Demographic Analysis and Projection Orlando MSA

Transcription

1 P a g e 1 Demographic Analysis and Projection Orlando MSA Nicholas Sofoul Florida Atlantic University December 9, 2009 Planimetrics Dr. Li

2 P a g e 2 Acknowledgements I would like to take this time to thank everyone for their support while writing this final paper of the fall 2009 semester. Specifically, I would like to thank my parents for understanding why I couldn t see them all semester, my partner, Michael, for getting me materials from the Florida International University Library, and my classmates for keeping me motivated. Professionally, I would like to thank the National Historical Geographic Information System of the University of Minnesota for providing me free historical census data, CensusScope, and Tetrad Systems, for providing me telephone assistance with PCensus Demographics. Last, I would like to thank the School of Urban and Regional Planning and FAU for granting me the funding to embark on this educational experience.

3 P a g e 3 Executive Summary The census designated Orlando Metropolitan Statistical Area is comprised of four counties in Central Florida. The region which started as a series of small agricultural and trading places has grown into a sprawling tourist destination. Although tourism remains the bread and butter of the region, the area is actively engaging in economic development aimed at the diversifying the local economy. By creating economic clusters for simulation/training, digital arts, aerospace/defense, and life science the area has been successfully working toward its goal of becoming a global competitor and model city. The Orlando MSA is developing a young and diverse populous. Population distributions based on age and sex show a small senior citizen population, a fairly large baby boomer cohort, and a steady flow of young people in the region. There are very few notable differences in proportions of males and females across cohorts. Meanwhile the area has experience a dramatic rise in the percentage of Hispanics and Asians in the area. This diversification might suggest that Orlando is emerging as a more global city. In addition to the diversification of the area, Orlando has seen increases in the number of college graduates and median household income. Some possible reasons for these changes include the expansion of the University of Central Florida (now the 3 rd largest university in the nation), and the region s economic development plan to get higher tech-higher wage jobs. The Orlando MSA is also a growing region. Based on population projections the total population of the MSA should increase between million by The coupled affect of continued population growth, diversification, and increasing wages will give Orlando an interesting mix of strengths and weaknesses. While the area will benefit from increased tax rolls and a younger/high tech people, there will be a continued stress to improve the infrastructure and provide more services to the region. This study concludes that if Orlando can meet the challenges of population growth while maintaining its dominance of a tourist-mecca and diversifying its economy, the region will be closer to its goal of emerging as a major world player enabling Orlando to compete with some of the world s biggest and best cities.

4 P a g e 4 Table of Contents List of Figures 5 Introduction 6 Population Distribution by Age and Gender 7 Population by Race 9 Educational Attainment 10 Income 11 Housing Characteristics 12 Migration 13 Poverty 14 Population Projections 15 Summary of Population Projection Models 18 Conclusion 19 References 21 Appendix 22

5 P a g e 5 List of Figures Pyramid Table by Age and Sex: Orlando MSA Orlando MSA Population Distribution by Sex and Age Cohort 8 Population Distribution by Race (1980, 1990, 2000) 9 Population Distribution by Educational Attainment 10 Median Household Income 1990 & Total Number of Housing Units by Occupancy, 1990 & Percentage of Housing Units by Occupancy, 1990 & Orlando MSA Migration, 1990 & Poverty Rates in Orlando MSA, 1990 & Summary of Extrapolation Curves 16 Summary of Ration Model Curves 17 Combined Ratio/Extrapolation Models for Orlando MSA 18

6 P a g e 6 Introduction The Greater Orlando MSA is located in central Florida and consists of 4 counties. The counties contained within the MSA are, Osceola, Orange, Seminole, and Lake Counties. Some of the major cities are Orlando, Kissimmee, Winter Park, and Clermont. The largest city in the region is Orlando (City of Orlando, 2009). The city was incorporated in 1875, like all early central Florida cities, served primarily as a small agricultural town specializing in cattle ranching and citrus (City of Orlando, 2009). The most significant event in the history of the Orlando MSA was the construction of Walt Disney s Florida Project. This plan created one of the world s premier tourist attractions and was a catalyst for further development of the tourist economy (Orlando Travel and Visitor's Bureau, 2009). These developments created a need for improved infrastructure and an increased population to meet the demands of new booming economy. Recently, especially in wake of the loss of business after September 11, the area is very interested in diversifying their economy (Orlando Chamber, 2009). Some industries that have been targeted expansion include aerospace/defense, simulation/training, digital media, and life sciences (Orlando Chamber, 2009). The Orlando MSA contains the University of Central Florida which, as of fall semester 2009, is the 3 rd largest university in the country with over 53,000 students (University of Central Florida, 2009). UCF originally started as an engineering technical school, called Florida Technological University, designed to train scientist to work at NASA and along the Space Coast. As the Orlando MSA continues to grow many, there is a focus to make Orlando more than a tourist destination and more of a major competitor and example for other cities to follow. They area is currently vying for mass transit projects, and is in the process of revitalizing downtown by creating a number of new public venues designed to make the area more attractive, especially Orlando, and better able to compete with Tampa, Jacksonville, and South Florida (Orlando Chamber, 2009). To conduct the demographic analysis of the area, I used data provided by the US Census Bureau through CensusScope.Org and the National Historic Geographic Information System. I examined several categories of data including: population distribution by age and sex, race, educational attainment, income, housing occupancy, housing growth, and poverty. For better visualization, I created a number of charts of the data. I included pyramid tables and pie charts for population distribution by age and sex, pie charts for distribution by race and educational attainment, and a bar chart for income distribution. Perhaps most importantly I used previous census data to create population projections of the MSA to the year I used a variety if projection models in my projection including extrapolation and ratio models. The following pages will discuss each demographic category in detail for the Orlando Florida MSA. Finally this report will end with general conclusions, speculation, and planning implications derived from the demographic analysis.

7 P a g e 7 Population Distribution by Age and Sex The pyramid tables below show that distribution of the population by age cohort and sex in Orlando for the years 1990 and There are some notable differences between the two tables. First, there has been a shift in the age cohort that represents the largest percentage of the population. In 1990 the largest age cohorts were and and in 2000 the largest is and This change is likely due to the ageing of the population. In 1990 the population was mostly skewed toward more population in cohorts 20-24, 25-29, Although a population bubble still exits, there is not as much variance between that these cohorts and the younger cohorts. Second, the population appears to be acquiring a sizable and steady younger population. Last, the male and female sides of the population pyramid are, with exception of the oldest cohorts, practically symmetrical indicating that there is an equal percentage of males and female in each age cohort. Tables 1 &2: Pyramid Table by Age and Sex Orlando MSA 1990 and 2000 Orlando Age Distribution by Sex, 1990 Age Cohort Female Male Percent of Population Orlando Age Distribution by Sex, 2000 Age Cohort Percentage Females Males

8 P a g e 8 Pie Charts by Age and Sex The following pie charts represent the male and female distribution of the population for the years 1990 and From my analysis of the charts it is apparent that there are some differences in the population for each sex in 1990 and Of the male population, there has been a significant increase in percentage in the 5-9 cohorts between 1990 and As in the pyramid tables, shift in the ageing baby boomers in the male population is also apparent. In the female population, with the exception of the boomers and a greater percentage in the younger population, there are few differences in the distribution between 1990 and Comparatively, it should be noted that there is a consistent greater population of females in the older age brackets than males, possibly due to the higher survival rate of women the older cohorts. Charts 3-6: Orlando MSA Population Distribution by Sex and Age Cohort Male Distrubution by Age, 2000 Male Distribution by Age, % 4% 4% 8% 3% 2%1%1% 3% 9% 8% 8% % 4% 6% 4% 8% 3% 2%1%1% 8% 0-4 4% 1% % % % 85+ 9% 10% Female Distrubution by Age, % 2%2% 4% 5% 6% 5% 4% 6% 6% 5% 5% 8% 9% 8% 9% Female Distrubution by Age, % 3% 2% 2% 6% 4% 4% 5% 6% 8% 8% 8%

9 P a g e 9 Population by Race The three pie charts below show that there have been significant changes in the demographics of the greater Orlando area in terms of race. The greatest changes have been in the percentage of Hispanics. The pie charts show that for each census, 1980, 1990 and 2000, the percentage of Hispanics doubled. This is huge difference and is more than a 300% increase in the percentage of Hispanics within the Orlando Metropolitan Statistical Area. It is important to note a majority of the increases of Hispanics has occurred in the southern part of the Orlando MSA. Another notable change is the rising percentage of Asians to the region (less than 1% in 1980 to 3% in 2000). Interestingly, despite substantial gains in the minority population the population of African Americans has remained between percent. Likewise, and due to the rising prevalence of Hispanics and other minority sub-groups, the percentage of the white population has decreased year after year. It is clear from the pie charts that the Orlando is becoming an area of greater racial diversity, especially in term of the Hispanic population. Charts 7-9: Population Distribution by Race (1980, 1990, 2000) Population Distribution by Race/Hispanic, 1980 Population Distrubution by Race/Hispanic, % 0%1% 4% 12% 0%2%0% 8% Total Hispanics Total Hispanics White* White* Black* Black* American Indian and Eskimo* Asian* American Indian and Eskimo* Asian* 82% Other* 78% Other* Population Distribution by Race/Hispanic, % 3%0% 2% 0% 1 Total Hispanics White* Black* American Indian and Eskimo* Asian* Other* Two or More Races* 65%

10 P a g e 10 Educational Attainment The pie charts below show the percentage of the Orlando population by educational attainment. It is clear from the charts that there have been fairly significant changes in this area. The percentages of higher-educated people (Associates degree and higher) have increased. This could be due to a number of reasons. First, the University of Central Florida, located within the Orlando MSA, experienced a dramatic increase in enrollment during this time period. Second, the Orlando area has been actively engaging in economic development aimed at diversifying it predominantly tourist-based economy. Much of this diversification has been aimed at graphic arts, aerospace/defense, and simulation/training. These areas usually have a higher percentage of workers that require a degree to work when compared to the tourism industry. Charts 10 & 11: Population Distribution by Educational Attainment 6% Educational Attainment, % 14% Less than 9th grade Some high school, no diploma High school graduate* Some college, no degree Associate degree 21% 31% Bachelor's degree Graduate or professional degree Educational Attainment, % 5% 12% Less than 9th grade 1 Some high school, no diploma High school graduate* Some college, no degree 8% 2 Associate degree Bachelor's degree 23% Graduate or professional degree

11 P a g e 11 Income Distribution The bar chart below shows median household income for the years 1990 and The chart indicates that there has been a dramatic increase in the median household incomes from 1990 to For example, in 1990, less than 5% of households had an income between thousand dollars. This compares to the nearly 10% of the households in the same income bracket in the year Likewise, the opposite has occurred in the lower income brackets. For income brackets below 35 thousand dollars, there has been a decrease in the percentage of households 1990 to Other than inflation and minimum wage increases, it is possible that this increase in income is related to the rise of higher educated people and the economic activity described in the previous section. Quite often careers that require college degrees also include higher salaries. Overall, this chart proves that Orlando is becoming a more affluent area and should have benefited from higher tax roles as a result of this income increase. Chart 12: Median Household Income Year 1990 and 2000 Comparative Median Household Income US Dollars $150,000 and above $100,000 - $149,999 $75,000 - $99,999 $50,000 - $74,999 $35,000 - $49,999 $25,000 - $34,999 $15,000 - $24,999 $10,000 - $14,999 Less than $9,999 Year 1990 Year % 5.00% 10.00% 15.00% 20.00% 25.00% Percentage of Households

12 P a g e 12 Housing Characteristics and Trends The charts below show changes in the housing occupancy and units from the 1990 and 2000 census. From the first chart it is apparent that the overall number of housing units has risen considerably from 1990 to In the past ten years, there has been at 25% increase in the total number of housing units. This percentage is consistent with the near 26% population growth in the same time frame. The second chart show that there have not been considerable changes in the overall makeup of housing units by occupancy. Generally, the percentage of owners has increased 2% from showing that slightly more people are owning their homes than renting. It should be noted that this housing data is almost 10 years old and in light of the housing boom and bust could be radically different. Chart 13: Total Number of Housing Units by Occupancy 1990 & 2000 Chart 14: Percentage of Housing Units by Occupancy 1990 & 2000

13 P a g e 13 Migration The two charts below show changes in migration for the Orlando MSA. Migration, in this case, is defined as the location of the residence of the respondent five years ago. Comparing the two charts it is apparent that although the population continues to increase the migration rate (all categories except same house ) is decreasing. This is shown by the increasing percentage of same house by 3.6% between the years 1990 and The greatest percentage difference is in the number of different state respondents. Overall this chart suggests that the population growth of the Orlando MSA is starting to shift from migration induced to births induced. People are starting to stay in the region and the percentage of population increasing from inside the region births is growing. Charts 15 & 16: Orlando MSA Migration 1990 & 2000

14 P a g e 14 Poverty Considering the rapid population growth and effort to diversify the economy away from the tourist industry, it is interesting to look how poverty rates have changed in 1990 and The chart below shows no real significant change in the level of poverty in the region. The only difference is a slight 0.58% increase in poverty. This chart indicates that the poverty rate is increasing in proportion with the population increase in the region. It also shows that despite efforts to bring a higher proportion of high wage, high tech jobs to the area, there have been no marked differences in the poverty rates of the region. Despite that the Orlando MSA has a poverty rate less than the national average, the city should look into finding way to reduce this number. Furthermore, as with housing, these statistics could be very different if updated. As the economic collapse has forced unemployment to over 10%, there is a significant possibility that poverty rates have increased. Chart 17: Poverty Rates in Orlando MSA 1990 & 2000

15 P a g e 15 Population Projections A wide variety of extrapolation and ratio models were used in order to make population projection for the Orlando Metropolitan Statistical Area. In order to make population projections I obtained historical census data from University of Minnesota s National Historic Geographic Information System (NHGIS) for the year Since Metropolitan Statistical Areas were not defined in 1920 and many of the earlier censes years, I obtained county data from each of the four counties and added the totals to derive the MSA populations. It is important to note that Lake County was not apart of the MSA until the 1990s; to control for this I have included Lake in all my historical MSA totals. Additionally, I was unable to procure the advanced crosstabs (by age, sex, and race) for three time periods. This is resulted in my inability to conduct a Cohort- Component Model for the Orlando MSA. Below, I will outline my extrapolation and ratio approaches in detail followed by a summary of my projection findings. Extrapolation Models Models are very common models for basic population projection. Most extrapolation models, except parabolic curves, use some variant of the linear model to make projections. These models are convenient because they do not require a great amount of data, they are inexpensive to conduct, and are pretty straightforward in methodology. It is important to note that while relatively easy to conduct, these models only use aggregate data and assumes that past trends will predict the future. These basic disadvantages can lead to misleading projections when using extrapolation models. In addition to the possibility of methodological error, the data available for population is almost 10 years old. The very outdated nature of the data sets used in this projection might distort the outcome. To conduct the extrapolation models for population projection I used the spreadsheet provided by Dr. Li in the course homepage. This Excel file has a data input sheet where I entered in historical aggregate population figures for each decade Once models were produced, I used the coefficient of relative variation, mean error, and mean absolute percentage error measures to make a determination of the top three models. The chart below shows the output curves of all the extrapolation methods with asterisks over the models chosen to use in my final determination of population projection. The models chosen in my extrapolation model group include Modified Exponential, Gompertz, and Logistical curves. The next part of this section will go into more detail on the advantages, disadvantage, and results from each of these curves.

16 P a g e 16 Chart 18: Summary of Extrapolation Curves Modified Exponential The modified exponential curve is derived from a family of asymptotic curves, all three of which have been chosen as the best extrapolation models. This method is good because it does not assume infinite population growth. On the other hand it involves a more complex formula than simile linear regression. In addition, the growth limit can sometimes distort the projection if it is too high or low. Gompertz The Gompertz curve is in the same family as the Modified Exponential Curve. It is a commonly describes growth patterns as slow then increases before tapering off towards the upper limit. It is typically on of the better models because it predicts a pattern of growth that has been frequently observed. Like the modified exponential growth curve, it s a more complex model and assumed growth limits can sometimes cause unreasonable outcomes. Logistical The Logistical curve is very similar to the Modified Exponential and Gompertz except that it used the reciprocals of observed values. This is a very popular curve that has a proven history as a projection tool and is considered more stable than the Gompertz. Logistical does not contain a sometimes misleading growth limit however, it is one of the most complex extrapolation methods. In general, this is the best model.

17 P a g e 17 Ratio Models Another way to make projections is through the use of a ratio model. This technique uses the population data from a larger geographic area to project the population of a smaller region or county. For example, if I wanted to predict the population of Broward County, FL. I would compare it to the South Florida region or to the State of Florida. There are three simple techniques contained within the category of ratio models. This analysis used all three including Constant Share, Shift Share, and Share of Growth. To make these projections, I utilized the spreadsheet provided by Dr.Li. Like all projection models this one is limited by the age of the data and by assumptions made in calculations. Chart 19: Summary of Ratio Models Constant Share A constant share ratio is calculated by finding the percentage of population of a smaller to its larger whole. In this instance I was comparing the Orlando MSA to the state of Florida. Due to its overly simplistic nature and reliance upon projected figures for the larger whole this method should be used carefully. It makes the assumption that the population growth will keeps its share constant and this in many cases is not realistic. Shift Share: Shift Share uses the same basic methodology as the Constant Share but accounts for changes in population share over time. This method is a slight bit more realistic than Constant Share; however, it makes the assumption that the share change between the launch year and the base will predict future outcomes.

18 P a g e 18 Share of Growth The Share of Growth method primarily focuses on population growth as opposed to shifts and shares of growth. This method uses the smaller areas share of growth as the primary means of making a population prediction. Once again, this model makes the assumption that the past share of growth will predict future outcome, which is not always the case. Summary of Demographic Projections The chart below shows the best extrapolation models (with the linear model added), and all three ratio models used to create thirty-year projections of population for the Orlando MSA. The final output shown on the chart below shows the lack of Chart 20: Combined Ratio and Extrapolation Population Models for Orlando MSA consensus of the different packages of models. Once track, the one taken by the asymptotic curves, brings the 2030 population to at least 4.5 million by The others, linear and ratio models bring the population to a maximum of 2.8 million. A possible reason for lack of a clear answer could be my inability to perform the cohort component analysis. Perhaps if I found this data the model of choice would be more apparent. In addition to the lack of a major model, this data is very old. For instance, this report predicts a population for 2010 (in the year 2009). It is very possible that even with additional models my outcomes would still be skewed. Without a clear consensus with the projection models, I created my own consensus line by averaging the outputs of each model. This line, named consensus, is my adopted population projection for the region. It estimates that that the 2030 population will be close to 3.5 million. Due to equal weighting, it is very possible that this projection could be misleading or wrong; however, without a oblivious model consensus and taken in light of the old (2000) data and the

19 P a g e 19 economic collapse I feel that this is a best alternative for a population projection for the Orlando MSA. Conclusions After a thorough review of the several key demographic variables I can make some general conclusions about the Orlando MSA. The Orlando MSA population is increasing the percentage of younger people in the population. This could be a result of new families moving to the area, a thriving state university, or the relocation of many senior citizens to outside the MSA. Like most areas Orlando will have to allocate additional social resources to the ageing of the baby boomer class. There few notable conclusions with reference to the distribution of males versus females across all age cohorts. Like in most areas, women have a higher survival rate in the older cohorts and tend to contribute to a significantly larger percentage of the oldest cohorts. Perhaps of greater interest is the significant changed that have occurred in the distributions of race, education, and household income among the populous. Over the past 20 years Orlando has become significantly more diverse in term of race. These changes are especially prevalent in the rising percentages of Hispanic, Asian, and multicultural groups. Interestingly, there is no population change among the African Americans. The dramatic increase of minorities in the region suggest that this area might one day rival South Florida, Los Angeles, and other highly multicultural areas with high percentages of Hispanics. In addition to the diversification of the area there are also marked increases in the percentage of college educated people possibly due the dramatic enrollment increases at University of Central Florida and industry diversification. These increases is both diversity and education are also coupled with rising median household incomes. Since 1990, there have been extreme differences in median household incomes. As low wage earners declined and higher wages increased, this finding is indicated by every income bracket. This massive rise in household income has probably contributed to Orlando s recent successes of building new community venues, creating massive incentive packages to lure in new high tech and high wage companies to the region. In addition, to rising household incomes the rate of new housing units continues to rise and is in line with the rate of population growth. Lastly, it is clear from all the population projections that the region will continue to increase in population; however, there are inconsistencies on predicting the extent of the population increase. Based on projections outlined in this report the population will reach between million by In order to create a model consensus, I averaged the models and predict that 2030 population will be near 3.5 million. The variance in the population models will make it very difficult for planners to allocate future resources. In addition, this increase of population, although providing additional tax base, will continue to stress this region to improve infrastructure, especially transportation, to accommodate continued population growth. For the best interest of the people in this region I recommend additional, more complex, modeling of population projection to ensure planners and city leaders are able to provide adequate service delivery. Overall, if the area continues to gain population, continues the track of economic diversification, and continues providing a world class travel experience the region should be on a path to

20 P a g e 20 meeting the goal of becoming a model city and a competitor to larger cities such as Houston, Boston, Los Angeles, and Seattle.

21 P a g e 21 References CensusScope. (2000). CensusScope. Retrieved November 3, 2009, from City of Orlando. (2009). Orlando: About US. Retrieved November 3, 2009, from Orlando Chamber. (2009). Orlando Chamber of Commerce. Retrieved November 4, 2009, from Orlando Travel and Visitor's Bureau. (2009). Retrieved November 3, 2009, from University of Central Florida. (2009). UCF Office of Institutional Research. Retrieved November 4, 2009, from UCF: At a Glance: University of Minnesota (2009) National Historic Geographic Information System. Data Retrieved December 7,

22 P a g e 22 Appendix Data Tables by Category Age Distribution by Sex, 2000 Male Female Number Percent Number Percent Total Population 809, % 835, % , % 52, % , % 56, , % 57, % , % 54, % , % 55, % , % 59, % , % 62, % , % 70, , % 67, % , % 58, % , % 51, % , % 39, % , % 33, % , % 31, % , , % , % 25, % , % 16, % 85+ 6, % 14, % Age Distribution by Sex, 1990 Male Female Number Percent Number Percent Total Population 602, , % , % 42, % , % 40, % , % 37, % , % 40, % , % 46, % , % 56, % , % 56, % , % 48, % , % 43, % , % 34, %

23 P a g e , % 28, % , % 27, % , % 28, % , % 29, % , % 22, , % 17, % , % 11, % 85+ 4, % 9, % Hispanic Population and Race Distribution for Non-Hispanic Population Number Percent Number Percent Number Percent ,224, ,644, Total Population 804,925 % 2 % 1 % Total Hispanics 28, % 100, % 271, % 1,070,46 White* 665, % 956, % % Black* 102, % 143, % 219, % American Indian and Eskimo* 1, % 3, , % Asian* 5, % 20, % 44, % Other* 1, % % 4, % Two or More Races* , % Hawaiian and Pacific Islander* , % Educational Attainment in Population 25 Years and Over, Number Percent of Total Number Percent of Total Total Population Age , % 1,083, % Less than 9th grade 56, % 55, % Some high school, no diploma 115, , % High school graduate* 240, % 298, % Some college, no degree 168, % 244, % Associate degree 57, % 85, Bachelor's degree 114, % 185, % Graduate or professional degree 48, % 83,

24 P a g e 24 Household Income, 2000 (1999 Income) Percent of Total Number Households Total Households 625, % Less than $9,999 45, % $10,000 - $14,999 35, % $15,000 - $24,999 83, % $25,000 - $34,999 89, $35,000 - $49, , % $50,000 - $74, , % $75,000 - $99,999 60, % $100,000 - $149,999 43, % $150,000 and above 11, % Household Income, 1990 (1989 Income) Percent of Total Number Households Total Households 466, % Less than $9,999 53, % $10,000 - $14,999 41, % $15,000 - $24,999 93, $25,000 - $34,999 84, $35,000 - $49,999 88, % $50,000 - $74,999 68, % $75,000 - $99,999 20, % $100,000 - $149,999 10, % $150,000 and above 5, % Population, Total 394, , ,925 1,224,852 1,644,561 Change 127, , , ,709 Percent Change 32.33% 54.03%

25 P a g e 25 Migration, 2000: Residence 5 Years Prior to Census Residence in 1995 Number Percent Same house 676, % Different house 861, % Same county 358, % Different county 423, % Same state 203, % Different State 220, % Elsewhere in 1995* 78, % Total Population Age 5+ 1,537, % Migration, 1990: Residence 5 Years Prior to Census Residence in 1985 Number Percent Same house 458, % Different house 680, % Same county 258, % Different county 385, % Same state 136, % Different State 248, % Elsewhere in 1985* 36, % Total Population Age 5+ 1,139, % Poverty by Age, 1990 and Number Percent Number Percent Total Population* 1,193, % 1,614, % In Poverty 120, % 172, % Not in Poverty 1,073, % 1,442, % 11 Years and Under 194, % 267, % In Poverty 29, % 41, % Not in Poverty 165, , % 12 to 17 Years 89, % 132, % In Poverty 11, % 17, % Not in 78, % 114,

26 P a g e 26 Poverty 18 to 64 Years 756, % 1,019, % In Poverty 64, % 97, Not in Poverty 692, % 921, % 65 Years and Above 152, , % In Poverty 15, % 15, % Not in Poverty 137, % 180, %

27 P a g e 27 Orlando County Name MSA Base Period CURVE EVALUATIONS Evaluating the Various Curves CRV ME MAPE Linear % Geometric % Parabolic % Mod Exp* % BEST Mod Exp UL % Gompertz* % BEST Gomp UL % Logistic* % BEST Log UL % *Used "Best Fitting Curve"

28 P a g e 28 Ratio Technique Orlando MSA Example Orlando MSA SUMMARY OF POPULATION PROJECTIONS RATIO TECHNIQUES, MEDIUM SERIES State Orlando Constant Shift Share , , ,949,163 2,104, , ,204,149 2,556, , ,523,993 2,525, ,511 Summary of Population Projection Models Mod Exp 2,472,481 3,576,175 5,162,748 Gomportz 2,474,645 3,509,180 4,911,219 Logistic 2,509,745 3,434,261 4,476,255 Linear 1,881,723 2,202,783 2,202,783 Shift Share 2,104,970 2,556,562 2,525,614 Constant Share 1,949,163 2,204,149 2,523,993 Share of Growth 2,052,617 2,394,234 2,822,746 Consensus 2,206,478 2,839,621 3,517,908 ** Consensus formed by taking the mean ourput of the models For each year 2010, 2020, and 2030