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

doi:10.1088/1742-6596/2287/1/012019

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

Department of Industrial Engineering

Research output: Contribution to journal › Conference article › peer-review

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 language	English
Article number	012019
Journal	Journal of Physics: Conference Series
Volume	2287
Issue number	1
DOIs	https://doi.org/10.1088/1742-6596/2287/1/012019
Publication status	Published - 2022
Event	2022 12th International Conference on Applied Physics and Mathematics, ICAPM 2022 - Singapore, Virtual, Singapore Duration: 2022 Feb 18 → 2022 Feb 20

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© Published under licence by IOP Publishing Ltd.

All Science Journal Classification (ASJC) codes

General Physics and Astronomy

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10.1088/1742-6596/2287/1/012019

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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.",

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PY - 2022

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N2 - 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.

AB - 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.

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ER -

Deep Neural Network As a Tool for Appraising Housing Prices: A Case Study of Busan, South Korea

Abstract

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