Automated Assessment of Housing Quality with the Use of Wordscores Algorithm

Michal Hebdzynski

ERES from European Real Estate Society (ERES)

Abstract: The aim of this paper is to address the problem of unavailability or inaccuracy of information on the quality of housing in the existing data sources. This may lead to obtaining biased results in the hedonic analyses of the market conducted for macroprudential and statistical purposes. We target this problem by proposing a supervised machine learning framework that base on the Wordscores algorithm. We try to answer the question, whether it is possible to reliably, automatically assess the quality of apartment, based solely on the textual description of its listing posted in the internet advertisement site. The accuracy of the method has been tested on the example of the Polish-language apartment sales and rental listings from 2019-2021. The obtained point estimates of the quality level show a high correlation with the human assessments. The results indicate that the application of the Wordscores algorithm gives 71% effectiveness in categorizing the apartments for rent into three quality groups: low, medium and high. For the apartments for sale, the effectiveness equals 64%. The study indicates that textual descriptions of apartments’ listings convey usable, yet most often unused information on the housing quality. The usage of the fruits of the method may lead to the increased accuracy of the performed analyses of the market, thus to its better understanding. The relative easiness of application of the algorithm and its high interpretability make the proposed method advantageous over the already developed, more econometrically sophisticated approaches.

Keywords: Hedonic methods; Housing quality; supervised machine-learning; Textual Analysis (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2022-01-01
New Economics Papers: this item is included in nep-big and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:arz:wpaper:2022_114

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