Deep neural network as a tool for appraising housing prices: a case study of Busan, South Korea

S. An, Y. Song, H. Jang, K. Ahn*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

This study examines whether the number of hidden layers in a deep neural network significantly influences the model accuracy and efficiency for appraising housing prices. We provide empirical evidence that the deep neural network can achieve high accuracy with a small number of hidden layers on our dataset, which contains various hedonic variables. Furthermore, we show that adding layers does not necessarily guarantee the model's accuracy and effectiveness of the computing time.
Original languageEnglish
Title of host publicationICAPM 2022Conference Proceedings
PublisherIOP Publishing
Number of pages5
Volume2287
DOIs
Publication statusPublished - 1 Jun 2022
Event12th International Conference on Applied Physics and Mathematics - Online, Unknown
Duration: 18 Feb 202220 Feb 2022
http://icapm.org/2022.html (Link to conference website)

Publication series

NameJournal of Physics: Conference Series
PublisherIOP Publishing
ISSN (Print)1742-6588

Conference

Conference12th International Conference on Applied Physics and Mathematics
Abbreviated titleICAPM 2022
Country/TerritoryUnknown
Period18/02/2220/02/22
Internet address

Keywords

  • deep neural network
  • housing prices
  • South Korea

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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