In this paper we explore the role of case adaptation for feature vector prediction problems. We focus on software project effort. We study three data sets that range from small (less than 20 cases) through medium (approximately 80 cases) to large (approximately 400 cases). These are typical sizes for this problem domain. We compare two variants of a linear size adjustment technique and (as a baseline) a simple k-NN approach. Our results show that the linear scaling techniques studied result in statistically significant improvements to predictions. However, the size of these improvements is relatively small, typically about 10%. The results include a number of extreme outliers which might be problematic if the techniques are to be used in practice. This suggests further work is required to cope better with the outlier problem.Null adaptation, the simplest, involves directly applying the solution from the retrieved case(s) to the target case. This is the approach adopted by a simple