Exploring alternative measures of net rents to farmland through the econometric capitalization formula for farmland price

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Date

1988

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Montana State University - Bozeman, College of Agriculture

Abstract

Farmland prices began to diverge from farm income trends during the mid-1950's. Traditional capitalization theory is the accepted mechanism with which to value farmland. The central idea to this theory is that land values must derive from net rents to land. The divergence between the time path of farm income and land prices has focused economists' attention on the validity of traditional capitalization theory in explaining farmland prices, and the appropriateness of net farm income as a measure of net returns imputed to farmland. This study explores three alternative measures of returns imputed to farmland. The measures are the United States Department of Agriculture (USDA) accounting data, cash rents, and gross revenue from corn and soybeans production. Each of these measures is fitted to essentially a second order non-stochastic difference equation framework; an economic capitalization formula for farmland price that is developed in a different study and is tested empirically using Illinois crop-share rent data as the measure of net rents to farmland. Problems are encountered concerning two of the three measures explored; data problems for the USDA accounting data measure and a joint dependency problem with farmland prices for the cash rent measure. Encouraging results are encountered when gross revenue from corn and soybeans is explored. The regression results for this measure are similar to the results of the study that uses cropshare rents. An implicit capitalization rate cannot be computed from the estimated land price model. But it appears that the estimated model can be used for conditionally forecasting short to intermediate time periods. Additional research on exploring other alternative measures or methods of imputing a net return to farmland is suggested, but the need for a good set of farm accounts data seems to be more pressing.

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