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Cost estimation is a vital task in most important software project decisions such as resource allocation and bidding. Analogy-based cost estimation is particularly transparent, as it relies on historical information from similar past projects, whereby similarities are determined by comparing the projects' key attributes and features. However, one crucial aspect of the analogy-based method is not yet fully accounted for: the different impact or weighting of a project's various features. Current approaches either try to find the dominant features or require experts to weight the features. Neither of these yields optimal estimation performance. Therefore, we propose to allocate separate weights to each project feature and to find the optimal weights by extensive search. We test this approach on several real-world data sets and measure the improvements with commonly used quality metrics. We find that this method 1) increases estimation accuracy and reliability, 2) reduces the model's volatility and, thus, is likely to increase its acceptance in practice, and 3) indicates upper limits for analogy-based estimation quality as measured by standard metrics.