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How Does Air Pollution Influence Housing Prices in the Bay Area?

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  • Minmeng Tang

    (Department of Land, Air and Water Resources, University of California, Davis, One Shields Ave, Davis, CA 95616, USA)

  • Deb Niemeier

    (Department of Civil and Environmental Engineering, University of Maryland, 1173 Glenn Martin Hall, College Park, MD 20742, USA)

Abstract

In this paper we examine the effects of localized air pollution measurements on the housing prices in Oakland, CA. With high-resolution air pollution measurements for NO, NO 2 , and BC, we can assess the ambient air quality on a parcel-by-parcel basis within the study domain. We combine a spatial lag model with an instrumental variable method to consider both the spatial autocorrelation and endogeneity effects between housing prices and air pollution concentrations. To the best of our knowledge, this is the first work in this field that combines both spatial autocorrelation and endogeneity effects in one model with accurate air pollution concentration measurements for each individual parcel. We found a positive spatial autocorrelation with housing prices using Moral’s I (value of 0.276) with the total sample number of 26,386. Somewhat surprisingly, we found a positive relationship between air pollution and housing prices. There are several possible explanations for this finding. Homeowners in high demand, low-stock housing areas, such as our study, may be insensitive to air pollution when the overall ambient air quality is relatively good. It is also possible that under clean air conditions, low variability in pollutant concentrations has little effect on property values. These hypotheses could be verified with more high-resolution air pollution measurements with a diversity of regions.

Suggested Citation

  • Minmeng Tang & Deb Niemeier, 2021. "How Does Air Pollution Influence Housing Prices in the Bay Area?," IJERPH, MDPI, vol. 18(22), pages 1-13, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:22:p:12195-:d:683877
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    2. Dongwon Ko & Seunghoon Park, 2024. "Investigating the Correlation between Air Pollution and Housing Prices in Seoul, South Korea: Application of Explainable Artificial Intelligence in Random Forest Machine Learning," Sustainability, MDPI, vol. 16(11), pages 1-23, May.

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