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Table 2. Data Sources for Random Variables * City of Minneapolis, Comprehensive Annual Financial Report. t Joyce Man and Mark Rosentraub, "Tax Increment Financing: Municipal Adoption and Effects on Property Value Growth," Public Finance Review 26 (1998): 523-547. * Richard Dye and David Merriman, "The Effects of Tax Increment Financing on Economic Development," journal of Urban Economics 47 (2000): 306-328. ~ Minneapolis Community Development Agency, Comprehensive Annual Financial Report. records.2 Finally, the operating subsidy was set at zero. Operating subsidies vary widely by project and usually have the characteristics of a loan, repaid out of future tax increlnent. It should be noted, however, that this assumption likely biases the result of this analysis somewhat in the direction of showing a n10re positive financial effect from TIE The pre-TIF growth rate of the prop- erties that will eventually be part of the TID can be treated either as a paralneter or as a policy variable (that is, a variable that can be changed easily by a govern- 111ental entity). I treated the growth rate as a paralneter and assumed a zero growth rate. (For states such as Minnesota that allow the base to increase by an amount equal to the pre- TIF growth rate, this factor should drop out.) Another variable that can be treated as either a policy variable or a 2 City of Minneapolis, C01Jzprehensive Annual Finan- cial Report. The tax rate is an effective one, found by dividing property tax revenue by total market value for the most recent available year. Table 3. Monte Carlo Simulation Base Results (10,000 iterations) parameter is the discount rate. Again, without any prior knowledge of when proj ects will be developed, I treated this as a parameter and assumed a 70/0 discount rate. The final variable in the model is the probability that a parcel will develop without the use of TIE I chose to treat this variable as a policy variable. Given that TIF allows local governments to tenlporarily take properties off local tax rolls, an important question is, which properties should be incorporated into l'II)s? For Iny initial estimates, I assumed a 100/0 probability of develop- ment. This translates into one TIF prop- erty being developed without 'rII~~ 011 average, within the next 10 years. Results The model outlined in the previous section was simulated through 10,000 iterations using the @Risk add-in package for Microsoft Excel. Table 3 and Figure 1 show the results. Table 3 shows descriptive statistics and the distribution of the results. The first column of the table shows the net present value for the project as a whole evaluated at the discount rate of 7<Yo specified above. This column shows that the most likely result (point estimate) of the effect of this $15 million project (with $30 million captured market value) is slightly more than a $5.5 million net financial loss to the local government. (Figure 1 shows that the distribution of results is skewed, therefore it is appro- priate to cite the median value as the point estimate.) The mean is statistically significantly greater than its standard error (p < .001), meaning that we can say with a high level of confidence that there is a negative financial effect for the local government. Table 3 and Figure 1 also present an estimate of the Figure 1. Probability Distribution of Net Present Value, Base Estimates (10,000 iterations) 450/0 400;u 35%) 30<*, >, 250/0 u ~ OJ ;::s 0- OJ ;.... 20eyo ~ 15%) 10eyo 5 <Yo 00/0 -25 -22.5 -20 -17.5 -15 -12.5 -10 -7.5 -5 -2.5 0 2.5 7.5 10 12.5 15 17.5 20 22.5 25 Net Present Value (in millions of dollars) SUMMER 2003 5