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Forecasting US real house price returns over 1831-2013: evidence from copula models

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  • Rangan Gupta
  • Anandamayee Majumdar

Abstract

Given the existence of nonnormality and nonlinearity in the data generating process of real house price returns over the period of 1831-2013, this article compares the ability of various univariate copula models, relative to standard benchmarks (naive and autoregressive models) in forecasting real US house price over the annual out-of-sample period of 1874-2013, based on an in-sample of 1831-1873. Overall, our results provide overwhelming evidence in favour of the copula models (Normal, Student's t , Clayton, Frank, Gumbel, Joe and Ali-Mikhail-Huq) relative to linear benchmarks, and especially for the Student's t -copula, which outperforms all other models both in terms of in-sample and out-of-sample predictability results. Our results highlight the importance of accounting for nonnormality and nonlinearity in the data generating process of real house price returns for the US economy for nearly two centuries of data.

Suggested Citation

  • Rangan Gupta & Anandamayee Majumdar, 2015. "Forecasting US real house price returns over 1831-2013: evidence from copula models," Applied Economics, Taylor & Francis Journals, vol. 47(48), pages 5204-5213, October.
    • Handle: RePEc:taf:applec:v:47:y:2015:i:48:p:5204-5213
    DOI: 10.1080/00036846.2015.1044648
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    Cited by:

    1. Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2021. "High-Frequency Volatility Forecasting of US Housing Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 62(2), pages 283-317, February.
    2. Huthaifa Alqaralleh & Gazi Salah Uddin & Canepa, Alessandra, 2022. "Time-frequency connectedness across housing markets, stock market and uncertainty: A Wavelet-Time Varying Parameter Vector Autoregression," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202204, University of Turin.
    3. Alqaralleh, Huthaifa & Canepa, Alessandra & Salah Uddin, Gazi, 2023. "Dynamic relations between housing Markets, stock Markets, and uncertainty in global Cities: A Time-Frequency approach," The North American Journal of Economics and Finance, Elsevier, vol. 68(C).
    4. Kang, Sang Hoon & Uddin, Gazi Salah & Ahmed, Ali & Yoon, Seong-Min, 2018. "Multi-scale causality and extreme tail inter-dependence among housing prices," Economic Modelling, Elsevier, vol. 70(C), pages 301-309.
    5. Roman Matkovskyy, 2019. "Extremal Economic (Inter)Dependence Studies: A Case of the Eastern European Countries," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 667-698, September.
    6. Luis A. Gil-Alana & Rangan Gupta & Fernando Perez de Gracia, 2016. "Persistence, mean reversion and non-linearities in the US housing prices over 1830--2013," Applied Economics, Taylor & Francis Journals, vol. 48(34), pages 3244-3252, July.
    7. Sinha, Ankur & Kedas, Satishwar & Kumar, Rishu & Malo, Pekka, 2019. "Buy, Sell or Hold: Entity-Aware Classification of Business News," IIMA Working Papers WP 2019-04-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    8. Sun, Tianyu & Chand, Satish & Sharpe, Keiran, 2018. "Effect of Aging on Urban Land Prices in China," MPRA Paper 89237, University Library of Munich, Germany.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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