Address-based sampling (ABS) refers to the use a list of addresses derived from the U.S. Postal Service's Computerized Delivery Sequence File as a sampling frame. While most residential addresses are included on an ABS frame, it still suffers from undercoverage. Undercoverage is problematic only if the uncovered units have different attributes than covered units. In this article, we assess the risk of observing coverage bias on a variety of univariate point estimates in an in-person survey. We accomplish this task using a combination of survey data and Monte Carlo simulation models to vary the coverage rate and the target geography. For some variables, we found low risk of observing coverage bias. But, for others, the risk of observing bias was high for nearly all levels of coverage. Finally, we did not observe any trends by variable type that would give us the ability to predict a priori which variables may suffer bias.