---
_id: '27507'
abstract:
- lang: eng
  text: 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.
author:
- first_name: Tim
  full_name: Heuwinkel, Tim
  last_name: Heuwinkel
- first_name: Jan-Peter
  full_name: Kucklick, Jan-Peter
  id: '77066'
  last_name: Kucklick
- first_name: Oliver
  full_name: Müller, Oliver
  id: '72849'
  last_name: Müller
citation:
  ama: 'Heuwinkel T, Kucklick J-P, Müller O. Using Geolocated Text to Quantify Location
    in Real Estate Appraisal. In: <i>55th Annual Hawaii International Conference on
    System Sciences (HICSS-55)</i>. ; 2022.'
  apa: Heuwinkel, T., Kucklick, J.-P., &#38; Müller, O. (2022). Using Geolocated Text
    to Quantify Location in Real Estate Appraisal. <i>55th Annual Hawaii International
    Conference on System Sciences (HICSS-55)</i>. Hawaii International Conference
    on System Science (HICSS), Virtual.
  bibtex: '@inproceedings{Heuwinkel_Kucklick_Müller_2022, title={Using Geolocated
    Text to Quantify Location in Real Estate Appraisal}, booktitle={55th Annual Hawaii
    International Conference on System Sciences (HICSS-55)}, author={Heuwinkel, Tim
    and Kucklick, Jan-Peter and Müller, Oliver}, year={2022} }'
  chicago: Heuwinkel, Tim, Jan-Peter Kucklick, and Oliver Müller. “Using Geolocated
    Text to Quantify Location in Real Estate Appraisal.” In <i>55th Annual Hawaii
    International Conference on System Sciences (HICSS-55)</i>, 2022.
  ieee: T. Heuwinkel, J.-P. Kucklick, and O. Müller, “Using Geolocated Text to Quantify
    Location in Real Estate Appraisal,” presented at the Hawaii International Conference
    on System Science (HICSS), Virtual, 2022.
  mla: Heuwinkel, Tim, et al. “Using Geolocated Text to Quantify Location in Real
    Estate Appraisal.” <i>55th Annual Hawaii International Conference on System Sciences
    (HICSS-55)</i>, 2022.
  short: 'T. Heuwinkel, J.-P. Kucklick, O. Müller, in: 55th Annual Hawaii International
    Conference on System Sciences (HICSS-55), 2022.'
conference:
  end_date: 2022-01-07
  location: Virtual
  name: Hawaii International Conference on System Science (HICSS)
  start_date: 2022-01-03
date_created: 2021-11-17T07:12:03Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '195'
keyword:
- Real Estate Appraisal
- Text Regression
- Natural Language Processing (NLP)
- Location Intelligence
- Wikipedia
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://scholarspace.manoa.hawaii.edu/bitstream/10125/80039/0561.pdf
oa: '1'
publication: 55th Annual Hawaii International Conference on System Sciences (HICSS-55)
status: public
title: Using Geolocated Text to Quantify Location in Real Estate Appraisal
type: conference
user_id: '77066'
year: '2022'
...