The National Agricultural Statistics Service (NASS) conducts quarterly multiple frame (MF) hog inventory surveys, using both a list and an area sampling frame. The MF survey direct expansion (DE) estimator for hogs is used as an indication by the National Board. A Robust Estimator (RE) is also currently being used by the National Board to augment the survey direct expansion total hog estimate. The first component of the RE is a DE non-outlier component. The second component of the RE is an average outlier component determined using data from several surveys. This report examines characteristics of those outlier records that are fundamental to computing the RE’s outlier component. Individual characteristics for outlier non-overlap (NOL) records for five states were studied. Three major causes of NOL outlier occurrence were found. They include increased expansion factors due to subsampling in follow-on surveys, the transitory and varying nature of hog production, and the location of hog operations on land with little or no agriculture. Individual list and NOL hog operation often produced more than one outlier within a frame-year. Results indicate that at the state level, some survey frame-year outlier totals and number of outliers occurring differ significantly. (The frame-year runs from June through May.) At the national level, no significant effects were found. Also, distributional differences for outliers were found between states. This is due in large part to the outlier cutoff values assigned each state. With no trends in outliers at the national level, the Robust Estimator should continue to be used without modification. However, the presence of a frame-year effect in outlier totals and outlier number of occurrences at the state level not only justifies the need for the RE but implies a modified RE might be investigated to help in making state level estimates. State level outlier cutoff values should be investigated in an attempt to locate optimum values for the RE’s ability to measure state and national board estimates.