# Logistic regression with multiple dependent variables in a single model

Imagine I have objects with 5 different properties which are either present (1) or not (0). Further, I have some other variables that I expect to predict the presence of a property.

Focusing on a single property out of the five, I could use a logistic regression to infer the influence of my variables on the properties presence. This, however, would give me five different models and I'd need to assume that the properties are independent of each other.

Is there an elegant way to combine all five attributes in a single model? Probably using some hierarchical model? For the implementation I use rstan, but some theoretical idea where to start would be helpful.