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TRANSCRIPT
Analysis of Gas Prices in Howard County, Maryland
Prepared for
Talkin and Oh, LLP
Daraius Irani, Ph.D., Executive Director
Susan Steward, Economist
Jessica Varsa, Senior Research Associate
Rebecca Ebersole, Senior Research Associate
Kierran Sutherland, Research Analyst
December 27, 2013
Towson, Maryland 21252 | 410-704-3326 | www.towson.edu/resi
Table of ContentsTable of Figures4Acronyms and Abbreviations61.0Executive Summary71.1Economic Analysis Findings71.2Land Planning Analysis Findings81.3Conclusion82.0Introduction93.0Economic Analysis103.1Annual Analysis103.22012 Cross-Sectional Analysis124.0Land Planning Analysis154.1Zoning Considerations154.2Examples of Gas Station Approvals and Denials in Howard County194.3Number and Location of Gas Stations204.4Underlying Zoning245.0Conclusion276.0References29Appendix A—Economic Modeling Assumptions and Explanations35A.1Assumptions35A.2Results36A.3Technical Discussion—Variables37A.42012 Cross-Sectional Analysis Variable Discussion42Appendix B—Summary of County Zoning Regulations44B.1Howard County44B.2Anne Arundel County44B.3Baltimore City45B.4Baltimore County45B.5Carroll County46B.6Frederick County46B.7Harford County46B.8Montgomery County46Appendix C—County Zoning Regulations Pertaining to Gas Stations48C.1Anne Arundel County48C.2Baltimore City53C.3Baltimore County56C.4Carroll County61C.5Frederick County63C.6Harford County65C.7Howard County67C.8Montgomery County73Appendix D—List of Relevant News and Cases79D.1Anne Arundel County79D.2Baltimore City79D.3Baltimore County79D.4Carroll County79D.5Frederick County80D.6Harford County80D.7Howard County80D.8Montgomery County81
Table of Figures
Figure 1: Acronyms and Abbreviations6
Figure 2: Average Annual Retail Prices, 201210
Figure 3: Annual Analysis Regression on Margin11
Figure 4: Margin Differences by County12
Figure 5: Regression Analysis for All Counties, 201214
Figure 6: Gas Station Density in Howard County, 201221
Figure 7: Howard County Gas Station Locations22
Figure 8: Regulation of Gas Stations by Jurisdiction, 201225
Figure 9: Regional Gas Station Locations26
Figure 10: Annual Analysis Regression on Margin for 2002–201236
Figure 11: Annual Analysis Regression on Margin for 2002–2012 Statistical Parameters36
Figure 12: Cross-Sectional Analysis Regression on Margin for 201237
Figure 13: Cross-Sectional Regression on Margin for 2012 Statistical Parameters37
Figure 14: Correlation Matrix for Retail and Rack Prices37
Figure 15: Correlation Matrix for Margin and Rack Price38
Figure 16: Example of Lagged Rack Variable40
Figure 17: Gas Stations per Capita (1,000) by County, 2007–201141
Figure 18: Zones permitting Gasoline Service Stations in Anne Arundel County48
Figure 19: §18-3-104 Parking Space Requirements50
Figure 20: Allowed Gasoline Service Station Signage in Anne Arundel County51
Figure 21: Bulk Regulations—Minimum and Maximum Lot and Use Area Requirements52
Figure 22: Bulk Regulations—Minimum Setbacks and Floor Area Ratios (FAR)52
Figure 23: Zones permitting Gasoline Service Stations in Baltimore City53
Figure 24: §10-405 Parking Space Requirements for Gasoline Service Stations54
Figure 25: Allowed Gasoline Service Station Signage in Baltimore City55
Figure 26: Bulk Regulations—Minimum Setbacks and Floor Area Ratios (FAR)55
Figure 27: Zones Permitting Fuel Service Stations in Baltimore County56
Figure 28: §405.4 Parking Space Requirements for Fuel Service Stations61
Figure 29: Bulk Regulations—Minimum Setbacks and Floor Area Ratios (FAR)61
Figure 30: Zones permitting Fuel Stations in Carroll County61
Figure 31: §103-24 Parking Space Requirements for Fuel Stations62
Figure 32: §223-138 Allowed Fuel Station Signage in Carroll County62
Figure 33: Lot and Use Size Regulations for Fuel Stations by Zone62
Figure 34: Zones permitting Automobile Filling and Service Stations in Frederick County63
Figure 35: (§1-19-6.100) Lot and Use Size Regulations for Automobile Filling and Service Stations by Zone64
Figure 36: (§1-19-6.220) Parking Space Requirements for Automobile Filling and Service Stations64
Figure 37: (§1-19-6.320) Allowed Automobile Filling and Service Station Signage in Frederick County64
Figure 38: Zones Permitting Gas Stations in Harford County65
Figure 39: Lot and Use Size Regulations for Gasoline Stations by Zone67
Figure 40: Bulk Regulations—Minimum Setbacks and Floor Area Ratios (FAR)67
Figure 41: Zones Permitting Gasoline Service Stations in Howard County68
Figure 42: §133.D.04 Parking Space Requirements for Gasoline Stations71
Figure 43: Allowed Gasoline Service Station Signage in Howard County71
Figure 44: Lot and Use Size Regulations for Gasoline Stations by Zone72
Figure 45: Bulk Regulations—Minimum Setbacks and Floor Area Ratios (FAR)72
Figure 46: Zones permitting Automobile Filling Stations in Montgomery County73
Figure 47: (§59-E-3.7) Parking Space Requirements for Automobile Filling Stations78
Figure 48: Allowed Gasoline Service Station Signage in Montgomery County78
Acronyms and Abbreviations
A listing of economic and fiscal impact terminology frequently used throughout this report can be found in Figure 1.
Figure 1: Acronyms and Abbreviations
Term
Definition
CUP
Percentage of jurisdiction that is zoned to allow gas stations under a “conditional use permit.”
CUP_Zone
Dummy variable. “1” if the gas station is located within this zone, “0” otherwise.
Distance Road
Distance of a particular station to a major highway such as I-695 or State Highway in miles.
Distance Station
Distance of a particular station to its nearest competitor in miles.
Dummy Variables
Variables that are characteristic identifiers of the data, but are assigned the value 0 or 1 depending on the characteristic. For example, one may use “sex” in a regression and set 1 for “female” and 0 for “male.” If the respondent is female then the dummy variable will be 1, and and results will be the difference between male and females for the dependent variable.
Household Income per Capita
The average reported annual income for a region defined by the U.S. Census estimates divided by the recorded population estimates for the region as defined by the U.S. Census.
Margin
Average recorded margins for a given region or station over a period of time (annual or weekly). Margins reflect the difference between retail and rack, less taxes and freight.
Nonconform
Dummy variable. “1” if the gas station is located in a nonconforming zone, and “0” otherwise.
Permit
Percentage of jurisdiction that is fully zoned for gas stations.
Population
The average annual recorded or estimated population for a given region as determined by the U.S. Census.
Rack Change
The difference in rack prices between the current period and the previous period.
Rack (Wholesale) Prices
Average rack prices recorded over a period of time (annual or weekly) for a given region or station.
Retail prices
Average retail prices recorded over period of time (annual or weekly) for a given region or station.
Stations per Capita
The total number of stations within a given region during a period of time divided by the total population estimate as recorded by the U.S. Census.
Tax
The average excise tax reported for that region during that time.
Unbranded Percent
The percentage of unbranded stations within a given region during a period of time (annually) divided by the number to total stations during that same period.
Source: RESI
1.0Executive Summary
The Regional Economic Studies Institute (RESI) of Towson University has been tasked with analyzing the retail gasoline market in Howard County, Maryland. RESI compared Howard County’s retail gasoline market to those in Baltimore City and Anne Arundel, Baltimore, Carroll, Harford, Frederick, and Montgomery Counties. RESI examined the structure of gasoline prices in Howard County through two separate and complementary analyses—an economic analysis and a land planning analysis—to fully answer the question, “Are gas prices in Howard County statistically different from gas prices in the surrounding jurisdictions? If so, why?”
1.1Economic Analysis Findings
Through regression analysis, RESI found the following factors determine, within a 95 percent significance level, annual changes in gas price markups:
· Percent of unbranded gasoline stations within a region;
· Station density per capita;
· Changes in rack (wholesale) prices;
· Population changes; and
· Current gasoline taxes.
The analysis focuses on data collected between 2002 and 2012 for eight jurisdictions: Baltimore City and Anne Arundel, Baltimore, Carroll, Frederick, Harford, Howard, and Montgomery Counties. Across counties, RESI found that, with the exception of Montgomery County, Howard County’s unleaded gasoline retail markup was the highest of the remaining seven jurisdictions, all other things being equal. Montgomery County, historically, had the highest recorded retail price for gasoline across all jurisdictions observed in the study.
Using 2012 weekly recorded data on gas prices by stations across eight jurisdictions, RESI ran a cross-sectional regression analysis and determined the following additional factors were statistically significant at the 95 percent confidence level in the short term:
· Regional population;
· Household income per capita;
· Distance from station to a major road;
· Distance to nearest competitor;
· Percentage of permitted gas stations zones;
· Percentage of permitted under conditional use permits;
· Difference between station’s and competitor’s prices;
· Zoning (conditional use zone, prohibited zone, or permitted zone); and,
· Being located within Columbia, Maryland.
Across counties, RESI determine that retailers located within Columbia, Maryland, had higher margins than those not within Columbia, Maryland. Overall, margins for those located within permitted zones were statistically less than those located within conditional use permit zones and prohibited zones. RESI concluded that the margin variation across gas stations was related to the direct and indirect zoning regulations across jurisdictions.
1.2Land Planning Analysis Findings
Through qualitative and spatial analysis, RESI used gas station locations and zoning to create inputs for the economic analysis to analyze their influence on gas prices in Howard County.
The findings for Howard County are consistent with economic theory regarding barriers to entry and competitiveness. Barriers to entry can include permit costs, length of time spent in the development review process, economic impact analysis fees, and new infrastructure construction. Zoning is a major barrier to entry for gas stations in particular. Gas stations require a conditional use permit in most zoning districts throughout the study area.
In particular, RESI examined the way gas stations are regulated in Columbia, a planned, unincorporated community within Howard County. Columbia is laid out in a series of villages, each of which include a “center” containing services and amenities that are often not visible from main roads. Columbia’s development, which is almost wholly contained in the New Town District, requires approval of a Final Development Plan. Gas stations are typically included as part of the proposed development, which must meet the criteria specified in the Final Development Plan, the primary source of zoning requirements for any specific property in the New Town District. The remaining land in Howard County uses standard zoning districts where gas stations are either prohibited are subject to approval of a conditional use permit.
1.3Conclusion
Historically, unleaded gasoline retail markup tends to be higher in both Howard County and Montgomery County, all other things being equal. RESI found the following:
· As the percentage of unbranded gas stations increases in a jurisdiction, the unleaded gasoline retail markup tends to decline over time;
· Gas stations located within conditional use permit zones and nonconforming zones will have margins higher than those in permitted zoning regions; and,
· Gas stations located in Columbia, Maryland, will have higher margins than those within the study region.
Overall, margins are affected greatly though land planning. The percentage of the region zoned for gas stations without barriers and with minimal barriers significantly impacts the retailer’s margins. The greater the barriers to enter the market in a given region, the higher the margins become for retailers since competition will be lessened or condensed. The further away from their competitors or the lack of competitors will also drive margins upwards for retailers. Through economic analysis, RESI found that gas stations in Columbia maintain a higher margin than other places in Howard County and elsewhere in the study region. Intentional planning and zoning decisions in Columbia’s New Town District may have an influence on the number and location of gas stations within its borders. If the zoning regulations are left unchanged, the trend may continue over time.
2.0Introduction
The Regional Economic Studies Institute (RESI) of Towson University has been tasked with analyzing the retail gasoline market in Howard County, Maryland. As part of the analysis, RESI compared Howard County’s retail gasoline market to those in neighboring jurisdictions (Baltimore City and Anne Arundel, Baltimore, Carroll, Harford, Frederick, and Montgomery Counties).
RESI examined the structure of gasoline prices in Howard County through two separate and complementary analyses to fully answer the question, “Are gas prices in Howard County statistically different from gas prices in the surrounding jurisdictions? If so, why?” An economic analysis was conducted to determine whether or not branding, competition, and other economic factors have affected gas prices in each jurisdiction. An analysis of land planning, or zoning regulations, was conducted to determine whether or not location and zoning impact gas prices in each jurisdiction.
RESI employed regression analysis to estimate a gas price margin model for the region. Using two separate Ordinary Least Squares regressions, RESI reviewed (1) the time-series effect on the regional gas stations and sustainability of the industry and (2) the cross-sectional effect across jurisdictions and stations to determine what factors contribute to the price differentiation. RESI ran the regressions and reviewed coefficient estimates to determine the size of the impact and if the impact has a positive or negative effect on price. Data regarding the retail prices, location, and wholesale markup were used in estimating the effect, if any, of factors such as branded or unbranded stations.
Annual data allowed RESI to create a variable, “gas stations per capita,” that took the number of establishments and divided it by the total population within that region during that given period. This allowed RESI on an annual basis to determine the potential competition within a region. Results of this regression analysis were reported on the 95 percent confidence level, indicating the most significant factors that impact retail gas prices.
RESI also investigated the influence the factors of location and zoning have on gas prices in Howard County. RESI conducted a spatial analysis of the location of gas stations, closest competitors, the distance to major highways, percentage of jurisdictions zoned outright allowing gas stations, and percentage of jurisdictions allowing gas stations through a conditional use permit.
RESI performed a zoning diagnostic for the eight jurisdictions, enabling ease of comparison across jurisdictions in how they regulate the siting and locating of gas stations within their borders. The findings from the qualitative zoning analysis were then used in the economic analysis to determine their influence on gas prices.
As part of the zoning analysis, RESI reviewed specific cases of the regulation of gas stations, primarily in Howard County. RESI reviewed documents including technical staff reports, conditional use permit and rezoning applications, and newspaper articles. The findings were further informed by discussions with staff at Howard County. Several cases are cited in the text. However, Appendix B contains a comprehensive list of cases reviewed.
In addition to the economic and land planning analyses, RESI has reviewed and included examples of comparative research on factors that lead to changes in retail gasoline prices as a basis of comparison.
3.0Economic Analysis
Traditional economic analysis would hint to researchers to investigate key elements such as gas prices, household income, expectations, and market share or competition to determine the potential gas price within an area. RESI took a traditional approach running an ordinary least squares model based on preconceived supply and demand factors that may influence gas prices. The results and statistical significance of each variable have been recorded in the following subsections.
3.1Annual Analysis
Retail prices of gas over eight counties within Maryland provided RESI with a potential dependent variable for analysis. RESI reviewed the prices across the eight counties for 2012 to determine if any difference existed through simple observation. Reviewing over 1,000 gas stations across eight counties, RESI determined an average annual retail price for each county during 2012 in Figure 2.
Figure 2: Average Annual Retail Prices, 2012
County
Average Retail Price
Anne Arundel
$3.56
Baltimore
$3.57
Baltimore City
$3.58
Carroll
$3.56
Frederick
$3.63
Harford
$3.55
Howard
$3.65
Montgomery
$3.70
Sources: RESI, OPIS
Reviewing Figure 2, RESI was able to determine that some price difference existed across counties, but further analysis was necessary to determine if this was a one-time event or an annual trend. RESI performed an econometric analysis of the change in prices over time across the eight counties on an annual basis first.
To determine the factors that may impact the price, RESI researched previous studies concerning the differentiation on gas prices across regions and brands. Studies stated that increases in input costs such as labor, wholesale costs, and taxes at times may add to the rapid rise in retail gasoline prices; however, as the costs decrease, gas prices will be slow to decrease in response.[footnoteRef:1] [1: Chesnes, “Asymmetric Pass-Through in U.S. Gasoline Prices,” 4.]
Other statistically significant variables included household income as a proxy for the wealth of a county, population change, stations per capita, and percentage of unbranded stations per capita.
To conduct the analysis, data was gathered for a period from 2002 to 2012. Data presented over time is termed as “time series” data in economics. The data collected spanned across Baltimore City and Anne Arundel, Baltimore, Carroll, Frederick, Harford, Howard, and Montgomery Counties. The review of more than a single jurisdiction is termed “cross-section,” meaning that the data is reported across various jurisdictions. Using this data, RESI ran the following time series cross-section panel data model:
Where represents the cross-section identifier, in this case the jursidiction, and represents the year. Variable descriptions and discussion are available in Appendices A.1 and A.3.
Figure 3: Annual Analysis Regression on Margin
Variable
Impact Multiplier
t-statistic
Statically Significant at 5% (Y/N)
Intercept
-3.011
-4.575
Yes
Household Income Change
0.144
0.330
No
Percent Unbranded
-0.177
-2.264
Yes
Stations per 1,000 Residents
0.466
3.381
Yes
Population Change
-10.023
-3.066
Yes
Dummy (Anne Arundel)
-0.393
-6.420
Yes
Dummy (Baltimore)
-0.372
-8.546
Yes
Dummy (Baltimore City)
-0.303
-5.278
Yes
Dummy (Carroll)
-0.385
-6.199
Yes
Dummy (Frederick)
-0.116
-1.421
No
Dummy (Harford)
-0.676
-9.727
Yes
Dummy (Montgomery)
0.406
5.586
Yes
Rack Change
0.545
8.500
Yes
Tax Current Period
0.584
1.649
No
Tax Lagged-One Period
-2.472
-4.388
Yes
Sources: RESI, OPIS, U.S. Census
Figure 3 highlights the annual analysis on the primary economic factors for the annual regression. The model predicts approximately 87 percent of variation based on non-land planning variables. At the 95 percent confidence level, RESI’s regression shows that there is some difference on the retailer’s margins for specific variables annually. When reading the table above for the variables such as “Percent Unbranded,” the results are the following: for every one percent increase in the percent of unbranded gas stations in a region, there is a 0.18 percent decrease in margins for a retailer.
The variations of the locations in this model are represented through dummy variables. For example, if a retailer is located in Anne Arundel County, then the dummy variable for Anne Arundel was equal to one. For all other dummy variables, the value was equal to zero. If all the dummy variables equal zero, then the retailer was located in Howard County. More information on interpreting dummy variables is located in Appendix A.3.
Since reading the impact of dummy variables is not as straightforward as the interpretation of other numerical figures, Figure 4 displays all the margin differences for each county dummy below in reference to their difference from Howard County. One would read Figure 4 as “retailers in X county would see margins X percent less (greater) than Howard County.”
Figure 4: Margin Differences by County
County
Impact Multiplier
Margin Difference from Howard
Anne Arundel
-0.393
-32.5%
Baltimore
-0.372
-31.1%
Baltimore City
-0.303
-26.1%
Carroll
-0.385
-32.0%
Frederick
-0.116
-11.0%
Harford
-0.676
-49.1%
Montgomery
0.406
50.1%
Sources: RESI, OPIS
In Figure 4, one would interpret the dummy variable for Anne Arundel as “retailer margins in Anne Arundel County are 32.5 percent less than those in Howard County” after using the conversion formula. The percentage differences are presented in Figure 4 for convenience.
The only jurisdiction where gasoline retailers recorded receiving higher margins on average compared to Howard County is Montgomery County. The estimates are consistent with what RESI saw in the data reported by the Oil Price Information Service (OPIS). The regression results show that the model could explain nearly 87 percent of the change in margins for a retailer. In the following section, RESI completed a more thorough analysis of a single year for 52 weeks across the eight jursidictions and added land planning components.
3.22012 Cross-Sectional Analysis
To determine the impact of the land planning characteristics on gas prices, RESI decided to look more closely at a one-year period across gas stations and jurisdictions. For this analysis, RESI created new variables to account for specific data within 2012. New variables introduced included the following:
· Percentage of the jurisdiction that permits gas stations outright;
· Percentage of the region that permits gas stations under a conditional use permit;
· Distance from a major road;
· Distance from another station;
· If the gas station was located within a conditional use permit zone;
· If the gas station was located within a nonconforming zone;
· The difference between a gas station and its closest competitor; and,
· If the gas station was located within Columbia, Maryland.
Unlike the other jurisdictions RESI examined, Howard County does not contain any incorporated areas. As a result, RESI omitted the gas stations in incorporated areas from the analysis.
Reviewing the data, RESI determined that the gas prices were highest within Montgomery County, followed by Howard County. Further review of Howard County revealed that Columbia, Maryland, had the highest gas prices within Howard County. The gas prices for stations in Columbia, Maryland, for 2012 at times equaled or exceeded those of Montgomery County retailers’ prices in some instances.
The station density variable, stations per 1,000 residents, was dropped from this analysis due to multicollinearity issues between the variable and “distance to next station.”[footnoteRef:2] [2: Multicollinearity is a property whereby two or more independent variables within a regression can be linearly predicted from one another. The presence of multicollinearity in a model would not negate the results of the model overall, but would lessen the degree of validity regarding the impact of an individual variable.]
Due to the limitation of annual data for the land planning piece, RESI decided to use observed weekly data for 2012 across the eight regions and averaged the pricing data over the 52 weeks. A total of over 900 gas stations were observed in the model for 2012.[footnoteRef:3] The model presented in Section 3.1 transformed into the following: [3: Gas stations in incorporated cities and those that had only reported for less than two weeks were dropped from the analysis.]
For more information or a reference to the definition of the variables above, please refer to beginning of the report for the acronyms and abbreviations list. RESI performed the basic analysis for all counties and all brands first. The results are reported in Figure 5.
Figure 5: Regression Analysis for All Counties, 2012
Variable
Impact Multiplier
t-statistic
Statically Significant at 5% (Y/N)
Intercept
3.386
6.814
Yes
Population in thousands
0.000
4.711
Yes
Household income per 1,000 residents
0.089
1.461
No
Difference in competitor prices
0.434
10.650
Yes
Percentage of Unbranded
0.212
3.577
Yes
Percent Permitted
-0.439
-8.598
Yes
Percent CUP
-0.097
-7.128
Yes
Distance to Major Road
-0.005
-5.040
Yes
Distance to Next Station
0.015
5.017
Yes
Rack Prices
-1.103
-6.580
Yes
Dummy—located within a conditional use permit zone
0.015
2.214
Yes
Dummy—located within a nonconforming zone
0.012
1.667
No
Dummy—located within Columbia, MD
0.107
9.358
Yes
Sources: RESI, OPIS, U.S. Census
Figure 5 shows the impact multipliers for given variables against the log variable “margin.” In this linear-linear model, one can read the above impacts as follows: “For every additional mile away from a major highway a gas station is, then the margins decrease by approximately $0.01.” Rack price is a little harder to determine, since rack prices hardly ever change by one dollar in the short term. Instead, one is more likely to see a 10 cent increase in the rack price of unleaded gasoline during a short period. Given this, one would say “If rack prices increase by $0.10, then gas retailer’s margins will decrease by approximately $0.11.”[footnoteRef:4] [4: Here, RESI multiplied the X variable ($0.10) by the impact multiplier presented in the output above (-1.103423) to get the approximate decrease to retailer’s margins. ]
To read the dummy variable “Dummy—located within Columbia, MD” above, the reader would interpret it as follows:
“If a retailer is located within Columbia, their margins will be approximately $0.11 higher than other retailers within the eight jurisdictions observed in this model.”
In interpreting the variable “Dummy—located within a conditional use permit zone” and “Dummy—located within a nonconforming zone,” the following can be stated:
“If a retailer is located within a conditional use permit zone, margins on average margins will be approximately $0.01 higher than those located within permitted zones.”
In conclusion, the variables included in both the 2012 cross-sectional and annual analyses are consistent across the two analyses, with the exception of percent unbranded.
4.0Land Planning Analysis
The regulation of gas stations in Maryland dates back to the mid-1960s when Spiro T. Agnew, as county executive, declared a moratorium on construction of new stations due to their proliferation. He directed the planning board to establish criteria for service stations. Although the moratorium was declared unconstitutional, the planning board developed its recommendations, which were made into law. The law essentially called for the establishment of districts where new gas stations could be located versus those where a public hearing was required.[footnoteRef:5] [5: Kane, “Maryland County Has Strict Law on New Gas Stations, Appearance,” 3.]
Almost fifty years later, gas stations face the same level of regulatory scrutiny in Maryland counties, where both the siting and operation of gas stations are regulated. As RESI will illustrate, Howard County, with its rapidly growing population, has several examples of gas station approval decisions and the accompanying public review process, indicating the ongoing control of their use.
4.1Zoning Considerations
Put simply by William Fulton—author, urban planner, and politician—“planning is the process by which our society decides what gets built where.” [footnoteRef:6] Zoning is one of the regulatory tools afforded to local government to regulate the use of land within its boundaries. Other examples include subdivision regulations and design review guidelines. [6: Fulton and Shigley. Guide to California Planning, 3rd ed., 7.]
The constitutionality of zoning was granted by the 1926 Supreme Court case Euclid v. Ambler, resulting in what is now known as Euclidean zoning, or “the division of all of the municipality’s land into use districts.”[footnoteRef:7] Zoning, as an exercise of the local police power, regulates the location of businesses and uses of land, which is justified on the grounds that it provides for the protection of public health, safety, and welfare. As a result, zoning’s effects on development patterns and property values are well documented. Academic studies and regulatory findings make up a large portion of the body of knowledge on how zoning can or has impacted the location of specific land uses, including gas stations. [7: Ibid, 54.]
Academic Studies
With rapidly fluctuating gas prices, academic research has centered on the influence of local and regional market conditions on the price of gas at the pump. A study authored by Barron, Taylor, and Umbeck in 2004 provides predictions of the effect of the number of competing gas stations on market prices and the variation in price across sellers of retail gasoline.[footnoteRef:8] The study suggests that price variation can result from “standard monopolistic competition,” or from consumers lacking knowledge on the location of the lowest price.[footnoteRef:9] Taking it one step further, and compounding international factors, the authors found that zoning laws allow some cities to build more stations per square mile, and with fewer restrictions, than other cities. [8: Barron, Taylor, and Umbeck, “Number of Sellers, Average Prices, and Price Dispersion,” 1041.] [9: Ibid, 1042.]
About ten years ago, Barron, Taylor, and Umbeck, conducted a study of gas stations in three California cities over a three-month period to determine why retail gas prices varied geographically. More gas stations and higher prices were witnessed in Los Angeles, which possesses more relaxed zoning laws, which in turn decreased sales volume more significantly because consumers were able to drive shorter distances to buy competitor’s gasoline. The authors found the inverse to be true in San Francisco, where more restrictive zoning resulted in fewer stations. Consumer behavior was less price elastic, thus less price sensitive, with consumers being more willing to purchase gas at higher prices than drive greater distances between stations. Not too surprisingly, gas station owners in Los Angeles were less likely to increase their prices than station owners in San Francisco.[footnoteRef:10] A study released by the U.S. Government Accountability Office in 2005 also found that higher per capita incomes, hence less price sensitivity, in San Francisco as compared with Los Angeles may have been a factor in explaining why gasoline prices were higher in San Francisco.[footnoteRef:11] [10: Nelson, “Fuel for the Fire.”] [11: US Government Accountability Office, “Motor Fuels: Understanding the Factors,” 37–47.]
Regulatory Factors
Because of the traffic and safety concerns generated by gas stations, they are heavily regulated, and consequently subjected to additional oversight. The two most common methods for approving a gas station in areas where they are not permitted outright are rezoning and conditional use permits (CUP). A rezoning takes place when an amendment is made to the zoning map to reclassify a property or parcel within a district and often involves a public hearing conducted by a regulatory body such as a zoning board. Decisions of zoning boards to grant or deny applications constitute quasi-judicial decisions of municipal administrative agencies. A CUP can provide flexibility within a zoning ordinance, allowing a city or county to consider special uses that may be essential or desirable. Conversely, it can also enable a municipality to regulate certain uses that could have detrimental effects on the community. Conditional use permits allow for specific and public considerations of each business development proposing to sell gas.
In Howard County, the CUP process entails applying to the Planning and Zoning Department, whereby the agency comments and provides a staff report with recommendations. The case is then heard by the Hearing Examiner. If no one appeals, the case is considered final. If appealed, it goes to the Board of Appeals, which consists of five members.[footnoteRef:12] [12: Robin Wegner, Assistant to the Hearing Examiner of Howard County, personal communication, April 23, 2013.]
Alternatively, gas stations and other uses can be regulated through Special Exception, which allows for additional considerations and regulations surrounding their use (residential, commercial, industrial, etc.), bulk (how much of the parcel the use takes up), and performance (what the impact of the use has on the property and surrounding properties, such as emitting smoke, noise, or odor). Some communities have even enacted the required spacing of gasoline stations along busy highways in an attempt to reduce traffic accidents. For example, the City of Elk Grove, California, south of Sacramento, permits a maximum of two service stations at any single intersection; stations must also be separated by a minimum of 500 feet.[footnoteRef:13] Muskego, Wisconsin, maintains a 1,500-foot separation requirement unless a special exception is granted by the Plan Commission.[footnoteRef:14] [13: City of Elk Grove, Title 23 Zoning Code, Chapter 23.72.] [14: City of Muskego, Chapter 17 Zoning, 14.03.06 Gasoline Service Stations, 246.]
Providing a source of relief from evolving zoning ordinances, gas stations can continue to operate as nonconforming uses in zoning districts where they are no longer permitted.[footnoteRef:15] When new zoning is established, the ordinance cannot eliminate structures already in existence. For instance, if a district is zoned residential, a preexisting gas station becomes a nonconforming use. This is the case throughout the study area, particularly in residential districts, and zones dedicated to accommodate population and economic growth. As long as the property containing nonconforming use status does not change, through expansion or a change in the nature of business, its status is protected. [15: Fulton and Shigley. Guide to California Planning, 3rd ed., 136–37.]
Other regulatory and market considerations may influence the price of gas. Costs and barriers to entry such as taxes, labor, and environmental compliance play into business owners’ decision making and are assumed to be fixed costs for the purpose of analysis. Many jurisdictions require gas station owners to acquire licenses to conduct business, including licenses for the operation of retail sales, cigarette sales, liquor sales, underground storage tanks, and fuel hoses, as well as Environmental Protection Agency licenses and public health permits, all of which may influence the price of gas.
Market Implications
A Canadian study by Eckert and West in 2005 documented that property values may influence the likelihood of a gas station to succeed, as well as influence gas prices, in a location on the urban periphery versus the urban core.[footnoteRef:16] Supporting those findings, a recent article in the Washington Post discusses the influence of rising property values on gas stations in Montgomery County.[footnoteRef:17] Demographic trends, fuel-efficient vehicles, and the economic downturn also play a part in the national decline in gas stations. The long-term implications for automobile owners living and working in the area are apparent. [16: Eckert and West, “Rationalization of Retail Gasoline Station Networks in Canada,” 19–20.] [17: Shaver, “Gas Stations are vanishing from Washington’s inner suburbs, 1–2.]
When gas profits can no longer keep up with rising real estate values, gas stations owners have sold their business in cities and inner suburbs near Washington, D.C., allowing them to be redeveloped for more profitable uses. Following this trend, at least two gas stations in Bethesda have stopped selling gas or closed in recent years with plans to be replaced with a high-rise apartment building and a new bank.[footnoteRef:18] On Wisconsin Avenue, Eastham’s Service Station’s lease expired in 2012 and a developer who purchased the land plans to replace the station with a large apartment complex. An owner of a BP station at the corner of Wisconsin and Highland Avenue recently that was losing money signed a twenty-year lease with TD Bank to build on the site.[footnoteRef:19] [18: Ibid.] [19: Ibid.]
Community Character and the Planning Process
Numerous jurisdictions cite traffic, safety, and community character as considerations in denying or approving gas stations in specific areas. Policies surrounding land use and community character are found in the municipality’s Comprehensive Plan. In Maryland, State law requires consistency between comprehensive plan recommendations and zoning. Howard County’s most recent plan update, PlanHoward 2030, contains policy recommendations for the treatment of gas stations. For example, Policy 5.4 deals with the Route 1 corridor revitalization strategy and implementing actions to reduce the strip commercial development by directing automobile-oriented uses away from the main road and residential areas.
Policy 10.4 involves the County’s responsibility to respond to changing market conditions and update conditional use regulations accordingly, and reads as follows:
The regulations should reflect current best practices and policies to minimize the impact of development on the environment. For example, the regulations regarding gasoline stations needs to reflect changes in the gasoline industry in the last decade and the challenges of blight and environmental mitigation required for redevelopment of abandoned gas stations.[footnoteRef:20] [20: Howard County Department of Planning and Zoning, PlanHoward 2030, 160–61. ]
Howard County has a history of public involvement in the regulation of new gas stations within its borders. In December 2012, the Howard County Independent Business Association, Inc., filed a Zoning Regulation Amendment Request to amend Section 131.G and 131.N.25 of the County’s Zoning Regulations. The petitioner requested that the conditional use regulations for gas stations applicable in the B-2, SC, M-1, and PEC zones be amended “to reflect current land use policies, to incorporate reasonable regulations that reflect changes in the gasoline industry, and to establish reasonable standards to address the environmental impact and potential blight.”[footnoteRef:21] [21: Howard County Department of Planning and Zoning, Zoning Regulation Amendment Request Form [Howard County Independent Business Association], 2. ]
The summarized requested changes by the petitioner are as follows:
· Extend applicability of conditional use regulations for gas stations into the New Town District,
· Require a needs analysis to demonstrate a public need for proposed stations,
· Impose distance requirements between stations to mitigate the environmental impacts caused by a concentration of gas stations,
· Impose tank size limits to decrease the risks of environmental contamination,
· Impose stacking requirements to protect the safety of consumers, and
· Establish the burden of proof for an applicant in a conditional use hearing.[footnoteRef:22] [22: Ibid.]
The petitioner justifies the requested changes on the basis that they are necessary to mitigate the potential harmful impact that a concentration of gas stations can have on the environment, and are consistent with Policy 10.4 of PlanHoward 2030, which stipulates updating the conditional use regulations for gas stations. Although whether the petitioner will be successful on the whole or in part has not yet been determined, the application proposes additional regulatory oversight of gas stations in Howard County.
4.2Examples of Gas Station Approvals and Denials in Howard County
Gas stations require a CUP in the majority of permitted districts in Howard County. As a result, there are many documented cases of gas station approvals and denials in Howard County in the past ten years. According to an analysis conducted by Tony Redman, AICP, testifying against the development of a gas station at Waverly Woods Village, there have been thirteen sites subject to Board of Appeals cases for conditional use applications for gas stations in Howard County during this period.
The most notable cases include the multiyear process to develop a gas station in Waverly Woods Village, a master planned community in northwestern Howard County. Beginning in 2007, Convenience Retailing, LLC, sought a rezoning of a one-acre property from B-1 to Planned Employment Center (PEC) and approval of a documented site plan for a gas station with a convenience store and car wash at the intersection of Warwick Way and Birmingham Way. The Planning Board unanimously recommended that the parcel adjacent to Waverly Woods Village Center be rezoned; however, the Zoning Board rejected the plan after appeals in 2007 and 2008, citing that there had not been a substantial change in the character of the neighborhood or a mistake in the original zoning.[footnoteRef:23] [23: Howard County Zoning Board, Decision and Order, Case No. 1067M [Convenience Retailing, LLC], 11–13.]
In 2009, after the unsuccessful attempt to rezone for the siting of the gas station in the Village, Convenience Retailing, LLC, petitioned for the development of a gas station in combination with a convenience store and car wash in the PEC zoning in the eastern portion of the Waverly Woods Village Center.[footnoteRef:24] Opponents of the proposal cited traffic and safety concerns in the residential neighborhood that may result from the development of a gas station.[footnoteRef:25] Through a minority decision, the petitioner was granted a CUP, with the dissent expressing traffic concerns. The gas station was constructed in November 2010 at 10781 Birmingham Way.[footnoteRef:26] [24: Howard County Board of Appeals, Decision and Order Case No. BA-08-049C [Convenience Retailing, LLC], 1.] [25: Carson, “Zoning Ok’d For Gas Station in Woodstock.”] [26: Howard County Board of Appeals, Decision and Order Case No. BA-08-049C [Convenience Retailing, LLC], 23.]
Another gas station in Waverly Woods Village proposed by the Waverly Woods Development Corporation at the corner of Marriottsville Road and Barnsley Way also met opposition. In November 2010, the Howard County Board of Appeals Hearing Examiner denied the CUP petition. A Department of Planning and Zoning technical staff report noted that a gas station and convenience store already exist nearby at the Waverly Woods Village Center, “so a second gasoline station and convenience store is unnecessary for the Waverly Woods neighborhood, and would likely mainly serve pass-through traffic, including traffic using I-70.”[footnoteRef:27] The County’s technical report also indicated potential problems with the proposed gas station’s access points.[footnoteRef:28] [27: Howard County Board of Appeals Hearing Examiner, Decision and Order Case No. BA10-023 [Waverly Woods Development Corporation], 9.] [28: Ibid.]
Following a series of public hearings by the Howard County Board of Appeals, Weis Markets successfully petitioned for a conditional use for a gasoline station in a B-2 Zoning District on the site of an existing Weis Market on a 12.24-acre parcel in April 2008. Two individuals who testified against the station claimed that the additional gas station would be detrimental to existing gasoline stations along Route 1 in Laurel, referencing Howard County’s General Plan, which recommends that gas stations and auto-oriented retail uses be directed away from Route 1 and from residential areas.
Currently, Giant of Maryland, LLC, is in the middle of an appeals process for a conditional use for a gas station in a B-2 zone located in the northeastern portion of the property in the Columbia Palace shopping center, which is anchored by a Giant Food Store, located at 8805 Centre Park Drive. The Department of Planning and Zoning recommended approval of the CUP request in 2012. The case was heard by the Hearing Examiner and appealed to the Board. According to the Assistant to the Hearing Examiner and Board of Appeals, there have been over 10 hearings on this particular case over the past two years.[footnoteRef:29] [29: Robin Wegner, Assistant to the Hearing Examiner of Howard County, personal communication, April 23, 2013.]
The previously described cases are exemplary of the trend in decisions of the regulation of gas stations in rapidly growing areas of Howard County. Although the above examples are from Howard County, gas stations are regulated through a public process throughout the state. A list of links to news coverage of notable zoning and conditional use approval decisions within the region and their associated findings is included in Appendix C.
4.3Number and Location of Gas Stations
As part of the land planning analysis, RESI performed a zoning diagnostic for each of the eight study area jurisdictions. Individual zoning codes were reviewed for relevant regulations involving the siting and location of gas stations. What follows is a summary of the way Howard County regulates gas stations within its borders. The relevant sections from each municipality’s zoning code can be found in Appendix B.
As part of the analysis, RESI examined the number of gas stations per zone in Howard County, both in terms of quantity and area. Howard County’s Zoning Code outlines special considerations for 59 conditional uses, including gas stations, in Section 131.N.25. The full section is included in Appendix B.
Figure 6 below depicts the density, or concentration, of gas stations by district in Howard County. For each district where gas stations are located, the number of gas stations found in that district is listed in the far right column. Gas stations are either permitted outright, conditionally, or through special exception, or exist as nonconforming uses, depending on the zoning in Howard County. Gas stations are most commonly located in zoning districts where they are permitted conditionally or through special exception, with the majority located in General Business (B-2) and New Town/Columbia (NT).
Figure 6: Gas Station Density in Howard County, 2012
Underlying Zoning District
Overlay[footnoteRef:30] [30: Please refer to Appendix B for a description of overlay zones.]
Gas Stations
Permitted Outright
MXD 3 & 6
Mixed Use Development > 75 acres
2
Total
2
Conditional Use or Special Exception
B-2
General Business
TNC
18
M-1
Light Manufacturing District
1
M-2
Heavy Manufacturing District
5
NT*
New Town/Columbia
17
PEC
Planned Employment Center
3
SC
Shopping Center District
0
Total
44
Nonconforming Use
CAC
Corridor Activity Center
CLI
2
CE
Corridor Employment
CLI
4
POR
Planned Office Research
2
R-20
Residential: Single
8
R-A-15
Residential: Apartments
1
R-SC
Residential: Single Cluster
3
RC
Rural Conservation
DEO
5
RSI
Residential: Senior-Institutional
1
Total
26
Total
72
*Gas stations permitted as part of Final Development Plan in New Town Zoning District and are subject to Sections 125 and the requirements in the Downtown Columbia Plan
Sources: Howard County, OPIS, RESI
As shown in Figure 6, Howard County had a total of 72 gas stations in 2012. Of that total, only two stations (3 percent of the total) were located within zones that permit the use of gas stations as a matter of right. Alternatively, over 61 percent of the County’s gas stations are located in districts that only allow gas stations through a conditional use permit or as part of a Final Development Plan in the New Town Zoning District. The remaining 36 percent of gas stations in Howard County are located in residential, rural, and corridor districts as nonconforming uses.
Figure 7, Howard County Gas Station Locations Map, portrays the information in Figure 6 geographically. Gas stations are concentrated in the New Town District in Columbia, northwest of I-95, and along major roadways, including US Routes 1, 29, and 40, in the central and eastern portions of the County. Several are located in and around Ellicott City. Districts where nonconforming gas stations are located are primarily residential and rural areas, which comprise the western and north-central portions of the County.
Figure 7: Howard County Gas Station Locations
Sources: Howard County Planning Office, OPIS, RESI
Howard County’s land use pattern also dictates where gas stations are located. The urban growth boundary creates a division between zones identified for development in the eastern third of the county and those reserved for the preservation of agricultural land in the western two-thirds. Maryland’s Smart Growth initiative requires counties to designate “Priority Funding Areas” (PFAs) that are eligible for future State financial assistance for growth, which correspond with areas to the east of the County’s Suburban-Rural Demarcation Line.
Howard County is also home to Columbia—the second most populous community in Maryland after Baltimore—an unincorporated, planned community comprised of ten self-contained villages.[footnoteRef:31] Each of these villages includes a “center” containing services and amenities, which is often not visible from the main road. The land use pattern in the villages is largely driven by the Downtown Columbia Plan and individual projects are overseen by the development review process. [31: Downtown Columbia Plan: A General Plan Amendment. Howard County, Maryland. Adopted February 1, 2010.]
The New Town (NT) Zoning District was created in 1965 and is the zoning classification for Columbia. In 1971, following shortly after the establishment of Columbia, a researcher interested in examining the influence of social goals on master planned communities found that the New Town zoning was a key element in establishing and guiding development of Columbia.[footnoteRef:32] The remaining land in Howard County uses standard zoning districts such as M-1, M-2, B-1, B-2, R-20, R-12, etc. [32: Brooks, Richard. “Social Planning in Columbia.” 373.]
Gas stations in the New Town Zoning District are subject to requirements in Sections 125 of the Howard County zoning code and must conform to the Downtown Columbia Plan. Development in the New Town zoning designation is subject to approval of a Final Development Plan.[footnoteRef:33] Gas stations are typically included as part of the proposed development, which must meet the criteria specified in the Final Development Plan, the primary source of zoning requirements for any specific property in the New Town District. Approval is at the discretion of the Planning Board, which is made up of five County citizens, and includes approval of a Preliminary Development Plan, a Comprehensive Sketch Plan, a Final Development Plan, and the Site Development Plan. Elsewhere in the county, Planning Board decisions serve as recommendations for the County Council, an elected body that also serves as the Zoning Board, which often makes the final decisions on many zoning and development issues. At each stage of the New Town District development process, public meetings are held. [33: Howard County Department of Planning and Zoning. The New Town District. ]
According to another journal article published in 1967 in the Journal of the American Institute of Planners in (reprinted in Journal of the American Planning Association in 2007), decisions involving the size and location of activity centers based on the overall community structure dictated the size of amenities such as schools, shopping, and entertainment.[footnoteRef:34] Columbia continues to experience the tension between the original goals of a post-industrial society and the forces of the open economic market and competition. However, attempts to revise the zoning requirements for the New Town District in Howard County have been unsuccessful to date. [34: Hoppenfeld, Morton. “A Sketch of the Planning-Building Process for Columbia, Maryland.” 405.]
Future analysis could evaluate the amount of “developable” land remaining for gas stations in Howard County, and whether it has an impact on gas prices. Such an analysis would entail examining zoning, the size of parcels, the assessed value of land and improvements, and the existing land use in order to determine the amount of land suitable for gas stations.
4.4Underlying Zoning
As part of RESI’s land planning analysis, zoning regulations pertaining to gasoline service stations were summarized for each of the eight counties including Howard County. Figure 8 below depicts the regulation of gas stations by jurisdiction, both in terms of number of zones and stations. For the full excerpts from each county’s zoning code, please refer to Appendix B. Figure 9 portrays this information in map form.
Analysis of Gas Prices in Howard County, Maryland
RESI of Towson University
1
81
Figure 8: Regulation of Gas Stations by Jurisdiction, 2012
County
Permitted Outright
%
CUP/SE
%
Nonconforming/ Prohibited
%
Incorporated Areas
%
Total[footnoteRef:35] [35: ]
Zones
Anne Arundel
9
10%
6
23%
19
67%
--
--
34
Baltimore City
0
0%
14
23%
6
77%
--
--
20
Baltimore
4
10%
0
0%
38
90%
--
--
42
Carroll
3
6%
1
2%
7
93%
--
--
11
Frederick
5
31%
0
0%
10
69%
--
--
15
Harford
5
31%
0
0%
11
69%
--
--
16
Howard
2
5%
6
16%
28
79%
--
--
36
Montgomery
6
7%
19
23%
56
69%
--
--
81
Stations
Anne Arundel
8
4%
156
82%
11
6%
16
8%
191
Baltimore City
0
0%
156
100%
0
0%
0
0%
156
Baltimore
222
94%
0
0%
14
6%
0
0%
236
Carroll
26
48%
0
0%
5
9%
23
43%
54
Frederick
30
35%
0
0%
5
6%
50
59%
85
Harford
59
74%
0
0%
21
26%
0
0
80
Howard
2
3%
44
61%
26
36%
0
0
72
Montgomery
12
6%
130
63%
36
17%
29
14%
207
Square Miles
Anne Arundel
3
0.8%
312
74.9%
91
21.9%
10
2.4%
416
Baltimore City
0
0.0%
65
80.3%
19
22.9%
0
0.0%
81
Baltimore
105
17.6%
0
0.0%
493
82.4%
0
0.0%
599
Carroll
6
1.3%
2
0.5%
425
9.6%
16
3.6%
449
Frederick
20
1.8%
0
0.0%
611
92.2%
32
4.8%
663
Harford
8
1.8%
0
0.0%
357
81.1%
75
17.1%
440
Howard
2
1.1%
35
14.1%
215
85.2%
0
0.0%
252
Montgomery
2
0.4%
16
3.2%
424
85.4%
54
11.0%
496
Sources: OPIS, RESI
Figure 9: Regional Gas Station Locations
Sources: County planning offices, OPIS, RESI
As shown in Figure 8, the greatest number of gas stations can be found in Baltimore City, followed by Montgomery County. Howard County has the fewest number of stations following Carroll County. Compared to the population in Harford and Frederick Counties, which are more rural in nature, Howard County has relatively few gas stations per capita. Gas stations per capita are represented in Figure 15, Gas Stations per Capita (1,000) by County, 2007–2011, in Appendix A.3. It is also apparent from the map that gas stations are primarily located near population centers and major roadways.
5.0Conclusion
RESI reviewed gas price data for gas stations across eight jurisdictions between 2002 and 2012. Using a time-series analysis, RESI determined that across counties over time there was a statistical difference between Howard County and the other jurisdictions within the model. Montgomery County was the only jurisdiction to exhibit margins higher than stations located within Howard County. Other factors such as stations per capita and population were highly statistically significant variables concerning gas retailers’ margins.
RESI found the following factors to determine, within a 95 percent significance level, annual changes in gas price markups:
· Percent of unbranded gasoline stations within a region;
· Station density per capita;
· Changes in rack (wholesale) prices;
· Population changes; and
· Current gasoline taxes.
A more detailed analysis using a one-year period, 2012, across gas stations was created to determine what regional factors may contribute to the statistical difference in retail margins. Variables for gas stations located in CUP and nonconforming zones were added into the model along with variables for percentage of the region in square miles that allowed for gas stations.
Results from the analysis could explain nearly 64 percent of the variation in the retailers’ margins, and land planning factors were highly statistically significant. Of the land planning variables that were statistically significant, RESI found that gas stations located within zones where gas stations are permitted conditionally (CUP/SE) had higher margins than gas stations in zones where gas stations are permitted outright, and an increase in the percentage of square miles designated as CUP/SE zoning or permitted zones would decrease retailer’s margins.
The inclusion of the permitting variables was to indicate the potential barriers to entry that gas stations may face when attempting to expand or open in a new jurisdiction. Permitted zones were viewed as having the fewest barriers to entry, CUP zones having the medium level, and nonconforming zones as having the most barriers to entry. Incorporated areas within the study area jurisdictions were excluded from the model and dropped from the analysis as they are regulated by their own zoning.
RESI concluded that the greater the restrictions, the higher the retailer’s margins would be for standard zoning. However, upon further research, Columbia, Maryland, was reviewed independently of Howard County since the development review process entailed the approval of a final development plan versus the traditional CUP review for standard zones. To account for this difference, RESI included a variable for Columbia, Maryland, into the analysis.
From this analysis, RESI concluded the following for gas retailers’ margins across the eight jurisdictions:
· The greater the distance between competing retailers, the higher the retailers’ margins;
· If the jurisdiction increases the percentage of land area permitting gas stations, then retailers’ margins would decrease; and
· Retailers located within Columbia, Maryland, will have margins approximately $0.11 higher on average than retailers located within the other study areas.
Overall, RESI concludes the case of higher gas prices can be attributed to the mark up by retailers in a given jurisdiction. The higher the barriers to entry, the higher the markup associated with those retailers. Other factors such as density of competition and location to a major highway can be associated with competition to gain consumers, but these factors are also indirectly driven by zoning. Zoning factors could limit the potential for some unbranded stations to enter the market if the costs to entrance resulted in negative or minimal margins and if the locations available would not drive the consumer base needed to continue operation. RESI can conclude that zoning does play a major factor in gas prices in the short term, which may result in a continued trend over the long term if current zoning restrictions remain unchanged.
6.0References
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Appendix A—Economic Modeling Assumptions and ExplanationsA.1Assumptions
RESI assumed the following when analyzing the changes in retail prices of gas stations.
1. Infrastructure changes did not occur during the period. The period reviewed in the annual analysis is too short to determine if there were major infrastructure improvements in an area, but RESI can determine that the stations’ locations have been in existence for quite some time. RESI assumes that to change infrastructure would take longer than the current study period, and therefore did not add a variable for this into the analysis.
2. Gas stations have perfect information about competitors’ prices. RESI assumes, since prices are posted for the public to view and information is available about gas prices in a region through website such as AAA and GasBuddy, that retailers in an area have perfect information about what their competitors’ prices are on a given day or time.
3. Rack prices are reported before the next period, and retailers will have a chance to change their prices accordingly. In the 2012 cross-sectional model, RESI assumes that the change is very instantaneous but that the declining prices may hinder changes in retail prices as retailers may worry about sudden shocks and price reversals to the higher historical values.
4. Gas stations’ competitors and locations are known. RESI assumes that any new placement of gas stations will do so with zoning restrictions and adhere to an optimizing Hotelling location model.[footnoteRef:36] Hotelling proposed that, given two firms located at separate ends of a line, one firm’s potential for sales to consumers would depend on the distance between that firm and its competitor, as well as which way consumers traveled. For example, take the line below with two gas stations. [36: Hotelling, “Stability in Competition,”46.]
β
α
B
A
Hotelling stated that if the firms are unable to change position and the distances are recorded as α and β, then those distances times the cost to transport the good (or in this case search for the good) must not exceed the price offered by either A or B.[footnoteRef:37] If the good is homogenous, consumers will tend to go to the closest firm to avoid extra costs, but if the distance is equal, then they will choose whatever firm is cheapest.[footnoteRef:38] [37: Ibid, 49.] [38: Ibid.]
A.2Results
Figure 10: Annual Analysis Regression on Margin for 2002–2012
Variable
Impact Multiplier
t-statistic
Statically Significant at 5% (Y/N)
Intercept
-3.011
-4.575
Yes
Household Income Change
0.144
0.330
No
Percent Unbranded
-0.177
-2.264
Yes
Stations per 1,000 Residents
0.466
3.381
Yes
Population Change
-10.023
-3.066
Yes
Dummy (Anne Arundel)
-0.393
-6.420
Yes
Dummy (Baltimore)
-0.372
-8.546
Yes
Dummy (Baltimore City)
-0.303
-5.278
Yes
Dummy (Carroll)
-0.385
-6.199
Yes
Dummy (Frederick)
-0.116
-1.421
No
Dummy (Harford)
-0.676
-9.727
Yes
Dummy (Montgomery)
0.406
5.586
Yes
Rack Change
0.545
8.500
Yes
Tax Current Period
0.584
1.649
No
Tax Lagged-One Period
-2.472
-4.388
Yes
Sources: RESI, OPIS, U.S. Census
Figure 11: Annual Analysis Regression on Margin for 2002–2012 Statistical Parameters
Statistic
Estimation
R-square
0.871
F-statistic
31.196
Durbin-Watson
2.201
Sources: RESI, OPIS, U.S. Census
Figure 12: Cross-Sectional Analysis Regression on Margin for 2012
Variable
Impact Multiplier
t-statistic
Statically Significant at 5% (Y/N)
Intercept
3.386
6.814
Yes
Population in thousands
0.000
4.711
Yes
Household income per 1,000 residents
0.089
1.461
No
Difference in competitor prices
0.434
10.650
Yes
Percentage of Unbranded
0.212
3.577
Yes
Percent Permitted
-0.439
-8.598
Yes
Percent CUP
-0.097
-7.128
Yes
Distance to Major Road
-0.005
-5.040
Yes
Distance to Next Station
0.015
5.017
Yes
Rack Prices
-1.103
-6.580
Yes
Dummy—located within a conditional use permit zone
0.015
2.214
Yes
Dummy—located within a nonconforming zone
0.012
1.667
No
Dummy—located within Columbia, MD
0.107
9.358
Yes
Sources: RESI, OPIS, U.S. Census
Figure 13: Cross-Sectional Regression on Margin for 2012 Statistical Parameters
Statistic
Estimation
R-square
0.637
F-statistic
137.747
Durbin-Watson
1.814
Sources: RESI, OPIS, U.S. Census
A.3Technical Discussion—Variables
On the whole, crude oil prices or “rack prices” are the largest contributor to the fluctuation in the pricing for gasoline. RESI conducted a correlation analysis on wholesale and retail price to demonstrate the dependency of the two variables. The results of this analysis are reported in Figure 14.
Figure 14: Correlation Matrix for Retail and Rack Prices
Variable
Correlation with Retail
Correlation with Rack
Retail
1.000
0.997
Rack
0.997
1.000
Sources: OPIS, RESI
The correlation matrix in Figure 14 highlights the relationship between the explanatory variable “rack price” and the dependent variable “retail price,” between 2002 and 2012 as an example of the relationship. Since they exhibit a very strong positive relationship, the use of the variables within the regression could create biased results and therefore render any findings invalid. Although correlation does not always indicate that there is a direct causation between retail prices and rack prices, the correlation does indicate that they move in the same direction when changed. If rack prices increase, according to the correlation coefficient matrix above, there will likely be an observed similar upward movement in retail if all other things remain equal.
Another factor to consider involves the structure of input prices for final goods in a retail setting. Traditionally, gas stations have no control over the wholesale price of gasoline. Factors that are within the retail station’s control include the number of pumps, pay-at-pump locations, entrances/exits, complementary goods, staff, and hours of operation.[footnoteRef:39] All of these items represent input costs that will take away from the overall profit margins of a retailer. Despite the homogenous good being gasoline, the differentiation among stations is witnessed through the physical characteristics of the station and the location. This differentiation allows for some price difference among stations within a region.[footnoteRef:40] Although the rack price is out of the retailer’s control, there do remain some factors of operation that alter gas prices that allow for retailers to dictate how much they budget or spend. RESI determined that using retail prices as the dependent variable may overlook some of those potential factors and create an omitted variable bias within the model. [39: Iyer, et al, “Too close to be similar:...,” 206.] [40: Ibid.]
In past economic studies, researchers have attempted to avoid the problematic correlation by using the variable “margin.” Margins would be able to fully reflect the change associated with the rack price, while capturing the underlying costs associated with daily operations for the retailer.[footnoteRef:41] RESI assumes that the stations can set their own retail prices and, given this control, will use the variable “margin” defined as the difference between the retail price and the rack price less taxes and freight as the endogenous variable in the models. [41: Hosken, et al. “Retail gasoline pricing: What do we know?” 10.]
A retailer’s margin will help to capture some of the input costs RESI cannot observe easily and vary across station and region. One input price that is readily available for observation is rack prices and should be kept within the model to accurately show the retailer’s perceived notions of future earnings. To avoid multicollinearity within the model, RESI reviewed margin and rack prices for correlation. Figure 15 shows the correlation matrix for the margin and rack prices.
Figure 15: Correlation Matrix for Margin and Rack Price
Variable
Correlation with Retail
Correlation with Rack
Margin
1.000
0.561
Rack Price
0.561
1.000
Sources: OPIS, RESI
Figure 15 highlights that the variable “rack price” is moderately positively correlated with margin and can be used in the model. Ordinarily, economists will avoid using variables with correlations above 0.7 as this would create issues within the analysis that may lead to biased estimates. The above can be read as “rack prices account for approximately 31 percent of the movement in retailer margins.”[footnoteRef:42] The higher the correlation coefficient is, the more the movement of the dependent variable can be explained by that singular variable, thereby creating potential omitted variable bias and multicollinearity within a regression. [42: When determining the effect from correlation upon an indicated variable, econometricians take the squared correlation coefficient.]
Preliminary research also indicates that price movements in retail gas prices have been known to move asymmetrically with their corresponding rack prices, therefore making rack price a good indicator of expectations for current retail prices. RESI used rack prices to capture the potential change to margins given the potential increase to retailers for their good with all other factors of cost remaining equal. Margin will act as the markup necessary to maintain business given the increasing or decreasing costs of rack prices.
Other input pricing variables including tax have been kept in the model to demonstrate the impact of those variables on retail prices. Taxes on gasoline purchases are collected by retailers and submitted during their business’s fiscal year. A potential change in the tax rate would be considered when determining retail markup by stations.
Income
Household income traditionally will impact both the supply and the demand for gasoline prices within a region. A higher average household income within a region may result in an equally higher retail gas price due to cost of living differentiation. In previous studies, income only became a statistically significant variable if firms within a region “are engaged in tacit collusion.”[footnoteRef:43] Eckert, et al, suggest that, given the localized region, if firms were actively pricing in a collective manner, a higher income in a given proximity may affect retail prices in that localized area.[footnoteRef:44] [43: Eckert, et al. “Price uniformity and competition in a retail gasoline market,” 229.] [44: Ibid.]
Changes in wages may also present a rising cost to employers. RESI attempts to capture the changing income within a region through a household income per capita change variable in the regression. As noted, despite firms having foresight about wage changes, wage contracts are often slow to change and therefore create at least a period lag between the proposed change and the real cha