Research on House Price Forecasting in Guiyang City Based on ARIMA and GARCH Models

Ruixuan Tian

doi:10.54097/fc6av449

Authors

Ruixuan Tian

DOI:

https://doi.org/10.54097/fc6av449

Keywords:

ARIMA Model, ARCH Test, GARCH Model, Mixed Model, Residential Transaction Average Price

Abstract

The purpose of this study is to forecast and analyze the house price of Guiyang city through ARIMA model and GARCH model, in order to improve the accuracy and reliability of house price prediction, and to provide references for real estate market analysis and policy formulation. As the capital city of Guizhou Province, Guiyang city's real estate market shows certain volatility in the context of economic recovery after the epidemic, and in-depth study of its house price trend is of great practical significance. This study will use the average residential transaction price data of Guiyang city from 2019 to 2024 to firstly analyze the smoothness and autocorrelation of the house price data by ARIMA model to capture the trend and seasonal changes in the time series; secondly, analyze the volatility of house price by GARCH model to capture the conditional heteroskedasticity phenomenon in the market. On this basis, a hybrid model is constructed by combining the advantages of ARIMA and GARCH models, and the performance of single model and hybrid model in house price forecasting is comparatively analyzed. The key issues of the study include how to effectively handle non-stationary data, capture house price volatility, assess the forecasting accuracy of the model, and the feasibility of constructing a hybrid model. Through ADF test, ACF and PACF test, ARCH test and other methods, this study will gradually build and optimize the ARIMA, GARCH and hybrid models, and finally forecast the house price trend in the coming year, and put forward the corresponding market analyses and policy recommendations. The innovation of this study lies in the application of ARIMA and GARCH models to the prediction of house prices in Guiyang City, and the enhancement of the prediction accuracy by the hybrid model, which provides a new analysis perspective for the real estate market.

Downloads

Download data is not yet available.

References

[1] Zhang, S. X. (2020). Application of ARIMA and ARIMAX models in private car ownership forecasting. Statistics and Decision Making, 37(5), 123–128.

[2] Divisekara, R. W., Jayasinghe, G. J. M. S. R., Kumari, K. W. S. N., et al. (2021). Forecasting the red lentils commodity market price using SARIMA models. SN Business & Economics, 1(20), 1–20.

[3] Sun, L. J., & Wang, J. (2018). Econometric analysis of real estate stock market return volatility. Economic Research, 52(2), 45–52.

[4] Aras, S. (2021). Stacking hybrid GARCH models for forecasting Bitcoin volatility. Expert Systems with Applications, 1(20), 1–20.

[5] Li, W. J. (2024). Volatility persistence of financial time series and optimal portfolio relationship—A perspective based on GARCH (1,1) model moments. Modern Business, 4, 105–108.

[6] Wang, Y. L. (2024). Research on RMB exchange rate volatility based on ARIMA-GARCH model. China Business Journal, 11, 9–12.

[7] Xiong, Z., & Che, W. G. (2022). Application of ARIMA-GARCH-M model in short-term stock forecasting. Journal of Shaanxi University of Technology (Natural Science Edition), 40(4), 69–74.

[8] Hu, X. Q. (2022). Research on real estate price forecasting based on ARIMA model—Taking Hefei City as an example. China Management Informatization, 5, 163–166.

[9] Fu, Q. W., Yi, Y. C., Zhang, S. J., et al. (2025). Forecasting house price in Hengyang city based on ARIMA model. Statistics and Applications, 12(1), 123–130.

[10] Chen, L. (2022). Price forecasting of commodity housing in Nanjing—Comparative analysis based on ARIMA and SARIMA models. Statistics and Applications, 11(2), 280–287.

[11] Jadevicius, A., & Huston, S. (2015). ARIMA modelling of Lithuanian house price index. International Journal of Housing Markets and Analysis, 8(1), 135–147.

[12] Zamri, A., Ahmad, B., Salleh, C., et al. (2024). Analysis of AIC and BIC through ARIMA modeling for house price prediction in Malaysia. Journal of Housing Economics, 12, 88–102.

[13] Cui, Y. S., & Liu, J. (2010). Research on the volatility of real estate market. North China Finance, 12, 11–13.

[14] Suleiman, A. A., Othman, M., Daud, H., et al. (2023). Forecasting the volatility of real residential property prices in Malaysia: A comparison of GARCH models. Real Estate Management and Valuation, 31(3), 20–31.

[15] Otto, P., & Schmid, W. (2023). A general framework for spatial GARCH models. Statistical Papers, 64(2), 1721–1747.