Co-viewing refers to the situation in which multiple people share the experience of watching video content and ads in the same room and at the same time. In this paper, we use online surveys to measure the co-viewing rate for YouTube videos that are watched on a TV screen. These simple one-question surveys are designed to optimize response accuracy. Our analysis of survey results identifies variations in co-viewing rate with respect to important factors that include the demographic group (age/gender) of the primary viewer, time of day, and the genre of the video content. Additionally, we fit a model based on these covariates to predict the co-viewing rate for ad impressions that are not directly informed by a survey response. We present results from a case study and show how co-viewing changes across these covariates.