Using Geolocated Text to Quantify Location in Real Estate Appraisal Heuwinkel, Tim Kucklick, Jan-Peter Müller, Oliver 2021-12-24T18:12:08Z 2021-12-24T18:12:08Z 2022-01-04
dc.description.abstract Accurate 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.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.700
dc.identifier.isbn 978-0-9981331-5-7
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Location Intelligence Research in System Sciences
dc.subject location intelligence
dc.subject natural language processing (nlp)
dc.subject real estate appraisal
dc.subject text regression
dc.subject wikipedia
dc.title Using Geolocated Text to Quantify Location in Real Estate Appraisal
dc.type.dcmi text
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