Residential housing price index forecasting via neural networks

During the past decade, the housing market in China has witnessed rapid growth and the significance of forecasting related to housing prices has undoubtedly elevated, which has become an important issue to the people in investment and policymakers in regulations. In this study, we explore neural networks for residential housing price index forecasts from ten major Chinese cities for July 2005-April 2021. We aim at constructing simple and accurate neural networks as a contribution to pure technical forecasts of the Chinese residential housing market. To facilitate the analysis, we investigate different model settings across algorithms, delays, hidden neurons, and data spitting ratios, and arrive at a simple neural network with three delays and three hidden neurons, which produces stable performance of about 0.75% average relative root mean square error across the ten cities for the training, validation, and testing phases. Our results can be used on a standalone basis or combined with fundamental forecasts to form perspectives of residential housing price trends and carry out policy analysis.

Automated summary

Over the past decade, the housing market in China has experienced rapid growth, leading to an increased significance in forecasting housing prices. In this study, we employed neural networks to forecast the residential housing price index in ten major Chinese cities from July 2005 to April 2021. Our aim was to develop simple and accurate neural network models that contribute to purely technical forecasts of the Chinese housing market. Through various model settings, including algorithms, delays, hidden neurons, and data splitting ratios, we identified a simple neural network with three delays and three hidden neurons that achieved stable performance with an average relative root mean square error of about 0.75% across the ten cities during the training, validation, and testing phases. Our findings can be utilized independently or in conjunction with fundamental forecasts to generate perspectives on residential housing price trends and conduct policy analysis.