Location-based queries are quickly becoming ubiquitous. However, traditional search engines perform poorly for a significant fraction of location-based queries, which are non-factual (i.e., subjective, relative, or multi-dimensional). As an alternative, we investigate the feasibility of answering location-based queries by crowdsourcing over Twitter. More specifically, we study the effectiveness of employing location-based services (such as Foursquare) for finding appropriate people to answer a given location-based query. Our findings give insights for the feasibility of this approach and highlight some research challenges in social search engines.