Date of Graduation


Document Type


Degree Type



Davis College of Agriculture, Natural Resources and Design


Agricultural and Resource Economics

Committee Chair

Alan R Collins

Committee Co-Chair

Xiaoli Etienne

Committee Member

Oleg Kucher


This study presents an empirical model that explains the impact of energy development activities on agricultural land values in north-central West Virginia. Based on the methodology and the data description, seven different models (linear and logs) are estimated and the results are analyzed to understand the contributions of each energy variable used in the models to the overall values of farmlands. We have examined 311 parcel-level sales of farmlands in 16 counties in the north-central of the state of West Virginia from January 2013 to July 2016. The results suggest that the selected model is statistically different from zero (prob> F= 0.000). Moreover, the model explains 40% in the variation of log per acre price. For the energy development variables, mineral rights transferred with agricultural land property have a positive, statistically significant (at the 10% level) coefficient in the model (as was expected). The more acres of mineral rights a parcel transfer contains, the higher its price should be compared to the one that has similar characteristics but not mineral rights.;Distance to active mining site contributes positively to total sale price (it has the expected sign). The further a parcel is from active mining site (mining area), the higher value it has compared to one that has similar characteristics, but is closer in distance to an active mining site. This coefficient is statistically significant at the 5% level. If the distance of a parcel of agricultural land from active mining site increases by one mile, the value of the parcel increases by 2.18% keeping other variables constant.;For the non-energy variables, the coefficient for log appraised building value and log of number of acres in a tract variables have their expected signs, positive and negative, respectively. Each coefficient is statistically significant at the 1% level. In addition, their coefficients represent elasticities, meaning that a 1% increase in appraised building value causes the per acre price to increase by 0.08% and if total acres increases by 1%, the price per acre decreases by 0.6%, holding other variables constant.;Distance to town with population between 10,000 and 25,000 has a negative sign and it is statistically significant at 5%. This is to say that the further a parcel of agricultural land is located from a city with a population between 10,000 and 25,000, the less attractive it is to buyers. This coefficient has the expected sign. If the distance increases by 1 mile, the price per acre decreases by 1.8% holding other variables constant.;Distance to stream has a positive coefficient (the expected sign) and it is significant at 1%. The further a parcel is from a stream, the more valuable it is compared to one with similar characteristics but in a closer distance to a stream. Probably, a parcel closer to a stream is highly likely to face flooding issues, which means that parcels that are located on areas of higher risks of flooding are less conducive to agricultural activities. Therefore, streams may cause a negative impact on agricultural land prices. If a distance of a parcel from a stream increases by 1/10 of a mile its price increases by 13.2% holding other variables constant.;To assess the aggregate impact of the energy development on agricultural lands, one county, Brooke County, had the available data on all agricultural land parcels to conduct this analysis. Based upon the selected model results, an aggregate property value loss is estimated at over {dollar}2.4 million for the owners of 176 parcels of farmland. This loss is based on an active coal mining site being located five miles from each parcel compared to the current distance. Five miles is double the average distance to an active coal mine for farmland parcels in Brooke County. While this study focused only on Brooke County, it is possible to see similar losses for property owners located near mining sites throughout the sixteen counties of north-central West Virginia. This analysis indicates property value losses from proximity to mining sites; however, these losses could be converted to property value gains if there was no mining sites or after these mining sites have been completed and remediated. There is a positive willingness to pay, as indicated by loss of property values, to have the mining site be move away from their locations.