Estimating the extent to which green swan events disrupt housing markets: Evidence from China
DOI: https://doi.org/10.3846/ijspm.2026.26143Abstract
Shocks from climate change and transitioning to a low-carbon economy can have a green swan effect on economies. To assess the extent to which green swan events may disrupt housing markets in the future, this study examines how the prices of carbon trading pilot sites in four Chinese cities (Beijing, Shanghai, Tianjin, and Chongqing) affect housing prices in these cities and whether the green swan effect increases with a rise in policy risks. This paper first performs simulations to determine the extreme risk of housing returns (value at risk) and whether this risk is affected by the exogenous shock of carbon returns. Then, this study examines the correlation between carbon prices and housing prices from the perspectives of returns and volatility. In terms of risk transmission effects, all four real estate markets are affected by carbon price risk spillovers. It verifies that China’s real estate market may be negatively impacted by “green swans” when carbon prices experience significant fluctuations. The findings provide investors with a means to evaluate whether the housing market is susceptible to a green swan effect, and also underscore the need for authorities to evaluate the impact of carbon reduction policies on the housing market.
Keywords:
green swans, climate change, low-carbon economy, carbon trading pilot, China’s housing marketHow to Cite
Share
License
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Adekoya, O. B., Oliyide, J. A., & Noman, A. (2021). The volatility connectedness of the EU carbon market with commodity and financial markets in time-and frequency-domain: The role of the US economic policy uncertainty. Resources Policy, 74, Article 102252. https://doi.org/10.1016/j.resourpol.2021.102252
Alqaralleh, H., Canepa, A., & Uddin, G. S. (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, 68, Article 101950. https://doi.org/10.1016/j.najef.2023.101950
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024
Balcilar, M., Hammoudeh, S., & Toparli, E. A. (2018). On the risk spillover across the oil market, stock market, and the oil related CDS sectors: A volatility impulse response approach. Energy Economics, 74, 813–827. https://doi.org/10.1016/j.eneco.2018.07.027
Bolton, P., Després, M., da Silva, L. A. P., Samama, F., & Svartzman, R. (2020). The green swan: Central banking and financial stability in the age of climate change. Bank for International Settlements.
Boudoukh, J., Richardson, M., & Whitelaw, R. F. (1998). The best of both worlds. Risk, 11, 64–67.
Bridge, G., Bulkeley, H., Langley, P., & van Veelen, B. (2020). Pluralizing and problematizing carbon finance. Progress in Human Geography, 44(4), 724–742. https://doi.org/10.1177/0309132519856260
Cai, M., Shi, Y., Ren, C., Yoshida, T., Yamagata, Y., Ding, C., & Zhou, N. (2021). The need for urban form data in spatial modeling of urban carbon emissions in China: A critical review. Journal of Cleaner Production, 319, Article 128792. https://doi.org/10.1016/j.jclepro.2021.128792
Campiglio, E., & van der Ploeg, F. (2022). Macrofinancial risks of the transition to a low-carbon economy. Review of Environmental Economics and Policy, 16(2), 173–195. https://doi.org/10.1086/721016
Chen, L., Du, Z., & Hu, Z. (2020). Impact of economic policy uncertainty on exchange rate volatility of China. Finance Research Letters, 32, Article 101266. https://doi.org/10.1016/j.frl.2019.08.014
Chen, Y., & Lee, C. C. (2020). The impact of real estate investment on air quality: Evidence from China. Environmental Science and Pollution Research, 27, 22989–23001. https://doi.org/10.1007/s11356-020-08874-2
Chow, S. C., Cunado, J., Gupta, R., & Wong, W. K. (2017). Causal relationships between economic policy uncertainty and housing market returns in China and India: Evidence from linear and nonlinear panel and time series models. Studies in Nonlinear Dynamics & Econometrics, 22(2), Article 20160121. https://doi.org/10.1515/snde-2016-0121
Christidou, M., & Fountas, S. (2018). Uncertainty in the housing market: Evidence from US states. Studies in Nonlinear Dynamics & Econometrics, 22(2), Article 20160064. https://doi.org/10.1515/snde-2016-0064
Chu, F. N., & Tsai, I. C. (2020). Do higher house prices indicate higher safety? Price volatility risk in major cities in Taiwan. International Journal of Strategic Property Management, 24(3), 165–181. https://doi.org/10.3846/ijspm.2020.12159
Cova, S., Andrade, C., Soares, O., & Lopes, J. (2021). Evaluation of cost-optimal retrofit investment in buildings: The case of Bragança fire station, Portugal. International Journal of Strategic Property Management, 25(5), 369–381. https://doi.org/10.3846/ijspm.2021.15082
da Silva, L. A. P. (2020). Green Swan 2 - Climate change and Covid-19: Reflections on efficiency versus resilience. Bank for International Settlements.
Dou, Y., Li, Y., Dong, K., & Ren, X. (2022). Dynamic linkages between economic policy uncertainty and the carbon futures market: Does Covid-19 pandemic matter? Resources Policy, 75, Article 102455. https://doi.org/10.1016/j.resourpol.2021.102455
Ehrlich, M. V., Hiber, C. A. L., & Schöni, O. (2018). Institutional settings and urban sprawl: Evidence from Europe. Journal of Housing Economics, 42, 4–18. https://doi.org/10.1016/j.jhe.2017.12.002
Escanciano, J. C., & Pei, P. (2012). Pitfalls in backtesting historical simulation VaR models. Journal of Banking & Finance, 36(8), 2233–2244. https://doi.org/10.1016/j.jbankfin.2012.04.004
Fakhfekh, M., Jeribi, A., & Salem, M. B. (2023). Volatility dynamics of the Tunisian stock market before and during the COVID-19 outbreak: Evidence from the GARCH family models. International Journal of Finance & Economics, 28(2), 1653–1666. https://doi.org/10.1002/ijfe.2499
Fan, J. S., & Zhou, L. (2019). Impact of urbanization and real estate investment on carbon emissions: Evidence from China’s provincial regions. Journal of Cleaner Production, 209, 309–323. https://doi.org/10.1016/j.jclepro.2018.10.201
Funke, M., Loermann, J., & Tsang, A. (2022). Volatility transmission and volatility impulse response functions in the main and the satellite Renminbi exchange rate markets. Review of International Economics, 30(2), 606–628. https://doi.org/10.1111/roie.12577
Han, H. (2015). Asymptotic properties of GARCH-X processes. Journal of Financial Econometrics, 13(1), 188–221. https://doi.org/10.1093/jjfinec/nbt023
Huang, W. L., Lin, W. Y., & Ning, S. L. (2020). The effect of economic policy uncertainty on China’s housing market. North American Journal of Economics and Finance, 54, Article 100850. https://doi.org/10.1016/j.najef.2018.09.008
Huang, Y., Dai, X., Wang, Q., & Zhou, D. (2021). A hybrid model for carbon price forecasting using GARCH and long short-term memory network. Applied Energy, 285, Article 116485. https://doi.org/10.1016/j.apenergy.2021.116485
Hull, J., & White, A. (1998). Incorporating volatility updating into the historical simulation method for value at risk. Journal of Risk, 1(1), 5–19. https://doi.org/10.21314/JOR.1998.001
Jelito, D., & Pitera, M. (2021). New fat-tail normality test based on conditional second moments with applications to finance. Statistical Papers, 62, 2083–2108. https://doi.org/10.1007/s00362-020-01176-2
Kim, J. M., Son, K., & Son, S. (2020). Green benefits on educational buildings according to the LEED certification. International Journal of Strategic Property Management, 24(2), 83–89. https://doi.org/10.3846/ijspm.2020.11097
Krogstrup, S., & Oman, W. (2019). Macroeconomic and financial policies for climate change mitigation: A review of the literature (IMF Working Paper No. 19/185). International Monetary Fund. https://doi.org/10.5089/9781513511955.001
Lai, L. X., Wong, P. F., & Yong, F. Y. Y. (2023). Investigating the influence of homebuyers’ sociodemographic factors on preferences of sustainable affordable housing. International Journal of Strategic Property Management, 27(4), 261–274. https://doi.org/10.3846/ijspm.2023.20200
Lehtonen, H., Huan-Niemi, E., & Niemi, J. (2022). The transition of agriculture to low carbon pathways with regional distributive impacts. Environmental Innovation and Societal Transitions, 44, 1–13. https://doi.org/10.1016/j.eist.2022.05.002
Li, X., Li, Z., Su, C. W., Umar, M., & Shao, X. (2022). Exploring the asymmetric impact of economic policy uncertainty on China’s carbon emissions trading market price: Do different types of uncertainty matter? Technological Forecasting and Social Change, 178, Article 121601. https://doi.org/10.1016/j.techfore.2022.121601
Li, Y., Wang, Y., Wang, R., Liu, C., & Zhang, Z. (2024). Bibliometric review of research on green building assessment method by CiteSpace and HistCite. International Journal of Strategic Property Management, 28(3), 177–193. https://doi.org/10.3846/ijspm.2024.21455
Liu, X., Yu, Z., & Li, Y. (2024). House price volatility in China: A pervasive pattern with geographic disparity. International Journal of Strategic Property Management, 28(1), 45–63. https://doi.org/10.3846/ijspm.2024.21096
Liu, Z., & Huang, S. (2021). Carbon option price forecasting based on modified fractional Brownian motion optimized by GARCH model in carbon emission trading. North American Journal of Economics and Finance, 55, Article 101307. https://doi.org/10.1016/j.najef.2020.101307
Lucheroni, C., Boland, J., & Ragno, C. (2019). Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models. Applied Energy, 239, 1226–1241. https://doi.org/10.1016/j.apenergy.2019.02.015
Mei, D., Zhao, C., Luo, Q., & Li, Y. (2022). Forecasting the Chinese low-carbon index volatility. Resources Policy, 77, Article 102732. https://doi.org/10.1016/j.resourpol.2022.102732
Mercure, J. F., Paim, M. A., Bocquillon, P., Lindner, S., Salas, P., Martinelli, P., Berchin, I. I., de Andrade Guerra, J. B. S. O., Derani, C., de Albuquerque Junior, C. L., Ribeiro, J. M. P., Knobloch, F., Pollitt, H., Edwards, N. R., Holden, P. B., Foley, A., Schaphoff, S., Faraco, R. A., & Vinuales, J. E. (2019). System complexity and policy integration challenges: The Brazilian energy-water-food nexus. Renewable and Sustainable Energy Reviews, 105, 230–243. https://doi.org/10.1016/j.rser.2019.01.045
Miles, I. (2008). Patterns of innovation in service industries. IBM Systems Journal, 47(1), 115–128. https://doi.org/10.1147/sj.471.0115
Miller, N., & Peng, L. (2006). Exploring metropolitan housing price volatility. Journal of Real Estate Finance and Economics, 33, 5–18. https://doi.org/10.1007/s11146-006-8271-8
Moreno-Monroy, A., Gars, J., Matsumoto, T., Crook, J., Ahrend, R., & Schumann, A. (2020). Housing policies for sustainable and inclusive cities: How national governments can deliver affordable housing and compact urban development. Coalition for Urban Transitions, London and Washington, DC. https://urbantransitions.global/wp-content/uploads/2020/02/Housing_Policies_for_Sustainable_and_Inclusive_Cities_web_FINAL.pdf
Morris, A. C., Neill, H. R., & Coulson, N. E. (2020). Housing supply elasticity, gasoline prices, and residential property values. Journal of Housing Economics, 48, Article 101669. https://doi.org/10.1016/j.jhe.2020.101669
Nieto, J., Carpintero, Ó., Miguel, L. J., & de Blas, I. (2020). Macroeconomic modelling under energy constraints: Global low carbon transition scenarios. Energy Policy, 137, Article 111090. https://doi.org/10.1016/j.enpol.2019.111090
Ofek, S., & Portnov, B. A. (2020). Differential effect of knowledge on stakeholders’ willingness to pay green building price premium: Implications for cleaner production. Journal of Cleaner Production, 251, Article 119575. https://doi.org/10.1016/j.jclepro.2019.119575
Okorie, D. I., & Lin, B. (2020). Crude oil price and cryptocurrencies: Evidence of volatility connectedness and hedging strategy. Energy Economics, 87, Article 104703. https://doi.org/10.1016/j.eneco.2020.104703
Paolella, M. S., Polak, P., & Walker, P. S. (2019). Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns. Journal of Econometrics, 213(2), 493–515. https://doi.org/10.1016/j.jeconom.2019.07.002
Pritsker, M. (2006). The hidden dangers of historical simulation. Journal of Banking & Finance, 30(2), 561–582. https://doi.org/10.1016/j.jbankfin.2005.04.013
Ruddock, L., & Ruddock, S. (2022). Investment in infrastructure as a key to sustainable economic recovery: The role of the building industry. International Journal of Strategic Property Management, 26(6), 439–449. https://doi.org/10.3846/ijspm.2022.18430
Schellekens, G., & van Toor, J. (2019). Values at risk? Sustainability risks and goals in the Dutch financial sector. De Nederlandsche Bank.
Semieniuk, G., Campiglio, E., Mercure, J.-F., Volz, U., & Edwards, N. R. (2021). Low-carbon transition risks for finance. WIREs Climate Change, 12(1), Article e678. https://doi.org/10.1002/wcc.678
Sun, J., Shi, J., Shen, B., Li, S., & Wang, Y. (2018). Nexus among energy consumption, economic growth, urbanization and carbon emissions: Heterogeneous panel evidence considering China’s regional differences. Sustainability, 10(7), Article 2383. https://doi.org/10.3390/su10072383
Svartzman, R., Bolton, P., Despres, M., da Silva, L. A. P., & Samama, F. (2021). Central banks, financial stability and policy coordination in the age of climate uncertainty: A three-layered analytical and operational framework. Climate Policy, 21(4), 563–580. https://doi.org/10.1080/14693062.2020.1862743
Walls, M., Gerarden, T., Palmer, K., & Bak, X. F. (2017). Is energy efficiency capitalized into home prices? Evidence from three U.S. cities. Journal of Environmental Economics and Management, 82, 104–124. https://doi.org/10.1016/j.jeem.2016.11.006
Wang, S., & Hartzell, D. (2022). What influences real estate volatility in Hong Kong? An ARMA-GARCH approach. International Journal of Housing Markets and Analysis, 15(1), 19–34. https://doi.org/10.1108/IJHMA-08-2020-0099
Wang, S., Zeng, Y., Yao, J., & Zhang, H. (2020a). Economic policy uncertainty, monetary policy, and housing price in China. Journal of Applied Economics, 23(1), 235–252. https://doi.org/10.1080/15140326.2020.1740874
Wang, X. (2023). Unveiling the multiscale impact of economic policy uncertainty on China’s housing market spillovers: Evidence from different uncertainty measurements. Finance Research Letters, 58, Article 104603. https://doi.org/10.1016/j.frl.2023.104603
Wang, X., Luo, Y., Wang, Z., Xu, Y., & Wu, C. (2021a). The impact of economic policy uncertainty on volatility of China’s financial stocks: An empirical analysis. Finance Research Letters, 39, Article 101650. https://doi.org/10.1016/j.frl.2020.101650
Wang, Y., & Wu, C. (2012). Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models? Energy Economics, 34(6), 2167–2181. https://doi.org/10.1016/j.eneco.2012.03.010
Wang, Y., Fu, Q., & Chang, C. P. (2021b). The integration of carbon price between European and Chinese markets: What are the implications? International Journal of Environmental Research, 15, 667–680. https://doi.org/10.1007/s41742-021-00342-0
Wang, Z., Li, Y., & He, F. (2020b). Asymmetric volatility spillovers between economic policy uncertainty and stock markets: Evidence from China. Research in International Business and Finance, 53, Article 101233. https://doi.org/10.1016/j.ribaf.2020.101233
Watari, T., McLellan, B. C., Ogata, S., & Tezuka, T. (2018). Analysis of potential for critical metal resource constraints in the international energy agency’s long-term low-carbon energy scenarios. Minerals, 8(4), Article 156. https://doi.org/10.3390/min8040156
World Bank. (2021). State and trends of carbon pricing 2021. World Bank. https://openknowledge.worldbank.org/handle/10986/35620
Xia, T., Yao, C. X., & Geng, J. B. (2020). Dynamic and frequency-domain spillover among economic policy uncertainty, stock and housing markets in China. International Review of Financial Analysis, 67, Article 101427. https://doi.org/10.1016/j.irfa.2019.101427
Yin, X. C., Li, X., Wang, M. H., Qin, M., & Shao, X. F. (2021). Do economic policy uncertainty and its components predict China’s housing returns? Pacific-Basin Finance Journal, 68, Article 101575. https://doi.org/10.1016/j.pacfin.2021.101575
Yong, J. N. C., Ziaei, S. M., & Szulczyk, K. R. (2021). The impact of Covid-19 pandemic on stock market return volatility: Evidence from Malaysia and Singapore. Asian Economic and Financial Review, 11(3), 191–204. https://doi.org/10.18488/journal.aefr.2021.113.191.204
Yu, J., Shi, X., Guo, D., & Yang, L. (2021). Economic policy uncertainty (EPU) and firm carbon emissions: Evidence using a China provincial EPU index. Energy Economics, 94, Article 105071. https://doi.org/10.1016/j.eneco.2020.105071
Zhang, L., Luo, Q., Guo, X., & Umar, M. (2022). Medium-term and long-term volatility forecasts for EUA futures with country-specific economic policy uncertainty indices. Resources Policy, 77, Article 102644. https://doi.org/10.1016/j.resourpol.2022.102644
Zhang, L., Sun, C., Liu, H., & Zheng, S. (2016). The role of public information in increasing homebuyers’ willingness-to-pay for green housing: Evidence from Beijing. Ecological Economics, 129, 40–49. https://doi.org/10.1016/j.ecolecon.2016.05.010
Zhang, L., Xin, Y., Han, R., Zhang, X., Hao, N., Li, L., & Zhang, J. (2024). Do Chinese government policies affect performance of real estate enterprises? International Journal of Strategic Property Management, 28(1), 16–28. https://doi.org/10.3846/ijspm.2024.20944
Zuo, J., & Zhao, Z. Y. (2014). Green building research–current status and future agenda: A review. Renewable and Sustainable Energy Reviews, 30, 271–281. https://doi.org/10.1016/j.rser.2013.10.021
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.