Price is the main factor that has a direct impact on the main results of the enterprises, especially agricultural. The pricing policy is able to maintain the competitiveness of the enterprise, to ensure its solvency and stability. Pricing is carried out in a complex interaction of a set of internal and external factors. The corresponding complexity of the phenomenon leads to the need to include many Factors in the model, which can negatively affect the result. The numerous number of Factors necessitate the use of methods that allow the identification of the most statistically significant Factors and assess their relationship with the resulting feature. The aim of the article is to identify the main Factors and their influence on the formation and change of the price of agricultural products using Factor analysis. Different methods such as mathematical and statistical (factor analysis, principal component), economic and statistical (multiple regression method based on factor scores) and generalization were applied in the article. The application of Factor analysis and method of principal components determine the most significant Factors influencing the formation of agricultural products prices. The author used different dimensionality reduction methods and obtained three main components: supply, demand, and the solvency of the population. This helped to reveal the relationship between variables and price without multicollinearity problem. Conclusions: the author used Factor analysis and the principal component method; it helped to take into account the peculiarities of the relationships between the indicators that characterize potato pricing, to eliminate multicollinearity between independent indicators, to get fewer common Factors than the original number of variables, to obtain a quantitative assessment of the latent variable. The use of orthogonal rotation helped to distribute the load on all three Factors more evenly, which simplified their further interpretation and to save 95 % of the total primary information after rotation and to reduce the dimensionality of the feature space, which is a high indicator for practical tasks.

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