Forecasting Transportation Revenue Sources: Survey of State Practices (2015)

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33 APPENDIX B Case Studies of Selected State Practices Arizona California Michigan Minnesota Missouri Oregon Texas Washington

34 ARIzoNA Short-Term Forecasting Entity: Arizona’s DOT Long-Term Forecasting Entity: Arizona’s DOT Types of Revenue Forecast: Federal funds allocated to the state, state gasoline per gallon taxes, state diesel fuel per gallon taxes, state vehicle registration and/or license fees, local (county or regional) sales and property taxes Type of Forecasting: Expert Consensus, Econometric. “Since 1986, the Arizona Department of Transportation (the Department) has used a comprehensive regression-based econometric model to estimate Transportation Excise Tax revenues for Maricopa County. These revenues, which flow into the Regional Area Road Fund (RARF), are the major funding source for the Maricopa County Freeway Program.” Risk Analysis Panel: “The revenue forecast is highly dependent on estimates of independent variables. In order to deal with variability between estimated and actual values, the Department introduced the Risk Analysis Process (RAP) in 1992. The RAP relies on probability analysis and the independent evaluation of the model’s variables by an expert panel of economists. The process results in a series of forecasts, with specified probabilities of occurrence, rather than a single or ‘best guess’ estimate.” An example of the RAP output can be found in this report. Data Inputs: For Maricopa County excise tax, Arizona’s DOT uses the following data inputs: Maricopa County nominal personal income growth, Maricopa County population growth, Maricopa County construction employment growth, Phoenix consumer price index (CPI), Sky Harbor passenger traffic growth, Maricopa County total non-farm employment growth, 30-year mortgage rate. Innovative revenues that are typically forecast: none Shortcomings: “ADOT has a very robust forecasting process that has been in place since 1992, which has proven highly accurate in all but the most volatile of economic environments. Publication: Arizona DOT’s revenue forecasts and risk analysis panel results are published in the report entitled: “MARICOPA COUNTY TRANSPORTATION EXCISE TAX Forecasting Process & Results FY 2013–2026” and can be found online at http://www.azdot.gov/docs/default- source/businesslibraries/rarfcastproc1426.pdf?sfvrsn=4

35 CAlIFoRNIA Short-Term Forecasting Entity: Caltrans (California DOT). This agency must also use fore- casts developed by the California Department of Finance in certain official budget planning and budget implementation documents. Long-Term Forecasting Entity: Caltrans (California DOT) and others. The California Trans- portation Commission (CTC) determines the methodology and assumptions used in the State Transportation Improvement Plan (STIP) Fund Estimate. Types of Revenue Forecast: Federal, state gasoline tax, state diesel tax Type of Forecasting: Trend extrapolation, econometric Data Inputs: California’s STIP Fund Estimate is required by statute to “display revenues that are based on current statutes and the most recently enacted state budget. Revenue estimates for future periods utilize historic trends and the economic outlook as a basis” (STIP 2014, p. 5). Annual increases to current levels are assumed at a fixed rate. Innovative revenues that are typically forecast: none Shortcomings: Lack of sophistication in forecasting revenues leaves us with “long-term trends” basis rather than an economic basis. Publication: California’s revenue forecasts are not regularly published. For explanation of long term forecasting methodology and assumptions, see the 2014 Statewide Improvement Plan (STIP) Fund Estimate, Adopted by the California Transportation Commission Aug. 6, 2013. http://www. dot.ca.gov/hq/transprog/ctcliaison/misc%20OCTCL%20Info/Final_2014_STIP_FE.pdf

36 MIChIgAN Short-Term Forecasting Entity: Michigan’s DOT. The 1 to 2 year forecast is done in “consen- sus” with Michigan Department of Treasury. The DOT and Treasury meet to agree on the two-year revenue forecast. Years 3 to 5 are forecast by DOT entirely. Long-Term Forecasting Entity: Michigan’s DOT. Types of Revenue Forecast: Federal funds allocated to the state, state gasoline per gallon taxes, state diesel fuel per gallon taxes, state vehicle registration and/or license fees. Type of Forecasting: Forecasting is described as follows: “State revenue estimate is based on Michigan’s DOT’s share of the MTF, as estimated by the Department of Treasury, Economic and Revenue Forecasting Division. Future state revenue is forecast using a long-range forecast- ing model managed by MDOT’s Statewide Transportation Planning Division. Federal funding is assumed to remain at for FY 2014–2016 and then increase at a 2.5 percent rate in FY 2017–2018.” Specifics of the revenue forecasting methodology were not available at the time of this publication. Proposals: Within the last two years, the DOT has received proposals from elected officials regarding various revenue enhancements, and provided detailed estimates of revenue projections. Shortcomings: “Currently using unsupported software. In the coming year or two we will be attempting to rewrite the revenue model in MS Excel. It would be nice to generate ‘what if’ scenarios with more ease.” Publication: A description of the short-term revenue forecast is available online in the 2014–2018 Five-Year Transportation Program, which can be found online at: http://www.michigan.gov/ documents/mdot/MDOTFinal5YearPlan20114-2018_445737_7.pdf. However, the MDOT long- term model is not published online.

37 MINNESotA Short-Term Forecasting Entity: Minnesota’s DOT Long-Term Forecasting Entity: Minnesota’s DOT Types of Revenue Forecast: Federal funds allocated to the state, state gasoline per gallon taxes, state diesel fuel per gallon taxes, state vehicle registration and/or license fees, state sales taxes designated for transportation Types of Forecasting: Trend extrapolation based on historical trends and national macro- economic forecasts. To forecast the state per gallon gasoline excise tax, Minnesota uses national macroeconomic forecast of U.S. gasoline consumption from Global Insight, a consultant. MnDOT also reviews regional forecast information from the federal Energy Information Administration (EIA). Finally, a comparison is made of actual local consumption versus previous forecast infor- mation provided by GI and EIA. The estimated quantities are then multiplied by the excise tax rate to produce the final forecast. For the state vehicle registration fee estimate, MnDOT has a model to forecast revenue from passenger vehicles that is largely based on forecasts of the purchase of new passenger vehicles. Forecasts of the sales of new vehicles are provided by GI. Proposals: The following changes or proposals have been considered: Gas tax increases, increased motor vehicle sales taxes, increased registration taxes, variety of bonding proposals Shortcomings: “The process for forecasting each specific revenue source is fairly well defined, but not documented, only well known to a few key staff. Mostly standalone Excel files: one for each revenue type.” Publication: Revenue forecasts by biennium are released by Minnesota’s DOT. The latest fund summary can be found at the following link: http://www.dot.state.mn.us/funding/documents/ transportationfundsforecasts-feb2014.pdf

38 MISSouRI Short-Term Forecasting Entity: Missouri’s DOT Long-Term Forecasting Entity: Missouri’s DOT Types of Revenue Forecast: Federal funds allocated to the state, state gasoline per gallon taxes, state diesel fuel per gallon taxes, state vehicle registration and/or license fees, state sales taxes designated for transportation. Types of Forecasting: Trend extrapolation, expert consensus. Missouri’s DOT developed econometric regression models that looked at the following variables: Motor vehicle fees (net of refunds); Driver’s license fees (net of refunds); Motor vehicle sales tax revenue (net of refunds) deposited to the State Road Fund; Motor vehicle use tax revenue (net of refunds); Gross gasoline tax revenue. Socioeconomic data are from Department of Energy: an annual energy outlook that includes national fuel prices, and usage. In 2007, Missouri’s DOT hired a consultant to assess and recommend improvements. For a detailed look at this case study, please see chapter four of this report. Shortcomings: According to the survey respondent from Missouri’s DOT, state forecast rev- enue has come in with positive variance no more than 3.2% within the past five year. The real uncertainty, she says, lies at projecting future federal reimbursements. Publication: Revenue forecasts for Missouri’s DOT are not available in published form. A copy of the consultant’s report can be found in “A Review and Critique of MODOT’s State Revenue Forecasting Model: Final Report,” published by HDR|HLB in 2007 and available online here: http://library.modot.mo.gov/RDT/reports/Ri06024/or07013.pdf

39 oREgoN Short-Term Forecasting Entity: Department of Transportation and others Long-Term Forecasting Entity: Department of Transportation and others Types of Revenue Forecast: Federal, state diesel, state vehicle registration/license, state sales tax, weight-mile tax Type of Forecasting: Econometric Data Inputs: Oregon uses 6–8 explanatory variables, including national CPI, employment, housing starts, real GDP, real fuel price, and new automobile sales. Also uses Oregon-specific inputs—employment, housing starts, populations, Portland CPI, real personal income. Oregon uses fuel efficiency national rate from IHS Global Insights “because light-duty vehicle efficiency is hard to measure at the state level.” The independent variables of Oregon’s regression equations are quarterly econometric vari- ables. The model is updated every six months with new data. Oregon DOT estimates quantities; e.g., vehicle registrations or gallons of motor fuel, to determine the total volume of transactions. The current rate of tax/fee is then multiplied by the quantities. Finally, to even out the variations Oregon DOT runs monthly time series accounts for seasonal effects. The monthly outputs are aggregated to quarterly forecasts. To date, Oregon DOT provides revenue forecasts only for traditional revenues, which include vehicle transaction fees (registration, title, transfer) and driver-related fees. Oregon’s forecast does not include federal allocations, which are considered “other funds.” Innovative revenues: As a formal exercise, revenue forecasting is based on “current law,” DOT does not speculate on proposals until implementation. Therefore the agency’s modeling does not include forecasts of innovative revenue proposals. Shortcomings: “Major shortcoming of the revenue forecasting techniques currently used by the department is the ongoing inability to more accurately estimate economic downturns and associated detrimental impacts on transportation revenues and more accurately consider fuel effi- ciency and vehicle miles of travel growth uncertainty in future years.” Publication: Revenue Forecast is published twice a year. This document is also available online at: http://www.oregon.gov/ODOT/TD/EA/reports.shtml and scroll down to “Transportation Revenue Forecasts.”

40 tEXAS Short-Term Forecasting Entity: Texas’ DOT Long-Term Forecasting Entity: Texas’ DOT Types of Revenue Forecast: Federal funds allocated to the state, state gasoline per gallon taxes, state diesel fuel per gallon taxes, state vehicle registration and/or license fees, tolls, alloca- tions of state general revenues, state sales taxes on lubricants designated for transportation, Local participation, Interest on cash balances, other agency and miscellaneous revenue deposited to the highway fund, Taxes and fees dedicated to the Texas Mobility Fund Types of Forecasting: In Texas, different agencies have responsibility for forecasting different types of revenues. TxDOT forecasts motor fuel tax and sales tax and miscellaneous revenues, while the Department of Motor Vehicles forecasts vehicle registration fees. Toll revenues for TxDOT toll roads are forecast through consultants working with the Toll Operations division of TxDOT. Toll revenues for non-TxDOT toll roads that do not share revenue with TxDOT are fore- cast by the tolling authority or entity and are not communicated with TxDOT. Future revenues are projected based on financial analysis that includes historical trends, current statutes, the Comptroller’s Biennial Revenue Estimate, and other sources. A set of fixed assump- tions are used to estimates of future cash balances. In addition, TxDOT and Texas A&M University jointly manage a scenario analysis program called TRENDS, which is used to estimate revenues under changing assumptions about demographics, fuel price, and other variables. The program is available to the public here: http://trends-tti.tamu.edu/ A set of revenue and expenditure assumptions, reviewed and updated at least annually, along with projections for active and future projects, are used to make estimates of future cash balances. Shortcomings: Revenue forecasts were reported to be very accurate. Publication: An Executive Summary of the revenue forecasts and assumptions can be found here: http://ftp.txdot.gov/pub/txdot-info/fin/cash_forecast.pdf

41 WAShINgtoN Short-Term Forecasting Entity: Washington’s DOT is lead agency with assistance from other state economists and some consulting firms Long-Term Forecasting Entity: Washington’s DOT is lead agency with assistance from other state economists and some consulting firms Types of Revenue Forecast: Federal funds allocated to the state, state gasoline per gallon taxes, state diesel fuel per gallon taxes, state vehicle registration and/or license fees, tolls, allo- cations of state general revenues, state sales taxes designated for transportation, local (county or regional) sales and property taxes, rental car fee, driver related fees, aviation fees, ferry fares and miscellaneous business related revenues Types of Forecasting: Econometric, travel demand models and some trend analysis Washington’s DOT uses multiple models for short and long term forecasts of diesel and gaso- line consumption in the state of Washington. Until recently, Washington’s DOT used a multi-step regression with econometric independent variables for both their quarterly and annual models. The quarterly model has since been revised, and Washington’s DOT now uses time series regres- sion to forecast quarterly consumption. This change was made in 2012 for both gasoline and diesel quarterly forecasts. According to Washington’s DOT staff, using time series analysis for quarterly consumption has led to less optimistic and therefore more accurate results. Additionally, it is better able to account for seasonal variability, which occurs at a quarterly level. This change is new, and the DOT staff have not yet published or described the changes in forecasting techniques in their technical manuals or publications. The equation for annual gasoline consumption in Washington is logarithmic in its formula- tion. The final model is a log-log functional model, with first difference of natural logs used on both sides of the equation. The independent variables in the annual gas consumption model are Washington non-agricultural employment and a composite variable of Washington gas prices and US on-road fuel efficiency. Shortcomings: The respondents said that, in certain tolling area, Washington’s DOT models have relied on travel demand models and some of these models have been prone to overestimate traffic and revenue. In the diesel consumption forecast model, the lack of importance of the truck fuel economy in the forecast models could result in overestimating diesel consumption in the future. Publication: The study that resulted in an update to the fuel consumption forecast model is published online and can be found here: http://www.ofm.wa.gov/budget/info/Nov10transpo fuelconsumptionsummary.pdf

Abbreviations used without definitions in TRB publications: A4A Airlines for America AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers MAP-21 Moving Ahead for Progress in the 21st Century Act (2012) NASA National Aeronautics and Space Administration NASAO National Association of State Aviation Officials NCFRP National Cooperative Freight Research Program NCHRP National Cooperative Highway Research Program NHTSA National Highway Traffic Safety Administration NTSB National Transportation Safety Board PHMSA Pipeline and Hazardous Materials Safety Administration RITA Research and Innovative Technology Administration SAE Society of Automotive Engineers SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005) TCRP Transit Cooperative Research Program TEA-21 Transportation Equity Act for the 21st Century (1998) TRB Transportation Research Board TSA Transportation Security Administration U.S.DOT United States Department of Transportation

TRANSPORTATION RESEARCH BOARD 5 0 0 F ifth S tre e t, N W W a s h in g to n , D C 2 0 0 0 1 A D D R ESS SER VICE R EQ UESTED NO N-PRO FIT O RG . U.S. PO STAG E PA ID CO LUM BIA, M D PER M IT NO . 88 ISBN 978-0-309-27188-2 9 780309 271882 9 0 0 0 0