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Forecasting house prices using dynamic model averaging approach: Evidence from China

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  • Wei, Yu
  • Cao, Yang

Abstract

Forecasting house price has been of great interests for macroeconomists, policy makers and investors in recent years. To improve the forecasting accuracy, this paper introduces a dynamic model averaging (DMA) method to forecast the growth rate of house prices in 30 major Chinese cities. The advantage of DMA is that this method allows both the sets of predictors (forecasting models) as well as their coefficients to change over time. Both recursive and rolling forecasting modes are applied to compare the performance of DMA with other traditional forecasting models. Furthermore, a model confidence set (MCS) test is used to statistically evaluate the forecasting efficiency of different models. The empirical results reveal that DMA generally outperforms other models, such as Bayesian model averaging (BMA), information-theoretic model averaging (ITMA) and equal-weighted averaging (EW), in both recursive and rolling forecasting modes. In addition, in recent years it is found that the Google search index, instead of fundamental macroeconomic or monetary indicators, has developed greater predictive power for house price in China.

Suggested Citation

  • Wei, Yu & Cao, Yang, 2017. "Forecasting house prices using dynamic model averaging approach: Evidence from China," Economic Modelling, Elsevier, vol. 61(C), pages 147-155.
  • Handle: RePEc:eee:ecmode:v:61:y:2017:i:c:p:147-155
    DOI: 10.1016/j.econmod.2016.12.002
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    Cited by:

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    3. Mariam Camarero & Laura Montolio & Cecilio Tamarit, 2019. "Determinants of German outward FDI: variable selection using Bayesian statistical," Working Papers 1906, Department of Applied Economics II, Universidad de Valencia.
    4. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    5. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    6. Nuri Hacıevliyagil & Krzysztof Drachal & Ibrahim Halil Eksi, 2022. "Predicting House Prices Using DMA Method: Evidence from Turkey," Economies, MDPI, vol. 10(3), pages 1-27, March.
    7. Doojav Gan-Ochir & Luvsannyam Davaajargal, 2023. "Forecasting Inflation in Mongolia: A Dynamic Model Averaging Approach," Journal of Time Series Econometrics, De Gruyter, vol. 15(1), pages 27-48, January.
    8. Dong, Xiyong & Yoon, Seong-Min, 2019. "What global economic factors drive emerging Asian stock market returns? Evidence from a dynamic model averaging approach," Economic Modelling, Elsevier, vol. 77(C), pages 204-215.
    9. Oguzhan Cepni & Rangan Gupta & Yigit Onay, 2022. "The role of investor sentiment in forecasting housing returns in China: A machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1725-1740, December.
    10. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
    11. Dong, Xiyong & Song, Li & Yoon, Seong-Min, 2021. "How have the dependence structures between stock markets and economic factors changed during the COVID-19 pandemic?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    12. Juan Manuel García Sánchez & Xavier Vilasís Cardona & Alexandre Lerma Martín, 2022. "Influence of Car Configurator Webpage Data from Automotive Manufacturers on Car Sales by Means of Correlation and Forecasting," Forecasting, MDPI, vol. 4(3), pages 1-20, July.
    13. Sakar Hasan Hamza & Qingna Li, 2023. "The Dynamics of US Gasoline Demand and Its Prediction: An Extended Dynamic Model Averaging Approach," Energies, MDPI, vol. 16(12), pages 1-13, June.
    14. Camarero, Mariam & Montolio, Laura & Tamarit, Cecilio, 2019. "What drives German foreign direct investment? New evidence using Bayesian statistical techniques," Economic Modelling, Elsevier, vol. 83(C), pages 326-345.
    15. Sun, Tianyu & Chand, Satish & Sharpe, Keiran, 2018. "Effect of Aging on Urban Land Prices in China," MPRA Paper 89237, University Library of Munich, Germany.
    16. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.

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    More about this item

    Keywords

    C53; C22; G14; G13; R21; R31; House price forecasting; DMA; Rolling forecasting; MCS;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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