Mineral Rights & Shale Development: A Hedonic Valuation of Drilling in Western Colorado

Andrew BoslettPhD Candidate

University of Rhode Island

Environmental & Natural Resource Economics

aboslet1@gmail.com

Todd Guilfoos & Corey LangAssistant 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 valuation2007 2008 2009 2010 2011 2012 2013

0

2,000

4,000

6,000

8,000

10,000

12,000

U.S. Shale Production (Billion Cubic Feet)

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 Developm

ent

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

itiitit XWellsp '1)ln(

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 statisticsFull Sample (N =

47,033)Split Estate (N =

783)

Variable Mean Std. Dev. Mean Std. Dev. 

Sale Price ($000s) 250.6 109.9 183.3 81.4Acres 1.4 8.0 6.4 26.7Age at time of sale (years) 18.3 24.1 17.8 16.4Beds 3.2 0.7 3.0 0.7Baths 2.1 0.6 2.0 0.6Finished squared feet (000s) 1.8 0.7 1.6 0.7Distance to municipality 0.5 1.5 1.8 3.4Distance to NPS area 20.1 23.4 28.4 16.9% Agricultural 7.4 23.3 3.0 14.4# of vertical wells < 1 mile 0.1 0.6 0.9 1.1

# of horizontal wells < 1 mile 0.3 3.1 2.1 7.9# of horizontal wells < 2 miles 1.5 12.0 12.1 35.6

% of properties with horizontal well < 1 mile 2.2 14.7 12.5 33.1

% of properties with horizontal well < 2 miles 7.4 26.2 34.6 47.6

% of properties with horizontal well < 3 miles 10.6 30.8 38.2 48.6

Table 2: The effect of unconventional development on the residential property market (N = 47,033), Binary Treatment

(1) (2) (3) (4) (5) (6)

VariablesProperty &

Location Var.+ Year FE

Year FE + County FE

Year * County FE

Year FE + Tract FE

Year * Tract FE

Wells < 1 Miles0.006 -0.009 -0.024 -0.027 -0.024 0.029

(0.0444) (0.050) (0.064) (0.081) (0.051) (0.054) R-Squared 0.406 0.493 0.493 0.496 0.562 0.590

Wells < 2 Miles0.011 -0.027 -0.052 -0.049 -0.031** -0.003

(0.048) (0.050) (0.067) (0.076) (0.015) (0.027)

R-Squared 0.406 0.493 0.493 0.497 0.562 0.590Notes: 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)

VariablesProperty &

Location Var.+ Year FE

Year FE + County FE

Year FE + Tract FE

Wells < 1 Miles -0.123*** -0.288*** -0.332*** -0.340***(0.0151) (0.0464) (0.0484) (0.0607)

R-Squared 0.418 0.507 0.521 0.552

Wells < 2 Miles0.0227 0.0153 -0.0425 -0.129

(0.0576) (0.0734) (0.0739) (0.119)

R-Squared 0.412 0.482 0.491 0.523Notes: 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 FEYear FE +

Tract FE

Wells < 1 Miles-0.015*** -0.019*** -0.019*** -0.020***(0.0007) (0.003) (0.003) (0.003)

R-Squared 0.468 0.544 0.556 0.589

Wells < 2 Miles-0.003*** -0.004*** -0.005*** -0.005***(0.0002) (0.0007) (0.00065) (0.0006)

R-Squared 0.467 0.550 0.565 0.597Notes: 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)

VariablesSplit Estate Definition Garfield

County OnlyVertical Well

Count> 0% > 25% > 50% > 75%

Well < 1m.-0.216*** -0.316*** -0.326*** -0.329*** -0.390 -0.332***(0.074) (0.057) (0.061) (0.061) (0.235) (0.050)

#Obs. 1,581 971 919 882 363 783R-Squared 0.474 0.512 0.516 0.507 0.644 0.535

Well < 2m.-0.089* -0.092 -0.105* -0.102 -0.054 -0.059(0.045) (0.058) (0.053) (0.066) (0.198) (0.072)

# Obs. 1,581 971 919 882 363 783R-Squared 0.468 0.493 0.493 0.484 0.613 0.505Notes: 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] > 0-0.356*** -0.352*** -0.358***(0.080) (0.067) (0.059)

Mean Normalized Bias 6.4 6.7 4.1Pseudo R² 0.042 0.033 0.016Likelihood Ratio Test 0.903 0.969 0.999Notes: 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 2000. 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 Specifications Alternative Datasets

+ Squared Terms

- Vertical Well Count

- Year F.E. + NPS 2000 - 2015Garfield County

Wells < 1 Mile -0.339*** -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 6.9 6.5 5.3 16.2 5.4 13.2Pseudo R² 0.065 0.039 0.023 0.291 0.021 0.115Likelihood Ratio Test 0.951 0.901 0.735 < 0.001   0.756 0.793Notes: 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