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DRAFT FBRMPO SE Data Development Report Overview In preparation for the French Broad River Metropolitan Planning Organization (FBRMPO) 2040 Long Range Transportation Plan (LRTP) Update it is necessary to produce residential and non-residential growth forecasts for input into the regional travel model. The travel model will be used to forecast traffic and transit ridership and will be utilized to determine project priorities. The Model—a growth allocation model constructed using CommunityViz; an extension to ArcGIS Desktop; (the Model) was used to forecast residential and non-residential growth for MPO planning area. The Model was created by updating the GroWNC Model, used for scenario planning for the GroWNC Regional Plan. Updates included adding the ability to allocate additional types of non-residential development needed for the regional travel demand model. As part of the forecasting process certain components of the model needed to be reviewed, validated and updated where appropriate. This report is meant to provide an overview of the components of the Model and the outputs of the SE data development process.

Model Data Review Schedule Below is schedule that outlines specific milestones in the future year socio-economic data development process. Individual model components were reviewed and validated by TCC SE Data Work Group members.

Table 1: Model Review Milestones

Task # Model Component Date Data Provided for Review Task 2.1 Land Supply August 8th Task 2.1 Future Land Use August 16th Task 2.1 Suitability August 16th Task 2.1 Environmental Constraints August 26th Task 3.2 Control Totals September 12th

Task 4.2 Draft Forecasts presented to SE Data Working Group October 31th and January 31st,

2013 Task 4.5 Draft Forecasts for TCC Expected March 2014

Model Components The Model forecasts were created using a modified version of the GroWNC Model. The Model was updated to include additional employment types needed for the travel demand model. It uses functionality available in CommunityViz Scenario 360, an extension to ArcGIS Desktop, to allocate county projections to parcels based on the supply of land, environmental constraints, land use suitability(probability of development) and capacity of the land (determined by adopted land use plans). Land use suitability, environmental constrained land, and capacity was calculated on a parcel level using a modified parcel dataset. This dataset was provided by Land Of Sky Regional Council circa spring of 2012. Large parcels were modified—split by a 40 acre grid, in order to ensure that parts of large parcels received suitability scores and allocations representing growth potential. Once allocations of control totals were made to parcels, pending development was added in and then total growth was aggregated to townships and Traffic Analysis Zones (TAZ) for review and input into the travel model.

Control Totals County population projections were based on the Office of State Budget and Management Projections (from 4/11/2012) extrapolated to 2040, except for Madison County, where the linear growth projection was used. In total the county control totals for the region project a population increase of 178,510 between 2010 and 2040 (an

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increase of 39%). Non-seasonal dwelling unit totals were based on the population estimates, existing persons per household by county (from the Census) and a vacancy rate of 0.1. Multi-family and single family housing percentages were based on existing housing stock and recent trends.

Non-seasonal Dwelling Units = ((Population Projection / Persons Per Household) * 1.10) Seasonal dwelling units were estimated by extrapolating seasonal home growth between 2000 and 2010 to the horizon year of 2040. Employment forecasts were developed based on Woods & Poole growth rates for retail, highway retail, office employment. Positive industrial employment growth was assumed at a rate consistent with the state average (from NC Department of Commerce projections). Service employment growth was assumed to be in line with the Woods & Poole forecasts less industrial sector growth. This method assumes that a portion of the service sector growth will be more industrial in nature—reflecting potential for logistics and transportation growth as well as breweries and other production processes. Total employment growth is projected to be 92,735 jobs. This is a growth of 50%. Lodging forecasts were made based on applying an average rooms per person rate to future population growth and adjusting downward by 10% to factor in a shift to bed and breakfasts and vacation homes. The rooms per person rate that was applied to future population growth was 0.017. Control totals used in the modeling effort are shown in Appendix A: Projection Tables.. Technical Note: Within the Model the control totals (minus pending development) have been coded as Assumptions (accessed via Scenario 360 Content Toolbar/360 Setup/Assumptions).

Land Supply The Land Supply determines where growth can go in the model. Essentially it is an inventory of available (vacant), underutilized land (land that can potentially absorb additional development), protected and utilized (built) lands. Growth (in terms of residential units or jobs) can only be allocated to available or underutilized parcels. The existing Land Supply in the model was derived using parcel information, an existing land use inventory and with assistance from local governments. Vacant land without structures was classified as available. An improvement value/land value ratio was used to determine underutilized lands. Park lands and lands with conservation easements were classified as protected. Lands with structures not likely to redevelop were classified as utilized. These parcels included schools and churches. Parcel extents of Pending Developments are also denoted in this dataset. These parcels are removed from the Land Supply due to the fact that expected housing units and employment are added separate from the growth allocation.

Maps of the Land Supply by county are included in the Appendix B: Land Supply Maps.

Technical Note: The modified parcel dataset in the Model can be symbolized and/or summarized by the LandSupply field. The LandSupply_With_Pending.lyr file located in the Data\Symbology\ folder of the CV Analysis folder can be used for symbology.

Suitability In addition to the availability of land—economic and environmental factors influence the location of future growth and development these factors can influence the “suitability” of land. Suitability can be thought of as the probability of a piece of land to develop in a certain way.

Suitability is defined as, “the fitness of a given unit of land for a specified use.” Smart Land-Use Analysis

, Margaret Carr and Paul Zwick

A number of economic and environmental suitability factors were taken into account in the development of the GroWNC Model. Suitability surfaces for four types of land use were created (Single Family Residential, Multi-

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family Residential, Commercial, and Industrial). These suitability maps will be used in the FBRMPO SE Data forecasting effort as well. The Commercial suitability analysis will be used for the allocation of retail and service employees. In addition, suitability analyses have been conducted for highway retail, office and lodging land uses. Raw suitability values were set to a 100 point scale using the CommunityViz Scenario 360 Suitability Wizard. Suitability maps by land use type are included in Appendix C: Suitability Maps. A set of tables that specifies suitability factors considered by land use type is included in Appendix D: Suitability Input Tables. Technical Note: The modified parcel dataset in the Model can be symbolized by suitability fields (SFSUIT100, MFSUIT100, COMSUIT100, HWYRETSUIT100, OFFSUIT100, INDSUIT100, LODGSUIT100). The Raster Calculator was used to calculate suitability on raster grids with cells 200x200ft. Mean suitability values were calculated by parcel using the Zonal Statistics as Table tool (and/or the Extract Values to Points tool) available in the ArcGIS Desktop Spatial Analyst extension. The suitability values were then transferred to the working parcel dataset in the CommunityViz model based on a unique ID. The suitability values by land use for each parcel are used in the growth allocation. For instance, parcels with higher industrial suitability are more likely to receive industrial employment. This process is discussed further in the Forecasts portion of this document.

Environmental Constraints Areas not available for development, due to environmental regulations or level of investment/engineering required to overcome site constraints were identified. These areas, including wetlands, lakes, floodways, areas with over 50% slope, and regulated hydrology buffers, will be removed from the Land Supply to determine the amount of land that is buildable and available or underutilized. A composite environmental constraints inventory was created based on merging GIS datasets. The following list contains all of the features included in the environmental constraints dataset.

• FEMA Floodway ("FLDWAY_LID" = '1000') • Hydrology (24k USGS Blueline Streams and Lakes/Ponds) + 50ft Buffer • Wetlands (NWI) + 50ft Buffer • Unbuildable slopes (20 ft DEM, Slopes over 50% selected, converted to polygon, donuts removed)

After a merge, the data was dissolved, and then converted to a single-part feature class. All polygons less than 0.05 acres were removed to improve processing speed. A map of this environmental constraints inventory is included in Appendix E: Environmental Constraints and Future Land Use. These constrained areas were removed from the Land Supply prior to calculating how much land is available for development.

Generalized Future Land Use Local land use policy dictates the maximum allowable development capacity of parcels. Such policies are implemented through local regulations. Most of the local governments throughout the 5-county area have adopted land use plans and administer land use regulations to limit the intensity of site development measured in residential dwelling units per acre and non-residential square footage (expressed as a ratio of a building footprint to the total acreage of the parcel, or floor-area ratio). This study will base allowable densities and uses on adopted land use and zoning plans, where available, in order to establish the capacity of each parcel. Where capacities were not explicitly specified in adopted policy, allowable uses and densities were estimated based on development trends in the study area. Since the land use plans and regulations vary from one jurisdiction to the next, a set of generalized future land use categories (GENFLU) were developed. Each locally-defined land use category was assigned a GENFLU category to simplify the coding necessary in the Model. For example, the Institutional designation in Mars Hill’s zoning ordinance and the Medical, Institutional, and Cultural District designation in Laurel Park’s zoning ordinance are assigned the institutional (INST) generalized future land use category. A map of generalized

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future land use classes is included in Appendix E: Environmental Constraints and Future Land Use. A table of generalized future land use classes is also included in the Appendix. The capacity (in terms of allowable dwelling units and non-residential square footage and employees) of each parcel in the study is calculated based the Generalized Future Land Use per adopted land use plans. These calculations are done using Dynamic Attributes in the CommunityViz software. Capacity is only calculated for available and underutilized parcels—not for built parcels unlikely to redevelop and protected areas. Dynamic Attributes are fields in a feature class attribute table that are automatically updated based on adjustments to other attributes (i.e. LandSupply and GENFLU fields). This is a unique capability of CommunityViz and was incorporated into the Model in order to facilitate future updates. For instance, changes to the future land use of a parcel (or group of parcels), due to a rezoning or comprehensive plan update, can be made.

Forecasts Allocation Once capacity is calculated it is necessary to distribute future growth. First, pending development is subtracted from total projected growth. Pending development (dwelling units and employees), defined as development that is anticipated to occur based on approved projects, is hard coded to individual parcels based on data provided by MPO staff. The remainder is distributed using a probability-based formula that distributes more growth to parcels that are more suitable. This step is accomplished using the CommunityViz Scenario 360 Allocator Wizard. For lodging, hotel rooms expected in pending development were subtracted from projected hotel rooms. The remaining hotel rooms were allocated by county in 25 room allotments based on lodging suitability. Maps of the allocation results (dwelling unit and employment forecasts) are included in the Appendix as Map 15.

Aggregation to TAZ After future growth is allocated to parcels a series of geoprocessing models is ran to summarize forecasts by TAZ and then update a TAZ template polygon with the new housing, employment and hotel rooms. Maps showing forecasts by TAZ are included in the Appendix as Map 16 and Map 17.

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Appendix A: Projection Tables

Table 2: Population Projections By County*

County 1990 2000 2010 2020 2030 2040 Δ 2010-2040 %Δ Buncombe 174,778 206,365 239,179 271,537 301,941 331,911 92,732 39% Haywood 46,942 54,025 59,148 64,243 69,338 74,438 15,290 26% Henderson 69,326 89,184 107,177 124,898 142,446 159,956 52,779 49% Madison 16,953 19,612 20,795 22,736 24,677 26,618 5,823 28% Transylvania 25,520 29,316 33,189 37,152 41,115 45,075 11,886 36% Total 333,519 398,502 459,488 520,566 579,517 637,998 178,510 39% *Based on NC Office of State Budget and Management Projections (4/11/2012) extrapolated to 2040 *Based on linear growth rate for Madison County

Table 3: Non-seasonal Dwelling Unit Projections By County**

2010 Forecasted 2010-2040 2040

County 2010 Dwelling Units New SF New MF Total New %Δ Total 2040 Buncombe 113,365 36,124 9,031 45,155 40% 158,520 Haywood 34,954 7,150 622 7,772 22% 42,726 Henderson 54,710 23,830 2,072 25,902 47% 80,612 Madison 10,608 2,490 216 2,706 26% 13,314 Transylvania 19,163 5,439 473 5,912 31% 25,075 Total 232,800 75,033 12,414 87,447 38% 320,247

**2010 Dwelling Units based on 2010 Census **New dwelling unit totals based on population projection, existing persons per household by county **Multi-family/Single Family percentages based on existing housing stock and recent trends **Vacancy rate of 0.1 assumed

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Table 4: Seasonal Home and Hotel Room Projections By County

County Seasonal Home Growth (2000-2010) Seasonal Homes Projected* Hotel Rooms (2010)** Projected Hotel Rooms***

Buncombe 1,343 4,029 6,749 1,576 Haywood 2,543 7,629 1,950 260 Henderson 1,033 3,099 1,534 897 Madison 239 717 124 99 Transylvania 847 2,541 259 202 Total 6,005 18,015 10,616 3,035 *Seasonal home growth based on 00-10 trends **Baseyear hotel rooms provided by MPO Staff (only includes hotels, motels, and resorts, does not include campsites, cabins and B&Bs, as these may be included in seasonal home estimates) ***Based on rooms per person rate of 0.017 (average of 2010 rates by county * 90%)

Table 5: Baseyear (2010) Employment By County**

IND RET HWYRET OFF SVC Total

Buncombe 27,184 15,791 16,133 16,832 47,801 123,741 Haywood 4,717 2,381 2,840 2,787 8,345 21,070 Henderson 10,654 4,855 4,220 4,704 15,533 39,966 Madison 956 331 365 959 2,027 4,638 Transylvania 2,605 1,220 1,366 2,254 6,296 13,741 Total 46,116 24,578 24,924 27,536 80,002 203,156 **Source: InfoUSA & FBRMPO

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Table 6: Employment Projections By County (Growth 2010-2040)*

IND RET HWYRET OFF SVC Total %∆

Buncombe 8,337 2,939 3,001 10,457 29,487 54,221 48% Haywood 1,446 430 511 1,452 2,266 6,105 35% Henderson 3,265 1,750 1,520 3,008 15,299 24,842 68% Madison 293 78 84 695 1,252 2,402 55% Transylvania 798 236 265 1,181 2,685 5,165 43% Total 14,139 5,433 5,381 16,793 50,989 92,735 50% *Based on Woods & Poole AGR for RET, HWYRET, OFF, SVC, Based on AGR of 0.96% for IND

Table 7: Employment Projections By County (2040 Total)**

IND RET HWYRET OFF SVC Total

Buncombe 35,521 18,730 19,134 27,289 77,288 177,962 Haywood 6,163 2,811 3,351 4,239 10,611 27,175 Henderson 13,919 6,605 5,740 7,712 30,832 64,808 Madison 1,249 409 449 1,654 3,279 7,040 Transylvania 3,403 1,456 1,631 3,435 8,981 18,906 Total 60,255 30,011 30,305 44,329 130,991 295,891 **Based on Woods & Poole AGR for RET, HWYRET, OFF, SVC, Based on AGR of 0.96% for IND

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Appendix B: Land Supply Maps Map 1: Buncombe County Land Supply

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Map 2: Haywood County Land Supply

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Map 3: Henderson County Land Supply

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Map 4: Madison County Land Supply

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Map 5: Transylvania Land Supply

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Appendix C: Suitability Maps Map 6: Single Family Suitability Map

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Map 7: Multi-family Suitability Map

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Map 8: Commercial Suitability Map (Used for Retail and Service Allocation)

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Map 9: Industrial Suitability Map

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Map 10: Office Suitability Map

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Map 11: Highway Retail Suitability Map

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Map 12: Lodging Suitability Map

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Appendix D: Suitability Input Tables Table 8: Single Family Suitability Inputs

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Table 9: Multi-family Suitability Inputs

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Table 10: Commercial Suitability Inputs

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Table 11: Industrial Suitability Inputs

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Table 12: Office, Institutional, Government Suitability Inputs

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Table 13: Highway Retail Suitability Inputs

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Table 14: Lodging Suitability Inputs

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Appendix E: Environmental Constraints and Future Land Use Map 13: Environmental Constraints

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Map 14: Generalized Future Land Use

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Table 15: Generalized Future Land Use Classes

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Map 15: Parcel Based Forecasts Map

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Map 16: TAZ Dwelling Unit Forecasts

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Map 17: TAZ Employment Forecasts