This document discusses local bias in parametric estimation models and its impact on model performance. It defines local bias as the deviation between parameters calibrated from local data versus general model defaults. An analysis of a software cost estimation model finds local bias varies between data groups and is positively correlated with decreased model accuracy and increased uncertainty, as measured by mean and variance of magnitude of relative error. The implications are that local bias should be identified and addressed to improve model evolution and balance accuracy versus stability.