Using Geolocated Text to Quantify Location in Real Estate Appraisal

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dc.contributor.authorHeuwinkel, Tim
dc.contributor.authorKucklick, Jan-Peter
dc.contributor.authorMüller, Oliver
dc.date.accessioned2021-12-24T18:12:08Z
dc.date.available2021-12-24T18:12:08Z
dc.date.issued2022-01-04
dc.description.abstractAccurate real estate appraisal is essential in decision making processes of financial institutions, governments, and trending real estate platforms like Zillow. One of the most important factors of a property’s value is its location. However, creating accurate quantifications of location remains a challenge. While traditional approaches rely on Geographical Information Systems (GIS), recently unstructured data in form of images was incorporated in the appraisal process, but text data remains an untapped reservoir. Our study shows that using text data in form of geolocated Wikipedia articles can increase predictive performance over traditional GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to automatically extract geographically weighted vector representations for text is established and used alongside traditional structural housing features to make predictions and to uncover local patterns on sale price for real estate transactions between 2015 and 2020 in Allegheny County, Pennsylvania.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.700
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/80039
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectLocation Intelligence Research in System Sciences
dc.subjectlocation intelligence
dc.subjectnatural language processing (nlp)
dc.subjectreal estate appraisal
dc.subjecttext regression
dc.subjectwikipedia
dc.titleUsing Geolocated Text to Quantify Location in Real Estate Appraisal
dc.type.dcmitext

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