This paper examines challenges in algorithmic intermediation and proposes a framework to mitigate adverse selection when private information about product quality is intertwined with private information about preferences. I examine these issues in the context of iBuyers—firms that offer instant home purchases using big-data-driven pricing models—and analyze why they have struggled to achieve sustainable profitability. I develop a model in which home sellers choose between selling to an iBuyer and listing on the open market based on two dimensions of private information: unobserved house quality and the hassle costs of traditional selling. Sellers may select an iBuyer either to avoid the time and effort of listing or because the iBuyer’s offer exceeds their expected market price, with the latter case generating adverse selection against the iBuyer. Using detailed transaction and listing data, I estimate the joint distribution of these factors, identified from repeated sales and seller choice following iBuyer entry. Counterfactual analyses show that a revenue-sharing contract mitigates adverse selection by improving selection incentives, while incorporating an LLM-based text score derived from unstructured listings further reduces informational frictions by providing a standardized signal of unobserved house quality. Together, these mechanisms enhance the viability of algorithmic transaction markets.

This paper examines the timing of product offerings as an additional dimension of competition, expanding the understanding of firms’ positioning decisions. The analysis exploits a novel setting in the U.S. coffee shop industry during the COVID-19 pandemic, when labor shortages sharply increased operating costs and induced firms to compete on which days to open. Using mobile tracking and sales data, I estimate a structural model of demand, pricing, and operating-day choices under sticky (uniform) pricing. The results show that higher labor frictions reduce the number of operating days, and that price stickiness amplifies this effect by linking daily operations to weekly pricing incentives. Counterfactual simulations reveal that ignoring this interaction understates the welfare losses from higher operating costs, underscoring the importance of accounting for interdependent competitive dimensions—time and price—in assessing market outcomes.

We offer a theory of anticompetitive tying in two-sided markets when below-cost or negative pricing is possible. With the coexistence of two consumer groups (one regarding tying and tied goods as complementary and the other as independent), a tying-good monopolist may face difficulties in extracting rent under separate sales and wish to use tying to directly capture the large advertising revenue created in the complementary segment. We uncover two distinct mechanisms by which tying raises monopoly profits but reduces social welfare. Our theory of tying can be applied to real-world antitrust law enforcement, such as the Google Android case.

This project examines how private equity (PE) firms engage in two-sided bargaining with local governments to navigate regulatory processes and shape housing supply. Using permit, lobbying, and legislative data from the United States, I study whether PE ownership facilitates faster permitting, more successful rezoning applications, and ultimately more efficient development outcomes. Preliminary evidence from Chicago shows that permits for PE-owned properties are processed more quickly, underscoring how scale, financing capacity, and lobbying strength influence bargaining dynamics in urban development.