In catastrophe risk modeling, a defensible estimation of impact severity and its likelihood of occurrence to a portfolio of assets can only be made through a rigorous treatment of uncertainty and the consideration of multiple alternative models. This approach, however, requires repeating lengthy calculations multiple times. To limit the demand on computational time and resources, a frequent practice in the industry is to estimate the distribution of earthquake-induced portfolio losses using a simulated catalog of events from a single representative mean ground motion hazard model for the region. This simplified approach is faster but may provide biased estimates of the likelihood of occurrence of the large and infrequent losses that drive many risk mitigation decisions. Investigation through case studies of different portfolios of assets located in the San Francisco Bay Region shows the potential for both a bias in the mean loss estimates and an underestimation of their central 70% interpercentile. We propose a simplified and computationally practical approach that reduces the bias in the mean portfolio loss estimates. This approach does not improve the estimate of the inter-percentile range, however, a quantity of no direct practical use.