---
_id: '27506'
abstract:
- lang: eng
  text: Explainability for machine learning gets more and more important in high-stakes
    decisions like real estate appraisal. While traditional hedonic house pricing
    models are fed with hard information based on housing attributes, recently also
    soft information has been incorporated to increase the predictive performance.
    This soft information can be extracted from image data by complex models like
    Convolutional Neural Networks (CNNs). However, these are intransparent which excludes
    their use for high-stakes financial decisions. To overcome this limitation, we
    examine if a two-stage modeling approach can provide explainability. We combine
    visual interpretability by Regression Activation Maps (RAM) for the CNN and a
    linear regression for the overall prediction. Our experiments are based on 62.000
    family homes in Philadelphia and the results indicate that the CNN learns aspects
    related to vegetation and quality aspects of the house from exterior images, improving
    the predictive accuracy of real estate appraisal by up to 5.4%.
author:
- first_name: Jan-Peter
  full_name: Kucklick, Jan-Peter
  id: '77066'
  last_name: Kucklick
citation:
  ama: 'Kucklick J-P. Visual Interpretability of Image-based Real Estate Appraisal.
    In: <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>.
    ; 2022.'
  apa: Kucklick, J.-P. (2022). Visual Interpretability of Image-based 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{Kucklick_2022, title={Visual Interpretability of Image-based
    Real Estate Appraisal}, booktitle={55th Annual Hawaii International Conference
    on System Sciences (HICSS-55)}, author={Kucklick, Jan-Peter}, year={2022} }'
  chicago: Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate
    Appraisal.” In <i>55th Annual Hawaii International Conference on System Sciences
    (HICSS-55)</i>, 2022.
  ieee: J.-P. Kucklick, “Visual Interpretability of Image-based Real Estate Appraisal,”
    presented at the Hawaii International Conference on System Science (HICSS), Virtual,
    2022.
  mla: Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate Appraisal.”
    <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>,
    2022.
  short: 'J.-P. Kucklick, 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: 2021-11-17T07:08:15Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '195'
- _id: '196'
keyword:
- Explainable Artificial Intelligence (XAI)
- Regression Activation Maps
- Real Estate Appraisal
- Convolutional Block Attention Module
- Computer Vision
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://scholarspace.manoa.hawaii.edu/bitstream/10125/79519/0149.pdf
oa: '1'
publication: 55th Annual Hawaii International Conference on System Sciences (HICSS-55)
status: public
title: Visual Interpretability of Image-based Real Estate Appraisal
type: conference
user_id: '77066'
year: '2022'
...