Home range estimates have been a key geographic unit for understanding the link between animals and their habitat/resource choices since the term was first described by Burt (1943) and formally quantified by Mohr (1947)—who introduced minimum convex polygons (MCP) as a method to delineate individual home ranges. Numerous methods have subsequently been developed to estimate home ranges. However, depending on the method used, widely different estimations can be found with the same animal location dataset. With different home range delineations, inferences in a heterogenous landscape about animal resource and habitat preferences with different delineations can impact wildlife management. In this research, time-based home range methods that account for autocorrelation in animal movement were evaluated for accuracy in terms of area, shape, and location in response to sample size and common wildlife GPS-point patterns. These characteristics of home range estimation are important for inferring animal habitat and resource use. Despite the improved accuracy of time-based methods compared to traditional point-based methods like MCP, location was often inaccurate for all GPS-point patterns, as were shape and area for GPS-point patterns with perforations (common for areas with large physical barriers like mountains or lakes). These findings are important to wildlife managers using time-based home range methods for analysis.