This case follows Nate Shepard, a fictionalized data analyst for John Deere, as he is tasked with finding a suitable method for predicting used tractor prices. Nate uses hedonic price theory to specify and
estimate a regression equation that can be used to evaluate marginal values of specific tractor attributes and predict out-of-sample tractor prices. Beyond price prediction, Nate must also consider the
inflationary environment the used tractor market has been experiencing of late in his regression
specification as well as compare the John Deere brand to rival manufacturers. The case allows readers
to go along with Nate in the journey as he completes the process of data collection and cleaning, initial
model specification based on relevant literature and theory, model estimation, evaluation of the model
for misspecification issues, model revision and re-estimation, and model interpretation and use. The case
provides an excellent example of empirical regression analysis in an agribusiness setting and gives
readers an opportunity to familiarize themselves with hedonic price theory using a data set of actual
used tractor auction results from 2020–2022.

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