The nonlinear relationship between air quality and housing prices by machine learning
Weiwen Zhang1,2, Sheng Pan1, Zhiyuan Li1
1School of Public Affairs, Zhejiang University, 866 Yuhangtang Road, Zhejiang, 310058, China.
Environmental Science and Pollution Research International
|October 20, 2023
Summary
Air pollution impacts housing prices nonlinearly, with its negative effect diminishing as pollution worsens. Air quality
Area of Science:
- Environmental Economics
- Urban Studies
- Econometrics
Background:
- Air quality significantly impacts urban environments and resident well-being.
- Understanding the relationship between environmental factors and housing prices is crucial for urban planning and policy.
- Endogeneity issues often complicate the analysis of air quality's effect on housing markets.
Purpose of the Study:
- To investigate the nonlinear relationship between air quality and housing prices in Chinese cities.
- To address endogeneity using instrumental variable and machine learning methods.
- To quantify the importance of air quality compared to other urban amenities.
Main Methods:
- Utilized a dataset of 228 Chinese cities from 2005-2019.
- Employed instrumental variable and machine learning techniques to mitigate endogeneity.
- Applied SHapley Additive exPlanations (SHAP) for feature importance analysis.
Main Results:
- A diminishing relationship was found: increased air pollution had a decreasing negative impact on housing prices.
- This effect was more pronounced in Eastern China, less land-constrained, and more populous cities.
- Air quality's influence on housing prices surpassed that of educational and medical resources.
Conclusions:
- Air quality is a critical determinant of housing prices, with a complex, nonlinear relationship.
- Policy interventions aimed at improving air quality can yield significant economic benefits.
- The study highlights regional disparities and the increasing importance of air quality in recent years.
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