This paper provides a first application of the techniques of benefits transfer to the health economics literature. These techniques seek to transfer the value of some good from one 'survey' context to a new 'policy' context so avoiding the need for new valuation surveys each time a new policy question arises. Two approaches to benefits transfer are assessed: the simple transfer of mean values and the transferral of value functions. We develop a new methodology for the latter approach in which value functions are iteratively built up from theoretical principles with transfer errors being tested each time a new variable is added. Through a novel application of advanced statistical tests we show that this approach outperforms the transferral of statistically driven Best-fit functions. The case study presented focuses upon the transfer of contingent valuation (CV) willingness to pay (WTP) estimates and associated value functions for reducing the health risks associated with solar ultraviolet (UV) exposure. Common format studies are conducted in four countries with transfers between all of these being undertaken. By calculating errors in predicted versus actual values across countries we show that, when transferring between similar contexts, simple mean-value transfers outperform more complex value function transfers (with the magnitude of the former errors being encouragingly small). However, this result is reversed when transfers are undertaken across dissimilar contexts where value functions partially adjust for these differences. In summary these findings provide support and guidance for future applications. © 2004 Elsevier B.V. All rights reserved.