Information Value of Property Description: A Machine Learning Approach

Lily Shen and Stephen Ross
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Lily Shen: Clemson University

No 2019-20, Working papers from University of Connecticut, Department of Economics

Abstract: This paper employs machine learning to quantify the value of “soft” information con-tained in real estate property descriptions. Textual descriptions contain information that traditional hedonic attributes cannot capture. A one standard deviation increase in the uniqueness of a property based on this “soft” information leads to a 15% increase in property sale price in a hedonic price model and a 10% increase in a repeat sales price model. The effects in the hedonic model appear to arise through two channels: the unobserved quality of the housing unit, and the market power of the housing unit relative to competing properties. The effects in the repeat sales model appear to be driven entirely by the market power of the unit. Further, an annual hedonic price index ignoring our measure of unobserved quality overstates real estate prices by be-tween 10% to 23% and mistimes the stabilization of housing prices following the Great Recession. Similar, but smaller effects, are observed for the repeat sales price index.

Keywords: Natural Language Processing; Unsupervised Machine Learning; Soft Information; Housing Prices; Price indexes; Property Descriptions (search for similar items in EconPapers)
JEL-codes: C45 G12 G14 R31 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2019-12, Revised 2020-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:uct:uconnp:2019-20

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