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Agenda 03/03/2015 W (Affordable Housing) AGENDA BCC WORKSHOP MEETING March 3 , 2015 NM to' VI, —47"ziem ' . al --711" 111111111111111111 1111 ....,112Mirmenammaimm Affordable Housing Workshop March 3 , 2015 COLLIER COUNTY Board of County Commissioners r Plow , r )l WORKSHOP AGENDA Board of County Commission Chambers Collier County Government Center 3299 Tamiami Trail East,3rd Floor Naples FL 34112 March 3,2015 1:00 P.M. Commissioner Tim Nance,District 5 -BCC Chair Commissioner Donna Fiala,District 1-BCC Vice-Chair;CRAB Chair Commissioner Georgia Hiller,District 2 -Community&Economic Development Chair Commissioner Tom Henning District 3 -Public Safety Coordinating Council Chair Commissioner Penny Taylor,District 4-TDC Chair;CRAB Vice-Chair 1. Pledge of Allegiance 2. Affordable/Workforce Housing 3. Public Comment 4. Adjourn Notice: All persons wishing to speak must turn in a speaker slip. Each speaker will receive no more than three(3)minutes. 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''''E - ' - 4 .r71 0 .f.54 8 . .4 Zzj4 I=r O co co a) au a) E ci, -5 c...) c.)w E ' 1_4' .54 c.) _i) ri C7111 ri White Paper IfrerCou.nty DRAFT AFFORDABLE/WORKFORCE HOUSING POPULATION BASED INDEX MODEL METHODOLOGY NOTE 2/24/15: THE DATA IN THIS WHITE PAPER IS PRIMARILY FROM THE PERIOD IN TIME STUDIED PRIOR TO INITIAL PUBLICATION IN THE FALL OF 2014. FIGURES IN THE PRESENTATION FOR THE MARCH 3, 2015 WORKSHOP HAVE BEEN UPDATED IN SOME CASES FOR A MORE CURRENT PERSPECTIVE. THE METHODS AND SOURCES REMAIN THE SAME. Collier County Community and Human Services Kimberley Grant, Director October 8, 2014 Update February 24, 2015 1 TABLE OF CONTENTS Background 3 Purpose of the Model and Considerations in Model Development .4 Core Model: Population Based 5 Projected Gross Future Demand 6 Available Inventory and Net Demand for Owner Occupied and Rental Housing 8 Supplemental Information 10 The Housing Opportunity Index . 11 Cost Burdened Levels 12 Additional Rental Information 16 Responding to the Model and Model Operations 17 Future Planned Enhancements 18 Development of the Model and Other Models Considered 18 Summary and Recommendation 20 Appendices and Map Appendix 1 —Collier County Housing Statistics 2012 22 Appendix 2—HUD 2014 Income Limits 23 Appendix 3—Support of Who Will Rent and Who Will Buy 24 Appendix 4-Naples Area Board of Realtors(NABOR) Information 25 Appendix 5—NABOR Units Sold by Bedroom Data 27 Appendix 6—Model Assumptions 28 Appendix 7—Employment 29 Appendix 8—2012 SHIP Incentive Review and Recommendation Report 30 Appendix 9—Organizational Information Definition Sheet 39 Appendix 10—Board of County Commissioners Statistics 47 Appendix 11 —Collier County Commuter Statistics 49 Appendix 12—Collier County Homeless Statistics 49 Appendix 13—Affordable Housing Needs Assessment: Population and Household Projection Methodology .. 50 2 Background Through a cooperative partnership between the Affordable Housing Advisory Committee (AHAC), Collier County Community and Human Services (CHS) Department, fka Collier County Housing, Human and Veteran Services Department (HHVS) and the Comprehensive Section in the Growth Management Division, the Housing Element of the Collier County Growth Management Plan (GMP) was amended as part of the 2011 Evaluation and Appraisal Report- based GMP amendments adopted in 2013 to require development of a method of indexing the demand, availability and cost for affordable/workforce housing throughout the County. The Housing Index Model herein is meant to replace the arbitrary number previously identified in the Housing Element to construct 1,000 affordable/workforce housing units each year to meet the County's demand for affordable/workforce housing units. The outcome of the collaborative effort between County staff and AHAC is to meet the following Goal, Objective and Policies of the Housing Element: Goal 1: To create an adequate supply of decent, safe, sanitary and affordable/workforce housing for all residents of Collier County. Objective 1: Provide new affordable housing units in order to meet the current and future housing needs of legal residents with very low, low and moderate and affordable workforce incomes, including households with special needs such as rural and farinworker housing in rural Collier County. Policy 1.1: By January 14, 2014, the Department of Housing, Human and Veteran Services shall establish a method of indexing the demand for very low, low, moderate and affordable workforce housing. Policy 1.2: By January 14, 2014, the Department of Housing, Human and Veteran Services shall establish a method of indexing the availability and costs of very low, low, moderate and affordable workforce housing. Policy 1.3: By January 14, 2014, the Department of Housing, Human and Veteran Services shall develop methods to predict future need, based on the Indexes established in Policies 1.1 and 1.2 above. Policy 1.4: By January 14, 2014, the Department of Housing, Human and Veteran Services shall establish necessary strategies, methods and tools to support this Objective. 3 Purpose of the Model and Considerations in Model Development The fundamental purpose of the model was to develop a method to predict the need for affordable/workforce housing. Based upon the outcome of the predictive model, information would be made available to decision makers in order to develop strategies to best meet the needs identified. For instance, if a large need was identified, the decision makers may wish to activate certain development incentives in order to encourage the development of additional affordable/workforce housing. Staff developed a logic flow for model development that assisted in understanding the purpose of the model and its intended outcome: Direction Factors Current State • Study demographics, available information • Determine key factors in establishing a demand and projection model Discern Need and Goals of • What is and who needs affordable/workforce housing Model • Stratify by owned and rental constituencies Develop Projection Model • Determine key factors and planning assumptions, sources for current data • Project demand; create baseline for future planning The following framework or set of assumptions was developed over the timeframe of model development: • Assume all current residents are housed, recognizing not necessarily in optimal conditions or in affordable situations • Use of a 3 persons per household standard was used in order to correlate with HUD figures; which also approximates the County wide statistic that households have an average of 2.7 persons per household • Housing is affordable at the median sales price to the family earning 80% of the area median income or above (Appendix 1) • Those persons earning over 80% of the area median income (AMI) are not considered "in need" of affordable/workforce housing (i.e. below market rate housing) (Appendix 2) • For prediction purposes to identify the need for owned versus rental housing, those households earning less than 50% of the AMI are considered most likely to rent and 4 those households earning more than 50.1% of the AMI are the population for the population that could qualify for homeownership (Appendix 3) • Data used for the model is already available and validated by reliable sources Core Model: Population Based After development of six draft models, the seventh one (now considered to be the official first generation model) was selected which identified that the most critical factor for predicting the need for future affordable/workforce housing was population (growth or decline). Population is the main driver that is quantifiable and commonly utilized to project future demand for affordable/workforce housing. The key secondary factors are area median income, housing prices,persons per household, and the Housing Opportunity Index. The objective is to create a simple model based on accepted principles and available and validated data, with the following notations: • The model cannot contain all possible variables affecting affordability of homes (such as individual income or debt considerations) • The model is the framework, but the supplemental information and factors would be considered in unison in order to create recommendations for the decision makers • The use of available, current data increases validity and currency • Model would be refined over time as market conditions change • Market and other conditions would be reviewed at least on a annual as the population figures are updated to create a need/surplus inventory in "real time" in order to react to the market 1 Affordable Housing Needs Assessment, Population and Household Projection Methodology,Prepared by the Shimberg Center for Affordable Housing, Rinker School of Building Construction,College of Design,Construction and Planning, University of Florida,September 2006 5 The population based model is a very simple model, as shown below. Population Based Model Formula Projected Gross Future Demand Less: Available inventory (owner occupied and rental) Results in: Projected Net Future Demand Projected Gross Future Demand In order to identify the future demand, we start with the current population and determine the households that are in need of affordable/workforce housing(those less than 80% AMI2). 2 AMI is defined as an estimate from the US Department of Housing and Urban Development(HUD)of how much money families in a given area earn,on average. 6 2015 Number of Households (HH) by Income Category (Population 350,286) 37%of our population Is less than 80%AM) •Provides baseline for population change/need in future 50.1-80% Above 120% AMI,27,968 •Model uses the HUD standard AMI,64,115 income categories based on 0-50%AMI, Area Median Income(AMI) 26,805 •Model assumes those at 80% 80.1%-120% AMI and above can compete in AMI,30,931 the marketplace for housing. 'Therefore,the need for additional affordable housing will be centered on those Using Shimberg 2015 Projections households earning less than 80%AMI The 2015 households by income category provide a baseline for population change and/or need in the future. The model uses the HUD standard income categories based on AMI. The model assumes those persons earning 80% of AMI and above can compete in the marketplace for housing. Therefore, the need for additional affordable/workforce housing will be centered on those households earning less than 80% of AMI. In order to obtain the gross demand for the future one year hence, the population is projected forward one year at the growth factor used by Comprehensive Planning in the Growth Management Division (currently 1.02%). Further, for planning purposes, it is assumed those making less than50% AMI are in need of rental units, and those earning more than 50.1% of AMI could qualify for homeownership. The group recognizes there are many that cross one way or the other,but this is a reasonable basis for planning. The gross demand for 2016 in this example (see below chart) is a need for 225 owned units and 216 rental units. 7 Projecting Gross Collier County 2016 Demand Projected Demand for Affordable Housing Units by Income Category (Based on 3573 Population Increase) 'Net population change(growth)= current population times 1.02% comprehensive management growth 50.1-80% factor. Above AMI, 225 •Then this is divided by 3 to 120%AMI, New 0-50% accommodate for the average 3 0 persons per household to derive AMI 216 the#of households. New *Then shown in the income 80.1420% categories as stratified by Schimberg AMI,0 *There is a recognition that factors other than population may impact the affordability of housing. The primary additional tool to be utilized will be the HOl information. If the HOI is below SO(much less 2015 Estimated population increased affordable,or above 60 much more by 1.02%growth factor affordable,projected gross demand will be rationally adjusted.) Available Inventory and Net Demand for Owner Occupied and Rental Housing We will now adjust the gross demand by available inventory to obtain the net demand for owner occupied and rental units. For owner occupied units, the model uses the available units on the Multiple Listing Services (MLS), and the data is provided by NABOR (Appendix 4). The model selects the number of available single family and condominium units for sale in the under $300,000 category. This is selected because the average sales price of homes in this category is approximately $170,000 which is considered affordable to a three (3) person household earning 80% of the AMI and approximates the standard housing expense at 30% of total income. The total number of homes for sale under $300,000 at present is 1,384 (Appendix 4). However, not all homes on the MLS under$300,000 would be available to meet the needs of a 3 person household. Those with 2 or 3 bedrooms will suit this model household. The data on the current MLS was not available by bedroom count, so a proxy has been created to determine the percentage of the available units from the current MLS listing that would meet the needs of the model household (i.e. have 2 or 3 bedrooms). The MLS data by bedroom counts is available for homes sold in the last twelve months and will be used as a proxy. So, it was calculated that 85% of the units sold were a 2 or 3 bedroom units for the twelve months ending April 2014 (Appendix 5). This ratio is then applied to the number of current units available to determine the current available inventory of affordable/workforce housing for this market(i.e. under 80% AMI); 1,384 8 * .85 is 1,176. We then adjust the gross demand by this available inventory to calculate the net demand. The gross demand for owned (225) LESS the available inventory (1,176) results in a negative number (-951). Therefore, at present, there is no population based demand for additional affordable/workforce housing for owner occupied units. For determination of the need for rental units, the model uses the vacancy rate of apartments in Collier County to adjust the gross to a net demand figure. Unfortunately, this is not a published figure, but is available from the Southwest Florida Apartment Association, who reported in July, 2014 that the vacancy rate is approximately 2%. The model only considers vacancy rates in excess of 10% (to be discussed) to result in available inventory for model purposes. Therefore the net demand is calculated as the gross demand for rental (216) LESS the available inventory (0) and results in a positive number (216). Therefore, at present, there is a need for 216 additional rental units to meet the 2016 population based demand for additional affordable/workforce rental housing. The entire population based housing index model is below shown in numerical format. Appendix 6 contains detailed notes explaining the calculations and data sources for the model. 9 Projected Net Demand for New Affordable Housing in 2016 FOR GROSS DEMAND: Uses o 3 Pelson Household:HUD 2014 AMI:100%$56,880,and 50%:$29,650 Population(A)'Growth Rate(B)= C. Net Pop Cl. Net pop D. it of NEW Population Increase(C) A. 2015 Est. B. Net Population Growth divided by 3 Households County Growth Percent- (persons) persons per Needing (Cl/3=B New Households(C1) Population Annual between 2015 household to Affordable and 2016 determine HH Housing in 2016 (37%<80%AMI) (C1)•%of HH less than 80%AMI (37%]=#New Households needing 350,286 1.02% 3,573 1,191 441 affordable housing(0) To break down the 441 into need for single family and rental units needed: FOR OWNED 150-80%AMII: 50.1-80%AMI: Owned _ 0-50%AMI* Rental E. Demand for F. Demand Rental 441(D).%of HH SO 80%AMI 225 Owned Housing 216 Housing Units [51%) of all HH<80%AMI=225 Units Demand for Owned Housing Units (49%of those<80%AMI) (E) (51%of those<80%AMR (6)—Available inventory(G)=Net G. Less Available SF projected demand=0 Units(I) and Condo H. Vacancy Rate -1,176 2% (deemed negligible Inventory(<5300K.2 availability) FOR RENTAL 10-50%AMII: or 3 bedrooms) 441(D)*%of HH 0-50%AMI[49%1 of all HH<80%AMI=216 Demand � I. Net Projected 216 J. Net Projected for Owned Housing V R Units ` O Demand: Owned Demand: Rental (F)—Adjusted d for Vacancy Rate(H)_ Net projected demand=216 Units U) Detailed Model Notes are on pages 30 and 31. Supplemental Information As noted earlier in this white paper, it is recognized that population changes alone may not determine the need for affordable/workforce housing. It is commonly held that market conditions and income conditions greatly impact the availability of housing in general, and more specifically, affordable/workforce housing.3 Through extensive research and discussion, additional supplemental data and facts that affect the need for affordable/workforce housing have been identified. Such factors as the Housing Opportunity Index, cost burdened rates of households, occupancy rates, and housing prices were examined. The Housing Opportunity Index 3 Reforming America's Housing Finance Market,A Report to Congress, US Department of the Treasury and US Department of Housing and Urban Development, February 2011 10 The working group (staff and AHAC) finds the Wells Fargo published Housing Opportunity Index (HOI) to be a very relevant data set to review and consider in addition to the population based core model because it is a reliable indicator of overall affordability of housing in our community available to the households earning 100% AMI. This is presented as meeting the requirements under Policy 1.1 and 1.2 noted earlier in this paper. As shown in the graphic illustration below, in simple terms, when income stays the same and the housing prices go up, affordability is decreased. Due to the nature of the recent drastic housing market fluctuations, the dramatic chart illustrates that following this data on a real time basis can be an indicator of demand for and availability of additional affordable/workforce housing units in our community. 400 $370K 350 300 $252K 250 f -- I Average Median ,00 !_ — — Housing Price — $166K Housing Opportunity 1=,0 —_ Index —_ Average Median 100 In{_ome $65K $66.1K $62.9K 50 67.5 HOI 18.3 52.6 ■1 M L�^ �", r �. rte+ G - N ,-.•. Cr .ti (o " G G G o G G G N G •`- G I .11 ^1 ^l •..I 1�.1 •^I ty •N N N ^l The HOI is defined as the "share of housing sold in the area that would have been affordable to a family earning local median income based on standard mortgage underwriting criteria (assumes 30% of gross income is spent on housing with 10% down payment)". Therefore, there are two major components — income and housing cost used to determine the HOI of an area.4 For income, County staff uses the annual median family income estimates for the Naples/Marco Island Metropolitan Area published by the US Department of Housing and Urban Development (HUD). Based on Collier County's HOI, staff made the following assumptions: 4 Source: National Association of Home Builders(NAHB)and Wells Fargo Housing Opportunity Index 11 If, the HOI is used as a key indicator of demand and availability of affordable/workforce housing in the community and if the HOI is over 50, the County is deemed to have sufficient availability. The HOI over 60 indicates a surplus. However, if the HOI falls below 50, it can be assumed that there is not enough affordable/workforce housing inventory to meet demand. For the first time in more than 5 '/2 years, the HOI fell below 50 in the fourth quarter of 2013 in Collier County, but has risen back above 50 in 2014. Naples-Marco Island MSA Measure 01 02 03 04 Q1 Q2 Q3 Q4 Q1 Q2 Q3 04 01 Q2 2011 2011 2011 2011 2012 2012 2012 2012 2013 2013 2013 2013 2014 2014 Housing Opportunity Index 66.3 62 68.2 67.9 66.4 66.3 71.8 69.7 65.7 58.3 59.2 49.8 53.6 51.5 Source: National Association of Home Builders—Wells Fargo Housing Opportunity Index based on information provided from sales transaction records from CoreLogic. The data includes information on state,county,date of sale and sales price of homes sold. Cost Burdened Levels As noted earlier, the core population based model is built on an assumption that all residents are currently housed. However, there is information that indicates certain households are "cost burdened"` or "severely cost burdened"6. Housing cost burden reflects the percent of income paid for housing by each household living in a geographic area. Based on recent US Census Bureau survey's, the number and percent of households paying more than thirty percent (30%) of their income for housing are reported for communities with populations of 20,000 or more. Housing cost burden is a distinctly different measure than the housing opportunity indexes that are based on the typical housing cost and the median income. Households spending more than 50 percent are considered to be "severely cost-burdened." Housing is generally considered to be affordable if the household pays less than 30 percent of income. The housing cost burden measure provides the actual "affordability outcome" of the housing choices made by individual households. Clearly these choices are constrained by each household's income and preferences, as well as by the housing available in the County. The 5 HUD defines"cost burdened"as a household paying more than 30%of their annual income for a mortgage payment. 6 HUD defines"severely cost burdened"as a household paying more than 50%of their annual income for a mortagepayment Florida Housing Data Clearinghouse,Shimberg Center for Housing Studies, University of Florida derived from figures produced from University of Florida Bureau of Economic and Business Research 12 housing cost burden measure reflects the preferences, budgets and housing units available to each individual household. Some households might obtain lower-cost housing by doubling up with relatives or accepting crowded living conditions, while other households might accept higher cost burdens to obtain larger units or more desirable locations. Below are the 2015 projections for cost burdened levels by income levels provided by the Shimberg Institute, which are then graphed by each income level. iousehold Demographic Data-Households by Tenure,Age of Householder,Income,and Cost Burden:Results OTHER REPORT ACTIONS Household Demographic Data-Households by: kbownload Excel data Jurisdiction Year Household Income Housing Cost Burden Household Count VIEW OMR DATA FOR THE Collier 2015 30%AMI or less i 30%or less 1626 SELECTED GEOGRAPHIC AREA Collier 2015 ! 30%AMI or less 1 30 1-50% 16021 General Unit Characteristics Copier 2015 30%411 or less more than 50% 12675 Population Projections Collier 2015 301-50%AMi, 30%or less 39481 Statewide Comparisons Collier 2015 301-50%AMl j 30150% 55601 START OVER Collier 2015 301-50%Awl more than 50% 6603 Collier 2015 50.1.80%AMI11 30%orless 102951 ',Different geographic area, same indicators Copier 2015 50 1-80%AM 30150% 9035 I Collier 2015 501-80%AMI' more than 50% 58571 A Create a New Profile 1 0 Different indicator same Collier 2015 801-120%AM 30%or less 16526 geographic area Copier 2015 1 80 1-120%AM 301-50% 6153 2015 801-120%AM I more than 50% 3326 Collier 2015 more than 120%AMI 30%or less 49416 Collier 2015 more Man 120%AMII 30.1-50% 7895 Copier 2015 more than 120%AMI I more than 50% 2612 i Notes: dable Sources: EstimateAras and projections by Shimberg Center for Housing Studies.based on 2000 and 2010 U S Census data and population projections by the Bureau of Economic and Business Research.UnNersiy of Florida 13 $29,650 1009f, 14,477 HH 90% 80% 12,163 HH TI 70% 14,892 HH eu 6o% ^d 50% 0.1 9,479 HH 40% U 3o% 10,507 HH •2o% 10% o% 3o%AMI or 30.1-5o%AMI 50.1-8o%AMI 80.1-12o%AM more than less 120%AMI 9o% 75% 59% 36% 18% The table below shows both the owner occupied and renter households in Collier County that are severely cost burdened by year. Income 2009 2010 2015 2020 2025 2030 Owner Renter Owner Renter Owner Renter Owner Renter Owner Renter Owner Renter 0-30% 3779 2965 3789 2968 4186 3225 4694 3544 5207 3850 5694 4133 AMI 30.1- 2338 1344 2347 1348 2627 1485 2996 1663 3388 1839 3772 2007 50% AMI 50.1- 2570 524 2579 526 2856 588 3209 668 3569 754 3911 839 80% AMI Total 8687 4833 8715 4842 9669 5298 10899 5875 12164 6443 13377 6979 Source:Florida Housing Data Clearinghouse, Shimberg Center for Housing Studies,University of Florida derived from figures produced from University of Florida Bureau of Economic and Business Research 14 HOUSING AFFORDABILTY A measure of housing affordability is the percentage of homes sold that are affordable to families earning the area's median income,commonly called the Housing Opportunity Index,or HOI. Naples-Marco Island Cape Coral-Fort Myers Median Q2 2014 $260,000 $157,000 Price(1) Q2 2009 $183,000 $96,000 Housing Q2 2014 51.5% 68% Opportunity Q2 2009 68.196................._....,_.......... ...• 81.4% Index(H01)(2) Median Q2 2014 $62,900 $58,000 Income(3) Q2 2009 $70,800 $60,700 National Q2 2014 197 143 Rank(4) Q2 2009 172 82 (1)Includes new and existing homes:(2)HOt is a measure of the percentage of homes sold that are aft ordable tc families earning the area's median income.: (3)Median household income as determined by Census:(4)Out of 224 metro areas nation:side(most to least affordable:loner is better) Source:NAHB/WMIs Fargo SCRIPPS NEWSPAPERS • 15 Additional Rental Information As indicated in the following chart, the wages of many workforce positions are insufficient to afford the rental rates in Collier County. Rental Rates Out of Line with Incomes Apt Income Required to Afford Rents Wage Index Annual Salary prices $90,000 $80,000 Income needed $32,400 $808 for 1 BD $70,000 Income needed $60,000 for 2 BD $40,300 $1,006 $50,000 Income needed $40,000 for 3 BD $52,800 $1,314 $30,000 $20,000 *Renters are cost burdened *Less affordable rental housing than $10,000 needed s_ *It is assumed that many"double up" , , 0o r e`°' •Mid level workers are not able to �0c10��o�� ya°` ��e `a4s,e'b a ey;e� ,,a e 1,5 a a a afford available rentals e eQ ca� °� � c e ae • a c"v OZ4 r oFe owe F d r e' e a •S`'\c`" `' e(s ti G 0�` L°� ,o P 5 h`ac Qc P Qte Source: Florida Jobs.org OES Occupational Employment Statistics Additional information on wages earned by workforce level households, see Appendix 7. The purpose for providing this supplemental information is to illustrate the additional information to be considered in addition to the outcome of the core population based model, when considering recommendations for decision makers regarding the need for additional affordable/workforce housing. The supplemental information for owner occupied housing units is provided as a means of rounding out the data/outcome from the core population based model. For instance, even if the population based model may not predict a need for additional owner occupied affordable/workforce housing units, it is important to consider that as many as 25% of the families in our community are in a cost burdened situation, and could benefit from more affordable/workforce units. 16 Likewise, if the population based core model were to show a need for additional affordable/workforce housing units a year hence, but the HOI showed our MSA was at a level of 60 or more, it may not be necessary to take immediate action. The point is that the population based model creates a good estimation of the units needed based on population changes, but other factors may be presented to the decision makers with recommendations to take actions. Responding to the Model and Model Operations While the core model is population based, decisions concerning the need for additional affordable/workforce housing are not one dimensional. Therefore it is important that the response to the model be considered at the same time as the model development in order to reflect an understanding of the basis for eventual recommendations and decision making. Semi- annually, the model will be updated and the supplemental factors already noted will be reviewed. In addition, it is envisioned that the semi-annual review would also include other relevant factors such as: • Future planned inventory for affordable/workforce units (affordable/workforce units planned but not permitted, units permitted but not built) • What incentives exist now, and which are active or could be employed, or additional incentives not currently available (for example: Impact fee deferrals, Fast track permitting, Density bonuses, Other) (Appendix 8) • Other assistance or incentives currently being offered such as down payment assistance or other forms of assistance offered to keep homes affordable or to maintain the existing stock • Current market and other economic conditions Once all the factors are calculated within the core population based model, and supplemental information has been gathered, how and when will the information be used? It is recommended that the information be compiled and reviewed on an annual basis to maintain currency with market experience. The main reason for suggesting an annual update is because the population figures are updated annually. The model can be updated at any time if market conditions shift radically, or for some other reason. On an annual basis, we will be able to review the trends and react reasonably quickly to the changing markets. In addition, the index model will allow recommendations to be made with the AHAC and planners from Growth Management which may eventually be presented to the Board of County Commissioners. The ultimate objective of all of this work is for the information to be available to decision makers to determine whether there is a gap between the availability and need for affordable/workforce housing; then what actions will be taken to close the gap. Should there be a gap, the most likely recommendations would be to activate, re-activate or modify the various incentives available in our community (Appendix 8 details the existing incentives.) It is 17 certainly also possible that new incentives or programs may be recommended or developed in response the identified need. It is important to note at this point, that the focus of the working group has been on the development of the model. As this model is put into action, the responses to the model will develop and mature over time. Future Planned Enhancements The working group envisions additional future planned enhancements to this first generation model. They are: • Stratify the information by commissioner district—Target February 2015 • Include the Future planned inventory for affordable/workforce units (affordable/workforce units planned but not permitted, units permitted but not built) — Target February 2015 • Review the Land Development Code Section 2.06 to determine if changes/updates need to made—Target March 2015 • Develop a list of incentives for developers to construct affordable/workforce housing already permitted—Target June 2015 • CHS will establish necessary strategies, methods and tools to support Objective 1 of the Housing Element—Target January 2016 Development of the model and other models considered The working group developed and discarded six draft models prior to recommending the population based core model. The affordable/workforce housing demand model started with the identification of specific factors/elements that affects affordable/workforce housing. Environmental Factors Factors Purpose for Inclusion Job growth Creates need Persons per household Reduce overcrowding Interest rates Consumer and lender interest in purchase/development Core Elements Elements Purpose for Inclusion Area median income Determines affordability for homes/rental Population growth If stagnant, harder to demonstrate need Median sales price Helps to understand what is affordable Housing opportunity Are people being afforded the opportunity in index our community? 18 With the exception of job growth, staff started with a twenty (20) year history of each of the factors/elements. In order to get a full, unbiased picture of the factors/elements, staff collected information from a number of local, state, and national sources. The following is a partial list of information sources used for data collection. Appendix 9 provides more detail on these organizations and the data they publish: National Association of Home Builders Wells Fargo Bank Housing Opportunity Index US Census Bureau American Community Survey Florida Housing Data Clearinghouse, Shimberg Center for Housing Studies, University of Florida University of Florida, Bureau of Economic and Business Research(BEBR) Federal Reserve Bank of Atlanta Federal Financial Institutions Examination Council www.Mortgage-x.com/trends Florida Department of Economic Opportunity Collier County Comprehensive Planning—Growth Management Division US Department of Housing and Urban Development(HUD) Collier County Naples Area Board of Realtors (NABOR) Apartment Ratings.coin Florida Jobs.org Occupational Employment Statistics (OES) Below is a list of the various draft models and their key factors/elements: • Model 1 —Census tract based — Build affordable/workforce housing where the demographics have the greatest need/demand • Model 2—Current inventory based — Current inventory based by HUD AMI levels and projected forward • Model 3—Seven factor based — AMI, population growth, median sales price, HOI, interest rates, persons per household and job growth • Model 4—Four factor based — AMI, HOI,population growth and median sales price • Model 5 — HOI, AMI,population growth and inventory 19 • Model 6—Net migration based — Net migration, HOI and inventory • Model7 — Population growth, persons per household and inventory Thanks go to the Affordable Housing Advisory Committee(AHAC) for championing this project. Special commendation is given to AHAC Chair, Steve Hruby and Vice Chair, Christian Davis for all the additional time they spent with CHS staff in reviewing and discussing the data and helping to evolve the model. Other AHAC members contributed greatly to the development of the model: Clyde Quinby, John Cowan, Patricia Fortune, Albert Batista, Alan Hamisch, and Mark Strain. CHS staff also wishes to recognize David Weeks and Michele Mosca from the Comprehensive Planning Section of the Planning & Zoning Department in the Growth Management Division; along with their director Mike Bosi and administrator, Nick Casalanguida. CHS staff Kim Grant and Elly Soto McKuen, along with Public Services staff, Jason Rummer sifted through the massive amounts of data and information to provide guidance and development of the model. Summary and Recommendation Development of a model to determine the need for affordable/workforce housing has been a challenging endeavor. This process started more than two years ago during the Growth Management Plan's Evaluation and Appraisal Report-based amendments process. County staff identified that there should be a better way to identify the need for affordable/workforce housing. Over the succeeding two years many people were involved in the development of this model. The working group recommends use of the population based core model and review of supplemental information as presented herein as the first generation affordable/workforce housing index model. 20 APPENDICES 21 III Appendix 1 Collier County Housing Statistics 2012 2012 http://www.homefacts.com/demographics/Florida/Collier-County.htm I Property Type Rent Payment Amount Rent Payment as%of Income Specified Owner-Occupied Units 91,797 Specified Renter-Occupied Units 29,920 Less than 15.0% 3,551 12.1% Less then$50,000 3,732 4.1% Less than$200 395 1.3% 15.0%to 19.9% 2,815 9.6% $50,000 to$99,999 10,357 11.3% $200 to$299 354 1.2% 20.0%to 24.9% 4,081 13.9% $100,000 to$149,000 12,643 13.8% $300 to$499 889 1.2% 25.0%to 29.9% 2,225 7.6% $150,000 to$199,000 12,690 13.8% $500 to$749 4,020 13.4% 30.0%to 34.9% 2,480 8.4% $200,000 to$299,999 16,248 17.7% $750 to$999 8,534 28.5% 35.0%or more 14,221 48.4% 56.8%of renters pay more than 30%of $300,000 to$499,999 17,643 19.2% $1,000 to$1,499 9,763 32.6% Income $500,000 to$999,999 12,091 13.2% $1,500 or more 5,965 19.9% $1,000,000 or more 6,393 7.0% 81%of Rentals cost more than$750 per month 233,50 Median/Dollars) 0 Owner-Occupied make up 60.7%of$0 to$300,000 of Housing Units Collier County,FL Structure Type Statistics Collier County,FL Rooms per Residence Collier County,FL Housing Occupancy Total Housing Units 19�39 Total Housing Units 199,39 Total Housing Units 199,397 1-unit,detached _ 81,490 40.9% 1 room 1,637 0.80% Occupied Housing Units 123,714 62.0% 1-unit,attached 10,704 5.4% 2 rooms 4,216 2.10% Owner Occupied 91,797 74.2% 2-units 4,716 2.4% 3 rooms 30,944 15.50% Renter Occupied 31,917 25.8% 3 or 4 units _ 14,093 7.1% 4 rooms 46,056 23.10% Vacant Housing Units 75,683 38.0% 5 to 9 units _ 20,047 10.1% 5 rooms 48,375 24.30% Homeowner Vacancy Rate 7.0% 10 to 19 units 20,961 10.5% 6 rooms 31,294 15.70% Rental Vacancy Rate 13.0% Average household size of 2 4 20 or more units 34,004 17.1% 7 rooms 17,342 8.70% owner occupied unit Average household size of 3.3 Mobile home 13,339 6.7% 8rooms 9,430 4.70% renter-occupied unit Boat,RV,van,etc. 43 0.0% 9 rooms or more 10,103 5.10% Median Rooms 5 58.5%of Housing Units have 5 or more rooms 22 Appendix 2 HUD Income Limits: 2014 1 2 3 4 5 6 7 8 Person Persons Persons Persons Persons Persons Persons Persons 30% $13,850$15,800 $17,800 $19,750 $21,350 $22,950 $24,500 $26,100 AMI AMI AMA $23,050$26,350 $29,650 $32,900 $35,550 $38,200 $40,800 $43,450 Collier $62,900 80% $36,900$42,150 $47,400 $52,650 $56,900 $61,100 $65,300 $69,500 County AMI 100% $44,280$50,580 $56,880 $63,180 $68,280 $73,320 $78,360 $83,400 AMI 23 Appendix 3 Support of who will rent and who will buy 100 The percent of 90 overall renters based on Income. 80 a, Se bk' to At the 50%HUD AMI 70 ,, y11° _ Level for 3 persons fr ($29,650), 55.5% will rent. 60 -,55% 50 .N, At the 100%HUD --Percent of AMI Level for 3 40 persons($56,880), Apartment 27.5%will rent 30 Home Renters \„..--7,,,,z7.57.:, 20 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 o rn o LA o IA o LA' o LA o 1.1+ o LA Source: Florida Demographic Estimating Conference July 13, 2012 24 Appendix 4 Naples Area Board of Realtors (NABOR) Information NABOR Housing Units Inventory (July 2014) and Sold (2014 full year) Combined Naples & Marco Island 12 Months Ending April Inventory 2014-07-30 2014 Condo Count Condo #Sold $0-$100K 72 $0-$100K 648 $100K-$150K 192 $100K-$150K 1064 Condo $150K-$200K 272 $150K-$200K 978 Inventory $200K-$250K 227 $200K-$250K 704 Apr-14 $250K-$300K 182 Difference Percent $250K-$300K 456 1,136 945 -191 -16.8% 3850 Inventory 2014-07-30 12 Months Ending April 2014 Single Family Count Single Family #Sold $0-$100K 64 $0-$100K 287 $100K-$150K 50 $100K-$150K 458 Single Family $150K-$200K 99 $150K-$200K 570 Inventory $200K-$250K 93 $200K-$250K 511 Apr-14 $250K-$300K 133 Difference Percent $250K-$300K 457 524 439 -85 -16.2% 2283 25 • Inventory of Housing Unit's Priced below $300,000 — April, 2013 • Single Family Units 530 • Condos 1,429 • Total 1,959 — April, 2014 • Single Family Units 512 • Condos 1,035 • Total 1,547 • (please note: overall inventory April, 2013 compared to April, 2014 decreased by 412 units or 21.1%) • Source—NABOB,April 2014 26 Appendix 5 NABOR Units Sold by Bedroom Data Overall April 2013 April 2014 12 Months Ending April 2013 12 Months Ending April 2014 Beds Count Beds Count Beds Count Beds Count O Bed 1 O Bed 8 O Bed 9 1 Bed 23 1 Bed 17 1 Bed 163 1 Bed 202 1+Den 2 1+Den 24 1+Den 14 2 Bed 236 2 Bed 248 2 Bed 1,893 2 Bed 1,954 2+Den 128 2+Den 95 2+Den 839 2+Den 810 3 Bed 239 3 Bed 211 3 Bed 2,329 3 Bed 2,183 3+Den 36 3+Den 25 3+Den 389 3+Den 283 4 Bed 32 4 Bed 26 4 Bed 338 4 Bed 248 4+Den 8 4+Den 5 4+Den 86 4+Den 53 5 Bed 1 5 Bed 3 5 Bed 34 5 Bed 23 5+Den 1 5+Den 1 5+Den 8 5+Den 11 6 or More 3 6 or More 1 27 Appendix 6 Model Assumptions A) Source: BEBR [Growth Management Division Comprehensive Planning Section number used, in order to match planning methods] B) Source: BEBR [Growth Management Division Comprehensive Planning Section number used, in order to match planning methods] C) 2015 population X 1.02% population increase projection for one year to 2016 D)The 441 represents the number of 3 person households below 80%AMI needing housing based on a 1.02% population growth [3573/3pph *.37 to reflect those below 80%AMI] Under the basis that those earning over 80%AMI are not the population we are concerned about in terms of need for affordable/workforce housing- use HOI as the rationale to support that. Source for conclusion that those below and above 80%AMI splits county into 37%/63%. Using 3 pph because the overall county is 2.7, and HUD produces numbers in whole increments, so use of 3 is reasonable and convenient for development of projected need. E)The 225 is a subset of the 441, representing those whose income is between 51%and 80% AMI [or 51%of the 0-80%AMI sector] and most likely seeking home ownership in 2016 F)The 216 is a subset of the 441, representing those whose income is between 0%and 50% AMI [or 49%of the 0-80%AMI sector] and most likely seeking rental in 2016 G) Source: NABOR. Under$300K chosen because average sales price within this category is $167K,which is roughly 3 x the AMI ($48K) keeping with the typical 30%of income rule of thumb for affordability. 48% of homes sold in last 12 months ending April 2014 are 2 bedrooms, so used this ratio for current MLS. (see Appendix 2 -$62,900) Calculation = 1,384 single family and condos available for sale as of July 2014 times 48% results in the inventory available for our 3 person HH. H) Source: Southwest Florida Apartment Association -verbal - 2-3%vacancy as of 6/27/14. I)A negative ( - ) number equals-0- need/DEMAND J)A negative ( - ) number equals -0- need/DEMAND. While the projection does not show a large need for additional units for new population, in fact there is an issue now with the rising home prices and the rising rental rates that will be discussed under the rental portion of this model. 28 Appendix 7 Employment Number of People Employed in (oilier County in the Jobs listed below Availabk Jobs Fast Food Worker(irriude Amusement and Recreation Atterdant;.Combined Food Pre ratior arc Scwing'Workers,includir;fart Food,Cook: Insbtutior and Ca}exria Cooks Restaurant Cooks,Short Order.Dishwashers,Faoc Preparaton Workers,Food Servers-Nonres urar. 'RUaixr;: c 14,700 Waitresses;Caur erAttencan;,Curia,FoodCahaession,andCoffeeShop} Account Collector(includes Billirr and Pastirg Clerks and Machine Operators,Bookkeeping,Accounting,and Auditir6(lerks) 1,680 Network Administrator 60 Purcha:irg Manager 20 Cashier 3,310 Auto Mechanic(includes Aszrrliers and fabricators,Automotive Body and Related Repairers,Service Tec hniciard.Bus arid Truck Meth;^c arc G e e 710 EngineSpetialistl Conruction laborer(incudes Carperters.Brdrmasor:and Bocknasons•Roder:.Csn en:Masons and Concrete finishers,Cor truct+an and Related Workers.Hex:`or Brickmasors.Boanasons Storemasans,and Tile and Marble Setters,P pars—Carpenters 1an.tor:;'c Cleaners 1Eacept Maids and Housekeeping Cearers) 1,710 fire Fr:r 4c0 Pre xh,o Tear er(irxludeTeatherA±sistnts) 350 In the neseard it vas found that there are 65jobtypes that make up65,91O available joos. 55.5%of total available jobs earn$29286 per ye or 510S per hour. Those jobs include the abate blur.Duurlg Roam and Cafeteria Atterdartti and Bartender Helpers,Emergency Medical Techr iciars and Paramxdcs, Frma+orkers one laborers.Crop.Nursery,and Green house,Firs-I.ne Supervisors of Food Preparation and Servny Workers.first-lint Supervisor:of Housekeeping and larrtoral Workers first-line Supervisors of landscapn;,laver•Service,and Groundskeeper.First-line Supervisors of Retail Sales Workers,Heating,Air Condition%and Ref+ige•:::"'.r' ^'c and Installers.l eavy and Tractor-Tr:ivTr ck Drivers,Helpers-Installation.Mantet:-ce and Repair Workers.Hosts and Horesse.Rent. -: .:.°re and Coffee Shop,Hole'.Motel.and Resat Desk Clerks,installation,Maintenance.arc Repair Workers,laborers and Freight Smock.arc :eca Movers.Hand Landscaping and Grourdskeeing Workers,Laurdry and Dry{leanir : r: Maids and Housekeeping Cearers,Maintenarce;lc Repair Workers,General Nursing Ass start,,Office aerie:.Plumbers.PipeFrtters. ^c':e: :rem Police and Sheriffs Patrol OfScers,Poke,fire.and Ambularce Dispatchers,Pmcunenert Cerks Receptionists and Information Clerks,Recreation Workers, Read Salepersons,Secretaries and AdrrinisoaaveAssistants'EEcept legs'and Medal. Security Gads.Stock Cierks ac Order Filers,leathers and Irrtructors are Tellers htts.,'aww.rvicajosscrOt nerket=niorratior d atatertr/stotgicalrogrormloccupeta hsranrert-stetistcsindlwrr;e: 201411 a 3,-mete:I5a:ec or 20013:ur+er,cite'Ne3t0.4erro Weal,rl 29 Appendix 8 2012 SHIP Incentive Review and Recommendation Report Incentives &Recommendations a. Incentive: Expedited Permitting — The processing of approvals of development orders or permits, as defined in Sec. 163.3164(7) and (8), F.S. for affordable housing projects is expedited to a greater degree than other projects. Current Incentive: Permits as defined in ss. 163.3164(7) and (8), Fla. Stat. for affordable housing projects are expedited to a greater degree than other projects. Review Synopsis: The committee reviewed and discussed the current resolution governing Expedited Permitting in Collier County. The committee noted the following: In 2010-2011, Collier County refined the building permit process and performance measures were developed to facilitate the expedited performance and allow the county to closely monitor its performance and adherence to this policy. The current permit process is review within 5, 10, or 15 days for various permit types, or a portion of the permit fee will be refunded. Development projects are given priority for processing. Developers in the community are aware of the process through public meetings. The committee concluded that the current Expedited Permitting process is sufficient and is adequately expediting the review of development orders and permits for affordable housing projects. The committee notes that while the process is currently operating as intended, an increase in development review requests may increase processing times and cause undesired delays. One possible solution the County may consider is using third-party permit reviewers. The cost to employ the third party reviewer would be paid by the agency seeking approval and the County would implement random auditing to ensure program compliance. Committee Recommendation: The current building permit process is 5, 10, 15 days. The committee recommends providing for single family permits using state or federal funds be moved to the top of the permitting process (i.e. receive priority). The committee considers the program to be operating as intended and the incentive effectively benefiting providers of affordable housing. In preparation of a future 30 increase in expedited permitting requests, the committee recommends the County begin research on the use of third-party reviewers. Amended Incentive: Collier County expedites permits as defined in Sec. 163.3164(7) and (8), F.S. for affordable housing projects; follow the 5, 10, 15 day processing assigning a higher priority to single family affordable housing . Affordable housing will be identified as those projects assisted with state or federal housing funds. b. Incentive: Impact Fee Waivers and Modifications — The modification of impact-fee requirements, including reduction or waiver of fees and alternative methods of fee payment for affordable housing. Current Incentive: Individuals or organizations constructing new affordable housing units to benefit very low- and low-income persons and households are eligible for the deferral of impact fees. Review Synopsis: Collier County Resolution No. 2008-97, provided Board of County Commissioner direction on restricting the use of the remaining funds for deferral of County Impact Fee for single family homeowners who occupied affordable housing units. The County has suspended the program. Committee Recommendation: The committee recommends exploring options to establish a funding source. Such fund may be used for future deferred impact fees for owner occupied dwelling units. Amended Incentive:No proposed changes at this time. c. Incentive: Density Flexibility— The allowance of flexibility in densities for affordable housing. Current Incentive: The developer may request increased density when including a certain percentage of affordable housing in the proposed development. Review Synopsis: The committee reviewed and discussed the current Affordable Housing Density Bonus Program, contained in Ordinance No, 90-89, now codified by Ordinance No. 04-41, as Land Development Code (LDC) 2.06.00 et seq, which density bonus can only be granted by the Commission and utilized by the Developer in accordance with the strict limitations and applicability of said provisions. 31 The committee concluded that the current Affordable Housing Density Bonus program lacks the certainty developers require when contemplating a new development. The number of allowable units often times is not determined until after the developer has undergone a timely and costly approval process. Under the current process, the public has no certainty that the product resulting from granting such a bonus will be appropriately located, configured and designed to be in compatible with the surrounding area. Finally, the current methodology for determining acceptable ranges of density bonuses is confusing and overly complex. In order to better educate and involve the public in the process, the committee recommends that a Community Planning Process, similar to the current Neighborhood Information Meeting, become a mandatory prerequisite for any density bonus request. The process should be led by the applicant and monitored by the County with the aim of making a best-case effort in reaching consensus between the community and the developer. The committee recognizes the need for the developer to secure the stakeholders prior to submittal to the county and should include all municipalities, county personnel and community members and neighbors to reach consent. Committee Recommendation: AHAC will continue to work with County staff in reviewing the current Affordable Housing Density Bonus program and the process of a mandatory Community Planning Process. The results will be presented to the Board of County Commissioners at a later date for consideration. Amended Incentive: No proposed changes at this time. d. Incentive: Reservation of Infrastructure— The reservation of infrastructure capacity for housing for very-low income persons, low income persons, and moderate income persons. Current Incentive: Not a current incentive. Review Synopsis: Collier County desires, as stated in Policy 2.11 of the Housing Element of the GMP, to "coordinate with local utility providers to ensure that the necessary infrastructure and facilities for new housing developments are in place, consistent with the County's Concurrency Management System". The committee discussed the need for the reservation of infrastructure and found that an appropriate amount of capacity should be reserved for future affordable housing. The committee acknowledges that a program to reserve infrastructure for future development will require a more detailed study and further consideration. The committee identified that this was not a critical priority however the committee may choose to re-visit as there is current adequate capacity. The committee discussed the need for a linkage between the need for reservation of infrastructure and the annual AUIR tasks. As the annual AUIR monitoring will act as an indicator identifying the need for reservation of infrastructure. 32 Committee Recommendation: AHAC will work with County staff in drafting a proposal and report for the reservation of infrastructure to ensure there is sufficient capacity for future development of affordable housing. The proposal and report will be presented to the Board of County Commissioners at a later date for consideration. Proposed Incentive: Committee does not propose an incentive to Reservation of Infrastructure at this time. e. Incentive: Accessory Dwelling Units- The allowance of affordable residential units in residential zoning districts. Current Incentive: Not a current incentive. Review Synopsis: The committee discussed the concept of accessory dwelling units and how they may assist in providing safe, decent and affordable housing in Collier County. The allowance of accessory dwelling units would not be appropriate in all areas of Collier County. The committee concluded that the use of these units, sometimes referred to as mother-in-law suites, should be encouraged in areas that have sufficient capacity to support them; new construction developments may provide the best opportunity for inclusion of accessory dwelling units. Further research and discussion is required to determine how the inclusion of an accessory unit would affect density on the subject property. Additionally, the Land Development Code (LDC) and the Comprehensive Plan would have to be examined to identify current policies that would negatively impact or restrict the use of accessory dwelling units. The committee acknowledged that is difficult to evaluate infrastructure needs with accessory dwelling and difficult to determine the impact on roads. However, the committee identifies the potential to collect impact fees on the accessory dwelling. The committee recognizes that this is a best practice across the country. Committee Recommendation: Direct the AHAC to identify areas within Collier County suitable for affordable accessory residential dwelling units. The AHAC would also seek a requirement on accessory dwellings to register address compliance and ensure accountability. The results will be presented to the Board of County Commissioners at a later date for consideration. Proposed Incentive: Committee does not propose an incentive to Accessory Dwelling Units at this time. 33 f. Incentive: Parking and Setbacks — The reduction of parking and setback requirements for affordable housing Current Incentive: Not a current incentive. Review Synopsis: The committee discussed the incentive of reduced parking and setback requirements for affordable housing. It was noted that a reduction in parking requirements in the urban areas, with mass transportation available, may be appropriate. In the rural areas the committee felt that the occupants of affordable housing would have parking needs similar to market rate housing, if not a greater need. Therefore, a reduction in parking requirements may not be an inventive. When considering the potential incentive of a reduction in the parking and set requirements, the committee noted that such a request should be made in the form of a deviation request, rather than a variance request. The committee believes consideration should be given to deviations that further assist with the production of affordable housing so long as the end product's appearance is not significantly affected. Committee Recommendation: Parking and setback are permissible on a case-by-case basis. Deviations occur when the reduction of parking and setback requirements may contain the costs of providing affordable housing. Collier County recognizes the importance of providing affordable housing and has a Planned Unit Development (PUD) approval process that allows a developer to request necessary deviations. The committee also recommends that staff consider an administrative process to allow projects in the process of converting to affordable housing projects, also to allow for a deviation in this requirement to incentivize development. Proposed Incentive: Reductions in parking and setback requirements for affordable housing are considered as deviations in the PUD approval process and variances in the conventional zoning process, on a case by case basis. g. Incentive: Flexible Lot Configurations — The allowance of flexible lot configurations, including zero-lot-line configurations for affordable housing. Current Incentive: Not a current incentive. Review Synopsis: Collier County desires, as stated in Policy 2.4 of the Housing Element of the GMP, to "review existing codes and ordinances and amend them as needed to allow for flexible and innovative residential design". The committee believes that Collier County should continue to evaluate local ordinances and codes to ensure that flexibility in the design is available to those producing affordable housing. The committee encourages the adoption of administrative deviations as a right of option with 34 requirements to enhance the development of flexible lot configurations, while not impacting the health and safety. Committee Recommendation: Flexibility in lot configurations of affordable housing should be evaluated on an on-going case-by-case basis. The committee recommends that the process continues to be embraced by Collier County during the Planned Unit Development(PUD) annual process. Proposed Incentive: Not a proposed incentive; Zero lot configuration requirements for affordable housing are considered as deviations in the PUD approval process and variances in the conventional zoning process, on a case by case basis. Proposed Incentive: Committee does not propose an incentive to Flexible Lot Configurations at this time. h. Incentive: Street Requirements—The modification of street requirements for affordable housing. Current Incentive: Not a current incentive. Review Synopsis: Modification of street requirements within affordable housing developments may more easily be encouraged with new construction. The modification of street requirements is addressed in the Collier County Community Character Plan which should continue to be embraced. Committee Recommendation: The modification of street requirements for affordable housing should be embraced. The committee supports an amendment to the land development code to address street width and sidewalk modifications. The committee also recommends a land development code amendment be considered for common deviations and allow this to occur for affordable housing projects without an application process. The modification of street requirements may be appropriate on a case-by-case basis. Collier County recognizes the importance of providing affordable housing and has a Planned Unit Development (PUD) approval process that allows a developer to request necessary deviations. Proposed Incentive: Street requirements for affordable housing re considered as deviations in the PUD approval process and variances in the conventional zoning process, on a case by case basis. 35 i. Incentive: Oversight (Ongoing) — The establishment of a process by which a local government considers, before adoption, policies, procedures, ordinances, regulations, or plan provisions that increase the cost of housing. Current Incentive: An ongoing process for review of local policies, ordinances, regulations and plan provisions that increase the cost of housing prior to their adoption Collier County requires all items which have the potential to increase the cost of housing to be prepared and presented to the Collier County Board of County Commissioners with the amount of the increase or decrease mentioned in the executive summary under fiscal impact. Review Synopsis: Collier County currently has an established process whereby local government, as well as the AHAC, considers, before adoption, policies, procedures, ordinances, regulation or plan provisions that increase the cost of housing. The committee determined the current process is functioning as intended. However, there may be additional opportunities to capture data relevant to providing affordable housing. The committee believes there are current controls in place to ensure adequate oversight of actions which increase the cost providing affordable housing. The committee recommends exploring additional options to capture information that has a financial impact on providing affordable housing. The committee also discussed the participation of its members in the Development Services Advisory Committee to allow for continuity and increased oversight of those policies, procedures and ordinances eta 1. effecting affordable housing. The process, by which items are prepared for the BCC Agenda includes a vast approval hierarchy to ensure that all proposed actions impacting affordable housing are reviewed on an ongoing basis. Furthermore, the Collier County Affordable Housing Advisory Committee regularly performs subcommittees to review impediments to affordable housing, as well as new affordable housing incentive. Committee Recommendation: The committee recommends an amendment of the current incentive to include the increase or decrease to affordable housing to be included in the executive summary under fiscal impact. The committee requests the appointment of an AHAC member to the Development Services Advisory Committee to allow for continuity and increased oversight of those policies, procedures and ordinances effecting affordable housing. Amended Incentive: Collier County requires all items which have the potential to increase the cost of housing to be prepared and submitted to DSAC for review and presented to the Collier County Board of County Commissioners with the amount of the increase or decrease mentioned in the executive summary under fiscal impact section. The Commissioners shall appoint a member of the AHAC to the DSAC. 36 j. Incentive: Land Bank Inventory — The preparation of a printed inventory of locally owned public lands suitable for affordable housing. Current Incentive: Collier County prepares an Inventory of Locally Owned Public Lands Suitable for Affordable Housing every three years. Review Synopsis: Florida Statute 125.379, Disposition of County property for affordable housing, requires the preparation of a printed inventory of locally owned public lands suitable for affordable housing. Collier County has completed this process and maintains a list of locally owned properties. The committee reviewed the incentive and found that the most recent list should be provided to the AHAC on a quarterly basis. Resolution 2007-172 directs the use of surplus land and directs those funds derived from the sale of such property be placed in the Affordable Housing Trust Fund. Committee Recommendation: The committee recommends that an updated printed inventory of locally owned public lands suitable for affordable housing be provided to the AHAC on an annual basis prior to wide spread distribution to allow for recommendations to further the development of affordable housing in Collier County. . Amended Incentive: Collier County will prepare an annual inventory of all real property owned by Collier County and the City of Naples that may be appropriate for use as an affordable housing development. k. Incentive: Proximity— The support of development near transportation hubs and major employment centers and mixed-use developments. Current Incentive: Not a current incentive Review Synopsis: The committee considered the topic of proximity in Collier County and found that the lack of major transportation hubs makes this issue of little significance at this time. However, the committee concluded that consideration should be given to providing affordable housing near transportation when possible. Additionally, the committee concluded that Collier County currently encourages the use of mixed-use developments and locating affordable housing near major employment centers. Permitting greater density when locating affordable housing near major employment centers should be encouraged. Committee Recommendation: The committee recommends that during discussion on density flexibility as discussed in section (c) of this report consideration be given to the proximity of affordable housing to major employment centers, schools and public 37 transportation. The committee would also encourage the review of the density bonus levels in the land development code. Proposed Incentive: Committee does not propose an incentive for Proximity at this time. 38 Appendix 9 Organizational Information Definition Sheet Affordable/workforce Unit—Housing for which the occupant(s) is/are paying no more than 30 percent of his or her income for gross housing costs, including utilities. Source: HUD.gov Housing Opportunity Index - The share of homes sold in that area that would have been affordable to a family earning the local median income, based on standard mortgage underwriting criteria. Therefore, there are really two major components -- income and housing cost. Federal Financial Institutions Examination Council - In the United States, the Federal Financial Institutions Examination Council, also referred to simply as the FFIEC, is a special council comprised of representatives from multiple agencies including the Federal Reserve Bank, the National Credit Union Administration, the Office of Thrift Supervision, the Office of the Comptroller of the Currency and the Federal Deposit Insurance Corporation. As a body, the Federal Financial Institutions Examination Council provides recommendations on the ethics by which each agency represented in the council will adhere to. The FFIEC is further responsible for the creation of the Uniform Bank Performance Report(UBPR),which is used to analyze how current economic climates and executive organization practices impact a bank's bottom line. The Federal Financial Institutions Examination Council also assures that the American public has access to information relating to mortgages, which must be disclosed by law by various financial institutions within the mortgage industry. Originally established in 1979, the Federal Financial Institutions Examination Council also makes it possible for the American public to view metropolitan banking data, which is organized by specific census zones. Within the FFIEC also exists a State Liaison Committee, which is made up of representatives from five separate state agencies. These representatives serve the efforts of the FFIEC in an advisory capacity. Another subcommittee of the FFIEC,known as The Appraisal Subcommittee, was created in 1989 to help advise the council. National Association of Home Builders/Wells Fargo Housing Opportunity Index (HOI) - NAHB is a trade association that helps promotes the policies that make housing a national priority and that all Americans have access to safe, decent and affordable housing, whether they choose to buy a home or rent. The NAHB/Wells Fargo Housing Opportunity Index (HOI) is a measure of the percentage of homes sold in a given area that are affordable to families earning the area's median income during a specific quarter. Prices of new and existing homes sold are collected from actual court records by Core Logic, a data and analytics company. Mortgage financing conditions incorporate interest rates on fixed- and adjustable-rate loans reported by the Federal Housing Finance Agency. The NAHB/Wells Fargo HOI is strictly the product of NAHB Economics, and is not seen or influenced by any outside party prior to being released to the public. 39 Federal Reserve Bank of Atlanta - The Federal Reserve Bank of Atlanta is part of the central bank of the United States. The Federal Reserve System—the Fed, as it is often called consists of twelve Reserve Banks located around the country and the Board of Governors in Washington, D.C. The Atlanta Fed territory covers the Sixth Federal Reserve District, which includes Alabama, Florida, and Georgia, and portions of Louisiana, Mississippi, and Tennessee. The Atlanta Fed and the other Reserve Banks play an important part in all three of the Fed's functions: monetary policy, bank supervision and regulation, and the operation of a nationwide payment system. In its monetary policy role, the Bank seeks to keep prices stable and economic growth at its maximum sustainable rate. The Bank's Board of Directors drawn from the business community, banks, and labor and consumer organizations makes recommendations every two weeks on the level of the discount rate, which is the rate at which the Bank lends to commercial banks. The Atlanta Fed's Supervision and Regulation staff seek to promote the safety and soundness of the banking system, foster stability in financial markets, ensure compliance with applicable laws and regulations, and encourage banking institutions to responsibly meet the financial needs of their communities. Through its six branch offices, the Atlanta Fed provides cash to banks, savings and loans, and other depository institutions;transfers money electronically through the FedWire® funds transfer system and automated clearinghouse; and clears millions of checks every day. The Federal Bank of Atlanta is a decentralized central bank and public policy institution and been in existence for over ninety years. Foresclosure-Response.org - is an online guide to foreclosure prevention and neighborhood stabilization developed and maintained by the Center for Housing Policy, the Local Initiatives Support Corporation (LISC), and the Urban Institute. The site includes easily accessible information on a broad range of state and local policy solutions, as well as tools to create customized data reports and maps and participate in interactive online discussions. The Foreclosure-Response.org is not the definitive word on state and local housing responses to the mortgage foreclosure crisis, but rather as a framework for organizing the knowledge and experience of policymakers and practitioners from around the country. Florida Housin2 Finance Corporation - Florida Housing was created by the Florida Legislature more than 25 years ago to help Floridians obtain safe, decent affordable housing that might otherwise be unavailable to them. Florida Housing administers the bulk of affordable housing resources available at the state level and works with local governments, non-profits, elected officials and others to affordable housing in Florida's communities. All of Florida Housing's state funds are appropriated through the Trust Funds created by the Sadowski Act and commonly known as the State Housing Initiatives Partnership(SHIP)program. University of Florida Shimber2 Center for Housin2 Studies - The Shimberg Center conducts research into housing policy and planning, with a special focus on housing affordability for Florida residents. Shimberg provides data and applied research to state agencies, local planners, the housing industry, non-profits, and others involved in shaping housing policy in Florida. Their current research focuses on documenting housing market conditions and affordable housing needs in Florida's counties, cities and neighborhoods; preserving Florida's affordable rental housing; linking affordable housing with land use and transportation decisions through GIS modeling; and supporting the development of energy efficient and healthy homes. The Center 40 also produces the Florida Housing Data Clearinghouse, which provides public access to data on housing needs and supply for Florida's cities and counties. Florida Agency for Workforce Innovation - The Florida Department of Economic Opportunity promotes economic opportunities for all Floridians through successful workforce, community, and economic development strategies. US Census American Community Survey — An ongoing statistical survey by the US Census Bureau sent to approximately 250,000 addresses monthly. It regularly gathers information previously contained only in the long form of the decennial census. It is the largest survey other than the decennial census that the Census Bureau administers. University of Florida Bureau of Economic and Business Research (BEBR) —The Bureau of Economic and Business Research (BEBR) is an applied research center in the College of Liberal Arts and Sciences at the University of Florida. BEBR conducts studies and releases statistical date on Florida's population and economy. WWW.Mortgage-x.com — Tracks and complies current values for almost all Adjusted Rate Mortgage (ARM) indexes available today. In addition provides historical data and forecasts for future impacts on the housing market. Mortgage-X is an independent information service and is not affiliated with any lending institution. Florida Department of Economic Opportunity (FDEO) — The Florida Department of Economic Opportunity promotes economic opportunities for all Floridians, formulating and implementing successful workforce, community and economic development policies and strategies. Naples Area Board of Realtors (NABOR) — Provides real estate related information for the Collier County area. Also provides access to the Multiple Listing Services (MLS) for housing units for sale. ApartmentRatings.com — An independent resource for renters nationally. The site evaluates apaitinent complexes, rental rates and does not accept advertisement from apartment owners and managers. Median Family Income Calculation Methodology Estimates of median family income for metropolitan and non-metropolitan areas are developed as follows: 1. 2007-2011 ACS estimates of median family income calculated by the Census Bureau for HUD's Fair Market Rent and Income Limit areas are used as the basis for FY 2014. 41 2. In areas where the 2011 5-year ACS estimate is smaller than the reported margin of error,the state non-metro estimate of median family income is used. 3. In areas where there is a 2011 1-year ACS estimate of median family income that exceeds its margin of error,the 1-year ACS estimate becomes the basis for median family income. 4. Once the appropriate 2011 ACS data has been selected,the data are set as of December 2012 using the December national CPI value divided by the 2011 National CPI value. 5. All estimates are then trended from December 2012 to April 2014(1 `/ year) with a trending factor of 0.982%per year. The trend factor is calculated as the annual growth rate in the national 1-year estimate of median family income between the 2006 and 2011 American Community Surveys. Collier County,FL has published local area 1-year 2011 ACS Survey results. The results of the Median Family Income Step by Step Process Collier County Results 1. The following are the 2011 American Community Survey 5-year median income estimate and margin of error for Collier County,FL: ACS2on 5- ACS2ou 5- Year Year Area Ratio Result Median Margin of Income Error 0.026< 1 $1,719/ Collier $67,362 $67,362 $1,719 Use ACS2011 County,FL Collier County,FL 0.026 Median Income 2. Since there is an ACS2011 1-year estimate available, a comparison is made in order to determine if the 1-year ACS2o11 estimate is different from zero: 42 ACS2011 1- ACS2011 1- Year Year Area Ratio Result Median Margin of Income Error 0.071 < 1 $4,280/ Collier $60,465 $60,465 $4,280 Update to County,FL = ACS2011 1-Year 0.071 Median Income 3. The calculation of the CPI Update Factor is as follows: 2012 December 2011 Annual CPI Update Area CPI CPI Factor (231.137 / Collier County, 224.939) 231.137 224.939 FL 1.02755 } i 4. The FY 2014 median family income is estimated as follows: ACS2011 1- CPI Trending FY 2014 Area Year Update 0.982%for Area MFI Estimate Factor 1.25 years Estimate ($60,465 * 1.00981 25 1.02755 * Collier $60,465 1.02755 = 1.01228) County,FL 1.01228 = $62,894 43 5. In keeping with HUD policy, the median family income estimate is rounded to the nearest$100: Unrounded Rounded Area FY 2014 MFI Estimate FY 2014 MFI Estimate Collier County,FL $62,894 $62,900 Code of Federal Regulations Title 24 - Housing and Urban Development Volume: 1 Date: 2014-04-01 Original Date: 2014-04-01 Title: Section 5.609-Annual income.Context: Title 24- Housing and Urban Development. Subtitle A-Office of the Secretary, Department of Housing and Urban Development. PART 5-GENERAL HUD PROGRAM REQUIREMENTS;WAIVERS. Subpart F- Section 8 and Public Housing, and Other HUD Assisted Housing Serving Persons with Disabilities: Family Income and Family Payment; Occupancy Requirements for Section 8 Project-Based Assistance. - Family Income. § 5.609 Annual income. (a)Annual income means all amounts, monetary or not, which: (1) Go to, or on behalf of, the family head or spouse(even if temporarily absent)or to any other family member; or(2)Are anticipated to be received from a source outside the family during the 12-month period following admission or annual reexamination effective date; and (3)Which are not specifically excluded in paragraph (c)of this section. (4)Annual income also means amounts derived (during the 12-month period)from assets to which any member of the family has access. (b)Annual income includes, but is not limited to: (1)The full amount, before any payroll deductions, of wages and salaries, overtime pay, commissions, fees, tips and bonuses, and other compensation for personal services; (2)The net income from the operation of a business or profession. Expenditures for business expansion or amortization of capital indebtedness shall not be used as deductions in determining net income.An allowance for depreciation of assets used in a business or profession may be deducted, based on straight line depreciation, as provided in Internal Revenue Service regulations. Any withdrawal of cash or assets from the operation of a business or profession will be included in income, except to the extent the withdrawal is reimbursement of cash or assets invested in the operation by the family; (3) Interest, dividends, and other net income of any kind from real or personal property. Expenditures for amortization of capital indebtedness shall not be used as deductions in determining net income. An allowance for depreciation is permitted only as authorized in paragraph (b)(2)of this section. Any withdrawal of cash or assets from an investment will be included in income, except to the extent the withdrawal is reimbursement of cash or assets invested by the family. Where the family has net family assets in excess of$5,000, annual income shall include the greater of the actual income derived from all net family assets or a percentage of the value of such assets based on the current passbook savings rate, as determined by HUD; (4)The full amount of periodic amounts received from Social Security, annuities, insurance policies, retirement funds, pensions, disability or death benefits, and other similar types of periodic receipts, including a lump-sum amount or prospective monthly amounts for the delayed start of a periodic amount(except as provided in paragraph (c)(14)of this section); 44 (5) Payments in lieu of earnings, such as unemployment and disability compensation, worker's compensation and severance pay(except as provided in paragraph (c)(3)of this section); (6) Welfare assistance payments. (i)Welfare assistance payments made under the Temporary Assistance for Needy Families(TANF)program are included in annual income only to the extent such payments: (A)Qualify as assistance under the TANF program definition at 45 CFR 260.31; and (B)Are not otherwise excluded under paragraph (c)of this section. (ii) If the welfare assistance payment includes an amount specifically designated for shelter and utilities that is subject to adjustment by the welfare assistance agency in accordance with the actual cost of shelter and utilities, the amount of welfare assistance income to be included as income shall consist of: (A)The amount of the allowance or grant exclusive of the amount specifically designated for shelter or utilities; plus (B)The maximum amount that the welfare assistance agency could in fact allow the family for shelter and utilities. If the family's welfare assistance is ratably reduced from the standard of need by applying a percentage, the amount calculated under this paragraph shall be the amount resulting from one application of the percentage. (7) Periodic and determinable allowances, such as alimony and child support payments, and regular contributions or gifts received from organizations or from persons not residing in the dwelling; (8)All regular pay, special pay and allowances of a member of the Armed Forces(except as provided in paragraph (c)(7)of this section). (9) For section 8 programs only and as provided in 24 CFR 5.612, any financial assistance, in excess of amounts received for tuition, that an individual receives under the Higher Education Act of 1965(20 U.S.C. 1001 et seq.), from private sources, or from an institution of higher education (as defined under the Higher Education Act of 1965 (20 U.S.C. 1002)), shall be considered income to that individual, except that financial assistance described in this paragraph is not considered annual income for persons over the age of 23 with dependent children. For purposes of this paragraph, "financial assistance"does not include loan proceeds for the purpose of determining income. (c)Annual income does not include the following: (1) Income from employment of children (including foster children)under the age of 18 years; (2) Payments received for the care of foster children or foster adults(usually persons with disabilities, unrelated to the tenant family,who are unable to live alone); (3) Lump-sum additions to family assets, such as inheritances, insurance payments(including payments under health and accident insurance and worker's compensation), capital gains and settlement for personal or property losses(except as provided in paragraph (b)(5)of this section); (4)Amounts received by the family that are specifically for, or in reimbursement of, the cost of medical expenses for any family member; (5) Income of a live-in aide, as defined in §5.403; (6) Subject to paragraph (b)(9)of this section, the full amount of student financial assistance paid directly to the student or to the educational institution; (7)The special pay to a family member serving in the Armed Forces who is exposed to hostile fire; (8)(i)Amounts received under training programs funded by HUD; (ii)Amounts received by a person with a disability that are disregarded for a limited time for purposes of Supplemental Security Income eligibility and benefits because they are set aside for use under a Plan to Attain Self-Sufficiency(PASS); (iii)Amounts received by a participant in other publicly assisted programs which are specifically for or in reimbursement of out-of-pocket expenses incurred (special equipment, clothing, transportation, child care, etc.)and which are made solely to allow participation in a specific program; (iv)Amounts received under a resident service stipend. A resident service stipend is a modest amount (not to exceed$200 per month)received by a resident for performing a service for the PHA or owner, on a part-time basis, that enhances the quality of life in the development. Such services may include, but are not limited to, fire patrol, hall monitoring, lawn maintenance, resident initiatives coordination, and serving as a member of the PHA's governing board. No resident may receive more than one such stipend during the same period of time; (v) Incremental earnings and benefits resulting to any family member from participation in qualifying State or local employment training programs(including training programs not affiliated with a local government)and training of a family member as resident management staff. Amounts excluded by this provision must be received under employment training programs with clearly defined goals and objectives, and are excluded only for the period during which the family member 45 participates in the employment training program; (9)Temporary, nonrecurring or sporadic income (including gifts); (10) Reparation payments paid by a foreign government pursuant to claims filed under the laws of that government by persons who were persecuted during the Nazi era; (11)Earnings in excess of$480 for each full-time student 18 years old or older(excluding the head of household and spouse); (12)Adoption assistance payments in excess of$480 per adopted child; (13)[Reserved] (14) Deferred periodic amounts from supplemental security income and social security benefits that are received in a lump sum amount or in prospective monthly amounts. (15)Amounts received by the family in the form of refunds or rebates under State or local law for property taxes paid on the dwelling unit; (16) Amounts paid by a State agency to a family with a member who has a developmental disability and is living at home to offset the cost of services and equipment needed to keep the developmentally disabled family member at home; or(17)Amounts specifically excluded by any other Federal statute from consideration as income for purposes of determining eligibility or benefits under a category of assistance programs that includes assistance under any program to which the exclusions set forth in 24 CFR 5.609(c)apply. A notice will be published in the Federal Register and distributed to PHAs and housing owners identifying the benefits that qualify for this exclusion. Updates will be published and distributed when necessary. (d)Annualization of income. If it is not feasible to anticipate a level of income over a 12- month period (e.g., seasonal or cyclic income), or the PHA believes that past income is the best available indicator of expected future income, the PHA may annualize the income anticipated for a shorter period, subject to a redetermination at the end of the shorter period. [61 FR 54498,Oct. 18, 1996, as amended at 65 FR 16716, Mar. 29, 2000; 67 FR 47432,July 18, 2002; 70 FR 77743, Dec. 30, 2005] 46 Appendix 10 Board of County Commissioners Statistics Average Population Number of Persons per Area Home BCC Districts Median Price 2013* Households* Household* Income* (AHP) 2010* 1(Fiala) 55,102 23,318 2.3 $64,330 $351,393 2(Hiller) 63,315 26,782 2.3 $82,866 $519,167 3(Henning) 68,116 23,110 3.0 $57,559 $308,000 4(Taylor) 53,983 24,865 2.2 $87,024 $570,975 5(Nance) 81,004 21,442 3.8 $48,466 $233,870 *The#of Persons per household is derived from census population divided by the#of households Affordable Housing Units by Commissioners Districts (with15 year affordability requirements within PUD's and Affordable Housing Developments; it is not intended to capture single units that may be affordable based on the market price of the housing) BCC Approved Built Approved Number of Affordable District versus Built Housing Multi-Unit as of 2011 Developments 1 232 232 100% 4 2 328 313 95% 3 3 833 769 92% 3 4 88 64 73% 17 5 3037 1507 50% 5 Total 4,158 2,885 69% 32 47 Appendix 11 Collier County Commuter Statistics • Collier County Commuter Statistics — 17.4% of workforce commute from outside County — Average commute time - 24.4 minutes • Within Collier County Commuters Percent of Population Drive alone 79.3 Car Pool 9.7 Work at home 5.0 Motorcycle/bicycle 5.0 Public Transportation (not taxi) 2.2 Walk 1.6 Other means 2.3 • Sources: American Fact Finder (2012), FL Agency for Workforce Innovation, Labor Market Statistics (2011) 48 Appendix 12 Collier County Homeless Statistics Individuals Homeless Needs Table Needs Currently Available Emergency Shelters 156 170 Transitional Housing 50 176 Permanent Supportive 105 24 v Housing Families Emergency Shelters 52 51 Transitional Housing 27 30 Permanent Supportive o Housing 0 3 Source: Point in Time Homeless Survey by Collier County Continuum of Care -January 24, 2014. 49 Appendix 13 AFFORDABLE HOUSING NEEDS ASSESSMENT Population and Household Projection Methodology 50 AFFORDABLE HOUSING NEEDS ASSESSMENT Population and Household Projection Methodology Prepared by the Shimberg Center for Affordable Housing Rinker School of Building Construction College of Design, Construction and Planning University of Florida September, 2006 AHNA Methodology— Population and Household Projections 1 Housing Demand A. Population and Population by Age Projections—the basic building block While the variables of greatest interest in the Affordable Housing Needs Assessment (ANNA) are the household estimates, those estimates are an outgrowth of a more fundamental building block— population and particularly population by age. Since the Assessment methodology assumes a constant household formation rate by age over the projection horizon the dynamic component of the household estimation process is population. Thus we begin a discussion of the Assessment's housing demand methodology by first describing the AHNA's population estimates. Population projections for jurisdictions and the unincorporated portions of counties are based on extrapolation of trends since 1990 and adjusted to the University of Florida's Bureau of Economic and Business Research (BEBR) population projections.' The BEBR's 2005 population estimate for each jurisdiction is used as the launch year population and projections are made for the years 2010-2030 in five-year intervals. To estimate and project housing demand, the next step is to divide the population into households. Finally, these households are allocated across tenure classes, age, size, income groups and cost burden. The methodology assumes that household formation rates and the distribution of household characteristics remain constant in their 2000 proportions across the entire projection horizon. However, changes in the age distribution of the population would be expected to lead to shifts in average household size as 1 BEBR is the state demographer and produces Florida's official population projections. AHNA Methodology— Population and Household Projections 2 different age groups have different propensities to form households. Therefore, the number of households is estimated using age-specific headship rates to reflect the projected changing age structure. 1. Population Projections Following the University of Florida's Bureau of Economic and Business Research (BEBR) approach to small area population forecasts, six methods were used to project the population of jurisdictions in the county, including the unincorporated portion of the county. The highest and lowest of the results of these six methods is dropped, and the remaining four are averaged. Finally, the results are adjusted to sum to the mid-range county projection, which is obtained from the BEBR. The population projections form the basis for the projection of population by age and ultimately the projection of households by age of householder. Assumptions The methodology uses the most currently available year, in this case 2005, as the benchmark or launch year and develops projections for the years 2010-2030 in five-year increments. The Bureau of Economic and Business Research (BEBR) provides the launch year population for each jurisdiction and county as well as the 2010-2030 county projections based on that launch year. Population for the base years (1990 and 2000) comes from the U.S. Census. County population projections prepared by BEBR control the population projections for each jurisdiction within a county. The methodology uses the BEBR's middle (medium) range population projections. AHNA Methodology— Population and Household Projections 3 Population projections are based on previous trends in a jurisdiction, and as such are not able to account for a particular community having limited land availability. Other local conditions not reflected in the estimates would be aggressive annexation policy (the BEBR estimates of population herein do include annexations as of the date of the estimate), recent commencement of large development projects, or dramatic and recent changes in local institutional facilities with large populations such as prisons. Description of Population Projections The most important base data for preparing estimates and projections of housing demand is population data. Population is the basis of estimates and projections of households, and the difference between households and housing inventory, when adjusted for the need for vacancies to allow a smoothly functioning housing market, is equal to the basic construction need for housing units. Population estimates and projections for small areas such as cities, as compared to the nation or a state, are difficult because of the influence of in- and out- migration of population, annexation, land availability, zoning, infrastructure availability, and other factors that have a large impact at the local level. In addition, in a smaller city the impact of growth is magnified under certain projection techniques. To overcome this problem, four techniques are used to project population. In addition, in the application of two of these techniques two different time periods are used resulting in six estimates. The highest and lowest AHNA Methodology— Population and Household Projections 4 estimates are dropped to eliminate extreme numbers, and the remaining four are averaged. The four approaches to population projection consist of two ratio techniques, relating one area to a larger area, and two mathematical extrapolation techniques that project population based on historical trends. We use the following terminology to describe each technique in the methodology: 1. Base year - the year of the earliest observed population used to make a projection; 2. Launch year - the year of the latest observed population used to make a projection; 3. Target year - the year for which population is projected; 4. Base period - the interval between the base year and the launch year; 5. Projection horizon - the interval between the launch year and the target year; 6. Medium, high and low projections - the BEBR county projections based on a variety of projection techniques; the high and low projections are derived from the Bureau's analysis of projection forecast errors for approximately 3,000 counties in the U.S.; the high and low projections are two-thirds confidence intervals around the medium projection. Data requirements include jurisdiction and total county population for base and launch years (1990, 2000 and 2005) using census data or BEBR estimates. For target years (2010, 2015, etc.) BEBR medium range county projections are used. The four basic projection techniques used in the methodology include the linear, exponential, share and shift methods. The linear and exponential techniques use the mathematical extrapolation approach; they take the jurisdiction's population from the base period and extrapolate it into the future. The shift and share methods use the ratio approach; they express the data as ratios or shares of the larger, parent population, for which a projection already AHNA Methodology— Population and Household Projections 5 exists. Therefore, these techniques require a county or parent population projection. The linear and share techniques use both 5 and 15-year base periods, resulting in a total of six projections. The base periods change over time as the launch year moves forward in time; the current base periods reflect the 1990 and 2000 base years and the 2005 launch year. A more detailed account of each technique is provided below. There is one final twist to the projection methodology. It is only the resident population of the jurisdiction that we want to project, so institutional populations such as prison inmates, military personnel or college students are removed from total county and jurisdiction populations prior to the calculations. (At a different point in the methodology the household-forming portion of this institutional population will be added back to the resident population to create a total household-forming population. However, only off-base military and off- campus college populations are considered household forming in this methodology.) Sources for institutional population are the Florida Departments of Corrections and Children and Families, U.S. Department of Defense, and the State Universities, as compiled by the Bureau of Economic and Business Research and the Shimberg Center. Population Projection Formulas The four projection techniques are patterned after the University of Florida Bureau of Economic and Business Research's (BEBR) county population projections. The trends established during a particular base period (e.g. 1990- 2005) are measured and continued through a growth period or projection horizon AHNA Methodology— Population and Household Projections 6 (e.g. 2010-2015) to establish the population projection. Though the techniques are simple, more sophisticated projection methodologies do not necessarily produce more accurate results. Attributes of each of the four techniques are as follows: Technique Attributes Mathematical Extrapolation Linear Bottom-up Approach Exponential Extrapolation of Small-Area Population Ratio Shift Top-down Approach Share Ratio of Parent Population Projection Formulas for each of the techniques are as follows: Linear (Amount of Change) Linear projection = (((launch year pop - base year pop)/(launch year-base year)* (target year- launch year)) + launch year pop Two linear projections are developed by using two different base years. The population change between each base year and the launch year is divided by the difference in the two periods to compute an average annual population increase (or decrease). This annual increase is multiplied by the number of years in the projection horizon to generate the total population growth for the area. This growth is added to the area's launch year population to establish its population. Exponential (Percent of Change) Exponential = launch year pop *EXP(LN(percent pop change)) where: LN(percent pop change)=LN(launch year pop/base year pop) *((target year-launch year)/(launch year-base year)) The template breaks this equation into two parts: a) computation of an average growth rate (using natural logarithms), and b) extrapolation of this rate to produce projected population. The former calculates the average rate of change in population between the oldest base year and the launch year. This rate is applied to the launch year population to project the population in the target year. The technique divides the area's launch year population by that for the base year AHNA Methodology— Population and Household Projections 7 to compute the percent change. This is multiplied by the projection period adjustment: (target year - launch year)/(launch year-base year). Share Share = ((area's launch year pop - area's base year pop)/(county launch pop - county base year pop)*(county target year pop - county launch year pop)) + area's launch year pop Two share projections are developed by using two different base years. This method computes the area's share of the county's population growth between launch year and the two base years, and then allocates to it an equal share of the county's projected population growth over the projection period. Shift Shift = county's target year pop * ((launch year area pop/launch year county pop) + ((target year - launch year)/launch year-base year) * ((area's launch year pop /county's launch year pop)- (area's base year pop/county's base year pop))) The shift method combines elements of the linear and share methods, making a linear extrapolation of the change in each area's share of the county population between the oldest base year (1990) and launch year. Average Average = (linear proj.1 + linear proj.2 + exponential projection + share proj.1 + share proj.2 + shift proj. - highest proj. - lowest proj.)/4 The accuracy of the four previously discussed techniques will vary according to the time period of the projection and the size of the area. No single technique is the most accurate, and certain techniques may yield rather explosive projections. To avoid producing the largest possible error we sum the six projections minus the lowest and highest of the six and take the average of the remaining four. Adjusted Average Adjusted Average = area projection * (county projection / sum of area average projections) The shift and share methods use apportionment techniques which generate county totals consistent with the overall county projection. However, the linear and exponential techniques ignore the county population projection, relying instead on extrapolation of the historic area trends. Since the Average includes the results of all four techniques, it is unlikely that it will produce county totals identical to the BEBR's county projection. The Adjusted Average computes the ratio of the projected county population to total area averages and then applies the ratio to each area average projection. The sum of the adjusted projections equals the county projection. AHNA Methodology— Population and Household Projections 8 rn C 0 +� 4- 0 4- —, C 0 • O O 0 a) • 4— C ca) +� O Q O C O cu Cl) D • to O 0- = L 0 • CDU a$ o - -a0 • • -0 0 CU O O a. t c a) • • o I— — = 0 0) • • _ •E 0 � z }' < CO• • N J— C —� O • • O O F—U • � co QC • • CO C O V a) = O = CD a) NCO a O• • E • o , - U — U v o = — • c3 _c N —) CCO co C • 2 O W O W u) • Um Um c • o • j • • • O • • d v • • • i N • cn L O co • = N 0 .— = a.., 0 i • O a C _c C a) � a• N O OCD CU 0 O a _ Q � OD N � ca a) C >" _a a L- O O a) al = to -' a F_ vLO N 0) _ m U) C -0O O o .0� .L CO 0 U 0. N o E a� o 0 = - 0 U O - O O a)CD CU CU (/) O ED. ci O a Wo_ o z� it 0_ , ca 2. Population by Age - Background The age distribution of the population serves as the basis for projecting the number of households and other aspects of housing demand. This is a fundamental assumption and the estimates and projections of population by age are a crucial component of the Assessment methodology. Several avenues are closed off to a method that must project an age distribution at the jurisdiction (or other small area) level. Cohort-component and econometric techniques require detail generally lacking at this geographic level. Small area techniques appropriate to total population projection are not so for age projections. Similarly, extrapolating trends in age groups may not be appropriate for rapidly growing areas like Florida. The Assessment's methodology produces sub-county estimates and projections with age detail, using data sources and techniques that are readily available, reliable, and relatively inexpensive. Since the United States conducts its population census every ten years, there is a substantial need for current information in the years between censuses. Population estimation techniques have been created to fill this need. Methods fall into three broad categories: 1) extrapolation of past trends, 2) allocation of current trends from other geographic areas, and 3) use of symptomatic data about the particular geographic area of interest. Extrapolation methods utilize data previously collected about an area to calculate a trend over time and then carry that trend forward to the present. Estimates can be created easily using extrapolation methods since the calculations are often simple and census data is commonly available. AHNA Methodology— Population and Household Projections 10 Extrapolation techniques do not work well in places that are increasing or decreasing in population at an unpredictable rate. Also, extrapolation techniques are not applicable for geographic areas whose boundaries are defined by the user (such as a 2 mile radius around a bank) rather than by a typical political and analysis geography for which data are regularly collected (such as cities or counties). Allocation methods produce population estimates by applying trends in one area to a second area. For example, if a reliable estimate exists for a state in 2005, then a 2005 estimate could be produced for a county by applying the state's average annual growth rate since 2000 to the 2000 population of the county. Ratios are often used to allocate population change from larger areas to smaller areas. For example, the absolute increase in population that occurred in the state since the last census can be divided among the constituent counties based on their share of the state's population at some prior point. Similar to extrapolation, allocation methods are fairly easy to calculate, but allocation is limited in that it requires data for two places, not just one. Also, allocation of trends is only reliable if there is continuity over time in the relationship between the two places. If the underlying ratios change over time, but there is no data available to detect that change, then an estimate produced by an allocation method will be unreliable. Collection of symptomatic data about the place of interest is going to produce the most reliable estimates of population, but this approach has the highest costs. Data sources for small areas vary greatly in terms of availability, AHNA Methodology— Population and Household Projections 11 cost, and precision. Some researchers use data on vital statistics (births and deaths), housing units, water usage, special surveys, and property appraiser parcels. Any consistent series that reflects the underlying demographic change occurring in the area is useful in calculating a trend and updating the results from the last census. Once an estimate is created for the total population, detail can be generated for different segments of the population and the current trends can be projected into the future. Since projections are based on historical data and trends in an area, projection methods fall into the extrapolation classification. For national estimates and projections, numerous data sources are available that generate quality results. Data availability and reliability are roughly proportionate to the size of place under investigation. There are far fewer options for calculating estimates and projections for counties than for the nation as a whole— and even fewer are available for sub-county areas. In general, the arduousness of a calculation and its potential error are increased by adding levels of detail (total population vs. age, sex, and income detail), decreasing the size of the place (nation vs. county vs. census tract), and increasing the time since the last base point (estimate for 5 years since the last census vs. 20 year projection vs. 50 year projection). Estimating and projecting a population's composition is especially problematic for small geographic areas. That objective crosses all three areas of difficulty—detail, size, and horizon. No single method has been the authoritative choice for detailed sub- county population estimates and projections. Cohort-component techniques AHNA Methodology— Population and Household Projections 12 (which fall into the extrapolation classification) have been the primary method used for national and state-level projections of the population by age. Cohort- component applies historical fertility, mortality, and migration patterns to a base population to produce a detailed depiction of the population at some subsequent point. Since fertility, mortality, and migration do not happen on a daily basis to all age segments of the population, accurate measurement of those demographic events in smaller populations is nearly impossible. Cohort-component has been used successfully for counties, but rarely for sub-county areas due to its data requirements. In the next section we examine the usefulness of a variation of the cohort-component method employed in the Assessment. 3. Hamilton-Perry Ratios There are no population by age estimates or projections available at the local level to the extent needed for this model. In fact there are no population projections for all Florida jurisdictions, so development of these numbers was a critical first step in the methodology. The population age projection used in the housing needs assessment is a technique in which survival rates (births and deaths)are combined with net migration rates into a single ratio for each age group. This survival/net migration ratio is then used to project the age group into the future. This methodology is, in turn, a simplified application of the cohort- component method of projection in which births, deaths, and migration (the components of population change) are projected separately for each age-sex group in the population (Hamilton and Perry, 1962; Smith and Shahidullah, 1995). AHNA Methodology— Population and Household Projections 13 The choice of this approach for use in the Assessment is notable, in part, because of what can't reasonably be done at a small geographic level that meets the objectives of low cost and accessibility. The conventional cohort-component approach requires individual detail for births, deaths, and migration not available at the jurisdiction level; for econometric modeling the jurisdiction is generally too small a unit of measure; typical small area population projection techniques like shift and share are not appropriate for age projections; and extrapolating trends in age groups is not appropriate for rapidly growing areas with volatile migration patterns. To calculate population by age, a net migration/survival ratio is determined for each age group. Two points in time are needed to construct the survival/net migration ratio --- in our case the jurisdiction's population by age group for 1990 and 2000. The sources for this data are the respective census counts. The third set of data needed for this methodology is the jurisdiction's population for each of the projection years. Since we are interested in projecting our resident population we subtract out the institutional population to give us an adjusted population. It is the adjusted population that we will project and, where necessary, add back the institutional population to give a final total population by age group. The data for institutional population by age group comes from the Florida Departments of Corrections and Children and Families, the U.S. Department of Defense, and the State Universities as compiled by the Bureau of Economic and Business Research (BEBR) and the Shimberg Center(The institutional population for two AHNA Methodology— Population and Household Projections 14 counties, Alachua and Leon, are special cases, please see the appendix for a description of how those two counties are handled). The Hamilton-Perry ratio is the change in the population of a particular set of birth years between two dates (an age cohort). The ratio is designed to capture the change in the size of an age cohort over a ten-year period. For example, the population aged 10-14 in 2000 is divided by the population ten years earlier, that is, the population aged 0-4 in 1990. The ratio is then applied to the population aged 0-4 in 2000 to project the population aged 10-14 in 2010 and to the population aged 0-4 in 2010 to project population aged 10-14 in 2020. The population in a cohort changes as a result of both the survival of the population in the cohort at the beginning of the ten-year period and the in- or out-migration of population in the particular set of birth years. In most age groups, migration is the dominant factor affecting changes in the population of an age group. Further, many parts of Florida have experienced large net in-migration. Calculation of the migration/survival ratio reflects the past impact of migration on various age groups and uses that trend as a basis to project the population by age group, with the total adjusted to the previously calculated jurisdiction total. Finally, the projections are "tweaked" slightly by making an adjustment to the projections of the population age 0-9 and 75+. To accomplish this slight adjustment, the Bureau of Economic and Business Research's estimates and projections of age group totals for each county are employed. AHNA Methodology— Population and Household Projections 15 Adiustment To The 0 - 9 and 75+ Age Ranges Two age groups require a modification to the general calculation, children aged 0-9 and persons aged 75 and older. To create the ratio for population aged 75+, divide that population in 2000 (75+) by the sum of populations age 65 to 75+ in 1990. The population less than ten years old is projected by calculating the ratio of children age 0-9 to the population age 15-44 in 2000 (0-9/15-44) and applying that ratio to the population age 15-44 ten years later. We still have to divide the population age 0-9 into the two population groups age 0-4 and 5-9. To do that we make an assumption that the share of children age 0-4 to those age 0-9 in the jurisdiction is the same as that of the county as a whole. 4. Finalize the population by age projections The preceding calculations have given us a preliminary projection for the year 2010. But the total jurisdiction population projected using this methodology may be inconsistent with that of the population projection methodology in Part 1. So, to complete the projection for 2010, the population of each age group is adjusted to reflect the total jurisdiction population calculated previously. The controlled age projection for 2010 computes the ratio of the projected jurisdiction population (control total) to the sum of age group populations (the jurisdiction's total uncontrolled population) and applies that ratio to each age group population. Age group projections for 2020 and 2030 are calculated in the same fashion. The survival/net migration ratio is applied to the age group population in the year 2010 (using the final or controlled age projection figure, rather than the AHNA Methodology— Population and Household Projections 16 uncontrolled figure) to produce a 2020 projection and that step is repeated again for the 2030 projection using 2020 as a base. The preliminary (or uncontrolled) age group projection is then adjusted using the ratio of the projected population (from the preceding methodology -- Part 1) to the sum of age group populations (total controlled population) to produce a final (or controlled) projection. We derive the projections for the launch year(2005), and the mid-decade points, 2015, etc., by using the compound growth rate between decades. The function is: Pop of year 2000+n = pop2000 * e ^ (n/10 * In(pop2000/pop2010)) (n = 2 or n = 5) Pop of year 2015 = pop2010 * e A (5/10 * In(pop2010/pop2020)) Pop of year 2025 = pop2020 * e A (5/10 * In(pop2020/pop2030)) The Hamilton-Perry ratios seem less able to capture the volatility in young adult and elderly populations. In counties like Charlotte, for example, the accelerated in-migration of elderly in the 1980's and 1990's and the corresponding shift in the age structure fell outside the rates captured by the H-P ratios. The use of the BEBR county age projections provides a way to recapture that important shift. So, the last step in the population by age projection methodology is to control the sum of jurisdictions by age group to the BEBR county age group projection. This is an iterative mathematical procedure that produces a best fit between the jurisdiction's total population and the county age group total. AHNA Methodology— Population and Household Projections 17 B. Householder by Age and Tenure 1. A fundamental assumption: headship rates Households are the basic unit of demand for housing. They are the way in which the population divides itself to occupy housing units. One member of a household is considered the representative of that household and is referred to as the householder. The percentage of the population in a given age group that are householders is the headship rate in that age group, or the propensity of persons in that age group to be household heads. Therefore, headship rates allow the conversion of the population of an age group into households. Different age groups have different propensities for forming households, so that as the age structure of the population shifts, the number of households that a given population would yield would also change. The way in which the population divides itself into households is related to a number of economic and social factors including income, housing prices, governmental assistance, marriage and divorce rates, and the mobility of the population. While household sizes declined significantly in the 1970s and continued to decline more slowly in the 1980s, the rate of decline slowed significantly during the 1990s. Further, factors that lead to changes in household size do not exhibit a clear and convincing pointer to the direction of future change. The fundamental assumption in the construction of household estimates in the Assessment is that household formation rates and the distribution of household characteristics remained constant in their 2000 proportions across the projection horizon. Estimates and projections of households are therefore based ANNA Methodoliogy— Population and Household Projections 18 on age-specific householder(headship) rates. These headship rates are applied to the age-specific population projections calculated in the previous section. The projection of householder by age, tenure, and size (headship) builds on the age group projections developed in Part 2. Three data sets are needed -- householder by tenure and age (at a minimum), population by age from the 2000 Census for each jurisdiction and the age group projections previously calculated. A headship rate is calculated from the 2000 census data by dividing the number of householders in each tenure/age group by the total population of that age group. The projection of householder by age/tenure is then calculated by applying that ratio (headship rate) to the age group projections of population for each projection period. The numbers of households in each age group are summed to the projected number of households. However, to meet the twin objectives of housing plan- and housing program-friendly formats in conjunction with more accurate household projections, the AHNA model requires complex cross-tabulations. 2. Household Projection Methodology In order to produce a complex cross-tabulation of household characteristics such as — Tenure X Age X Size X Income X Cost Burden projections (for a projection horizon of 2010-2030) — the data requirements of the methodology are: 1. Population by age estimates/projections (2000-2030); 2. 2000 Household Count by Tenure X Age X Size X Income X Cost Burden AHNA Methodology— Population and Household Projections 19 Methodology: Step 1: Calculate the household formation rate for year 2000 (or the most recent census). Household Count of Tenure X Age X Size X Income X Cost Burden Household formation rate = Population by age For example, the household formation rate for the following household type: renter/15-24years old/1 person per household/Income of 30.1-50% of Area Median Income (AMI)/cost burden less than 30% = #of renter households/15-24 years old/1 pph/Inc=50%AMI/<30% CB (year 2000) #of persons 15-24 years old (year 2000) Step 2: The 2005 projection of the example household type is: Household formation rate X population of persons 15-24 years of age in 2005 AHNA Methodology— Population and Household Projections 20 .... N O wo O -0 .0m _c • o O O U O N c C 7 a) _c C Q E X N N N ca U) Q 5 N O f6 N a) O N c N o O N o Q N 0 - >, 0-_o O N o o w E V o m Do (r) C U CD , 7 C o .0 a) O O " Z L ?, > O 03.n - O E ' N a O N O . c4 p N N W O N L C 0) 7 2 7 Q O 03 p p C ^ N(a N ...._CI O 7 Q a E Q N N Q O O C C E , U a) Q _ O O C - V o o V VF TC 2 >-. N n C 0 O .V N o Q 7 V O O N Q +- C O ) Q U) p 03 p o O > C 1M Q O CO O p ? N N N N V 1 z I CO r N L O C C O U o o_ O O p O a N O N (0 CI) N N N Q 7 co O 7 L - N > 0 c aC@ Q O o O O O L O E Q N Q t/7 N Q.w. 10 f/1 U C O C p .O t0 p . O p O _ o 0 0 `- U ` c • V O U O .O N 7 O Q N a 0• N N LO m L Q C 0 7 o Q N 0 C Q O N o 0 o cp.°- o c Q - m o 0 N V O7 Cp '0 a a j O c N c .- O O (n 7 V Q O + co C O .0 p 0 O � O- N U C U 0 0 N Q O) CA O O 0 Q 3 0)',7_ O O c >. O C >' O 0) 7 7 N p O O 'O ^ a 0 o O m 7 _C o- r 7 (1) N o Q 0 N a) N Q L Q CD Z LL Q APPENDIX A discussion of the FSU/FAMU and UF enrollment figures The FSU/FAMU and UF enrollment figures for three universities — Florida State University and Florida A&M University in Leon County and University of Florida in Alachua County — their distribution by age and their distribution by on- and off-campus population, have a significant influence on the household projections contained in the Needs Assessment (AHNA) for Leon and Alachua counties. This is an explanation of how that was accomplished. Planning officials in these two counties should pay close attention to the assumptions and the resulting population and household estimates and projections. Institutional populations such as major university enrollments, inmate populations, and the armed forces are subtracted from total population estimates before the AHNA projections of"permanent" population are made. Projections of the institutional populations are made separately and these populations are added back to the permanent population projections to produce a final population total. Household estimates and projections are made from the "permanent" population figures, i.e., the permanent population is the household-forming population and does not generally include the institutional population. In certain counties the institutional population or some part of it is considered a household- forming population. In Alachua and Leon Counties a portion of the university headcount, the off-campus portion, is added back to the permanent population (by age) and the total is used to project households. The FSU/FAMU and UF headcounts include all students and, if the information is available, the spouses and children of students residing in on- campus family housing. The actual and projected headcounts, the distribution of headcount by age, and on-campus occupancy were obtained from various sources at the three universities. In certain cases projections had to be extrapolated by assuming an average annual increase derived from the last year of projected headcount that the Shimberg Center could obtain from university sources. To distribute the university headcounts geographically we attributed all the on-campus student population plus a varying percentage of the off-campus to Tallahassee or Gainesville; the remainder was attributed to the unincorporated area. The percentage of off-campus UF headcount attributed to Gainesville was: 40%-1989/90, 45%-thereafter. The off-campus distribution for Leon County was derived from data obtained from the Tallahassee-Leon County Planning Department. AHNA Methodology— Population and Household Projections 22 • • • Florida Statistical Comparison Affordable/Workforce Housing February 2015 Tim C. Durham Executive Manager Corp. Business Ops. 1 This report is a survey of housing affordability data taken from publicly available federal sources. The first table and chart focus on coastal south Florida using U.S. Census Bureau estimates for 2013, which are the most current estimates available. Disposable Income Dedicated to Monthly Mortgage Payment - Coastal South Florida Disposable Median Disposable Median Value 30-Year Fixed Income Household Income per of Owner at 4.0%w/ 10% Dedicated to Income Month (est.) Occupied Down Mortgage (2009-2013 ACS) Housing Units Monthly Payment (2009-2013 ACS) Payment Miami-Dade Co. 34.3% $43,100 $2,521.24 $201,000 $863.64 Collier Co. 33.5% $55,843 $3,354.82 $261,300 $1,122.74 Palm Beach Co. 27.4% $52,432 $3,131.69 $199,700 $858.06 Sarasota Co. 25.8% $49,052 $2,910.59 $175,000 $751.93 Broward Co. 25.5% $51,251 $3,054.44 $181,500 $779.86 Florida 24.8% $46,956 $2,773.48 $160,200 $688.34 Charlotte Co. 23.7% $44,378 $2,604.84 $143,700 $617.44 Lee Co. 22.9% $47,439 $2,805.08 $149,400 $641.93 Sources and Notes: Median Home Value and Median Household Income are taken from the 2009-2013 American Community Survey Estimates (Selected Housing Characteristics, DP04), a product of the U.S. Census Bureau. Disposable Income is the Median Household Income less Federal Income Tax,Social Security Tax and Medicare Tax. Monthly mortgage payments do not include insurance costs or property taxes. 36% 34.3% 33.5% Disposable Income Dedicated to Monthly 34% I Mortgage Payment - Coastal South Florida 32% 30% 28% 27.4% 26% 25.8% 25.5% 24.8% 24% 23.7% 22.9% --- 22% [7___.. 1 20% I j 18% Go. Go. G°- Go. Go. .a6 G°. Go oar ```e�00 ca oc, too OP ota 0, o'�`� \.�� Nr Q 2 The next table, which is taken from the same source, ranks coastal south Florida counties based on the percentage of owners who pay 35%or more of their household income towards their housing unit. Percentage of Owners Paying 35% or More of Household Income for Housing Unit Rank County Percentage of Owners Median Cost Paying 35%or More of (with a mortgage) Household Income for Housing Unit (with a mortgage) 1 Miami-Dade County 46.7% $1,800 2 Broward County 42.1% $1,844 3 Charlotte County 42.1% $1,354 4 Collier County 41.9% $1,810 5 Palm Beach County 41.0% $1,799 6 Sarasota County 38.3% $1,506 7 Lee County 36.5% $1,483 8 Florida 36.3% $1,530 Sources and Notes: The information provided above is taken from the 2009-2013 American Community Survey Estimates (Selected Housing Characteristics, DP 04),which is produced by the U.S.Census Bureau. The table below ranks the percentage of renters paying $1,500 or more in gross rent per month in coastal south Florida. The cost of a median rental in coastal south Florida counties is also included. Percentage of Renters Paying $1,500 or More for Gross Rent Rank County Percentage of Renters Paying Median Gross Rent $1,500 or More for Gross Rent 1 Broward County 25.2% $1,171 2 Palm Beach County 24.8% $1,149 3 Miami-Dade County 20.8% $1,085 4 Collier County 17.9% $1,020 5 Sarasota County 15.8% $1,003 6 Florida 14.8% $990 7 Charlotte County 12.7% $912 8 Lee County 11.0% $943 Sources and Notes: The information provided above is taken from the 2009-2013 American Community Survey Estimates (DP 04, Selected Housing Characteristics),which is produced by the U.S.Census Bureau. 3 The table below ranks the increase in the House Price Index (HPI) from the 3rd Quarter of 2013 to the 3rd Quarter of 2014. The table includes the national top ten Metropolitan Statistical Areas followed by the other Florida Metropolitan Statistical Areas. All rankings are national. Change in FHFA Metropolitan Area House Price Indexes (All Transactions Index, 2014Q3) Rank Metropolitan Area 1-Year (percentage) 1 Vallejo-Fairfield, CA 18.26 2 Naples-lmmokalee-Marco Island, FL 17.75 3 Reno, NV 17.67 4 Modesto, CA 17.60 5 Salinas, CA 16.99 6 Las Vegas-Henderson-Paradise, NV 16.88 7 Merced, CA 16.59 8 Bend-Redmond, OR 16.18 9 Riverside-San Bernardino-Ontario, CA 14.44 10 Napa, CA 14.42 13 Fort Lauderdale-Pompano Beach-Deerfield Beach, FL (MSAD) 13.32 18 Miami-Miami Beach-Kendall, FL (MSAD) 12.35 21 Deltona-Daytona Beach-Ormond Beach, FL 11.68 23 West Palm Beach-Boca Raton-Delray Beach, FL (MSAD) 11.24 30 Port St. Lucie, FL 10.32 34 Lakeland-Winter Haven, FL 10.02 35 Sacramento--Roseville--Arden-Arcade, CA 9.86 36 Tampa-St. Petersburg-Clearwater, FL 9.81 38 Punta Gorda, FL 9.75 50 North Port-Sarasota-Bradenton, FL 9.10 51 Palm Bay-Melbourne-Titusville, FL 9.08 56 Orlando-Kissimmee-Sanford, FL 8.74 60 Cape Coral-Fort Myers, FL 8.50 71 Ocala, FL 7.56 72 Jacksonville, FL 7.52 138 Crestview-Fort Walton Beach-Destin, FL 4.15 146 Pensacola-Ferry Pass-Brent, FL 3.93 159 Tallahassee, FL 3.61 Source: Federal Housing Finance Agency. See http://www.fhfa.gov/DataTools/Tools/Pages/House-Price-Index-(HPII.asox. The average change for Florida for the one-year period is 7.85. The average change nationally for the one-year period is 4.55 4 American Community Survey - 2013 Subject Definitions Gross rent is the contract rent plus the estimated average monthly cost of utilities (electricity, gas, and water and sewer) and fuels (oil, coal, kerosene,wood, etc.) if these are paid by the renter (or paid for the renter by someone else). Selected monthly owner costs are the sum of payments for mortgages, deeds of trust, contracts to purchase, or similar debts on the property (including payments for the first mortgage, second mortgages, home equity loans, and other junior mortgages); real estate taxes; fire, hazard, and flood insurance on the property; utilities (electricity, gas, and water and sewer);and fuels (oil, coal, kerosene, wood, etc.). Selected monthly owner costs provide information on the monthly housing cost expenses for owners. When the data is used in conjunction with income data, the information offers an excellent measure of housing affordability and excessive shelter costs. The data also serve to aid in the development of housing programs to meet the needs of people at different economic levels. Income in the Past 12 Months "Total income" is the sum of the amounts reported separately for wage or salary income; net self- employment income; interest, dividends, or net rental or royalty income or income from estates and trusts; Social Security or Railroad Retirement income; Supplemental Security Income (SSI); public assistance or welfare payments;retirement, survivor, or disability pensions; and all other income. Receipts from the following sources are not included as income: capital gains, money received from the sale of property (unless the recipient was engaged in the business of selling such property); the value of income "in kind" from food stamps, public housing subsidies, medical care, employer contributions for individuals, etc.; withdrawal of bank deposits; money borrowed; tax refunds; exchange of money between relatives living in the same household; gifts and lump-sum inheritances, insurance payments, and other types of lump-sum receipts. 5 Newspaper Articles Article in Naples News June Fletcher 6:00 PM, Sep 6, 2014 Collier's soaring housing prices shutting out many working-class people Short supply of affordable homes Naples, FL. - Orphaned as a teenager, Conrad Mountjoy works two jobs as a chef to support himself. Though the hardworking 25-year-old can afford $650 a month for rent, he can't find anything in that price range close to either of his jobs in Old and North Naples. Because he doesn't own a car, proximity to work is important. Since he moved to the area from Alaska last month, he's been living out of his backpack, crashing with friends and putting "housing wanted" ads on Craigslist and Roomster, a roommate-matching website. "Renting here is near impossible," he said. "I didn't expect it would be so hard." Mountjoy is part of the Naples that doesn't get much attention in glossy tourist brochures and lifestyle magazines—the low- and moderate-income residents who struggle to find affordable housing to rent or buy. Outbid by cash-rich investors and retirees, as well as visitors willing to pay high short-term rents for seasonal rentals, these residents are the backbone of Southwest Florida's workforce. But since median incomes have actually fallen over the last five years — by 11.2 percent, to $62,900 in Naples-Marco Island, and by 4.4 percent, to $58,000 in Cape Coral-Fort Myers—they simply can't keep up, especially in light of extremely tight supply, rising rents and skyrocketing home prices. According to the latest statistics from the U.S. Census, average gross monthly rents in 2012 were $1,098 in Naples-Marco Island and $941 in Cape Coral-Fort Myers. Nationally, renters paid $884 a month. Aspiring homebuyers face similar challenges: An analysis by the National Association of Home Builders and Wells Fargo for the second quarter of 2014 showed both metro areas rank low when it comes to affordability. Out of 224 cities nationwide, Naples-Marco Island ranked 197 and Cape Coral-Fort Myers ranked 143. Consequently, the report showed, only about half of families earning the area's median income can afford to buy homes at the median price of$260,000 in Naples-Marco Island. Because median-priced homes are cheaper in Cape Coral-Fort Myers at $157,000, about seven out 10 households can afford to buy. The housing crunch affects married and single people alike, in all age groups, said Michael Puchalla, executive director of the Housing Development Corp. of Southwest Florida, a nonprofit counseling agency promoting financial education and affordable housing. HOUSING AFFORDABILTY A measure of housing affordability is the percentage of homes sold that are affordable to families earning the area's median income, commonly called the Housing Opportunity Index, or HOI. Naples-Marco Island Cape Coral-Fort Myers Median Q2 2014 $260,000 $157,000 Price (1) Q2 2009 $183,000 $96,000 Housing Q2 2014 51.5% 68% : Opportunity Q2 2009 68.1% 81.4% Index (H01) (2) Median Q2 2014 $62,900 $58,000 Income (3) Q2 2009 $70,800 $60,700 National Q2 2014 197 143 Rank (4) Q2 2009 172 . ... ............. 82 (I)Includes new and existing homes: (2)HOl is a measure of the percentage of homes sold that are affordable to families earning the area's median income.: (3)Median household income as determined by Census:(4)Out of 224 metro areas nationwide(must to least affordable:lower is better) Source:NAHB/Wells Fargo SCRIPPS NEWSPAPERS It's especially tough for those who went through a foreclosure or job loss during the recession and saw their credit ruined, since lending standards have become more stringent, he said. Others, like first-time buyers and students seeking temporary rentals, are also at a disadvantage, since they haven't had much time to amass savings or establish their credit histories. So usually their choices are to take on multiple roommates or live in outlying areas, where prices and rents are cheaper. But even long-term homeowners who have amassed equity are affected by the lack of affordable housing in the most convenient and established areas. Artist and gallery owner Mary Moran recently sold the three-bedroom house she owned in Lake Park in central Naples for 10 years, so she could downsize into an easy-care condo. But Moran, who is in her'70s, hasn't been able to find a one- or two-bedroom apartment near her gallery in downtown Naples in her"ideal" price range of$1,100 a month. She's now worried that she will have to pay closer to what she paid for her old mortgage, around $1,700 a month, which wipes out her goal to save money for her eventual retirement. "I've never been in a position like this before," she said. "I've put my rental needs on my church's prayer list." For the poor: long waits and no guarantees For those who must depend on subsidized housing, the situation is even more dire. Those who qualify for the Section 8 program, which provides rental assistance for low-income people, face long waits and no guarantee of finding a project or landlord who will house them, said Esmeralda Serrata, executive director of the Collier County Housing Authority. Serrata said the waiting list for Section 8 housing in the county is so long — currently at 417 people —that it hasn't been opened for new applicants in more than two years. The average waiting time for housing once a person makes the list is two to four years. 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