How to correctly specify a mixed model

You should get exactly the same answer whichever way you do it (try 
it!).  The only thing you lose by aggregating is the estimate of the 
variance among observations within areas (which you might not care about 
anyway).  The advantage is a simpler model, which is easier to do 
inference on and harder to screw up. This is the idea of Murtaugh's 2007 
paper in Ecology, "Simplicity and Complexity in Ecological Data 
Analysis".  The only reasons *not* to aggregate would be:

- you're interested in the within-area variance;
- you're doing a GLMM (count/binary responses can't always be aggregated 
as simply as Normal responses)
- you have individual-level covariates that vary within areas
- you have unbalanced data (this can be often be handled by assigning 
non-equal weights)

   A sample size of 7 is indeed somewhat low for a regression with 2 
inputs, but whether you aggregate or not won't make a difference.

How to correctly specify a mixed model

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