Microaggregation by individual ranking is one of the most commonly applied disclosure control techniques for continuous microdata. The paper studies the effect of microaggregation by individual ranking on the least squares estimation of a multiple linear regression model in continuous variables. It is shown that the naive parameter estimates are asymptotically unbiased. Moreover, the naive least squares estimates asymptotically have the same variances as the least squares estimates based on the original (non-aggregated) data. Thus, asymptotically, microaggregation by individual ranking does not induce any efficiency loss on the least squares estimation of a multiple linear regression model.