Finding a house to buy, using statistics

Atma Mani, a geospatial engineer for ESRI, imagined shopping for a house with data, maps, and analysis. Basically, a personalized recommendation system:

The type of recommendation engine built in this study is called ‘content based filtering’ as it uses just the intrinsic and spatial features engineered for prediction. For this type of recommendation to work, we need a really large training set. In reality nobody can generate such a large set manually. In practice however, another type of recommendation called ‘community based filtering’ is used. This type of recommendation engine uses the features engineered for the properties, combined with favorite / blacklist data to find similarity between a large number of buyers. It then pools the training set from similar buyers to create a really large training set and learns on that.

I love going all nerd on these sort of things. The most interesting part for me though is that it always seems to come down to a gut feeling. You have to see the house and get a feel for the area, which is much harder to get through data. So then, how do you couple the information you get from the data with more fuzzy emotions?