Analogy-based estimation has, over the last 15 years, and particularly over the last 7 years, emerged as a promising approach with comparable accuracy to, or better than, algorithmic methods. In addition, it is potentially easier to both understand and apply; these two important factors can contribute to the successful adoption of estimation methods within Web development Companies We believe therefore, analogy-based estimation should be examined further. This paper replicates previous work that investigated the use of two types of adaptation rules as a contributing factor to better estimation accuracy. In addition, it also investigates the use of Feature Subset Selection, in addition to Adaptation rules. Two datasets are used in the analysis; results show that adaptation rules improved estimation accuracy for the less "messy" dataset. Feature Subset Selection also seems to help improve the adaptation results.