Real Estate Dictionaries Across Space and Time

Adam D. Nowak, Bradley S. Price and Patrick S. Smith ()
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Adam D. Nowak: West Virginia University
Bradley S. Price: West Virginia University
Patrick S. Smith: San Diego State University

The Journal of Real Estate Finance and Economics, 2021, vol. 62, issue 1, No 6, 139-163

Abstract: Abstract Leveraging high-dimensional variable selection methods, we show the textual information provided in real estate agents’ remarks about a property can be used to address spatial and temporal heterogeneity in housing markets. Including the textual information in the pricing model decreases in-sample prediction errors by as much as 18.7% at the MSA-level and 39.1% at the zip code level. These results are robust to transforming the raw text using a real estate specific word list, the choice of n-grams, word stemming, and heteroscedasticity in the hedonic and repeat-sales models. These findings suggest the raw text in the remarks can be included directly in predictive pricing models.

Keywords: House prices; Machine learning; Real estate dictionary; Textual analysis (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11146-019-09740-w

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