On comparing algorithmic cost estimates prepared in di fferent organisations

Roughly summarized in the algorithmic cost estimation the cost is estimated as a mathematical function linking costs or inputs with metrics to produce an estimated output. This function arise from the analysis of historical cost information which relates some commonly used attributes for cost (usually product size, function points, object points, etc.), to the project cost:

Effort = A x Size ^B x M

where A is an organization-dependent constant, B reflects the disproportionate effort for large projects and M is a multiplier reflecting product, process and people attributes. Due to the constant A beeing a organization-dependant constant, the algorithmic cost model will differ from one organization to another. Another great disadvantage of this models is the inconsistency of the estimates, studies show that estimates vary from 85% - 610% between predicated and actual values. By adjusting the weightings of the attributes (in the formula - the multiplier M), also called calibrating to the specific environment, the accuracy of the model can be greatly improved. This calibration however is in a sense customization of the generic model for a specific environment (situation, organization, etc) and would result in a model, which is not useful outside of this particular environment (e.g. organization) it was calibrated for.
Conclusion: The estimates of factors contributing to B and M are subjective and thus organization and situation-dependant. Due to this algorithmic cost models are not directly comparable form one organization to another.