General Principles Of Financial Planning Pdf

General Principles Of Financial Planning Pdf – Guide to Developing Financial Plans and Performance Measures for Transportation Asset Management (2019) Chapter: Chapter 4 – Financial Projections

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General Principles Of Financial Planning Pdf

General Principles Of Financial Planning Pdf

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Important Financial Skills That Employers Value

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41 Revenue and expense forecasting helps agencies plan ahead and anticipate their ability to meet long-term goals. With the sources and uses identified in the previous chapter, the next step is to plan for future income and expenses to build a sustainable financial plan. There are many different methods for forecasting income and expenses. This chapter provides background information on methods commonly used by transportation organizations and outlines the steps that TAM practitioners should follow when forecasting a TAM financial plan. 4.1 Fundamentals of Forecasting Introduction To develop a comprehensive asset management financial plan, agencies need to identify current and estimated sources of revenue 10 years into the future. Fortunately, state DOTs are already in the practice of forecasting sources of income and expenses. In particular, state DOTs include financial projections in their STIP and their long-term plan. The 10-year look ahead required by the FHWA’s TAMP Rule extends beyond STIP’s four- to six-year time horizon, but falls within the lifetime of the state’s long-term plan. Thus, TAM specialists do not have to develop the forecasts themselves, but they still need to understand the methodologies and assumptions used in the forecasts. While this chapter provides background information and key foundations for revenue and expense forecasting, the asset manager who develops the asset management financial plan is encouraged to use the agency’s already existing efforts to forecast future project revenue. At this point, the TAM specialist should receive a list of asset management resources and uses, populated with budget figures for the current year. The steps at the end of this chapter guide the practitioner through the forecasting process to produce a 10-year projection of agency revenue and non-asset management use. The Asset Management Utilization Forecast will then be discussed in the next chapter. The end product will vary by agency, but Table 4-1 shows an example of a forecast worksheet based on data from the Texas Department of Transportation (Texas Department of Transportation 2017). The Texas DOT projects 11 years into the future, and Table 4-1 shows three of those 11 years. Sources of Forecasts Various methods of revenue forecasting have been developed before. NCHRP 479 Synthesis: Forecasting Sources of Transportation Revenue: An Examination of State Practices contains CHAPTER 4 Financial Forecasting

42 Guide to Developing Financial Plans and Performance Measures for Transportation Asset Management Key insights into government practices to forecast future revenues. In general, forecasting efforts at the state level focus on “traditional sources of revenue: federal fund spending or expense reimbursements, state fuel and excise taxes, state vehicle registration fees, tolls, and local taxes and fees” (Wachs and Heimsath 2015). The report describes the three most common methods of predicting future earnings from these traditional sources: • Trend extrapolation, • Expert analysis and • Econometric models. Each of these methods from the NCHRP report is summarized below. Trend extrapolation Trend extrapolation “is the application of a mathematical formula to past income levels to determine future income” (Wachs and Heimsath 2015). In this way, historical funding trends can be extrapolated into the future in a linear fashion or with some adjustment to account for expected future changes in underlying conditions (Wachs and Heimsath 2015). For example, historical data may indicate a certain rate of increase in sales tax used for transportation. Assuming the same growth in the future allows agencies to easily predict future revenue. Trend extrapolation is a simple application of basic statistical methods. In its simplest form, this can simply be a one-dimensional linear regression of the dependent variable over time, although multivariate and/or non-linear models can also be used. According to the NCHRP Synthesis 479, states generally use trend extrapolation to project revenues from funding sources that are “historically stable”, that is, the trend extrapolation method is a reliable way to forecast year 1 and year 2. 3 Year 4 Year 5 Year 6 Actual Actual Actual Projected Projected Cost Plan 1, 420 1, 496 1, 731 2, 037 1, 837 1, 539 Construction 3, 132 2, 961 2, 971 4, 1, 5 , 755 Enter 586 4, 84 4, 885 5, 166 427 56 1840 1840 186 186 166 247 166 189 192 1826 240 306 614 1 2, 331 Short-term loan D/S 376 752 1 1 1 1 Inflation / Additional programs – 1 – 5 – Total expenses 8.910 10.320 10.243 11,900 11.884 61 State Fund Fund 8,100 9.472 9.401 10.340 10.782 9.793 State Mobility Fund – revenues from bonds 220 158 169 347 300 179 State Mobility Fund – taxes and fees 229 77 9 Build America 9 0 40 96 98 88 123 GR Debt service 2 1250 134 309 -100 – General revenue 3 5 4 2 2 2 National Infrastructure Bank (SIB) 3 42 30 48 35 3 Total funds 0 2 0 1 0 2 1 11 900 11,884 10,664 Actual forecast Table 4-1. Sample high-level overview of the forecast worksheet ($ million).

Financial projections 43 income from sources such as general fund allocations and local contributions, which are determined according to a fixed formula (Wachs and Heimsath 2015). Expert consensus Another method used for project revenues is expert consensus. It “is based on the professional judgment of a select panel or conference of economists, analysts, academics and others who discuss and attempt to agree on future income projections or critical inputs that will affect those projections” (Wachs and Heimsath 2015). NCHRP Synthesis 479 notes that this method can be used in conjunction with other methods when expert panels are asked to review already made forecasts and make necessary adjustments. To obtain expert consensus, the agency may convene a panel of experts to discuss the forecast, or the agency may choose to conduct a Delphi survey. In the Delphi survey, the agency sends information about the forecast and asks various experts for opinions. The feedback is then summarized by the agency and sent back to the experts. The iterative nature of the Delphi study helps to reach consensus on the prediction. Econometric models Econometric models, such as regression analysis, are used to quantify relationships between variables. This involves identifying the independent and dependent variables that affect a particular source of income, and then establishing the relationship between them. The agency may have a different set of regression equations for each revenue source (Wachs and Heimsath 2015). Econometric models can be as simple as linear regression, which defines the relationship between a single independent variable and a dependent variable. For example, the relationship between the price per gallon of gasoline and the gallons of gasoline used can be described using linear regression. Models can also be complicated by including multiple independent (also called multivariate) variables to predict the outcome. These models find a match to the input data and predict the income from the resource based on statistically estimated coefficients of independent variables (Wachs and Heimsath 2015). NCHRP Synthesis 479 cites the use of econometric models by the Oregon Department of Transportation. Oregon DOT uses over 200 equations to forecast revenue. In order to understand the types of variables included in the econometric model, the forecasting equation for motor fuel, the sale of which is the main source of state revenue, contains the following independent variables: Index of real retail prices of motor fuel fuel, ⢠Fuel efficiency of the existing fleet of light vehicles, ⢠Total employment outside Oregon Agriculture, ⢠Oregon Real Aggregate Personal Income, ⢠Oregon Labor Force Participation Rate, ⢠Consumer Sentiment Index, and ⢠Ethanol Blend Mandate Implementation Variable. Again, the econometric model can be as sophisticated as the agency wants by including independent variables describing a particular outcome. NCHRP Synthesis 479 case studies (Wachs and Heimsath 2015) contains case studies of how different states are meeting their projections. Table 4-2 summarizes the three z-states

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