Let $G_n$ be an $n \times n$ matrix with real i.i.d. $N(0,1/n)$ entries, let $A$ be a real $n \times n$ matrix with $\Vert A \Vert \le 1$, and let $γ\in (0,1)$. We show that with probability $0.99$, $A + γG_n$ has all of its eigenvalue condition numbers bounded by $O\left(n^{5/2}/γ^{3/2}\right)$ and eigenvector condition number bounded by $O\left(n^3 /γ^{3/2}\right)$. Furthermore, we show that for any $s > 0$, the probability that $A + γG_n$ has two eigenvalues within distance at most $s$ of each other is $O\left(n^4 s^{1/3}/γ^{5/2}\right).$ In fact, we show the above statements hold in the more general setting of non-Gaussian perturbations with real, independent, absolutely continuous entries with a finite moment assumption and appropriate normalization. This extends the previous work [Banks et al. 2019] which proved an eigenvector condition number bound of $O\left(n^{3/2} / γ\right)$ for the simpler case of {\em complex} i.i.d. Gaussian matrix perturbations. The case of real perturbations introduces several challenges stemming from the weaker anticoncentration properties of real vs. complex random variables. A key ingredient in our proof is new lower tail bounds on the small singular values of the complex shifts $z-(A+γG_n)$ which recover the tail behavior of the complex Ginibre ensemble when $\Im z\neq 0$. This yields sharp control on the area of the pseudospectrum $Λ_ε(A+γG_n)$ in terms of the pseudospectral parameter $ε>0$, which is sufficient to bound the overlaps and eigenvector condition number via a limiting argument.