Effects of air quality on house prices: evidence from China’s Huai River Policy

Abstract

We employ a regression discontinuity (RD) design to estimate the impact of air pollution on house prices across a river that demarcates regions with and without coal-fired heating resulting from the Huai River Policy. This policy was decreed by the Chinese government in the 1950s and mandated the burning of coal for indoor heating at subsidised prices north of the Huai River. Employing quasi-experimental variation in particulate matter of 10 micrometres or less in aerodynamic diameter (PM₁₀) generated by this arbitrary policy and a regression discontinuity (RD) design based on distance from Huai River, we estimate the local average treatment effect (LATE) to provide new evidence on the capitalisation of PM₁₀ air pollution into house values. By using panel data covering 30 large cities on either side of the river for the period 2006–2015, we found that 1 µg/m³ (micrograms per cubic metre) reduction in average PM₁₀ is associated with an approximately 1% increase in house prices. The results are robust to using parametric and nonparametric estimation methods, adjustment to a rich set of covariates, and using a subsample excluding first-tier cities.

KEYWORDS:

• Air quality
• house prices
• PM₁₀
• Huai River Policy
• regression discontinuity design
• China

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Total suspended particles (TSP) measures the mass concentration of particulate matter in the air. Within TSP, PM₁₀ stands for particles with a diameter of 10 µm or less, and PM_2.5 stands for those with a diameter of 2.5 µm or less. Particulates that are 10 µm or greater are filtered and generally do not enter the lungs. Particulates smaller than 10 µm are likely to enter the lungs. Particulate matter that is smaller than 2.5 µm (PM_2.5) can enter into the Alveoli where gas exchange occurs. Throughout the world, ambient monitoring now focuses on PM₁₀ and PM_2.5.

2 We exclude Lhasa (capital city of Tibet) in our sample and restrict the sample to the year 2006–2015 due to availability of certain covariates.