The cities of Paris, London, Chicago, and New York (among many others) have set up large scale bike-share systems to facilitate the use of bicycles for urban commuting. This paper estimates the impact on bike-share ridership of two facets of system performance: accessibility (how far the user must walk to reach stations) and bike-availability (the likelihood of finding a bicycle). Our analysis is based on a structural demand model for spatially differentiated products that includes distinct mechanisms for the short and long-term effects of bike-availability (via lost sales and increased user-interest, respectively). The bike-share context, and the distinct mechanisms require us to go beyond past work in incorporating real time changes in product (bike)-availability information, and including much finer data on potential demand sources. These enhancements render traditional estimation methods computationally infeasible; we transform our estimation from the time domain to the “local-stock out- state” domain to address this. Our estimates for the Vélib’ bike-share system in Paris suggest that a 10% increase in station density would increase ridership by 5.09% (±0.45%), while a 10% increase in bike-availability would increase ridership by 12.29% (±0.39%), three-fourths of which comes from fewer lost-sales, and the rest from increased user interest. We illustrate the use of our estimates in identifying neighborhoods and times to target for improvements, and in comparing alternate operational improvements and station networks.