Multicollinearity Using VIF and Condition Indeces

I am testing my dataset for multicollinearity using VIF and condition indices(CI).My dataset is cross-sectional macroeconomics data. I have 6 independent variables ($$x_1$$,$$x_2$$,$$x_3$$,$$x_4$$,$$x_5$$,$$x_6$$) plus 2 dummies ($$d_1,d_2$$) plus 2 interactions terms ($$d_1*x_1$$,$$d_2*x_1$$).

regression t-test : seven statistical significant variables F: statistical significant overall

VIF&CI

Mean VIF : 10.63 (with very high R-squared (>85%) in all dummies and interaction terms) CI : 48.3

When I remove dummies and interactions from the model the results are much more better (Mean VIF : 1.62 , CI: 19.34 R-squared <50%).

I am expecting -due to the nature of dummies and interaction terms- that my results would present multicollinearity.

Are the above results serious evidence for multicollinearity in my model?