Master of Science / Department of Animal Sciences and Industry / Michael D. MacNeil / Jennifer M. Bormann / Genetic and phenotypic data are often provided to bull buyers at time of sale to aid producers in establishing economic value (pricing) of candidates for selection. This study evaluates the association between the information provided to bull buyers at time of sale and prices paid for bulls sold by two large seedstock operations located in Kansas (KS Ranch) and Colorado (CO Ranch). Data were gathered from 15 sale catalogs that documented bulls sold at auctions taking place from 2009 to 2013. In total, there were 39 potential predictor variables recorded for 2,601 Angus bulls for the KS Ranch; while 14 plausible predictor variables were recorded for 504 purebred and 1,399 Stabilizer bulls at the CO Ranch. Due to extensive multicollinearity between predictors, principal component (PC) analyses were conducted on the standardized predictors to reduce dimensionality within each ranch and genetic group. Eleven PC were considered to provide important meaningful information in summarizing the 39 predictors originally available to buyers at the KS Ranch. For both the purebred and Stabilizer bulls from each set of breed type data in the CO ranch, 6 principal components had eigenvalues greater than 1.0. Similar to the findings for the KS Ranch, these PCs also explained approximately 75% of the cumulative variability of the predictors. Sale prices were then regressed on the corresponding PC using a stepwise selection to identify the PC subset that most significantly explained the behavior of bull sale prices (P < 0.05). The final models explained approximately 63%, 37% and 58% of the variation in sale prices received for Angus, purebred and Stabilizer bulls, respectively. Interpretation of the eigenvectors for the PC having the greatest eigenvalues led to the conclusion that buyers put the most weight on growth traits followed by carcass characteristics and economic selection indices. However, no distinction of a specific variable’s numerical impact on price was determined.