WiFi access points that provide Internet access to users have been steadily increasing in urban areas. Different access points differ from one another in terms of services that they provide, including available upstream and downstream bandwidths, overall network capacity, open/blocked ports, security features, and so on. However, there is no reliable service available at present that can aid a user in selecting an access point from the many that are available. The primary research challenge is how to accurately estimate the current backhaul bandwidth of different access points in an efficient manner without requiring any installation of special software on the access points, and not burdening the WiFi subscribers to perform any communication or computation intensive task. This paper presents a new highly scalable bandwidth estimation technique that is suitable for efficiently estimating the backhaul bandwidth of a large number of APs. This technique has been extensively evaluated via a prototype implementation in an indoor testbed and in the Amazon EC2 platform. The evaluation demonstrates that the proposed technique exhibits high measurement accuracy, low latency, high scalability, and minimal intrusiveness.