Forecasting Residential Real Estate Prices via Machine Learning for Taizhou City of Zhejiang Province in China

The Chinese real estate market has grown at such a fast rate over the last several decades, up to the present decline patterns that began at the end of 2021. As a result, projecting future property prices has become more challenging for investors and the government. This is a result of the state of the economy right now. In this work, we examine monthly residential property prices for Taizhou City, Zhejiang Province, China, using Gaussian process regressions with a variety of kernels and basis functions, spanning from April 2015 to the July 2024. Our forecasting activities employ estimated models, which we train using a combination of cross-validation and Bayesian optimisations for endowing the constructed model with good flexibility. The models that were created were successful in precisely predicting the prices from September 2022 to July 2024 outside the sample. These models had a relative root mean square error of 0.1120%, a root mean square error of 19.1482, a mean absolute error of 14.3875, and a correlation coefficient of 99.986%. It is plausible that our findings may be used alone or in combination with further projections to formulate conjectures about fluctuations in the residential real estate prices and conduct additional policy analysis, which will help investors and policymakers in better understanding the evolving market condition.