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We note that in each case the r-squared, which is a measure of correlation, is very high, indicating <br /> a strong correlation between the two variables. <br /> We then apply the regression analyses toward our concluded NO] for the subject, which results <br /> in an indication of price per SF for the subject, as follows: <br /> Subject's NOI per SF $10.18 <br /> Forecast (Excel Function) $119.61 <br /> Linear Regression $119.61 <br /> Polynomial Regression $129.14 <br /> Power Regression $119.95 <br /> Average Indication: $122.79 <br /> While this analysis provides support for a clear correlation between NO] per SF and sale price per <br /> SF, we do not apply the precise analysis directly in the economic characteristics adjustment, but <br /> rather, we will use the indicated correlation to apply qualitative adjustments to each comparable <br /> consistent with the trend indicated. <br /> A summary of all the adjustments applied to the comparables is located on the following table: <br /> 85 1 Page <br />