Residential Building Lifespan and Community Turnover

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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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