Rent-to-own (RTO) firms rent products in exchange for a fee and offer the already-rented products for purchase at buyout prices to their renters. Prediction of demand requires a decision model that captures the renters' decision-making process and their ownership and rental utilities. We develop a modular framework that separates utility estimation from identifying the renter's decision process. We build renter decision models, reflecting different degrees of sophistication and alertness.

Digital goods firms routinely utilize renting models for product differentiation: one can try a free or cheap version first, then subscribe to unlock additional functionality. Recent technological advances enable physical goods firms to create products with identical hardware that are digitally controlled to allow for similar differentiation. We present a stylized model to explore when physical goods firms should adopt such differentiation instead of the traditional high- and low-end products.

Consider a platform facilitating matches in the presence of supply congestion. A key attribute of supply is how competitive it will be for demand to obtain the supply after the match. Should the platform reveal current competition levels? To answer this, we partnered with a marketplace and propose a structural model in which workers account for future competition. We conduct counterfactual analysis to study the impact of signaling competition on workers' lead purchasing decisions and revenue.