Despite its success, the delete relaxation has significant pitfalls. In an attempt to overcome these pitfalls, recent work has devised so-called red-black relaxed plan heuristics, where red variables take the relaxed semantics (accumulating their values), while black variables take the regular semantics. These heuristics were shown to significantly improve over standard delete relaxation heuristics. However, the experiments also brought to light a major weakness: Being based on repairing fully delete-relaxed plans, the returned estimates depend on arbitrary choices made in such plans. This can lead to huge over-estimation in arbitrary subsets of states. Here we devise a new red-black planning method not based on repairing relaxed plans, getting rid of much of this variance. Our experiments show a significant improvement over previous red-black relaxed plan heuristics, and other related methods.