Office real estate price index forecasts through Gaussian process regressions for ten major Chinese cities

Abstract

During the last decade, the Chinese housing market has seen fast expansion, and the importance of housing price forecasts has surely increased, becoming an essential problem for policymakers and investors. In this article, we explore Gaussian process regressions across different kernels and basis functions for monthly office real estate price index forecasts for ten major Chinese cities from July 2005 to April 2021 using cross-validation and Bayesian optimizations that could endow the forecast models with higher adaptability and better generalization performance. The models constructed offer precise out-of-sample forecasts from May 2019 to April 2021 with relative root mean square errors ranging from 0.0205 to 0.5300% across the ten price indices. Benchmark analysis against the autoregressive model, autoregressive-generalized autoregressive conditional heteroskedasticity model, nonlinear autoregressive neural network model, support vector regression model, and regression tree model suggests that the Gaussian process regression model leads to statistically significant higher accuracy. Our findings might be utilized independently or in conjunction with other projections to create views on office real estate price index movements and undertake further policy research.