Andreas Graefe is a research fellow at the Department of Communication Science and Media Research at LMU Munich, Munich, Germany. J. Scott Armstrong, Alfred Cuzán, Michael Lewis-Beck, Andreas Murr, and Christopher Wlezien provided helpful comments. The author also received valuable suggestions when presenting the manuscript at the 2013 Annual Conference of the International Communication Association in London, the 2013 International Symposium on Forecasting in Seoul, and the 2013 American Political Science Association Annual Meeting in Chicago. Bettina Zerwes helped with collecting data. Jamie Graefe and Nancy Elfant did editorial work. This work was supported by an LMUexcellent research fellowship from the Center for Advanced Studies at LMU Munich.
Simple surveys that ask people who they expect to win are among the most accurate methods for forecasting US presidential elections. The majority of respondents correctly predicted the election winner in 193 (89 percent) of 217 surveys conducted from 1932 to 2012. Across the last 100 days prior to the seven elections from 1988 to 2012, vote expectation surveys provided more accurate forecasts of election winners and vote shares than four established methods (vote intention polls, prediction markets, quantitative models, and expert judgment). Gains in accuracy were particularly large compared to polls. On average, the error of expectation-based vote-share forecasts was 51 percent lower than the error of polls published the same day. Compared to prediction markets, vote expectation forecasts reduced the error on average by 6 percent. Vote expectation surveys are inexpensive and easy to conduct, and the results are easy to understand. They provide accurate and stable forecasts and thus make it difficult to frame elections as horse races. Vote expectation surveys should be more strongly utilized in the coverage of election campaigns.