  Motivated by real-world applications such as rental and cloud computing
services, we investigate pricing for reusable resources. We consider a system
where a single resource with a fixed number of identical copies serves
customers with heterogeneous willingness-to-pay (WTP), and the usage duration
distribution is general. Optimal dynamic policies are computationally
intractable when usage durations are not memoryless, so existing literature has
focused on static pricing, whose steady-state reward rate converges to
optimality at rate $\mathcal{O}(c^{-1/2})$ when supply and demand scale with
$c$. We show, however, that this convergence rate is suboptimal, and propose a
class of dynamic "stock-dependent" policies that 1) preserves computational
tractability and 2) has a steady-state reward rate converging to optimality
faster than $c^{-1/2}$. We characterize the tight convergence rate for
stock-dependent policies and show that they can in fact be achieved by a simple
two-price policy, that sets a higher price when the stock is below some
threshold and a lower price otherwise. Finally, we demonstrate this "minimally
dynamic" class of two-price policies to perform well numerically, even in
non-asymptotic settings, suggesting that a little dynamicity can go a long way.
