The external cost of asbestos in the housing market

ABSTRACT

This study uses a Generalized Method of Moments spatial error hedonic model to estimate the potential impact of asbestos on residential property prices. The analysis examines a large sample of 32,403 housing sales occurring between 2008 and 2015 in a fast-growing Coastal Alabama region. Results indicate the presence of asbestos depreciates house values by 13.44%, translating into an external cost of approximately $25,300 per property.

KEYWORDS:

• Asbestos
• housing price impacts
• spatial error modelling
• hedonic modelling

JEL CLASSIFICATION:

• C12
• D12
• K32
• Q53
• R21

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ For example, Watterson et al. (2006), report that annual treatment costs for asbestos-related lung cancers increased from $3 billion in 2000 to approximately $5 billion in 2015.

² A few examples include vinyl sheet flooring and floor tiles; asphalt floor tiles and backing; roofing shingles; cement wallboard; acoustical and decorative plaster; caulking; spackling; adhesives; cement siding and pipes; and heating and electrical ducts. Information available at: http://www.asbestos.net/exposure/risks/eight-things-to-know-about-asbestos/.

³ Proprietary data available from the authors.

⁴ Data available at www.census.gov.

⁵ Data available at www.bls.gov.

⁶ ZI = 66.42*** (99% confidence) rejected H₀: No Spatial Correlation.

⁷ LM_λ* = 3045*** > LM_ρ* = 28*** (99% confidence) rejected the Spatial Autoregressive Model in favour of the Spatial Error Model.

⁸ Given the large sample used in this study, to perform the computational procedure we utilized the statistical software program, R, release 3.2.3 (https://www.r-project.org/). The entire computational process ran for a total of 21 h, 14 min on the Ultraviolet Dense Memory Cluster of the Alabama Super Computer Authority (https://www.asc.edu), and necessitated 56.94 gigabytes of memory.

⁹ The objective function was minimized using the optim solver in the statistical programme, R, release 3.2.3 (https://www.r-project.org/), based on the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm.