Forecasting trading volume in local housing markets through a time-series model and a deep learning algorithm

Abstract

PurposeIt 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/approachFour 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.FindingsThe 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/valueWhereas 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.

Reaksi Pasar Terhadap Peristiwa Right Issue Di Bursa Efek Indonesia

“…Trading volume activity merupakan perbandingan antara jumlah saham yang diperdagangkan pada waktu tertentu dengan jumlah saham perusahaan yang beredar pada periode tertentu. Besar kecilnya perubahan rata-rata trading volume activity antara sebelum dan sesudah pemecahan saham merupakan ukuran besar kecilnya akibat yang ditimbulkan oleh adanya pemecahan saham terhadap volume perdagangan saham (Lee & Park, 2021). "…”

Training and Interpreting Machine Learning Models: Application in Property Tax Assessment

“…However, the real estate market is always characterized as a localized market; thus, a corresponding local approach is desirable to improve the valuation accuracy. For example, most property statistics are produced at the local level instead of the global level: Case-Shiller home price indices are measured at the metropolitan area level (Lee & Park, 2022) the real estate market appreciate and depreciate in a substantially different manner depending on the region (Clapp, 2004).…”

The Burr XII Autoregressive Moving Average Model

“…Articles on trading volume forecasts have also been published recently, in the stock market in India [23], ref. [24] in the real estate market. Therefore, time series analysis is a strategic tool used to predict future trends and moreover, it helps investors to make decisions that lead to good results and more profits.…”