ARIMA and Exponential Smoothing Models in Forecasting the Macau Property Price Index During COVID-19

Abstract

The real estate market plays an important role in the economies of many countries, and the future trend of the market has long been a topic of concern to both academics and practitioners. This paper attempts to study the effectiveness and superiority of two univariate time series models, Autoregressive Integrated Moving Average  (ARIMA) model and exponential smoothing, to forecast the Macau residential property price index during the COVID-19 pandemic. Based on 1- and 2-year holdout samples during the shock of COVID-19 in Macau, the results show that the out-of-sample forecasting performances of both models are better than the baseline model of classical decomposition. There is also evidence that the ARIMA models outperform the Winters three-parameter exponential smoothing models in the two out-of-sample periods. Therefore, in the context of unprecedented events such as COVID-19, the ARIMA method is more effective than the Winters exponential smoothing method in making rapid and accurate adjustments when the Macau residential property price index is significantly affected. Our findings provide important implications for relevant government departments, home buyers and sellers, and property market participants in their selections of reliable models to forecast future property market behavior.