We consider the kernel estimator of conditional density and derive its asymptotic bias, variance and mean-square error. Optimal bandwidths (with respect to integrated mean-square error) are found and it is shown that the convergence rate of the density estimator is order n-2⁄3. We also note that the conditional mean function obtained from the estimator is equivalent to a kernel smoother. Given the undesirable bias properties of kernel smoothers, we seek a modified conditional density estimator which has mean equivalent to some other nonparametric regression smoother with better bias …