This paper compares different methods of obtaining individual-level scores for MaxDiff surveys at the individual level: Simple counting, individual-level logit, and HB. Key to the success for all these methods was having enough information available for each respondent to estimate stable scores.
The author (Orme) finds that counting analysis provides reasonable population estimates of scores, but that the individual-level scores can lack precision. Precision is better under the logit model estimation methods: either individual-level logit or HB, which "borrows" information across the sample to improve the individual-level logit scores for individuals.
Despite the simplicity of the counting approach and its weaknesses, it tends to do quite well in predicting responses to holdout choices. But, across-respondent variance (heterogeneity) tends to be weaker than the other methods studied.