Searching for a Better Forecast: San Francisco's Revenue Forecasting Model

By Agostini, Stephen J. | Government Finance Review, December 1991 | Go to article overview

Searching for a Better Forecast: San Francisco's Revenue Forecasting Model


Agostini, Stephen J., Government Finance Review


Placing forecasting in the context of the local economy resulted in more accurate revenue projections for San Francisco and a better understanding of the forces determining the city's available resources.

In public-sector budgeting, the availability of resources usually circumscribes discussions about expenditures. As these discussions intensify in the face of mounting fiscal duress, reliable and informative revenue forecasts become critical elements of the budgeting process. This is increasingly the case for all state and local governments, and has been particularly true for the city and county of San Francisco since 1988.

In the spring of 1988, San Francisco faced the daunting task of closing an unprecedented general fund deficit - for FY1989 the general fund deficit was projected at $192 million, an amount equal to 17 percent of the prior year's general fund budget. Understandably, the size of the deficit prompted the mayor's office to carefully review all revenue and expenditure estimates, in order to understand the underlying causes of the problem and fashion the mayor's budget proposal accordingly.

The Need for a Better Model

In the course of this review it became clear that the city's method for revenue forecasting was inadequate. For example, there was little systematic reliance on national, regional or local economic variables to inform the forecasts. Consequently, economic trends or events affecting the underlying tax base of the city, such as changes in employment, had little impact on, or relevance for, the forecasts. In addition, isolating trends which were particularly relevant when discussing tax increases, or new levies, to alleviate the deficit was impossible. Instead, these trends and events simply became components of the anecdotal library which was, in reality, the city's principal forecasting tool. Most importantly, the revenue forecasts were off from actual receipts by wider and wider margins.

Given the rising discomfort over these revenue forecasts, and the prospect of another deficit in FY1990, the mayor's office endeavored to create a forecasting tool which would * improve the accuracy of the city's

forecasts, * enable the mayor's office to analyze and

identify economic trends and events

shaping the underlying tax base and * be relatively easy to use.

By placing forecasting in the context of the local economy, the mayor's office hoped to gain a better understanding of the forces determining the city's available resources and improve the accuracy of the forecasts. Accurate and informed forecasts also offered the prospect of greatly enhancing decision making during the budget process.

Developing a model that was relatively inexpensive to create and easy to use was also an important consideration. The model would have to be developed so that it could run on software compatible with an Apple Macintosh 512K personal computer, the only data processing resource available to the mayor's office. The data which would be used in generating these forecasts had to be readily accessible from inexpensive government publications, since staff had no funds available for purchasing large data bases. The variables selected for use in the model would have to be easily available from newspapers and magazines, so as not to incur additional costs for specialized data. With these goals and constraints in mind, the financial analyst in the mayor's office set out to build a revenue forecasting model.

Components of the New Model

San Francisco's revenue model is a set of 23 econometric equations, which are divided into three discrete, yet interrelated, components. The first component consists of 10 equations which forecast San Francisco's underlying tax base, defined here as output. The second component consists of five equations to forecast key local or regional economic variables, such as San Francisco's total assessed value. …

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Searching for a Better Forecast: San Francisco's Revenue Forecasting Model
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