A Gaussian process regression machine learning model for forecasting retail property prices with Bayesian optimizations and cross-validation

The real estate market in China has been growing rapidly during the past decade, with different property price patterns across various regions. Among different types of properties, prices of retail properties have not been sufficiently analyzed. This study focuses on forecasting problems of price indices of retail properties across ten major cities in China from 2005 to 2021 through monthly data and Gaussian process regression models. We examine ten kernels, four basis functions, and two predictor standardization options for constructing the forecast model through Bayesian optimization and cross-validation for each price index. The ten models built lead to accurate out-of-sample forecast results for a two-year window from 2019 to 2021. Relative root mean square errors across the ten indices range from 0.0113% to 0.4835%. Market participants and policymakers could utilize forecast models here for assessing retail property prices.