JEANNEAUX, Philippe; DESJEUX, Yann; ENJOLRAS, Geoffroy; LATRUFFE, Laure

doi:10.1002/agr.21752

DESJEUX, Yann
Bordeaux Sciences Economiques [BSE]

ENJOLRAS, Geoffroy

Langue

Article de revue

Ce document a été publié dans

Agribusiness. 2022-06

Résumé en anglais

Farm transfer is increasingly seen as fundamental to the development of agriculture. One of the major challenges is to assess farm value in the context of an opaque market for farms. We contribute to the scarce literature on farm valuation by empirically applying three valuation methods to the Farm Accountancy Data Network database for France in 2017 and 2018 and for five types of farming. The three methods—the fundamental method, the patrimonial method, and the financial method—are well known for the valuation of companies, but have yet to be implemented widely for farms in the empirical literature. The results show that wine-growing farms have the highest values on average. Pig and beef farms have high average patrimonial values, reflecting their high capital intensity, but beef farms have the lowest average values calculated with methods based on cash flows, revealing unfavorable market conditions for these farms. The results further reveal that total farm output drives the value upward, but that high farm labor, indebtedness, and age contribute to reducing value. Our findings also highlight that, in practice, the differences in values across methods may be substantial.< Réduire

Mots clés en anglais

agricultural production

FADN

France

farm value

valuation methods

URI

https://oskar-bordeaux.fr/handle/20.500.12278/140652

DOI

http://dx.doi.org/10.1002/agr.21752

Lien vers les données de la recherche

https://www.casd.eu/en/

Project ANR

Etude de la toxicité des médicaments pris pendant la grossesse : évaluation de modèles statistiques et d'indicateurs de santé périnatale - ANR-15-CE36-0006

Unités de recherche

Bordeaux Sciences Economiques / Bordeaux School of Economics (BSE) - UMR 6060

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Farm valuation: A comparison of methods for French farms