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The Impact of Energy Production on Farmland Markets: Evidence from New York’s 2008 Hydraulic Fracturing Moratorium
Abstract
Future conventional and renewable energy production will predominantly occur on farmland, resulting in economic gains as well as potential negative externalities for farmland owners and rural communities. However there is limited research on the economic impact of energy production that takes place on farmland. This study uses the discrete change in expectations caused by the 2008 New York State moratorium on hydraulic fracturing to investigate the net impact of shale gas development on farmland values. We use a difference-in-differences empirical design with a hedonic pricing model. We find that the moratorium led to net economic losses for rural landowners in New York’s Southern tier, as reflected in farmland values declining approximately $1,400/acre.
The shale gas boom revolutionized the energy sector through hydraulic fracturing (fracking). High levels of energy production force communities, states, and nations to consider the externalities and potential risks associated with this unconventional oil and natural gas development (UOGD). In this review, we systematically outline the environmental, economic, and anthropogenic impacts of UOGD, while also considering the diverse methodological approaches to these topics. We summarize the current status and conclusions of the academic literature, in both economic and related fields, while also providing suggested avenues for future research. Causal inference will continue to be important for the evaluation of UOGD costs and benefits. We conclude that current economic, global, and health forces may require researchers to revisit outcomes in the face of a potential shale bust.
Expected final online publication date for the Annual Review of Resource Economics, Volume 13 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
There are frequent and increasing conflicts between energy development and agricultural land use. In these debates, claims are commonly made that energy income can improve farm viability. We develop a conceptual model that provides a framework for when and how energy income could improve farm viability. If farms face difficulty accessing credit, they invest at below optimal levels. In such cases, energy income may be used to increase investment, thus strengthening the farm economy. We test the predictions of this model with detailed, nationally representative U.S. Department of Agriculture farm survey data. This large, cross sectional dataset is well suited to propensity score matching and allows us to test whether observationally similar farms with and without energy income have different levels of credit access and capital investment. We are able to test whether model outcomes are similar under different farm and energy income definitions. Overall, we find little evidence that energy income improves credit access or increases capital investments. This suggests that on average the benefits of energy income for farm household consumption do not necessarily extend to the farm economy or agricultural production.
Given the expansive water consumption of the agricultural sector in the western United States, irrigation practices have increasingly been restricted as a way to combat water scarcity. Using segment-level panel data on land values and irrigation status, we measure the extent to which irrigation restrictions are capitalized into irrigated and nonirrigated cropland values in Nebraska. On average, irrigation restrictions did not have a measurable impact on farmland values across 1999-2012. However, our results show that the effects of the restrictions vary considerably over time and that basins that were more dependent on irrigation were disproportionately impacted by the restrictions.
The goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of apartment transactions referring to real estate sales from 2008–2013 in the city of Ljubljana, the capital of Slovenia, was used in order to test and compare the predictive performances of both models. Apparent challenges faced during modelling included (1) the non-linear nature of the prediction assignment task; (2) input data being based on transactions occurring over a period of great price changes in Ljubljana whereby a 28% decline was noted in six consecutive testing years; and (3) the complex urban form of the case study area. Available explanatory variables, organised as a Geographic Information Systems (GIS) ready dataset, including the structural and age characteristics of the apartments as well as environmental and neighbourhood information were considered in the modelling procedure. All performance measures (R2 values, sales ratios, mean average percentage error (MAPE), coefficient of dispersion (COD)) revealed significantly better results for predictions obtained by the random forest method, which confirms the prospective of this machine learning technique on apartment price prediction.
Farmland has long represented a significant component of both farm sector and farm household assets. This chapter provides a comprehensive overview of significant developments in modeling farmland values, with attention to methodological challenges and recent modeling innovations. After outlining the capitalization model that provides the theoretical underpinnings for most farmland value studies, the merits and efficacy of dynamic models using aggregate data, as well as increasingly popular cross-sectional hedonic models that use spatially disaggregate data are presented. Estimation issues in hedonic models are reviewed, with a focus on those deserving special consideration in the context of farmland values such as spatial dependence and heterogeneity and sample selection bias. Promising future research directions include greater use of nonparametric approaches, quasi-experimental designs, panel data analyses, and structural econometric models, which take advantage of spatially explicit farmland values data but avoid the restrictive assumptions of standard spatial lag and spatial error models.
This paper explores the way community understanding of the impact and infrastructure associated with hydraulic fracturing (fracking) has translated into social action within the social, political, and ecological context of rural New York State, USA. How have residents confronted the boom-bust development of fracking, in the face of both actual drilling and the construction of supporting energy infrastructure? What avenues are open to residents as they plan for the long term sustainability of townships? Finally, on the level of both energy and rural development: if not gas, then what? This paper uses both actual and virtual interviews and oral histories to provide a case study of a struggle for environmental justice amidst conflicting visions and experiences of rural life in one New York region.
Key words: fracking, home rule, New York, social movements , Zapatistas
This article estimates the impact of the 2007-2008 residential housing market bust on farmland values, using parcel-level farmland sales data from 2001-2010 for a 50-county region under urbanization pressure in western Ohio. Hedonic model estimates reveal that farmland was not immune to the residential housing bust; the portion of farmland value attributable to proximity to urban areas was almost cut in half shortly after the bust in 2009-2010. Nonetheless, total farmland prices remained relatively stable in the 2000s, likely due to increased demand for agricultural commodities. Our results are robust to different assumptions about the structure of the unobserved spatial correlation.
Thousands of rural landowners in New York State have joined together to form grassroots organizations aimed at collectively bargaining with natural gas companies. The leverage afforded by acting collectively allows these landowner coalitions to potentially influence the economic, environmental, and community impacts of gas development across hundreds of thousands of acres. In-depth interviews with coalition leaders conducted for this article reveal the scope, motivations, and benefits of membership in these groups. Our work examines these elements using multiple frameworks for understanding collective natural resource management. The coalitions are primarily concerned with the advancement of private member benefits, while public benefits of the collective action are poised to accrue indirectly. Group leaders are also contemplating how to use their leverage to secure direct benefits for the wider community – actions that may give communities a modicum of local control over gas development.
Pennsylvania is not only the birthplace of the modern petroleum industry but also the focus of the modern Marcellus Shale gas play. For more than 150 yr, Pennsylvania has experienced a rich history of oil and gas exploration and production, witnessed the advent of modern petroleum regulations, and now sits deep in the heart of the largest domestic shale gas play the United States has ever seen. Although a known source rock for decades, the Marcellus Shale was not considered a viable gas reservoir until Range Resources Corporation (Range) discovered the play with its completion of the Renz No. 1 well in Washington County in October 2004. Using horizontal drilling and hydraulic fracturing techniques used by operators working the Barnett Shale gas play, Range has gone on to complete hundreds of horizontal shale gas wells in Washington County alone. Other operators have followed suit in counties from one corner of the state to the other, and as of June 2011, the Commonwealth has issued nearly 6500 Marcellus Shale gas well permits. Based on publicly reported well completion and production data, an average Marcellus Shale gas well requires 2.9 million gal of water during the hydraulic fracturing process and produces 1.3mmcf gas/day. Furthermore, theU.S. Energy Information Administration has estimated that as of mid- 2011, daily Marcellus Shale gas production in Pennsylvania exceeds 2.8 bcf. Because of the level of drilling activity and production associated with the Marcellus play, Pennsylvania has become the nexus of shale gas production and water management issues. Copyright ©2011. The American Association of Petroleum Geologists/Division of Environmental Geosciences. All rights reserved.
Significance
We identified a significant regional flux of methane over a large area of shale gas wells in southwestern Pennsylvania in the Marcellus formation and further identified several pads with high methane emissions. These shale gas pads were identified as in the drilling process, a preproduction stage not previously associated with high methane emissions. This work emphasizes the need for top-down identification and component level and event driven measurements of methane leaks to properly inventory the combined methane emissions of natural gas extraction and combustion to better define the impacts of our nation’s increasing reliance on natural gas to meet our energy needs.
Although shale energy development can bring infusions of money and jobs to local communities, an array of risks to community-level assets and institutions is also possible. Sociological research dating back to the 1970s links rapid oil and gas development with overburdened municipal services, upended social and cultural patterns, and volatile economic growth. Research on technological risk has demonstrated communities can come to be associated with pollution and contamination, resulting in out-migration, declining amenity-led development, and decreased financial investment. Emerging shale energy case studies in Wyoming, Pennsylvania, North Dakota, and Texas show a similar, although nuanced, picture of these concerns. Yet, little data exists on the prevalence or magnitude of these risks in the current context of shale gas development. The existing research has largely remained case-based in nature, has not been synthesized across various disciplines, and has not been updated to account for various social and technological trends that have occurred since its publication. This paper offers a critical review of major research endeavors that inform our knowledge of risk to communities from shale energy development, while identifying gaps in our understanding of these risks and areas of research need.
The pace of development of shale gas plays varies greatly among US states and globally. Through analysis of telephone survey responses, we explore support for natural gas drilling in residents of Washington County (WC), PA (n=502) vs. residents of Allegheny County (AC), PA (n=799). WC has had intense Marcellus Shale (MS) drilling activity, in comparison to adjacent AC, which has had little drilling activity. WC residents are marginally more supportive of MS drilling than are AC residents (p=0.0768). Residents of WC are more likely to perceive MS as an economic opportunity than are AC residents (p=0.0015); to be in a family that has signed a MS lease (p<0.0001); to follow the MS issue closely (p=0.0003); to get MS information from neighbors, friends, and relatives (p<0.0001); and are marginally less likely to perceive MS as an environmental threat (p=0.1090). WC leaseholders are significantly more supportive of MS drilling than WC non-leaseholders and AC non-leaseholders (p=0.0024). Mediation analyses show that county-based differences in support of MS drilling are due to WC residents seeing more of an economic opportunity in the MS and their greater likelihood of having a family-held lease.
We investigate the influence of future land development on current agricultural land values. From a theoretical model of land markets, we derive a reduced-form expression for agricultural land values in terms of observable variables. This result dictates the specification of our econometric model and we find strong support for the model in an application to New York State. The estimated model, together with a spatial interpolation algorithm, is used to generate a surface of estimated development rights values for Orange County. This approach overcomes several problems that arise with the use of standard appraisal methods to value conservation easements.
The experiments aimed to compare the performance of random subspace and random forest models with bagging ensembles and single
models in respect of its predictive accuracy were conducted using two popular algorithms M5 tree and multilayer perceptron.
All tests were carried out in the WEKA data mining system within the framework of 10-fold cross-validation and repeated holdout
splits. A comprehensive real-world cadastral dataset including over 5200 samples and recorded during 11 years served as basis
for benchmarking the methods. The overall results of our investigation were as follows. The random forest turned out to be
superior to other tested methods, the bagging approach outperformed the random subspace method, single models provided worse
prediction accuracy than any other ensemble technique.
Keywordsrandom subspaces–random forest–bagging–property valuation
Extracting gas from shale increases the availability of this resource, but the health and environmental risks may be too high.
Understanding the economic impact of surface temperatures is an important question for both economic development and climate change policy. This study shows that in 28 Caribbean-basin countries, the response of economic output to increased temperatures is structurally similar to the response of labor productivity to high temperatures, a mechanism omitted from economic models of future climate change. This similarity is demonstrated by isolating the direct influence of temperature from that of tropical cyclones, an important correlate. Notably, output losses occurring in nonagricultural production (-2.4%/+1 degrees C) substantially exceed losses occurring in agricultural production (-0.1%/+1 degrees C). Thus, these results suggest that current models of future climate change that focus on agricultural impacts but omit the response of workers to thermal stress may underestimate the global economic costs of climate change.
National land use policies and strategies worldwide have attempted to establish a healthy housing rental market towards urban sustainability. Monitoring fine-scale housing rental prices should provide essential implications for equitable housing policies. However, doing so remains a challenge because aggregated data were traditionally collected at a coarse scale through census or social surveys. On-line housing rental websites (OHRWs) have become popular social media platforms in the housing studies. This paper attempts to demonstrate how to monitor fine-scale housing rental prices based on OHRWs using the case of Shenzhen in China. Employing hedonic model, a set of housing rental determinants are initially selected from three characteristics (neighborhood, location and structure) and at three levels (nearest accessibility, 15-minute walking distance availability and sub-district availability). Housing rent prediction models are then established (respectively for October 2017 and February 2018) using the training samples collected from the OHRWs and six machine-learning algorithms, including random forest regression (RFR), extra-trees regression (ETR), gradient-boosting regression (GBR), support vector regression (SVR), multi-layer perceptron neural network (MLP-NN) and k-nearest neighbor algorithm (k-NN). Thereafter, the relative importance of the determinants is calculated and visualized using partial dependence plots. Finally, the models are used to monitor housing rental price dynamics for all of the communities within Shenzhen. Results show that all of the algorithms except SVR generally present good performance. Among them, RFR and ETR are the best one in October 2017 and February 2018, respectively. Concerning the spatial pattern of housing rental, the high-high clusters merge in the central districts, whereas the low-low clusters are located in the outskirts, and the growth rate is the greatest in the farthest outskirts from the central districts. Each determinant affects the housing rent across different scale and sub-district availability and nearest accessibility are more important than 15-minute walking distance availability. The two most influential determinants are sub-district job opportunity and nearest accessibility to health care facilities. The case of Shenzhen shows that the demonstrated framework, which integrates machine-learning algorithms and the hedonic modeling, is practical and efficient. The approach is believed to provide an essential tool to inform equitable housing policies.
We quantify the externalities associated with unconventional oil and gas development using hedonic valuation. One complication in determining local impacts is that some but not all properties are unified with mineral rights, which enable the residents to financially benefit from drilling, and this information is typically unobserved by researchers. To overcome this issue, we exploit the mineral severance legacy of the homestead act extensions of the twentieth century to identify properties in western Colorado that do not have mineral rights and are therefore only impacted negatively by drilling. We find housing prices decline about 35% when drilling occurs within 1 mile. Treated properties are affected by highly intensive drilling (∼16 wells drilled within a mile, on average), and there is suggestive evidence of nonlinear impacts on a per-well basis. Our estimate of local costs is larger than those found elsewhere in the literature, which demonstrates the critical importance of mineral ownership. © 2019 by The Association of Environmental and Resource Economists.
We propose a new method for estimation in linear models. The ‘lasso’ minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree‐based models are briefly described.
We build on the intuitive (static) modeling framework of Rosen (1974) and specify a simple, forward-looking model of location choice. We use this model, along with a series of graphs, to describe the potential biases associated with the static model and relate these biases to the time series of the amenity of interest. We then derive an adjustment factor that allows the potentially biased static estimates to be converted into forwardlooking estimates. Finally, we illustrate these concepts with two empirical applications: The marginal willingness to pay to avoid violent crime and the marginal willingness to pay to avoid air pollution. © 2019 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.
Who owns an area's natural resources affects the local financial gains from extraction and participation in resource governance. We develop a typology of ownership regimes using two dimensions of ownership, private versus public and local versus absentee, and apply it to unconventional natural gas development in the United Kingdom (UK) and the state of Pennsylvania in the United States. We find that local residents in Pennsylvania own 53% of the acreage leased for development and capture an estimated 8.5% of the value of production of the typical well, more than double what is expected from a well in the UK's public-absentee regime despite revenue-sharing policies. The dollar amount of local payments is also larger in Pennsylvania, with the difference reflecting policies and institutions, not differences in the value of production. The Pennsylvania case provides a benchmark for public-absentee owners considering policies to direct payments to communities hosting extraction: it gives the local payments corresponding to the case where subsurface owners voluntarily negotiate lease terms with energy firms and roughly half of ownership revenues accrue locally.
This paper examines the local impacts of shale gas development (SGD). We use a hedonic framework and exploit a discrete change in expectations about SGD caused by the New York State moratorium on hydraulic fracturing. Our research design combines difference-in-differences and border discontinuity, as well as underlying shale geology, on properties in Pennsylvania and New York. Results suggest that New York properties that were most likely to experience both the financial benefits and environmental consequences of SGD dropped in value 23% as a result of the moratorium, which under certain assumptions indicates a large and positive net valuation of SGD.
We study farm real estate values in the Barnett shale (Texas) and the northeastern part of the Marcellus shale (Pennsylvania and New York). We find that shale gas development caused appreciation in real estate values in both areas but the effect was much larger for the Marcellus, suggesting broader ownership of oil and gas rights by surface owners. In both regions, the greatest appreciation occurred when land was leased for drilling, not when drilling and production boomed. We find evidence that effects vary by farm type, which may reflect correlation between farm type and ownership of oil and gas rights. Copyright © 2015 Northeastern Agricultural and Resource Economics Association.
We consider statistical inference for regression when data are grouped into clusters, with regression model errors independent across clusters but correlated within clusters. Examples include data on individuals with clustering on village or region or other category such as industry, and state- year differences- in- differences studies with clustering on state. In such settings, default standard errors can greatly overstate estimator precision. Instead, if the number of clusters is large, statistical inference after OLS should be based on cluster- robust standard errors. We outline the basic method as well as many complications that can arise in practice. These include cluster- specifi c fi xed effects, few clusters, multiway clustering, and estimators other than OLS. © 2015 by the Board of Regents of the University of Wisconsin System.
We study how much private mineral owners capture geologically-driven advantages in well
productivity through a higher royalty rate. Using proprietary data from nearly 1.8 million leases,
we estimate that the six major shale plays generated $39 billion in private royalties in 2014.
There is limited pass-through of resource abundance into royalty rates. A doubling of the
ultimate recovery of the average well in a county increases the average royalty rate by 1 to 2
percentage points (a 6 to 11 percent increase). Thus, mineral owners benefit from resource
abundance primarily through a quantity effect, not through negotiating better lease terms from
extraction firms. The low pass-through likely reflects a combination of firms exercising market
power in private leasing markets and uncertainty over the value of resource endowments.
The recent appreciation in agricultural land values across the United States has raised a number of important questions for
farmers, farmland owners, lenders, and policy makers. While traditional economic theory suggests that farmland values are
determined by the discounted stream of expected returns, previous research has shown that agricultural land values are actually
driven by a complex set of factors. This study leverages the unique characteristics of a national land-based USDA survey on
farmland values and cash rents to estimate broad, national-level determinants of the market value of cropland and pastureland.
Our results support past research findings that indicate farmland values are only partially explained by agricultural returns.
We find that multiple nonagricultural attributes of farmland also contribute to the market value.
New technologies for accessing energy resources, changes in global energy markets, and government policies have encouraged growth in the natural gas and wind industries in the 2000s. The growth has offered new opportunities for wealth creation in many rural areas. At a local level, households who own land or mineral rights can benefit from energy development through lease and royalty payments. Using nationally-representative data on U.S. farms from 2011, we assess the consumption, investment, and wealth implications of the $2.3 billion in lease and royalty payments that energy companies paid to farm businesses. We estimate that the savings of current energy payments combined with the effect of payments on land values added $104,000 in wealth for the average recipient farm.
Public concerns about hydraulic fracking are growing and scientists continue to analyze and evaluate its associated environmental impacts. However, a rigorous spatial analysis of environmental impacts is necessary to provide a perspective on risk based on proximity to fracking wells. This comment describes the environmental impacts of fracking within a spatial context. It emphasizes five key points: (1) the closer to a hydraulic fracking well, the higher the risk of groundwater and drinking water well contamination; (2) residents living nearest to a fracking well experience a higher human health risk due to exposure to gas emissions during the fracking process; (3) huge and high density gas emissions are detected and recorded close to fracking wells; (4) fracking induces seismicity and small earthquakes are recorded close to fracking wells; and (5) hydraulic fracking directly changes local environment and landscape characteristics. Spatial impact assessments are critical for improving understanding of the impacts of hydraulic fracking on the environment and society.
This article offers the first national examination of the determinants of adoption of wind and solar energy generation on U.S. farming operations. The inclusion of state policies and characteristics in a multilevel modeling approach distinguishes this study from past research utilizing logit models of technology adoption which focus only on the characteristics of the farm operation. Results suggest the propensity to adopt is higher for livestock operations, larger farms, operators with internet access, organic operations, and newer farmers. The results find state characteristics such as solar resources, per capita income levels, and predominantly democratic voting increasing the odds of farm adoption. This research suggests the relevance of state policy variables in explaining farm level outcomes is limited, although in combination best practice net metering and interconnection policies—policies designed to encourage the development of small scale distributed applications—are shown to increase the likelihood of farm solar and wind adoption. The prevalence of electric cooperatives—which are often not subject to state renewable energy policies and often service farms—is negatively related with the propensity to adopt and suggests that policy design may be a factor.
Based on the theoretical foundation of hedonic methods, positive relationships between various types of environmental amenities and house sales price have been investigated. However, as hedonic theory does not provide any arguments in favor of specific sets of independent variables, this lack of theoretical support led researchers to select independent variables from empirical results and intuitive information of previous studies. In previous hedonic studies, the most widely used selection criterion was stepwise selection for multiple regression with ordinary least square (OLS) regression for model fitting. The objective of this study is to apply machine learning approaches to the hedonic variable selection and house sales price modeling. Two rule-based machine learning regression methods including Cubist and Random Forest (RF) were compared with the traditional OLS regression for hedonic modeling. Each regression method was applied to analyze 4469 house transaction data from Onondaga County, NY (USA) with two different neighborhood configurations (i.e., 100 m and 1 km radius buffers). Results showed that the RF resulted in the highest accuracy in terms of hedonic price modeling followed by Cubist and the traditional OLS method. Each regression method selected different sets of environmental variables for different neighborhood. Since the variables selected by RF method led to make an in-depth hypothesis reflecting the preferences of house buyers, RF may prove to be useful for important variable selection for the hedonic price equation as well as enhancing model performance.
Improvements in technology have made it profitable to tap unconventional gas reservoirs in relatively impermeable shale and sandstone deposits, which are spread throughout the U.S., mostly in rural areas. Proponents of gas drilling point to the activity's local economic benefits yet no empirical studies have systematically documented the magnitude or distribution of economic gains. I estimate these gains for counties in Colorado, Texas, and Wyoming, three states where natural gas production expanded substantially since the late 1990s. I find that a large increase in the value of gas production caused modest increases in employment, wage and salary income, and median household income. The results suggest that each million dollars in gas production created 2.35 jobs in the county of production, which led to an annualized increase in employment that was 1.5% of the pre-boom level for the average gas boom county. Comparisons show that ex-ante estimates of the number of jobs created by developing the Fayetteville and Marcellus shale gas formations may have been too large.
IntroductionThe Role of Leasing in Farmland MarketsOwnership Turnover Rates and Market EfficiencySpecialized Institutional Features of Farmland MarketsPerformance of Farmland InvestmentsOther Issues in Farmland MarketsConclusion
References
The hedonic property value model is among our foremost tools for evaluating the economic consequences of policies that target the supply of local public goods, environmental services, and urban amenities. We design a theoretically consistent and empirically realistic Monte Carlo study of whether omitted variables seriously undermine the method’s ability to accurately identify economic values. Our results suggest that large gains in accuracy can be realized by moving from the standard linear specifications for the price function to a more flexible framework that uses a combination of spatial fixed effects, quasi-experimental identification, and temporal controls for housing market adjustment.
Nonagricultural factors impact land values to cause a divergence of discounted cash rents for agricultural land and land values in Georgia. General economic factors are represented by per capita income in nonmetro areas. Cash rents for cropland and pasture have positive impacts on land values. Nonagricultural factors are stronger influences on land values than are cash rents. Greater effective demand exists for pasture than for cropland because pasture is subject to relatively more pricing pressure in northern counties with higher incomes and population. Increased land values have led to increased net wealth for Georgia agricultural producers.
This paper deals with some unresolved issues in interpreting empirical property value/air pollution studies. A model is developed in which it is shown that in general regressions of property values on pollution variables do not identify the demand curve for clean air. However since the first derivative of the observed rent function is a locus of household equilibrium marginal willingnesses to pay, aggregate benefits can be calculated directly. The possibilities for obtaining approximate measures of aggregate benefits for nonmarginal changes in air quality are also discussed.
A theoretical model of farmland valuation is developed to explicitly account for three effects of urban sprawl: conversion
of farmland to urban uses, effect on agricultural returns, and speculative effect as represented by farmland conversion risk.
This model is estimated using county-level data in the continental United States. Evidence is found for all three effects
of urban sprawl on farmland values. Counties more accessible to major urban centers have higher net agricultural returns.
Subsidiary evidence supports that the latter effect may be attributed to survival of (or conversion to) high-valued agriculture
around urban centers.
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