is based on a broader range of data sources. (See discussion
of use factors in Chapter 3.) This helps to identify and de-emphasize
data which is at odds with other data sources. In contrast, the pooled
model relies on a single data set (one also used for the use factor
model) using a single technique (linear regression) which requires
little understanding of underlying relationships in the data. The
importance of using diverse data sources, when they exist, is that a
higher degree of confidence can be placed in estimates of future
conditions (forecasts) when a model is not tied to a single account of
present conditions. Also, the process of justifying diverse sources
contribute to a more thorough understanding.
Both demand forecasting models contribute to a better understanding
of demand in the EIS area, now and in the future. However, in light of
the eventual uses of the model, to serve as a basis for conservation
analyses, the use factor model is chosen as the most appropriate for
this EIS.
As discussed in Chapter 6, the series 1 data were selected as the
most likely for the EIS area. Therefore, using the preferred use factor
model and the preferred demographic and socioeconomic data (series 1) ,
the following projected water demands are considered as the best esti-
mates available and will be used in the subsequent conservation analy-
sis. The final demand forecasts are presented in table 115.
Appendix 2
273
Table 115
Unconstrained Water Demand Forecasts •
EIS Forecast
Year Demand!/
(acre-feet)
1980 314,000
1990 414,000
2000 515,000
2010 598,000
2035 703,000
1/ These forecasts have not been adjusted for natural retrofit.
r
Appendix 2
274
• -a
EXHIBIT 2-A
WATER DEMAND FORECASTS BY
WATER SUPPLIER
Table 1
Eater Demand Forecasts by Water Supplier
Pooled Model, Series 1 1/
(acre-feet per year)
Water Supplier 1 80I/ 120 2000 2010 2215
Arapahoe 83 1,752 3,734 4,265 4,741
Arvada 15,195 19,205 23,851 28,377 34,692
Aurora 29,534 42,590 61,077 77,106 98,760
Beverly Hills 12 12 12 12 15
Brighton 2,759 4,446 7,223 9,426 12,851
Brook Forest 31 37 46 49 52
Broomfield 3,090 5,670 8,628 10,577 13,470
Charlou Park 68 83 107 110 114
Castle Pines 0 660 1,528 2,197 2,341
Castle Rook 988 4,523 9,313 13,154 19,423
Chaparral 0 199 479 503 546
Con. Mutual 12,955 14,842 17,349 18,165 19,613
Cottonwood 49 1,531 3,323 3,685 4,078
Crestview 2,243 3,127 4,198 1,440 4,796
DWD Service Area?/ 188,779 237,370 283,332 323,529 361,617 _
Denver S.R. Sub 512 1,635 3,237 4,382 5,124
Dolly-O-Denver 0 9 25 34 49
Eastlake 25 25 25 28 28
E Cherry Creek Val. 423 4,673 10,353 12,731 15,130
East Valley 31 64 104 107 114
Englewood 8,147 9,270 10,540 11,734 12,396
Erie 132 221 325 405 528
Evergreen 752 871 1,003 1,114 1,123
Florence Gardens 9 37 71 95 114
Forest Hills 18 18 18 18 18
Genesee 184 230 276 316 451
Glendale 782 1,721 2,676 3,173 3,562
Golden 2,716 3,882 5,146 6,122 6,919
Hazeltine Heights 31 34 37 37 40
Hi-Land Acres 52 58 68 71 77
Holly Mutual 31 49 74 77 80
Ideldale 18 25 34 40 43
Lafayette 773 1,356 2,099 2,657 3,713 --.
Louisville 1,102 2,228 3,605 4,673 6,017
Louviers 83 120 166 175 187
Maple Grove 12 12 12 12 12
Mission Viejo
(Highlands Ranch) 15 4,026 9,178 13,167 15,968
Morrison 175 264 353 414 451
Mt. Carbon 0 8o 166 215 261
North Table Mt 1,283 1,724 2,191 2,581 3,102
Northglenn 5,324 5,888 6,658 6,843 7,312
Orchard Hills 52 68 83 86 92
Parker 117 875 1,850 2,476 2,752
Ro:borough Park 28 393 890 1,270 1,596
Sedalia 28 49 77 101 123
Silver Heights 52 52 55 55 61
South Adams Co. 3,882 5,170 6,658 7,901 8,745
Stonegate 0 801 1,789 2,338 2,633
Thornton 7,923 12,163 17,091 22,538 30,804
Thunderbird 92 92 92 95 101
View Ridge 9 9 12 12 15
Weisner Estate 21 28 31 31 34
Westminster 8,251 11,568 15,848 19,527 25,357
Willows 2,123 3,025 3,964 4,317 4,750
Balance of Area 7.094 11.905 17.429 28.082 55.545
Total (acre-feet) 308,088 420,765 548,509 652,645 792,536
1/ End use level, does not include system losses. Adjustments for water
savings attributable to natural metering and retrofit have not been made
to the above projections because of difficulties in allocating such sav-
ings to individual districts.
2/ Does not include Arvada, North Table Mountain, Consolidated Mutual,
Broomfield, or Crest View; does not include Sable and Cherry Creek Valley
districts after 1980. _
p Not directly comparable to actual 1980 consumption. Assumes average
weather conditions, conservation programs existing in 1981 and 1982, and
1982 marginal price for water. •
Appendix 2
275
Table 2
Water Demand Forecasts by Water Supplier
Pooled Model, Series 2 1/
0 ' (acre-feet per year)
Water Suoolier 121122/ 1222 2000 2010 1.2.11
Arapahoe 83 1,752 3,734 4,299 5,121
Arvada 15,195 19,205 23,851 28,512 36,637
Aurora 29,534 42,590 61,077 77,628 106,217
Beverly Hills 12 12 12 12 15
Brighton 2,759 4,446 7,223 9,435 14,130
Brook Forest 31 37 46 49 52
Broomfield 3,090 5,670 8,628 10,601 14,547
Charlou Park 68 83 107 110 114
Castle Pines 0 660 1,528 2,197 2,341
Castle Rock 988 4,523 9,313 13,164 21,740
Chaparral 0 199 479 503 546
Con. Mutual 12,955 14,842 17,349 18,177 19,755
Cottonwood 49 1,531 3,323 3,704 4,302
Crestview 2,243 3,127 4,198 4,409 4,817 -
DWD Service Area?/ 188,779 237,370 283,332 323,882 365,213
Denver S.E. Subs 512 1,635 3,237 4,422 5,127 _
Dolly-O-Denver 0 9 25 34 49
Eastlake 25 25 25 28 28
E Cherry Creek Val. 423 4,673 10,353 12,731 15,118
East Valley 31 64 104 107 114
Englewood 8,147 9,270 10,540 11,767 12,470
Erie 132 221 325 408 568
Evergreen 752 871 1,003 1,114 1,135
Florence Gardens 9 37 71 98 120
Forest Hills 18 18 18 18 18
Genesee 184 230 276 319 494
Glendale 782 1,721 2,676 3,203 3,903
Golden 2,716 3,882 5,146 6,161 7,156
Hazeltine Heights 31 34 37 37 40
Hi-Land Acres 52 58 68 71 77
Holly Mutual 31 49 74 77 80
Ideldale 18 25 34 40 43
Lafayette 773 1,356 2,099 2,657 3,995
Louisville 1,102 2,228 3,605 4,710 6,545
Louviers 83 120 166 175 190
Maple Grove 12 12 12 12 12
Mission Viejo
(Highlands Ranch) 15 4,026 9,178 13,188 16,226
Morrison 175 264 353 417 463 .
Mt. Carbon 0 80 166 215 261
North Table Mt 1,283 1,724 2,191 2,593 3,289
Northglenn 5,324 5,868 6,658 6,843 7,340
Orchard Hills 52 68 83 86 92
Parker 117 875 1,850 2,479 2,801
Roxborough Park 28 393 890 1,283 1,596
Sedalia 28 49 77 101 126
Silver Heights 52 52 55 55 61
South Adams Co. 3,882 5,170 6,658 7,944 8,785
Stonegate 0 801 1,789 2,350 2,789
Thornton 7,923 12,163 17,091 22,544 33,446
Thunderbird 92 92 92 95 101
View Ridge 9 9 12 12 15
Weisner Estate 21 28 31 31 34
Westminster 8,251 11,568 15,848 19,534 26,913
Willows 2,123 3,025 3,964 4,333 4,922
Balance of Area 7.094 11.905 17.429 29,442 74,624,
Total (acre-feet) 308,088 420,765 548,509 658,416 836,713
1/ Bud use level, does not include system losses. Adjustments for water
savings attributable to natural metering and retrofit have not been made
to the above projections because of difficulties in allocating such sav-
ings to individual districts.
2/ Does not include Arvada, Worth Table Mountain, Consolidated Mutual,
Broomfield, or Crest View; does not include Sable and Cherry Creek Valley
districts after 1980.
j/ Not directly comparable to actual 1980 consumption. Assumes average
weather conditions, conservation programs existing in 1981 and 1982, and
1982 marginal price for water.
Appendix 2
276
Table 3
Meter Demand Forecasts by Water Supplier
Pooled Model, Series 3 1/
(acre-feet per year)
Water Supplier 1 8 / ..-..190 2000 2010 2012
Arapahoe. 83 1,752 3,734 4,330 5,545
Arvada 15,195 19,205 23,851 28,647 38,613
Aurora 29,534 42,590 61,077 76,153 113,802
Beverly Hills 12 12 12 12 15
Brighton 2,759 4,446 7,223 9,442 15,422
Brook Forest 31 37 46 49 52
Broomfield 3,090 5,670 8,628 10,626 14,876
Charlou Park 68 83 107 110 114
Castle Pines 0 660 1,528 2,197 2,344
Castle Rock 988 4,523 9,313 13,176 23,587
Chaparral 0 199 479 503 546
Con. Mutual 12,955 14,842 17,349 18,190 19,923
Cottonwood 49 1,531 3,323 3,722 4,547
Crestview 2,243 3,127 4,198 4,412 4,842
DWD Service Area 188,779 237,370 283,332 324,259 369,353
Denver S.R. Subs 512 1,635 3,237 4,458 5,140
Dolly-C-Denver 0 9 25 37 46
Eastlake 25 25 25 28 28
I Cherry Creek Val. 423 4,673 10,353 12,731 15,112
East Valley 31 64 104 107 114
Englewood 8,147 9,270 10,540 11,804 12,556
Erie 132 221 325 408 608
Evergreen 752 871 1,003 1,129 1,148
Florence Gardens 9 37 71 98 126
Forest Hills 18 18 18 18 18
Genesee 184 230 276 322 525
Glendale 782 1,721 2,676 3,234 4,277
Golden 2,716 3,882 5,146 6,198 7,419
Hazeltine Heights 31 34 37 37 40
Hi-Land Acres 52 58 68 71 77
Holly Mutual 31 49 74 77 80
Ideldale 18 25 34 40 43
Lafayette 773 1,356 2,099 2,660 4,284
Louisville 1,102 2,228 3,605 4,750 7,097
Louviers 83 120 166 175 193
Maple Grove 12 12 12 12 12
Mission Viejo
(Highlands Ranch) 15 4,026 9,178 13,213 16,520
Morrison 175 264 353 420 476
Mt. Carbon 0 80 166 218 313
North Table Mt 1,283 1,724 2,191 2,608 3,480
Northglenn 5,324 5,888 6,658 6,846 7,373
Orchard Hills 32 68 83 86 89
Parker 117 875 1,850 2,485 2,857
Roxborough Park 28 393 890 1,295 1,596
Sedalia 28 49 77 104 129
Silver Heights 52 52 55 55 61
South Adams Co. 3,882 5,170 6,658 7,987 8,837
Stonegate 0 801 1,789 2,363 2,961
Thornton 7,923 12,163 17,091 22,553 36,109
Thunderbird 92 92 92 95 101
View Ridge
15
Weisner Estate 21 28 12
31 31 34
Westminster 8,251 11,568 15,848 19,543 27,861
Willows 2,123 3,025 3,964 4,348 5,112
Balance of Area 7.094 11.905 17.429 29.871 91.838
Total (acre-feet) 308,088 420,765 548,509 660,355 878,286
1/ End use level, does not include system losses. Adjustments for water
savings attributable to natural metering and retrofit have not been made
to the above projections because of difficulties in allocating such sav-
ings to individual districts.
2/ Does not include Arvada, North Table Mountain, Consolidated Mutual,
Broomfield, or Crest View; does not include Sable and Cherry Creek Valley
districts after 1980.
2/ Not directly ocmparable to actual 1980 consumption. Assumes average
weather conditions, conservation programs existing in 1981 and 1982, and
1982 marginal price for water.
Appendix 2
277
•
•
Table 4
6KYtA Water Demand Forecasts: Series 1
(acre-feet)
Water District 1980 1990 2000 2013 2035
ALAMEDA 17792 309 — 2133944 _— sN37733 —322— 394
ARAPAH01 374? 4115
ARVADA AURORA DE It 26 12]31 57657 70220 33579
DEM OF AR
VEIDONVULTTHILLS 5720
,743 9164
E132 3115 12771
61R6 2670 +9,43
7716
2011-MAR 22 6/ 2 8318 11711
BRIGHTON FOREST p 2839{ 4170
150 150
0 MF 177NF1f 77'36 139
075 10176 12405 *5437
A97Lp64211 19 12599 17691
E ROCK 10818
MERRY
OU►ARKL 137 24gfp0 190 197 718
CCCCso
.p.pERRYMOOR !T a90 227 1660s2 578 774 61 181
CRE974IEYDl 141,4
3069 1+1+5s61p0 130006152 17611 18448
16513
DENVER S.E. S 608 lb30 2785 7911 3993
BD,ug c-O-DENVE 0 14314? 154517 16554699773 1{{584736
BR
TOTAL}li SER 129798 31621 41191 19961 21031 16995 20569
E CHERRY LL CRK. s
EIAASTTlVALLEY 423 49 76 76 76
EDOEVATER 771 85i 929 911 908
fN01 EYOOD 9190 10009 20938 11661 11764
ERIE 19d 320 472 593 754
EVERGREEN 1196 1664 2150 2537 2858
FLORENCE BARD 11 48 83 111 125
FOREST HILLS 4 24 24 24 24
Mud
6g !79 294 374 468
gOFuK7CE 063 1113 '164 2500 2687
6 !"EN JJ4? 1�3'� 6090 6494
GREEN MTN 4576 3177 7939 13966
HAZELTINE HEI 78 42 45 45 45
HI-LAND ACRES 75 84 94 96 .n3
995 961 1091 1147
!!1 OH VIEW 789
HIGHLANDS RAN 19 4323 9731 5
Mil, MUTUAL
25694 15- 23 31 31 3
IOIEOALE 7B 47 {{6{ZZ0 6 6
279
KEKEN-CARYLON IRNT 1087 12140 296, 3763 . 16287
LAFAYETTE 1455
LAKEHURST If 191 2871 3269 70 3496 0 7687
V`LOUISVILLE 11111 3168e7 5136 6690 0
y0 8716
MMEEADOVBROOK 290 717 44 161 776
MORRISON
NT. CARSON 140 1M91 76 263 309
NORTH TABLE 9 1772 1787 117 2542 3023
NORTH WASHNOT 2109 4528 28883 2T7 3121
NORTHOLENN - 3713 4005 4763 4403 1269
NORTHEIDE 444 630 863 13
ORCHARD HILLS 54 9
PANORAMA PARK 19 p 7
PARKER 139 1066 d 2117 2797 3053
9E^6116U8N PA 26 134 17`a' 229 265
68 68
SILVER HEIGHT
C 31020 6`744y7 8416 497 10583
3NORNTDM 7866 11716 13917 20210 2.4479 THUNDERBIRD 91 91 3!
ppV�A�EELLEY EE 936 1171 1321 1722 1795
YEISNEPDEgg4TAT t4 �p77p999�2 2 210 78 0
S 19
18
YVIEEATTRMSTER ROOR 17461 12775 15710 17]17 1685
•+6LOV 2286 2876 7419 7605 3171
TOTALS 313,749 413,586 5144, 18 597,614 703043
'figures are for 1980 with typical weather and should not be compared directly
with actual 1980 demands.
Appendix 2:
278
Table 5
MU Water Demand Forecasts: Series 2
(acre-feet)
' _ _ i4atxr Xistrlct_ 1990 2000 _ 2010 2035
ee 30 -31 �1p1 ��P3 37777- _. 394
AR63 43541
VADA 219'6 �2 i1 254006 19061 35570
AURORA 299 6 7733 57 70697 92559
ANOFT-CLOV 5720
73 81622 !63557 26003 67162
NVUEY HILLS
NNAAggy lit jj 336
eNTFNREDT "tp' 4111 6;�2 ins 1790'0
DROOMFIELD 88 7150 7055 II 6 150 150
T I 8 ! 16659
CCCNETE2Rµ00RyL 1028! I7iNARLOU PARK 131t�g222 922 12666608 *9773
2e9
CHERRYNO R ST 12}0 lz7 1787 166 Isi
90 651
X RRY CRK 8s
CON. MUTUAL 14124
15660 13000
eCeggOEET9TOOTgNqWEOEOD 8 17218 13727
DEB9FRTE. $ { +s, 3291 `37 77
DOLLY-0-8EMVE 3049
601 lilt 2721 2464 1923
8YD CITY+COUN 8 145147 154517 36
DOD READ 4 DI 11873 154744 162141
DOD TOTAL SER 14374 31621 MR 21031 52910 0 20697
E CHERRY CRK. 173 5144 11079 13455 16206
EgEAAAyST VAEELI EY 23 49 76 76 76
EEEDB`E` 2T£R32 ''22 3
ERIE 9190 9 90 10096 01
109]0 32 33117 109
76
13
ERIgp EEEE {771 JJ 11693 11813
fLORENCENDARD 1186 1644 2150 25552
2876 FOREST HILLS 24 1 112
eSEEENEMp9^LE 0i 131718 2164 2 9 24
378 s2'+
BOLDEN IR63 1794 25 6114 6811
GREEN MTM 1576 5177 5730 7938 13967
NHAAZELTINE HEI 9
NI-LAND ACRES 75 e14 H
NIGH VIEW e♦♦ 9d !0.
HIGHLANDS RAM 71q 4577 97951091
MOLLY MUTUAL 13597 17891
IlLEDALE 30 91 t6Z0 368 1 111
KEELTTON MEIBXT 127 135 143 5373 lnseDi
REM-[ARYL 1507 1955 5861
LAFAYETTE E { p CS
LAREYO0DT 4 3459 4626
LOUISVILLE 1552 327 23
'700 19 1687
LOUVIERS�Ep Igi1k 31447 519732 202
9218
"'RIS0N��pp 29149 E 0 22207 203 364 304
NORTH
Ma N 177? 187 222!16 2532 3333
205
NN�pOORRRTHOOyLLIENNENRT - 1713 40025 4365 4405 888 2976 4785
ORCM�RODMIL`f 444 610 801 1107 1217
PANORAMA PARK 6t 25
PPAARRKKEERRQ 159 1066 '211 2800 3093
"�D�ALI^ M} 426 f$$ 51175 1250 1266
MINESOUTH ADAMS C 8102 6`711, 8416 11313168 120620
7866 11791 15913 20215 78704
TNUNDERDIRB 1
84
�VOiELLELiy1LL91ERYR yygg���� 933 1127„1 - 13*78
1` 1713 1822
_____IN5TERA7 1 aj 177776 177!! 2101820 217+7422
Imo` 2406 2876 04 2780
17118 5213 +027
3405
I LLSYlR
TOTALS )13,749 413,586 514,618 539,933 739,822
TMfigures are for 1960 with typical weather and should not be compared directly
with actual 1980 demand.
Appendix 2 •
279
Table 6
^ 6KY&A Water Demand Forecasts: Series 3
(acre-feet)
_ Water District 19801/ 1990 2000 2010 2035
ALAPANOE 3II09 344 373 377
394
AULLROR29
AA
EE 79932 1500 OI R .25406 2922s0e* 3401 3784 7474
AMCROFT-CLOY 5720 4 71174 6803 '71700
1164 12771 7 77919
� RULEY MILLS 116 7616 63145 t}737 6 16 17
BRIGHTON 2934 41,0 6;23 _^t�3109 423
179
OON ELD B9 9
7950 7079
3 1097608 'S0 31 14130
16717
OOK FOREST
7¢i
:ITII Ms
0
1949
N R`OCUA►AIIKL 1080 '0 III;
7105
12617445
595 216391 +
HENRY CRK s. 1 ' 378 197
il1
NE MO T 4278 570 7
162
MUTUAL 13069 124460 1308 a0 te1
TTOM 17625 1 15
E9TVTEY 1333 6152 5 X1,9 645P
DENVER S.E. 9 {
DOLLY-0-DENVE 409 10 21 4004
. ICITY*COUN 129799 143147 154517 16 76
aBwYnn READ + DI 18736 31621 41181 15299 111750
DODMTOTAL CSER 14574 19309 21038 52992 9 827
A TTT VALLEY 475 2 5144 AKE 328 110776 13456 16220
EINDI 32
WATER 9770 10099 10979 * 7 9 76
11
ERIE BREEN 196 328 472 11724 4 I1 873
FLORENCE BARD 1186 1648 2150 2566 7131 FOREST HILLS11 1s24 2!9 24 s24
'^ 111111IE 3R63 •797 21644 2091 3111
GREEN MTN
NAZELTINE NEI 4576 5172 5730 6147 13960
NI-LAND II `` yyACRES VIEW .5 41 �4 96
45 45
HOHILLYAMUTUALN 715 472, 7711 1361e9 995 961 4 161099
IDLEDALE 39 • 60 1 31 31
KELTON HEIGHT s3� 135 II!!
263 279
LA[MERRE 105 1755 786+ J95J 10746
tAK9Y0GDTT y6
LOUISVILLE OI`tE[( 9RL 1552 2676 3269 8 J 02 ]1 7
IP100YORL101E( 1 f 0e 77
1441/7 0003 690y0y 9585
MORRISON 290 387 4 6.0 771
NT. CANON 149 227
p 366
yy NORTH ►ECOS 311 7`)} 914 360
434
MONTH TABLE 9 1372 17 7 2196 2568
NORTH WASHNOTzees 3396
RORTHOLENN ' 2109 7 4005 4365 4407 4 11
NORTMSIDE 444 630 801 1167 1271
ORCHARD HILLS 54 69 IS
PANORAMA PARK 119 22 21
PARKER
PA 526 1066 117 . 25 27
X77 1172 SEDALIA 48IGHT l0174
$ 69
ILVER
ADAMS C 5102 6747 9476 9470 769 1pls 2�t269
T61
HUNDERBIRD 766 11;1 15903 202'7 7112,
YY 99
VtEEAtLLEY EEB8gg[ 936 11}7711 1335 17+g8
WESTMINSTERAT 1 ±4 13776 177 fb 1481
VILLSR1960K 77401 27831 33� 2704 2e+e6
2286 287` 7419• , 362 8125
TOTALS 313,749 113,586 314,618 601,478 773,623
*Figures are for 1930 with typical weather and should not be compared directly
_with actual 1980 demand.
Appendix 2
280
�. _
EXHIBIT 2-B -
POOLED !DDEL RESIDUALS
AND INCOME ADJUSTED'? FACTORS
Table 1
Pooled Model Residuals, 1981 and 1982 Average
Pooled
Model
Water Supplier Residual
(g.h.d.)
Arapahoe 0
Arvada 13.627
Aurora 42.40067
Beverly Hills 0
Brighton 96.8518
Brook Forest 0
Broomfield -62.4754
Charlou Park 0
Castle Pines 0
Castle Rock 88.28565
Chaparral 0
Con. Mutual -24.3071
Cottonwood 0
Denver S.E. Suburbs -
Dolly-0-Denver 0
Eastlake 0
E. Cherry Creek 69.62205
East Valley 0
Englewood 71.3164
Erie -12.2973
Evergreen 0
Florence Garden -79.8727
Genesee 0
Glendale -85.8494
Golden -61.5024
Hazeltine Heights 47.5058
Hi-Land Acres -86.3415
Holly Mutual -287.267
Idledale 0
Lafayette 0
Louisville -87.7686
Louviers 0
Maple Grover 0
Morrison 0
Mt. Carbon 0
Northglenn 190.525
Orchard Hills 0
Parker -62.0352
Roxborough Park 0
Sedalia -272.82
Silver Heights -91.2837
South Adams County -34.7283 ^
Stonegate 0
Thornton 76.75775
Appendix 2
281
Table 1 (Continued)
Pooled
Model
Water Supplier Residual
(g.h.d.
Thunderbird 0
View Ridge 0
Westminster -32.8498
Willows 33.3694
Balance of Area 0
DWD City + County 72.01421
DWD Total Serve 65.4017
DWD Read + Bill 79.91875
Green Mountain -43.8136
Wheatridge -31.2777
Edgewater 3.79372
Bow-Mar 12.71081
Panorama Park -252.442
Alameda 0
Kelton Heights -54.5231
Bonvue 0
High View 0
Valley 2.33475
Lakehurst -18.9166
North Washington 132.2136
Cherry Creek Village 128.004
Meadowbrook -75.9929
Willowbrook -55.0607
Northside 22.15884
North Pecos 0
Cherrymoore Sth 0
Key Caryl 151.014
Bancroft-Clover -26.9038
Lakewood -119.975
North Table Mountain 68.5763
Crestview -63.2132
Notes: A description of the residuals and their application to the pooled
model is provided in Chapter 4 and Chapter 8. Suppliers with zero
either (1) did not submit data for 1981 and 1982; (2) were outliers;
or (3) were not yet in existence. Water demand forecasts for several
of the outlier suppliers were adjusted to reflect reported 1981 and
1982 data.
Appendix 2
282
Table 2
Pooled Model Income Adjustment Factors
Series 1 Series 2 Series
1980 1.000 1.000 1.000
1990 1.006 1.006 1.006
2000 1.041 1.041 1.041
2010 1.032 1.031 1.031
2035 0.937 0.934 0.931
Note: These constants should be multiplied by each district's income forecasts
to create a system-wide weighted average household income projection at
growth rates specified in Chapter 7. This adjustment is necessary
because the pooled model is based on a per unit, GHD calculation without
regard to the number of households in district itself.
Appendix 2
283
EIRIBIT 2-C
ALTERNATIVE POOLED
IADEL PROJECTIONS
ALTERNATIVE POOLED MODEL PROJECTIONS
Water demand forecasts applying the alternative interaction term
pooled regression model are presented in table 1 . This model was iden-
tified and described in Chapter 4. 'The underlying economic and demo-
graphic forecasts are the same as those utilized for the preferred
pooled water model forecasts reported earlier.
Table 1
EIS Demand Area Water Demand Forecasts
Applying the Alternative Pooled Model,
Thousands of Acre-Feet
Year Series 1 Series 2 Series
1980 307.5 307.5 307.5
1990 415.0 415.0 415.0
2000 534.5 534.5 534.5
2010 632.1 634.9 636.8
2035 747.1 790.0 830.3
Appendix 2
284
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