Forecasts of Residential Real Estate Price Indices for Ten Major Chinese Cities through Gaussian Process Regressions

Due to the rapid growth of the Chinese housing market over the past ten years, forecasting home prices has become a crucial issue for investors and authorities alike. In this research, utilising Bayesian optimisations and cross validation, we investigate Gaussian process regressions across various kernels and basis functions for monthly residential real estate price index projections for ten significant Chinese cities from July 2005 to April 2021. The developed models provide accurate out-of-sample forecasts for the ten price indices from May 2019 to April 2021, with relative root mean square errors varying from 0.0207% to 0.2818%. Our findings could be used individually or in combination with other projections to formulate theories about the trends in the residential real estate price index and carry out additional policy analysis.