# CONTEXT CURRENT DEMOGRAPHICS & GROWTH FORECAST

ADOPTED | APRIL 2016 

# APPENDIX SOUTHERN CALIFORNIA ASSOCIATION OF GOVERNMENTS

## INTRODUCTION 1 FORECASTING PROCESS 1 GROWTH TRENDS 2 REGIONAL GROWTH FORECAST 12 FORECAST METHODOLOGY AND ASSUMPTIONS 16 SMALL AREA FORECAST AND ALLOCATION 21 REFERENCES 43



## ADOPTED | APRIL 2016 

# APPENDIX CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST

# INTRODUCTION The Regional Growth Forecast is used as a key guide for developing regional plans and strategies mandated by federal and state governments such as the Regional Transportation Plan/Sustainable Communities Strategy (RTP/SCS), the Program Environmental Impact Report (PEIR) for the RTP/SCS, the Air Quality Management Plan (AQMP), the Federal Transportation Improvement Program (FTIP), and the Regional Housing Needs Assessment (RHNA). The Growth Forecast Appendix to 2016 RTP/SCS is intended to provide more details on the development of the regional growth forecasts for the 2016 RTP/SCS. The Growth Forecast Appendix is comprised of five major sections. Section I summarizes the forecasting process focusing on the forecasting timeline and milestones. Section II provides an overview of the recent trends in the region’s growth of population, households, and employment. Section III explores the regional growth forecast with its socio-economic characteristics. Section IV discusses the forecast methodology and the major assumptions for the regional growth forecast. Section V describes the small area forecast and allocation. 

# FORECASTING PROCESS The regional growth forecast reflects recent and past trends; key demographic and economic assumptions; and local, regional, state or national policies. The SCAG’s regional growth forecast also emphasizes the participation of local jurisdictions and other stakeholders in the growth forecast development process. **TABLE 1** 

### lists the forecasting timeline and milestones for the development of the regional growth forecast for the 2016 RTP/SCS. The first major milestone for the growth forecast development was the SCAG panel of demographic and economic experts meeting. On June 27, 2013, the SCAG panel of demographic and economic experts meeting was held to review SCAG’s methodology and assumptions for its population, household, and employment growth forecast for the 2016 RTP/SCS. Twenty academic scholars and leading practitioners were invited to participate on the panel. The panel of experts reviewed demographic and economic trends in the national and regional growth context, discussed the key assumptions underlying the regional and county growth forecast, and provided responses to survey questions on major assumptions (see (1) 2016 RTP/SCS) growth forecast development: information from panel of experts meeting and range of regional growth projections at http://www.scag.ca.gov/committees/ CommitteeDocLibrary/cehd080113fullagn_3.pdf ; (2) panel survey results and tabulation at http://www.scag.ca.gov/Documents/SurveyAnswersSummary062713.pdf ). On September 12, 2013, SCAG incorporated the recommendations of the panel of experts into the refined range of regional growth forecasts and developed a recommended, preliminary set of regional and county growth forecasts for 2012, 2020, 2035 and 2040, reflecting recent trends and updated assumptions. In November 2013, the preliminary small area (e.g., jurisdiction and transportation analysis zone) growth forecasts, reflecting recent trends and controlling for the updated county controls, were released to local jurisdictions for their comments and input in November 2013. SCAG provided local jurisdictions with the preliminary set of growth forecasts at the jurisdiction and transportation analysis zone levels. Between February 2014 and October 2014, SCAG conducted the first round of local review through one-on-one meetings with local jurisdictions. As with the 2012 RTP/SCS, SCAG sought verification of the existing land use, general plan land use, and zoning information; and approval of jurisdictional level population, households and employment forecasts for the years 2020, 2035 and 2040. Jurisdictions were allowed to submit sub-jurisdictional input (e.g., input at the census tract or transportation analysis zone level). However, the sub-jurisdictional level input would only be treated as advisory. SCAG held one-on-one meetings with 195 of the 197 local jurisdictions in the SCAG region to explain the methods and assumptions of how the small area growth forecasts were developed. The local jurisdictions provided SCAG with their input on those growth forecasts along with the proper documentation by end of September 2014. SCAG updated the local growth forecasts and revised them as necessary. There was a SCAG staff assessment of the draft local input growth forecast in September 2014 (http://www.scag.ca.gov/committees/CommitteeDocLibrary/cehd100214fullagn.pdf ). As of September 2014, 81 percent of 197 jurisdictions provided input on SCAG’s preliminary growth forecasts. The key findings from the aggregated local input data included: 1) All three (3) growth figures were within the preliminary range of growth forecasts; 2) All three growth figures from local jurisdictions were lower than the preliminary mid forecasts, but higher than the preliminary low forecasts, in 2040; and 3) the 2040 regional unemployment rate would be 5.4 percent for the SCAG region. The population to household ratio was 3.0 and is consistent with that of the preliminary growth forecasts. The local input growth forecast at the regional level was found to be technically sound. The local input was primarily existing general plan-based. In November 2014, SCAG produced the draft small area (e.g., jurisdiction and transportation analysis zone) growth forecasts reflecting local input, and further developed the alternative growth forecasts reflecting different land use scenarios (trend baseline and three policy scenarios) in subsequent months. As part of the scenario planning exercise, SCAG developed a policy growth scenario. The goal of this scenario is to maximize the benefits of

## 2


# GROWTH TRENDS **POPULATION** According to the 2015 population estimates from the California Department of Finance, the population of the Southern California region is 18.9 million, which represents 5.9 percent of the 321 million people of the U.S., and 48.3 percent of California’s population. With the region’s land area of 38,000 square miles, the region’s population density is now 490 people per square mile. The Southern California region is the 5 th 

### highest in population among states in the nation, behind New York and ahead of Illinois, and the second largest combined statistical area (CSA) in the nation behind the New York CSA. Greenhouse Gas/Vehicle Miles Traveled (GHG/VMT) reductions, public health, and other co-benefits from the large transportation investments in our region focusing on transit and first/last mile strategies. This is done by identifying opportunity areas with current and/or future transit investments where mixed use and high density housing are mostly likely to occur in the future. Between June 2015 and July 2015, SCAG conducted a second and final round of local review of both the draft local input and draft policy growth forecasts. All the comments received were incorporated into the draft 2016 RTP/SCS to ensure accuracy and reasonableness. After developing the draft 2016 RTP/SCS between August 2015 and November 2015, SCAG released the draft Plan in December 2015. The Regional Council is scheduled to adopt the 2016 RTP/SCS in April 2016.

**T**

**able 1**  Forecasting Timeline and Milestones 

### Milestone Date/Period Reference Materials 1 Adopted the 2012 RTP/SCS jurisdictional level growth forecast. April 2012 http://gisdata.scag.ca.gov/Pages/SocioEconomicLibrary.aspx 2 Developed an initial range of preliminary 2016-2040 RTP/SCS regional growth forecast with major demographic and economic assumptions. June 2013 http://www.scag.ca.gov/committees/CommitteeDocLibrary/ cehd080113fullagn.pdf 3 Held a panel of experts meeting to assess U.S.Bureau of Labor Statistics (BLS), U.S. Census Bureau, and California Department of Finance (DOF) projections and to discuss demographic and economic trends and assumptions. June 2013 http://www.scag.ca.gov/committees/CommitteeDocLibrary/ cehd080113fullagn.pdf 4 Developed a recommended preliminary set of region and county growth forecasts. September 2013 http://www.scag.ca.gov/committees/CommitteeDocLibrary/ cehd080113fullagn.pdf 5 Developed the initial draft of the small area forecast at jurisdiction/TAZ level and released it to local jurisdictions for comments. November 2013 6 Started the one-on-one meeting with the local jurisdictions for local review across the region. February 2014-October 2014 http://www.scag.ca.gov/committees/CommitteeDocLibrary/ cehd020614fullagn.pdf 7 Released preliminary draft local input/general plan growth forecast. November 2014 http://www.scag.ca.gov/ Documents/2016DraftGrowthForecastByJurisdiction.pdf 8 Released both draft local input/general plan growth forecast and the draft policy growth forecast for 2016-2040 RTP/SCS for comments and local input. June 2015-July 2015 9 Release of the draft 2016-2040 RTP/SCS. December 2015 10 Scheduled adoption of the 2016-2040 RTP/SCS. April 2016

## 3

The recent population growth of the region from 2010-2015 is an extension of the existing slow growth pattern observed during the 2000-2010 period (see **TABLE 2** 

### ). Although the regional economy has recovered from the Great Recession by adding 800,000 jobs with the lower unemployment rate, the regional population continues to show slow growth. The average annual growth rate for the 2010-2015 period was only 0.8 percent, which was lower than the 0.9 percent growth rate of the 2000-2010 period. California and the U.S. have also experienced slow growth over the last 15 years, which will continue over the next 25 years. The average annual growth rate of the SCAG region, California, and the U.S. during the 2015-2040 period is consistent with or lower than the growth rate for the 2010-2015 period. The Great Recession had a significant impact on the regional population growth. The Great Recession resulted in the lowest number (75,000) and the lowest percent change (0.4 percent) in the 2008-2009 annual population growth of the Southern California region since 2000. The number and the percent change in the annual population growth after the Great Recession has steadily increased, up to 144,000 and 0.7 percent in 2014-2015 (see **FIGURE 1** 

### and **FIGURE 2** 

### ). The post-recession growth was much lower than that of 2000- 2005: the annual growth and the average percent change of population were 144,000 and 0.7 percent in the in the 2010-2015 period, while population growth and the average percent change of population in the 2000-2005 period were 200,000 and 1.2 percent. The region’s population growth is mainly determined by two major components: natural increase (births-deaths) and net migration (net domestic migration and net immigration) (see **FIGURE 3** 

### ). There was a significant change in net domestic migration and net immigration after the Great Recession (see **FIGURE 4** 

### and **FIGURE 5** 

### ). During the 2007-2010 period, morepeople moved out of the region than into the region. The average annual number of outmigrants from the region was 150,000 people more than that of in-migrants from the other parts of the country. During the same period, 83,000 legal immigrants and undocumented immigrants from foreign countries immigrated annually to the region. However, only 60,000 people annually left the region for other parts of the nation and 63,000 people immigrated annually to the region between 2010-2015.

**T**

**able 2**  Average Annual Growth Rate of Population, 2000-2040 

### 2000-2010 2010-2015 2015-2040 SCAG Region 0.9% 0.8% 0.7% California 1.0% 1.0% 0.9% United States 1.0% 0.8% 0.8% Source: U.S. Census Bureau, CA DOF, SCAG 

### 15.5 16.0 16.5 17.0 17.5 18.0 18.5 19.020002001200220032004200520062007 20082009201020112012201320142015In Millions



### Year **F igure**  **1** Population Growth, SCAG Region, 2000-2015 (in Millions)

Source: CA DOF, SCAGAnnual Percent Change



### 0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 1.6%00-0101-0202-0303-0404-0505-0606-0707-0808-0909-1010-1111-1212-1313-1414-15 **F igure**  **2** Annual Percent Change of Population, SCAG Region, 2000-2015

Source: CA DOF, SCAG

## 4

-100 -50

0 50 100 150 200 250 30000-0101-0202-0303-0404-0505-0606-0707-0808-0909-1010-1111-1212-1313-1414-15



### Natural Increase Net MigrationIn Thousands **F igure**  **3** Components of Population Change, SCAG Region, 2000-2015 (in Thousands)

Source: CA DOF, SCAGIn Thousands



### -180 -140 -100 -60 -20 2000-0101-0202-0303-0404-0505-0606-0707-08 0 8-0909-1010-1111-1212-1313-1414-15 **F igure**  **4** Net Domestic Migration, SCAG Region, 2000-2015 (jn Thousands)

Source: CA DOF, SCAG 

### Although more migrants have come into this region after the Great Recession, the number of births has continued to decline in recent years. The average annual number of births decreased from 266,000 during the 2007-2010 period to 243,000 during the 2010- 2015 period (see **FIGURE 6** 

### ). During the same period, the total fertility rate decreased from 2.1 to 1.9. Whether the fertility rate in the future will continue to decline is a challenging question for demographers. With changing components (births, domestic migration, immigration) to population growth since 2010, the demographic characteristics of the regional population changed accordingly (see **TABLE 3** 

### ). First, the region’s population has become older. The median age increased from 34.6 in 2010 to 35.4 in 2015. The percentage of the population 65 years old and over increased from 10.9 percent in 2010 to 12.3 percent in 2015, while the percentage of the working-age population of 16-64 years old decreased slightly from 66.7 percent in 2010 to 66.3 percent in 2015. As a result, the old-age dependency ratio increased from 16.4 in 2010 to 18.5 in 2015 by 2.1. The old-age dependency ratio is defined as the ratio of those 65 years old or more to the working-age population—those ages 16-64. It is usually measured as the proportion of dependents per 100 working-age population.In Thousands



### 0 20 40 60 80 100 12000-0101-0202-0303-0404-0505-0606-07 07-0808-0909-1010-1111-1212-1313-1414-15 **F igure**  **5** Net Immigration, SCAG Region, 2000-2015 (in Thousands)

Source: CA DOF, SCAG

## 5

**T**

**able 3**  Demographic Characteristics of Regional Population, 2000-2040 

### 2000 2010 2015 2040 Difference (2010-2015) Difference (2015-2040) Total population (in Thousands) 16,574 18,075 18,779 22,138 700 (Annual Average % Change: 0.8%) 3,559 (Annual Average % Change: 0.7%) **Annual Natural Increase (00-10, 10-15, 35-40)** 163,260 136,805 110,715 -26,455 -26,090 Annual Births (00-10, 10-15, 35-40) 270,283 247,101 274,493 -23,182 27,392 Annual Deaths (00-10, 10-15, 35-40) 107,023 110,296 163,778 3,273 53,482 **Annual Net Migration (00-10, 10-15, 35-40)** -13,128 3,828 15,043 16,956 11,000 Annual Net Immigration 81,628 62,941 95,950 -18,687 33,000 Annual Net Domestic Migration -94,756 -59,114 -89,907 35,642 -30,000 **Components of Population Growth (00-10, 10-15, 35-40)** Natural Increase 108.7% 99.8% 87.9% -8.9% -11.9% Net Migration -8.7% 0.2% 12.1% 8.9% 11.9% 

### Total 100.0% 100.0% 100.0% **Age Composition of Population** Persons Under 16 Years Old 25.6% 22.4% 21.4% 19.3% -1.0% -2.1% Persons 16–64 Years Old 64.4% 66.7% 66.3% 62.5% -0.4% -3.8% Persons 65 Years Old And Over 9.9% 10.9% 12.3% 18.2% 1.4% 5.9% 

### Total 100.0% 100.0% 100.0% 100.0% **Median Age** Male 31.3 33.4 34.3 36.9 0.9 2.6 Female 33.3 35.7 36.5 38.9 0.7 2.4 

### Total 32.3 34.6 35.4 37.9 0.8 2.5 **Dependency Ratio** Child Dependency Ratio* 39.8 33.6 32.3 28.0 -1.3 -4.3 Old-Age Dependency Ratio** 15.4 16.4 18.5 28.2 2.1 9.7 

### Total Dependency Ratio*** 55.2 50.0 50.8 56.2 0.8 5.4 **Ethnic Composition of Population** White (NH) 39.6% 33.6% 31.4% 22.4% -2.2% -9.0% Black (NH) 7.4% 6.6% 6.3% 5.4% -0.3% -0.8% Asian & Others (NH) 12.5% 14.6% 15.6% 19.1% 1.0% 3.5% Hispanic 40.6% 45.3% 46.7% 53.1% 1.4% 6.4% 

### Total 100.0% 100.0% 100.0% 100.0% **Entropy Index (Normalized)****** **0.860 0.856**  **0.853 0.826**  **-0.003 -0.027**  Note: * The number of children per hundred people of working age. ** The number of seniors (65+) per hundred people of working age. ***The number of children (age 0-15) and aged persons (age 65 and over) per hundred people of working age (age 16–64).   The enropy index (normalized) ranges from 0 (less diverse) to 1 (more diverse). NH - Non-Hispanic. Source: U.S. Census Bureau, CA DOF, SCAG

## 6In Thousands



### 210 220 230 240 250 260 270 280 29000-0101-0202-0303-0404-0505-0606-0707-0808-0909-1010-1111-1212-1313-1414-15 ** F** **igure 6**  Births, SCAG Region, 2000-2015 (in Thousands)

Source: CA DOF, SCAG 

### 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6.020002001200220032004200520062007 20082009201020112012 2013 20142015 

### YearIn Millions **F igure**  **7** Household Growth, SCAG Region, 2000-2015 (in Millions)

Source: CA DOF, SCAGAnnual Percent Change



### 0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4%00-0101-0202-0303-0404-0505-0606-0707-0808-0909-1010-1111-1212-1313-1414-15 **F igure**  **8** Percent Change of Households, SCAG Region, 2000-2015

Source: CA DOF, SCAG 

### Second, the region is currently one of the most ethnically diverse regions in the nation. Hispanic and NH Asian/Other populations increased their share from 59.9 percent in 2010 to 62.3 percent in 2015, while NH White and NH Black populations decreased their share from 40.2 percent in 2010 to 37.7 percent in 2015. The region’s ethnic composition has moved toward bigger Hispanic and Asian/Other populations and smaller White and Black populations over time. The ethnic diversity remains high. The normalized entropy index was used to measure the ethnic diversity. The normalized entropy index ranges from 0 to 1, and approaches its maximum of 1 when four race/ethnic groups are equally present. The normalized entropy index for the region was 0.855 in 2010 and 0.853 in 2015. The region already reached the highest entropy index of 0.858 in 2001, and was higher than the nation. The normalized entropy index for the nation was 0.748 in 2000. 

## HOUSEHOLDS The Great Recession had a more significant impact on regional household growth than population growth. Only 100,000 households (20,000 households per year) were added to the region during the 2010-2015 period, while 800,000 people (150,000 people per year) were added to the region during the 2010-2015 period (see **FIGURE 7** 

### ). The slower household growth could be explained in part by demographic factors, including population growth, age composition of population, and household formation. Natural increase was a key driving force of the recent population growth. Additionally, most of the immigrants were of Asian and Hispanic population who were showing a lower household formation level.

## 7

rates by sex also extended their historical trends between 2010-2015. During the 2010- 2015 period, the male headship rates continued to decline, while the female headship rates continued to increase. The NH Asian/Other headship rates increased slightly, while the other racial/ethnic headship rates declined during the 2010-2015 period. (See **TABLE 10** 

### ). The housing shortage is another major factor contributing to the slower household growth. The housing supply was sluggish from 2010-2014, although there was an increasing pattern of housing production on an annual basis. In 2014, 40,000 residential building permits were approved (see **FIGURE 10** 

### ). In particular, the permits for multiple housing units account for over 60 percent of total residential building permits from 2010-2015. The share of multiple housing permits in the most recent five years is much higher than that of 2000-2010. 

## EMPLOYMENT After losing 800,000 jobs between 2007 and 2010, the SCAG region has returned to the pre-recession level of eight million jobs in 2015 with a much lower unemployment rate of 6.6 percent in 2015 than 12.3 percent in 2010 (see **FIGURE 11** 

### and **FIGURE 12** 

### ). In order to achieve the pre-recession level of jobs, the region needed to add the jobs at an annual growth rate of 2.1 percent starting in 2010 (see **FIGURE 13** 

### ). The regional share of national jobs increased from 5.1 percent in 2010 to 5.3 percent in 2015. The changing unemployment rate is directly correlated with the change of the population-employment (P-E) ratio. The P-E ratio is high The annual average growth rate of households was only 0.3 percent from 2010-2015 (see **FIGURE 8** 

### ). The average household size increased from 3.0 in 2010 to 3.1 in 2015 due to the growth of NH Asian/Other and Hispanic groups (see **FIGURE 9** 

### ). The average household size of Hispanics increased from 4.0 in 2010 to 4.1 in 2015, and average household size of Asian and Others increased from 3.1 in 2010 to 3.2 in 2015. The age and racial/ethnic composition of householders changed according to the changing demographic characteristics of the population between 2010 and 2015. Householders are getting older and remain diverse in 2015. Householders who were 55 years and older increased their share from 35.8 percent in 2010 to 40.2 percent in 2015, while householders who were 15-54 years old decreased their share from 64.2 percent in 2010 to 59.8 percent in 2015. Householders of Hispanics and NH Asian/Others increased their share from 48.2 percent in 2010 to 50.6 percent in 2015, NH White and NH Black Householders decreased their share from 51.7 percent in 2010 to 49.4 percent in 2015 (see **TABLE 4** 

### ). The overall headship rates (the number of people 15 years old and over who are counted as heads of households divided by the number of people 15 years old and over) measuring household formation behavior have declined from 41.3 percent in 2010 to 40.3 percent in 2015. The headship rates continued to decline in the 2010-2015 period as well as between 2000-2010. The greatest decline in the headship rate between 2010-2015 was observed in the 75+ age group (-3.1 percentage points), while the 25-34 age group showed the greatest decline in the headship rate between 2000-2010 (-3.9 percentage points). The headship

Source: Construction Industry Research BoardBuilding Permits (In Thousands)



### 0 10 20 30 40 50 60 70 80 90 100

Single Family Multi Family00-0101-0202-03 03-0404-0505-0606-0707-08 08-0909-1010-1111-1212-1313-1414-15 **F igure**  **1 0**  Residential Building Permits Issued by Housing Types, SCAG Region, 2000-2015 (in Thousands)



### 3.00 3.02 3.04 3.06 3.08 3.10 3.1220002001200220032004200520062007 20082009201020112012201320142015Average Household Size



### Year **F igure**  **9** Average Household Size, SCAG Region, 2000-2015

Source: CA DOF, SCAG

## 8

2000 2010 2015 2040 Difference (2010-2015) Difference (2015-2040) Total Households (in Thousands) 5,400 5,848 5,947 7,412 99 (Annual Average % Change: 0.3%) 1,465 (Annual Average % Change: 1.0%)AGE COMPOSITION OF HOUSEHOLDERS 15-24 4.3% 3.9% 2.9% 2.4% -0.9% -0.5% 25-34 19.2% 16.4% 15.6% 13.1% -0.2% -2.5% 35-44 25.6% 21.3% 19.8% 18.1% -1.3% -1.8% 45-54 20.3% 22.7% 21.5% 18.7% -1.4% -2.8% 55-64 12.6% 17.0% 18.6% 16.2% 1.4% -2.4% 65-74 9.5% 10.1% 12.4% 14.8% 1.9% 2.5% 75+ 8.6% 8.7% 9.2% 16.8% 0.5% 7.6% 

### Total 100.0% 100.0% 100.0% 100.0%ETHNIC COMPOSITIONOF HOUSEHOLDER**S** White (NH) 49.4% 43.7% 41.9% 28.9% -2.2% -13.0% Black (NH) 8.1% 8.0% 7.5% 6.6% -0.6% -0.9% Asian & Others (NH) 12.7% 13.7% 15.1% 20.1% 1.8% 5.0% Hispanic 28.8% 34.5% 35.5% 44.4% 1.2% 8.9% 

### Total 100.0% 100.0% 100.0% 100.0%AVERAGEHOUSEHOLD SIZE White (NH) 2.4 2.3 2.3 2.3 0.0 0.0 Black (NH) 2.7 2.6 2.6 2.5 0.0 -0.1 Asian & Others (NH) 3.2 3.1 3.2 2.8 0.1 -0.4 Hispanic 4.3 4.0 4.1 3.6 0.1 -0.5 

### Total 3.0 3.0 3.1 3.0 0.1 -0.1 **T**

**able 4**  Characteristics of Regional Households, 2000-2040

Note: NH - Non-Hispanic. Source: U.S. Census Bureau, CA DOF, SCAG

## 9

0% 2% 4% 6% 8% 10% 12% 14%20002001 20022003200420052006200720082009201020112012 201320142015 

### YearUnemployment Rate **F igure**  **1 1**  Unemployment Rate, SCAG Region, 2000-2015

Source: CA EDD, SCAG



### 6.0 6.5 7.0 7.5 8.0 8.5 9.02000200120022003200420052006200720082009201020112012201320142015In Millions



### Year **F igure**  **1 2**  Job Growth, SCAG Region, 2000-2015 (in Millions)

Source: CA EDD, SCAGAnnual Percent Change



### -8% -6% -4% -2% 0% 2% 4%00-0101-0202-0303-0404-0505-0606-0707-0808-0909-1010-1111-1212-1313-1414-15 **F igure**  **1 3**  Annual Percent Change of Jobs, SCAG Region, 2000-2015

Source: CA EDD, SCAG



### 2.0 2.1 2.2 2.3 2.4 2.52000200120022003200420052006200720082009201020112012201320142015 

### YearPopulation to Employment Ratio **F igure**  **1 4**  Population to Employment Ratio, SCAG Region, 2000-2015

Source: CA DOF, CA EDD, SCAG

## 10

population and employment distribution using the county in the region as a unit of analysis. If HIOC equals 0, then population and employment are perfectly deconcentrated across the region. If HIOC equals 100, then population and employment are concentrated into one county in the region. Considering the suburbanization of population and employment in the region, the historical pattern of the HIOC tends to move toward the lower level, which means more deconcentration. The SCAG region showed a downward trend of HIOC from 62.09 in 2000 to 58.34 in 2010 to 58.19 in 2015 for population, and from 67.41 in 2000 to 64.91 in 2010 to 63.43 in 2015 for employment. The suburbanization of population and employment (in particular, population) in the post-recession period has slowed down compared to the 2000-2010 period. While there has been a downward change in HIOC along with spatial changes in population and employment in the region, the gap of HIOC between population and employment became smaller after the recession compared to the 2000-2010 period. The Index of Divergence (IOD) was used to measure the gap of HIOC between population and employment. If IOD equals 0, then there is no gap between two HIOCs. This means that the county distribution of both population and employment is more balanced and there is a convergence of the county distribution of population and employment. For example, the share of both Riverside and San Bernardino Counties’ population increased from 23.4 percent in 2010 to 23.5 percent in 2015 by 0.1 percent, while the share of both Riverside and San Bernardino Counties’ employment increased from 17.2 percent in 2010 to 18.4 percent in 2015 by 1.2 percent. The county distribution of population and employment indicates that faster growth of employment in Riverside and San Bernardino Counties, and Imperial County reduced the gap in the suburbanization level of population and employment observed in 2010. The IOD decreased from 0.066 in 2010 to 0.052 in 2015. This change will have a positive implication for regional transportation and air quality. The population to employment (P-E) ratio was used to measure the balance of county population and employment. All counties in the region experienced a decline in P-E ratio between 2007 and 2010. The regional P-E ratio declined from 2.5 to 2.3 during the same period. Riverside, Imperial, and San Bernardino Counties experienced a faster decline in the P-E ratio than other counties: 3.7 in 2010 to 3.1 in 2015 for Riverside County; 3.1 in 2010 to 2.4 in 2015 for Imperial County; 3.1 in 2010 to 2.9 in 2015 for San Bernardino County. in a recession, while it is low in a better business cycle. The P-E ratio was highest at 2.5 in 2010, and moved toward the lower level of 2.4 in 2015. The Great Recession greatly influenced all of the industrial sectors and contributed to the fast restructuring of the industrial sectors (see **TABLE 5** 

### ). Eighteen major industrial sectors experienced a loss of jobs from 2007-2010 due to the Great Recession. Only two major industrial sectors did not experience a loss: accommodation and food service, public administration. Both the construction and manufacturing sectors were heavily impacted during the Great Recession and accounted for 40 percent of the total job losses that occurred during the Great Recession. The construction sector lost 170,000 jobs (36 percent of 470,000 in 2007) and the manufacturing sector lost 150,000 jobs (18 percent of 810,000 jobs in 2007). Other heavily impacted sectors during the same period were: retail trade (–100,000 jobs); administrative and support services (–90,000 jobs); professional, scientific and technical services (–70,000 jobs); and finance and insurance (–60,000 jobs). The wage level of seriously impacted industrial sectors (i.e., construction, manufacturing, the professional, scientific and technical services sectors) was relatively high. With such a loss in the higher wage sector jobs, the economic quality of the region’s residents was negatively impacted. Two industrial sectors accounted for nearly 30 percent of the total job growth (800,000) from 2010-2015: health care and social assistance (+130,000 jobs) and professional, scientific, and technical services (+100,000 jobs). Other industrial sectors adding a significant number of jobs were: accommodation and food service (+80,000); construction (+70,000 jobs); retail trade (+70,000 jobs); and administrative and support service and waste service (+70,000 jobs).As a result of job growth by industrial sectors, the industrial structure remained serviceoriented. There was an increase in the share of professional, scientific, and technical services from 6.6 percent to 7.2 percent, followed by health care from 12.3 percent to 12.8 percent, and construction from 4.2 percent to 4.6 percent. There was also a decline in the share of industrial sectors including manufacturing from 9.1 percent in 2010 to 8.4 percent in 2015, and educational services from 9.5 percent in 2010 to 8.9 percent in 2015. 

## SUBURBAN GROWTH The region continued its slow population growth in the post-recession period (2010-15), adding only 800,000 people, while experiencing rapid employment growth, adding 800,000 jobs since 2010. Although suburbanization of population and employment continued, there was a little change in the county distribution of the regional population and employment during the same period (see **TABLE 8** 

### ). The Hoover Index of Concentration (HIOC; Plane and Rogerson, 1994) was used to measure the concentration of intra-regional

## 11

**T**

**able 5**  Regional Employment by Industry Sectors, 2007-2040

Source: CA EDD, SCAG



### Jobs by 2 Digit NAICS Sector 2007 2010 2015 2040 Difference (2010-2015) Difference (2015-2040) 2013 Wage Level ($) Number (1,000) % Number (1,000) % Number (1,000) % Number (1,000) % Total Employment (in Thousands) 8,002 100% 7,257 100% 8,006 100% 9,872 100% 749 (Annual Average % Change: 2.1%) 1,866 (Annual Average % Change: 0.9%) 52,126 Total Farm 69 0.9% 62 0.9% 65 0.8% 57 0.6% 0.0% -0.2% 27,811 Natural Resources and Mining 8 0.1% 7 0.1% 7 0.1% 5 0.1% 0.0% 0.0% 126,750 Utilities 49 0.6% 47 0.6% 48 0.6% 45 0.5% 0.0% -0.1% 99,700 Construction 470 5.9% 302 4.2% 369 4.6% 582 5.9% 0.5% 1.3% 55,585 Manufacturing 810 10.1% 658 9.1% 673 8.4% 638 6.5% -0.7% -1.9% 62,157 Wholesale Trade 410 5.1% 364 5.0% 397 5.0% 483 4.9% -0.1% -0.1% 61,782 Retail Trade 874 10.9% 775 10.7% 846 10.6% 967 9.8% -0.1% -0.7% 31,405 Transportation and Warehousing 316 3.9% 300 4.1% 326 4.1% 379 3.8% -0.1% -0.2% 51,539 Information 278 3.5% 254 3.5% 269 3.4% 308 3.1% -0.1% -0.2% 93,022 Finance and Insurance 322 4.0% 265 3.7% 284 3.5% 320 3.2% -0.1% -0.3% 95,719 Real Estate and Rental and Leasing 172 2.1% 154 2.1% 166 2.1% 204 2.1% -0.1% 0.0% 57,418 Professional, Scientific, and Technical Services 544 6.8% 477 6.6% 598 7.5% 864 8.8% 0.9% 1.3% 83,006 Management of Companies and Enterprises 103 1.3% 87 1.2% 96 1.2% 107 1.1% 0.0% -0.1% 94,986 Administrative and Support and Waste Services 622 7.8% 532 7.3% 598 7.5% 712 7.2% 0.1% -0.2% 35,515 Educational Services 692 8.6% 688 9.5% 715 8.9% 867 8.8% -0.6% -0.1% 49,719 Health Care and Social Assistance 910 11.4% 891 12.3% 1,021 12.8% 1,512 15.4% 0.5% 2.6% 43,678 Arts, Entertainment, and Recreation 159 2.0% 134 1.8% 152 1.9% 194 2.0% 0.1% 0.1% 63,060 Accommodation and Food Service 635 7.9% 690 9.5% 766 9.6% 875 8.9% 0.1% -0.7% 19,784 Other Services 314 3.9% 304 4.2% 340 4.2% 419 4.3% 0.1% 0.0% 33,415 Public Administration 246 3.1% 267 3.7% 270 3.4% 308 3.1% -0.3% -0.3% 74,118 **Entropy Index (Normalized) 0.914**  **0.910 0.909**  **0.899**

## 12


# REGIONAL GROWTH FORECAST **REGIONAL GROWTH FORECAST** SCAG projects that the region will add 3.8 million residents, 1.5 million households and 2.4 million jobs over the RTP/SCS planning horizon (2012-2040) (see **FIGURE 15** 

### and **FIGURE 16**  

### ). Population and households are projected to grow at the annual average growth rate of 0.7 percent during the same period, while employment grows faster at two percent until 2020, and then stabilizes at 0.7 percent. (see **FIGURE 17** 

### ). The SCAG region’s population is projected to grow slower than that of the previous years. The slow growth pattern is not present only in the SCAG region, but is also observed from U.S. and California population projections by U.S. Census Bureau and California DOF, respectively (see **TABLE 2** 

### ). 

## POPULATION The slow population growth pattern experienced in the post-recession period is expected to continue into the future. Between 2015 and 2040, the annual population growth rate will be only 0.7 percent, which is similar to the post-recession period, but much lower than that experienced between 2000-2010. The region will grow mainly through natural increase (see **TABLE 3** 

### and **FIGURE 19** 

### and **FIGURE 21** 

### ). Nearly 90 percent of the population growth will be due to natural increase (e.g., births minus deaths) in the region rather than net migration (e.g., inmigration minus outmigration) (see **FIGURE 20** 

### ). The average number of babies per woman

18.3

7.4 5.9 18.8

8.0 5.9 22.1

9.9 7.4

Population Employment Households 2012 2015 2040 **F igure**  **1 5**  Population, Employment, and Households, SCAG Region, 2012, 2015 and 2040 (in Millions)

Source: CA DOF, CA EDD, SCAG 

### Population Employment Households 0 5 10 15 20 25

2000 2020 2040In Millions **F igure**  **1 6**  Population, Employment, and Household Growth, SCAG Region, 2000-2040 (in Millions)

Source: CA DOF, CA EDD, SCAG



### -8% -6% -4% -2%

0 2% 4%

Population Employment

2000-2001 2014-2015 2039-2040Annual Percent Change **F igure**  **1 7**  Annual Percent Change of Population and Employment, SCAG Region, 2000-2040

Source: CA DOF, CA EDD, SCAG

## 13

5% 3% 1% 1% 3% 5% 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+

2040 2015 (Shaded) Males Females **F igure**  **1 8**  Population Pyramids, SCAG Region, 2015 and 2040

Source: SCAG

**F igure**  **1 9**  Components of Population Change, SCAG Region, 2000-2040 (in Thousands)

Source: CA DOF, SCAG 

### 0 50 100 150 200 250 300

2000-2001 2014-2015 2039-2040 Births DeathsIn Thousands **F igure**  **2 0**  Births and Deaths, SCAG Region, 2000-2040 (in Thousands)

Source: CA DOF, SCAG



### Natural Increase Net Migration -100 -50

0 50 100 150 200

2000-2001 2014-2015 2039-2040In Thousands **F igure**  **2 1**  Net Immigration and Net Domestic Migration, SCAG Region, 2000-2040 (in Thousands)

Source: CA DOF, SCAG 

### Net Immigration Net Domestic Migration -200 -150 -100 -50

0 50 100 150

2000-2001 2014-2015 2039-2040In Thousands

## 14

**HOUSEHOLDS** As the population ages and remains diverse in the region during the projection period, the householders are also aging and showing the change in the racial/ethnic distribution (see **TABLE 4** 

### ). Given the cohort size of the baby boomer generation, the effect of aging population on the number of households is enormous. The number of households will reach more than 7.4 million in 2040 with the net addition of over 1.4 million households in the next 25 years. Older householders (65 years and older) will account for 75 percent of the projected household growth in the region and will increase their share from 21.6 percent in 2015 to 31.6 percent in 2040 by ten percent. However, the share of householders 15-64 years old will decline from 78.4 percent to 68.4 percent. In particular, householders 15-24 years old will show the smallest increase among all age groups. Following the changing dynamics of population projections, the region’s householders also experience a shift in the racial/ethnic composition during the projection period (see **TABLE 4**  

### ). Hispanic householders will be the largest ethnic group in the region in 2040, and will continue to show the most growth among four racial/ethnic groups during the projection period. Hispanic householders will increase their share of total householders by 8.9 percent from 35.5 percent in 2015 to 44.4 percent in 2040. NH Asian/Other householders will have the highest growth mainly through immigration. The share of NH Asian/Other householders increases by 5.2 percent from 15.1 percent in 2015 to 20.1 percent in 2040. However, NH White householders will experience a net decline of 335,000 from 2.5 million in 2015 to 2.1 million in 2040. The share of NH White householders will go down by 13 percent points from 41.9 percent in 2015 to 28.9 percent in 2040. NH Black householders will also experience the smaller share of household growth (7.5 percent in 2015 vs. 6.6 percent in 2040). In contrast to the normalized entropy index for population, the normalized entropy index for householders will slightly increase from 0.875 in 2015 to 0.879 in 2040. There was an increase in the average household size from 3.0 in 2010 to 3.1 in 2015, but the household size will eventually decline from 3.1 in 2015 to 3.0 in 2040 as a result of the increase in the older householders and the increased headship rates of Hispanic and Asian/ Other populations. A smaller household size of both Hispanic and Asian/Other populations were made possible with an assumption that Hispanic and Asian/Other immigrants will have higher headship rates as they live in the U.S. for a longer period of time. 

## EMPLOYMENT With an increase in jobs in the post-recession period (2010-15), the SCAG region’s economy returned to 2007 levels with an unemployment rate of 6.6 percent in 2015. The region is expected to add 1.9 million jobs, from 8 million in 2015 to 9.9 million in 2040. of child bearing age remains the same at 1.9 in both 2015 and 2040. The life expectancy of people in the region is expected to increase. International migration also plays an important role in population growth. Seven of ten new residents will be arriving in the region through international migration. 2.2 million more persons leave the region for the rest of the nation than persons migrating to the region between 2015-2040. The most noticeable demographic characteristics of the projected population in the region will be the aging of the population and shifts in the racial/ethnic distribution (see **TABLE 3** 

### and **FIGURE 18** 

### ). First, the region’s median age is 35.4 in 2015 which is younger than the nation’s median age of 36.8. The region’s population is aging due to the aging of the baby boomer generation (born between 1946 and 1964) and the lower birth rate. The median age of the population is projected to increase by 2.5 years to 37.9 in 2040. The share of the population 65 years old and over is projected to increase from 12 percent in 2015 to 18 percent in 2040, while the share of the population of 64 years old or less decreases from 88 percent in 2010 to 82 percent in 2035. In particular, both children 15 years old or less and the working age population of 16-64 years old have shown a decline from 21 percent to 19 percent, and from 66 percent to 63 percent, respectively, during the projection period. The decline of the working age population may result in a potential shortage of workers and slower job growth unless the older population extends their retirement age. With the increasing share of the older population and the decreasing share of the working age population, the old-age dependency ratio is projected to increase from 19 percent in 2015 to 28 percent in 2040 by 9 percent. The older population will grow over six and half times faster than that of working age groups (16-64) during the same period. The older population, mainly composed of the baby boomer generation, will constitute 51 percent of the population growth between 2015 and 2040. The region’s already high racial/ethnic diversity changes over time during the projection horizon (see **TABLE 3** 

### ). The Hispanic population will become the majority ethnic group in the region around 2027 and will continue to show the greatest growth due to births and immigration. The Hispanic population will increase its share of the population by 6.4 percent from 46.7 percent in 2015 to 53.1 percent in 2040. NH Asian/Other population, which includes the multiracial groups, will have the fastest growth mainly through immigration. The share of NH Asian/Other population increases from 15.6 percent in 2015 to 19.1 percent in 2040 by 3.5 percent. However, the NH White population will experience a net decline of 940,000 from 5.9 million in 2015 to 5 million in 2040. The share of NH White population will decrease from 31.4 percent in 2015 to 22.4 percent in 2040 by 9 percent. NH Black population will also experience the smaller share of population growth (6.3 percent in 2015 vs. 5.4 percent in 2040). As a result of the changing racial/ethnic composition, the normalized entropy index will decline from 0.853 in 2015 to 0.826 in 2040.

## 15

With a transformation of the region’s industrial structure, the economic quality of life of the region’s residents is severely affected. The distribution of jobs by wage level indicates that the region will increase the share of jobs in the lower 25 percent category, while there is a decrease in the share of jobs in the other job categories (note: the wage level is categorized into four levels: (1) bottom 25 percent, (2) lower 25 percent, (3) upper 25 percent, (4) top 25 percent). The jobs in the lower 25 percent category include (1) construction, (2) transportation and warehousing, (3) real estate and rental and leasing, (4) educational services, and (5) health care and social assistance. The share of the jobs in the bottom 50 percent category increases from 65.2 percent in 2015 to 67.1 percent in 2040, while the share of the jobs in the top 50 percent category decreases from 34.8 percent to 33.0 percent in 2040. The economic and job creation analysis appendix documents an analysis of the economic impacts of the 2016 RTP/SCS. 

## SUBURBAN GROWTH The region continued slow population growth in the post-recession period, adding only 3.3 million people, while having fast jobs growth, adding nearly 1.9 million jobs from 2015-2040. The HIOC, a measure of concentration, shows a decline from 58.19 in 2015 to 55.00 in 2040 for population, and from 63.43 in 2015 to 59.53 in 2040 for employment (see **TABLE 8** 

### ). The declining HIOC indicates that there will be a deconcentration trend toward more growth of population and employment in Riverside and San Bernardino Counties. The share of both Riverside and San Bernardino Counties’ population increased from 23.5 percent in 2015 to 26.6 percent in 2040 by 3.1 percent, while the share of both Riverside and San Bernardino Counties’ employment increased from 18.4 percent in 2015 to 22.2 percent in 2040 by 3.8 percent. The fast growth of population and the faster growth of employment in these two counties made a major contribution to the downward change in the HIOC of the region from 2015-2040. The gap of HIOC between population and employment becomes smaller in 2040 than in 2015. The IOD decreased from 0.052 in 2015 to 0.045 in 2040. The region’s industrial mix, however, will experience continuous change over time due to the region’s relative competitiveness and globalization (see **TABLE 5** 

### ). The region’s relative competitiveness comes from the diversity of jobs in the region. The normalized entropy index for measuring the region’s job diversity is 0.909 in 2015. The region’s few jobs are relatively competitive compared to the national level. The location quotient (LQ) is used to measure the relative competitiveness (see **TABLE 6** 

### ). The region includes competitive high wage jobs such as (1) information, (2) manufacturing, (3) professional, scientific, and technical services, (4) wholesale trade and (5) arts, entertainment and recreation. The region also shows a strong competitiveness in low wage jobs including (1) accommodation and food service, (2) administrative and support services and (3) transportation and warehousing. The employment in the manufacturing sector is losing ground in the region as well as in the U.S. Globalization plays an important role in transforming the industrial structureof the region. It is clear that the region’s industrial structure evolves from productionoriented industries to service-oriented industries. For example, the share of employment in the manufacturing sector will continue to decrease from 8.6 percent in 2015 to 6.4 percent in 2040. A few selected sectors are expected to have rapid growth. The construction sector will regain its normal share by increasing from 4.6 percent in 2015 to 5.9 percent in 2040. This growth translates into 213,000 jobs from 2015-2040. The following four industrial sectors: (1) health care and social assistance (+491,000 jobs), (2) professional and business services (+266,000 jobs), (3) construction (+213,000), and (4) education services (+152,000) are key industrial sectors that are projected to add more than 1.1 million jobs by 2040 and account for 61 percent of total job growth from 2015-2040. The four industrial sectors increase their share from 34 percent in 2015 to 39 percent in 2040. While many servicejobs require minimal skills and pay low wages, service jobs also include high-paying, highskill work, such as investment banking and computer operations. This changing composition of industrial sectors requires diverse skill needs for our industries.

**T**

**able 6**  Regional Employment by Wage Level, 2015 and 2040

Note: 1 = (1) Natural Resources and Mining, (2) Utilities, (3) Information, (4) Finance and Insurance, (5) Management of Companies and Enterprises; 2= (1) Manufacturing, (2) Wholesale Trade, (3) Professional, Scientific and Technical Services, (4) Arts, Entertainment, and Recreation, (5) Public Administration; 3= (1) Construction, (2) Transportation and Warehousing, (3) Real Estate and Rental and Leasing, (4) Educational Services, (5) Health Care and Social Assistance; 4= (1) Total Farm, (2) Retail Trade, (3) Administrative and Support and Waste Services, (4) Accommodation and Food Service, (5) Other Services. 

### 2013 Wage Level 2007 2010 2015 2040 Difference (2010-2015) Difference (2015-2040) 1 - top 25% 9.5% 9.1% 8.8% 7.9% -0.3% -0.9% 2 - upper 25% 27.1% 26.2% 26.0% 25.1% -0.2% -0.9% 3 - lower 25% 32.0% 32.2% 32.5% 36.2% 0.3% 3.7% 4 - bottom 25% 31.4% 32.6% 32.7% 30.9% 0.2% -1.8%

## 16


# FORECAST METHODOLOGY AND ASSUMPTIONS **GROWTH FORECAST APPROACH** SCAG’s Regional Growth Forecast includes three major indicators: population, households and employment. As past practice, SCAG uses the BULA (Balance, Uncertainty, Latest, and Adaptive) and Collaborative approach toward developing the regional growth forecast for 2016 RTP/SCS. SCAG’s growth forecast process has been open, transparent, and extensive. Such an inclusive process involves broad participation from experts and stakeholders All counties in the region experienced a decline in P-E ratio from 2015-2040. The regional P-E ratio declined from 2.3 to 2.2. Riverside and San Bernardino Counties experienced a faster decline in the P-E ratio than other counties: 3.1 in 2015 to 2.7 in 2040 for Riverside County; 2.9 in 2015 to 2.7 in 2040 for San Bernardino County. If the region continues to experience faster employment growth in Riverside and San Bernardino Counties, where an abundant labor force is available, the region’s serious transportation and air quality problems may be reduced due to more balanced county distribution of population and employment.



### Jobs by 2 digit NAICS sector 2007 2010 2015 2040 Difference (2010-2015) Difference (2015-2040) Total Farm 0.635 0.565 0.591 0.591 0.026 0.000 Natural Resources and Mining 0.210 0.198 0.182 0.127 -0.016 -0.055 Utilities 1.130 1.111 1.139 1.173 0.028 0.034 Construction 0.925 0.813 0.855 1.064 0.042 0.210 Manufacturing 1.068 1.084 1.100 1.150 0.016 0.050 Wholesale Trade 1.237 1.263 1.256 1.312 -0.007 0.056 Retail Trade 0.996 0.992 0.996 0.992 0.004 -0.004 Transportation and Warehousing 0.992 1.069 1.056 1.035 -0.013 -0.021 Information 1.646 1.737 1.737 1.869 0.000 0.132 Finance and Insurance 0.933 0.871 0.862 0.840 -0.009 -0.022 Real Estate and Rental and Leasing 1.213 1.282 1.262 1.327 -0.020 0.065 Professional, Scientific and Technical Services 1.156 1.093 1.129 1.169 0.036 0.040 Management of Companies and Enterprises 1.032 0.915 0.951 0.922 0.036 -0.029 Administrative and Support and Waste Services 1.256 1.248 1.241 1.142 -0.007 -0.099 Educational Services 0.965 0.976 0.927 0.881 -0.049 -0.046 Health Care and Social Assistance 0.986 0.948 0.915 0.887 -0.033 -0.028 Arts, Entertainment, and Recreation 1.253 1.139 1.165 1.169 0.027 0.004 Accommodation and Food Service 1.013 1.186 1.229 1.172 0.043 -0.057 Other Services 0.801 0.837 0.854 0.881 0.017 0.026 Public Administration 0.480 0.526 0.509 0.514 -0.017 0.005 Source: U.S. BLS, CA EDD, SCAG **T**

**able 7**  Regional Location Quotients for Industry Sectors, 2007-2040

## 17

**LOCAL INPUT** The initial mid-range regional growth forecast was further disaggregated into the small area level. SCAG provided local jurisdictions with the SCAG’s multi-level small area growth forecast for their review and comments. SCAG’s staff conducted one-on-one meetings with 195 of 197 jurisdictions to review the forecast and to receive local input. This local input process provided an opportunity for jurisdictions to offer their local knowledge and input to inform SCAG’s regional datasets. After SCAG received input from local jurisdictions, SCAG assessed the reasonableness of the aggregated local input data at the regional level by using the unemployment rate, and SCAG also evaluated the comments and incorporated the adjustments into the population, household and employment growth distributions. The resulting final local input growth forecast serves as a basis for further developing the policy growth forecast. Additional refinements to the final local input growth forecast were made to reflect land use-transportation coordination through the scenario planning process in the development of the policy growth forecast (see **EXHIBITS 1-9** 

### ). 

## GROWTH FORECAST METHODOLOGY The regional growth forecast for the 2016-2040 RTP/SCS was developed using the regional forecast methodology used in the development of the 2012 RTP growth forecast and updated demographic-economic assumptions (see SCAG’s growth forecast report for 2012 RTP/SCS: http://rtpscs.scag.ca.gov/Documents/2012/final/SR/2012fRTP_ GrowthForecast.pdf ). The following is the methodology for developing the regional growth forecast for the 2016 RTP/SCS. SCAG projects regional employment using a shift-share model. The shift-share model computes employment comprised of 20 broad NAICS sectors, at a future point in time using a regional share of the nation’s employment. The regional employment forecasts are based on a set of national employment forecasts. The national employment forecasts have two components: 1) forecasts of the number of total jobs and 2) forecasts of jobs by industry sector. The regional job projections depend both on the number of total jobs in the U.S. and the distribution of these jobs among industry sectors. The forecast of total U.S. jobs is based on a forecast of total population, population by age group, labor force participation rates, assumed unemployment, and the ratio of jobs to workers (employed residents) reflecting assumptions about multiple job holding for individuals. The population by age group and labor force participation rate forecasts are quantitatively more important than the other assumptions in developing national projections of total jobs. specifically. The following three major activities (panel of experts meeting, range of regional growth forecasts, and local input) were essential in developing the regional growth forecast with demographic-economic assumptions. 

## PANEL OF EXPERTS MEETING (2013)The collective expert opinions are a useful reference to reduce the short-term and longterm projection errors. SCAG held the 2013 SCAG panel of demographic and economic experts meeting on June 27, 2013 to review SCAG’s methodology and assumptions for its population, household, and employment growth forecast for the 2016-2040 RTP/SCS. Twenty (20) academic scholars and leading practitioners were invited to participate on the panel. The panel of experts reviewed demographic and economic trends in the national and regional growth context, discussed the key assumptions underlying the regional and county growth forecast, and provided responses to survey questions on major assumptions. 

## A RANGE OF REGIONAL GROWTH FORECASTS SCAG initially sets a range of regional growth forecasts (population, employment, and households) to address the uncertainty of a certain set of growth forecasts. A set of regional growth forecasts are developed in the following order: employment, population and households (Field and MacGregor, 1987). The regional employment forecast is initially developed and followed by the population forecast, and then by the household forecast. First, a range of the regional employment forecasts (low, mid, high) is derived using a range of the regional shares of the national jobs as suggested by the expert panel. Second, 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.) remain unchanged for three different employment levels. Third, a range of the regional population forecasts are translated into a range of the regional household forecasts using a mid-trend method to convert population into households. It is based on the trend extrapolation of headship rates by age, sex, and race/ethnicity with a consideration of the assimilation assumptions of the Hispanic and Asian headship rates.

## 18

**T able**  **8** Regional Population and Employment by County, 2000-2040 

### 2000 2010 2015 2040 Difference (2010-2015) Difference (2015-2040) County/Region Number (1,000) % Number (1,000) % Number (1,000) % Number (1,000) % Number (1,000) % Number (1,000) %POPULATION Imperial 143 0.9% 176 1.0% 182 1.0% 282 1.3% 7 0.0% 100 0.3% Los Angeles 9,544 57.6% 9,827 54.4% 10,159 54.1% 11,514 52.0% 332 -0.3% 1,355 -2.0% Orange 2,854 17.2% 3,017 16.7% 3,157 16.8% 3,461 15.6% 140 0.1% 304 -1.2% Riverside 1,557 9.4% 2,192 12.1% 2,316 12.3% 3,183 14.4% 125 0.2% 867 2.1% San Bernardino 1,719 10.4% 2,039 11.3% 2,111 11.2% 2,731 12.3% 72 0.0% 620 1.1% Ventura 757 4.6% 825 4.6% 853 4.5% 966 4.4% 28 0.0% 113 -0.2% SCAG Region 16,574 100.0% 18,078 100.0% 18,779 100.0% 22,138 100.0% 703 3,359 HIOC* 62.09 58.34 58.19 55.00 -0.1 -3.2EMPLOYMENT Imperial 54 0.7% 56 0.8% 76 0.9% 125 1.3% 20 0.2% 49 0.4% Los Angeles 4,445 59.7% 4,140 57.1% 4,463 55.7% 5,226 52.9% 323 -1.3% 763 -2.4% Orange 1,517 20.4% 1,493 20.6% 1,633 20.4% 1,899 19.2% 140 -0.2% 266 -1.1% Riverside 514 6.9% 592 8.2% 742 9.3% 1,175 11.9% 150 1.1% 433 2.5% San Bernardino 587 7.9% 653 9.0% 729 9.1% 1,028 10.4% 76 0.1% 299 1.3% Ventura 323 4.3% 323 4.4% 363 4.5% 420 4.3% 40 0.1% 57 -0.2% SCAG Region 7,440 100.0% 7,257 100.0% 8,006 100.0% 9,872 100.0% 749 1,866 HIOC* 67.41 64.91 63.43 59.53 -1.48 -3.9 IOD** 0.054 0.066 0.052 0.045 -0.013 -0.007P-E RATIO Imperial 2.6 3.1 2.4 2.3 -0.7 -0.1 Los Angeles 2.1 2.4 2.3 2.2 -0.1 -0.1 Orange 1.9 2.0 1.9 1.8 -0.1 -0.1 Riverside 3.0 3.7 3.1 2.7 -0.6 -0.4 San Bernardino 2.9 3.1 2.9 2.7 -0.2 -0.2 Ventura 2.3 2.6 2.4 2.3 -0.2 -0.1 SCAG Region 2.2 2.5 2.3 2.2 -0.1 -0.1 Note: * HIOC (Hoover Index of Concentration) measures the distribution of population and employment. If HIOC equals 0, then population and employment are perfectly de-concentrated. If HIOC equals 100, then the county’s share in comparison with the entire SCAG region’s population or employment would be concentrated to a single county of the SCAG region. However, if the HIOC drops to 0, then each county’s share would be equal. **IOD (Index of Divergence) measures the intra-regional segregation of population and employment. If ID equals 1, then the population and employment of a county are unbalanced. If IOD equals 0, then the population and the employment of a county are spatially proportioned. Source: CA DOF, CA EDD, SCAG

## 19

SCAG projects regional population using the cohort-component model. The model computes population at a future point in time by adding to the existing population the number of group quarters population, births and persons moving into the region during a projection period, and by subtracting the number of deaths and the number of persons moving out of the region. The patterns of migration into and out of the region are determined by the number of forecasted jobs. Households are forecasts by multiplying the projected residential population by projected headship rates. The headship rate is the proportion of a population cohort that forms the household. Age-sex-racial/ethnic specific household formation levels are used to translate projected residential population into projected households. The regional growth forecast is further disaggregated to the county and the smaller geographies. The preliminary county level growth forecast was derived using the county share of the regional growth forecast from 2012 RTP/SCS county growth forecast, and was later refined as a result of local input. 

## REGIONAL DEMOGRAPHIC-ECONOMIC ASSUMPTIONS Demographic and economic assumptions play a decisive role in determining the size of population, households, and employment in the future (see **TABLE 9** 

### ). 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 1.9, which is lower than the replacement level of 2.1, during the projection period. The region’s life expectancy at birth improves at the same rate as that of the nation’s life expectancy improvement as assumed by the U.S. Census Bureau’s 2014 population projection. Domestic migration fluctuates and is directly influenced by labor demand derived from regional employment forecasts. International net immigration will be 63,000 per year until it increases to 96,000 per year in 2020. The share of Hispanic and Asian migrants in the nation will increase along with the increasing Hispanic and Asian population size. In addition to demographic assumptions, three translation factors are needed to link regional employment forecasts to regional population forecasts. They are labor force participation rates, the implied unemployment rates and multiple jobholding rates. First, labor force participation rates play an important role in translating the labor force demand into labor force supply. The projected pattern of national labor force participation rates developed by Pitkin and Myers in 2013 was used to project SCAG region’s labor force participation rates. The overall labor force participation rate is projected to decline from 64.5 percent in 2010 to 62.2 percent in 2040. Second, some workers may keep two or more jobs. The double jobholding rate will be 4.5 percent of the workers during the projection period. Third, the implied unemployment rate will range from five percent to eight percent during the projection period. The implied unemployment rate is derived by matching labor supply estimated **T able**  **9** Regional Demographic-Economic Assumptions

Note: NH - Non-Hispanic Source: CA DOF, SCAG 

### Race/Ethnicity 2010-2015 (Annual Average) 2015-2040 (Annual Average) Difference (2015-2040)TOTAL FERTILITY RATE White (NH) 1.5 1.5 0 Black (NH) 1.7 1.7 0 Asian & Others (NH) 1.6 1.6 0 Hispanic 2.1 2.1 0 

### Total 1.9 1.9 0CRUDE DEATH RATE White (NH) 11.4 11.9 0.5 Black (NH) 9.1 9.4 0.3 Asian & Others (NH) 2.6 4.0 1.4 Hispanic 2.8 3.5 0.7 

### Total 6.2 6,4 0.2INTERNATIONAL NETIMMIGRATION Total 62,941 95,950 33,000 White (NH) 11% 11% 0% Black (NH) 3% 3% 0% Asian & Others (NH) 19% 19% 0% Hispanic 68% 68% 0% 

### Total 100% 100% 0%LABOR FORCEPARTICIPATION RATE White (NH) 63.5% 60.7% -2.8% Black (NH) 59.6% 56.7% -2.9% Asian & Others (NH) 63.3% 59.8% -3.5% Hispanic 66.4% 64.4% -3.0% 

### Total 64.5% 62.2% -2.3%

## 20

from population projections with workers estimated from job projections. Finally, the most important consideration is the reasonable regional share of national jobs. The SCAG region’s share of the national jobs in 2040 is assumed to remain at the 5.3 percent observed in 2015. **TABLE 10** 

### shows the projected headship rates by age, sex and race/ethnicity between 2015 and 2040, which are the basis for deriving the household forecast. The headship rate projections were developed using the trend extrapolation of headship rates with an assumption of the assimilation of Hispanic and NH Asian/Other headship rates. The overall headship rates will increase from 40.3 percent in 2015 to 41.3 percent in 2040 (see **TABLE 10**  

### ). As a result of the assimilation of Hispanic and NH Asian/Other headship rates, Hispanic headship rates increase from 33.1 percent in 2015 to 36.2 percent in 2040 by 3.1 percent, and NH Asian/Other headship rates increase from 38.3 percent in 2015 to 42.7 percent in 2040 by 4.5 percent. The female headship rates also increase due to the higher labor force participation, marriage postponement, and longer life expectancy.

Note: *2015 headship rates were derived using 2010 Census and 2014 Annual ACS data. **The 2040 Asian and Hispanic headship rates reflect an assumption of 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. NH - Non-Hispanic. Source: U.S.Census Bureau, CA DOF, SCAG 

### 2000 2010 2015 2040 Difference (2010-2015) Difference (2015-2040)AGE 15-24 9.9% 7.1% 6.4% 6.3% -0.4% -0.1% 25-34 40.1% 36.2% 34.3% 33.2% -1.0% -1.1% 35-44 50.4% 48.8% 47.5% 46.9% -1.4% -0.6% 45-54 54.6% 52.8% 51.2% 50.3% -1.7% -0.9% 55-64 56.3% 54.3% 52.3% 50.9% -1.8% -1.4% 65-74 58.5% 56.2% 55.3% 53.6% -1.9% -1.7% 75+ 60.4% 60.9% 57.8% 55.1% -2.2% -2.7%SEX Male 58.3% 45.6% 42.7% 42.9% -1.1% 0.2% Female 28.5% 37.2% 37.9% 39.7% -0.7% 1.8%RACE/ETHNICITY White (NH) 51.2% 50.5% 49.4% 49.7% -0.9% 0.3% Black (NH) 49.1% 48.6% 47.2% 48.4% -0.4% 1.2% Asian & Others (NH) 38.7% 38.4% 38.5% 42.5% -0.1% 4.0% Hispanic 34.2% 33.3% 32.7% 36.0% -0.2% 3.3% 

### Total 43.1% 41.3% 40.3% 41.3% -0.8% 1.0% **T**

**able 1**  **0** Regional Headship Rates by Age, Sex, and Race/Ethnicity, 2000-2040

## 21


# SMALL AREA FORECAST AND ALLOCATION A critical input driving SCAG’s planning process is the Regional Growth Forecast. The Regional Growth Forecast at the jurisdictional and TAZ levels are 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). SCAG’s 2016 RTP/SCS growth forecast includes six counties’ jurisdictional level population, household, and employment for years 2012, 2020, 2035 and 2040. 

## JURISDICTIONAL GROWTH FORECASTING Based on the growth forecast at the regional level described in the previous charters, SCAG further projects jurisdictional level population, household and employment. The latest jurisdictions’ existing and general plan land use serve as the basis for future year population and household allocations. Household growth rates and household size are estimated based on historical trends and the developable capacity from the local jurisdiction’s general plan. Population projections are calculated based on household growth and household size. Future jurisdictional employment is estimated based on the share of the county’s employment by sector. It is further adjusted to account for population serving jobs, such as Retail and Service, which are highly correlated with population growth. The following major data sources are considered and used in the development of the growth forecast: z 

### California Department of Finance (DOF) population and household estimates; z 

### California Employment Development Department (EDD) jobs report by industry; z 

### Regional Housing Needs Assessment (RHNA) growth projections for years 2014 through 2021; z 

### 2012 existing land use and General Plans from local jurisdictions; z 

### 2010 Census and the latest American Community Survey (ACS) data; and z 

### 2011 Business Installment data from InfoGroup. After the initial growth forecast was developed, SCAG’s staff conducted one-on-one meetings with 195 of 197 jurisdictions in the region to review the forecast and receive local input. This local input process provided an opportunity for jurisdictions to offer their local knowledge and input to inform SCAG’s regional datasets. SCAG evaluated the comments and incorporated the adjustments into the population, household, and employment growth distributions. These adjustments also include the incorporations of approved projects provided by the local jurisdictions. The resulting Draft 2016 RTP/SCS growth forecast served as the basis for the initial 2016 RTP/SCS evaluation. Below are the guiding principles, which are the basis for developing the draft Policy Growth Forecast (PGF): z 

### Principle #1: The draft PGF for the 2016 RTP/SCS shall be adopted by the Regional Council at the jurisdictional level, thus directly reflecting the population, household and employment growth projections derived from the local input and previously reviewed and approved by SCAG’s local jurisdictions. The draft PGF maintains these projected jurisdictional growth totals, meaning future growth is not reallocated from one local jurisdiction to another. z 

### Principle #2: The draft PGF at the TAZ level is controlled to be within the density ranges* of local general plans or input received from local jurisdictions in this most recent round of review. z 

### Principle #3: For the purpose of determining consistency for California Environmental Quality Act (CEQA), lead agencies such as local jurisdictions have the sole discretion in determining a local project’s consistency with the 2016 RTP/SCS. z 

### Principle #4: Transportation Analysis Zone (TAZ) level data or any data at a geography smaller than the jurisdictional level is included in the draft PGF only to conduct the required modeling analysis and is therefore, only advisory and non-binding because SCAG’s sub-jurisdictional forecasts are not to be adopted as part of the 2016 RTP/SCS. After SCAG’s adoption of the PGF at the jurisdictional level, the TAZ level data may be used by a jurisdiction in local planning as it deems appropriate and there is no obligation by a jurisdiction to change its land use policies, General Plan, or regulations to be consistent with the RTP/SCS. SCAG staff plans to monitor the use of this data after the adoption of the RTP/SCS to encourage appropriate use. z 

### Principle #5: SCAG staff continues to communicate with other agencies whouse SCAG subjurisdictional level data to ensure that the “advisory & nonbinding” nature of the dataset is appropriately maintained. (See Attachment 1 for information regarding SCAG’s communications with SCAQMD and ARB about the use of SCAG’s sub-jurisdictional level data). Consistent with the above stated principles, the preferred scenario and corresponding forecast of population, household and employment growth is adopted at the jurisdictional level as part of the 2016 RTP/SCS, and sub-jurisdictional level data and/or maps associated with the 2016 RTP/SCS is advisory only. For purposes of qualifying for future funding opportunities and/or other incentive programs, sub-jurisdictional data and/or maps used to determine consistency with the Sustainable Communities Strategy shall only be used at the discretion and with the approval of the local jurisdiction. However, this does not otherwise limit the use of the sub-jurisdictional data and/or maps by SCAG, County Transportation Commissions, Councils of Governments, SCAG Subregions, Caltrans, and other public *With the exception of the 6 percent of TAZs that have average density below the density range of local general plans.

## 22

agencies for transportation modeling and planning purposes. Any other use of the subjurisdictional data and/or maps not specified herein, shall require agreement from the Regional Council, respective policy committees and local jurisdictions. 

## LOCAL INPUT PROCESS Local input plays an important role in developing a reasonable growth forecast for the 2016 RTP/SCS. SCAG’s Bottom-Up Local Input Process began in March 2013 and has been designed to engage local jurisdictions in establishing the base geographic and socioeconomic projections including population, household and employment. z 

### March 2013: Each jurisdiction was contacted individually and was requested to provide their base general plan, land use, and zoning data to SCAG. z 

### June 2013: With approval from SCAG’s Community Economic Human Development (CEHD) Committee, the protocol for local jurisdictions to provide input and approval of SCAG’s geographic and socioeconomic datasets was established. z 

### October 2013: Based on guidance from the CEHD, the Technical Working Group (TWG), and our subregional partners, SCAG staff distributed the schedule, protocol, and summary descriptions of SCAG’s base datasets in a letter to all regional city managers, planning directors, city clerks (for forwarding to all elected officials), subregional executive directors, and subregional coordinators. z 

### November 2013 through January 2014: With input from the CEHD, TWG, and subregional staff, SCAG staff rolled-out the draft growth forecast including Population, Household, and Employment. z 

### December 2013 through August 2014: With support from our subregional partners and oversight from the CEHD, staff met with 99 percent of SCAG’s 197 jurisdictions one-on-one and received feedback from 93 percent of jurisdictions on all or a portion of our information requests. z 

### June 2015 through July 2015: SCAG distributed the draft policy growth forecast to the local jurisdictions again to seek additional feedback. z 

### During the following month, SCAG staff incorporated all the comments received in the draft policy growth forecast as part of the draft plan. The close collaboration enables us to form the growth projection which reflects the locals’ visions. The **TABLE 11** 

### presents the local input based jurisdictional level growth forecast.

## 23

County City Name 2012 Population 2040 Population 2012 Household 2040 Household 2012 Employment 2040 Employment Imperial Brawley city 25,800 42,900 7,600 15,000 8,000 16,800 Imperial Calexico city 40,200 62,200 10,200 19,300 8,300 17,500 Imperial Calipatria city 7,600 9,600 1,000 1,600 1,300 2,200 Imperial El Centro city 44,100 61,000 13,100 19,900 20,300 43,800 Imperial Holtville city 6,100 8,000 1,800 2,500 1,000 2,000 Imperial Imperial city 15,800 25,400 4,600 8,800 3,400 9,500 Imperial Westmorland city 2,300 2,700 600 700 300 500 Imperial Unincorporated 37,700 70,300 10,400 24,700 16,400 32,300 Los Angeles Agoura Hills city 20,500 22,700 7,300 8,200 12,500 15,300 Los Angeles Alhambra city 84,000 88,800 29,300 31,900 28,000 33,500 Los Angeles Arcadia city 56,700 65,900 19,600 22,900 28,900 34,400 Los Angeles Artesia city 16,600 18,000 4,500 5,000 5,000 5,800 Los Angeles Avalon city 3,800 5,100 1,500 2,100 2,500 3,000 Los Angeles Azusa city 47,100 55,000 12,800 15,600 16,600 20,600 Los Angeles Baldwin Park city 76,100 83,600 17,200 19,300 16,500 19,500 Los Angeles Bell city 35,700 36,900 8,900 9,200 12,400 13,700 Los Angeles Bellflower city 77,100 79,600 23,700 24,400 13,600 14,700 Los Angeles Bell Gardens city 42,300 44,000 9,700 10,100 9,400 10,500 Los Angeles Beverly Hills city 34,400 37,200 14,900 16,200 57,700 68,900 Los Angeles Bradbury city 1,100 1,200 400 400 100 200 Los Angeles Burbank city 103,300 118,700 42,500 48,400 106,800 145,000 Los Angeles Calabasas city 23,800 24,500 8,700 9,100 16,700 17,300 Los Angeles Carson city 92,000 107,900 25,300 30,800 58,500 69,700 Los Angeles Cerritos city 49,300 50,900 15,500 16,000 30,400 33,700 Los Angeles Claremont city 35,500 39,400 11,700 13,200 17,400 19,700 Los Angeles Commerce city 12,900 13,500 3,400 3,600 44,600 49,100 Los Angeles Compton city 97,300 100,900 23,100 24,000 25,400 28,200 Los Angeles Covina city 48,200 51,600 15,900 17,200 25,300 29,500 Los Angeles Cudahy city 23,800 23,800 5,600 5,600 2,900 2,900 Los Angeles Culver City city 39,100 40,700 16,800 17,500 44,100 53,000 Los Angeles Diamond Bar city 56,000 63,900 17,900 21,200 15,400 19,300 Los Angeles Downey city 112,500 121,700 33,900 37,300 47,500 53,000 **T**

**able 1**  **1** Jurisdictional Forecast 2040

## 24

County City Name 2012 Population 2040 Population 2012 Household 2040 Household 2012 Employment 2040 Employment Los Angeles Duarte city 21,500 24,300 7,000 8,200 10,100 11,900 Los Angeles El Monte city 114,200 137,200 27,800 34,700 28,000 35,700 Los Angeles El Segundo city 16,700 17,300 7,100 7,400 38,400 45,400 Los Angeles Gardena city 59,400 68,700 20,600 24,200 28,900 33,500 Los Angeles Glendale city 193,200 214,000 72,400 81,100 111,300 127,000 Los Angeles Glendora city 50,500 54,300 17,200 18,900 20,000 23,000 Los Angeles Hawaiian Gardens city 14,300 15,900 3,600 4,000 4,800 5,600 Los Angeles Hawthorne city 85,300 87,000 28,600 30,000 27,200 32,100 Los Angeles Hermosa Beach city 19,600 20,400 9,500 9,800 7,400 10,000 Los Angeles Hidden Hills city 1,900 2,000 600 600 300 300 Los Angeles Huntington Park city 58,500 67,400 14,600 17,400 15,600 18,600 Los Angeles Industry city 500 500 100 100 67,700 74,700 Los Angeles Inglewood city 110,900 129,000 36,600 43,300 31,100 37,400 Los Angeles Irwindale city 1,400 2,000 400 500 18,800 21,500 Los Angeles La Cañada Flintridge city 20,400 21,600 6,900 7,300 6,500 8,300 Los Angeles La Habra Heights city 5,400 6,200 1,800 1,900 200 400 Los Angeles Lakewood city 80,600 84,700 26,600 28,200 18,900 21,400 Los Angeles La Mirada city 48,800 52,100 14,700 15,800 17,400 20,200 Los Angeles Lancaster city 158,300 209,900 47,400 65,300 45,800 59,600 Los Angeles La Puente city 40,100 50,200 9,500 12,400 6,300 8,700 Los Angeles La Verne city 31,800 32,900 11,400 12,100 12,200 14,300 Los Angeles Lawndale city 33,000 33,900 9,700 10,100 6,700 8,200 Los Angeles Lomita city 20,500 21,200 8,100 8,400 4,600 5,400 Los Angeles Long Beach city 466,300 484,500 163,800 175,500 153,200 181,700 Los Angeles Los Angeles city 3,845,500 4,609,400 1,325,500 1,690,300 1,696,400 2,169,100 Los Angeles Lynwood city 70,300 76,100 14,700 16,200 9,200 10,900 Los Angeles Malibu city 12,700 14,100 5,300 5,600 8,500 10,300 Los Angeles Manhattan Beach city 35,300 37,100 14,000 14,800 18,000 20,700 Los Angeles Maywood city 27,500 28,900 6,600 6,900 3,600 4,000 Los Angeles Monrovia city 36,800 40,300 13,800 15,300 19,700 23,300 Los Angeles Montebello city 63,000 67,300 19,100 21,000 27,500 30,800 Table 11 Jurisdictional Forecast 2040 Continued

## 25

County City Name 2012 Population 2040 Population 2012 Household 2040 Household 2012 Employment 2040 Employment Los Angeles Monterey Park city 61,300 65,000 20,200 21,500 32,500 36,500 Los Angeles Norwalk city 105,900 106,300 27,100 27,200 24,100 27,300 Los Angeles Palmdale city 154,200 201,500 43,100 59,300 29,300 40,300 Los Angeles Palos Verdes Estates city 13,600 13,900 5,100 5,200 2,300 2,900 Los Angeles Paramount city 54,500 58,000 13,900 14,800 19,600 22,300 Los Angeles Pasadena city 140,300 150,700 58,900 62,400 111,000 144,800 Los Angeles Pico Rivera city 63,400 69,100 16,600 18,400 18,900 22,400 Los Angeles Pomona city 150,500 190,400 38,600 51,100 55,100 67,200 Los Angeles Rancho Palos Verdes city 42,000 42,300 15,600 15,700 5,800 6,200 Los Angeles Redondo Beach city 67,200 74,400 29,000 33,000 24,000 29,800 Los Angeles Rolling Hills city 1,900 2,000 700 700 100 100 Los Angeles Rolling Hills Estates city 8,100 8,600 3,000 3,100 5,900 6,800 Los Angeles Rosemead city 54,300 60,800 14,300 16,400 13,700 16,200 Los Angeles San Dimas city 33,600 34,500 12,000 12,400 11,200 12,700 Los Angeles San Fernando city 23,900 26,900 6,000 7,000 10,900 12,700 Los Angeles San Gabriel city 40,100 46,900 12,600 15,300 14,100 16,800 Los Angeles San Marino city 13,200 13,300 4,300 4,400 3,600 4,200 Los Angeles Santa Clarita city 202,000 262,200 67,300 90,300 73,500 95,900 Los Angeles Santa Fe Springs city 16,600 21,700 4,800 6,500 54,600 62,000 Los Angeles Santa Monica city 90,700 103,400 47,100 53,900 89,600 103,700 Los Angeles Sierra Madre city 11,000 11,200 4,800 5,000 1,900 2,100 Los Angeles Signal Hill city 11,200 12,000 4,200 4,600 13,800 16,500 Los Angeles South El Monte city 20,300 22,500 4,600 5,200 15,700 17,800 Los Angeles South Gate city 94,700 111,800 23,200 28,300 20,400 24,000 Los Angeles South Pasadena city 25,800 27,100 10,500 11,100 9,300 10,500 Los Angeles Temple City city 35,900 40,600 11,600 13,500 6,900 8,400 Los Angeles Torrance city 146,500 159,800 56,100 62,000 102,300 117,600 Los Angeles Vernon city 100 300 0 100 43,200 46,100 Los Angeles Walnut city 29,800 33,800 8,700 10,400 8,400 9,900 Los Angeles West Covina city 107,000 116,700 31,700 35,000 29,500 34,300 Table 11 Jurisdictional Forecast 2040 Continued

## 26

County City Name 2012 Population 2040 Population 2012 Household 2040 Household 2012 Employment 2040 Employment Los Angeles West Hollywood city 34,800 41,800 22,600 27,800 29,800 37,300 Los Angeles Westlake Village city 8,300 8,800 3,300 3,500 13,300 15,900 Los Angeles Whittier city 85,900 96,900 28,300 32,600 26,900 31,700 Los Angeles Unincorporated 1,040,700 1,273,700 292,700 392,400 222,900 288,400

Orange Aliso Viejo city 49,300 51,000 18,500 19,400 18,900 20,900 Orange Anaheim city 345,300 403,400 99,200 122,600 177,900 245,600 Orange Brea city 41,100 50,600 14,500 18,100 46,700 53,700 Orange Buena Park city 81,800 92,500 24,000 27,900 34,300 39,800 Orange Costa Mesa city 111,200 116,400 40,000 42,500 84,400 93,200 Orange Cypress city 48,500 49,700 15,700 16,300 22,100 27,700 Orange Dana Point city 33,800 35,800 14,200 15,300 11,900 14,100 Orange Fountain Valley city 56,000 59,300 18,700 19,900 30,400 34,900 Orange Fullerton city 138,000 160,500 45,500 55,200 60,800 94,100 Orange Garden Grove city 172,900 178,200 46,200 48,200 51,700 58,500 Orange Huntington Beach city 193,200 207,100 74,900 81,200 75,800 87,000 Orange Irvine city 227,100 327,300 81,800 123,400 224,400 320,000 Orange Laguna Beach city 23,100 23,100 10,800 11,000 12,100 14,100 Orange Laguna Hills city 30,600 31,500 10,400 10,900 18,500 19,400 Orange Laguna Niguel city 63,900 72,000 24,300 27,700 18,300 22,100 Orange Laguna Woods city 16,500 17,100 11,400 11,700 4,400 6,500 Orange La Habra city 61,100 68,500 19,000 21,700 17,300 19,900 Orange Lake Forest city 78,500 90,700 26,300 30,500 39,200 49,000 Orange La Palma city 15,800 15,800 5,100 5,100 7,700 8,500 Orange Los Alamitos city 11,600 12,100 4,100 4,200 14,200 15,600 Orange Mission Viejo city 94,500 96,600 33,200 34,100 37,100 39,100 Orange Newport Beach city 86,300 92,700 38,800 41,700 76,000 79,100 Orange Orange city 138,500 153,000 43,600 49,300 94,100 105,500 Orange Placentia city 51,500 58,400 16,600 18,900 19,000 23,500 Orange Rancho Santa Margarita city 48,500 48,700 16,700 16,800 17,200 19,500 Orange San Clemente city 64,400 68,000 24,000 25,300 24,800 29,500 Orange San Juan Capistrano city 35,200 39,500 11,500 13,300 14,700 17,900 Table 11 Jurisdictional Forecast 2040 Continued

## 27

County City Name 2012 Population 2040 Population 2012 Household 2040 Household 2012 Employment 2040 Employment Orange Santa Ana city 329,200 343,100 73,300 78,000 154,800 166,000 Orange Seal Beach city 24,400 24,800 13,000 13,300 11,000 12,300 Orange Stanton city 38,700 41,600 10,700 11,800 7,200 8,500 Orange Tustin city 77,300 83,000 25,600 27,900 37,600 66,400 Orange Villa Park city 5,900 6,100 2,000 2,000 1,500 1,700 Orange Westminster city 91,000 92,800 26,200 26,800 24,200 26,400 Orange Yorba Linda city 66,200 70,500 21,900 23,400 15,600 17,700 Orange Unincorporated 120,700 180,100 37,800 56,900 20,700 41,200 Riverside Banning city 30,100 37,600 10,800 14,000 7,300 14,200 Riverside Beaumont city 39,400 80,600 12,400 27,200 5,900 18,000 Riverside Blythe city 20,000 24,600 4,500 6,200 3,700 6,600 Riverside Calimesa city 8,100 24,800 3,300 10,900 1,300 5,900 Riverside Canyon Lake city 10,700 11,300 3,900 4,100 1,200 2,700 Riverside Cathedral City city 52,200 68,100 17,100 26,000 10,800 21,200 Riverside Coachella city 42,400 146,300 9,200 40,100 8,500 34,400 Riverside Corona city 156,000 172,300 45,300 52,000 66,400 88,400 Riverside Desert Hot Springs city 27,800 58,900 9,100 21,900 3,700 12,900 Riverside Eastvale City 56,500 65,400 14,100 16,500 4,300 9,800 Riverside Hemet city 80,800 126,500 30,300 52,200 21,000 45,500 Riverside Indian Wells city 5,100 7,200 2,800 4,400 4,000 7,000 Riverside Indio city 78,800 123,300 23,800 39,300 16,000 36,800 Riverside Lake Elsinore city 54,100 111,400 15,200 35,000 11,800 31,700 Riverside La Quinta city 38,300 47,700 14,900 19,100 12,400 21,500 Riverside Menifee city 81,600 121,100 28,400 48,100 10,300 23,500 Riverside Moreno Valley city 197,600 256,600 51,800 73,000 31,400 83,200 Riverside Murrieta city 105,600 129,800 32,800 43,500 23,200 45,100 Riverside Norco city 26,900 32,100 7,000 9,200 13,200 25,700 Riverside Palm Desert city 49,800 61,700 23,400 31,400 36,900 53,600 Riverside Palm Springs city 45,600 56,900 22,900 31,300 26,300 45,800 Riverside Perris city 70,700 116,700 16,600 32,700 15,100 32,200 Riverside Rancho Mirage city 17,600 25,000 8,900 13,600 12,300 20,500 Riverside Riverside city 310,700 386,600 92,400 118,600 120,000 200,500 Table 11 Jurisdictional Forecast 2040 Continued

## 28

County City Name 2012 Population 2040 Population 2012 Household 2040 Household 2012 Employment 2040 Employment Riverside San Jacinto city 45,100 79,900 13,200 27,600 5,900 17,800 Riverside Temecula city 104,100 137,400 32,500 42,900 43,000 63,500 Riverside Wildomar city 33,000 56,200 10,100 18,100 5,000 13,500 Riverside Jurupa Valley City 97,000 114,500 25,000 30,400 24,500 32,600 Riverside March JPA* 500 4,000 400 2,100 700 3,600 Riverside Unincorporated 359,000 499,200 112,300 162,900 70,500 156,600 San Bernardino Adelanto city 31,100 70,000 7,900 18,100 3,900 7,800 San Bernardino Apple Valley town 70,200 100,600 23,700 34,800 15,400 27,600 San Bernardino Barstow city 23,100 35,100 8,100 12,900 8,100 16,800 San Bernardino Big Bear Lake city 5,100 6,900 2,200 3,000 3,800 5,400 San Bernardino Chino city 79,400 120,400 21,000 34,000 42,600 50,600 San Bernardino Chino Hills city 75,800 94,900 23,000 28,300 11,500 18,600 San Bernardino Colton city 52,800 69,100 15,000 20,800 16,800 29,200 San Bernardino Fontana city 200,200 280,900 49,600 74,000 47,000 70,800 San Bernardino Grand Terrace city 12,200 14,200 4,400 5,700 2,200 5,300 San Bernardino Hesperia city 91,100 129,100 26,400 39,100 14,900 28,300 San Bernardino Highland city 53,700 66,900 15,500 20,600 5,500 10,200 San Bernardino Loma Linda city 23,400 29,300 8,800 11,800 16,700 21,100 San Bernardino Montclair city 37,200 42,700 9,600 11,600 16,500 19,000 San Bernardino Needles city 4,900 7,000 1,900 2,800 2,200 3,800 San Bernardino Ontario city 166,300 258,600 45,100 75,300 103,300 175,400 San Bernardino Rancho Cucamonga city 170,100 204,300 55,400 73,100 69,900 104,600 San Bernardino Redlands city 69,600 85,500 24,800 32,400 31,700 53,400 San Bernardino Rialto city 100,800 112,000 25,400 31,500 21,100 30,500 San Bernardino San Bernardino city 211,900 257,400 59,300 77,100 88,900 128,900 San Bernardino Twentynine Palms city 25,900 37,300 8,300 11,400 4,300 8,500 San Bernardino Upland city 74,700 81,700 25,900 28,900 31,700 43,500 San Bernardino Victorville city 119,600 184,500 33,100 55,400 29,800 52,700 San Bernardino Yucaipa city 52,300 72,500 18,400 28,200 8,200 15,000 San Bernardino Yucca Valley town 21,000 26,300 8,300 12,200 6,100 10,000 San Bernardino Unincorporated 295,600 344,100 94,200 111,300 57,400 91,100 Table 11 Jurisdictional Forecast 2040 Continued

*The March JPA (Joint Powers Authority) is designated as the federally recognized reuse authority for the former active duty base.

## 29

County City Name 2012 Population 2040 Population 2012 Household 2040 Household 2012 Employment 2040 Employment Ventura Camarillo city 66,300 79,900 24,800 30,200 35,800 47,300 Ventura Fillmore city 18,800 21,800 5,200 6,300 3,000 5,300 Ventura Moorpark city 34,800 43,000 10,600 13,100 11,300 16,600 Ventura Ojai city 7,500 8,400 3,100 3,300 5,100 5,300 Ventura Oxnard city 200,100 237,300 50,100 60,100 58,100 79,200 Ventura Port Hueneme city 21,800 22,400 7,100 7,300 6,400 6,700 Ventura San Buenaventura (Ventura) city 106,700 125,300 40,700 48,400 60,700 66,000 Ventura Santa Paula city 29,800 39,600 8,500 11,500 7,800 11,700 Ventura Simi Valley city 125,100 142,400 41,300 47,400 44,000 61,100 Ventura Thousand Oaks city 127,800 131,700 45,900 47,200 68,200 81,900 Ventura Unincorporated 96,700 113,600 32,100 37,500 31,800 38,700 Table 11 Jurisdictional Forecast 2040 Continued

Note: All figures are rounded to the nearest 100. *The March JPA (Joint Powers Authority) is designated as the federally recognized reuse authority for the former active duty base.

## 30

**TAZ LEVEL PROJECTIONS** The socioeconomic input data for the transportation model are processed at the Transportation Analysis Zone (TAZ) level in two different formats: 1. The marginal total of person and household attributes and 2. The joint distributes of person and household attributes. TAZ is often referred to as TIER 2, are generally equivalent to Census block groups, and there are 11,267 TAZs in the region. A total of 65 socioeconomic variables and 8 joint tables are developed as input for the transportation demand model (see **TABLE 12** 

### and **TABLE 13** 

### ). These variables include population, households by type, household income by category, employment by sector, etc. The eight joint tables, each with two or more dimensional attributions, are required by SCAG’s enhanced transportation demand model. One of these joint distributions is 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 distribution of socioeconomic attributes at the TAZ level using diverse public and private sources of data and advanced estimation methods. The major data sources include the 2000 and 2010 Census, 2006-2010 Census Transportation Planning Package (CTPP), American Community Survey (ACS), California Department of Finance (DOF), California Employment Development Department (EDD), Firm based info Group 2011, Existing Land Use, 2012 County Assessor’s Parcel Database, and jurisdictional general plans. The development of the TAZ level socioeconomic input involves three major processes: 1. Development of three major variables: population, households, and employment; 2. Development of secondary variables: socioeconomic attributes of persons, households, and employment sectors; 3. Development of joint distributions of selected attributes. The TAZ level projections are all consistent to the local general plan capacity. 

## DEVELOPMENT OF MAJOR VARIABLES The initial TAZ level household projections started from the household and employment estimates at the Minimum Planning Unit (MPU) level within the TAZ. The MPU is the smallest geographic computing unit at which our calculations can take place. In general, the MPUs are equivalent to parcels. The 2012 parcel data, the 2010 Census and the 2011 firm based employment data are the key databases used for the initial MPU level household and employment estimates. The aggregation of the MPU level household and employment became the first draft of the TAZ level estimates. Total population is calculated based on the TAZ household forecast. The two components for the total population are group quarters population and residential population. The average number of persons per household (PPH) is projected using the recent estimates and trends, and is calculated using the updated jurisdictional totals for population and households. Group quarters population is projected relying on the censuses and historical trends. TAZ level household and employment projections are controlled to the jurisdictional level projections. Which means the sum of TAZ level household and employment within a jurisdiction are the same as this jurisdiction’s growth projections. The initial TAZ level jobs are projected using a constant-share method. The current TAZ’s share of jurisdictional level 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 a jurisdiction. The initial TAZ population, household, and employment forecasts become a basis for the local review process. 

## DEVELOPMENT OF SOCIAL ECONOMIC VARIABLES FOR **TRANSPORTATION MODELS** SCAG develops additional attribute variables such as population by age, household by income range, employment by sector, and etc. Please refer to **TABLE 12** 

### for entire variable list. The joint distributions of households are developed into joint distributions of selected secondary variables using the Population Synthesis (PopSyn). It generates synthetic population and households with attribute distributions, which become the basis for computing the joint distributions. SCAG uses the 2010 Census SF1 (Summary File 1) aggregated data at the TAZ level and 2007-2011 five-year PUMS (Public Use Microdata Sample) based individual data at the PUMA (Public Use Microdata Areas) level as seed data to produce synthetic population and households at the TAZ level.

## 31

Variables Description of VariablesPOPULATION(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”. Group Quarters Population living in student dormitories (1 variable): Population living in college dormitories (includes college quarters off campus). Population by Age (4 variables): the number of population for different age groups: 5-17, 18-24, 16-64, and 65+.HOUSEHOLDS(26 VARIABLES) Total Households (1 variable): Household refers to all of the people who occupy a housing unit. By definition there is only one household in an occupied housing unit. Households by Household Size (4 variables): the number of one-person households, two-person households, three-person households, and four or more person households. Households by Age of Householder (4 variables): the number of households with age of householder between 18 and 24 years old, 25 and 44, 45 and 64, and 65 or older. Households by Number of Workers (4 variables): the number of households with no worker, with one worker, with two workers, and with 3+ worker. Households by Household Income (4 variables): the number of households with annual household income (in 2011 dollar) of less than $35,000, $35,000-74,999, 75,000- 149,999, and 150,000 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.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”. **T**

**able 1**  **2** Description of Socioeconomic Variables 

### Variables Description of VariablesWORKERS(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 $35,000, $35,000-$74,999, and $75,000 or more.MEDIAN HOUSEHOLDINCOME (5 VARIABLES): Median Household Income (1 variable): median household income is the median value of household income for all households within a zone. Household Income includes the income, from all sources, for all persons aged 15 years or older within a household. Median Household Income by Income Categories (4 variables): the median income is estimated for each of four different income categories: less than $35,000, $35,000- $74,999, $75,000-$149,999, and $150,000 or more.EMPLOYMENT(17 VARIABLES) Total Employment (1 variable): total number of jobs including full and part-time 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 selfemployed 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) agricultur e & mining, 2) construction, 3) manufacturing, 4) wholesale trade, 5) r etail trade, 6) tr ansportation, warehousing, and utilities, 7) information, 8) financial activities, 9) pr ofessional and business services, 10) education and health services, 11) leisur e and hospitality services, 12) other services, and 13) public administration. Employment related variables: 1. Light/Gener al warehouse area 2. High cube warehouse area 3. Light/Gener al warehouse employment 4. High cube warehouse employment Employment by wage level (3 variables): total number of jobs by three wage levels: of less than $35,000, $35,000 to $74,999, $75,000 or more. TABLE 12 Description of Socioeconomic Variables Continued

## 32

**T able**  **1 3**  Joint Distributions of Population, Households, and Workers by Selected Demographic Attributes 

### Major Variables Demographic AttributesHOUSEHOLD 1 Household income (less than $35,000, $35,000 to $74,999, $75,000 to $149,999, $150,000 or more) Household size (1,2,3,4 or more persons in household) Number of workers (0,1,2,3 or more workers in household) Type of dwelling unit (single-family detached, other)HOUSEHOLD 2 Household income (less than $35,000, $35,000 to $74,999, $75,000 to $149,999, $150,000 or more) Number of workers (0,1,2,3 more workers in household) Age of head of household (18-24, 25-44, 45-66, 65 or more years old).HOUSEHOLD 3 Household income (less than $35,000, $35,000 to $74,999, $75,000 to $149,999, $150,000 or more) Household size (1,2,3,4 more persons in household)HOUSEHOLD 4 Household income (less than $35,000, $35,000 to $74,999, $75,000 to $149,999, $150,000 or more)
Number of college students (0, 1, 2 or more)HOUSEHOLD 5 Household income (less than $35,000, $35,000 to $74,999, $75,000 to $149,999, $150,000 or more) Number of children age 5-17 (0,1,2,3 or more) 

### Major Variables Demographic AttributesHOUSEHOLD 6 Household income (less than $35,000, $35,000 to $74,999, $75,000 to $149,999, $150,000 or more) Population Age (0-4, 5-17, 18-24, 25 or more)HOUSEHOLD 7 Household income (less than $35,000, $35,000 to $74,999, $75,000 to $149,999, $150,000 or more) Worker’s earnings (less than $24,999, $25,000-$49,999, $50,000 or more)HOUSEHOLD 8 Household income (less than $35,000, $35,000 to $74,999, $75,000 to $149,999, $150,000 or more) Household size (1,2,3,4 or more persons in household) Number of workers (0,1,2,3 or more workers in household) Type of dwelling unit (single-family detached, other) Age of head of household (18-24, 25-44, 45-66, 65 or more years old) **TABLE 13** Joint Distributions of Population, Households, and Workers by Selected Demographic Attributes Continued

## 33

**A NOTE FOR THE MOUNTAIN AREA SEASONAL CHARACTERISTICS**  Reporting of socio-economic data and analysis of transportation needs for the mountain areas of San Bernardino County are a challenge given significant seasonal variation due to recreation activities and tourism. SCAG’s forecast of future population, households, and employment for purposes of economic, infrastructure and transportation planning are built primarily from U.S. Census and state employment data for a “typical” season of the year. In the San Bernardino Mountain communities, such as the City of Big Bear Lake, or areas like Lake Arrowhead, Crestline, Wrightwood, and Running Springs, the full-time population and employment of the area are relatively low, but significant increases are experienced during the peak winter and summer seasons due to the added seasonal residents and tourism. As a result, standard socio-economic growth forecasts for these areas tend not to reflect the significant seasonal variations experienced due to visitor/recreational activities. Seasonal characteristics in mountain and some desert communities are not captured by conventional methods that are utilized to forecast growth and analyze transportation needs. Therefore, special attention must be given to these communities to acknowledge the unique demographic conditions and travel needs of these areas. As an example, **TABLE 14** 

### presents peak seasonal characteristics for the City of Big Bear Lake, illustrating the impact of seasonal fluctuations. For transportation planning and facility design in these areas, special consideration and studies are required to ensure seasonal impacts are properly captured.



### Population Households Employment Visitors 2012 Off-Peak 5,100 2,200 3,800 10,000 

### 2012 Peak --- --- 5,800 60,000 

### 2040 Off-Peak 7,000 3,000 5,400 14,000 

### 2040 Peak --- --- 7,400 76,000 **T**

**able 1**  **4** Seasonal Comparison of Activity in the City of Big Bear Lake

Note: Visitors and Peak Season forecasts provided by City of Big Bear Lake Planning Staff**Kern**

**San Diego Camarillo**  **Moorpark Ojai** 

**Simi Valley**

**Thousand**

**Oaks Oxnard** 

**Arcadia Azusa**  **Burbank**

**Carson Downey**  **El Monte**  **Glendale Glendora** 

**La Mirada**  **Los Angeles Monrovia** 

**Monterey Park** 

**Norwalk Pasadena** 

**Pomona**

**Torrance Walnut**  **West Covina** 

**Whittier Calabasas** 

**Long Beach**  **Adelanto Apple Valley**  **Barstow**

**Chino Chino Hills**  **Colton Fontana**  **Hesperia**

**Highland**
**Loma Linda**  **Ontario Rancho**  **Cucamonga**

**Redlands Rialto**  **San Bernardino**  **Upland Victorville** 

**Yucaipa Yucca Valley**  **Lancast er** 

**Palmdale**

**S anta Clarita** 

**Eastvale**

**Lake Elsinore Hemet**  **Indio Indian**  **Wells**

**Temecula Perris**  **Moreno Valley**

**Murrieta Wildomar**  **Rancho Mirage**  **Corona Jurupa Valley**  **Desert Hot Springs**

**San Jacinto**  **Beaumont Banning**  **Calimesa**

**Palm Desert**  **Riverside**

**Menifee Norco** 

**La Quinta**  **Palm Springs**  **Anaheim Brea** 
**Buena Park** 

**Costa Mesa**  **Fullerton**

**Garden Grove** 

**Huntington Beach**  **Irvine**

**Laguna Niguel**  **Mission Viejo**  **Newport Beach**  **Orange**

**San Clemente**  **Santa Ana Seal**  **Beach Tustin**  **Yorba Linda 91**  **14**

**60 126** 

**71 57**  **60 110**  **134**

**91**

**22 134**  **101**
**101**

**101**

**15 215**  **10**

**15**

**10 210**  **405 5** 

**605**

**710**

**5 15** 

**405 110**  **105 5**  **10**

**405**

**210 210**  **San Bernardino County** 

**Riverside County**  **Orange County**  **Los Angeles**

**County Ventura**  **County**



### Population Density in 2012 (Persons per Square Mile) **Less Than or Equal to 1,000 1,001 - 10,000**  **10,001 - 20,000 20,001 - 30,000**  **Greater Than 30,000**
**(Source: SCAG, 2015)** 

### O:\=RTP\=rtp2016\mxds\SED\POP_Density_2012.mxd | Date: 2/4/2016 **Map Title: Population Density in 2012 8**  **10**
**10**

**0 5 10 2.5**  **Miles** 

### Note: Transportation Analysis Zone (TAZ) level data or any data at a geography **smaller than the jurisdictional level is included in the draft PGF for regional modeling purpose only, and is advisory and non-binding.**  

## E **xhibit 1**  2012 Population**Kern**

**San Diego Camarillo**  **Moorpark Ojai** 

**Simi Valley**

**Thousand**

**Oaks Oxnard** 

**Arcadia Azusa**  **Burbank**

**Carson Downey**  **El Monte**  **Glendale Glendora** 

**La Mirada**  **Los Angeles Monrovia** 

**Monterey Park** 

**Norwalk Pasadena** 

**Pomona**

**Torrance Walnut**  **West Covina** 

**Whittier Calabasas** 

**Long Beach**  **Adelanto Apple Valley**  **Barstow**

**Chino Chino Hills**  **Colton Fontana**  **Hesperia**

**Highland**
**Loma Linda**  **Ontario Rancho**  **Cucamonga**

**Redlands Rialto**  **San Bernardino**  **Upland Victorville** 

**Yucaipa Yucca Valley**  **Lancast er** 

**Palmdale**

**S anta Clarita** 

**Eastvale**

**Lake Elsinore Hemet**  **Indio Indian**  **Wells**

**Temecula Perris**  **Moreno Valley**

**Murrieta Wildomar**  **Rancho Mirage**  **Corona Jurupa Valley**  **Desert Hot Springs**

**San Jacinto**  **Beaumont Banning**  **Calimesa**

**Palm Desert**  **Riverside**

**Menifee Norco** 

**La Quinta**  **Palm Springs**  **Anaheim Brea** 
**Buena Park** 

**Costa Mesa**  **Fullerton**

**Garden Grove** 

**Huntington Beach**  **Irvine**

**Laguna Niguel**  **Mission Viejo**  **Newport Beach**  **Orange**

**San Clemente**  **Santa Ana Seal**  **Beach Tustin**  **Yorba Linda 91**  **14**

**60 126** 

**71 57**  **60 110**  **134**

**91**

**22 134**  **101**
**101**

**101**

**15 215**  **10**

**15**

**10 210**  **405 5** 

**605**

**710**

**5 15** 

**405 110**  **105 5**  **10**

**405**

**210 210**  **San Bernardino County** 

**Riverside County**  **Orange County**  **Los Angeles**

**County Ventura**  **County**



### Population Density in 2040 (Persons per Square Mile) **Less Than or Equal to 1,000 1,001 - 10,000**  **10,001 - 20,000 20,001 - 30,000**  **Greater Than 30,000**
**(Source: SCAG, 2015)** 

### O:\=RTP\=rtp2016\mxds\SED\POP_Density_2040.mxd | Date: 2/4/2016 **Map Title: Population Density in 2040 8**  **10**
**10**

**0 5 10 2.5**  **Miles** 

### Note: Transportation Analysis Zone (TAZ) level data or any data at a geography **smaller than the jurisdictional level is included in the draft PGF for regional modeling purpose only, and is advisory and non-binding.**  

## E **xhibit 2**  2040 Population**Kern**

**San Diego Camarillo**  **Moorpark Ojai** 

**Simi Valley**

**Thousand**

**Oaks Oxnard** 

**Arcadia Azusa**  **Burbank**

**Carson Downey**  **El Monte**  **Glendale Glendora** 

**La Mirada**  **Los Angeles Monrovia** 

**Monterey Park** 

**Norwalk Pasadena** 

**Pomona**

**Torrance Walnut**  **West Covina** 

**Whittier Calabasas** 

**Long Beach**  **Adelanto Apple Valley**  **Barstow**

**Chino Chino Hills**  **Colton Fontana**  **Hesperia**

**Highland**
**Loma Linda**  **Ontario Rancho**  **Cucamonga**

**Redlands Rialto**  **San Bernardino**  **Upland Victorville** 

**Yucaipa Yucca Valley**  **Lancast er** 

**Palmdale**

**S anta Clarita** 

**Eastvale**

**Lake Elsinore Hemet**  **Indio Indian**  **Wells**

**Temecula Perris**  **Moreno Valley**

**Murrieta Wildomar**  **Rancho Mirage**  **Corona Jurupa Valley**  **Desert Hot Springs**

**San Jacinto**  **Beaumont Banning**  **Calimesa**

**Palm Desert**  **Riverside**

**Menifee Norco** 

**La Quinta**  **Palm Springs**  **Anaheim Brea** 
**Buena Park** 

**Costa Mesa**  **Fullerton**

**Garden Grove** 

**Huntington Beach**  **Irvine**

**Laguna Niguel**  **Mission Viejo**  **Newport Beach**  **Orange**

**San Clemente**  **Santa Ana Seal**  **Beach Tustin**  **Yorba Linda 91**  **14**

**60 126** 

**71 57**  **60 110**  **134**

**91**

**22 134**  **101**
**101**

**101**

**15 215**  **10**

**15**

**10 210**  **405 5** 

**605**

**710**

**5 15** 

**405 110**  **105 5**  **10**

**405**

**210 210**  **San Bernardino County** 

**Riverside County**  **Orange County**  **Los Angeles**

**County Ventura**  **County**



### Population Growth, 2012 - 2040 (Persons per Square Mile) **Less than or Equal to 500 501 - 1,000**  **1,001 - 2,500 2,501 - 5,000**  **Greater than 5,000**
**(Source: SCAG, 2015)** 

### O:\=RTP\=rtp2016\mxds\SED\POP_Growth_2012_2040.mxd | Date: 2/4/2016 **Map Title: Population Growth, 2012 - 2040 8**  **10**
**10**

**0 5 10 2.5**  **Miles** 

### Note: Transportation Analysis Zone (TAZ) level data or any data at a geography **smaller than the jurisdictional level is included in the draft PGF for regional modeling purpose only, and is advisory and non-binding.**  

## E **xhibit 3**  Population Change, 2012-2040**Kern**

**San Diego Camarillo**  **Moorpark Ojai** 

**Simi Valley**

**Thousand**

**Oaks Oxnard** 

**Arcadia Azusa**  **Burbank**

**Carson Downey**  **El Monte**  **Glendale Glendora** 

**La Mirada**  **Los Angeles Monrovia** 

**Monterey Park** 

**Norwalk Pasadena** 

**Pomona**

**Torrance Walnut**  **West Covina** 

**Whittier Calabasas** 

**Long Beach**  **Adelanto Apple Valley**  **Barstow**

**Chino Chino Hills**  **Colton Fontana**  **Hesperia**

**Highland**
**Loma Linda**  **Ontario Rancho**  **Cucamonga**

**Redlands Rialto**  **San Bernardino**  **Upland Victorville** 

**Yucaipa Yucca Valley**  **Lancast er** 

**Palmdale**

**S anta Clarita** 

**Eastvale**

**Lake Elsinore Hemet**  **Indio Indian**  **Wells**

**Temecula Perris**  **Moreno Valley**

**Murrieta Wildomar**  **Rancho Mirage**  **Corona Jurupa Valley**  **Desert Hot Springs**

**San Jacinto**  **Beaumont Banning**  **Calimesa**

**Palm Desert**  **Riverside**

**Menifee Norco** 

**La Quinta**  **Palm Springs**  **Anaheim Brea** 
**Buena Park** 

**Costa Mesa**  **Fullerton**

**Garden Grove** 

**Huntington Beach**  **Irvine**

**Laguna Niguel**  **Mission Viejo**  **Newport Beach**  **Orange**

**San Clemente**  **Santa Ana Seal**  **Beach Tustin**  **Yorba Linda 91**  **14**

**60 126** 

**71 57**  **60 110**  **134**

**91**

**22 134**  **101**
**101**

**101**

**15 215**  **10**

**15**

**10 210**  **405 5** 

**605**

**710**

**5 15** 

**405 110**  **105 5**  **10**

**405**

**210 210**  **San Bernardino County** 

**Riverside County**  **Orange County**  **Los Angeles**

**County Ventura**  **County**



### Household Density in 2012 (Households per Square Mile) **Less Than or Equal to 400 401 - 4,000**  **4,001 - 8,000 8,001 - 12,000**  **Greater Than 12,000**
**(Source: SCAG, 2015)** 

### O:\=RTP\=rtp2016\mxds\SED\HH_Density_2012.mxd | Date: 2/4/2016 **Map Title: Household Density in 2012 8**  **10**
**10**

**0 5 10 2.5**  **Miles** 

### Note: Transportation Analysis Zone (TAZ) level data or any data at a geography **smaller than the jurisdictional level is included in the draft PGF for regional modeling purpose only, and is advisory and non-binding.**  

## E **xhibit 4**  2012 Households**Kern**

**San Diego Camarillo**  **Moorpark Ojai** 

**Simi Valley**

**Thousand**

**Oaks Oxnard** 

**Arcadia Azusa**  **Burbank**

**Carson Downey**  **El Monte**  **Glendale Glendora** 

**La Mirada**  **Los Angeles Monrovia** 

**Monterey Park** 

**Norwalk Pasadena** 

**Pomona**

**Torrance Walnut**  **West Covina** 

**Whittier Calabasas** 

**Long Beach**  **Adelanto Apple Valley**  **Barstow**

**Chino Chino Hills**  **Colton Fontana**  **Hesperia**

**Highland**
**Loma Linda**  **Ontario Rancho**  **Cucamonga**

**Redlands Rialto**  **San Bernardino**  **Upland Victorville** 

**Yucaipa Yucca Valley**  **Lancast er** 

**Palmdale**

**S anta Clarita** 

**Eastvale**

**Lake Elsinore Hemet**  **Indio Indian**  **Wells**

**Temecula Perris**  **Moreno Valley**

**Murrieta Wildomar**  **Rancho Mirage**  **Corona Jurupa Valley**  **Desert Hot Springs**

**San Jacinto**  **Beaumont Banning**  **Calimesa**

**Palm Desert**  **Riverside**

**Menifee Norco** 

**La Quinta**  **Palm Springs**  **Anaheim Brea** 
**Buena Park** 

**Costa Mesa**  **Fullerton**

**Garden Grove** 

**Huntington Beach**  **Irvine**

**Laguna Niguel**  **Mission Viejo**  **Newport Beach**  **Orange**

**San Clemente**  **Santa Ana Seal**  **Beach Tustin**  **Yorba Linda 91**  **14**

**60 126** 

**71 57**  **60 110**  **134**

**91**

**22 134**  **101**
**101**

**101**

**15 215**  **10**

**15**

**10 210**  **405 5** 

**605**

**710**

**5 15** 

**405 110**  **105 5**  **10**

**405**

**210 210**  **San Bernardino County** 

**Riverside County**  **Orange County**  **Los Angeles**

**County Ventura**  **County**



### Household Density in 2040 (Households per Square Mile) **Less Than or Equal to 400 401 - 4,000**  **4,001 - 8,000 8,001 - 12,000**  **Greater Than 12,000**
**(Source: SCAG, 2015)** 

### O:\=RTP\=rtp2016\mxds\SED\HH_Density_2040.mxd | Date: 2/4/2016 **Map Title: Household Density in 2040 8**  **10**
**10**

**0 5 10 2.5**  **Miles** 

### Note: Transportation Analysis Zone (TAZ) level data or any data at a geography **smaller than the jurisdictional level is included in the draft PGF for regional modeling purpose only, and is advisory and non-binding.**  

## E **xhibit 5**  2040 Households**Kern**

**San Diego Camarillo**  **Moorpark Ojai** 

**Simi Valley**

**Thousand**

**Oaks Oxnard** 

**Arcadia Azusa**  **Burbank**

**Carson Downey**  **El Monte**  **Glendale Glendora** 

**La Mirada**  **Los Angeles Monrovia** 

**Monterey Park** 

**Norwalk Pasadena** 

**Pomona**

**Torrance Walnut**  **West Covina** 

**Whittier Calabasas** 

**Long Beach**  **Adelanto Apple Valley**  **Barstow**

**Chino Chino Hills**  **Colton Fontana**  **Hesperia**

**Highland**
**Loma Linda**  **Ontario Rancho**  **Cucamonga**

**Redlands Rialto**  **San Bernardino**  **Upland Victorville** 

**Yucaipa Yucca Valley**  **Lancast er** 

**Palmdale**

**S anta Clarita** 

**Eastvale**

**Lake Elsinore Hemet**  **Indio Indian**  **Wells**

**Temecula Perris**  **Moreno Valley**

**Murrieta Wildomar**  **Rancho Mirage**  **Corona Jurupa Valley**  **Desert Hot Springs**

**San Jacinto**  **Beaumont Banning**  **Calimesa**

**Palm Desert**  **Riverside**

**Menifee Norco** 

**La Quinta**  **Palm Springs**  **Anaheim Brea** 
**Buena Park** 

**Costa Mesa**  **Fullerton**

**Garden Grove** 

**Huntington Beach**  **Irvine**

**Laguna Niguel**  **Mission Viejo**  **Newport Beach**  **Orange**

**San Clemente**  **Santa Ana Seal**  **Beach Tustin**  **Yorba Linda 91**  **14**

**60 126** 

**71 57**  **60 110**  **134**

**91**

**22 134**  **101**
**101**

**101**

**15 215**  **10**

**15**

**10 210**  **405 5** 

**605**

**710**

**5 15** 

**405 110**  **105 5**  **10**

**405**

**210 210**  **San Bernardino County** 

**Riverside County**  **Orange County**  **Los Angeles**

**County Ventura**  **County**



### Household Growth, 2012 - 2040 (Households per Square Mile) **Less than or Equal to 200 201 - 500**  **501 - 1,000 1,001 - 2,000**  **Greater than 2,000**
**(Source: SCAG, 2015)** 

### O:\=RTP\=rtp2016\mxds\SED\HH_Growth_2012_2040.mxd | Date: 2/4/2016 **Map Title: Household Growth, 2012 - 2040 8**  **10**
**10**

**0 5 10 2.5**  **Miles** 

### Note: Transportation Analysis Zone (TAZ) level data or any data at a geography **smaller than the jurisdictional level is included in the draft PGF for regional modeling purpose only, and is advisory and non-binding.**  

## E **xhibit 6**  Household Change, 2012-2040**Kern**

**San Diego Camarillo**  **Moorpark Ojai** 

**Simi Valley**

**Thousand**

**Oaks Oxnard** 

**Arcadia Azusa**  **Burbank**

**Carson Downey**  **El Monte**  **Glendale Glendora** 

**La Mirada**  **Los Angeles Monrovia** 

**Monterey Park** 

**Norwalk Pasadena** 

**Pomona**

**Torrance Walnut**  **West Covina** 

**Whittier Calabasas** 

**Long Beach**  **Adelanto Apple Valley**  **Barstow**

**Chino Chino Hills**  **Colton Fontana**  **Hesperia**

**Highland**
**Loma Linda**  **Ontario Rancho**  **Cucamonga**

**Redlands Rialto**  **San Bernardino**  **Upland Victorville** 

**Yucaipa Yucca Valley**  **Lancast er** 

**Palmdale**

**S anta Clarita** 

**Eastvale**

**Lake Elsinore Hemet**  **Indio Indian**  **Wells**

**Temecula Perris**  **Moreno Valley**

**Murrieta Wildomar**  **Rancho Mirage**  **Corona Jurupa Valley**  **Desert Hot Springs**

**San Jacinto**  **Beaumont Banning**  **Calimesa**

**Palm Desert**  **Riverside**

**Menifee Norco** 

**La Quinta**  **Palm Springs**  **Anaheim Brea** 
**Buena Park** 

**Costa Mesa**  **Fullerton**

**Garden Grove** 

**Huntington Beach**  **Irvine**

**Laguna Niguel**  **Mission Viejo**  **Newport Beach**  **Orange**

**San Clemente**  **Santa Ana Seal**  **Beach Tustin**  **Yorba Linda 91**  **14**

**60 126** 

**71 57**  **60 110**  **134**

**91**

**22 134**  **101**
**101**

**101**

**15 215**  **10**

**15**

**10 210**  **405 5** 

**605**

**710**

**5 15** 

**405 110**  **105 5**  **10**

**405**

**210 210**  **San Bernardino County** 

**Riverside County**  **Orange County**  **Los Angeles**

**County Ventura**  **County**



### Employment Density in 2012 (Jobs per Square Mile) **Less than or Equal to 500 501 - 5,000**  **5,001 - 10,000 10,001 - 20,000**  **Greater Than 20,000**
**(Source: SCAG, 2015)** 

### O:\=RTP\=rtp2016\mxds\SED\EMP_Density_2012.mxd | Date: 2/4/2016 **Map Title: Employment Density in 2012 8**  **10**
**10**

**0 5 10 2.5**  **Miles** 

### Note: Transportation Analysis Zone (TAZ) level data or any data at a geography **smaller than the jurisdictional level is included in the draft PGF for regional modeling purpose only, and is advisory and non-binding.**  

## E **xhibit 7**  2012 Employment**Kern**

**San Diego Camarillo**  **Moorpark Ojai** 

**Simi Valley**

**Thousand**

**Oaks Oxnard** 

**Arcadia Azusa**  **Burbank**

**Carson Downey**  **El Monte**  **Glendale Glendora** 

**La Mirada**  **Los Angeles Monrovia** 

**Monterey Park** 

**Norwalk Pasadena** 

**Pomona**

**Torrance Walnut**  **West Covina** 

**Whittier Calabasas** 

**Long Beach**  **Adelanto Apple Valley**  **Barstow**

**Chino Chino Hills**  **Colton Fontana**  **Hesperia**

**Highland**
**Loma Linda**  **Ontario Rancho**  **Cucamonga**

**Redlands Rialto**  **San Bernardino**  **Upland Victorville** 

**Yucaipa Yucca Valley**  **Lancast er** 

**Palmdale**

**S anta Clarita** 

**Eastvale**

**Lake Elsinore Hemet**  **Indio Indian**  **Wells**

**Temecula Perris**  **Moreno Valley**

**Murrieta Wildomar**  **Rancho Mirage**  **Corona Jurupa Valley**  **Desert Hot Springs**

**San Jacinto**  **Beaumont Banning**  **Calimesa**

**Palm Desert**  **Riverside**

**Menifee Norco** 

**La Quinta**  **Palm Springs**  **Anaheim Brea** 
**Buena Park** 

**Costa Mesa**  **Fullerton**

**Garden Grove** 

**Huntington Beach**  **Irvine**

**Laguna Niguel**  **Mission Viejo**  **Newport Beach**  **Orange**

**San Clemente**  **Santa Ana Seal**  **Beach Tustin**  **Yorba Linda 91**  **14**

**60 126** 

**71 57**  **60 110**  **134**

**91**

**22 134**  **101**
**101**

**101**

**15 215**  **10**

**15**

**10 210**  **405 5** 

**605**

**710**

**5 15** 

**405 110**  **105 5**  **10**

**405**

**210 210**  **San Bernardino County** 

**Riverside County**  **Orange County**  **Los Angeles**

**County Ventura**  **County**



### Employment Density in 2040 (Jobs per Square Mile) **Less than or Equal to 500 501 - 5,000**  **5,001 - 10,000 10,001 - 20,000**  **Greater Than 20,000**
**(Source: SCAG, 2015)** 

### O:\=RTP\=rtp2016\mxds\SED\EMP_Density_2040.mxd | Date: 2/4/2016 **Map Title: Employment Density in 2040 8**  **10**
**10**

**0 5 10 2.5**  **Miles** 

### Note: Transportation Analysis Zone (TAZ) level data or any data at a geography **smaller than the jurisdictional level is included in the draft PGF for regional modeling purpose only, and is advisory and non-binding.**  

## E **xhibit 8**  2040 Employment**Kern**

**San Diego Camarillo**  **Moorpark Ojai** 

**Simi Valley**

**Thousand**

**Oaks Oxnard** 

**Arcadia Azusa**  **Burbank**

**Carson Downey**  **El Monte**  **Glendale Glendora** 

**La Mirada**  **Los Angeles Monrovia** 

**Monterey Park** 

**Norwalk Pasadena** 

**Pomona**

**Torrance Walnut**  **West Covina** 

**Whittier Calabasas** 

**Long Beach**  **Adelanto Apple Valley**  **Barstow**

**Chino Chino Hills**  **Colton Fontana**  **Hesperia**

**Highland**
**Loma Linda**  **Ontario Rancho**  **Cucamonga**

**Redlands Rialto**  **San Bernardino**  **Upland Victorville** 

**Yucaipa Yucca Valley**  **Lancast er** 

**Palmdale**

**S anta Clarita** 

**Eastvale**

**Lake Elsinore Hemet**  **Indio Indian**  **Wells**

**Temecula Perris**  **Moreno Valley**

**Murrieta Wildomar**  **Rancho Mirage**  **Corona Jurupa Valley**  **Desert Hot Springs**

**San Jacinto**  **Beaumont Banning**  **Calimesa**

**Palm Desert**  **Riverside**

**Menifee Norco** 

**La Quinta**  **Palm Springs**  **Anaheim Brea** 
**Buena Park** 

**Costa Mesa**  **Fullerton**

**Garden Grove** 

**Huntington Beach**  **Irvine**

**Laguna Niguel**  **Mission Viejo**  **Newport Beach**  **Orange**

**San Clemente**  **Santa Ana Seal**  **Beach Tustin**  **Yorba Linda 91**  **14**

**60 126** 

**71 57**  **60 110**  **134**

**91**

**22 134**  **101**
**101**

**101**

**15 215**  **10**

**15**

**10 210**  **405 5** 

**605**

**710**

**5 15** 

**405 110**  **105 5**  **10**

**405**

**210 210**  **San Bernardino County** 

**Riverside County**  **Orange County**  **Los Angeles**

**County Ventura**  **County**



### Employment Growth, 2012 - 2040 (Jobs per Square Mile) **Less than or Equal to 200 201 - 500**  **501 - 1,000 1,001 - 2,000**  **Greater than 2,000**
**(Source: SCAG, 2015)** 

### O:\=RTP\=rtp2016\mxds\SED\EMP_Growth_2012_2040.mxd | Date: 2/4/2016 **Map Title: Employment Growth, 2012 - 2040 8**  **10**
**10**

**0 5 10 2.5**  **Miles** 

### Note: Transportation Analysis Zone (TAZ) level data or any data at a geography **smaller than the jurisdictional level is included in the draft PGF for regional modeling purpose only, and is advisory and non-binding.**  

## E **xhibit 9**  Employment Change, 2012-2040

## 43


# REFERENCES Field, Brian, and B. MacGregor. 1987. Forecasting Techniques for Urban and Regional Planning. London: UCL Press. Plane, David, and P. Rogerson. 1994. The Geographical Analysis of Population: With Applications to Planning and Business. New York: Wiley.

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## ADOPTED | APRIL 2016 WWW.SCAGRTPSCS.NET 

# APPENDIX CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST

please recycle 2347 2016.03.31 

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