Here's a post that may be helpful
'One of my favorite uses of zip code data is to look up demographic variables based on zipcode that may not be available at the individual level otherwise...
For instance, with http://www.city-data.com/ you can look up income distribution, age ranges, etc., which might tell you something about your data. These continuous variables are often far more useful than just going based on binarized zip codes, at least for relatively finite amounts of data.
Also, zip codes are hierarchical... if you take the first two or three digits, and binarize based on those, you have some amount of regional information, which gets you more data than individual zips.
As Zach said, used latitude and longitude can also be useful, especially in a tree based model. For a regularized linear model, you can use quadtrees, splitting up the United States into four geographic groups, binarized those, then each of those areas into four groups, and including those as additional binary variables... so for n total leaf regions you end up with [(4n - 1)/3 - 1] total variables (n for the smallest regions, n/4 for the next level up, etc). Of course this is multicollinear, which is why regularization is needed to do this.'
