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This report presents the concept of the resilience dividend as a useful metric for community resilience planning. The report suggests defining the resilience dividend as the net co-benefit (or co-cost) of investing in enhanced resilience, in the absence of a disruptive incident. In order for this definition to be useful, the report reviews literature on co-benefits. The main lessons from this review are that: (1) there is no consensus on the use of the meaning of cobenefit; (2) much of the literature on co-benefits is focused on climate change; and (3) there exist opportunities for the development of co-benefit measurement and assessment methodologies in the context of resilience planning. This report provides guidance and direction on the study and use of the resilience dividend by categorizing the various definitions of co-benefits to provide a frame of reference, reviewing measurement and assessment efforts, and summarizing the review in an annotated bibliography that can serve as a "quick guide" for researchers and practitioners looking for existing work on co-benefits. The report also highlights potential directions for future research.

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Presidential Executive Order 13717 (EO 13717), Establishing a Federal Earthquake Risk Management Standard, encourages federal agencies to "enhance resilience... [to] future earthquakes" by evaluating and retroftting existing federal buildings based on current existing building codes. However, while guidance on evaluation and retroft practices is readily available, a standard approach to estimating retroft costs does not exist. Moreover, the absence of easily obtainable estimates can make retrofts prohibitive for decision-makers.This paper develops a cost-estimating methodology for seismic retrofts that (1) captures the essential factors that drive seismic retroft costs, such as building construction and square footage; and (2) is reproducible using data available to decision-makers.The methodology builds on FEMA 156 and 157, Typical Costs for Seismic Rehabilitation of Existing Buildings, Volumes 1 and 2. A series of regression models is ft to the data used for the FEMA reports, with the models varying in the level of data required; e.g., a decision-maker may not have information on building construction for each asset in their inventory. Thus, the trade-off from estimating retroft costs subject to data limitations can be quantifed in terms of prediction error, providing decision-makers with a set of options for estimating costs together with a measure of predictive performance. We fnd that a simple model, in terms of data requirements, can deliver reliable predictions.