Predicting property price index using artificial intelligence techniques : evidence from Hong Kong

Abstract

Purpose: Booms and bubbles are inevitable in the real estate industry. Loss of profits, bankruptcy and economic slowdown are indicators of the adverse effects of fluctuations in property prices. Models providing a reliable forecast of property prices are vital for mitigating the effects of these variations. Hence, this study aims to investigate the use of artificial intelligence (AI) for the prediction of property price index (PPI). Design/methodology/approach: Information on the variables that influence property prices was collected from reliable sources in Hong Kong. The data were fitted to an autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and support vector machine (SVM) models. Subsequently, the developed models were used to generate out-of-sample predictions of property prices. Findings: Based on the prediction evaluation metrics, it was revealed that the ANN model outperformed the SVM and ARIMA models. It was also found that interest rate, unemployment rate and household size are the three most significant variables that could influence the prices of properties in the study area. Practical implications: The findings of this study provide useful information to stakeholders for policy formation and strategies for real estate investments and sustained growth of the property market. Originality/value: The application of the SVM model in the prediction of PPI in the study area is lacking. This study evaluates its performance in relation to ANN and ARIMA.