Working Paper Series,
Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance

Abstract: Online-to-offline platforms increasingly use buyer-facing visibility systems to allocate consumer attention, but transactions often remain dependent on human agents who execute offline work. This paper studies whether buyer-facing visibility also shapes the supply of offline execution capacity. We assemble high-frequency data from a large housing brokerage platform in Beijing that link daily listing visibility, traffic, agent participation, showing trips, closing-agent identities, and sale-related listing records. To estimate the effect of visibility-induced buyer attention, we use a listing-level mechanical rank-shock instrument based on the entry and exit of other properties above the focal listing in the relevant search set. The evidence shows that higher visibility-induced traffic mobilizes offline labor: listings attract more participating agents on the extensive margin and receive more showing trips from participating agents on the intensive margin. This labor expansion, however, does not translate into a systematically stronger observable closing agent, suggesting that visibility expands contest participation more than winner selection. We develop a model in which buyer-facing visibility raises agents' expected returns to offline execution, inducing entry and effort under fixed commission shares. Calibrated counterfactual simulations suggest that execution-aware visibility allocation can substantially improve platform outcomes: a marginal transaction-value rule raises platform transaction value by 36.9% while mobilizing 11.0% more participating agents, and a welfare-aware rule preserves similar value gains while tempering agent-participation expansion. The paper identifies cross-side labor effects of buyer-facing information systems and shows that recommendation design in online-to-offline platforms should account for offline executability.

Keywords: Platform visibility; recommendation systems; online-to-offline platforms; algorithmic management; offline execution

JEL-codes: D83; L86; M15

Language: English

34 pages, June 22, 2026

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