Air Pollution and House Price Spillovers in Chinese cities
FANG Yi, YUAN Yan, WANG Yanru
National Academy of Development and Strategy, Renmin University of China; School of Management and Economics, The Chinese University of Hong Kong, Shenzhen; School of Finance, Dongbei Univerisy of Finance and Economics
Summary:
in recent years, The global climate and environment have been deteriorating continuously, bringing huge negative impacts on the economic development and financial stability of various countries and drawing the world's attention. This paper takes the most common environmental shock (air pollution) and the financial sub-market most closely related to air pollution (the real estate market) as its research objects, using samples of 150 cities within 8 major urban agglomerations in China from July 2010 to December 2021 to study the impact of air pollution on the risks of urban real estate markets. Specifically, this paper, referring to Silva-Buston (2019), constructs synchronism indicators that reflect the connectivity between the house price return of one city and that of the other cities within the same urban agglomeration for 8 major urban agglomerations respectively, and uses the indicators to measure house price spillovers. Then, this paper conducts an analysis of the validity of indicators, analyzing the evolution and distribution laws of core indicators from both time and spatial dimensions. Next, this paper adopts a panel regression model to examine the impact of the Air Quality Index(AQI) of a city on its own house price spillover, and replaces the independent variable and dependent variable respectively for robustness tests. This paper also uses instrumental variables to alleviate the possible endogeneity problem, and tests the existence of the two impact paths, physical diffusion and industrial synergy, by group-based regression. Finally, this paper empirically evaluates the effects of China's past environmental protection policies on air pollution control and financial stability. The spillover indicators constructed in this paper perform in line with the real-world experience in both time and spatial dimensions, and the validity of the indicators has been largely verified. Time dimension analysis reveals that the constructed house price spillover indicators can provide additional information for measuring real estate market risks from the perspective of network node synchrony. Spatial dimension analysis finds that the overall housing price spillovers of urban agglomerations may be related to factors such as their own geographical area, spatial compactness, and the gap in the economic development level of internal cities, where most central and large cities are systemically important. The regression results indicate that the deterioration of urban air quality will lead to a decline in urban house prices and an increase in the urban house price spillover effect. On average, for every one standard deviation increase in the urban air pollution, the urban house price return will decrease by 12.29%, and the urban house price spillover will increase by 22.74%. This result might be because air pollutants emitted by a certain city will impose a common negative shock on the cities in the adjacent area, thereby increasing its own house price spillover. After conducting the robustness test and the endogeneity test, we find that the baseline results in this paper are very robust. Moreover, it has been empirically verified that physical diffusion and industrial synergy are two important paths through which air pollution affects the urban house price spillover. We also find that the implementation of environmental protection policies in China in the past has played a positive role in maintaining financial stability. This paper has the following three contributions. Firstly, compared with many existing studies that investigate the impact of air pollution on house price or house price volatility from the perspective of a single city, this paper studies the impact of air pollution on house price spillover from the perspective of overall synchronicity, focusing on analyzing the network spillover mechanism of real estate market risk caused by air pollution. Thus, our study provides a supplement to the research in the field of real estate market risk. Secondly, this paper analyzes and verifies two mechanisms by which air pollution affects the spillover effect of urban house pncing, namely physical diffusion and industrial synergy. Specifically, the physical diffusion mechanism refers to the connectivity of real estate markets among cities caused by the diffusion of air pollutants avnong cities through meteorological factors. The industrial synergy mechanism refers to the phenomenon that under the industrial division of urban agglomeration, big cities are responsible for the production with high added value and low pollution, while small cities are responsible for the production with low added value and high pollution, which leads to the simultaneous change of urban air quality in the face of market demand and collaborative production. This paper has reference significance for alleviating the spatial negative externalities of air pollution through regional collaborative governance. Finally, this paper constructs urban house price spillover indicator that capture the characteristics of the house price network in urban agglomerations with low data frequency ceqvirements. Referring to Silva-Buston (2019), this paper uses the regression method to estimate the mean dependence of house prices among cities in the network. The indicator construction has almost no model risk and requires low data frequency, which enables this paper to include all 150 cities that meet the requirements of data availability in the 8 major urban agglomerations as research samples, and almost completely retain the internal hierarchical structure of each urban agglomeration.
方意, 袁琰, 王晏如. 空气质量、产业协同与城市群内房价溢出效应[J]. 金融研究, 2024, 531(9): 95-113.
FANG Yi, YUAN Yan, WANG Yanru. Air Pollution and House Price Spillovers in Chinese cities. Journal of Financial Research, 2024, 531(9): 95-113.
Broner, F., P. Bustos and V. M. Carvalho, 2012, “Sources of Comparative Advantage in Polluting Industries”, NBER Working Paper, No.18337.
[11]
Cotter, J., G. Stuart and R. Richard, 2015, “Can Housing Risk be Diversified？ A Cautionary Tale from the Housing Boom and Bust”, Review of Financial Studies, 28(3), pp.913～936.
[12]
Heyes, A. and M. Zhu, 2019, “Air Pollution as a Cause of Sleeplessness: Social Media Evidence from a Panel of Chinese Cities”, Journal of Environmental Economics and Management, 98.
[13]
Jones, C. and C. Leishman, 2006, “Spatial Dynamics of the Housing Market: An Interurban Perspective”, Urban Studies, 43(7), pp.1041～1059.
[14]
Jordà, Ò., M. Schularick, A. M. Taylor and F. Ward, 2019, “Global Financial Cycles and Risk Premiums”, IMF Economic Review, 67(1).
[15]
Silva-Buston, C., 2019, “Systemic Risk and Competition Revisited”, Journal of Banking ＆ Finance, 101(C), pp.188～205.
[16]
Yang, J., Z. Yu and Y. Deng, 2018, “Housing Price Spillovers in China:A high-dimensional Generalized VAR Approach”, Regional Science and Urban Economics, 68, pp.98～114.
[17]
Yang, J., M. Tong and Z. Yu, 2021, “Housing Market Spillovers through the Lens of Transaction Volume: A New Spillover Index Approach”, Journal of Empirical Finance, 64, pp.351～378.
