# 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 **1 7**  Forecasting Framework 17 Forecasting Methodology and Assumptions 22 

### Section IV: SCAG PECAS Land Use Model **3 8** 
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 contribu

tors 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 eco

nomic 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 assump

tions 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 statisti

cal 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 

### 1assumptions, 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 informa

tion (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 recom

mended 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/subre

gional 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 participa

tion 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 reduc

tion between December 2010 and January 2011. This SED data was used for the pre

liminary 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 alloca

tion plan in February 2012. The Regional Council will be adopting the 2012–2035 RTP/ 

### 2 Growth ForecastSCS in April 2012. The regional growth forecast is adopted as part of the 2012–2035 RTP/SCS and RHNA process.

**TABLE 1** 

### F orecasting 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 demo

graphic 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 con

ducted 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 evalu

ate local input, and evaluate new BLS and Census projections. May 2010

11 Local input regional growth forecast with 2035 employ

ment 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 con

ducted 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 rep

resents 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 

### 3The 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 metropoli

tan 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 aver

age 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** 

### A nnual 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 con

sequent 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 ForecastThe 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 strug

gling 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 per

cent 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** 

### R egional 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) trans

portation 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 imbal

ance of jobs and housing in the SCAG region, and poses serious transportation and air quality challenges.

**TABLE 4** 

### T he 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 domes

tic 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 compo

nents 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** 

### U nemployment Rate, SCAG Region, 1990–2010

**0% 4%**  **8% 12%**  **16%**

**1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010**

**FIGURE 2** 

### J ob Growth, SCAG Region, 1990–2010

0 2 4 6 8 10199019911992199319941995199619971998199920002001200220032004200520062007200820092010Millions

Jobs **FIGURE 3** 

### Pe rcent 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** 

### P opulation Growth, SCAG Region, 1990–2010

0 4 8 12 16 20199019911992199319941995199619971998199920002001200220032004200520062007200820092010Millions

Population Growth Forecast 

### 7**FIGURE 5** 

### Pe rcent 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** 

### C omponents of Population Change, SCAG Region, 1990–2010

-150 -100 -50

0 50 100 150 200 250 300 35090-9191-9292-9393-9494-9595-9696-9797-9898-9999-0000-0101-0202-0303-0404-0505-0606-0707-0808-0909-10Thousands

Natural Increase Net Migration **FIGURE 7**  

### Net Domestic Migration, SCAG Region, 1990–2010

-300 -250 -200 -150 -100 -50

0 5090-9191-9292-9393-9494-9595-9696-9797-9898-9999-0000-0101-0202-0303-0404-0505-0606-0707-0808-0909-10Thousands

Net Domestic Migration
**FIGURE 8** 

### N et Immigration, SCAG Region, 1990–2010

0 20 40 60 80 100 120 140 160 18090-9191-9292-9393-9494-9595-9696-9797-9898-9999-0000-0101-0202-0303-0404-0505-0606-0707-0808-0909-10Thousands

International Migration 

### 8 Growth Forecast**FIGURE 9** 

### B irths, SCAG Region, 1990–2010

0 50 100 150 200 250 300 35090-9191-9292-9393-9494-9595-9696-9797-9898-9999-0000-0101-0202-0303-0404-0505-0606-0707-0808-0909-10Thousands

Births **FIGURE 10** 

### P opulation to Employment Ratio, SCAG Region, 1990–2010

1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6199019911992199319941995199619971998199920002001200220032004200520062007200820092010

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** 

### P opulation, 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 HouseholdsMillions

2008 2035 **FIGURE 12** 

### P opulation, Employment, and Household Growth, SCAG Region, 1970–2035

**0 5**  **10 15**  **20 25** **1970 1975** **1980 1985** **1990 1995** **2000 2005** **2010 2015** **2020 2025** **2030 2035** **Millions**

**Population Employment**  **Households** 

### 10 Growth Forecast**FIGURE 13** 

### Pe rcent 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 aHispanic 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 changetoward 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** 

### De mographic 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** 

### P opulation 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** 

### P opulation 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**  

### C omponents 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 2034Thousands

Natural Increase Net Migration

**FIGURE 17** 

### B irths and Deaths, SCAG Region, 1970–2035

0 50 100 150 200 250 300 350

1971 1978 1985 1992 1999 2006 2013 2020 2027 2034Thousands

Births Deaths Growth Forecast 

### 13**FIGURE 18** 

### N et 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 Thousa** nds

**Net Immigration Net Domestic Mi**  g **ration**



## HOUSEHOLDS As the population ages and diversifies in the region during the projection period, house

holders 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 house

holders 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** 

### C haracteristics 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 eco

nomic quality of life by maintaining the manufacturing sector and other traditional high income job sectors. **TABLE 7** 

### R egional 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 popula

tion to employment ratio in each of the six counties will converge toward the regional average ratio.

**TABLE 8** 

### R egional 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 compo

nents (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** 

### P opulation 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 2035Millions

2008 RTP 201 2-2035 RTP /SCS Source: CA DOF and SCAG 

### 16 Growth Forecast**FIGURE 20** 

### E mployment 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 2035Millions

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 pro

jections. The major sources of potential projection errors include: (1) the unstable/uncer

tain 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 popula

tion 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 immigra

tion. 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 behav

ior, 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 conver

gence 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 popula

tion 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 avail

able 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 agen

cies 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 agen

cies (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 pro

vided 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 expec

tancy, 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 bot

tom 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 unem

ployment 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 Forecastresponses 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, dur

ing 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 dif

ferent immigration assumptions. The 2009 national population projections are a supple

mental 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 reason

able 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 migra

tion 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 projec

tions would be negligible. International immigration, in particular, unauthorized immi

grants 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 immi

gration 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 mem

bers 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 revi

sions 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 demograph

ics 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 house

holds) 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 popu

lation 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, assimila

tion method, 2008 headship rate method, trend method, mid-trend method, mid-assim

ilation). 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** 

### R egional 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: SCAGGrowth 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 employ

ment 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 per

cent) 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** 

### R egional 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** 

### R egional 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** 

### R egional 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** 

### R egional 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 devel

oping the regional growth forecast for the 2012–2035 RTP/SCS.


## FORECASTING METHODOLOGY SCAG projects regional population using the cohort-component model. The model com

putes 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 projec

tion 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 populationGrowth 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 jurisdic

tion level household size and the group quarters population from the recent DOF esti

mates 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 fertil

ity 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 dur

ing 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 dur

ing 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 lat

est 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 match

ing 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 impor

tant 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** 

### K ey 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 immigrantsGrowth 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 house

hold forecast.

**TABLE 15** 

### H eadship 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 transpor

tation 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 attri

butes. 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 attri

butes 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) devel

opment 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 ini

tially 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 house

hold 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 hous

ing 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 popula

tion plus the product of households and average persons per household (PPH). The aver

age 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 statisti

cal 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 syn

thetic 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 signifi

cance 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 consistentGrowth 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** 

### Des cription 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 vari

able): Population living in college dormitories (includes college quarters off campus). Population by Age (4 variables): the number of population for dif

ferent 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 house

hold in an occupied housing unit.Households by Household Size (4 variables): the number of oneperson households, two-person households, three-person house

holds, 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 cate

gories: 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** 

### J oint 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 imbal

ance 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 employ

ment figures. SCAG updated the base year (2008) data with the growth delta of the pre

liminary forecast unchanged. SCAG made an appropriate adjustment of growth forecast as needed.

**TABLE 18** presents a proposed growth forecast of population, households, and employ

ment 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** 

### P roposed 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 20,600Growth 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 92,900 108,700 122,600Growth 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 10,400Growth 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 15,700Growth 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 transporta

tion 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 param

eters 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 process

ing 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 param

eters 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 mod

eling 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 con

sumption location, based on variable technical coefficients and a market clearing mecha

nism 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 repre

sented in the SCAG SD module.

 Transport Model (TR Module): This is one of the ‘non-PECAS’ modules. It repre

sents the transport system connecting locations, including at a minimum a trans

port 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 esti

mated 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 appli

cation, this is a regional control total of households by 14 types and employment by 42 industrial sectors. **FIGURE 21** 

### P ecas 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 pre

sentation 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 finan

cial 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** 

### S cenario 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 move

ment 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 commodi

ties 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 realloca

tion of activity. **FIGURE 23** shows the variation over time that is caused by the changed travel cost.

**FIGURE 23** 

### S cenario Test Result: Travel Distance of Commodities in Value * Mile Transported

2005 4.0+13 4.4+13

4.2+13 4.6+13

Base VMT Fee 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 aver

age 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** 

### S cenario 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 mea

sures. 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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