Online auctions are inherently dynamic. Online auction designs that internalize temporal changes in the economic environment are generally expected to perform better than static designs. This is because providing opportunities for both buyers and sellers to inform each other about preference changes over time can increase market transparency and lead to more efficient markets. In this paper, we focus on a feature that is unique to online auctions, the buyout price. We introduce a dynamic buyout model and show analytically how the buyout price should change over time in order to maximize seller profit and buyer surplus. Based on our theoretical results, we suggest that online auction performance can be improved with the addition of more dynamic features. Finally, we describe an experimental design that can be used to estimate the benefits of a dynamic buyout option.