Modern home-range estimation typically relies on data derived from expensive radio- or GPS-tracking. Although trapping represents a low-cost alternative to telemetry, evaluation of the performance of home-range estimators on trap-derived data is lacking. Using simulated data, we evaluated three variables reflecting the key trade-offs ecologists face when designing a trapping study: 1) the number of observations obtained per individual, 2) the trap density and 3) the proportion of the home range falling inside the trapping area. We compared the performance of five home-range estimators (MCP: Minimum Convex Polygon, LoCoH: Local Convex Hull, KDE: Kernel Density Estimation, AKDE: Autocorrelated Kernel Density Estimation, BicubIt: Bicubic Interpolation). We further explored the potential benefits of combining these estimators with asymptotic models, which leverage the saturating behavior of changes in the estimated home-range area as the number of observations increases to improve accuracy, as well as different data-ordering procedures. We then quantified the bias in home-range size under the different scenarios investigated. The number of observations and the proportion of the home range within the trapping grid were the most important predictors of the accuracy and the precision of home-range estimates. The use of asymptotic models helped to obtain accurate estimates at smaller sample sizes, while distance ordering improved the precision and asymptotic consistency of estimates. While AKDE was the best performing estimator under most conditions evaluated, bicubic interpolation was a viable alternative under common real-world conditions of low trap density and area covered. A case study using empirical data from white-tailed deer in Florida and another from jaguars in Belize demonstrated support for the findings of our simulation results. Although researchers with trap data often overlook home-range estimation, our results indicate that these data have the capacity to yield accurate estimates of home-range size. Trapping data can, therefore, lower the economic costs of home-range analysis, potentially enlarging the span of species, researchers and questions studied in ecology and conservation.