The automatic construction of new features from raw data
'high predictive value' is only defined if you a target which you are trying to predict upon. It seems that you don't, and your goal is to cluster data points according to some scale defined by a variety of factors. These can undoubtedly be used to cluster data points, and I'd advise you to look into the various methods available: some that may be interesting for you are Agglomerative and Hierarchical clustering.

Now to answer the question, you can surely generate new features from the ones present in your dataset that may or may not help you achieve your goal. You can: 


Bin your data: define some categories such as 'rich', 'average', 'poor' with specified ranges and create a new feature that maps a numerical value (wealth) to a bin
One-hot-encode categorical variables


After these pre-processing steps are done, you could go ahead an apply the clustering methods I mentioned to group data together into their respective 'Social Status'. Of course, lots of tweaking and experimentation will be needed. As far as what I've encountered, there are not really automagic ways of generating new features, and mostly the available methods will depend greatly on the type of data and problem you are working on.