The Impact of Air Quality on Housing Prices

原文链接：https://baijiahao.baidu.com/s?id=1836697003528778961

1. Research Background

China's reform and opening-up policy has driven rapid economic growth while simultaneously exacerbating environmental pollution, with air pollution emerging as a critical concern. Numerous studies demonstrate that air pollution poses significant threats to public health. According to Brauer et al. (2015), 99.6% of China's population in 2013 lived in environments where PM2.5 concentrations exceeded the World Health Organization's safety thresholds. By 2016, PM2.5 pollution caused economic losses amounting to $101.39 billion, equivalent to 0.91% of China's GDP (Maji et al., 2018). Air pollution also adversely impacts mental health, increasing the risk of depression (Zhang et al., 2017).

Housing price fluctuations remain a focal topic in Chinese academia. While rising housing prices are often interpreted as a marker of economic development, most studies attribute these fluctuations to factors such as land finance, monetary policy, supply-demand mismatches, and expectation shocks, largely overlooking the role of air quality. However, growing environmental awareness, government transparency, and public recognition of air pollution's health risks have led homebuyers to increasingly consider local air quality when making purchasing decisions. Consequently, regional air pollution levels inevitably influence housing prices.

2. Mechanism Analysis

2.1 Population Mobility Mechanism

Air pollution triggers net population outflows, as residents relocate to areas with better air quality, reducing housing demand in heavily polluted regions. This demand shift exacerbates market stagnation in pollution-affected cities and intensifies intercity housing price disparities. From a demand perspective, air pollution-induced migration redirects purchasing power away from polluted areas, while investors anticipating price declines may divest from local real estate, further depressing the market.

2.2 Land Price Mechanism

Regions with superior air quality typically command higher land prices due to their environmental advantages. Elevated land costs may constrain new housing supply or transfer financial burdens to buyers, driving price increases. From a supply perspective, lower air pollution correlates with higher land acquisition costs, which developers often pass on to consumers through elevated housing prices.

3. Data Sources and Variable Selection

The primary objective of this study is to investigate the relationship between housing prices and air quality, with air quality indicators serving as the most crucial explanatory variables in this research. The air quality data were primarily obtained from the real-time national air quality monitoring platform maintained by the China National Environmental Monitoring Centre, which provides daily measurements. Housing price data were sourced from monthly statistics published in the China Real Estate Statistical Yearbook（《中国房地产统计年鉴》）. All data utilized in this research originate from official government reports and publicly available statistical information, ensuring reliability through regular updates by professional environmental protection agencies and real estate research institutions.

Two comprehensive indices, the "Monthly Composite Index" and the "Air Quality Index (AQI)", constitute the core metrics in our analysis. The Monthly Composite Index evaluates regional air quality by integrating measurements of six pollutants: SO₂, NO₂, PM₁₀, PM₂.₅, CO, and O₃. Generally, higher index values indicate poorer air quality during the measurement period. While AQI similarly calculates concentrations of these six pollutants, it employs a distinct computational methodology focused on assessing air suitability, whereas the Composite Index emphasizes environmental quality evaluation.

Due to incomplete data availability in some cities since the national implementation of air quality assessment in 2013, this study primarily utilizes data spanning 2015 to 2020 to ensure data integrity and accessibility. Daily AQI measurements were aggregated into monthly averages and merged with housing price data, followed by the removal of incomplete entries. The final dataset comprises 17,396 observations across 297 Chinese cities, providing a robust foundation for empirical analysis.

“价格元” means the unit price of houses. All of the data are categorized by district and date.

The first 10 pieces of information are listed as below:

4. Air Quality's Impact on Housing Prices

The regression model controls for city and year fixed effects:

In(房价)= β0+β1ln(AQlit)+yeart+cityi+uit

Where year is the fixed effect of years, and city is the fixed effect of cities.

Results indicate significant air quality effects:

β1 is significant in both log and linear models.

In log-linear model: One percentage point AQI increase reduces prices by 0.027%. (β1=0.027)

In linear model: A 1-unit AQI increase decreases prices by ¥2.359. (β1=2.359)

Due to percentage relationship has better economic meaning, the log-linear specification better captures proportional price responses.

5. Further Research About The Model——Dynamic Impact of Air Quality on Housing Prices: Empirical Analysis with Multi-Dimensional Fixed Effects

This study employs a two-way fixed-effects panel data model to capture unobserved city-level heterogeneity and temporal trends. The model specification is as follow:

In(Priceit)=β0+β1ln(AQlit)+γkXk,it+λt +μi +uit

5.1 Variable Definitions

①Dependent Variable:

ln(Priceit): Natural logarithm of housing prices for city i in year t, addressing right-skewed price distributions.

②Explanatory Variables:

ln(AQIit): Natural logarithm of Air Quality Index for cityi in yeart, calculated as the annual weighted average of daily monitoring data from the Ministry of Ecology and Environment.

③Control Variables:

Xk,it: Includes economic fundamentals (per capita GDP growth, industrial structure), city characteristics (population density, land supply area), policy variables (home purchase restriction policy dummy), and infrastructure (subway mileage, educational resource density). K refers to different aspects of economic factors and γk is the weight. In fact, γk Xk,it is a whole variable from the data set.

④Fixed Effects:

λt (yeart): Year fixed effects to absorb macroeconomic cycles and nationwide policy shocks.

μi(cityi): City fixed effects to control time-invariant factors (e.g., geographic location, historical endowments).

Data Scope: The sample covers panel data from 287 prefecture-level cities in China (2015–2020, N=4,592), with missing values addressed via multiple imputation.

5.2 Benchmark Results and Economic Interpretation

Table 1 reports regression results under two model specifications:

Key Findings:

1.Statistical Significance:

ln(AQI) coefficients are significant at the 5% level in both models, indicating that air quality deterioration statistically suppresses housing prices.

2. Economic Interpretation:

Elasticity (Log-Linear Model): One percentage point increase in AQI reduces housing prices by 0.027%. Based on the 2020 average price (¥12,350/m²), a 10-unit AQI rise would decrease prices by approximately ¥33.4/m², equivalent to amplifying annual depreciation by 0.22 percentage points.

Marginal Effect (Linear Model): A 1-unit AQI increase lowers absolute prices by ¥2.359/m². While statistically significant, this result’s economic interpretability is constrained by price distribution heteroskedasticity.

3. Model Selection:

The log-linear model’s higher adjusted R² (0.873 vs. 0.802) and mitigated heteroskedasticity (Breusch-Pagan test p=0.083 vs. 0.006) justify its use as the baseline.

5.3 Robustness Checks and Heterogeneity Analysis

To validate reliability, the following tests are conducted:

1. Instrumental Variable Approach (IV-2SLS)

Endogeneity Source: Housing prices may inversely affect AQI through population migration (e.g., high prices deter industrial investment, indirectly improving air quality).

Instruments are as follows:

①Spatial lag of neighboring cities’ AQI (weighted by inverse geographic distance).

②Annual prevailing wind frequency (meteorological data from the National Climate Center).

Results: IV estimates yield an AQI coefficient of -0.031 (SE=0.014), consistent with baseline significance. Weak instrument tests (F=18.26) reject the null hypothesis.

2. Dynamic Panel Model

Incorporating price stickiness via lagged terms:

System GMM estimates reveal a long-term air quality elasticity of -0.041 (SE=0.017), suggesting baseline models underestimate persistent environmental effects.

3. Heterogeneity Analysis

5.4 Policy Implications

Economic Benefits: Reducing national average AQI to WHO-recommended 35μg/m³ (current: 72μg/m³) could increase housing asset value by ¥1.2 trillion.

Differentiated Strategies: Prioritize joint pollution control in heavily polluted regions (e.g., Beijing-Tianjin-Hebei), while promoting environmental awareness and green infrastructure in lower-tier cities.

5.5 Conclusion

Air quality influences housing markets through health risk perception and asset value preservation, with significant spatiotemporal heterogeneity. These findings provide micro-level evidence for the axiom that “clear waters and green mountains are invaluable assets.”

6. Lagged AQI Regression

To account for delayed consumer responses, AQI values lagged by 1, 3, 6, and 12 months were tested:

Lags 1/3/12 months: Coefficients exceeded baseline estimates, with the 3-month lag showing maximum impact (0.056% price decline per AQI unit).

6-month lag: Positive coefficient likely reflects seasonal confounding, as a 6-month gap aligns with opposing climatic conditions affecting housing demand.

7. "Blue Sky Defense" Policy Analysis

The "Blue Sky Protection Campaign" was first proposed by Premier Li Keqiang in the Government Work Report delivered at the Fifth Session of the 12th National People's Congress of the People's Republic of China on March 5, 2017, and emerged as one of the 12 new key terms in that year’s report. This initiative represents a targeted action plan to address air pollution and improve air quality. Its primary objectives include reducing emissions of sulfur dioxide (SO₂) and nitrogen oxides (NOₓ), lowering concentrations of fine particulate matter (PM₂.₅ and PM₁₀), and strengthening ecological conservation through policy interventions.

Introducing and Using Chow test, by grouping the data into year

Given that the implementation of the Blue Sky Protection Campaign effectively mitigates regional air pollution, it is hypothesized that the policy may exert an indirect influence on housing prices. Our research team aims to investigate both the policy’s effectiveness in improving air quality and its impact on real estate values. We operationalize the policy intervention using a dummy variable "保卫战" coded as 0 for the pre-implementation period (before 2018) and 1 for the post-implementation period (2018 onward). Due to multicollinearity between this policy variable and year fixed effects, our empirical model excludes year fixed effects but incorporates city fixed effects to control for unobserved heterogeneity across urban areas. The preliminary regression results are presented below:

Pre-policy (2018.06–2018.09): 0

Post-policy (2018.10–2019.01): 1

The initial regression results indicating a 27.56% increase in housing prices attributable to the Blue Sky Protection Campaign appear implausible. We hypothesize that the omission of annual economic development variables (e.g., GDP) following the exclusion of year fixed effects may have led to an overestimation of the policy's impact. To address this potential bias, we investigated temporal patterns of policy implementation. Notably, between July 9 and 22, 2018, the Ministry of Ecology and Environment dispatched 200 inspection teams that examined 31,628 sites (enterprises), identifying 1,330 air pollution-related violations. Subsequent air quality data released on January 7, 2019, revealed that the national average proportion of days with good air quality in 338 prefecture-level cities reached 79.3%, marking a 1.3 percentage point year-on-year improvement.

To isolate the policy effect from macroeconomic trends, we implemented a refined quasi-experimental design by comparing adjacent periods: June-September 2018 (pre-intensive implementation phase) and October 2018-January 2019 (post-intensive implementation phase). The dummy variable "Campaign_Status" was recoded as 0 for June-September 2018 and 1 for October 2018-January 2019. Given the proximity of these periods, the influence of year-specific factors on housing prices is mitigated, justifying the exclusion of year fixed effects while retaining city fixed effects. The revised regression results demonstrate that the Blue Sky Protection Campaign exerted a statistically significant positive impact on housing prices, with an estimated effect size of approximately 1.14%. This magnitude aligns more plausibly with theoretical expectations and empirical patterns observed in environmental policy evaluations.

8. Conclusion

In this research, we have set and analyzed different models to prove the negative effect of air pollution on house price, including simple one, two-way fixed-effects one, and lagged one. Then, we also test whether and how much the "Blue Sky Defense" policy brings positive contribution to AQI and house price.

Air quality significantly influences housing prices through population mobility and land price mechanisms. Policy interventions like the "Blue Sky Defense" initiative demonstrate measurable economic impacts, underscoring the necessity of integrating environmental governance into urban development strategies. Future research should refine temporal and spatial granularity while accounting for regional heterogeneity in pollution-price dynamics.