Using photographs and metadata to estimate house prices in South Korea
Data Technologies and Applications
ISSN: 2514-9288
Article publication date: 24 November 2020
Issue publication date: 12 April 2021
Abstract
Purpose
Most prior attempts at real estate valuation have focused on the use of metadata such as size and property age, neglecting the fact that the building workmanship in the construction of a house is also a key factor for the estimation of house prices. Building workmanship, such as exterior walls and floor tiling correspond to the visual attributes of a house, and it is difficult to capture and evaluate such attributes efficiently through classical models like regression analysis. Deep learning approach is taken in the valuation process to utilize this visual information.
Design/methodology/approach
The authors propose a two-input neural network comprising a multilayer perceptron and a convolutional neural network that can utilize both metadata and the visual information from images of the front view of the house.
Findings
The authors applied the two-input neural network to Guri City in Gyeonggi Province, South Korea, as a case study and found that the accuracy of house price estimations can be improved by employing image information along with metadata.
Originality/value
Few studies considered the impact of the building workmanship in the valuation process. The authors revealed that it is useful to use both photographs and metadata for enhancing the accuracy of house price estimation.
Keywords
Citation
Lee, C. and Park, K.-H. (2021), "Using photographs and metadata to estimate house prices in South Korea", Data Technologies and Applications, Vol. 55 No. 2, pp. 280-292. https://doi.org/10.1108/DTA-05-2020-0111
Publisher
:Emerald Publishing Limited
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