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        <title>Using Twitter Language to Predict the Real Estate Market</title>
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        <namePart type="given">Mohammadzaman</namePart>
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            <namePart type="given">Mirella</namePart>
            <namePart type="family">Lapata</namePart>
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    <abstract>We explore whether social media can provide 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 Twitter 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 training on the residual error of the traditional models leads to more accurate overall assessments. Finally, we discover that it is Twitter language related to business (e.g. ‘company’, ‘marketing’) and technology (e.g. ‘technology’, ‘internet’), among others, that yield predictive power over economics.</abstract>
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%0 Conference Proceedings
%T Using Twitter Language to Predict the Real Estate Market
%A Zamani, Mohammadzaman
%A Schwartz, H. Andrew
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F zamani-schwartz-2017-using
%X We explore whether social media can provide 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 Twitter 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 training on the residual error of the traditional models leads to more accurate overall assessments. Finally, we discover that it is Twitter language related to business (e.g. ‘company’, ‘marketing’) and technology (e.g. ‘technology’, ‘internet’), among others, that yield predictive power over economics.
%U https://aclanthology.org/E17-2005
%P 28-33

Markdown (Informal)

[Using Twitter Language to Predict the Real Estate Market](https://aclanthology.org/E17-2005) (Zamani & Schwartz, EACL 2017)

• Using Twitter Language to Predict the Real Estate Market (Zamani & Schwartz, EACL 2017)

ACL

• Mohammadzaman Zamani and H. Andrew Schwartz. 2017. Using Twitter Language to Predict the Real Estate Market. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 28–33, Valencia, Spain. Association for Computational Linguistics.