This paper establishes the power of dynamic collaborative task completion among workers for urban mobile crowd-sourcing. Collaboration is defined via the notion of peer referrals, whereby a worker who has accepted a location-specific task, but is unlikely to visit that location, offloads the task to a willing friend. Such a collaborative framework might be particularly useful for task bundles, especially for bundles that have higher geographic dispersion. The challenge, however, comes from the high similarity observed in the spatio-temporal pattern of task completion among friends. Using extensive real-world crowd-sourcing studies conducted over 7 weeks and 1000+ workers on a campus-based crowd-sourcing platform, we quantify the effect of such "task completion homophily", and show that incorporating such peer-preferences can improve worker-specific models of task preferences by over 30%. We then show that such collaborative offloading works in spite of such spatio-temporal similarity, primarily because workers refer tasks to their close friends, who in turn perform such peer-requested tasks (with over 95% completion rate) even if they experience detours that are significantly larger (often more than twice) than what they normally tolerate for platform-recommended tasks.