Forecasting Property Price Indices in Hong Kong Based on Grey Models

Abstract

The real estate market in Hong Kong plays an important role in its economy. The property prices have been increasing a lot since 2009, which have become a major concern. However, few studies have been done to forecast the property price indices in Hong Kong. In this paper, two grey models, GM(1,1) and GM(0,N), are introduced for the forecasting. The results show that GM(1,1) has a better performance when forecasting with stable trend data, while GM(0,N) is more suitable for forecasting data in fluctuating trend. The sensitivity analysis for GM(0,N) shows that Population(POP) and Best Lending Rate(BLR) are significantly sensitive factors for data in stable trend. While for the fluctuating data, sensitivity of each factor presents uncertainties. This study also compares the forecasting performance of grey models with the ANN model and ARIMA model. The study demonstrates that grey models are more suitable for forecasting the Hong Kong property price indices than others.

The Impact Of Macroeconomic Variables On Real Estate Price Forecasting Modelling In Abuja Nigeria

“…Moreover, the ARIMAX model performance also revealed that macroeconomic variables contain high information content concerning future development of real estate market in Abuja Nigeria. The paper also affirmed the findings of [18] that prediction of a single residential price changes is difficult but aggregate real estate price changes can be forecasted. The limitation of this research is that, there is limited data of all categories of residential price in Abuja.…”

“…The studies shows many types of forecasting classification methods, which comprises univariate and multivariate models, simple and complex model, conventional and advanced models, artificial intelligence and statistical models etc. The autoregressive integrated moving average (ARIMA) models can provide accurate forecasting performance, particularly in the short-term forecasting [18]. ARIMA models provide excellent forecasting capability for short-term forecast [19].…”

“…Similarly, ARIMA modelling technique exist in the imperfect character of real estate market which make them to apply the Box-Jenkins method and the problem connected with revolving points identification that can be eliminated with the procedure of moving average [20]. [21] Attempted forecasting Hong Kong real estate price and shows that ARIMA model is capable to signal short-term market trend [18].…”

Predicting property price index using artificial intelligence techniques

“…The study found that the 2011 bubble experienced in Hong Kong could be attributed to demand pressure for a medium-sized apartment by small-sized property owners. Tan et al (2017) also investigated the forecasting performance of grey models for PPIs in Hong Kong and reported that grey models is a good tool to forecast property prices indices in Hong Kong. However, investigations into the performance of ANN been benchmarked with the SVM machine learning approach and ARIMA econometric approach in Hong Kong is limited, especially for the prediction of PPI.…”

The Behavioral Mechanism and Forecasting of Beijing Housing Prices from a Multiscale Perspective

“…erefore, the research on machine learning has recently emerged as a trend in the field of real estate price forecasting because it can capture the nonlinearity and nonstationarity that exist widely in real estate prices. Specifically, support vector regression [17], C4.5 [18], Naïve Bayesian [18], AdaBoost [18], Boltzmann machine [19], and grey models [20] have been wildly used to forecast house prices. For instance, Wang et al [17] proposed a hybrid model that integrates support vector machines and particle swarm optimization for real estate price forecasting.…”