Mineral Rights & Shale Development: A Hedonic Valuation of Drilling in Western Colorado Andrew Boslett PhD Candidate University of Rhode Island Environmental & Natural Resource Economics Todd Guilfoos & Corey Lang Assistant Professors University of Rhode Island Environmental & Natural Resource Economics

Background  Significant macroeconomic impact  “Game Changer”  “Renaissance”  “Security”  What are the local economic benefits vs. environmental impacts of SD?  Hedonic valuation

Background  Hedonic valuation  Negative impact of SD  (Unobserved) Mineral estate ownership variation  Mineral rights severance  No financial benefits to split estate owners  Minerals > Surface  Dissatisfaction with local SD

Background  Homestead Act of 1862  Disbursed both land and minerals  Enlarged Homestead Act of 1909 and the Stock-Raising Homestead Act of 1916  Disbursed only land, not minerals  Significant legacy of mineral rights severance in the western U.S.  ~60 million acres

Research Question & Hypothesis  The previous literature has estimated net impacts by using both full and split estate properties  Ignored the alternative valuations of full versus split estate properties  Unobserved mineral ownership distribution?  Relationship between severance and level of local drilling?  Endogenous treatment?  Question: What is the value of the local environmental costs of shale development, as valued in the housing market?  We can learn much about the value of the environmental costs of shale development by focusing on split estate properties  12-34% of a property’s sale value in western Colorado

Study Area & Data  Western Colorado  Garfield, Mesa, and Rio Blanco counties  2000 to 2014  Choice of study area  (1) Disclosure state  (2) ~4,500 horizontal wells  (3) Split estate with federal government Available Sales Data Mineral Severance O&G Development

Methodology  Hedonic valuation  (1) OLS Regression w/ all properties  S.E. classification = Omitted Variable  (2) OLS Regression w/ split estate properties  (3) Robustness checks  (4) Propensity score matching w/ all properties  (5) Propensity score matching w/ split estate properties  (6) Robustness checks

Assumptions  Our interpretation of the results is reliant on a series of assumptions 1.Close proximity to a horizontal well is exogenous  Surface estate is subordinate, no pre-drilling differences in sale price 2.Property buyers and sellers are aware of shale development  Ramp-up of planning activity, large size of installations 3.Property buyers and sellers are aware of mineral severance  Long history of O&G development, BLM-focused effort at providing more information 4.Estimates are not impacted by spillover effects  Small enough area to not worry about regional effects, location F.E. 5.The financial benefits of local development are negligible for split estate owners  Definition of split estate, limited benefits from surface use agreements

Table 1: Summary statistics Full Sample (N = 47,033)Split Estate (N = 783) VariableMeanStd. Dev.MeanStd. Dev. Sale Price ($000s) Acres Age at time of sale (years) Beds Baths Finished squared feet (000s) Distance to municipality Distance to NPS area % Agricultural # of vertical wells < 1 mile # of horizontal wells < 1 mile # of horizontal wells < 2 miles % of properties with horizontal well < 1 mile % of properties with horizontal well < 2 miles % of properties with horizontal well < 3 miles

Table 2: The effect of unconventional development on the residential property market (N = 47,033), Binary Treatment (1)(2)(3)(4)(5)(6) Variables Property & Location Var. + Year FE Year FE + County FE Year * County FE Year FE + Tract FE Year * Tract FE Wells < 1 Miles (0.0444)(0.050)(0.064)(0.081)(0.051)(0.054) R-Squared Wells < 2 Miles ** (0.048)(0.050)(0.067)(0.076)(0.015)(0.027) R-Squared Notes: Observations represent single family residential properties sold from 2000 to early 2015 in Garfield, Mesa, and Rio Blanco counties. We truncate the data set to exclude the 5 and 95 percentiles of sale price. The dependent variable is the natural log of sale price (CPI-adjusted to 2014 values). Property variables include # of bedrooms and bathrooms, parcel acreage, property finished living area, property age, and squared terms. Location variables include distance to the closest National Park Service Area, distance to the closest municipality, and the percentage of the property in an agricultural use, along with associated squared terms. Census tracts are based on U.S. Census 2010 boundaries. Standard errors are shown in parentheses and are estimated using tract-level cluster- robust inference: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Table 3: The effect of unconventional development on the split estate properties (N = 783), Binary Treatment (1)(2)(3)(4) Variables Property & Location Var. + Year FE Year FE + County FE Year FE + Tract FE Wells < 1 Miles ***-0.288***-0.332***-0.340*** (0.0151)(0.0464)(0.0484)(0.0607) R-Squared Wells < 2 Miles (0.0576)(0.0734)(0.0739)(0.119) R-Squared Notes: Observations represent single family residential properties sold from 2000 to early 2015 in Garfield, Mesa, and Rio Blanco counties. We truncate the data set to exclude the 5 and 95 percentiles of sale price. The dependent variable is the natural log of sale price (CPI-adjusted to 2014 values). Property variables include # of bedrooms and bathrooms, parcel acreage, property finished living area, and property age, along with squared terms. Location variables include distance to the closest municipality and National Park Service Area, and the percentage of the property in an agricultural use, along with squared terms. Census tracts are based on U.S. Census 2010 boundaries. Standard errors are shown in parentheses and are estimated using tract-level cluster-robust inference: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Table 4: The effect of unconventional development on the split estate properties (N = 783), Continuous Treatment (1)(2)(3)(4) Variables Property & Location Var. + Year FE Year FE + County FE Year FE + Tract FE Wells < 1 Miles ***-0.019*** *** (0.0007)(0.003) R-Squared Wells < 2 Miles ***-0.004***-0.005*** (0.0002)(0.0007)( )(0.0006) R-Squared Notes: Observations represent single family residential properties sold from 2000 to early 2015 in Garfield, Mesa, and Rio Blanco counties. We truncate the data set to exclude the 5 and 95 percentiles of sale price. The dependent variable is the natural log of sale price (CPI-adjusted to 2014 values). Property variables include # of bedrooms and bathrooms, parcel acreage, property finished living area, and property age, along with squared terms. Location variables include distance to the closest municipality and National Park Service Area, and the percentage of the property in an agricultural use, along with squared terms. Census tracts are based on U.S. Census 2010 boundaries. Standard errors are shown in parentheses and are estimated using tract-level cluster-robust inference: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Table 5: Robustness checks, Binary Treatment (1)(2)(3)(4)(5)(6) Variables Split Estate Definition Garfield County Only Vertical Well Count > 0%> 25%> 50%> 75% Well < 1m ***-0.316***-0.326***-0.329*** *** (0.074)(0.057)(0.061) (0.235)(0.050) #Obs.1, R-Squared Well < 2m * * (0.045)(0.058)(0.053)(0.066)(0.198)(0.072) # Obs.1, R-Squared Notes: Standard errors are shown in parentheses and are estimated using tract-level cluster-robust inference: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Table 6: Matching estimates of the effect of unconventional development on split estate properties Nearest Neighbor (1)Nearest Neighbor (3)Kernel Wells[1 Mile] > ***-0.352***-0.358*** (0.080)(0.067)(0.059) Mean Normalized Bias Pseudo R² Likelihood Ratio Test Notes: Property variables include # of bedrooms and bathrooms, parcel acreage, property finished living area, property age, distance to closest municipality, and the percentage of the property in an agricultural use. We also include a count variable of the number of vertical oil and gas wells drilled within a mile of the property from 1980 to The dependent variable is the natural log of sale price (CPI-adjusted to 2014 values). All statistics are post-matching. Bootstrapped standard errors are shown in parentheses: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Table 7: Matching robustness checks (1)(2)(3)(4)(5)(6) Alternative P.S. Model SpecificationsAlternative Datasets + Squared Terms - Vertical Well Count - Year F.E.+ NPS Garfield County Wells < 1 Mile ***-0.348***-0.313***-0.465***-0.239***-0.350* (0.085)(0.070)(0.063)(0.168)(0.062)(0.185) Mean Normalized Bias Pseudo R² Likelihood Ratio Test < Notes: The dependent variable is the natural log of sale price (CPI-adjusted to 2014 values). All statistics are post-matching. Bootstrapped standard errors are shown in parentheses: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Discussion & Conclusions  The previous literature that has heretofore focused on net valuations of shale development  We avoid a number of issues by only analyzing split estate properties in western Colorado  12 – 36% decrease, robust across various specifications  ~ $60,000 or $3,400 per well  Notes  Remote setting of western Colorado?  Information issues?  No financial benefits?

Acknowledgements  Garfield, Mesa, and Rio Blanco County  Assessment  GIS  Bureau of Land Management  Colorado office  Steven Hall, Martin Hensley, Deanna Masterson & Courtney Whiteman  Local experts  Lois Dunn, real estate agent  Cameron Grant, lawyer  Local BLM officials