Abstract Estimating the exposure of the coastal systems to natural hazards using coastal vulnerability models, which benefits from index‐based approaches and utilize information about the characteristics of the system, has become extensively adopted in the past few decades in coastal management and planning. However, the explanatory power of index‐based approaches and subjective selection of vulnerability factors are still in dispute. This study aims to introduce a stochastic coastal vulnerability model and assess its skill in characterizing and preserving simultaneous information about various comprising factors. Two common coastal vulnerability indices, Additive Coastal Vulnerability Index and Multiplicative Coastal Vulnerability Index are formed, and then their performances are compared to the proposed Probabilistic Coastal Vulnerability Index (PCVI) for the coastal counties of South Carolina. PCVI is developed based on the joint‐probability analysis of vulnerability factors using copula functions, which makes it capable of preserving the importance of multivariate information, and in turn, forms a more informative index. The performance of indices is benchmarked against post‐hazard flood maps and the cost of fatalities from Hurricane Florence (2018) and Hurricane Matthew (2016). The PCVI revealed more accurate results in terms of explaining the importance of vulnerability associated with biophysical and socio‐economic factors. The capability of PCVI to preserve multivariate vulnerability information offers a more pragmatic approach to reflect the exposure and adaptive capacity of coastal communities facing coastal hazards.
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