Discrete choice experiments (DCEs) addressing adaptation to climate-related risks may be subject to response biases associated with variations in risk exposure across sampled populations. Systematic adjustments for such biases are hindered by the absence of rigorous, standardized selection-correction models for multinomial DCEs, together with a lack of information on non-respondents. This paper illustrates a systematic approach to accommodate risk-related non-response bias in DCEs, where variations in risk exposure may be linked to observable landscape characteristics. The approach adapts reduced form response-propensity models to correct for survey non-response, capitalizing on the fact that indicators of risk exposure may be linked to the geocoded locations of respondents and non-respondents. An application to coastal flood adaptation in Connecticut, USA illustrates implications for welfare estimation. Results demonstrate that the proposed approach can reveal otherwise invisible, systematic effects of survey response patterns on estimated WTP.