Online real estate platforms make listings broadly accessible, yet agents’ repeated interactions create informal collaboration networks that can segment buyers’ effective information sets. We study how agents’ informal collaboration networks shape buyer search and matching outcomes in the housing market. Using detailed buyer-agent-property records covering property showings and transactions, we apply an unsupervised machine learning method to recover agent networks from historical collaboration patterns. We find that buyers served by agents embedded in larger networks view more properties, search longer, and purchase higher-priced properties, whereas those served by agents in more exclusive networks search less and purchase lower-priced properties. We develop and estimate a structural model of buyer search and purchase in which agent networks shape buyers’ effective information sets through listing awareness and network-based steering. Counterfactual simulations quantify how expanding information access and removing network-based steering affect buyer search, match quality, and welfare.

Star employees are among a firm’s scarcest resources, making it important to understand how they create value and where firms should deploy them. While prior research emphasizes supply-side spillovers such as knowledge transfer, peer learning, and collaboration, we study a different mechanism arising when customer demand attaches to individual employees. Using intra-firm reassignments of agents across shops of a large real estate brokerage, we find that shop performance rises immediately and persistently when a star arrives and reverses when she departs. Star arrival sharply expands the receiving shop’s customer pool. Yet the star does not increase her own service volume; instead, coworkers absorb the additional demand, lifting their output by 11.8 percent. Tracing customer flows across shops, we find that about two-fifths of the receiving shop’s customer gain is redirected from the star’s origin shop, while most of the inflow is net new demand the firm would not otherwise have captured. But shop capacity is bounded: as coworkers take on more buyers, conversion does not improve and buyers search longer, with the strain sharpest where the shop is already congested. The same star creates more value in shops with room to serve additional customers and the ability to convert them. These findings reframe star deployment from fostering post-arrival coworker interaction to matching scarce demand-drawing talent to the units best positioned to capitalize on the demand it attracts.