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The objective of this work is to provide a statistical tool that can drive local policies on the basis of urban specificities. For this purpose, a very detailed and updated statistical information at finer geographic level is necessary. Typically, the first aspect has always been assured by the Census that until now had the limit of providing data on an annual basis. A temporal discrepancy is no longer acceptable nowadays. The timeliness of the information is on the contrary assured by the sample surveys, which, unfortunately, have limitations on territorial level dissemination, the estimates are, usually, produced at regional level. From these considerations it emerges the need to provide solutions that exploit the availability of new sources of information, such as administrative data. The integration of this information with survey data can overcome the lack of information at a more detailed territorial level, assuring simultaneously timely and accurate estimates. Actually, Istat produces social and economic indicators using administrative data (Archimede) at municipal level. However, due to a different taxonomy, these indicators do not coincide with those usually computed by sample surveys. Therefore, this information is often not consistent with the information officially produced by Istat at the regional level. The idea is, first of all, to compare the indicators computed by the two sources of information, for all the metropolitan cities, for some large municipalities and for functional aggregations of small municipalities. The next step is to use the proxy information from the administrative data to fit unit and area small area level models or to compute projection type estimates. The output will allow to empirically evaluate the results obtained on important indicators of social exclusion and well-being, typically produced with the EuSilc survey. Furthermore, the results will provide important indications for the sample allocation, in view of a possible coordination of the social survey modules to be drawn from the Census master sample.