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The value of land dominates the financial structure of most American agriculturalproduction firms, and land values are an important factor in long-term agricultural planning andrisk management. As the primary source of collateral for farm loans, farmland values havesignificant implications for both producers as well as bankers financing agricultural loans. TheFederal Reserve Bank of Kansas City’s Survey of Agricultural Credit Conditions is an expertopinion survey in which agricultural bankers provide land value forecasts. As the survey hasdrawn increased attention, the survey has drawn criticism regarding its use qualitative data toforecast land values. Our research examines the value of the survey data with respect to itsability to forecast movement in land values. Three techniques are used in the analysis.Interpreting the aggregate forecasts as probability estimates, Brier’s probability scores are usedto evaluate aggregate bankers’ predictions. Next, turning points are evaluated using contingencytables. Finally, Granger causality tests are used to determine the dynamic relationship betweenland value predictions and actual land value changes reported by bankers. Bankers’ forecastspredict land values for irrigated and ranchland well, but non-irrigated forecasts were onlymarginally helpful in prediction non-irrigated farmland values. Forecasts provided in the surveymay be beneficial, especially considering the scarcity of other publicly available data.