# Independent variable collinear with intercept

I have high collinearity between one of the covariates (credit score variable with values ranging from 600 to 800) and the intercept term, when regressing a continuous dependent variable on some behavioral attributes. I checked multiple multicollinearity metrics and the story is the same. Correlation coefficeint is 0.952, condition index is 80 with proportion of variation of intercept & FICO being 0.98 and 0.97 respectively. The VIF is also very high. Credit score does have a very flat relationship with the dependent variable but that probably would not have any implication here. Can someone please explain why this might be happening? And if this happens with an independent variable that has a more significant relationship with the dependent variable, should I consider dropping the intercept term from my model?

• Assuming $Y = \alpha + \beta*X_1 + \gamma*X_2$ is a regression equation, one could talk about correlation between $X_1$ and $X_2$ i.e the variables OR $\beta$ and $\gamma$ i.e. their slopes. I am trying to make the same distinction in my comment above. Question is- when I have a high condition index for intercept and another variable, is it due to correlation between $\alpha$ and $X_1$ OR $\alpha$ and $\beta$? – Nagesh Rathi Jul 5 at 19:27