Fiscal Impact Analysis: New Methods, New Data and Best Practices
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On October 18, 2016 Senior Advisors Sidney Wong and Steven Nelson, alongside Senior Vice President and Principal, Peter Angelides, led a continuing professional education session at the Pennsylvania Chapter of the American Planning Association (APA) 2016 Annual Conference to discuss their research on Fiscal Impact Analysis using multipliers based on the current 2014 ACS-PMS. This revolutionary new method of providing Fiscal Impact Analysis is the basis for Community Data Analytics (CDA), which provides up-to-date customized demographic projections for communities.
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Fiscal Impact Analysis: New Methods, New Data and Best Practices
- 1. PA-APA, October 2016 New Methods, New Data and Best Practices Fiscal Impact Analysis: New Methods, New Data and Best Practices Speakers Steven L. Nelson, AICP, Econsult Solutions, Inc. Peter Angelides, AICP, Ph.D., Econsult Solutions, Inc. Sidney Wong, Ph.D., Community Data Analytics American Planning Association The Pennsylvania Chapter Conference October 18, 2016 Session E1
- 2. PA-APA, October 2016 New Methods, New Data and Best Practices Introduction • Development Impacts • Fiscal Impact Analysis Methods and Issues • Data Sources & Multipliers in Pennsylvania • Reverse Fiscal Impact Analysis • Discussions
- 3. PA-APA, October 2016 New Methods, New Data and Best Practices Why Do We Care about Development Impacts? • Fiscal • Economic • School • Traffic • Environment • Social • Political • Others
- 4. PA-APA, October 2016 New Methods, New Data and Best Practices Fiscal Benefits Which is the most important? • Property tax revenues • Local wage tax revenues • Sales tax revenues • Other levies • User charges, fees and fines • Increment of property values / tax base expansion • Others
- 5. PA-APA, October 2016 New Methods, New Data and Best Practices Fiscal Costs • School expenditures • Government operating expenses • Capital improvement costs • Traffic improvement expenditures • Debt financing • Others Which is the most important?
- 6. PA-APA, October 2016 New Methods, New Data and Best Practices Fiscal Impact Analysis Practices & Steps
- 7. PA-APA, October 2016 New Methods, New Data and Best Practices Fiscal Impact Analysis “[a] projection of the direct, current, public costs, and revenues associated with residential or nonresidential growth to the local jurisdiction(s) in which this growth is taking place.” Page 1, Burchell, Robert W. and David Listokin, 1978. The Fiscal Impact Handbook Source: http://www.transactionpub.com/title/The- Complete-Illustrated-Book-of-Development-Definitions- 978-1-4128-5504-4.html
- 8. PA-APA, October 2016 New Methods, New Data and Best Practices “Is growth good or bad for my for community?” • It depends… • Development generates a host of new costs for a municipality. • Also generates new revenues. • It is important that municipalities determine if the new revenues offset the associated costs. • FIA can help elected officials make fiscally sound land use decisions.
- 9. PA-APA, October 2016 New Methods, New Data and Best Practices The Uses of FIA • Planning Applications of FIA include: – Land use policies – Rezonings – Annexations – Redevelopment • Budget and Finance Applications – Capital improvement programming – Revenue forecasting – Fiscal planning – Level of service changes
- 10. PA-APA, October 2016 New Methods, New Data and Best Practices Methods of Fiscal Impact Analysis • There are number of standard approaches to choose from. • The two most common include: – The Average Cost approach – The Marginal Cost approach • The distinction between the two is fundamental to FIA. • They may result in dramatically different estimates of the fiscal impacts.
- 11. PA-APA, October 2016 New Methods, New Data and Best Practices The Hybrid Approach • Combines the Average Cost approach with a case study analysis. • The Average Cost approach is used to calculate per-capita costs and revenues. • The case studies are used to identify areas of capacity constraints. – This helps bring in the benefits of the Marginal Approach.
- 12. PA-APA, October 2016 New Methods, New Data and Best Practices Steps of Fiscal Impact Analysis
- 13. PA-APA, October 2016 New Methods, New Data and Best Practices FIA Steps • Step 1: Estimate the number of residents and/or employees – Total population – School-Age Children Based on the type of housing units – PUMS data provides the most up-to-date information
- 14. PA-APA, October 2016 New Methods, New Data and Best Practices FIA Steps • Step 2: Estimate the costs associated with the development. – Not all spending categories will be impacted. • Step 3: Allocate costs between residential and non-residential uses – The method depends on the cost categories.
- 15. PA-APA, October 2016 New Methods, New Data and Best Practices FIA Steps • Step 4: Derive per-capita, per-student, per-employee expenditure estimates • Step 5: Sum Total Costs – Operating costs vs. capital costs – Use case studies and interviews to understand potential capital costs • Assess need for new capacity
- 16. PA-APA, October 2016 New Methods, New Data and Best Practices FIA Steps • Step 7: Estimate the revenues associated with the project. – Property tax vs. other revenues • Step 8: Allocate other revenue to land uses – Estimate per-capita and per-employee revenues
- 17. PA-APA, October 2016 New Methods, New Data and Best Practices FIA Steps • Step 9: Sum Total Revenues – Property taxes – Other revenues – One-time revenue • Step 10: Calculate the Net-fiscal impacts – Revenues - costs
- 18. PA-APA, October 2016 New Methods, New Data and Best Practices Potential Issues • Property Tax Abatements – Could pose an issue for the period where any taxes are abated. • As if first “fully” occupied. • The outputs are only as good as the inputs. • Critical data for estimating impacts.
- 19. PA-APA, October 2016 New Methods, New Data and Best Practices FIA Data Needs Timeliness Specific geographical coverage Relevant housing configurations
- 20. PA-APA, October 2016 New Methods, New Data and Best Practices Data Needs • At a minimum a good FIA requires: – Description of the development – Local revenue and expenditure data – Local property value data and tax rates – Number of estimated future residents and workers
- 21. PA-APA, October 2016 New Methods, New Data and Best Practices Critical Information & Multipliers Occupants • Age • School-Age Children • Public School Attendees • Household Income • Number of Cars Available • Year of Moving In • Other Information Housing Units • Structure Types • Number of Bedrooms • Rental or Owned • Year Structure Built • Other information School Traffic
- 22. PA-APA, October 2016 New Methods, New Data and Best Practices Possible Data Sources • Census (Summary 1 File) • American Community Survey • American Housing Survey • Customized Survey • Administrative Records • Public Use Microdata Sample X X X X X
- 23. PA-APA, October 2016 New Methods, New Data and Best Practices 2006 Fannie Mae Demographic Multiplier Series • 2000 PUMS, i.e. survey data in the 1990s • State level data • Occupied units built between 1990 and 1999 • Average number of occupants • Average number of School-Age Children (SAC)
- 24. PA-APA, October 2016 New Methods, New Data and Best Practices Issues of the 2000 Multipliers • Statewide averages cannot reflect local characteristics • Took 3 to 4 years to prepare • Not updated afterward • Drastic demographic changes • Estimation biases • Insufficient data
- 25. PA-APA, October 2016 New Methods, New Data and Best Practices 2000 to 2016 Many Changes Between 2000 and 2010, the average household size remained constant or declined in every state. Only in five states the average household size grew Texas, Delaware, Florida, California, Nevada (2.62 to 2.65) Pennsylvania: 2.48 to 2.45
- 26. PA-APA, October 2016 New Methods, New Data and Best Practices Average Household Size 2.2 2.3 2.4 2.5 2.6 2.7 2.8 Maine North Dakota Vermont Montana South Dakota Wisconsin New Hampshire Michigan US Louisiana New Mexico Alaska 2000 2010 US 11 States with Largest Absolute Decline in the 2000s Sources: Table H12, 2000 and 2010 Census SF1
- 27. PA-APA, October 2016 New Methods, New Data and Best Practices Public Use Microdata Sample Viable, robust and workable but not so easy to use
- 28. PA-APA, October 2016 New Methods, New Data and Best Practices Public Use Microdata Sample (PUMS) • ACS raw data: Un-tabulated records about individuals, households, and housing units. • Released every year. • 1-year, 3-year, and 5-year samples. • 1-percent per year. • Most recent: 2010-2014 5-Year ACS PUMS. • Geographic Areas: Region, Division, State, and PUMA.
- 29. PA-APA, October 2016 New Methods, New Data and Best Practices Public Use Microdata Areas (PUMAs) • A PUMA represents at least 100,000 residents, but can up to 190,000 (example: Pittsburgh north). • Boundaries depends on population density. • Revised after each decennial census. • 92 PUMAs in Pennsylvania, the 5-Year sample represents 12.7 million persons. • 11 PUMAs in Philadelphia, 2 in Pittsburgh.
- 30. PA-APA, October 2016 New Methods, New Data and Best Practices CDA Research of the 2014 PUMS • PUMA level analysis using 5-Year Sample • Demographic Multipliers and Planning Ratios – Number of persons or Average Household Size – School-Age Children – Public school attendees – Cars available – Average household income
- 31. PA-APA, October 2016 New Methods, New Data and Best Practices CDA Research of the 2014 PUMS Samples • All occupied units • Movers (new residents) vs. new units • Householders 55+ • Transit-commuter households • Condominium households • Low & moderate income households • Housing and rental values • Others
- 32. PA-APA, October 2016 New Methods, New Data and Best Practices CDA Research of the 2014 PUMS Housing Configurations • Number of bedrooms: studio & 1, 2, 3, etc. • Housing structure: Single family detached, single family attached, multifamily • Tenure: rental or owned • A combination of the above and crosstab with the samples
- 33. PA-APA, October 2016 New Methods, New Data and Best Practices Current Demographic Multipliers and Planning Ratios Changes between 2000 and 2014 Local Variations
- 34. PA-APA, October 2016 New Methods, New Data and Best Practices Findings of the Pennsylvania Multipliers 1. Changes between 2000 and 2014 2. Correlation between New Residents New Units Samples 3. Local Variations 4. Location-Specific Data Needed
- 35. PA-APA, October 2016 New Methods, New Data and Best Practices Changes between 2000 and 2014: Average Household Size Pennsylvania State Level, New Units Sample Sources: Community Data Analytics (2016), based on 2000 Census and 2010-2014 5-Year ACS-PUMS Fannie Mae Foundation (2006), Residential Demographic Multipliers, Pennsylvania Table 1 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 SFD 2B SFD 3B SFD 4B SFD 5B+ SFA 2B SFA 3B SFA 4B 2-4 1B 2-4 2B 2-4 3B 5+ Own 1B 5+ Own 2B 5+ Own 3B 5+ Rent 1B 5+ Rent 2B 5+ Rent 3B 2000 2014
- 36. PA-APA, October 2016 New Methods, New Data and Best Practices 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 SFD 2B SFD 3B SFD 4B SFD 5B+ SFA 2B SFA 3B SFA 4B 2-4 1B 2-4 2B 2-4 3B 5+ Rent 1B 5+ Rent 2B 5+ Rent 3B 2000 2014 Changes between 2000 and 2014: School-Age Children Pennsylvania State Level, New Units Sample Sources: Community Data Analytics (2016), based on 2000 Census and 2010-2014 5-Year ACS-PUMS Fannie Mae Foundation (2006), Residential Demographic Multipliers, Pennsylvania Table 2
- 37. PA-APA, October 2016 New Methods, New Data and Best Practices 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 New Residents vs New Units: Average Household Size 2014 Pennsylvania State Level Scatterplot • New units sample is a subset of new residents sample (1 to 3 or much less). • New units sample commonly does not have sufficient sample size. • New residents sample has less variability, so more reliable. • New residents sample reflects longer- term effects. • New residents sample has slightly larger value for rental, multifamily, and 1 to 3 bedroom units. • Pearson R = 0.9733 NewUnitsSample New Residents Sample Source: Community Data Analytics (2016), based on 2010-2014 5-Year ACS-PUMS
- 38. PA-APA, October 2016 New Methods, New Data and Best Practices 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 New Residents vs New Units: School-Age Children 2014 Pennsylvania State Level Scatterplot • Estimations for SAC has larger variability than those for household size. • New residents sample is more reliable. • New residents sample reflects longer- term effects. • New residents sample has slightly larger value for rental, single-family attached, multifamily, and 1 to 3 bedroom units. • Pearson R = 0.946 NewUnitsSample New Residents Sample Source: Community Data Analytics (2016), based on 2010-2014 5-Year ACS-PUMS
- 39. PA-APA, October 2016 New Methods, New Data and Best Practices Selected PUMAs in Pennsylvania • Geography: From different parts of the state • Counties: 16 PUMAs from 13 counties • Development density: City, suburb and exurb, rural • These 16 PUMAs represent a population of 2.25 million (2010) • Population ranges from 106,600 (Lancaster County central) to 195,600 (Pittsburg South)
- 40. PA-APA, October 2016 New Methods, New Data and Best Practices Selected PUMAs in Pennsylvania 00900 00702 00701 02901 02803 00102 01701 01807 02002 03502 03501 03002 0300303401 03103 03209
- 41. PA-APA, October 2016 New Methods, New Data and Best Practices Allentown and Lehigh Valley Selected PUMAs in Pennsylvania Scranton and Lackawanna
- 42. PA-APA, October 2016 New Methods, New Data and Best Practices Allegheny and Westmoreland Selected PUMAs in Pennsylvania Lancaster Region
- 43. PA-APA, October 2016 New Methods, New Data and Best Practices Northwest and Central PA Selected PUMAs in Pennsylvania 202 Corridor
- 44. PA-APA, October 2016 New Methods, New Data and Best Practices Southeastern Pennsylvania Selected PUMAs in Pennsylvania
- 45. PA-APA, October 2016 New Methods, New Data and Best Practices Pennsylvania Local Variations: School-Age Children 2014 PUMA Level, New Residents Sample Source: Community Data Analytics (2016), based on 2010-2014 5-Year ACS-PUMS 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 102 701 702 900 1701 1807 2002 2803 2901 3002 3003 3103 3209 3401 3501 3502 PA All Occupied Units Blended Housing Configuration
- 46. PA-APA, October 2016 New Methods, New Data and Best Practices 0 0.1 0.2 0.3 0.4 0.5 102 701 702 900 1701 1807 2002 2803 2901 3002 3003 3103 3209 3401 3501 3502 PA 2014 PUMA Level, New Residents Sample Pennsylvania Local Variations: School-Age Children 2-Bedroom Multi-family Units Source: Community Data Analytics (2016), based on 2010-2014 5-Year ACS-PUMS
- 47. PA-APA, October 2016 New Methods, New Data and Best Practices 0.0 0.2 0.4 0.6 0.8 1.0 1.2 102 701 702 900 1701 1807 2002 2803 2901 3002 3003 3103 3209 3401 3501 3502 PA 2014 PUMA Level, New Residents Sample Pennsylvania Local Variations: School-Age Children 3-Bedroom Single-family Units Source: Community Data Analytics (2016), based on 2010-2014 5-Year ACS-PUMS
- 48. PA-APA, October 2016 New Methods, New Data and Best Practices Reverse Fiscal Impact Analysis Innovative tool in property buy-outs and acquisition proposals Disaster prone areas
- 49. PA-APA, October 2016 New Methods, New Data and Best Practices Reverse Fiscal Impact Analysis • Reverse FIA is tool for decision-makers. • “Retreat” option • Targeted tool for targeted audience • Excludes public safety/health considerations
- 50. PA-APA, October 2016 New Methods, New Data and Best Practices Reverse Fiscal Impact Analysis • Buyouts for high-cost flood-prone properties • NFIP deficit = $19B • New Jersey = $5.32B in payouts
- 51. PA-APA, October 2016 New Methods, New Data and Best Practices Reverse FIA Framework • FIA estimates new construction & development • Reverse FIA estimates loss of development & “de- construction.” • Does not include “induced impacts.” • Does not include acquisition & demo costs. • Uses real data. • Reduced population = reduced demand for services • Typically uses avg. cost method
- 52. PA-APA, October 2016 New Methods, New Data and Best Practices Reverse FIA Framework Traditional • Revenue (taxes, fees, non-local) • Costs (municipal services, education, debt service) Reverse FIA • Costs (revenue lost) • Revenue (cost savings)
- 53. PA-APA, October 2016 New Methods, New Data and Best Practices Reverse Fiscal Impact Methodology 1. Determine geographic area. 2. Determine type of properties & values. 3. Estimate costs by type of property/# of residents/workers/school children. 4. Project losses due to buyouts. 5. Project revenue loss due to buyouts. 6. Project cost savings. 7. Apply sensitivity analysis.
- 54. PA-APA, October 2016 New Methods, New Data and Best Practices Example of Estimating Lost Revenue Projected pop. loss: 10 units x 3 (AVHH) = 30 persons Projected school children loss: 10 x 0.2 = 2 children Municipal revenue loss: 10 homes x $100,000 x 0.005 + 1 commercial property x $500,000 x 0.005 = $5,000 + $2,500 = $7,500 School District revenue loss: 10 homes x $100,000 x 0.1 + 1 commercial property x $500,000 x 0.01 = $5,000 + $10,000 = $15,000
- 55. PA-APA, October 2016 New Methods, New Data and Best Practices Example of Estimating Cost Savings Municipal: 30 persons x $1000 x .9 + 5 workers x $1000 x 0.1 = $27,000 + $500 = $27,500 School District: 2 children x $13000 x .5 = $13,000
- 56. PA-APA, October 2016 New Methods, New Data and Best Practices Potential Fiscal Impact & Sensitivity Analysis Potential impact (savings – loss) Muni: $27,500 - $7,500 = $20,000 potential savings If muni can save 50% of potential cost savings = $6,250 If muni can save 10% of potential savings = -$4,750 School District: $13,000 - $15,000 = -$2,000
- 57. PA-APA, October 2016 New Methods, New Data and Best Practices Example of Reverse FIA Sea Bright Borough • 198 residential units, 21 commercial properties, municipal impact only. • $85,000 (savings) (50% of savings realized) • -$208,000 (cost) (25% of savings realized) 2050 Sea Level Rise
- 58. PA-APA, October 2016 New Methods, New Data and Best Practices Example of Reverse FIA Mystic Island • 100 to 500 residential properties, municipal and primary school impact only. • Municipal impact: - 70,000 to -$515,000 (cost) • Primary School District impact: - $37,000 to - $363,000 (cost)
- 59. PA-APA, October 2016 New Methods, New Data and Best Practices Example of Reverse FIA South River • 54 to 126 residential properties, municipal and school district impacts. • Municipal impact: -$3,700 to -$6,500 (cost) • School District impact: -$26,500 to -$53,000 (cost)
- 60. PA-APA, October 2016 New Methods, New Data and Best Practices Reverse Fiscal Impact Analysis Conclusions • Very limited number of examples, varied results. • South River example probably overestimates municipal cost impact. • Characteristics of different towns result in significant differences in results. • Need to perform more analyses.
- 61. PA-APA, October 2016 New Methods, New Data and Best Practices SESSION E1 CONCLUSIONS • Fiscal Impact Analysis remains one of the important tools. • Hybrid approach should be used. • Current and geographically specific demographic multipliers & planning ratios from PUMS should be used. • FIA can be used to evaluate property buy-outs and acquisition proposals.
- 62. PA-APA, October 2016 New Methods, New Data and Best Practices Discussion Small group or open?
Editor's Notes
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- Peter or Sidney
- Peter This is a common question asked by local elected and planning officials when presented with development plans. The answer depends on the town’s capacity of absorb growth. Whenever land is developed, not matter if it is residential, commercial, or industrial, a whole host of costs are incurred by the municipality. This includes police fire, emergency service, trash removal, street maintenance, sewer and water. It is important for municipalities to determine whether or not the flow of new property tax revenue from a development will balance out the increased costs. For example, if a particular development proposal or land use alternative will generate more in tax revenue than it will in public service costs, the surplus revenues may enable a community to improve services or fund infrastructure maintenance/replacement backlogs. Conversely, if public service costs exceed revenues, it could increase pressure to raise taxes, find additional revenue sources, or decrease levels of service. This is where fiscal impact analysis comes in. Surprisingly or maybe not that surprisingly, the overwhelming majority of municipalities do not require an FIA. A fiscal impact analysis is a tool that compares the local government costs against local government revenues associated with development policies and projects. This allows local governments to weigh land use policy decisions, acceptable levels of public services provided, plans for capital investments, and long-term borrowing needs, in addition to prompting local officials to evaluate current and future revenue sources.
- Peter The beginnings of the use of fiscal impact analysis in government decision-making can be traced to the 1930’s, where it was used to examine the impacts of public housing and urban renewal programs. During the 1950’s and 1960’s, fiscal impact analysis began to be used for examining the impacts of private developments, as well as proposed annexation areas. By the 1980’s, fiscal impact analysis had become a more common planning and land use assessment tool, used for the evaluation of rezonings, comprehensive plan amendments and economic development decisions From a planning perspective, a fiscal impact analysis directly links proposed zoning and land uses with projected population and employment growth related to residential and nonresidential development. Can help ensure that planning and growth management policies are “legally defensible” and can withstand the close scrutiny of legal challenges, Land Use policies – should a jurisdiction encourage higher density development or add an overlay? Rezoning – most of the time rezoning cases are not evaluated from a fiscal perspective. Annexations – may not be that relevant here on the east coast. Some communities perceive annexation as a cash cow because of the instant revenues from the increased tax base, but they fail to considered the increase cost of serving the new residents. Redevelopment – what are the fiscal impacts of encouraging the development of a vacant/underused parcel? Fiscal Impact analysis can also address budget and finance questions. Capital Improvement Programming – can help forecast the need for additional capital facilities Revenue Forecasting – changes in revenues from land use change Fiscal Planning Level of Service Changes
- Peter A well-prepared fiscal impact analysis can go a long way in increasing the confidence of both elected officials and the general public for a proposed plan. However, an analysis prepared using a methodology inappropriate to the situation, faulty assumptions, or a “black-box” approach, can significantly erode the public’s trust and confidence in the plan. Average costing is the simpler more common procedure. It attributes costs to new development according to average cost per unit of service in existing development times the number of units the growth is estimated to create or the demand for that unit. It does not take into account excess or deficient capacity to deliver services, and it assumes that average costs of municipal services will remain stable in the future. Alternatively, marginal costing relies on analysis of the demand and supply relationships for public services. This procedure recognizes that excess and deficient capacity exits in communities. It views growth not in a linear manner, but as a more cyclical process in terms of the impact on expenditures. The distinction between average and marginal costing is fundamental to fiscal impact analysis. Marginal and average costing approaches may result in dramatically different estimates of fiscal impacts for the same development. This is due to the “lumpy” nature of certain public services, like sewage treatment plants and water supply systems. When such facilities are built in a community, they are typically financed with long-term debt and built with the expectation that they will also serve future population growth in the community. Therefore, the incremental cost of providing the service to one more resident is low. However, these facilities do have a threshold level where surplus capacity is eventually depleted. It is at this point that the new development or new growth requires new infrastructure investment and the marginal cost of serving a new resident may actually be higher than the average cost. The marginal cost approach focuses on defining a community’s marginal response to a new development or land use change through careful attention to existing demand and supply relationships in a community.
- Peter The most significant limitation of the straight per capita multiplier approach is that it does not account for excess or deficient capacity. It also assumes that the cost of services for new development is the same as existing; and this is not fully justified in all cases. The modified model detailed here requires you to calculate the operating per-capita costs and revenues associated with development and then to examine your capital facilities using a case study approach to allow for issues of capacity.
- Peter The first step in the analysis is to estimate the new population and employees associated with the development. The PUMS analysis that we discussed earlier is very useful. The most important factor affecting the fiscal impacts of development is the number of school-aged children. If the developer does not have an estimate of the number of new employees. This can be estimated using square feet per employee estimates.
- Peter Step 2 – The most important step here is determining which budget line items will be impacted by the development. Not all line items and departments will be impacted by the new development. For example, a new residential development is unlikely to generate significant administrative burden such that the mayors office will need to hire more staff. However, the municipality may have to hire more police officers or firefighters. The portion of costs associated with residential uses is generally estimated using one of two methods: either through local knowledge; or through the use of property value data and parcel data For example, if you know that all expenditures for health and human services are associated with residents only, then allocate all of these costs to residential land uses. If expenditures are associated with both population and workers in the community, use the fall-back method to allocate costs. the residential share of all service costs is estimated by dividing the residential property value and number of parcels by total value and total number of parcels, respectively. These two results are averaged and this value is applied to local costs to determine the residential share of costs. For example, if residential parcels comprise 60% of the total property value, then assume that 60% of the costs are associated with residential land uses and the remainder is associated with non-residential land uses. This is a little trickier for when it comes to school impacts. Typically a portion of the costs associated with educating each study is paid by the state. When estimating the costs per students, you should focus on the costs paid for from local funds. Most school district budgets break the costs down by who pays for them – local vs state vs federal government.
- Peter Derives the per capita cost estimates by dividing the costs by the number of residents and number employees. To operate the total operating costs, multiply the per-capita costs by the number of new residents and the number of new employees. In growing communities, it is often necessary to invest in capital facilities to accommodate new development. New streets, water and sewer systems and schools may be needed to serve additional population. Capital costs include but are not limited to: sewer and water infrastructure, new schools, road improvements, police cars, fire trucks and emergency equipment, recreational facilities, etc. The identification of infrastructure facilities necessary to accommodate the new development should occur in a systematic manner. This is where the hybrid approach comes in. This information can be identified in a number of ways. One would be to contact department heads for their expertise on necessary capital improvements to serve new development. Another would be to analyze any support documentation the community may have, such as a capital improvement plan.
- Peter Most local government have two sources of revenues: Property taxes and other revenues. Other revenues include fines and tickets, licenses and permits, special assessments, state funds. You need to determine which ones are likely to impacted by the new development. Then you follow a similar approach as was used with costs to allocate the revenue among uses. Then estimate the per-capita and per-employee revenues.
- Peter In order to estimate the property tax revenue, you need to arrive at the assessed value of the completed project. The exact methods will determine on the municipality and is likely going to be more art than science. Once the assessed value is determined, multiply the assessed value by the property tax millage rate. Estimate the other revenue by multiplying the new number new residents and employees by the respective per-capita revenue estimates. The project is also expected to generate one-time upfront revenue from building permit, utility connection fees, impact fees, ect. Those also need to be calculated. The number, type, and amount of upfront fees are liklet to differ from municipality to municipality. The final step is to calculate the net-fiscal impacts by subtracting the costs from the revenues. If the net-impacts are positive the project will generate more revenues than costs and if the net-impacts are negative, the project will cost more in expenditures than it will generate in revenue.
- Peter Property tax abatements can potentially cause a project to have negative impacts in the early years. The results of the analysis are only as good as the data used. That is why it is important to use the most up-to-date and geographically specific data, such as the PUMS multipliers that Rinoa discussed earlier. Now Sidney will discuss a fiscal impact analysis that he was involved in.
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- Sidney You need local revenue and expenditure data for both the local government and school district. This can be obtained from the respective annual budgets. The number of existing residents and workers can be obtained from Census Data.
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