Predictions of residential property price indices for China via machine learning models

The Chinese real estate market has expanded at such a quick rate over the last two decades, up to the current decline patterns that began at the end of 2021. As a result, predicting future property prices has become a significant challenge for both the government and investors. Within the scope of this investigation, we investigate quarterly national residential property price indices for China with data sourced from Bank for International Settlements from the second quarter of 2005 to the first quarter of 2024 by using Gaussian process regressions with a variety of kernels and basis functions. For the purpose of model training and conducting forecasting exercises using the estimated models, we make utilisation of cross-validation and Bayesian optimisations based upon the expected improvement per second plus algorithm. Use of Bayesian optimisations could help endow Gaussian process regression models with good flexibility for forecasting into the future. With a relative root mean square error of 0.1291 percent, root mean square error of 0.1816, mean absolute error of 0.1527, and correlation coefficient of 99.901%, the created models were able to reliably anticipate the price indices from the third quarter of 2020 to the first quarter of 2024 out of sample. The constructed Gaussian process regression models also outperform several alternative machine learning models and econometric models. Their forecast performance is robust to different out-of-sample evaluation periods as well. In order to build hypotheses about trends in the residential real estate price index and to carry out more policy research, our findings might be used either alone or in combination with other projections.