Nonidentifiability prevents unbiased coefficient estimation

If a design matrix $X$ has a nonzero null vector $z$, the normal linear model has identical observation distributions at $\beta$ and $\beta +z$. Any estimator has the same expectation at those two parameters, and therefore cannot be an unbiased estimator of both coefficient vectors. Full column rank is required for unbiased estimation of the entire unconstrained coefficient vector; identifiable linear combinations can still be estimated without it.