Predicting Field Value with Interpretable Models

Abstract

This thesis explores predicting current prices of individual agricultural fields in Finland based on historical data. The task is to predict field prices accurately with the data we have available while keeping model predictions interpretable and well explainable. The research question is to find which out of several different models we try out is most optimal for the task. The motivation behind this research is the growing agricultural land market and the lack of publicly available field valuation services that can assist market participants to determine and identify reasonable asking prices. Previous studies on the topic have used standard statistics to establish relevant factors that affect field prices. Rather than creating a model whose predictions can be used on their own in every case, the primary purpose of previous works has indeed been to identify information that should be considered in manual field valuation. We, on the other hand, focus on the predictive ability of models that do not require any manual labor. Our modelling approaches focus mainly but not exclusively on algorithms based on Markov–Chain Monte Carlo. We create a nearest neighbors model and four hierarchical linear models of varying complexity. Performance comparisons lead us to recommend a nearest neighbor -type model for this task.