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Chapter 3
Common Analytical Methods and Their Shortcomings

Typically, a fiscal impact analysis is prepared by an analyst with a background in public finance or economics. The analyst may begin by obtaining a recent budget of the jurisdiction whose regulatory approval is being sought. Additional data for the jurisdiction's population, employment, housing units, and commercial and industrial space may also be gathered. In order to achieve a clear understanding of the proposed project or development scenario, the analyst would also need to gather information regarding the timing of development, market and taxable property values, occupancy and employment characteristics, and other pertinent economic and demographic factors. After this initial data-gathering phase, the analyst embarks on what interests us the most -- calculating the likely consequences of the proposed development on the jurisdiction's budget.

Depending on the chosen methodology, the outcomes can vary greatly. Below, we describe and review the most common methods of calculating revenues and costs, dividing the discussion in two because of some basic differences between jurisdictions' operating and capital budgets. We discuss how several frequently employed techniques can lead to inaccuracies in estimating revenue streams, cost projections, or both. Equipped with this information, an interested stakeholder reviewing a fiscal impact analysis should seek to discover which method or methods have been used in preparing it before evaluating its reliability.


Many fiscal impact analyses begin with the operating side of a jurisdiction's budget. Operating costs typically include personnel, office, administration, and sometimes minor equipment costs relating to municipal services such as police and fire protection and trash pickup. Operating revenues include general revenues such as those derived from real and personal property, income, business and sales taxes, user fees and charges, and state and federal revenue contributions.

Although there are a variety of methods to forecast operating costs and revenues, most analyses rely on one or a combination of four basic methods: (1) average per capita, (2) adjusted per capita, (3) disaggregated per capita, and (4) dynamic. Each has its own characteristics and each may yield different outcomes. Some of these methods can also be applied to capital facilities such as roads, parks and recreation facilities, and school buildings; however, additional considerations become relevant with regard to capital facilities, and these are discussed in a separate section.

Average per capita method

This is the simplest and most common method, but it also tends to be among the least reliable. It divides the existing total local budget (or individual budget categories) by the existing population (or housing units) in the jurisdiction to determine an average per-capita or per-household cost for the jurisdiction. The result is then multiplied by the expected new population or housing units associated with the proposed new development. (To account for the non-residential component of a proposal, the expected number of additional jobs must be accounted for, perhaps with an assigned per-job equivalency value that equates jobs to residents or households.) Costs and revenues are then divided by the equivalent population or housing units.

This method is referred to as the "average" or "gross" per capita approach. It is often used for expenditures and most types of tax revenues, but not for real property tax revenues, which are usually calculated separately on the basis of the expected taxable value of the new development. Since the value of new housing and commercial space is frequently assumed to be higher than that of existing housing and commercial space, such an exception for property tax revenues can yield an overly optimistic outcome. The implicit (but incorrect) assumption behind such a distinction is that the only part of the local budget likely to be changed on a per capita basis by new development is the category of real property tax revenues.

Apart from this exception, the average per capita method uses the jurisdiction's current cost and revenue patterns to forecast the impact of its new population. The major shortcoming of this approach is that it fails to recognize that both cost and revenue patterns associated with new development can differ significantly from those of the existing population and development. A second shortcoming is that the average per capita method sometimes fails to "unbundle" or separate the local jurisdiction's budget into its residential and non-residential sectors. As a result, a strictly residential development would be "credited" with a share of revenues and expenditures attributable to the non-residential sector. The average per capita approach can be particularly unreliable when the new development differs significantly from the existing development pattern, such as when it introduces a new type of housing, commercial property, or neighborhood design to a community or when an exclusively or mostly commercial development is built in a predominantly residential community.

Adjusted per capita method

In a variation, the results of the average per capita approach may be adjusted up or down on the judgment of the analyst or local officials to reflect expected changes as a result of the new development. Many fiscal impact analyses use a combination of average and adjusted per capita methods.

The adjusted per capita method relies heavily on the subjective judgment of the analyst or of local officials whose advice is used to inform the particular adjustment used. To help overcome the limitations of subjectivity, some fiscal impact analyses use local income, density, or market value data to inform the adjustments. The adjustments can be somewhat more reliable when links between these variables and the affected budget categories can be demonstrated.

Disaggregated per capita method

Another step in the direction of sophistication is the so-called "disaggregated" approach. Most local governments receive revenues from, and provide services to, both the residential and non-residential (for example, commercial, industrial, or agricultural) sectors. Typically, the average or adjusted per capita method relies on the jurisdiction's aggregated, or blended, revenue and expenditures data from both sectors. But the per-unit costs and revenues from the two sectors are rarely identical.

Recognizing this, the disaggregated method "unbundles" the local budget by estimating the costs and revenues separately for each of the jurisdiction's major land use sectors. To determine the disaggregated per-unit amounts for each sector, the amounts relevant to each are then divided by the number of service units in each (for example, number of people or households in the case of the residential sector; jobs, acreage, or thousands of square feet of floor space for the non-residential sector). Some disaggregated approaches apply the resulting figures directly to the proposed new development, while others make adjustments to reflect expected differences between existing and new development for each sector.

The disaggregated method relies on various techniques (which we need not discuss here) to segregate the local government's budget into its residential and non-residential sectors. The resulting allocations can provide a reasonable estimate of these costs and revenues, but it is rarely possible to know the exact amount attributable to each sector for all revenue and expenditure categories.

Use of multipliers

An increasingly common method among the building industry and some governments for projecting fiscal impacts involves the use of multipliers derived from economic models. Using data from the models, an analyst might take the estimated direct economic activity in dollars associated with a project and "multiply" it by a given amount to account also for indirect, secondary impacts. The total measure of economic activity is then used to estimate revenues for the purpose of determining fiscal impacts.

Such multiplier approaches to fiscal impact analysis suffer from several shortcomings. First, the multipliers are usually obtained from economic models of large regions or states. But they are applied at the level of an individual local jurisdiction that is usually only a fraction of a region's or state's economy. The smaller the jurisdiction relative to the economic region for which the multipliers have been derived, the less reliable the multipliers will be for that jurisdiction.

Furthermore, while the multipliers are applied to the revenue side of the budget, few such analyses ever apply a multiplier to the cost side of the local budget. The implicit (but often wrong) assumption is that local governments can generate revenue from secondary, induced, or indirect development without incurring increased costs in providing services to that development. Another shortcoming of the multiplier approach is its tendency to "double-count" revenues. A multiplier-based fiscal analysis of a project might credit it with the additional revenue impacts as derived from 1,000 new jobs elsewhere in the jurisdiction. But, when the separate fiscal impact analysis of the development where these jobs are located is (or was) prepared by its developer, the revenues would also be claimed on behalf of that development.

Dynamic method

The most sophisticated of the four basic methods is the dynamic approach, which recognizes that, over time, significant new development can cause a change in a jurisdiction's economic, land-use, and demographic factors, and thus in its service levels, per capita costs, and per capita revenues. Dynamic methods apply statistical techniques to time-series data from the jurisdiction, or from others that have experienced a similar development pattern; alternatively, they may use cross-sectional data from multiple jurisdictions representing a variety of development patterns. On the basis of this analysis, dynamic approaches estimate how much of "this" (such as sales tax revenue per capita) a jurisdiction can expect to get from so much of "that" (such as per capita personal income, per capita market value of housing) generated by new development.

Dynamic approaches are ordinarily more data-intensive than others and require substantial time, effort, and expertise in preparing the required statistical analysis. To generate meaningful results, dynamic approaches may also require analysis of individual revenue and expenditure categories, because each one can be affected differently by the economic, demographic, and land-use characteristics of new development. Statistical approaches best capture the dynamic impact of the cumulative impacts of development on local governments. Due to their data intensity, however, they are far less common than per capita approaches.


Until now, the focus of this discussion has been mainly on operating costs and revenues. But local governments also have a capital component, which can be substantially affected by new development: studies have found that the capital costs associated with new development can potentially amount to tens of thousands of dollars per household. [6] Fiscal impact analysis can be highly sensitive to the assumptions and methodologies used in estimating capital costs, and the consideration of capital costs and revenues involves complicating factors that are not present on the operating side of the budget.

The challenge of accounting for shared infrastructure

First, the consideration of capital outlays required for a particular development can be complicated by the "lumpy" nature of capital investments. Major capital facilities - such as schools, arterial roads, or sewer-line extensions - are ordinarily not built to accommodate each new person or unit of development separately. Rather, the timing and location of new facilities is determined by the capacity of existing facilities and the long-range capital improvements and land use plans of the local jurisdiction. As a result, the cost of facilities required to serve any particular new development can be difficult to estimate, particularly if these facilities will serve both existing and new development. Sometimes the only way to determine an appropriate growth share of existing or new capital facilities is to obtain "guesstimates" from a knowledgeable local official.

In any event, it is important to consider at the beginning whether there is any functional excess capacity in existing facilities. For example, is the current water supply infrastructure adequate to absorb the proposed new development? If adequate capacity is available, then a particular development may not trigger the need for new facilities. Once existing functional capacity within the jurisdiction or service area approaches full use, however, additional capacity will be required to serve new development. [7]

But even existing capacity is not necessarily "free." Someone has paid, or is currently paying, for it. Deciding how to "charge" for existing capacity becomes an equity issue that can be resolved only after considering past and expected growth patterns. If a jurisdiction's excess capacity is a result of past decline in population or employment, it may not be appropriate to charge the "cost" of excess functional capacity to new development. In this case, new development helps to spread the cost of excess capacity and related operating and maintenance costs across a larger user base. Growing jurisdictions, however, often expand capacity specifically to accommodate or even attract expected new development. In this case, it may be appropriate to charge the cost of excess capacity to new development.

Basic methods for estimating capital costs

There are two basic approaches for estimating the impact of new development on a jurisdiction's capital budget. The first is analogous to the average per capita method described in the section on operating costs and revenues: since capital investments are usually paid for with bonds or other debt mechanisms designed to spread the cost over time, some fiscal impacts analyses divide all of the jurisdiction's existing debt service or the total cost of its capital facilities by its current population (or service units). The result is then multiplied by the anticipated new population or number of units in the proposed development to determine the portion of capital costs that may be attributed to the development. A serious shortcoming of this approach is that it tends to under-represent the cost of new capital facilities if the derived per-capita cost is based on the cost of such facilities constructed several years earlier or the cost of bonds related to their construction, since these amounts are rarely representative of current costs. In addition, focusing exclusively on debt service can exclude the cost of facilities with no outstanding debt or those paid for out of current revenues or reserves.

Another approach involves determining required capital facilities based on the service or design capacity of individual facilities. For example, one fire station may be required for every 10,000 residents or jobs. Dividing the cost of the fire station by its service population results in a per capita capital cost. The cost of a needed new facility may be based on the cost of similar facilities that have been recently constructed elsewhere, or the projected cost in the jurisdiction's capital improvements plan. For jurisdictions that rely on long-term debt to finance capital facilities, the net capital cost per capita can be annualized to determine the recurring debt service associated with the facility.

A special caution regarding school costs

Determining education-related capital costs can be especially tricky. In particular, newcomers to a community may have a higher (or lower) number of school-age children per household than the historical average or that for existing households. For example, Loudoun County, Virginia averages 0.45 pupils per household, including long-time residents, across the county. [8] But, based on survey and other data, the county estimates that the average new single-family, detached dwelling unit generates 0.90 pupils per household, twice the average for all households; it estimates that a new townhouse generates 0.45 pupils per household and a new multi-family apartment or condominium unit generates 0.20 pupils per unit. With Loudoun's future development expected to consist of 39 percent detached, 38 percent townhouses and 23 percent multi-family units, the average future housing unit in the county can be expected to generate 0.57 pupils, or 26 percent more enrollment than the current average household in the county. If a fiscal impact analysis were to apply the county's current average to estimate the number of new pupils from anticipated new residential development, it would underestimate the capital and operating cost for new schools by 26 percent, a significant error considering that the cost of local public schools can often exceed the cost of all other general-purpose local government services.

To complicate matters further, communities experiencing slow growth rates can deviate from this pattern, since the higher number of pupils per new household can sometimes be offset by a decline in enrollment of pupils from existing households. And a community experiencing substantial new retirement or second home development may have a lower number of pupils per new household than the current average.

Accounting for subsidies

Some jurisdictions provide capital or operating subsidies to new development. For example, a jurisdiction may use public funds collected from the jurisdiction as a whole to construct capital facilities that are required only or primarily to serve new development. In addition, some jurisdictions provide sales tax or property tax abatements to new development that reduce operating revenues. These subsidies should ordinarily be counted as additional costs in the fiscal analysis.

Revenues associated with capital investments

Where capital contributions are obtained from new development (such as impact fees and system development charges), the amount of such contributions also needs to be estimated and offset against the project's share of the jurisdiction's capital facility costs. Capital revenues can be estimated on the basis of local policies or practices in assessing them. It is important to remember, however, that some jurisdictions do not obtain any direct capital contributions at all from new development.

Some fiscal impact analyses also apply credits for the value of capital facilities, such as streets or sewer pipes, built within the new development. However, most on-site improvements are solely for the benefit of the development (and the developer), and they should ordinarily not be included as an offset against public-sector costs. Even off-site facilities required by the development, such as an improvement to a nearby intersection to accommodate additional project-related traffic, may not be appropriate as a "credit" unless the cost of the improvement is also included. Only if the developer invests in a capital improvement whose benefit clearly reaches beyond the development itself would the project ordinarily generate a capital revenue for the benefit of others.


These complexities aside, the calculation of net fiscal impact for any particular development proposal can be boiled down to a series of simple equations. A review of a fiscal impact analysis should take care that each of these elements has been properly identified and evaluated:

Cost Side Operating Costs + Capital Costs = Total Costs
Revenue Side Real Property Revenues + Other Operating Revenues = Operating Revenue

Operating Revenue + Capital Revenues and Credits = Total Revenue
Net Fiscal Impact Total Revenue - Total Costs = Net Fiscal Impact

The review should also assure that any public subsidies (see "Accounting for Subsidies" above) are properly accounted for:

Net Fiscal Impact - Public Subsidies = 'Net Net' Fiscal Impact


6. See, e.g., Fodor, E., The Cost of Growth in Oregon (1998).

7. This becomes further complicated when facilities serve separate subareas within the same jurisdiction. Capacity may be available within one subarea but not another.

8. Based on 1998 data.

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