Forecasting trading volume in local housing markets through a time-series model and a deep learning algorithm
Engineering, Construction and Architectural Management
ISSN: 0969-9988
Article publication date: 16 February 2021
Issue publication date: 10 February 2022
Abstract
Purpose
It is important to forecast local trading volumes as well as global trading volumes because the real estate market is always characterized as a localized market. The house trading volume at the local level is forecast through appropriate models to enhance the predictive accuracy.
Design/methodology/approach
Four representative housing submarkets in South Korea are selected, and their trading volumes are forecast. A well-established time-series model and a deep learning algorithm are employed: the autoregressive integrated moving average (ARIMA) model and the recurrent neural network (RNN), respectively. The trading volumes in adjacent areas are utilized as covariates, and an ensemble prediction is applied additionally to improve the model performance.
Findings
The results indicate no significant difference in prediction performance between the ARIMA model and the RNN, which can be attributed to the insufficient amount of data used. It is discovered that the spillover effects of trading volumes across the study areas can be exploited to improve the predictive accuracy, and that the diversity of the predicted values from the candidate models can be used to increase the forecasting accuracy further.
Originality/value
Whereas property prices have been investigated extensively, the discussion on forecasting trading activity of properties is limited in the literature. The results of this study are expected to promote more interest in adopting a local perspective and using a diversity of predicted values when forecasting house trading volumes.
Keywords
Citation
Lee, C. and Park, K.K.-H. (2022), "Forecasting trading volume in local housing markets through a time-series model and a deep learning algorithm", Engineering, Construction and Architectural Management, Vol. 29 No. 1, pp. 165-178. https://doi.org/10.1108/ECAM-10-2020-0850
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited