Residential Building Lifespan and Community Turnover
Publication: Journal of Architectural Engineering
Volume 26, Issue 3
Abstract
Environmental impact studies within the built environment rely on predicting building lifespan to describe the period of occupation and operation. Most life cycle assessments (LCAs) are based on arbitrary lifespan values, omitting the uncertainties of assessing service life. This research models the lifespan of American residential housing stock as a probabilistic survival distribution based on available data from the American Housing Survey (AHS). The log-normal, gamma, and Weibull distributions were fit to demolition data from 1985 to 2009 and these three models were compared with one another using the Bayesian information criterion. Analysis revealed that the estimated average housing lifespan in the United States is 130 years given model assumptions, although a probabilistic approach to lifespan can yield higher accuracy on a case-by-case basis. Parameters for modeling housing lifespan as log-normal, gamma, and Weibull survival functions are published with the intent of further application in LCA. The application of probabilistic housing lifespan models to community-wide turnover and integration with existing simulations of natural disaster are proposed as potential ways to achieve community sustainability and resilience goals.
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Acknowledgments
This material is based upon work supported by the National Science Foundation (NSF) under Grant No. 145675 titled, A Risk Informed Decision Framework to Achieve Resilient and Sustainable Buildings that Meet Community Objectives, with support from NSF's Research Undergraduate Experience (REU) program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. The research was developed in collaboration with faculty colleagues at Colorado State University and the University of Oklahoma. Special appreciation is due to Naiyu Wang at the University of Oklahoma and Michele Shaffer, Lucy Gao, and Anya Mikhaylova of the University of Washington's Department of Statistics.
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Published In
Journal of Architectural Engineering
Volume 26 • Issue 3 • September 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Aug 10, 2018
Accepted: Oct 24, 2019
Published online: Jun 4, 2020
Published in print: Sep 1, 2020
Discussion open until: Nov 4, 2020
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