Web users like sites that load quickly. Longer web page load times translate to reduced user satisfaction and loss of revenue and mindshare. The time required to load a given web page is difficult to predict because it is a complex function of many factors, such as the latencies associated with the network requests used to retrieve that content from remote servers. However, one of the most important factors is the page content, including the scripts, images, style sheets and other objects that are present on the page. In this paper we propose a simple metric for characterizing the content of a web page in terms of its impact on page loading times. This metric, called the latency amplification factor (LAF), characterizes the content of a web page in terms of how it affects the page load time. The LAF of a web page can be estimated quickly and easily, and we describe a lightweight method for doing so. In addition, we propose an extended version of the basic LAF metric, called CLAF, that relates page load time to underlying request latencies in the presence of content delivery networks. We estimated LAFs for a variety of popular web sites, and found that they varied substantially. To validate our approach for estimating LAFs, we compared estimated LAFs against measured LAFs and found that our methodology, though simple, gave reasonably accurate estimates.