The Impact of Energy Production on Farmland Markets: Evidence from New York's 2008 Hydraulic Fracturing Moratorium.

AuthorIfft, Jennifer

    The U.S. is on its way to becoming a net energy exporter. Most current and planned energy production, whether conventional or renewable, will take place on farmland (Hitaj et al., 2018). However, little is known about how energy production influences farmland markets. Farmland makes up over 80 percent of farm sector assets and serves as a primary source of collateral for farm loans (Nickerson et al., 2012). U.S. energy exports have been fueled by a boom in natural gas production from new technology, such as hydraulic fracturing (DiChristopher, 2018). In 2017 shale gas production accounted for 60% of U.S. natural gas production (EIA, 2017) and 18% of domestic energy production (EIA, 2018). With this growth, there have been multiple debates on the impacts of shale gas development (SGD) on the economy, environment, and social welfare. As such, the impact of SGD on property values has become the focus of a growing body of literature (Muehlenbachs et al., 2015). Other research has considered how areas with energy production activities tend to have increased population, employment, business activities and government revenue in the short term (Weber, 2012; Fetzer, 2014). Hitaj et al. (2018) found that oil and gas payments represent over 6% of net cash farm income and almost 5% of off-farm household wages earned by all U.S. farm operators and landlords in 2014. Payments of this magnitude can influence farmland values as well as rural economies.

    This study estimates the net valuation of future SGD, as reflected in farmland values, using farmland transactions data from New York state from before and after a moratorium was unexpectedly imposed on SGD in 2008 (figure A3). (1) Farmland market impacts are relevant both for the direct influence on agriculture as well as a measurement of the economic impact of energy production. Energy production can provide income for rural landowners and may be an important source of income for farm households (Hitaj et al., 2018). On the other hand, higher farmland prices can increase the capital investment necessary to begin farming or expand existing operations. However, there is limited research on how energy production influences farmland markets. Quantification of the capitalization of SGD into farmland values also contributes to understanding of the economic impacts of SGD for rural landowners, reflecting expected energy production revenues less any costs related to negative economic, environmental and amenity impacts. While farmland values may reflect some of the expected negative externalities of SGD, we expect this to be substantially less than residential property.

    There are two major components of compensation payments of SGD: signing bonuses and royalty payments. Signing bonuses can vary from $50/acre to $6000/acre (Weidner, 2013). Federal regulations mandate a minimum royalty payment of 12.5% of the value of gas removed, but the negotiated rate could be much higher and is typically based on multiple factors (Weidner, 2013). Brown et al. (2016) estimated that on average, landowners in Marcellus Shale (Pennsylvania) received a 13.2% royalty payment. Other sources of revenue include payments for pipeline construction (Messersmith, 2010) and compressor station construction (Boslett et al., 2016). Harleman and Weber (2017) estimated that an unconventional well would produce $9.1 million worth of gas and $774K payments to the local community over its lifespan. The aggregate impact of technological innovation and shale gas development on U.S. equity capitalization is estimated to be $3.5 trillion since 2012, which suggests that negative environmental effects are offset by the positive employment and stock market impacts (Gilje et al., 2016). The costs of SGD include environmental impacts such as water pollution and air pollution (Caulton et al., 2014; Sovacool, 2014; Howarth et al., 2011), as well as social impacts such as mental distress and conflicts within local communities (Kriesky et al., 2013; Simonelli, 2014).

    Several recent studies have focused on local effects of SGD, through analysis of residential housing values. Residential property value primarily reflects the value of buildings, as well as amenities associated with buildings. Muehlenbachs et al. (2015) found that shale gas development can decrease residential property values significantly due to the risk of water contamination. Gopalakrishnan and Klaiber (2013) found that the negative impact of SGD on residential property value depends on proximity and intensity of the nearby shale activity and is transitory. On the other hand, Boslett et al. (2016) showed that the New York State (NYS) SGD moratorium led to a 19%-27% decrease in housing prices in three counties in New York's southern tier. Boslett et al. (2016) studied housing prices in Steuben, Chemung and Tioga county. 'Southern tier' is often used to broadly refer to several southwestern New York counties. Our study includes several counties typically considered to be a part of the southern tier: Allegany, Steuben, Chemung, Tioga, Broome and Chenango For agricultural properties, the land value reflects the present value of the income stream generated from agricultural production, as well as potential non-agricultural influences or income sources (Borchers et al., 2014a). Weber and Hitaj (2015) found farmland in the Barnett Shale (Texas) and the Marcellus Shale (Pennsylvania) experienced significant appreciation in value when there was intense gas leasing and drilling. Weber et al. (2013) showed that U.S. farmland values are positively associated with royalty payments from wind, oil and gas production.

    This study makes two major contributions. The first is providing novel and robust evidence of local impacts of SGD on farmland values, which improves understanding of the economic impacts of energy production for rural landowners and the farm sector. To the best of our knowledge, this is the first study focused on the impact of the NYS shale gas moratorium on farmland values and one of the few studies on the impact of energy production on farmland. We use NY farmland transactions data and a difference-in-differences design to quantify the net value of expected SGD in farmland in the southern tier of New York in the late 2000s. Our identification strategy is similar to Boslett et al. (2016), but we focus on farmland and New York state only. Leading up to a SGD moratorium announced July 23, 2008, there was a general expectation that a statewide ban on SGD would not be passed, as reflected by reported investment in farmland and several leases being signed by rural landowners (Jacquet and Stedman, 2011;Wilber, 2014). This natural experiment allows us to quantify price changes for properties most likely to be affected by SGD relative to price changes for properties highly unlikely to have SGD, based on underlying geologic formations that largely follow county boundaries. (2)

    The second contribution is in using a machine learning technique-least absolute shrinkage and selection operator (LASSO) regression-to select variables to control for agricultural use value of farmland. Large datasets and complex spatial information are becoming the norm in property valuation research. ZTRAX and other public or private property transaction databases allow researchers to estimate models that contain an unprecedented level of information on property and surrounding characteristics, for example neighborhood attributes or soil properties. While these databases allow researchers to account for previously unobservable property characteristics, standard econometric models often cannot incorporate this new information. Appraisal methods are now regularly augmented by machine learning methods, i.e. Ceh et al. (2018); Lasota et al. (2011); Antipov and Pokryshevskaya (2012), and these methods are useful for informing housing policy (Hu et al., 2019). Machine learning models for hedonic estimation of residential property values have been shown to be useful for variable selection and increasing predictive power (Yoo et al., 2012), but research applications have been relatively rare. In this study, we have a large number of potential control variables for soil properties, but are limited by the number of transactions observed. Use of LASSO allows our hedonic price model to benefit from more robust controls for agricultural use value, while maintaining a tractable and interpretable specification.

    We begin with a description of our empirical model and identification strategy, followed by a description of New York farmland transactions and other geo-referenced data used in this study. We next select our preferred model and control variables, then present our results and provide a comparison with findings from other studies and several robustness checks. We find that farmland in the southern tier of New York experienced a value loss of approximately $1400/acre due to the moratorium. This estimate suggests that markets valued future benefits of shale gas production well above costs and a substantial loss of expected wealth for rural landowners due to the moratorium.


    We use a hedonic price model, a standard approach in the farmland valuation literature (Nickerson and Zhang, 2014), to model New York state farmland transactions. Hedonic pricing models are based on revealed preferences and assume that the price of a good is a function of the good's characteristics. Rosen (1974) developed the model for differentiated consumer goods and Freeman (1974) constructed a similar model to quantify the marginal effect of environmental characteristics on residential properties' value. By decomposing the price of the good into implicit prices of characteristics of the good, we recover the value of each characteristic of the good. We apply the hedonic pricing framework to farmland prices to incorporate the change in expectation of future SGD. The price function is...

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