ADOPTED APRIL 2012

Southern California Association of Governments

GROWTH FORECAST APPENDIX
GROWTH FORECAST

Introduction 1

Section I: Challenges, Forecasting Timeline and Milestones 1

Challenges 1

Forecasting Timeline and Milestones 1

Section II: Regional Growth: Past and Future 3

Growth Trends 3

Growth Forecast 10

Section III: Forecasting Framework, Methodology and Assumptions 17

Forecasting Framework 17

Forecasting Methodology and Assumptions 22

Section IV: SCAG PECAS Land Use Model 38

Introduction 38

Consulting Projects 38

PECAS Overview 38

Scenario Test 39

Coda 41

References 42
Introduction

The regional growth forecast represents the most likely growth scenario for the Southern 

California region in the future, taking into account a combination of recent and past 

trends, reasonable key technical assumptions, and local or regional growth policies. The 

Integrated Growth Forecast at the regional and small geographic area level is the basis 

for developing the Regional Transportation Plan (RTP), Sustainable Communities Strategy 

(SCS), Program Environmental Impact Report (PEIR), and the Regional Housing Needs 

Assessment (RHNA). The development of the Integrated Growth Forecast is driven by a 

principle of collaboration between SCAG and local jurisdictions who are major contributors to the process. The integration of the regional and local forecasts is achieved through 

the joint efforts and collaboration among the various contributors. SCAG’s Community, 

Economic and Human Development (CEHD) Committee provided direction to the growth 

forecast update process for the 2012–2035 RTP/SCS/PEIR/RHNA. SCAG’s Plans & 

Programs Technical Advisory Committee (P&P TAC) assisted in the forecasting process 

by providing valuable technical input.

The Growth Forecast Appendix is comprised of four major sections. Section I introduces 

the major source of challenges in producing the 2012–2035 RTP/SCS growth forecast, 

and summarizes the nineteen forecasting milestones of forecasting development. Section 

II describes the past growth trends and describes the size and characteristics of the 

projected population, households and employment. Section III discusses the forecasting 

framework, methodology and assumptions. Section IV introduces the SCAG PECAS land 

use model, including its structure, specifications, calibration, and an example scenario.

Section I: Challenges, Forecasting Timeline 

and Milestones

Challenges

The new state law requirement (i.e., SB 375) and the Great Recession (2007–2009) posed 

new challenges in the development of the 2012–2035 RTP/SCS growth forecast. With the 

introduction of SB 375 in California, federal program/requirement (RTP and conformity 

analysis) and two state programs (RHNA and Blueprint) as well as local general plans 

have become more strongly interlinked. Furthermore, as the foundation for developing 

these plans and programs, the 2012–2035 RTP/SCS growth forecast is required to 

be consistent. 

SCAG began developing the long term growth forecasts for the 2012–2035 RTP/SCS in 

the middle of the Great Recession (2007–2009). The traditional long term perspective, 

which might not reflect on-going economic trends and frequently updated short term economic forecasts, had the potential to result in a serious bias in the short term population 

projections. The most important source of potential projection errors was the unstable/

uncertain nature of the key economic-demographic assumptions. Two of the key assumptions were short-term unemployment and migration rates. 

Due to the current economic climate and the uncertain nature of the short term economic 

future in the region, the accuracy and the reasonableness of population projections (and 

assumptions) by the US Census Bureau and the California Department of Finance (DOF) 

were questioned by regional demographers and economists. In addition, relevant statistical data has not always been made available to regional planners in a timely manner. 

There was also a significant gap between the US Census Bureau and the DOF population 

estimates during the intercensal period. Both agencies have used their own methods in 

generating population estimates for this period. The gap in population estimates in the 

late 2000s was very high. In the SCAG region, the difference was 4.2 percent in 2009. 

SCAG generally uses the CA DOF population estimates for planning and forecasting 

purposes. The gap issue was resolved as DOF benchmarked its estimates to the 2010 

Census count. Nevertheless, the adjustment process of the preliminary growth forecast 

was quite challenging, due to the limited time available to produce the revised growth 

forecast for the 2012–2035 RTP/SCS. 

Forecasting Timeline and Milestones

TABLE 1 lists the forecasting timeline and milestones for the development of the regional 

growth forecast for the 2012–2035 RTP/SCS. SCAG began its forecast update process 

by conducting subregional workshops throughout the region in September 2008. When 

necessary, one-on-one meetings were arranged for local jurisdictions or subregions in the 

SCAG region. Through these workshops and one-on-one meetings, SCAG confirmed the 

accuracy of the small area socioeconomic data (SED), existing land use as of 2008, and 

local general plan information. As a result of the workshops and one-on-one meetings, 

SCAG revised and updated the 2008 regional growth forecast methodology and its key 

Growth Forecast 1
assumptions, and developed the framework for future dialogue between SCAG planners 

and local and subregional planners. 

During the first quarter of 2009, SCAG developed an initial range of the regional growth 

forecasts for the 2012–2035 RTP/SCS, which was released in April 2009. Since the Great 

Recession was in full force at that time, the region had experienced an enormous job 

reduction accompanied by high unemployment rates. With a high degree of uncertainty 

on the short term job growth and its potential impact on domestic and international 

migration, SCAG adjusted its forecasting framework for both the short term and long 

term perspectives. The direction of the short term economic perspective was considered 

important in the uncertain forecasting context. Three scenarios of job growth, high, mid, 

and low were used as a major driver of the region’s population growth, which eventually 

influences household growth. 

In May 2009, a first panel of experts meeting was held to review an initial range of 

regional growth forecasts and related assumptions. The panel of experts was composed 

of fifteen experts in the field of regional and national economics and demography. Experts 

were provided with a list of questions regarding assumptions with background information (e.g., historical data and a preliminary range of forecast by the moderator) before 

the panel of experts meeting. SCAG incorporated the recommendations of the panel of 

experts into the refined range of regional growth forecasts, and developed a recommended preliminary set of regional growth forecasts in June 2009. The preliminary 

county and sub-county sets of growth forecasts, reflecting recent trends, were released 

in July 2009.

Between July 2009 and February 2010, SCAG conducted a second round of local/subregional review through workshops and one-on-one meetings with local jurisdictions and 

subregions. First, SCAG provided local jurisdictions with the preliminary set of growth 

forecasts at the city and 2000 census tract, and transportation analysis zone levels. 

Second, SCAG held a workshop to explain the methods and assumptions of how the 

growth forecasts at jurisdictional/census tract/transportation analysis zone level were 

developed. Third, the local jurisdictions or subregions provided SCAG with their input 

on those growth forecasts along with proper documentation. In February 2010, SCAG 

released a local input/general plan growth forecast for 2012–2035 RTP/SCS. In fact, this 

SED data was used for developing the SCAG suggested emission reduction targets for the 

years 2020 and 2035. 

The local input SED data resulted in an imbalance of regional population and employment 

for the year 2035. As usually occurs, the local input SED data tends to have less regional 

population (labor force or workers) than required to meet the projected employment in 

2035. In April 2010, SCAG began analyzing the sensitivity of the labor force participation level, given population and employment. In May 2010, the second panel of experts 

meeting was held to evaluate local input, and new Bureau of Labor Statistics (BLS) and 

US Census projections. The expert panel recommended SCAG reduce employment to 

maintain a reasonable relationship between population and employment. The relationship 

between the two regional forecasts is usually assessed using the double jobbing rate, 

the implied unemployment rate, and labor force participation rates. These factors were 

assumed to remain unchanged from historical levels. SCAG focused on the uncertainty 

of the labor force participation rate due to (1) the increasing share of female workers, (2) 

a higher survival rate, and (3) the sensitivity to extra job opportunities and supply of a 

skilled workforce. SCAG found that the aging population would effectively respond to the 

shortage of workforce in the region by increasing their labor force participation rate.

SCAG adjusted the local input regional growth forecast with a 2035 employment reduction between December 2010 and January 2011. This SED data was used for the preliminary regional transportation model calibration and validation for the 2012–2035 RTP/

SCS. Between January 2011 and March 2011, SCAG conducted further data gathering 

workshops and, as necessary, made revisions to the local input growth forecast. 

In May 2011, SCAG conducted the third and final panel of experts survey to evaluate 

new 2010 Census data and existing demographic and economic assumptions. The third 

expert panel consisted of the same panel members, who had participated in the first and 

second panel of experts meeting. They provided SCAG with updated perspectives of the 

short term economic future of the region and its implication for population and household 

forecasts. SCAG continued collecting and updating the local growth forecasts and revised 

them as necessary between June 2011 and July 2011. During the same time period, 

SCAG consulted HCD/DOF for SCAG region growth forecasts and RHNA determination, 

and held RTP/SCS workshops across the region and conducted public outreach for review 

of the socioeconomic data.

After developing the draft 2012–2035 RTP/SCS between July 2011 and November 2011, 

SCAG released the draft plan in December 2011. SCAG distributed the draft RHNA allocation plan in February 2012. The Regional Council will be adopting the 2012–2035 RTP/

2 Growth Forecast
SCS in April 2012. The regional growth forecast is adopted as part of the 2012–2035 

RTP/SCS and RHNA process. 

TABLE 1 Forecasting Timeline and Milestones

Milestone Date/Period

1 Adopted 2008 RTP growth forecasts. May 2008

2 Conducted subregional workshops across the region in 

anticipation of 2012–2035 RTP/SCS growth forecast, 

data and tool requirements under the SB 375.

September 2008–

January 2009

3 Developed an initial range of preliminary 2012–2035 

RTP/SCS regional growth forecasts with major demographic and economic assumptions.

April 2009

4 Held the first panel of experts meeting to assess BLS, 

Census, and DOF projections and to discuss demographic 

and economic trends and assumptions.

May 2009

5 Developed a recommended preliminary set of regional 

growth forecasts.

June 2009

6 Developed a preliminary set of growth forecasts at the 

county and sub-county level, reflecting the recent trends.

July 2009

7 Held subregional workshops across the region and conducted outreach for local review.July 2009 – 

February 2010

8 Released local input/general plan growth forecast for 

2012–2035 RTP/SCS. This dataset was used for target 

setting recommendation.

February 2010

9 Observed the imbalance of 2035 regional population and 

employment from local input.

April 2010

10 Held the second panel of experts meeting to evaluate local input, and evaluate new BLS and Census 

projections.

May 2010

11 Local input regional growth forecast with 2035 employment reduction. December 2010–

January 2011

12 Conducted data gathering workshops & made revisions. January 2011–

March 2011

Milestone Date/Period

13 Conducted the third panel of experts survey to evaluate 

new 2010 Census data and existing demographic and 

economic assumptions.

May 2011

14 Collected local input and revised forecasting data as 

necessary.

May 2011–

August 2011

15 HCD/DOF consultation for SCAG region growth forecasts 

and RHNA determination.

June 2011–

August 2011

16 Held RTP/SCS workshops across the region and conducted public outreach for review.June 2011–

August 2011

17 Developed the draft 2012–2035 RTP/SCS. The dataset 

was used for regional transportation model calibration 

and validation.

July 2011–

November 2011

18 Released the draft 2012–2035 RTP/SCS. December 2011

19 Distributed the draft RHNA allocation plan. February 2012

20 Will adopt the 2012–2035 RTP/SCS. April 2012

Section II: Regional Growth: Past and Future

Growth Trends

POPULATION

The United States Census of 1850 counted the population of the Southern California 

Region to be 3,530. At that time, the population of the United States was 23,191,876. 

The regional share of the nation’s population was close to zero. According to the 2010 

Census, the population of the Southern California Region was 18,051,534, which represents over 5.8 percent of the US population of 308,745,538, and nearly 49 percent 

of California’s population of 37,253,956. With the region’s land area of 38,000 square 

miles, the region’s population density is now 475 persons per square mile. The Southern 

California region is the 5th highest in population among states in the nation, behind 

Florida, and the second largest combined statistical area (CSA) in the nation behind the 

New York CSA. 

Growth Forecast 3
The region’s population growth over the last 160 years can be categorized into four major 

periods using statewide growth as a reference: very rapid growth (1850–1910), rapid 

growth (1910–1960), average growth (1960–1990), and slow growth (1990–2010) (see 

TABLE 2). The very rapid growth (1850–1910) represents the early stage of urbanization 

in the region. The railroad lines were first introduced into this agrarian region, and they 

played an important role in the region’s growth and urbanization. The annual average 

growth rate of population in the region in this period was 311 percent, which is 60 times 

higher than the national rate, and 8 times higher than the California rate. 

The rapid growth (1910–1960) period represents the beginning and rapid stages of 

regional growth and urbanization. The region reached one million people in 1920 for the 

first time, and grew to five million people by 1950. It took only three decades for the 

region to add four million people. The annual average growth rate of population in the 

region was 22 percent, which is 10 times more than that of the nation, and twice that 

of California. 

The average growth (1960–1990) period represents regional growth and urbanization 

comparable to that of the state. In 1970, the SCAG region’s population reached 10 million 

and exceeded 50 percent of the California population. By adding 4.6 million people to the 

region between 1970 and 1990, the region has evolved into one of the largest metropolitan regions in the nation. There was an accelerated suburbanization during this period. 

Four of six counties in the region exceeded one million people in 1990. The annual average growth rate of population in the region was 2.9 percent, which is 2 times more than 

that of the nation, and similar to that of California. 

The slow growth (1990–2010) period represents the mature stage of population growth 

and urbanization. During this period, the region added 3.4 million people, which was a 

much slower growth pattern than in the previous decades. Although the regional growth 

stabilized during this period, urbanization and suburbanization continued. Orange County 

exceeded three million people, and Riverside and San Bernardino Counties exceeded two 

million people each in 2010. The annual average growth rate of population in the region 

was 1.2 percent, similar to that of California and the nation. Both the region and California 

became average growth areas from the perspective of the national growth. 

TABLE 2 Annual Average Growth Rate of Population, 1850–2010

Periods 1850–1910 1910–1960 1960–1990 1990–2010

SCAG Region 311.0% 21.6% 2.9% 1.2%

California 41.1% 11.2% 3.0% 1.3%

US 5.0% 1.9% 1.3% 1.2%

Source: US Census Bureau.

Two factors account for population change: natural increase and net migration. Natural 

increase is the balance between births and deaths in a period and net migration is the 

sum total of people coming to and leaving the region in the same period. Net migration 

is of two types: domestic and international. Domestic migration is the movement in and 

out of the region from other parts of the country, and immigration is the flow of people 

from other countries. Net migration greatly influenced the region’s past and recent 

population growth. 

The region’s economic growth is usually a major factor behind net migration and the consequent population growth. The availability of jobs attracts people to the region, whereas 

in times of recession, the reverse is true. Major economic recessions in the 1930s (1929–

1933, 1937–38), 1970s (1973–1975), 1990s (1990–1993), and 2000s (2007–2009) have 

had a negative impact on the region’s population growth. As a result, the annual average 

growth rate of population in the region during those periods was 2.5 percent, 1.5 percent, 

1.3 percent, and 0.9 percent, respectively. The growth of the motion picture, petroleum, 

and aircraft industries and the region’s reputation as the land of opportunity explain the 

tremendous growth in the region during the 1980s. It should be noted that the recession 

in the 1990s was the result of major cuts in the national defense budget which affected 

the region much more severely than the rest of the nation. The regional population over 

the last couple of decades has become increasingly home-grown Californians (Myers et 

al, 2010) as the major component of change has been natural increase.

4 Growth Forecast
The region currently faces serious challenges caused by the recent economic recession 

that began in December 2007. The region lost approximately 800,000 jobs from 2007 to 

2010. Although the economic recession officially ended in 2009, the region is still struggling to bring its economy back to the pre-recession level. The future growth will depend 

on how the region addresses its economic challenges. 

EMPLOYMENT

Both the economic recession and globalization have heavily affected the restructuring 

of the industrial sectors from manufacturing to more service oriented industries (see 

TABLE 3). In particular, both the construction and manufacturing sectors declined due to 

the recessions and globalization, respectively. The construction sector plays an important 

role in economic growth through diverse development activities. There is usually little 

development activity during the recession period. The overall share of the construction 

industry decreased from 5 percent in 1990 to 4 percent in 2010. At one point in 2005, 

its share reached 6 percent of all industry sectors and immediately declined to 4 percent in 2010 due to the economic recession. The manufacturing sector has consistently 

decreased its share from 17 percent in 1990 to 9 percent by 2000 and 8 percent in 2010. 

The manufacturing sector generally provides workers with higher pay than other sectors. 

With such a rapid decline in the manufacturing sector jobs, the economic quality of the 

region has declined. In contrast, services sectors including education, health services, 

and the leisure and hospitality sector showed an increase in their share of jobs in the 

region. Education and health service jobs increased its share from 17 percent in 1990 

to 22 percent in 2010, and leisure and hospitality sector jobs increased its share from 

8 percent in 1990 to 11 percent in 2010. 

TABLE 3 Regional Employment Trends, 1990–2010

1990 2000 2010 Difference 

(1990–10)

% Change 

(1990–10)

Jobs (‘000) 6,906 7,482 7,225 319 4.6

Jobs by NAICS

Agriculture & Mining (%) 1.4 1.1 1.0 -0.4 -24.3

Construction (%) 5.0 4.9 4.0 -0.9 -15.2

Manufacturing (%) 17.1 13.7 9.2 -8.0 -44.0

Wholesale Trade (%) 5.0 5.0 5.1 0.0 5.4

Retail Trade (%) 10.5 10.3 10.8 0.4 8.2

Transportation and 

Warehousing, and Utility (%) 4.4 4.7 4.8 0.3 12.2

Information (%) 3.6 4.3 3.5 -0.1 0.8

Financial Activity (%) 6.7 5.6 5.8 -0.9 -9.6

Professional and 

Business Services (%) 13.7 15.6 15.3 1.7 17.6

Education and Health 

Services (%) 17.0 19.1 22.0 5.0 35.3

Leisure and Hospitality (%) 8.3 8.9 10.6 2.3 33.2

Other Services (%) 3.8 3.9 4.0 0.2 10.5

Public Administration (%) 3.5 2.9 4.0 0.5 18.3

Total (%) 100.0 100.0 100.0 0.0

Note: (1) education and health; and (2) local ground transportation/USPS and utility sectors in Public 

Administration from CA EDD database were reassigned to (1) education and health services and (2) transportation and utility sector, respectively. Source: CA EDD and SCAG. 

Growth Forecast 5
URBANIZATION AND SUBURBANIZATION PATTERNS

Although the regional growth rate stabilized in the last 20 years, the urbanization and 

suburbanization of the region has continued (see TABLE 4). In 2010, Orange County 

exceeded three million people, and Riverside and San Bernardino Counties exceeded two 

million people each. Riverside County is now the third largest county in the region. The 

Counties of Riverside and San Bernardino increased their share of the population from 

17.7 percent in 1990 to 23.4 percent in 2010, while Los Angeles County decreased its 

share from 60.5 percent in 1990 to 54.4 percent in 2010. The fast growth of population, 

relative to employment, in Riverside and San Bernardino Counties has led to an imbalance of jobs and housing in the SCAG region, and poses serious transportation and air 

quality challenges.

TABLE 4 The County Share of the Regional Population, 1990–2010

County 1990 

Number

1990

%

2000 

Number

2000

%

2010 

Number

2010

%

Imperial 109,303 0.7% 142,361 0.9% 174,528 1.0%

Los Angeles 8,863,164 60.5% 9,519,338 57.6% 9,818,605 54.4%

Orange 2,410,556 16.5% 2,846,289 17.2% 3,010,232 16.7%

Riverside 1,170,413 8.0% 1,545,387 9.4% 2,189,641 12.1%

San 

Bernardino 1,418,380 9.7% 1,709,434 10.4% 2,035,210 11.3%

Ventura 669,016 4.6% 753,197 4.6% 823,318 4.6%

SCAG 14,640,832 100.0% 16,516,006 100.0% 18,051,534 100.0%

Source: US Census Bureau

ECONOMIC RECESSIONS AND GROWTH TRENDS

The period between 1990–2010 is characterized as a slow growth period. The population 

growth (3.4 million) and job growth (308,000) during the 1990–2010 period was seriously 

affected by two major economic recessions. 

The first recession started in 1990 and ended in 1993. This recession was caused 

primarily by federal defense budget cuts. Many defense workers in the region lost their 

jobs during the 1990–1993 period. The job losses in the region reached 500,000 during 

the period and the region’s unemployment rate ranged from 5.6 percent in 1990 to 9.7 

percent in 1993 (see FIGURES 1–3). This economic recession primarily affected domestic migration into and out of the region. During the 1990–1996 period, net outmigration 

reached nearly 1.1 million people as a result of net domestic out-migration, while net 

immigration was not affected (see FIGURES 7–8). As a result of the change in the components of population change, the percent change of population gradually declined from 2 

percent in 1990–1991 to 0.5 percent in 1994–1995 (see FIGURES 5–6). 

The second recession started in 2007 and ended in 2009. During the 2007–2010 period, 

the region lost 800,000 jobs. The region’s unemployment rate reached 12.3 percent in 

2010 (see FIGURE 1). The impact of this economic recession on migration was different 

from the recession in 1990–1993. This recession affected both net domestic and net 

immigration (see FIGURES 7–8). During the 2007–2010 period, nearly 420,000 people left 

the region as a result of net domestic migration. The level of net domestic migration in 

the recession of 2007–2010 was much smaller than in 1990–1993. The net immigrants in 

the recession of 2007–2009 are estimated at 250,000 (annual estimate of 83,000), which 

is much smaller than 767,000 (annual estimate of 128,000) during the 1990–1996. The 

second recession resulted in the lowest percent change (0.4 percent) in annual population 

during the past 20 year span. 

The number of births declined from 328,000 in 1990–1991 to 258,000 in 2009–2010 

(see FIGURE 9). Although there was a sign of increasing births from 2001–2002 to 

2007–2008, both recessions must have negatively affected the decision to have children.

With two recessions in 20 years, the region’s job estimates have moved up and down (see 

FIGURE 2). The region’s 6.9 million jobs in 1990 decreased to 6.4 million jobs in 1993. The 

region had an increase in jobs from 6.9 million in 1990 to 8 million in 2007. As a result 

of the second recession, the number of jobs was reduced from 8 million jobs in 2007 to 

7.2 million jobs in 2010. The percent change of jobs between 1990 and 2000 is only 4.5 

percent, which is much lower than the 23.4 percent population increase. Therefore, the 

population to employment ratio rapidly increased from 2.12 in 1990 to 2.51 in 2010 (see 

FIGURE 10).

6 Growth Forecast
FIGURE 1 Unemployment Rate, SCAG Region, 1990–2010

0%

4%

8%

12%

16%

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

FIGURE 2 Job Growth, SCAG Region, 1990–2010

0

2

4

6

8

10

199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Millions

Jobs

FIGURE 3 Percent Change of Jobs, SCAG Region, 1990–2010

-8%

-6%

-4%

-2%

0%

2%

4%

90-9191-9292-9393-9494-9595-9696-9797-9898-9999-0000-0101-0202-0303-0404-0505-0606-0707-0808-0909-10

Percent Change

FIGURE 4 Population Growth, SCAG Region, 1990–2010

0

4

8

12

16

20

199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Millions

Population

Growth Forecast 7
FIGURE 5 Percent Change of Population, SCAG Region, 1990–2010

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

90-9191-9292-9393-9494-9595-9696-9797-9898-9999-0000-0101-0202-0303-0404-0505-0606-0707-0808-0909-10

Percent Change

FIGURE 6 Components of Population Change, SCAG Region, 1990–2010

-150

-100

-50

0

50

100

150

200

250

300

350

90-9191-9292-9393-9494-9595-9696-9797-9898-9999-0000-0101-0202-0303-0404-0505-0606-0707-0808-0909-10

Thousands

Natural Increase Net Migration

FIGURE 7 Net Domestic Migration, SCAG Region, 1990–2010

-300

-250

-200

-150

-100

-50

0

50

90-9191-9292-9393-9494-9595-9696-9797-9898-9999-0000-0101-0202-0303-0404-0505-0606-0707-0808-0909-10

Thousands

Net Domestic Migration

FIGURE 8 Net Immigration, SCAG Region, 1990–2010

0

20

40

60

80

100

120

140

160

180

90-9191-9292-9393-9494-9595-9696-9797-9898-9999-0000-0101-0202-0303-0404-0505-0606-0707-0808-0909-10

Thousands

International Migration

8 Growth Forecast
FIGURE 9 Births, SCAG Region, 1990–2010

0

50

100

150

200

250

300

350

90-9191-9292-9393-9494-9595-9696-9797-9898-9999-0000-0101-0202-0303-0404-0505-0606-0707-0808-0909-10

Thousands

Births

FIGURE 10 Population to Employment Ratio, SCAG Region, 1990–2010

1.9

2.0

2.1

2.2

2.3

2.4

2.5

2.6

199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Population to Employment Ratio

Growth Forecast 9
Growth Forecast

The regional growth forecast is used as a key guide for future transportation investments 

in the SCAG region. The 2012–2035 RTP/SCS growth forecast was developed reflecting 

both the short term and long term perspectives. The latest 2010 Census data and 2011 

EDD data indicate lower population, households and employment for year 2010 than 

forecasted in the 2008 RTP. The region is still expected to grow over the RTP planning 

period (2008–2035)—adding 4.2 million new residents, 1.5 million new households, and 

1.7 million new jobs by 2035 (see FIGURES 11 and 12). 

FIGURE 11 Population, Employment, and Households, SCAG Region, 2008 

and 2035

17.9

7.7

5.8

22.1

9.4

7.3

0

5

10

15

20

25

Population Employment Households

Millions

2008 2035

FIGURE 12 Population, Employment, and Household Growth, SCAG Region, 

1970–2035

0

5

10

15

20

25

19701975198019851990199520002005201020152020202520302035

Millions

Population Employment Households

10 Growth Forecast
FIGURE 13 Percent Change of Population and Employment, SCAG Region, 

1970–2035

-6%

-4%

-2%

0%

2%

4%

6%

8%

1971 1980 1989 1998 2007 2016 2025 2034

Population Employment

POPULATION

The slower population growth pattern experienced in the last decade is expected to 

continue into the future. Between 2010 and 2035, the annual population growth rate will 

be only 0.9 percent, which is lower than the growth rate for the past 20 years. The region 

will grow mainly through natural increase (see FIGURES 16–18). 

The most salient demographic characteristics of the projected population in the region 

will be the aging of population and shifts in ethnic distribution (see TABLE 5 and 

FIGURES 14–15). With the aging of the baby boomer generation (born between 1946 and 

1964), the median age of the population is projected to increase from 34.2 in 2010 to 

36.7 in 2035. The share of the population 65 years old and over is projected to increase 

from 11 percent in 2010 to 18 percent in 2035, while the share of the population less 

than 65 years old decreases from 89 percent in 2010 to 82 percent in 2035. In particular, 

the share of the population of the working age 16–64 has its share sharply decline from 

65 percent to 60 percent during the projection period. This implies a future shortage of 

workers. With the increasing share of the older population and the decreasing share of 

the working age population, the aged dependency ratio (i.e., the number of aged people 

per hundred people of working age) is projected to increase from 17 percent in 2010 to 

30 percent in 2035 (an increase of 13 percent during the period). 

The other characteristic of the projected population is the racial/ethnic diversity (see 

TABLE 5). The region already has a high level of racial/ethnic diversity in 2010 with a 

Hispanic population of 45 percent, a non-Hispanic White population of 34 percent, a nonHispanic Asian population and others of 14 percent, and a non-Hispanic Black population 

of 7 percent. The region’s racial/ethnic composition is projected to exhibit a rapid change 

toward a majority Hispanic population of 56 percent in 2035, while the share of the nonHispanic White population is projected to drop sharply to 22 percent.

Growth Forecast 11
TABLE 5 Demographic Characteristics of Regional Population, 2010, 2020, and 2035

2010 2020 2035 Difference 

(2010–2035)

% Change 

(2010–2035)

Total population (‘000) 18,104 19,663 22,091 3,987 22.0

Births per 1,000 population (05–10, 15–20, 30–35) 15.4 15.3 14.5 -0.9

Total fertility rate (per woman) (05–10, 15–20, 30–35) 2.2 2.2 2.2 0.0

Deaths per 1,000 population (05–10, 15–20, 30–35) 6.2 6.4 7.6 1.4

Natural increase (%) (05–10, 15–20, 30–35) 143.9 111.0 95.4

Net migration (%) (05–10, 15–20, 30–35) -43.9 -11.0 4.6

Age composition of population 

Persons under 16 years old (%) 23.2 22.6 21.9 -1.3 15.2

Persons 16–64 years old (%) 65.9 63.6 60.1 -5.8 11.7

Persons 65 years old and over (%) 10.9 13.8 18.0 7.1 101.5

Total 100.0 100.0 100.0

Median age 34.2 35.2 36.7 2.4

Dependency ratio*

Child dependency ratio** 35.2 35.5 36.5 1.3

Aged dependency ratio*** 16.6 21.6 29.9 13.3

 Total 51.8 57.2 66.4 14.6

Ethnic composition of population

Non-Hispanic White persons (%) 33.8 28.8 22.3 -11.5 -19.0

Non-Hispanic Black persons (%) 7.0 6.7 6.2 -0.8 8.5

Non-Hispanic Asian & Other persons (%) 14.0 14.5 15.1 1.2 32.6

Hispanic persons (%) 45.3 49.9 56.3 11.0 52.2

Total 100.0 100.0 100.0

Note: *The number of children (age 0-15) and aged persons (age 65 and over) per hundred people of working age (age 16–64). ** The number of children per hundred people of working age. *** The number of aged 

people per hundred people of working age. Source: SCAG 

12 Growth Forecast
FIGURE 14 Population Pyramids, SCAG Region, 2010 and 2020

54321012345

 0- 4

 5- 9

10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84

85+

Percentages

Age

2010 (shaded) & 2020

Males Females

Males Females

FIGURE 15 Population Pyramids, SCAG Region, 2010 and 2035

54321012345

 0- 4

 5- 9

10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84

85+

Percentages

Age

2010 (shaded) & 2035

Males Females

Males Females

FIGURE 16 Components of Population Change, SCAG Region, 1970–2035

-150

-100

-50

0

50

100

150

200

250

1971 1978 1985 1992 1999 2006 2013 2020 2027 2034

Thousands

Natural Increase Net Migration

FIGURE 17 Births and Deaths, SCAG Region, 1970–2035

0

50

100

150

200

250

300

350

1971 1978 1985 1992 1999 2006 2013 2020 2027 2034

Thousands

Births Deaths

Growth Forecast 13
FIGURE 18 Net Immigration and Net Domestic Migration, SCAG Region, 

1990–2035

-300

-250

-200

-150

-100

-50

0

50

100

150

200

1991 1997 2003 2009 2015 2021 2027 2033

Thousands

Net Immigration Net Domestic Migration

HOUSEHOLDS

As the population ages and diversifies in the region during the projection period, householders follow the same path (see TABLE 6). The number of householders 65 years or 

older will reach more than 2 million in 2035 with the addition of one million households 

in the next 25 years. The growth of the senior householders will represent more than 

70 percent of the projected household growth in the region. However, the share of householders in the younger age groups will decline. In particular, householders 45–54 years 

old will show an absolute decline.

The racial/ethnic distribution of householders will also change. The non-Hispanic White 

householders will decrease from 44 percent in 2010 to 29 percent in 2035, while 

Hispanic householders will increase from 35 percent in 2010 to 47 percent in 2035. The 

household size will also decline from 3.04 in 2010 to 2.97 in 2035 as a result of the 

dynamics of the changing age and racial/ethnic composition of the projected population 

and declining birth rates.

TABLE 6 Characteristics of Regional Households, 2010, 2020, and 2035

2010 2020 2035 Difference 

(2010–35)

% Change 

(2010–35)

Total households (‘000) 5,853 6,458 7,325 1,472 25.1

Age composition of householders

15–24 (%) 3.9 3.4 3.4 -0.5 7.8

25–34 (%) 16.4 16.9 15.4 -1.0 17.8

35–44 (%) 21.3 18.5 18.4 -2.8 8.4

45–54 (%) 22.7 19.4 17.7 -5.0 -2.3

55–64 (%) 17.0 18.8 15.6 -1.4 14.8

65–74 (%) 10.1 13.4 14.9 4.8 84.7

75+ (%) 8.7 9.5 14.6 5.9 110.1

Total 100.0 100.0 100.0

Ethnic composition of householders

Non-Hispanic White householders (%) 43.7 37.3 28.8 -14.9 -17.6

Non-Hispanic Black householders (%) 8.0 7.9 7.5 -0.6 16.0

Non-Hispanic Asian & Other 

householders (%) 13.7 14.9 16.3 2.6 48.7

Hispanic householders (%) 34.5 39.9 47.4 12.9 72.0

Total 100.0 100.0 100.0

Average household size

Non-Hispanic White households 2.3 2.3 2.2 -0.1

Non-Hispanic Black households 2.6 2.5 2.4 -0.2

Non-Hispanic Asian & Other 

households 3.1 2.9 2.7 -0.4

Hispanic households 4.0 3.8 3.5 -0.5

Total 3.0 3.0 2.9 -0.1

Source: SCAG

14 Growth Forecast
EMPLOYMENT

Two economic recessions and globalization were the major factors behind the slow 

growth in the region over the past 20 years. Although recessions and further globalization 

are expected, the region is still expected to add 2.2 million jobs, from 7.2 million in 2010 

to 9.4 million in 2035. The annual average growth rate in jobs will be over 1 percent. 

The region will be recovered from the recent economic recession in the near future. In 

the long run, the regional economy will get back to normal with reasonable labor force 

participation rates and unemployment levels.

The region’s industrial mix, however, will experience continuous change over time due 

to globalization (see TABLE 7). The region will transform its industrial structure from 

manufacturing oriented industries to the service oriented industries. The construction 

sector will regain its normal share by increasing from 4 percent in 2010 to 6.5 percent in 

2035. Selected service sectors including professional and business services, education 

and health services will grow by more than one million and their share will increase from 

37 percent in 2010 to 40 percent in 2035. The share of employment in the manufacturing 

sector will continue to decrease from 9 percent in 2010 to 8 percent in 2035 as a result 

of continued globalization.

There has been a concern about the economic performance of the region in recent years. 

SCAG initiated a discussion of an economic growth strategy among local and diverse 

stakeholders in the region. The economic growth strategy intends to improve the economic quality of life by maintaining the manufacturing sector and other traditional high 

income job sectors.

TABLE 7 Regional Employment Projections by Industry Sectors, 

2010, 2020, and 2035

2010 2020 2035 Difference 

(2010–35)

% Change 

(2010–35)

Total jobs (‘000) 7,225 8,414 9,441 2,216 30.7

Jobs by NAICS

Agriculture & Mining (%) 1.0 0.9 0.8 -0.2 5.5

Construction (%) 4.0 6.0 6.5 2.4 108.6

Manufacturing (%) 9.2 8.6 7.6 -1.6 8.0

Wholesale Trade (%) 5.1 4.9 4.8 -0.3 23.3

Retail Trade (%) 10.8 10.5 10.2 -0.6 23.1

Transportation and 

Warehousing, and Utilities (%) 4.8 4.6 4.6 -0.2 26.5

Information (%) 3.5 3.3 3.1 -0.4 16.2

Financial Activity (%) 5.8 5.7 5.5 -0.2 25.1

Professional and 

Business Services (%) 15.3 16.3 16.8 1.5 43.1

Education and Health 

Services (%) 22.0 22.0 23.1 1.1 37.2

Leisure and Hospitality (%) 10.6 9.7 9.6 -1.0 18.4

Other Services (%) 4.0 4.1 4.1 0.2 36.2

Public Administration (%) 4.0 3.3 3.3 -0.7 8.3

Total (%) 100.0 100.0 100.0 0.0

Source: CA EDD and SCAG

Growth Forecast 15
COUNTY DISTRIBUTION OF POPULATION AND EMPLOYMENT

According to the SCAG growth forecast, Riverside and San Bernardino Counties will 

grow faster and increase their share of regional jobs while Los Angeles and Orange 

Counties decrease their share, between 2010 and 2035 (see TABLE 8). During the same 

period, in both Riverside and San Bernardino Counties the population to employment 

ratio will decline as relatively more jobs than population are added. Overall the population to employment ratio in each of the six counties will converge toward the regional 

average ratio.

TABLE 8 Regional Population and Employment Shares by County, 

2010, 2020, and 2035

County Share of Regional Population Share of Regional Employment

2010 2020 2035 2010 2020 2035

Imperial 1% 1% 1% 1% 1% 1%

Los Angeles 54% 53% 51% 57% 54% 51%

Orange 17% 17% 15% 21% 19% 19%

Riverside 12% 13% 15% 8% 11% 13%

San 

Bernardino 11% 12% 12% 9% 10% 11%

Ventura 5% 5% 4% 5% 5% 4%

SCAG 100% 100% 100% 100% 100% 100%

Source: US Census Bureau and SCAG

COMPARISON OF 2008 RTP GROWTH FORECAST 

AND 2012–2035 RTP/SCS GROWTH FORECAST

The growth forecast for the 2012–2035 RTP/SCS reflects the recent trends in components (e.g., births, deaths, domestic migration, international migration) of population 

growth and in job growth by sectors. The 2012–2035 RTP/SCS growth forecast for 2035 

is lower than that of the 2008 RTP growth forecast (See FIGURES 19–20).

FIGURE 19 Population Growth, SCAG Region, 2003–2035: 

2008 RTP vs. 2012–2035 RTP/SCS

17.6

18.9

21.5

24.1

17.1 17.9

19.7

22.1

0

5

10

15

20

25

30

2003 2008 2020 2035

Millions

2008 RTP 2012-2035 RTP/SCS

Source: CA DOF and SCAG

16 Growth Forecast
FIGURE 20 Employment Growth, SCAG Region, 2003–2035: 

2008 RTP vs. 2012–2035 RTP/SCS

7.5

8.1

9.2

10.3

7.5 7.7

8.4

9.4

0

2

4

6

8

10

12

2003 2008 2020 2035

Millions

2008 RTP 2012-2035 RTP/SCS

Source: CA EDD and SCAG

Section III: Forecasting Framework, Methodology 

and Assumptions 

Forecasting Framework 

SCAG began developing the long term growth forecasts for the 2012–2035 RTP/SCS in 

the middle of the Great Recession (2007–2009). The traditional long term perspective, 

which might not reflect the on-going economic trends and the frequently updated short 

term economic forecast, might result in a serious bias in the short term population projections. The major sources of potential projection errors include: (1) the unstable/uncertain nature of the key economic-demographic assumptions, in particular, unemployment 

rate and migration in the short term framework; (2) the timing and reasonableness of 

population projections (and assumptions) by the US Census Bureau and CA DOF; (3) the 

lack of relevant statistical data in a timely manner; and 4) the significant gap in population estimates between the US Census Bureau and CA DOF.

SCAG uses the BULA (Balance, Uncertainty, Latest, and Adaptive) approach toward 

developing the regional growth forecasts. First, the regional forecasts should maintain the 

Balance between employment, population, and households due to their interrelationship, 

assuming that employment growth is a driving force of regional population and household 

growth. The employment-population-household (EPH) forecast framework has been the 

basis for developing the growth forecast for the SCAG region.

Second, the regional forecasts embrace the forecast Uncertainty as the recent population 

projections of US Census Bureau (2008) and California Department of Finance (2007) are 

quickly outdated in their consideration of the actual and projected international immigration. A range of regional growth forecasts are derived to reflect the different paths of 

national population growth with different immigration assumptions and their impact on 

national growth.

Third, SCAG makes an effort to use the Latest demographic and economic assumptions 

to ensure that the growth forecasts are current. The normative assumptions are also 

introduced into the forecast process as needed. For example, household formation behavior, measured in headship rates, is used to measure the housing needs of the projected 

population. The headship rates of minority ethnic populations did not show a convergence toward the headship rates of the White population. SCAG introduces the socially 

acceptable assimilation pattern into the assumption of the convergence of headship rates 

toward the White headship rates.

Finally, SCAG approaches development of growth forecasts in an Adaptive and flexible 

way. Many demographic and economic statistics are unstable and quickly outdated as 

the recession continued. SCAG collected those information materials and regularly went 

through the expert panel review (2009, 2010, 2011). Through the BULA approach, SCAG 

developed more realistic and accurate regional growth forecasts. SCAG annually updated 

the short term forecasts with the quickly changing demographic and economic conditions 

between 2009 and 2011 before adopting the growth forecast in April 2012.

Growth Forecast 17
UNCERTAINTY IN A REGIONAL GROWTH FORECAST 

AND EXPERT OPINION

In a rapidly changing and volatile economic environment, the usual economic and population projection models do not produce accurate projections. This is particularly true of 

the short term projections due to the unstable nature of the economic and demographic 

assumptions. The average approach (e.g., average of the newly available economic or 

demographic projections) might be a preferred approach for updating the new short term 

economic and demographic projections (Smith et al, 2001). Timely developed private 

sources of the near term or long term economic and demographic projections are available with a cost, although the demographic projections tend to rely on the most recent 

series of projections by the U.S. Census Bureau or the state statistical agency. The 

collective expert opinion could be a useful reference to reduce the short term and long 

term projection errors. The following is a brief summary of the expert opinion on critical 

factors and key economic and demographic assumptions collected through three panels 

of experts meetings between 2009 and 2011.

The Panel of Experts Meeting (2009)

A first panel of experts meeting was held on May 15, 2009. The panel was composed of 

fifteen experts in the field of regional and national economics and demography. These 

experts have developed economic or demographic forecasts for a long time or the agencies that they work for might have produced economic or demographic forecasts. They 

came from a variety of public or private organizations. Nearly 50 percent of the panel 

members come from universities in California (e.g., University of Southern California, 

University of California Los Angeles, University of California, Riverside, University of 

California, Santa Barbara, California State University, Long Beach, California State 

University, Fullerton). Other panel members come from state or local government agencies (e.g., Los Angeles Economic Development Corporation, South Coast Air Quality 

Management District, California Department of Finance), private consulting firms (e.g., 

Regional Economic Models, Inc., Beacon Economics, DB Consulting). Experts were provided with a list of questions regarding assumptions with background information (e.g., 

historical data and a preliminary range of forecasts by the moderator) a few days before 

the panel of experts meeting.

The survey questions focused on three major aspects of job and population projections: 

1) short term economic outlook; 2) long term economic assumptions (e.g., regional share 

of the national job projections, retirement age of workers, labor force participation rate); 

and 3) long term demographic assumptions (e.g., fertility rate, life expectancy, and net 

international immigration). The survey questions included, but were not limited to: 1. 

How deep and long will the recession be? How will the recession affect the economy and 

prospects for housing in 2020?; 2. After the recession ends, will national job growth be 

equal to, greater than, or less than the U.S. job growth rate from the current U.S. BLS 

projection?; 3. Will workers retire at an older age in 2020/2035 than now?; 4. How will 

California’s share of U.S. jobs change in the future?; 5. How will the SCAG region’s share 

of California jobs change in the future?; 6. How does the panel evaluate the new Census 

Bureau U.S. population projections and related assumptions of fertility rates, life expectancy, and international immigration?; 7. Will labor force participation rates continue to 

increase for older workers?

First, the short term economic outlook is focused on understanding the timing of the bottom of the national and regional economic recession. According to the responses of the 

experts, the economic recession measured in job losses in the SCAG Region would most 

likely end in 2010 (2 respondents), 2011 (7 respondents), or 2012 (3 respondents). Once 

the economy is recovered from the recession, it might take several years for the unemployment rates to return to a normal range (5–8 percent). Five of seven responded that, 

after the recession ends, regional job growth would be equal to the annual average U.S. 

job growth rate (1.04 percent between 2006 and 2016) from the current 2007 US BLS job 

projection. Two respondents said that the regional job growth would be greater than the 

U.S. job growth rate from the current 2007 US BLS job projection. 

Second, the regional share of the national job projections was surveyed through two 

different but related questions about 1) California’s share of U.S. jobs for 2020 and 2035 

and 2) the SCAG Region’s share of California jobs for 2020 and 2035. Twelve experts 

responded to both questions above. The survey results imply that the regional share of 

the national job projection ranges from 4.3 percent (minimum) to 5.3 percent (maximum) 

in 2020 and 3.8 percent (minimum) to 5.5 percent (maximum) in 2035 (see TABLE 2). 

The gap between the minimum and maximum is much bigger in 2035 than in 2020. The 

median regional share remains constant at 5 percent for both 2020 and 2035, which is 

0.2 percent lower than the most current regional share (5.2 percent). The overall survey 

18 Growth Forecast
responses are not optimistic about the SCAG region’s relative economic competitiveness 

in the national economy, although the survey questions did not directly touch on “the 

regional share of the national job growth”. The labor force participation rate (retirement) 

trends in the SCAG region will be consistent with the national projection, and will support 

the assumption that workers in the region will tend to retire at an older age in the future.

Third, there is no or little concern about the national and regional assumptions of the 

future fertility rates and life expectancy. The current regional average total fertility rate of 

2.1 is assumed to decline slightly to 2.0 and 1.9 in 2020 and 2035, respectively, during the projection period. The regional life expectancy will increase consistent with the 

national life expectancy’s increase during the projection period. The national immigration 

assumptions are major concerns of the panel members. The US Census Bureau released 

one set of long-term population projections for the nation in August 2008. These baseline 

projections included higher immigration projections, which resulted in an increase in the 

projected population growth to 2050. The key question is whether SCAG will adjust the 

current international immigration upward in light of the higher Census Bureau projections. 

Ten of the thirteen panel members said No to the upward adjustment of the international 

immigration assumption. 

The Panel of Experts Meeting (2010)

Two major projections from the US Census Bureau and US Bureau of Labor Statistics 

(BLS) were released since the previous year’s panel of the expert meeting. In December 

2009, the US Census Bureau released alternative sets of population projections with different immigration assumptions. The 2009 national population projections are a supplemental series to the 2008 national population projections released on August 14, 2008, 

and provide results for differing assumptions of net international migration (http://www.

census.gov/population/www/projections/2009projections.html). All other methodology 

and assumptions of mortality and fertility rates, are the same as those used in the 2008 

national population projections. The lower immigration assumption, which looks reasonable in light of the recent trends, results in lower national population. When compared 

with the baseline projections released in August 2008, the gap between the low migration alternative and the baseline is 4.5 million (1.3 percent of the baseline population) in 

2020, 9.7 million (2.5 percent of the baseline population) in 2035. In December 2009, 

BLS released new job projections to 2018. These projections were based on the national 

population projections released by the US Census Bureau in August 2008. Since there is 

only 1.3 percent difference in 2020 population between the low migration alternative and 

the baseline, the potential impact of the new low immigration alternative on job projections would be negligible. International immigration, in particular, unauthorized immigrants show a rapid decline from 11.8 million in 2007 to 11.6 million in 2008, and to 10.8 

million in 2009. The decline in just one year between 2008 and 2009 reaches 800,000, 

which would be the likely impact of the recent economic recession.

A second panel of experts meeting was held on May 28, 2010, just one year after the 

first meeting held in 2009. Panel members, who participated in the first panel of experts 

meeting, were invited to the second panel of experts meeting. Eleven members attended 

the meeting to: 1) revisit the potential impact of economic recession and recovery in the 

national economy on the regional economy; 2) provide input on the recent trends in immigration and U.S. population growth; and 3) review the recent trends in the region’s share 

of the national jobs. 

With those newly available data in mind, the panel members participating in the second 

panel of experts meeting provided input to SCAG staff. First, the panel thought that job 

losses in the region would end in 2010 or 2011 in the 2009 meeting. While panel members differed on the size and timing of the recovery, the panel did not think the recession 

would affect the size of the region in 2020 and 2035. Some panel members thought there 

could be a lingering impact on unemployment rates, income growth and housing markets. 

Second, U.S. population growth affects the pool of people and jobs in the nation. For any 

given SCAG share of future growth, higher U.S. immigration and population growth will 

push the SCAG region growth higher and vice versa. U.S. immigration and population 

growth is likely to be maintained at the lower level for the next 5 to 10 years. 

Third, job shares dropped in 2008 and 2009, and state and regional job losses were 

larger than in the U.S. The majority of panel members supported the downward revisions of the regional shares of the national jobs. We are not sure if these declines in the 

regional job shares are temporary, based on the sharp decline in construction. There is 

a possibility that these declines might be a permanent shift because of the result of the 

long term demographic trends toward the aging of population, or because of the lack of 

the timely development and implementation of economic growth policy and strategy

Growth Forecast 19
20 Growth Forecast

The Panel of Experts Meeting (2011)

A third panel of experts survey was conducted through email on May 2011. Most of the 

panel members, who participated in the first and second panel of expert meeting, were 

requested to answer survey questions on the demographic and economic trends and 

assumptions. Thirteen members responded to questions on: 1) economic recovery of the 

nation; 2) immigration assumptions at the national level; 3) the projected region’s share 

of the national jobs; 3) household projections. There was an overall consensus on the 

following few issues: 1) panelists expect the nation to be fully recovered by 2020 from 

the recession; 2) panelists expect U.S. unemployment to be between 4.5 percent and 6.5 

percent; 3) panelists see no need to change the immigration assumptions from last year; 

4) while not all panelists gave clear answers, no panelist said the relationship between 

projected jobs, population and households was not reasonable. On whether the SCAG 

region would see job growth faster than the nation, six of ten respondents said yes, while 

only two said no. The housing questions are particularly hard for short email answers. 

The question on whether household projections should be based purely on demographics 

was not clear to panelists. Some panelists wanted to comment on changing demographics and the implications for housing. Most panelists think demographics are the major 

determinant in the long run but had some concerns about 2020. Seven of 10 respondents 

agreed that market conditions would prevent “enough” housing from being built by 2020.

A RANGE OF REGIONAL GROWTH FORECASTS AND LOCAL INPUT 

SCAG assumes that any set of growth forecasts (population, employment, and households) within a range of growth forecasts might be a plausible choice set. The scenario 

was developed in the following way: First, international immigration, one of the major 

demographic assumptions in US population projections by the US Census Bureau (2008), 

is not consistent with the recent trends, and is adjusted downward to reflect the recent 

trends. This downward adjustment of international immigration and the resulting population reduces the baseline US job projections due to the reduced labor supply. A mid 

national employment forecast is developed as a result of expert panel review (2009), is 

used as a key guide to develop a range of regional growth forecasts. 

Second, a range of the regional employment forecasts (low, mid, high) is derived using 

a range of the regional shares of the national jobs from the expert panel review. A range 

of regional employment forecasts is translated into a range of the regional population 

forecasts (low, mid, high) using a set of demographic assumptions. All related economic 

and demographic assumptions (e.g., unemployment rates, labor force participation rates, 

immigration level, fertility rates, and survival rates, etc) for three different employment 

levels remain unchanged during the conversion process. 

Third, a range of the regional population forecasts are translated into a range of the 

regional household forecasts using six different methods (e.g., cohort method, assimilation method, 2008 headship rate method, trend method, mid-trend method, mid-assimilation). A trend method produces the fewest households, while the assimilation method 

produces the highest number of households, given the same population level. SCAG uses 

a mid-trend assumption as a baseline method to convert population into households. It 

is based on a combination of the extrapolation of headship rates by race/ethnicity and 

gender and assimilation assumptions of the Hispanic and Asian headship rates. The mid 

term household projection is used with the population and employment scenarios to 

generate three scenarios of regional growth. TABLE 9 presents the three growth scenarios 

for population, households, and employment as well as the local input scenario. The local 

based scenario is consistent with the low scenario, and reflects the recent trends. All of 

these scenarios were derived before the 2010 Census redistricting data was available. 

TABLE 9 Regional Growth Forecasts (in Thousands): 

Three Scenarios and Local Input

Scenario EPH 7/1/2010 7/1/2020 7/1/2035 2010–2020

Low

EMP 7,458 8,526 9,423 1,068

POP 19,020 20,692 23,039 1,673

HHLD 5,925 6,569 7,341 644

Mid

EMP 7,458 8,735 9,783 1,277

POP 19,020 21,111 23,790 2,091

HHLD 5,925 6,692 7,581 767

High

EMP 7,458 9,172 10,426 1,714

POP 19,020 22,000 25,128 2,981

HHLD 5,925 6,969 8,020 1,044

Local Input 

(12/2010)

EMP 7,352 8,559 9,579 1,101

POP 18,997 20,600 22,930 1,580

HHLD 5,933 6,545 7,365 620

Source: SCAG
Growth Forecast 21

2010 DECENNIAL CENSUS AND RE-BENCHMARK 

OF REGIONAL GROWTH FORECASTS

City and County level demographic data (Redistricting Data [P.L. 94-171]) for the State 

of California was released by the US Census Bureau on March 8, 2011. TABLES 10 and 

11 show population and household counts from the 2000 and the 2010 Census (April 1st 

figures) for each county in the SCAG region. The tables also present SCAG’s preliminary 

projections of population and households by county for July 2010. Highlights from the 

table include: (1) Redistricting data from the 2010 Census showed that the population in 

the SCAG region was 18.05 million as of April 1, 2010, which is 1.5 million higher (9.3 

percent) than the regional population count for the 2000 Census (16.5 million). The 2010 

Census population figure for the SCAG region was almost 1 million lower (4.9 percent) 

than SCAG’s preliminary population projections for 2010, which were provided before the 

publication of 2010 Census and primarily resulted from population estimates from the 

California Department of Finance (DOF); and (2) The 2010 Census redistricting data also 

counts the number of households in the SCAG region at 5.8 million, which is 461,000 

higher (8.6 percent) than the regional household count for the 2000 Census (5.4 million). 

The 2010 Census household figure for the SCAG region was about 85,000 lower (1.4 

percent) than SCAG’s preliminary household projections for 2010, which are based on 

household estimates from the CA DOF. 

On March 4, 2011, EDD released state and county estimates of wage and salary jobs 

for 2010 and adjustments to its previously released 2008 and 2009 job estimates (see 

TABLE 12). Highlights from the table include: (1) The new job data indicates that employment in the region totaled 7.22 million in 2010, about 128,000 (1.7 percent) less than 

SCAG’s preliminary employment projections of 7.35 million; (2) Among counties in the 

SCAG region, job losses were much more severe in Los Angeles, Orange and Ventura 

Counties than previously projected; and (3) The region lost almost 800,000 jobs (7.9 percent) from 2007 to 2010. During this period, for every 100 jobs lost in the United States, 

17 were in California, and of those, 9 were lost in the SCAG region.

TABLE 10 Regional and County-Level Population Change, 2000-2010

County

Population

4/1/2000 Census 4/1/2010 Census 7/1/2010 SCAG*

Imperial 142,361 174,528 191,215

Los Angeles 9,519,338 9,818,605 10,451,374

Orange 2,846,289 3,010,232 3,181,814

Riverside 1,545,387 2,189,641 2,203,587

San Bernardino 1,709,434 2,035,210 2,123,624

Ventura 753,197 823,318 845,314

SCAG 16,516,006 18,051,534 18,996,928

Note: *Projected based on Local Input and California Department of Finance (DOF), and produced before 

the publication of 2010 Census. Source: US Census Bureau and SCAG

TABLE 11 Regional and County-Level Households, 2000 and 2010

County

Households

4/1/2000 Census 4/1/2010 Census 7/1/2010 SCAG*

Imperial 39,384 49,126 53,550

Los Angeles 3,133,774 3,241,204 3,270,353

Orange 935,287 992,781 1,012,627

Riverside 506,218 686,260 705,645

San Bernardino 528,594 611,618 621,772

Ventura 243,234 266,920 269,170

SCAG 5,386,491 5,847,909 5,933,117

Note: *Projected based on Local Input and California DOF, and produced before the publication of 2010 

Census. Source: US Census Bureau and SCAG
22 Growth Forecast

TABLE 12 Regional and County-Level Employment Estimates, 2008–2010 

County

Employment

SCAG Preliminary Projection* EDD Revised

2008 2009 2010 2008 2009 2010

Imperial 61,504 56,033 50,561 62,449 58,668 58,687

Los Angeles 4,340,344 4,284,475 4,228,607 4,460,171 4,184,002 4,123,262

Orange 1,624,061 1,620,241 1,510,928 1,621,910 1,499,723 1,490,318

Riverside 663,950 618,986 574,023 651,662 600,250 586,234

San 

Bernardino 700,603 677,794 654,985 702,424 652,840 640,497

Ventura 347,720 340,492 333,264 348,380 329,159 325,672

SCAG 7,738,182 7,598,021 7,352,368 7,846,995 7,324,642 7,224,670

Note: * Projected based on Local Input and CA EDD. Source: CA EDD and SCAG 

SCAG incorporated the 2010 Census (Redistricting Data [P.L. 94-171]) and 2011 

Employment Development Department data into the 2012–2035 RTP/SCS and RHNA 

process. Local jurisdictions within the SCAG region participated in the 20-month review 

and input process for developing the growth forecast dataset. SCAG updated the base 

year 2008 estimates by backcasting new 2010 Census data and updated 2020 and 2035 

growth forecast by using the growth increments for 2008–2020 and 2020–2035 from the 

most recent database (December 2010). 

TABLE 13 Regional Growth Forecasts (in Thousands): Before and After 

Incorporation of 2010 Census and 2011 EDD Database 

RTP 2000 2010 2020 2035 2010–2020

EMP

Before Census

7,440 7,352 8,559 9,579 1,207

After Census 7,225 8,431 9,451

POP

Before Census

16,517 18,970 20,600 22,930 1,630

After Census 18,052 19,681 22,012

HHLD

Before Census

5,386 5,918 6,545 7,365 628

After Census 5,848 6,476 7,296

POP/

HHLD

Before Census

3.07

3.21 3.15 3.11 2.6

After Census 3.09 3.04 3.02

POP/

EMP

Before Census

2.22

2.58 2.41 2.39 1.35

After Census 2.5 2.33 2.33

Note: Population and household estimates for 2000 and 2010 are based on the Decennial Census and 

benchmarked to April 1st for both Census years. Employment estimates and projections are based on the 

annual average. Source: US Census Bureau and SCAG 

Forecasting Methodology and Assumptions

The regional growth forecast for the 2012–2035 RTP/SCS was developed using the 

regional forecast methodology used in the development of the 2008 RTP growth forecast 

(see SCAG’s growth forecast report for 2008 RTP: http://www.scag.ca.gov/rtp2008/pdfs/

finalrtp/reports/fGrowthForecast.pdf). The following is an overall approach toward developing the regional growth forecast for the 2012–2035 RTP/SCS.

FORECASTING METHODOLOGY 

SCAG projects regional population using the cohort-component model. The model computes population at a future point in time by adding to the existing population the number 

of group quarters population, births, and persons moving into the region during a projection period, and by subtracting the number of deaths and the number of persons moving 

out of the region. The patterns of migration into and out of the region are influenced by 

the availability of jobs. The preliminary regional and county growth forecast of population 
Growth Forecast 23

and households for the 2012–2035 RTP/SCS was derived using the updated economic 

and demographic trends and perspectives. 

The preliminary city population and household forecast was derived by multiplying the 

2008 RTP jurisdiction’s share of the county growth delta during the forecast horizon by 

the updated county household growth delta during the same period. The jurisdictions’s 

relative growth pattern in the 2008 RTP growth forecast remained constant. The jurisdiction level household size and the group quarters population from the recent DOF estimates were incorporated into the new database. 

Regional employment is projected using a shift-share model. The shift-share model 

computes employment at a future point in time using a regional share of the nation’s 

employment. The preliminary regional and county growth forecast of employment for the 

2012–2035 RTP/SCS was derived using updated economic and demographic trends and 

perspectives. The preliminary jurisdiction employment forecast was derived by using a 

constant-share method. The jurisdiction’s share of county jobs for each sector for the 

base year is assumed to remain constant during the forecast years. By using the constant 

share method, the jurisdiction’s job growth by sector will be determined by the different 

growth of the specific sector by county. If a city in Los Angeles County is specialized in a 

specific industry (e.g., manufacturing), its future job growth will be severely affected by a 

declining pattern of manufacturing sector in Los Angeles County. The initial job forecasts 

became a basis for the future local input process. 

KEY REGIONAL ASSUMPTIONS

Demographic and economic assumptions play a decisive role in determining the size of 

population, households, and employment in the future. Population size is projected by 

identifying the demographic rates (e.g., fertility rate, survival rate, migration rate) of the 

population cohort. The region’s total fertility rate remains at 2.2, which is slightly higher 

than the replacement level of 2.1, during the projection period. The total fertility rate of 

Hispanic women gradually declines from 2.64 in 2010 to 2.49 in 2035, while other racial/

ethnic groups’ rates tend to remain constant during the projection period. The total fertility rate of recent Asian and Hispanic immigrants is assumed to be at a higher level than 

the long-term residents of the same ethnic origin. 

The region’s life expectancy at birth improves at the same rate as that of the nation’s life 

expectancy improvement as determined by the US Census Bureau’s 2008 Projection during the projection horizon. 

Domestic migration is fluctuating and is directly influenced by labor demand derived 

from regional employment projections. International net immigration is kept constant at 

104,000, which is an annual average of international net immigration of the region during the 1990–2010 period. The share of racial/ethnic domestic (from within the nation) 

migrants changes along with the changing population size of the racial/ethnic group. For 

example, the share of Hispanic migrants increases while that of the non-Hispanic Whites 

decreases. In addition to demographic assumptions, three additional translation factors 

are needed to link employment projections to population projections. They are labor force 

participation rates, the implied unemployment rates, and double jobbing rates. First, labor 

force participation rates play an important role in translating the labor force demand into 

labor force supply or vice versa. The labor force participation rates projected in the latest U.S. Bureau of Labor Statistics (BLS) projections for 2016 were used as the starting 

point for projecting labor force participation rates for 2020 and 2035. The projected labor 

force participation rates for the population over 55 are especially important as a large 

share of labor force growth will be in this age group. SCAG kept the projected 2016 labor 

force participation rates for the population under 55 for the years 2020 and 2035. SCAG 

increased the projected labor force participation rates for population groups age 55 and 

above for 2020 and 2035. These increases reflect a continuation of the large increases 

seen in recent years and projected by BLS to 2016. They reflect the better health and 

life expectancy of the workforce and the tendency for people to work longer for financial 

reasons. As a result of the projected increases in age-related labor force participation 

rates, the overall labor force participation rate is projected to slightly decline from 60.4 

percent in 2010 to 59.4 percent in 2035. Second, some workers might keep two or more 

jobs. The double jobbing rate is assumed to be around 4.5 percent, and remain constant 

during the projection period. Third, the implied unemployment rate is derived by matching labor supply estimated from population projections with workers estimated from job 

projections. The panel of experts suggested that the acceptable implied unemployment 

rate ranges from 5 percent to 8 percent. The current projection assumes a 5 percent 

unemployment rate during the projection period (2015–2035). Finally, the most important consideration is the reasonable regional share of national jobs. Currently the SCAG 
24 Growth Forecast

region’s share of the national jobs is assumed to be 5.2 percent, and remains constant 

during the projection period.

TABLE 14 Key Regional Assumptions

Race/Ethnicity 2005–10 2015–20 2030–35

Fertility Rate

White (NH**) 1.7 1.7 1.7

Black (NH) 2.05 2.05 2.05

Asian & Other (NH) 1.50 (2.06*) 1.51 (2.06*) 1.51 (2.06*)

Hispanic 2.64 (2.88*) 2.58 (2.82*) 2.49 (2.72*)

Total 2.18 2.2 2.2

Crude Death Rate

White (NH) 11.4 11.9 14

Black (NH) 9.1 9.4 10.4

Asian & Other (NH) 2.6 4 6.8

Hispanic 2.8 3.5 4.8

Total 6.2 6,4 7.5

Domestic In-Migration

White (NH) 55% 46% 33%

Black (NH) 8% 8% 8%

Asian & Other (NH) 16% 18% 22%

Hispanic 21% 28% 38%

Total 100% 100% 100%

Domestic Out-Migration

White (NH) 48% 46% 35%

Black (NH) 7% 7% 7%

Asian & Other (NH) 13% 14% 16%

Hispanic 31% 36% 43%

Total 100% 100% 100%

Race/Ethnicity 2005–10 2015–20 2030–35

International Migration

(annual average) 104,000 104,000 104,000

Labor Force Participation Rate

White (NH) 62.4% 61.1% 60.9%

Black (NH) 57.8% 57.7% 57.1%

Asian & Other (NH) 59.1% 58.3% 56.4%

Hispanic 59.6% 61.3% 60.0%

Total 60.4% 60.5% 59.4%

Note: *Total Fertility Rates of Asian And Hispanic Immigrants. **NH refers to Non-Hispanic. Source: CA 

DOF and SCAG 

The region’s households are projected by using projected headship rate. The projected 

households at a future point in time are computed by multiplying the projected residential 

population by projected headship rates. Headship rate is the proportion of a population 

cohort that forms the household. Age-gender-racial/ethnic specific household formation 

level was applied to the projected population to estimate households. It is specified by 

age, gender, and race/ethnicity. Headship rate is projected in 5 year intervals for seven 

age groups (e.g., 15–24, 25–34, 35–44, 45–54, 55–64, 65–74, 75+), both genders 

(male and female), and four racial/ethnic groups. 

The draft headship rate projections were developed using the extrapolation of the recent 

trends between 2005 and 2010 with different assumptions about the assimilation of 

Hispanic and Asian headship rates. The two plausible sets of headship rate assumptions 

were (1) age-sex-racial/ethnic specific household formation trends without assimilation 

and (2) age-sex-racial/ethnic specific household formation trends with assimilation. 

In fact, there has been a gradual decline in the overall headship rates of the SCAG region 

from 43.7 percent in 1990 to 42.2 percent in 2010, with a sharp decrease by 3 percent 

from 46.7 percent in 1980 to 43.7 percent in 1990. The region’s Hispanic headship rates 

generally did not converge toward the White headship rates between 1980 and 2000, 

but the gap of the White and Hispanic headship rates was growing over time and across 

generations, after controlling for socioeconomic factors. Although Hispanic immigrants 
Growth Forecast 25

experience a linear assimilation toward the Non-Hispanic White headship rates over time, 

U.S. born Hispanic residents do not show a linear assimilation toward the Non-Hispanic 

White headship rates (Choi, 2008). There was generally a stronger effect of the economic 

recession on headship rates of the minorities or immigrants than that of the native-born 

White. As a result, the gap in headship rates between minorities or immigrants and the 

White group is larger (Min & Choi, 2011).

Since there is a potential overestimate of the projected households due to assimilation 

assumption, SCAG produces an acceptable range of the projected households by using 

headship rates with/without assimilation assumption. The final projected households 

will be within the acceptable range. TABLE 15 shows the projected headship rates of the 

racial/ethnic groups between 2010 and 2035, which are the basis for deriving the household forecast. 

TABLE 15 Headship Rates by Race/Ethnicity

2010* 2020** 2035**

White (NH) 50.7% 51.6% 52.1%

Black (NH) 49.3% 51.6% 52.1%

Asian & Other (NH) 39.1% 39.6%–40.8% 39.4%–42.6%

Hispanic 34.8% 35.9%–36.5% 36.4%–37.8%

Total 42.2% 42.4%–42.8% 41.6%–42.9%

Note: *The base year (2010) headship rates were derived using ACS 05-07 3 year average and controlled 

for 2010 Census household estimates. **The 2020 and 2035 Asian and Hispanic headship rates include 

two assumptions of with/without assimilation. A headship rate assumption with assimilation is developed 

in the following way, Asian headship rates are reduced by 50 percent of the difference from 2010 White 

headship rates by 2050; Hispanic headship rates are reduced by 25 percent of the difference from 2010 

White headship rates by 2050. Source: SCAG

SMALL AREA FORECAST AND ALLOCATION

The socioeconomic input data for the transportation model are processed at the transportation analysis zone (TAZ) level in two different formats: (1) the marginal total of person 

and household attributes and (2) the joint distributions of person and household attributes. TAZ is often referred to as TIER 2, are generally equivalent to Census block groups, 

and there are 11,267 TAZs in the region. 

 A marginal total of 54 independent socioeconomic attributes and 7 joint distributions of 

two or more attributes are developed as input for the transportation demand model (see 

TABLES 16 and 17). Those variables include population, households, school enrollment, 

household income, workers, employment, etc. Joint distributions of two or more attributes are now needed as required by the enhanced transportation demand model. One 

of those joint distributions is the number of households by household income, household 

size, the number of workers, and the type of dwelling units, at the TAZ level. 

SCAG develops the marginal and joint distributions of socioeconomic attributes at the TAZ 

level using diverse public and private sources of data and advanced estimation methods. 

The major data sources include 2000 and 2010 Census, 2000 Census Transportation 

Planning Package (CTPP), American Community Survey (ACS), California Department of 

Finance (DOF), California Employment Development Department (EDD), InfoUSA, 2008 

Existing Land Use, and 2008 County Assessor’s Parcel Database. 

The socioeconomic input data at the TAZ level is estimated using three major processes: 

1) development of three major variables (population, households, employment; 2) development of secondary variables (socioeconomic attributes of persons and households, 

employment sectors); 3) development of joint distributions of selected attributes.
26 Growth Forecast

Development of Major Variables

The household estimates at the TAZ level are initially derived by summing the Minimum 

Planning Unit (MPU) level household estimates within the TAZ. The MPUs are generally 

equivalent to parcels. The MPU level household estimates are derived using the following 

process: (1) add the new residential construction between 2000 and 2008 to 2000 MPU 

level housing estimates from 2000 Census; and (2) convert housing units into households 

using the 2000 vacancy rate. The MPU level household estimates are controlled to the 

2008 city level household estimates. The employment estimates at the MPU level are initially derived by using 2008 InfoUSA database. The MPU level employment estimates are 

controlled to the 2008 county level employment estimates from CA EDD. TAZ level household and employment estimates are derived by aggregating the MPU level estimates.

TAZ population and household forecasts are derived using the housing unit (HU) method, 

as used in the city forecasts. The first step of the housing unit method is to project housing units at the TAZ level. Since SCAG focuses on the household forecast, SCAG derives 

the initial TAZ household forecasts by reflecting growth patterns incorporated in the 2008 

RTP forecasts, recent estimates and trends, and updated city household forecasts. The 

TAZ household forecast is converted into population by using the group quarters population plus the product of households and average persons per household (PPH). The average number of persons per household is projected using the recent estimates and trends, 

and is constrained by the updated city PPH. Group quarters population is projected using 

the TAZ’s share of the city population from the 2000 Census and 2008 DOF, which is 

assumed to remain constant during the projection horizon.

TAZ jobs are initially projected using a constant-share method. The current TAZ’s share 

to city jobs for each sector will remain constant during the forecast years. By using the 

constant share method, the TAZ’s job growth by sector will be simply determined by the 

different growth of the specific sector by city. The initial TAZ population, household, and 

employment forecasts become a basis for the local review process.

Development of Secondary Variables

Three major variables are further disaggregated into necessary attributes (e.g., age, 

persons per households, industry sectors, etc), as required in the transportation demand 

model development process. The additional attribute variables are called secondary 

variables. These secondary variables at the TAZ level are estimated using the Large Area 

Secondary Variables Allocation Model (LASVAM) and Small Area Secondary Variables 

Allocation Model (SASVAM) (Cho, 2006; Choi & Ryu, 2011). SCAG uses LASVAM to 

develop the county level secondary variables using the trend extrapolation or the statistical method. SCAG uses SASVAM to develop the TAZ level secondary variables using the 

probabilistic choice model, which reflects the temporal change of the individual attribute 

and the changing relationship of the related attributes.

Development of Joint Distributions of Selected Secondary Variables

The marginal distribution of secondary variables at the TAZ level processed by SASVAM 

is developed into joint distribution of selected secondary variables using the Population 

Generator (PopGen) 1.1, developed by Arizona State University. PopGen 1.1 generates synthetic populations and households with attribute distributions, which become the basis for 

computing the joint distributions. SCAG uses the 2000 Census SF3 driven aggregate data 

at TAZ level and 2000 Census PUMS based individual data at the PUMA level as seed data 

to produce 2008 synthetic populations and households at the TAZ level.

INCORPORATION OF MAJOR DEVELOPMENT PROJECTS

SCAG incorporates major long range major development projects with regional significance into the TAZ population, household, and employment forecast to accurately assess 

their impacts on the long term regional transportation system. These projects are often 

collected through the Intergovernmental Review (IGR) program. 

As required by the California Environmental Quality Act (CEQA), SCAG should review 

projects throughout its jurisdiction to monitor regional development as part of the IGR. 

Through the IGR, SCAG confirms that the regionally significant projects are consistent 
Growth Forecast 27

with a range of adopted regional plans and policies. This IGR process allows SCAG to 

identify the regionally significant major development projects. 

Once the small areas are identified as a major development project area through the 

IGR process, a set of preliminary population, household, and employment forecasts are 

processed using the information of proposed major development projects. The major 

development projects and related growth estimates generally do not influence the overall 

growth forecast of the local jurisdiction, but may influence the growth distribution within 

the local jurisdictional boundary.

TABLE 16 Description of Socioeconomic Variables

Variables Description of Variables

Population 

(8 variables)

Total Population (1 variable): total number of people living within 

a zone. Total population is composed of residential population and 

group quarters population. 

Group Quarters (Non-Institutional) Population (1 variable): is 

primarily comprised of students residing in dormitories, military 

personnel living in barracks, and individuals staying in homeless 

shelters. Group quarters (non-institutional) population does NOT 

include persons residing in institutions.

Residential Population (1 variable): the number of residents NOT 

living in “group quarters.”

Population 

(8 variables)

Group Quarters Population living in student dormitories (1 variable): Population living in college dormitories (includes college 

quarters off campus).

Population by Age (4 variables): the number of population for different age groups: 5–17, 18–24, 16–64, and 65+.

Variables Description of Variables

Households 

(26 variables)

Total Households (1 variable): Household refers to all of the people 

who occupy a housing unit. By definition there is only one household in an occupied housing unit. 

Households by Household Size (4 variables): the number of oneperson households, two-person households, three-person households, and four or more person households.

Households by Age of Householder (4 variables): the number of 

households with age of householder between 18 and 24 years old, 

25 and 44, 45 and 64, and 65 or older.

Households 

(26 variables)

Households by Number of Workers (4 variables): the number of 

households with no worker, with one worker, with two workers, 

and with three workers or more.

Households by Household Income (4 variables): the number of 

households with annual household income (in 1999 dollars) of 

less than $24,999, $25,000–$49,999, $50,000–$99,999, and 

$100,000 or more.

Households by Type of Dwelling Unit (2 variables): the number of 

households living in single-family detached housing, and living in 

other housing.

Households by Number of College Students (3 variables): the 

number of households with no college student, with one college 

student, with two college students or more. 

Households by Number of Children age 5–17 (4 variables): the 

number of households with no child, with one child, with two 

children, and three children or more.
28 Growth Forecast

Variables Description of Variables

School Enrollment 

(2 variables)

K-12 School Enrollment (1 variable): the total number of K-12 

(kindergarten through 12th grade) students enrolled in all public 

and private schools located within a zone. All elementary, middle 

(junior high), and high school students are included. This variable 

represents “students by place of attendance”.

College/University Enrollment (1 variable): the total number of 

students enrolled in any public or private post-secondary school 

(college or university) that grant an associate degree or higher, 

located within a zone. This variable also represents “students by 

place of attendance”.

Workers 

(4 variables):

Total Workers (1 variable): total number of civilian workers residing 

in a zone. Workers are estimated by the place of residence.

Workers by earning level (3 variables): the number of workers with 

earnings of less than $24,999, $25,000–$49,999, $50,000 or 

more.

Median Household 

Income 

(5 variables):

Median Household Income (1 variable): median household income 

is the median value of household income for all households within 

a zone. Household Income includes the income, from all sources, 

for all persons aged 15 years or older within a household. 

Median Household Income by Income Categories (4 variables): the 

median income is estimated for each of four different income categories: less than $24,999, $25,000–$49,999, $50,000–$99,999, 

and $100,000 or more.

Variables Description of Variables

Employment 

(17 variables)

Total Employment (1 variable): total number of jobs within a zone. 

The employment variables represent all jobs located within a zone 

(i.e., employment by place of work). Jobs are composed of wage 

and salary jobs and self-employed jobs. Jobs are categorized into 

13 sectors based on the North American Industry Classification 

System (NAICS) code definition.

Employment by 13 Industries (13 variables): the number of total 

jobs for 1) agriculture & mining, 2) construction, 3) manufacturing, 

4) wholesale trade, 5) retail trade, 6) transportation, warehousing, 

and utilities, 7) information, 8) financial activities, 9) professional 

and business services, 10) education and health services, 11) 

leisure and hospitality services, 12) other services, and 13) public 

administration. 

Employment by wage level (3 variables): total number of jobs 

by three wage levels: of less than $24,999, $25,000–$49,999, 

$50,000 or more.
Growth Forecast 29

TABLE 17 Joint Distributions of Population, Households, and Workers 

by Selected Demographic Attributes.

Major Variables Demographic Attributes

Households Household income (less than $24,999, $25,000 to $49,999, 

$50,000 to $99,999, $100,000+) 

Household size (1,2,3,4+ persons in household) 

Number of workers (0,1,2,3+ workers in household) 

Type of dwelling unit (single-family detached, other)

Households Household income (less than $24,999, $25,000 to $49,999, 

$50,000 to $99,999, $100,000+)

Number of workers (0,1,2,3+ workers in household) 

Age of head of household (18–24, 25–44, 45–66, 65+ years old).

Households Household income (less than $24,999, $25,000 to $49,999, 

$50,000 to $99,999, $100,000+)

Household size (1,2,3,4+ persons in household)

Households Number of college students (0, 1, 2+)

Household income (less than $24,999, $25,000 to $49,999, 

$50,000 to $99,999, $100,000+)

Households Number of children age 5–17 (0,1,2,3+)

Household income (less than $24,999, $25,000 to $49,999, 

$50,000 to $99,999, $100,000+)

Population Age (0–4, 5–17, 18–24, 25+)

Household income (less than $24,999, $25,000 to $49,999, 

$50,000 to $99,999, $100,000+)

Workers Worker’s earnings (less than $24,999, $25,000–$49,999, 

$50,000+)

Household income (less than $24,999, $25,000 to $49,999, 

$50,000 to $99,999, $100,000+)

LOCAL INPUT

Local input plays an important role in developing an accurate growth forecast for 

2012–2035 RTP/SCS. Although the local input is an on-going process, SCAG sought local 

input from local jurisdictions for at least three times during the development of the growth 

forecast. The first and preliminary local input process was conducted between September 

2008 and January 2009 in anticipation of 2012–2035 RTP/SCS growth forecast, data and 

tool requirements under SB 375. SCAG collected the 2008 existing land use, 2008 zoning, 

and the current general plan land use through the local input process. 

The second and major local input process was conducted between July 2009 and 

February 2010 to collect and update land use information and the preliminary growth 

forecast. The preliminary projection of population, household and employment growth at 

the jurisdictional and TAZ level was provided to all local jurisdictions for comments and 

inputs. SCAG conducted a series of one-on-one meetings with local jurisdictions. Over 90 

percent of 195 local jurisdictions provided SCAG with input on growth forecast, existing 

land use, zoning, and general plan land use. The local input result presented an imbalance of population and employment for the year 2035. The number of jobs in 2035 is 

larger than that of available labor supply. SCAG eventually adjusted the 2035 employment 

downward to achieve a balance of population and employment. 

The third and final local input process was conducted at the jurisdictional level between 

May 2011 and August 2011. With the availability of demographic data from the 2010 

Census (Redistricting Data [(P.L. 94-171]) and employment data from CA EDD in March 

2011, SCAG rebenchmarked the growth forecast’s base year demographic and employment figures. SCAG updated the base year (2008) data with the growth delta of the preliminary forecast unchanged. SCAG made an appropriate adjustment of growth forecast 

as needed.

TABLE 18 presents a proposed growth forecast of population, households, and employment for years 2008, 2020, and 2035 at the local jurisdictional level in the SCAG region. 

The proposed local growth forecast was primarily derived using the bottom-up local 

input process. 
30 Growth Forecast

TABLE 18 Proposed 2012–2035 RTP/SCS Growth Forecast

County City Population Households Employment

2008 2020 2035 2008 2020 2035 2008 2020 2035

Imperial Brawley 24,200 36,200 46,800 7,500 11,500 15,800 7,500 12,300 14,600

Imperial Calexico 37,800 50,800 62,800 10,100 14,100 18,800 9,000 15,300 18,500

Imperial Calipatria 7,400 9,000 9,900 1,000 1,200 1,500 2,300 4,000 5,000

Imperial El Centro 41,300 50,300 61,300 12,900 15,700 19,300 18,600 31,400 38,000

Imperial Holtville 5,800 6,600 7,300 1,800 2,100 2,400 1,600 2,600 3,000

Imperial Imperial 14,200 19,900 22,900 4,300 6,200 7,600 2,800 4,800 5,800

Imperial Westmorland 2,200 3,000 3,800 600 900 1,200 600 1,000 1,200

Imperial Unincorporated 36,900 67,900 73,400 10,400 19,900 24,000 18,900 30,300 35,100

County Total 170,000 244,000 288,000 49,000 72,000 91,000 62,000 102,000 121,000

Los Angeles Agoura Hills 20,300 20,400 21,400 7,300 7,500 7,900 11,600 12,100 12,700

Los Angeles Alhambra 83,000 87,000 92,400 29,200 31,300 33,300 29,600 31,000 32,500

Los Angeles Arcadia 56,200 59,600 64,300 19,500 21,000 22,700 26,700 28,100 29,500

Los Angeles Artesia 16,500 16,700 17,000 4,500 4,700 4,800 5,900 6,200 6,500

Los Angeles Avalon 3,600 4,300 5,100 1,400 1,700 1,900 4,400 4,600 4,800

Los Angeles Azusa 46,300 49,500 53,800 12,700 13,800 14,800 18,200 18,500 19,200

Los Angeles Baldwin Park 75,400 78,200 82,200 17,200 17,900 18,600 17,600 18,300 19,200

Los Angeles Bell 35,500 35,900 36,400 8,900 8,900 9,000 9,000 9,300 9,700

Los Angeles Bellflower 76,600 76,600 81,300 23,600 23,700 25,100 16,000 16,900 17,900

Los Angeles Bell Gardens 41,900 43,000 44,500 9,600 9,700 9,700 8,000 8,400 8,800

Los Angeles Beverly Hills 34,100 35,000 36,300 14,900 15,200 15,600 58,000 61,400 64,800

Los Angeles Bradbury 1,000 1,100 1,100 300 400 400 200 300 300

Los Angeles Burbank 103,300 112,400 115,300 41,900 46,000 47,000 90,300 102,300 114,700

Los Angeles Calabasas 23,000 23,800 24,400 8,500 9,000 9,200 14,800 15,400 16,200

Los Angeles Carson 91,700 97,500 106,000 25,500 27,400 29,600 51,900 52,500 54,000

Los Angeles Cerritos 49,000 49,400 49,800 15,500 15,600 15,800 35,900 37,100 38,600

Los Angeles Claremont 34,800 36,100 37,900 11,600 12,100 12,600 18,100 19,400 20,600
Growth Forecast 31

County City Population Households Employment

2008 2020 2035 2008 2020 2035 2008 2020 2035

Los Angeles Commerce 12,800 12,900 13,000 3,400 3,400 3,500 48,100 47,800 48,600

Los Angeles Compton 95,900 96,900 97,600 22,900 23,100 23,100 30,600 31,200 32,200

Los Angeles Covina 47,800 48,700 50,200 15,900 16,200 16,700 12,900 13,100 13,600

Los Angeles Cudahy 23,600 25,200 27,200 5,600 6,000 6,400 3,400 3,500 3,700

Los Angeles Culver City 38,900 39,300 40,000 16,800 17,000 17,300 45,400 47,900 50,400

Los Angeles Diamond Bar 55,300 58,700 63,300 17,800 19,300 20,800 15,500 16,200 17,000

Los Angeles Downey 111,800 116,200 122,700 33,900 35,000 36,200 40,200 42,200 44,200

Los Angeles Duarte 21,200 22,100 23,400 7,000 7,400 7,900 6,700 7,000 7,300

Los Angeles El Monte 113,400 124,300 140,100 27,800 30,400 33,300 36,300 37,100 38,400

Los Angeles El Segundo 16,700 16,900 17,000 7,100 7,200 7,200 53,800 54,000 55,400

Los Angeles Gardena 58,800 59,700 66,200 20,500 21,000 23,200 30,500 28,900 30,700

Los Angeles Glendale 191,600 198,900 209,300 72,200 75,200 78,600 93,600 98,200 103,000

Los Angeles Glendora 49,700 52,900 56,700 17,000 18,000 18,400 12,300 12,900 13,500

Los Angeles Hawaiian Gardens 14,300 14,800 15,600 3,600 3,700 3,900 2,900 3,000 3,200

Los Angeles Hawthorne 84,300 89,600 96,300 28,500 29,500 30,600 20,600 21,100 21,800

Los Angeles Hermosa Beach 19,400 19,600 19,700 9,500 9,600 9,600 7,000 7,300 7,700

Los Angeles Hidden Hills 1,800 1,900 1,900 600 600 600 30 30 30

Los Angeles Huntington Park 58,100 62,000 67,700 15,000 15,700 16,900 16,400 16,800 17,400

Los Angeles Industry 200 200 200 100 100 100 84,100 83,900 85,600

Los Angeles Inglewood 109,700 111,900 113,500 36,400 37,900 38,800 33,400 35,000 36,700

Los Angeles Irwindale 1,400 1,600 2,000 400 400 500 13,400 11,500 12,300

Los Angeles La Cañada Flintridge 20,200 20,400 20,600 6,800 7,000 7,100 9,500 10,200 10,300

Los Angeles La Habra Heights 5,300 5,700 6,500 1,800 1,900 2,200 800 800 900

Los Angeles Lakewood 80,000 80,500 80,600 26,600 27,100 27,400 15,700 16,800 17,800

Los Angeles La Mirada 48,500 50,300 52,800 14,700 15,000 15,300 19,400 19,100 19,300

Los Angeles Lancaster 154,500 174,800 201,300 46,300 52,200 58,800 49,700 51,900 54,200

Los Angeles La Puente 39,800 45,000 52,500 9,500 10,700 11,900 8,000 8,400 8,800

Los Angeles La Verne 31,100 33,000 35,600 11,300 12,000 12,900 9,400 10,100 10,800
32 Growth Forecast

County City Population Households Employment

2008 2020 2035 2008 2020 2035 2008 2020 2035

Los Angeles Lawndale 32,700 34,600 37,400 9,700 10,100 10,700 5,700 6,000 6,300

Los Angeles Lomita 20,300 21,000 21,900 8,100 8,100 8,200 4,700 5,000 5,200

Los Angeles Long Beach 462,200 491,000 534,100 163,500 175,600 188,900 168,100 176,000 184,800

Los Angeles Los Angeles 3,770,500 3,991,700 4,320,600 1,309,900 1,455,700 1,626,600 1,735,200 1,817,700 1,906,800

Los Angeles Lynwood 69,300 72,300 74,300 14,600 15,300 15,700 13,200 13,800 14,500

Los Angeles Malibu 12,600 13,800 14,800 5,300 5,400 6,100 8,900 8,900 9,900

Los Angeles Manhattan Beach 35,100 35,500 36,000 14,100 14,100 14,100 15,100 16,100 17,200

Los Angeles Maywood 27,400 27,600 28,000 6,600 6,600 6,700 3,700 3,900 4,000

Los Angeles Monrovia 36,300 37,700 39,400 13,600 14,300 14,800 17,700 18,300 19,100

Los Angeles Montebello 62,500 66,400 66,400 19,000 20,500 20,500 25,700 26,400 27,400

Los Angeles Monterey Park 60,100 67,900 77,700 19,900 20,900 21,700 30,400 32,000 33,700

Los Angeles Norwalk 105,500 109,100 114,200 27,100 27,400 27,700 24,600 25,700 27,000

Los Angeles Palmdale 149,200 179,300 206,100 41,900 51,300 58,800 32,700 38,900 47,200

Los Angeles Palos Verdes Estates 13,400 13,500 13,500 5,100 5,100 5,100 3,500 3,400 3,400

Los Angeles Paramount 54,100 57,100 62,600 13,900 14,100 14,400 18,300 18,500 19,100

Los Angeles Pasadena 135,300 143,400 152,500 54,500 58,400 61,400 117,300 124,400 131,300

Los Angeles Pico Rivera 62,900 65,900 70,100 16,600 17,600 18,700 16,100 16,400 16,900

Los Angeles Pomona 149,100 168,500 197,400 38,500 43,400 48,900 54,700 57,000 59,600

Los Angeles Rancho Palos Verdes 41,600 41,700 41,700 15,500 15,600 15,600 6,300 6,700 7,100

Los Angeles Redondo Beach 66,500 69,700 73,000 28,900 30,700 32,000 30,100 30,600 31,600

Los Angeles Rolling Hills 1,900 1,900 1,900 700 700 700 40 40 40

Los Angeles Rolling Hills Estates 8,100 8,100 8,200 3,000 3,000 3,000 3,800 4,000 4,200

Los Angeles Rosemead 53,600 55,500 58,100 14,200 15,000 15,800 16,400 16,900 17,600

Los Angeles San Dimas 33,400 35,000 35,600 12,000 12,600 12,900 13,100 13,600 14,100

Los Angeles San Fernando 23,600 24,400 25,500 5,900 6,200 6,600 15,000 15,300 15,900

Los Angeles San Gabriel 39,700 42,800 46,100 12,500 13,800 14,800 14,200 15,000 15,700

Los Angeles San Marino 13,100 13,200 13,300 4,300 4,300 4,300 4,800 5,000 5,300

Los Angeles Santa Clarita 175,900 201,100 237,100 59,300 70,100 81,900 92,900 108,700 122,600
Growth Forecast 33

County City Population Households Employment

2008 2020 2035 2008 2020 2035 2008 2020 2035

Los Angeles Santa Fe Springs 16,200 17,900 20,300 4,800 5,200 5,800 49,600 49,600 50,500

Los Angeles Santa Monica 89,100 92,400 94,700 46,600 49,200 51,400 99,500 101,600 104,200

Los Angeles Sierra Madre 10,900 10,900 11,000 4,800 4,900 5,000 3,400 3,400 3,400

Los Angeles Signal Hill 11,000 11,800 12,900 4,100 4,400 4,700 11,700 12,300 12,700

Los Angeles South El Monte 20,100 20,800 21,800 4,600 4,800 5,000 15,700 15,300 15,400

Los Angeles South Gate 94,400 101,200 110,000 23,400 24,800 26,100 19,700 20,000 20,600

Los Angeles South Pasadena 25,600 25,900 26,300 10,500 10,600 10,800 9,000 9,500 10,000

Los Angeles Temple City 35,400 36,900 39,000 11,600 12,300 13,000 6,700 7,000 7,300

Los Angeles Torrance 145,000 150,800 158,500 55,800 57,800 59,800 105,800 109,100 113,300

Los Angeles Vernon 100 100 100 30 30 30 44,600 45,700 47,300

Los Angeles Walnut 29,000 32,600 33,200 8,500 9,800 10,000 9,000 9,500 10,000

Los Angeles West Covina 106,100 112,200 120,200 31,600 32,600 33,900 27,700 29,300 30,900

Los Angeles West Hollywood 34,400 35,100 36,100 22,700 23,100 23,800 32,300 34,500 36,600

Los Angeles Westlake Village 8,300 8,600 9,000 3,300 3,300 3,400 9,300 9,600 10,000

Los Angeles Whittier 85,300 87,600 90,500 28,300 29,400 30,500 31,300 33,000 34,800

Los Angeles Unincorporated 1,052,800 1,159,100 1,399,500 298,100 336,100 405,500 237,000 266,100 318,100

County Total 9,778,000 10,404,000 11,353,000 3,228,000 3,513,000 3,852,000 4,340,000 4,558,000 4,827,000

Orange Aliso Viejo 47,200 51,500 51,000 18,000 19,000 19,000 17,200 19,600 19,700

Orange Anaheim 333,900 369,100 405,800 97,700 107,600 124,700 192,200 193,700 224,200

Orange Brea 39,200 48,300 49,800 14,300 17,000 17,800 48,900 49,100 49,500

Orange Buena Park 80,000 83,500 83,200 23,600 24,200 24,500 33,900 35,500 35,700

Orange Costa Mesa 109,100 113,700 114,000 39,700 40,100 40,900 94,200 88,300 88,800

Orange Cypress 47,800 50,300 51,400 15,700 16,000 16,500 26,900 27,500 28,200

Orange Dana Point 33,400 35,900 36,200 14,200 14,800 15,200 13,600 13,500 13,700

Orange Fountain Valley 54,900 58,300 59,500 18,500 19,100 19,700 34,400 32,900 33,000

Orange Fullerton 134,700 145,500 164,500 45,300 47,400 54,800 65,300 63,400 91,600

Orange Garden Grove 170,400 179,400 180,300 46,000 47,000 47,800 50,900 49,700 49,800

Orange Huntington Beach 189,700 199,800 205,500 74,300 75,800 79,200 82,900 80,100 80,600
34 Growth Forecast

County City Population Households Employment

2008 2020 2035 2008 2020 2035 2008 2020 2035

Orange Irvine 203,600 265,600 304,200 75,800 98,000 114,700 223,500 242,000 291,800

Orange Laguna Beach 22,700 23,500 23,400 10,900 10,900 10,900 14,000 14,000 14,100

Orange Laguna Hills 30,300 32,100 32,000 10,500 10,600 10,700 19,900 20,400 20,500

Orange Laguna Niguel 62,700 65,700 65,200 24,100 24,500 24,600 20,000 20,100 21,000

Orange Laguna Woods 16,200 17,000 16,900 11,300 11,500 11,600 5,500 6,200 6,700

Orange La Habra 60,100 62,800 62,300 19,000 19,200 19,300 17,700 17,500 17,600

Orange Lake Forest 77,200 88,100 87,400 26,200 30,000 30,100 44,500 40,600 45,800

Orange La Palma 15,500 15,600 15,600 5,100 5,100 5,100 7,800 7,300 7,400

Orange Los Alamitos 11,400 12,000 12,000 4,200 4,300 4,300 15,300 14,300 14,300

Orange Mission Viejo 93,200 96,600 97,000 33,200 33,400 33,900 37,200 38,000 38,800

Orange Newport Beach 84,200 88,700 90,300 38,400 39,500 40,700 82,500 77,000 77,700

Orange Orange 135,500 141,500 156,300 43,100 43,700 49,400 99,900 104,900 108,600

Orange Placentia 50,200 53,600 57,000 16,300 16,900 18,700 19,200 21,200 21,200

Orange Rancho Santa Margarita 47,800 49,500 49,000 16,700 16,700 16,700 17,700 16,300 16,600

Orange San Clemente 63,200 68,100 68,300 23,800 24,800 25,200 25,600 26,300 26,600

Orange San Juan Capistrano 34,400 38,100 37,800 11,300 12,300 12,300 15,700 15,700 15,800

Orange Santa Ana 323,900 337,600 336,700 73,100 73,900 74,800 168,400 146,000 149,400

Orange Seal Beach 24,100 25,000 24,800 13,000 13,100 13,100 10,600 11,500 11,800

Orange Stanton 38,100 40,800 43,400 10,800 11,200 11,900 8,400 8,100 8,300

Orange Tustin 74,700 81,300 82,900 24,900 26,600 27,800 42,100 51,900 66,800

Orange Villa Park 5,800 6,000 6,100 2,000 2,000 2,000 1,900 1,800 1,800

Orange Westminster 89,700 92,900 92,600 26,200 26,300 26,500 23,800 24,800 24,900

Orange Yorba Linda 63,500 69,700 69,400 21,400 22,600 22,800 19,000 17,200 17,300

Orange Unincorporated 121,200 159,100 189,300 38,500 44,000 57,600 23,800 29,700 39,500

County Total 2,989,000 3,266,000 3,421,000 987,000 1,049,000 1,125,000 1,624,000 1,626,000 1,779,000

Riverside Banning 28,900 42,200 61,900 10,800 15,600 22,900 7,800 11,300 15,900

Riverside Beaumont 33,600 56,500 79,400 11,000 18,800 26,200 5,100 8,600 13,400

Riverside Blythe 20,300 22,700 24,300 4,500 5,200 5,800 5,500 7,800 10,400
Growth Forecast 35

County City Population Households Employment

2008 2020 2035 2008 2020 2035 2008 2020 2035

Riverside Calimesa 7,700 14,800 25,800 3,300 6,300 11,000 1,900 2,800 4,300

Riverside Canyon Lake 10,300 11,000 11,700 3,900 4,100 4,300 1,200 1,300 1,400

Riverside Cathedral 50,200 57,000 64,600 17,100 19,600 23,900 13,800 18,900 23,900

Riverside Coachella 38,200 70,200 128,700 8,600 17,300 34,000 6,400 12,800 27,900

Riverside Corona 148,000 155,800 164,600 44,600 46,100 48,800 71,200 88,300 105,000

Riverside Desert Hot Springs 25,200 43,500 58,100 8,600 15,400 20,900 3,500 5,100 6,900

Riverside Eastvale 53,200 61,500 68,300 13,500 15,700 17,700 3,700 5,400 10,100

Riverside Hemet 76,400 83,400 110,300 29,900 33,700 45,900 26,200 39,800 52,500

Riverside Indian Wells 4,800 5,500 5,800 2,700 3,100 3,600 3,900 4,900 6,000

Riverside Indio 73,300 91,500 111,800 23,000 28,600 34,600 21,000 30,100 40,000

Riverside Jurupa Valley 94,400 103,700 126,000 24,500 27,100 33,300 28,100 34,400 53,500

Riverside Lake Elsinore 50,200 70,500 93,800 14,600 21,000 28,700 10,300 15,000 20,100

Riverside La Quinta 36,100 41,600 46,300 14,600 16,600 17,900 9,200 10,600 11,900

Riverside Menifee 74,800 93,100 119,400 27,100 35,900 45,900 8,800 10,500 12,600

Riverside Moreno Valley 187,400 213,700 255,200 51,100 60,000 72,800 32,300 48,000 64,400

Riverside Murrieta 101,200 109,300 121,100 32,700 35,100 39,200 17,400 50,100 86,500

Riverside Norco 26,500 30,300 32,700 7,000 8,000 8,700 12,400 17,000 21,600

Riverside Palm Desert 47,100 52,100 56,800 23,000 25,800 28,000 37,700 41,600 44,500

Riverside Palm Springs 43,400 48,900 56,100 22,700 25,700 30,400 36,300 44,400 52,300

Riverside Perris 65,900 82,000 114,000 16,100 22,000 30,900 14,400 21,700 26,700

Riverside Rancho Mirage 16,900 18,800 22,900 8,900 9,600 11,800 10,800 12,700 17,400

Riverside Riverside 295,500 339,000 382,700 91,400 104,000 117,800 151,500 198,300 217,800

Riverside San Jacinto 42,600 50,300 99,100 13,000 15,900 33,200 5,700 8,100 10,900

Riverside Temecula 95,100 109,800 118,900 30,900 34,300 37,200 46,600 59,800 72,800

Riverside Wildomar 31,500 42,100 53,700 10,000 13,100 16,800 3,400 5,900 9,300

Riverside Unincorporated 349,100 471,500 710,600 109,600 150,800 240,000 68,000 123,700 203,200

County Total 2,128,000 2,592,000 3,324,000 679,000 834,000 1,092,000 664,000 939,000 1,243,000

San Bernardino Adelanto 31,200 46,100 68,400 7,700 11,900 17,700 5,400 7,300 10,600
36 Growth Forecast

County City Population Households Employment

2008 2020 2035 2008 2020 2035 2008 2020 2035

San Bernardino Apple Valley 69,000 82,900 109,000 23,500 28,500 37,100 15,500 17,000 22,500

San Bernardino Barstow 22,500 27,300 36,200 8,000 9,900 13,000 13,300 14,900 19,100

San Bernardino Big Bear Lake 5,000 5,600 7,000 2,200 2,400 3,000 6,200 6,400 7,400

San Bernardino Chino 75,600 88,800 107,200 20,100 24,600 29,200 48,500 53,500 67,700

San Bernardino Chino Hills 74,600 76,600 78,400 22,900 24,000 25,600 9,300 10,500 12,900

San Bernardino Colton 52,100 60,700 71,700 15,000 17,800 21,100 24,000 25,500 29,600

San Bernardino Fontana 193,900 222,700 259,100 48,600 57,500 66,700 47,600 53,700 69,000

San Bernardino Grand Terrace 11,800 11,600 13,000 4,300 4,600 5,400 3,000 3,200 4,000

San Bernardino Hesperia 89,600 98,200 132,500 26,300 28,900 39,300 15,500 20,400 28,700

San Bernardino Highland 53,000 58,600 67,300 15,400 17,700 20,300 6,000 7,800 9,100

San Bernardino Loma Linda 23,000 26,700 31,700 8,700 10,500 12,600 17,600 23,300 32,600

San Bernardino Montclair 36,000 39,700 43,900 9,300 10,400 11,600 16,500 17,000 18,400

San Bernardino Needles 4,800 6,000 8,000 1,900 2,400 3,100 3,300 3,800 4,700

San Bernardino Ontario 162,900 203,800 307,600 44,600 57,700 87,300 114,300 142,900 214,400

San Bernardino Rancho Cucamonga 162,800 167,100 167,100 53,600 56,300 57,600 62,500 63,900 68,300

San Bernardino Redlands 68,600 75,500 87,900 24,700 28,300 32,500 41,400 46,700 60,100

San Bernardino Rialto 98,900 110,000 125,200 25,100 29,400 34,700 22,900 26,400 32,800

San Bernardino San Bernardino 209,900 231,200 261,400 59,300 66,900 76,800 101,300 113,400 145,300

San Bernardino Twentynine Palms 24,900 26,300 36,300 8,000 8,600 11,800 3,200 3,200 4,500

San Bernardino Upland 72,600 76,700 80,200 25,400 28,300 31,300 27,900 29,700 33,400

San Bernardino Victorville 111,900 145,300 190,100 31,400 43,700 56,900 33,700 45,900 66,400

San Bernardino Yucaipa 51,200 55,800 61,900 18,200 20,700 23,600 9,800 10,900 14,000

San Bernardino Yucca Valley 20,700 23,000 26,200 8,300 9,900 11,800 4,600 5,100 6,000

San Bernardino Unincorporated 289,400 301,600 372,600 93,300 97,700 117,500 47,200 58,300 77,700

County Total 2,016,000 2,268,000 2,750,000 606,000 698,000 847,000 701,000 810,000 1,059,000

Ventura Camarillo 64,500 72,200 76,700 24,400 27,500 29,700 32,200 37,800 40,600

Ventura Fillmore 14,900 18,000 20,800 4,100 5,100 5,900 3,200 3,500 3,900

Ventura Moorpark 33,900 39,300 41,500 10,400 12,000 12,700 12,000 14,200 15,700
Growth Forecast 37

County City Population Households Employment

2008 2020 2035 2008 2020 2035 2008 2020 2035

Ventura Ojai 7,400 8,400 9,400 3,100 3,600 4,100 6,300 7,100 7,800

Ventura Oxnard 193,900 216,700 244,500 49,100 58,800 70,600 59,000 64,000 69,800

Ventura Port Hueneme 21,600 22,100 22,500 7,100 7,200 7,400 10,900 10,500 10,800

Ventura San Buenaventura (Ventura) 105,300 116,900 128,800 40,300 45,200 50,100 63,100 70,500 77,400

Ventura Santa Paula 29,000 35,400 38,800 8,300 10,000 11,100 8,800 9,700 10,500

Ventura Simi Valley 123,200 129,700 133,200 41,200 42,800 44,000 41,400 46,200 50,700

Ventura Thousand Oaks 125,200 129,700 130,900 45,600 46,100 46,600 67,600 72,700 78,700

Ventura Unincorporated 94,200 100,500 107,200 31,900 33,700 35,300 43,400 42,800 44,900

County Total 813,000 889,000 954,000 266,000 292,000 318,000 348,000 379,000 411,000 

Region Total 17,895,000 19,663,000 22,091,000 5,814,000 6,458,000 7,325,000 7,738,000 8,414,000 9,441,000 

Note: * The City numbers may not sum to the County total due to rounding. City numbers were rounded to the nearest 10 for jurisdictions with small numbers or to the nearest 100 for all others, while county numbers 

were rounded to the nearest 1,000. ** The proposed growth forecasts of Eastvale and Jurupa Valley are subject to further revisions due to the recent incorporation, pending final approval from cities/county. Source: 

SCAG.
38 Growth Forecast

Section IV: SCAG PECAS Land Use Model

Introduction

SCAG made its initial step in developing an integrated, comprehensive transportation and land use model in early 2009. The model is based on the PECAS (Production 

–Exchange – Consumption Allocation System) framework. The model has not been 

officially peer-reviewed and is not fully operational. The model is used only as an internal 

scenario testing tool for the 2012–2035 RTP/SCS. However, the model will be the main 

tool to design and develop RTP scenarios in the near future when it is ready. This chapter 

introduces the related effort and summarizes a selected scenario test.

The following section briefly summarizes the status of consulting project for development 

of the model. Then the model is introduced in terms of its structure, SCAG specifications, 

and calibration. An analysis of the impact of the implementation of a 5 cents VMT fee 

is provided as an example scenario. A brief conclusion remark is provided at the end of 

the section.

Consulting Projects

The model was commissioned by SCAG and built by SCAG staff together with the Urban 

Land Use and Transportation (ULTRANS) Center at the University of California, Davis, and 

HBA Specto Incorporated in Calgary, Alberta. Over the 25 month contract period from 

February 2009 to March 2011, the staff tried to achieve two purposes. The first was to 

develop a working draft model and its related data within the given period of time. To 

achieve this objective, the development started with the statewide PECAS model, which 

was developed by ULTRANS and HBA for CalTrans to demonstrate the model. Once the 

model and the data was delivered, it was modified to make it more specific to the SCAG 

region through an iterative development approach. The draft model developed from this 

process represented the SCAG region in geographic sense, but its behavioral parameters and economic data represented the state of California. The second was to acquire 

knowledge to develop an integrated land use model. This involved staff work in processing raw data, development of the database, model setup and adjustment (calibration) and 

operation. After twenty-three 2 or 4 day workshops held at SCAG or UC Davis, model has 

been scrutinized down to the individual source code lines. 

Another portion of the consulting project was to establish the economic data and parameters for the SCAG region with MJ Consulting. Based on the transferred knowledge, 

regional economic data was processed to set up the AA (Activity Allocation) module of 

the PECAS model. The work was performed from September 2009 to May 2010. As a 

result of this project, staff was able to develop a more streamlined process to prepare 

regional economic data, and a calibrated set of the AA module for the region. In addition, 

this project allowed staff to have time and resource to produce the first SCAG parcel-level 

land use database.

PECAS Overview

PECAS is a generalized approach for simulating spatial economic systems. It is designed 

to provide a simulation of the land use component of land use transport interactive modeling systems.

PECAS uses an aggregate, equilibrium structure with separate flows of exchanges 

(including goods, services, labor and space) going from the production site to the consumption location, based on variable technical coefficients and a market clearing mechanism with exchange prices. It provides an integrated representation of spatially distinct 

markets for the full range of exchanges, with the transport system and the development 

of space represented in more detail with specific treatments.

Flows of exchanges from production to exchange zones and from exchange zones to 

consumption are allocated using the nested logit models, according to exchange prices 

and transport generalized costs (expressed as transport utilities with negative signs). 

These flows are converted to transport demands that are loaded to networks in order to 

determine congested travel utilities. Exchange prices determined for space inform the 

calculation of changes in space thereby simulating developer actions. Developer actions 

are represented at the level of land use zones using an aggregate flow treatment. The 

system is run for each year being simulated, with the travel utilities and changes in space 

for one year influencing the flows of exchanges in the next year. 

PECAS includes two basic modules that are linked together with two other modules to 

provide a representation of the complete spatial economic system. FIGURE 21 shows the 

relationship between modules.
Growth Forecast 39

The set of four basic modules includes:

 Activity Allocation Module (AA Module): This is one of the two PECAS modules. It 

represents how the production, exchange and consumption activities locate within 

the space provided by developers and how these activities interact with each other 

at a given point in time. For SCAG application, economic activities from 42 industrial 

sectors and 14 types of households consist of individual markets in each of 302 CSA 

(Community Statistical Area) zones.

 Space Development Module (SD Module): This is another of the two PECAS 

modules. It represents the actions of developers in the provision of different types 

of developed space where activities can locate, including the new development, 

demolition and re-development that occurs from one point in time to the next. This 

developed space is typically floor space of various types and is called ‘space’ in the 

PECAS framework. 9 residential types and 14 non-vacant land use types are represented in the SCAG SD module. 

 Transport Model (TR Module): This is one of the ‘non-PECAS’ modules. It represents the transport system connecting locations, including at a minimum a transport network, the transport demands that load onto this network (as a result of the 

economic interactions represented in the AA Module) and the congested times and 

costs for interactions between locations arising with the loading of these demands. 

For SCAG application, two sub-modules are employed to simulate the traditional 

4-step model. The first sub-module of trip generation converts the activity flow estimated by AA Module into Origin-Destination matrices between the CSA zones, and 

another sub-module performs a Frank-Wolfe style traffic assignment to estimate link 

traffic volume, zone-to-zone travel time, and VMT.

 Economic Demographic Aggregate Forecasting Model (ED Module): This is the 

other of the ‘non-PECAS’ modules: It is a form of model or approach used to develop 

aggregate economic forecasts for the study area being modeled. In the SCAG application, this is a regional control total of households by 14 types and employment by 

42 industrial sectors.

FIGURE 21 Pecas Module Structure

Zone-to-zone Travel

time and distance



Time T

ED

AA

TR

SD AA

TR

ED

Time T+1

 Import/Export by Commodity

 Activity totals by type



Space rent by type

and location

Updated oor space

by type and location



 Commodity ow by location

 Activity by type and location

Scenario Test

As stated above, the PECAS land use model produced as-is based, preliminary impact 

analysis results for various what-if scenarios. Therefore any measurements presented 

in this section are not necessarily intended to support any implementation of the plan. 

The purpose of this section is to demonstrate the functionality of the developing land use 

model based on the PECAS framework.

As depicted in FIGURE 21, each module in PECAS produces intermediate outputs, which 

are input to the subsequent processes. The AA module primarily estimates the spatial 

price of commodities, and allocates activities, which consists of households by types 

and employment by sector. The AA module also estimates commodity flows, between the 

sectors and locations of production and consumption, in dollars. The main output from 

the SD module is changes in land use and amount of floor space at parcel level. The presentation of this detail output would prevent finding regionally meaningful implications. 

It could also cause unnecessary misunderstanding as if that market-driven forecast was 
40 Growth Forecast

intended for the local land use plan. For SCAG’s planning purpose, microscopic outputs 

are aggregated over sectors and statistical zones.

A what-if scenario was selected to present the comprehensiveness of PECAS. Note that 

this scenario is independent of and does not reflect the assumptions underlying the financial plan as documented in the Transportation Finance Appendix and Chapter 3 of the 

2012–2035 RTC/SCS. Under the test, the following assumptions are applied.

 The current gasoline tax, 36.4 cents per gallon, would gradually increase until 2025 

to 50 cents per gallon due to nominal inflation. 

 After that, a 5 cents per mile VMT fee would replace the gasoline tax in year 2026.

 The auto operating cost would be 50 cents per mile, and 10 percent is fuel cost.

 The increase of the gasoline tax up to year 2026 would be the equivalent of about 

a 10 percent increase in operating costs, and the new VMT fee would result in an 

additional 10 percent surcharge.

FIGURE 22 shows the scenario of auto operating costs over the planning horizon. When 

evaluating (dis-)utility of locations, importance of travel cost is high with the increase 

in auto operating costs. In responding to the relatively increased travel cost, economic 

actors (households and firms) would change their behavior, including relocating the 

operation location, or job site according to their preference. Such a change would cause 

shorter travel distance on average, at a given transportation network capacity.

FIGURE 22 Scenario of Auto Operating Cost

2005

95%

100%

105%

110%

115%

120%

125%

Relative Operating Cost to 2007

2010 2015 2020 2025 2030 2035 2040

Average travel distances are calculated by three categories; Worker Compensation 

Distance (WCD) is the distance between a worker’s residence to work place, which is 

equivalent to Home-to-Work trip distance. In PECAS flow is reported with the value of 

the commodity, times the distance between where it originated (produced) to where it is 

destined (consumed). To calculate the average for WCD, the estimated wage at the place 

of work is used. Intermediate Goods movement Distance (IGD) is the delivery distance of 

commodity to the other industries for their production activities. Consumer Goods movement Distance (CGD) is the distance of the same commodities but its destination is to 

households, and it is equivalent to the distance of Home-to-Shop trips. For IGD and CGD, 

commodity price at production site is used to calculate average distance.

PECAS estimates the flow of commodities including goods, services, and labor by using 

the value of commodities multiplied by the movement distance of commodities from 

where they are produced to where they are consumed. The total transported commodities in the SCAG region were estimated to be 41.98 trillion dollar•miles in year 2007, and 

53.19 trillion dollar•miles in year 2035, if no additional auto operating cost is charged. 
Growth Forecast 41

This 26.7 percent increase is caused by regional economic growth and continuing sprawl. 

With the VMT fee replacing the current gasoline tax, the estimation is 3.55 percent 

less than the base case at year 2035. Given that economic growth remains constant in 

both cases, this reduction is due to the additional travel cost and subsequent reallocation of activity. FIGURE 23 shows the variation over time that is caused by the changed 

travel cost.

FIGURE 23 Scenario Test Result: Travel Distance of Commodities 

in Value * Mile Transported

2005

4.0+13

4.4+13

4.2+13

4.6+13

VMT Fee Base

4.8+13

5.0+13

5.2+13

5.4+13

$ * Miles

2010 2015 2020 2025 2030 2035 2040

-3.55%

26.71%

TABLE 19 summarizes the average travel distance by category. In the base case, the average travel distance of working trip (WCD) would increase 10.3 percent in year 2035, while 

intermediate goods delivery (IGD) and shopping trips (CGD) would increase 7.5 percent 

and 3.2 percent, respectively. The overall increase is 5 percent. With the new 5 cents per 

mile VMT fee, overall travel distance would increase 1.3 percent from year 2007. 

TABLE 19 Scenario Test Result: Average Travel Distance In Miles

Scenario Type 2007

(A)

2020 2035

(B) (B)/(A) (C) (C)/(A)

Base

WCD 12.858 13.543 1.053 14.176 1.103

IGD 42.784 44.284 1.035 46.006 1.075

CGD 25.515 25.846 1.013 26.341 1.032

Sum 32.517 33.257 1.023 34.157 1.050

VMT Fee

WCD 12.858 12.948 1.007 12.668 0.985

IGD 42.784 43.819 1.024 44.815 1.047

CGD 25.515 25.390 0.995 25.182 0.987

Sum 32.517 32.776 1.008 32.928 1.013

Coda

This section introduces the SCAG PECAS land use model, as an available tool to be used 

in the future RTP. After a 2-year development consulting project, SCAG commissioned 

a draft version of the PECAS model, and applied it to cursory test of various scenarios 

internally. At the current stage, the model produced reasonable sensitivity to the what-if 

scenario with increased auto operating cost.

With refined modeling parameters and input data, the model will be involved more 

actively in the scenario development process, by supporting region-wide impact measures. Also, with a mature model, its activity reallocation estimation could be the basis of 

scenario-sensitivity socioeconomic data development.
42 Growth Forecast

References

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Workbook. pp. 89–304.

Choi, Simon. 2008. Testing the Convergence of Hispanic Headship Rates: The Case of 

Southern California, Paper presented at the 47th Annual Meeting of the Western Regional 

Science Association (WRSA), The Big Island, Hawaii, February 17–20, 2008.

Choi, Simon. 2010. Projecting Regional Population in the Middle of an Economic 

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American Meetings of the Regional Science Association International, 2010, Denver, 

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Choi, Simon, S. Ryu. 2011. Linking the Regional Demographic Processes and the Small 

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presented at the 52nd Association of Collegiate Schools of Planning (ACSP) Conference, 

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Park, Heonsoo. 2009. Advanced Programming Support for Developing Traffic Analysis 

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Smith, Stanley K., J. Tayman, D. A. Swanson. 2001. State and Local Population 

Projections: Methodology and Analysis. New York: Kluwer Academic/Plenum Publishers.

Southern California Association of Governments (SCAG). 2008. 2008 Regional 

Transportation Plan: Making the Connection, Growth Forecast Report. http://www.scag.

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