Rental price index forecasts of residential properties using Gaussian process regressions

Purpose Since the Chinese real estate market has expanded so quickly over the past 10 years, investors and the government are both quite concerned about projecting future property prices. Design/methodology/approach This work aims to investigate monthly rental price index forecasts of residential properties for ten major Chinese cities from 3M2012 to 5M2020 by using Gaussian process regressions with a diverse variety of kernels and basis functions. The authors conduct forecast exercises through use of Bayesian optimizations and cross-validation. Findings With relative root mean square errors spanning the range of 0.0370%–0.8953%, the constructed models successfully forecast the ten price indices from 6M2019 to 5M2020 out of sample. Originality/value The findings might be used independently or in combination with other projections to create theories about the trends in the rental price index of the residential property and carry out additional policy analysis.