Data augmentation using a variational autoencoder for estimating property prices
ISSN: 0263-7472
Article publication date: 5 February 2021
Issue publication date: 28 April 2021
Abstract
Purpose
Prior studies on the application of deep-learning techniques have focused on enhancing computation algorithms. However, the amount of data is also a key element when attempting to achieve a goal using a quantitative approach, which is often underestimated in practice. The problem of sparse sales data is well known in the valuation of commercial properties. This study aims to expand the limited data available to exploit the capability inherent in deep learning techniques.
Design/methodology/approach
The deep learning approach is used. Seoul, the capital of South Korea is selected as a case study area. Second, data augmentation is performed for properties with low trade volume in the market using a variational autoencoder (VAE), which is a generative deep learning technique. Third, the generated samples are added into the original dataset of commercial properties to alleviate data insufficiency. Finally, the accuracy of the price estimation is analyzed for the original and augmented datasets to assess the model performance.
Findings
The results using the sales datasets of commercial properties in Seoul, South Korea as a case study show that the augmented dataset by a VAE consistently shows higher accuracy of price estimation for all 30 trials, and the capabilities inherent in deep learning techniques can be fully exploited, promoting the rapid adoption of artificial intelligence skills in the real estate industry.
Originality/value
Although deep learning-based algorithms are gaining popularity, they are likely to show limited performance when data are insufficient. This study suggests an alternative approach to overcome the lack of data problem in property valuation.
Keywords
Citation
Lee, C. (2021), "Data augmentation using a variational autoencoder for estimating property prices", Property Management, Vol. 39 No. 3, pp. 408-418. https://doi.org/10.1108/PM-09-2020-0057
Publisher
:Emerald Publishing Limited
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