Agricultural Productive and Consumptive Use Components of Rural Land Values in Texas: Comment

Previous studies have found that the market price of rural land generally exceeds the capitalized value of its returns from agricultural production (Castle and Hoch). In a recent article, Pope attempts to explain this disparity through an analysis of land values across school districts in rural Texas. Pope concludes that consumptive demand for land accounts for over 75% of the difference between agricultural land market prices and capitalized rental value in agricultural production. However, deficiencies in Pope's conceptualization of the problem and his empirical model raise doubts about the large importance of consumptive value. Pope identifies only two primary components to the value of surface rights to rural land: agricultural productive (including future productivity growth) and consumptive components. Hence, what is not agricultural productive value is, by this definition, consumption value. This conceptual framework is too simple and misleading. For example, demand for land resulting from expected future growth in nonagricultural uses (an expectation component) is not addressed in Pope's study. This may have been an important contributor to land values in Texas during the period of this analysis because of the large influx of population from the Midwest and the intense energy exploration and production activities. This expectation component, however, is empirically captured by Pope in variables representing consumptive value in model 2 (since model 1 did not have much explanatory power). Ignoring the role of expectations in land value determination, Pope interprets the proportion of land value that may have been attributable to expectations as arising from consumptive demand. Even without an expectations component, Pope has failed to properly estimate the consumptive value of rural land parcel price. Land is a heterogenous resource with many attributes. These attributes can be broken down into three general categories: soil or bare land, natural site characteristics (trees, streams, lakes, ponds, wild animals, mountain views, and geographic hazards such as hurricanes, floods, earthquakes), and availability of other nonland services (roads, power, water, sewer, etc.). Pope's analysis of parcel price includes soil or bare land, one site characteristic (number of white-tailed deer harvested) and one nonland service measure (access to metropolitan areas). The sum of the marginal valuations of these attributes is treated by Pope as contributing to consumptive value. There are two problems in his approach. First, the attributes that Pope used in the regression (number of deer harvested and access to metropolitan areas) contribute to productive as well as consumptive values. Second, marginal prices derived from the simple hedonic function, estimated by Pope, do not measure the willingness to pay for additional units of attributes except under restrictive and unlikely assumptions such as incomes and socioeconomic characteristics of all buyers and sellers are the same (Rosen, Follain and Jimenez). There are, however, other methods suggested and used in the literature to determine the marginal values of various attributes and their interrelationships (Rosen, Quigley, Diamond). Pardew, Shane, and Yanagida is an example of a study applying Rosen's hedonic framework to determine marginal values of parcel size, presence of trees, distance to mountain, presence of sewer lines, etc. Conceptually, Pope has estimated a simple hedonic equation (excluding on-site structures) for rural real property. Viewed as a hedonic price equation for rural property, the estimated model has measurement problems. The dependent variable, average market value per acre of rural land (AMV), is based on samples of actual sales data or appraisals of agricultural parcels that vary widely in access to nonland services (State Property Tax Board 1985). Annual net returns per acre from agriculture (ANR) are calculated on the basis of owner-operator budgets or typical lease agreements for a given Texas area (State Property Tax Board 1982). For this study, individual sales data are more appropriate because sales price, calculated net returns, and parcel characteristics have a one-to-one correspondence with one another. For the data set selected, the average ANR calculated for a county may represent a much different average farm than is represented by averRangesan Narayanan and Ronald L. Shane are associate professors in the Department of Agricultural Economics at the University of Nevada. Detailed review comments by an anonymous referee were extremely helpful. The authors are also grateful to Mike Houston for sharing his knowledge of Texas land characteristics and market conditions.