Forecasting Cash Rent Values

Stability in the farming sector provides stability in rural economies, with a varying but large portion of employment in rural communities across the nation directly related to agriculture or to the agricultural services and processing industries. Instability in the agricultural sector can send ripple effects throughout the economy through increased food and fiber prices. Additionally, there has been a movement towards land investment by equity firms. As rent i s the primary source of revenue, understanding movements in rent is useful for mitigating risk and understanding the market. The purpose of the following research is to address the deficit in recent forecast literature pertaining to land and cash rent prices and to identify the best methodology for forecasting. Tested methods include a Holt-Winters naive forecast, a structural model with lagged rent, farmland prices and crop prices as explanatory variables, an error correction model (ECM), an autoregressive integrated moving average (ARIMA) forecast model, and a composite forecast. Each model is evaluated using mean absolute percent error (MAPE) and root mean squared error (RMSE).