Homoscedasticity is a basic assumption in regression. This assumption means that the variance around the regression line is the same for all values of the predictor variable. If homoscedasticity does not hold then
1. Parameter estimates: If variances for the outcome variable differ along the predictor variable then the estimates of the parameters within the model will not be optimal.
2. Confidence intervals: unequal variances/heteroscedasticity creates a bias and inconsistency in the estimate of the standard error associated with the parameter estimates in your model.