Farm valuation: A comparison of methods for French farms
2022
Jeanneaux, Philippe | Desjeux, Yann | Enjolras, Geoffroy | Latruffe, Laure | Territoires (Territoires) ; AgroParisTech-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA) | Bordeaux Sciences Economiques (BSE) ; Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS) | Centre d'études et de recherches appliquées à la gestion (CERAG) ; Université Grenoble Alpes (UGA) | ANR-15-CE36-0006,EToMeG,Etude de la toxicité des médicaments pris pendant la grossesse : évaluation de modèles statistiques et d'indicateurs de santé périnatale(2015)
International audience
Show more [+] Less [-]English. 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.
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