Classifying multilabel images with TensorFlow
I suggest you try building multiple networks: one network for building type (outputs "house", "apartment", or "condominum"), another network for build year, another for garage (yes vs no), and so on.  This keeps the number of classes small for each network, and allows each network to tune itself for the specific task it is focusing on.

If you want to avoid multiple networks:

Conceptually, share the first n-1 layers, and have a separate nth layer for each of these tasks.  In other words, the last layer is made wider, with elements for building type, elements for building year, and so on.  You end with multiple softmax elements.  A simple example would be one softmax for building type outputs ("house", "apartment", or "condominum") and one softmax for build year ("new" or "old").  Thus, you'd have 5 output wires.

You can of course experiment with other splits, say, where the first n-3 layers are shared and then for the last 3 layers you have multiple different networks in parallel.