We explore whether social media can pro vide a window into community real estate — foreclosure rates and price changes — beyond that of traditional economic and demographic variables. We find language use in Twitter not only predicts real estate outcomes as well as traditional variables across counties, but that including Twit ter language in traditional models leads to a significant improvement (e.g. from Pearson r = .50 to r = .59 for price changes). We overcome the challenge of the relative sparsity and noise in Twitter language variables by showing that train ing on the residual error of the traditional models leads to more accurate overall as sessments. Finally, we discover that it is Twitter language related to business (e.g. 'company', 'marketing') and technology (e.g. 'technology', 'internet'), among oth ers, that yield predictive power over eco nomics.
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