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
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?