Cleared for Value: How Airport Characteristics Drive Hangar Value

Abstract

1. Introduction

The United States general aviation system includes 4,889 public-use airports, or 5,179 public-use landing facilities counting heliports, seaplane bases and the rest, supporting tens of thousands of aircraft hangars (Federal Aviation Administration, ADIP). Despite the aggregate value of that inventory, systematic analysis of which airport-level characteristics most affect hangar values is close to absent from the published literature. Appraisers, investors, lenders, and airport authorities rely on informal quality hierarchies and experiential judgment. The result is inconsistent valuation and, for capital allocators, decisions made without a documented basis for the location adjustment that often dominates the analysis.

The gap matters because the spread is large. A 40,000-square-foot corporate hangar at a well-served metropolitan airport can command rental rates several times those achievable by a structurally identical facility at a small regional field. Capitalized at prevailing rates, that difference produces value divergence measured in millions of dollars for a single property. The specific airport characteristics responsible, and their relative contributions, have never been decomposed in peer-reviewed work.

The analytical difficulty is compounded by the structure of aviation real estate. Conventional location analysis has well-established frameworks for demographics, transportation access, and competitive supply. Hangar valuation additionally requires assessment of runway length and configuration, instrument approach capability, air traffic control services, customs availability, and fixed-base operator service depth. None of these appear in standard commercial real estate texts. Further, the interaction between airport characteristics and the ground lease structures that govern most hangar properties creates value dynamics that physical or locational analysis alone cannot capture (Carter 2026a, 2026c).

This paper addresses the gap in three parts. First, it provides a taxonomy of airport-level value drivers organized by mechanism of impact: operational gating, service differentiation, and market demand generation. Second, it develops a weighted Airport Value Index that aggregates these factors into a composite score for cross-airport comparison, and positions that index against the Federal Aviation Administration's existing Runway Design Code, which already encodes two of the variables the index relies on most heavily. Third, it assembles the public evidence that does exist on the size of airport-level rent spreads and states plainly where evidence does not exist.

Section 2 reviews the existing literature. Section 3 develops the theoretical framework linking airport characteristics to hangar value through tenant willingness to pay and capitalization rate formation. Section 4 presents the taxonomy. Section 5 introduces the Airport Value Index and addresses the methodological literature on composite indicators. Section 6 presents the published rate evidence. Section 7 works an illustrative valuation case. Section 8 discusses implications for appraisers, investors, and airport authorities. Section 9 sets out limitations. Section 10 concludes.

2. Literature Review

The academic literature on aviation real estate valuation is thin. Lindsey (2008a, 2008b) produced the foundational and for many years the only peer-reviewed guidance on aircraft hangar appraisal, establishing frameworks for income capitalization and comparable sales analysis adapted to the aviation context. Lindsey identified airport characteristics as relevant to hangar values but did not decompose or weight them.

The broader real estate literature provides established frameworks for location analysis in conventional property types. Sivitanidou and Sivitanides (1999) showed that office capitalization rates respond to both macroeconomic conditions and property-specific location characteristics. Ambrose and Nourse (1993) set out the band-of-investment framework linking financing availability to capitalization rate determination, a mechanism directly relevant here because airport quality affects lender underwriting thresholds. Geltner et al. (2026) treat location as a value driver in commercial real estate at length, without addressing the operational factors specific to aviation facilities.

Simpson (1998) on marina valuation offers the closest structural parallel. Marinas, like hangars, are specialized-use properties whose values depend on location factors, in that case water depth, channel access, and proximity to open water, with no analog in conventional analysis. Simpson's method for evaluating use-specific location characteristics provides a template, though the factors themselves differ.

Federal design standards supply a further foundation that valuation practice has underused. Advisory Circular 150/5300-13B, Change 1, assigns each runway a Runway Design Code combining an Aircraft Approach Category, an Airplane Design Group based on wingspan and tail height, and the visibility minimum associated with the lowest approach serving that runway (Federal Aviation Administration 2024). The Runway Design Code therefore already encodes, in a standardized and publicly available form, two of the variables this paper treats as central: the aircraft the runway is designed to serve and the approach capability available to them. Any index of airport quality has to explain what it adds beyond the Runway Design Code. Section 5.3 takes up that question directly.

Airport operational data itself is available through the Airport Data and Information Portal, which replaced the paper Airport Master Record workflow when Form 5010-1 was cancelled in July 2024 (Federal Aviation Administration, ADIP). Instrument procedure inventories are published through the Instrument Flight Procedures Information Gateway. The National Business Aviation Association publishes the Business Aviation Fact Book, which documents fleet and activity levels but does not link facility characteristics to property values or capitalization rates.

Two further bodies of work bear on the method used here. The first is the literature on composite indicators. The OECD and the European Commission Joint Research Centre (2008) set out the standard construction sequence for a composite index, and Saisana, Saltelli, and Tarantola (2005) established uncertainty and sensitivity analysis as the accepted test of whether a composite ranking is robust to its own weighting choices. Paruolo, Saisana, and Saltelli (2013) go further, showing that the nominal weights assigned to an index rarely equal the importance those variables actually exert on the resulting ranking, and that publishing an index without measuring that gap invites exactly the misreading that its authors intend to avoid. Any scoring framework of the kind proposed here is subject to those critiques, and Section 5.3 addresses them rather than ignoring them.

The second is transaction cost economics. Williamson (1979, 1985) on asset specificity and the hold-up problem, and Klein, Crawford, and Alchian (1978) on appropriable quasi-rents, explain why airport characteristics affect hangar values through mechanisms distinct from conventional location premiums. A hangar is a single-purpose improvement on land the tenant does not own, held under a lease that reverts. That combination creates a dependency between tenant investment and airport-level conditions with no counterpart in conventional commercial real estate (Carter 2026c).

What does not exist is the study this field most needs. No published hedonic analysis regresses hangar rents or hangar transaction prices on airport characteristics. The empirical foundation for every premium and discount figure circulating in aviation appraisal practice, including those in this paper, is professional observation rather than estimated coefficients. That is stated here as a finding, not a caveat.

2.4 What Has Been Measured, and What Has Not

One published study decomposes airport capability into components close to this paper’s operational tier and tests them, and it should be stated before the framework is proposed.

Swartz, Donovan and Clower (2016) examined 112 general aviation airports in Texas using a structural equation model. They built an airport infrastructure component by principal components from master record data, comprising the number of runways, the length of the longest runway, the maximum weight-bearing capacity and whether a control tower is on the field; and an accessibility component from published landing minimums, comprising precision and non-precision approaches per runway, the best decision height and the best runway visual range. Both loaded on single components explaining 58.6 and 66 percent of variance respectively.

Their standardised path coefficients are the useful result: accessibility to activity at 0.387, infrastructure to activity at 0.205, infrastructure to economic activity at 0.351 and accessibility to economic activity at 0.212, all significant. In their words, “a one standard deviation increase in ACCESS results in a 38.7% increase in the number of operations, while a one standard deviation increase in INFRA only results in a 20.5%,” while infrastructure is “the most powerful single contributor to economic outcomes.”

What that study does and does not do for this paper should be stated plainly. It measures activity and economic outcomes at and around the airport. It does not measure hangar rent or hangar value, so it does not fill the gap this paper identifies. What it does is establish that the runway length, tower and approach minimum triad is a measurable driver of something, that the components are separable, and that accessibility dominates operations while infrastructure dominates economic outcomes. That ordering is the one this paper’s tiers assume.

A second study bears on whether the existing federal classification already carries the information. Johnson and Gu (2017) modelled annual operations at non-towered general aviation airports using master record data and the national plan categories, and found the categories statistically significant in the model. Any proposal to build a new index has to answer that finding, and Section 5.3 does.

A larger hedonic literature measures how airports affect the value of residential property nearby. It shares a method with the question here and not the question itself: in that literature the airport is a disamenity measured from outside the fence, and here it is the subject measured from inside. Nelson (2004) synthesises twenty hedonic studies and thirty-three noise discount estimates across twenty-three airports. McMillen (2004) found homes about nine percent lower inside the 65 decibel contour at Chicago. Cohen and Coughlin (2008) found in Atlanta that spatial effects magnify the noise penalty while houses farther from the airport nonetheless sell for less, so proximity and noise pull in opposite directions. Zolnik and Schondelmeyer (2026), on the Washington region, found values falling 2.12 percent per kilometre of distance from one airport and rising 1.42 percent per kilometre from another up to a peak at 8.6 kilometres, and attributed the divergence to the land use around each field. That last finding is this paper’s thesis observed in a different market: the sign of the airport effect depends on the airport’s own characteristics.

3. Theoretical Framework

3.1 The Gating Mechanism

The primary mechanism through which airport characteristics affect hangar values is the gating function of aircraft compatibility. Runway length, approach capability, and operational characteristics jointly determine which aircraft can use the field reliably. That determination defines the universe of potential tenants and, more to the point, the financial capacity of those tenants.

The relationship is not continuous. It has thresholds, and the thresholds are set by published aircraft performance rather than by convention. Takeoff field lengths at sea level under standard atmospheric conditions at maximum takeoff weight run to roughly 5,995 feet for the Gulfstream G700, 5,800 feet for the Bombardier Global 7500, 5,115 feet for the Dassault Falcon 6X, 4,810 feet for the Cessna Citation Longitude, and 3,209 feet for the Embraer Phenom 300E. The practical consequence is sharper than the usual framing suggests. A field with less than 4,000 feet of runway excludes every one of those aircraft except the Phenom, and does so before any consideration of density altitude, contaminated surfaces, or operator-specific safety margins, each of which pushes the required length higher.

This gating mechanism operates through willingness to pay. A corporate flight department basing a large-cabin aircraft is systematically less price-sensitive than a recreational owner basing a single-engine piston. The corporate tenant's willingness to pay reflects the aircraft's replacement cost, the cost of operational disruption from an inadequate facility, and the budget of the parent organization. A hangar that can accommodate those aircraft at a field capable of supporting their operations commands rents that are a multiple of what the same structure achieves at a field restricted to light aircraft.

3.2 Service Differentiation and Operational Reliability

Beyond the binary gate, service characteristics create a continuous quality gradient affecting tenant satisfaction, operational efficiency, and willingness to pay. Instrument approach capability affects the share of the year during which operations can be conducted reliably. A field that is closed to its tenants in low weather imposes real costs through missed appointments, diversions, and repositioning.

The conventional version of this argument, that an ILS commands a substantial premium over fields without one, needs qualification. Satellite-based approach capability has closed much of the gap. Localizer performance with vertical guidance procedures, enabled by the Wide Area Augmentation System, now number in the thousands across more than two thousand airports, and well over a thousand of those serve runways with no ILS at all (Federal Aviation Administration, Instrument Flight Procedures Information Gateway). Many reach 200-foot minimums, the same decision height as a Category I ILS. Weighting ILS presence heavily in a current valuation framework therefore risks double-counting a capability the field may already have by another means. The valuation-relevant variable is the lowest published minimum on the runway, not the type of navigation aid that produces it.

Fixed-base operator service depth, fuel availability and pricing, customs and immigration capability, and ground transportation access each contribute independently to the operational friction tenants experience. Business aviation runs on irregular schedules. Airports with extended tower hours, competing service providers, and on-site customs reduce that friction in ways tenants pay for.

3.3 The Capitalization Rate Channel

Airport characteristics reach value through two channels: rental rates and risk pricing. The rent channel is intuitive. The capitalization rate channel is less so.

Airport quality affects capitalization rates through at least four pathways. Airports with deeper and more diversified tenant demand exhibit lower vacancy risk, which investors price. Airports that attract institutional capital experience competitive bidding that compresses yields below levels observed where only individual investors participate. Airport quality affects lender underwriting, and properties at stronger fields typically qualify for higher loan-to-value ratios and better pricing, which the band-of-investment framework predicts will reduce the overall capitalization rate (Ambrose and Nourse 1993). And the perceived stability of an airport market affects required returns, with established facilities commanding lower risk premiums than emerging or declining ones.

Because the two channels move together, airport quality differences are amplified in capitalized value. A field that supports both higher rent and a lower capitalization rate produces a value premium larger than either channel would generate alone. That multiplicative interaction is the reason airport analysis cannot be treated as a secondary adjustment.

3.4 Competitive Supply Geography

The number and quality of alternative airports within a regional draw area affects the pricing power of hangar operators at any given field. An airport that is the sole provider of usable hangar space within a 50 to 75 mile radius exercises pricing power that sustains rents above levels prevailing in competitive multi-airport markets.

This explains an otherwise puzzling pattern. Some regional airports in geographically isolated markets command rents inconsistent with their operational tier. A mid-tier field that is the only facility within 90 miles capable of handling mid-size jets may price as though it were a premier facility, because for its tenants there is no alternative. The converse also holds. A strong field in a market with several competing strong fields faces pricing constraints its operational quality alone would not predict. Section 6 shows this effect operating inside a single airport system's own published rates.

4. Taxonomy of Airport Value Drivers

This section organizes airport characteristics across three tiers: operational factors, service and infrastructure factors, and market and economic factors. Each tier contributes independently, and the tiers interact. Strong service infrastructure at an operationally limited field produces a different outcome than the same services at a field without those limits.

4.1 Tier 1: Operational Factors

Runway length and configuration. Runway length is the dominant operational characteristic because it sets the ceiling on tenant quality and willingness to pay. The relationship exhibits threshold effects at operational boundaries defined by aircraft performance rather than by round numbers.

Table 1. Runway length thresholds and aircraft compatibility.

Segment boundaries derived from manufacturer published takeoff field lengths at sea level, standard atmosphere, maximum takeoff weight. Runway classification conventions follow FAA Advisory Circular 150/5300-13B, Change 1 (Federal Aviation Administration 2024). Field length requirements increase with elevation, temperature, runway contamination, and operator safety margins, so the boundaries shown are the most favorable case.

Airports crossing the 5,000-foot threshold become viable for the light and mid-size jet segment, which changes the composition of the demand pool rather than merely widening it. Appraisers should evaluate not only current runway length but the airport capital improvement program. A funded extension from 4,800 to 5,500 feet is a material forward-looking adjustment that an income approach based on current rents will not capture.

Multiple runway configurations add value by reducing weather-related operational restrictions and increasing capacity. Crosswind runways permit operations in wind conditions that effectively close single-runway fields. Parallel runways reduce delay at peak periods.

Instrument approach capability. The relevant variable is the lowest published minimum available on the runway serving the hangar, not the equipment that produces it. As discussed in Section 3.2, satellite-based procedures have brought many fields to minimums formerly available only with an ILS. Airports restricted to non-precision or circling approaches, or to visual operations alone, carry a genuine and measurable limitation. Airports with published vertically guided approaches to low minimums do not, whether the guidance comes from a localizer and glideslope or from the Wide Area Augmentation System.

Air traffic control services. Tower availability and hours materially affect operational flexibility. Extended or continuous tower operation supports irregular schedules more effectively than part-time coverage. Approach control services improve traffic flow and enable radar-based weather and traffic management that non-towered fields cannot offer. The effect is partly direct, through reliability, and partly through the perception of operational discipline that influences corporate basing decisions.

4.2 Tier 2: Service and Infrastructure Factors

Fixed-base operator services and competition. The fixed-base operator is the primary interface between an airport and its tenants. Multiple competing operators at a field produce better pricing, service quality, and operational flexibility than single-operator environments. Competition holds fuel pricing to market, keeps line service responsive, and gives tenants recourse when service deteriorates.

Full-service operators providing crew facilities, passenger terminals, conference space, and ground transportation create an ecosystem that raises the attractiveness of hangar tenancy at that field. Airports with that infrastructure support materially higher hangar rents than limited-service fields with restricted hours.

Customs and international services. On-site customs and immigration is a material differentiator for any field serving internationally active tenants. Clearing at the home base eliminates the cost and scheduling friction of a diversion to a port of entry, which adds fuel burn, crew duty time, and passenger delay. For flight departments with regular international operations, customs availability is frequently a basing requirement rather than an amenity.

Fuel availability and pricing. Continuous fuel availability supports the flexibility business aviation requires. Where fuel service is restricted to business hours, early departures and late arrivals become impractical, which reduces tenant utility for departments operating on executive schedules. Competitive fuel pricing, usually a function of multiple-operator competition, affects total operating cost and factors into basing decisions.

Ground transportation access. Drive time to the business district the airport serves, highway access, and ground transportation availability directly affect utility. A field requiring a 90-minute drive to the district it ostensibly serves will lose tenants to a closer alternative even when the closer field is operationally inferior. Proximity measured in drive time, not map distance, is the operative variable.

Electrical, data, and emerging infrastructure. Modern hangar operations depend on more than airfield capability. Electrical capacity sufficient for 480V service, climate control, and maintenance equipment is now a baseline expectation at stronger fields. High-speed connectivity, controlled access systems, and sustainable aviation fuel availability are emerging differentiators. As electric and hybrid aircraft move toward commercial operation, fields with adequate grid capacity and charging infrastructure will be better positioned for that tenant class.

4.3 Tier 3: Market and Economic Factors

The mechanism behind this tier has an empirical literature, and it should be named rather than assumed. Green (2007), writing in a real estate journal, examines the relationship between airports and economic development directly. Sheard (2014) estimates an elasticity of tradable-service employment with respect to airport size of about 0.22, and finds that airports redistribute sectoral employment within a metropolitan area rather than raising total employment. That distinction matters here: it implies a hangar’s demand base is the corporate activity the airport attracts to its own catchment, not the growth of the region as a whole.

Regional economic base. The strength and composition of the regional economy determines the depth and durability of aviation demand. Four variables at the metropolitan level warrant evaluation. Corporate presence, particularly headquarters and regional centers in aviation-intensive industries such as energy, finance, technology, and healthcare, generates consistent business aviation activity. High-net-worth household density correlates with personal aircraft ownership and demand for larger hangar configurations. Regional output growth indicates the demand trend. And industry concentration is a risk variable in its own right: markets dominated by a single sector carry exposure that diversified markets do not, as Texas aviation markets demonstrated during the 2014 to 2016 energy downturn.

Corporate relocation can alter regional demand patterns quickly. Headquarters migration shifts hangar occupancy, rents, and capitalization rates at airports in both the origin and destination markets, often before the relocation is complete.

Climate and usable flying days. The number of days per year on which visual operations are practical is among the more consequential and less discussed geographic value drivers. It matters not because instrument-rated pilots cannot fly in instrument conditions but because visual conditions expand the pilot population able to use the field, which broadens and deepens the tenant pool. The mechanism favors the Sunbelt and disadvantages markets with persistent low ceilings. Practitioners can quantify it for a specific market from published climate normals rather than from general impression (National Centers for Environmental Information).

Based aircraft trends and fleet composition. The number and type of aircraft based at a field provide direct evidence of current demand and a basis for forecasting. Positive based-aircraft growth, particularly in jet and turboprop registrations, signals strengthening demand for quality hangar space. The mix matters as much as the count. A field transitioning from a predominantly piston fleet to a growing jet fleet is one where willingness to pay is rising.

Competitive supply geography. As set out in Section 3.4, the competitive geography of the market affects pricing power independently of operational quality. Airports that are the sole provider of usable space within a meaningful radius command premiums that appear inconsistent with their operational tier when that tier is assessed in isolation.

4.4 Where the Data Comes From, and What Each Source Will Not Tell You

Every factor above has to be sourced from somewhere, and the sources differ in authority. The standard references on the subject are the Airport Cooperative Research Program volumes on developing and leasing airport property (Transportation Research Board 2011) and on estimating market value and market rent at small airports (Transportation Research Board 2020), with facility planning in Report 113 (Transportation Research Board 2014), through-the-fence competition in Report 114, the current fixed base operator baseline in Synthesis 129 (Transportation Research Board 2023), and general aviation lease terms in Legal Research Digest 46 (Transportation Research Board 2024). An analyst who takes a based aircraft count from one file and an operations count from another has combined a validated figure with an estimate.

Table 2. Federal data sources for airport characteristics.

Source: Author’s compilation from each program’s published documentation.

The operations figure deserves particular care because it is the one most often quoted and least often qualified. At a towered field it is a count. At a non-towered field, which is most of them, it is an estimate produced by a method that varies by state and by year, and research on automated counting at non-towered airports exists precisely because the estimates are known to be unreliable. A demand argument that rests on a non-towered operations figure rests on an estimate, and the report should say so.

5. The Airport Value Index

The taxonomy identifies the characteristics. Turning it into a usable tool requires a composite framework that weights and aggregates them into a single comparable metric. This section introduces the Airport Value Index, a 100-point scoring system for cross-airport comparison, and then subjects it to the standard critiques of composite indicators.

5.1 Scoring Architecture

The Index assigns weights to three factor categories. Operational factors receive the largest weight because they function as gates that set the ceiling on achievable rent. Service factors receive a lower weight, reflecting their role as continuous differentiators within an operational tier. Market factors receive an equal weight to service factors, reflecting their function as demand determinants that set the floor on sustainable occupancy and the trajectory of rent growth.

Table 3. Airport Value Index scoring dimensions and weights.

The 40/30/30 allocation is the author's professional judgment. It is not estimated from transaction data, and no dataset supporting an estimated allocation exists. Users should treat the weights as a starting point subject to the sensitivity testing described in Section 5.3.

Each sub-factor is scored on a 0 to 10 scale against observable, documented criteria, and the weighted total produces a composite score. The value of the exercise lies less in the score itself than in the discipline of requiring every factor to be scored against evidence that appears in the file.

5.2 Score Ranges and Classification

The composite score maps to a four-tier classification. The tiers are a communication device for organizing qualitative differences between fields, and the rent ranges shown are indicative of the ranges the author has observed in practice rather than summary statistics from a defined sample.

Table 4. Index score ranges and airport classification tiers.

Rent ranges are indicative of the author's observation in appraisal practice. They are not derived from a published dataset. Section 7 assembles the published state data that does exist, which reports rents by airport classification rather than at the building level. Ranges will differ by region, by building generation, and by lease structure, and should be tested against local evidence in every assignment. See Carter (2026b) on the limits of per-square-foot benchmarks.

5.3 What the Index Adds, and What It Cannot Do

Two objections have to be met before a framework of this kind is fit for use in a valuation report.

The first is redundancy. The Federal Aviation Administration already publishes a standardized code encoding the approach speed of the design aircraft, its wingspan and tail height, and the visibility minimum associated with the lowest approach serving the runway (Federal Aviation Administration 2024). Those variables overlap heavily with the operational tier that carries the largest weight in this index. The overlap is real but narrower than it looks, and the difference matters. The design code has three elements: the approach speed category of the critical aircraft, the wingspan and tail height group, and the approach visibility minimum. Runway length is not among them. A runway carrying a given design code may be three thousand feet or six thousand, and this paper treats length as the gating variable. So the code encodes two of the operational variables used here and not the one weighted most heavily.

The design code is nonetheless the better instrument for what it does cover, and should be recorded first in any assignment. What it does not do is combine that operational capability with service depth, competitive supply geography, and regional demand, which are the variables that separate two fields with identical design codes and very different rent levels. The index is a wrapper around the design code rather than a substitute for it, and an analyst who reports the index without reporting the design code has skipped the more defensible of the two.

The second objection is more fundamental. Paruolo, Saisana, and Saltelli (2013) demonstrate that the nominal weights of a composite index are frequently not the weights that actually drive its rankings, because the effective importance of a variable depends on its variance and its correlation with the others as much as on the coefficient attached to it. A framework that assigns 40 percent to operational factors may, across a real set of airports, produce rankings in which operational factors explain far more or far less than 40 percent of the variation. The OECD and Joint Research Centre (2008) handbook and Saisana, Saltelli, and Tarantola (2005) prescribe the remedy: uncertainty and sensitivity analysis run across plausible alternative weighting schemes, reported alongside the index, so that users can see which conclusions survive a change in assumptions and which do not.

That analysis cannot be performed here, because performing it requires the airport dataset and the rent observations that do not yet exist in published form. Until they do, this index should be used as a structured checklist that forces an analyst to address every relevant factor and to document the evidence for each, not as a measurement instrument whose output can be quoted to a decimal place. An index score is a summary of judgments. It does not convert those judgments into data.

6. Evidence on the Size of the Spread

Because no hedonic study exists, the question of how large airport-level differences actually are has to be approached through whatever public evidence is available. The most useful source is a multi-airport system that publishes its own rates, because it holds the operator, the lease form, the policy environment, and the regional economy constant while the airports themselves differ.

The Metropolitan Airports Commission operates six reliever airports in the Minneapolis and St. Paul region, and two independent documents cover all six on one basis.

The first is a market rent study prepared for the commission by an outside appraisal firm (Airport Business Solutions 2023), concluding unimproved aeronautical land rates of $0.80 per square foot a year for storage hangar sites at St. Paul Downtown against $0.50 at Lake Elmo, a spread of 60 percent, and $0.55 against $0.25 for commercial sites, a spread of 120 percent. That is an appraiser’s opinion of market rent developed under the reporting standard, not a schedule of rates charged.

The second is the rate ordinance the commission actually charges under (Metropolitan Airports Commission 2024), effective January 2025 and running through 2030. For calendar 2026 its storage tenant ground rent runs from $0.96 at St. Paul Downtown to $0.62 at Lake Elmo, a spread of 55 percent, and its commercial ground rent from $0.77 to $0.45 without percentage rent, a spread of 71 percent, or $0.57 to $0.28 where the lease also carries percentage rent, a spread of 104 percent.

The two documents agree in rank order across all six fields and roughly in magnitude, which is the useful result. Stated conservatively: storage ground rent varies by about 55 to 60 percent across one operator’s six fields, and commercial ground rent by about 70 to 120 percent depending on which rent structure applies. The operational difference tracks the rent difference in the direction the framework predicts: St. Paul Downtown has a primary runway of 6,491 feet, while Lake Elmo at the lower end has a 3,504 foot primary runway, no control tower, and satellite-based approaches to both ends with no vertically guided instrument landing system.

Three observations follow. First, airport-level spreads of this magnitude are real and are visible in published documents, not only in appraisal judgment. Second, they exist before building quality, hangar generation, or improvement condition enters the analysis at all, since ground rent is charged on land. Third, the two documents are independent of each other and were produced for different purposes, one to establish market rent and one to set the rates the commission bills, and they reach the same ordering.

One qualifier compresses the spread and belongs in the record. The appraisal states that its recommended rates are exclusive of an infrastructure preservation fee the commission imposes on all leased parcels, and concludes that the current fee of about $0.12 per square foot is reasonable. A flat charge applied uniformly narrows a proportional spread: adding it to both ends takes the storage spread from 60 percent to 48 and the commercial spread from 120 percent to 81. The spreads remain large. They are not as large as the headline rates suggest, and a practitioner quoting them should quote the all-in figure.

What the schedule cannot do is isolate the contribution of individual characteristics. St. Paul Downtown differs from Lake Elmo in runway length, approach capability, tower status, service depth, and proximity to the central business district simultaneously. The 60 percent spread is the joint effect of all of them. Decomposing it requires the study that has not been done.

Table 5. Airport characteristics and the direction of their effect on hangar rent.

Direction and relative magnitude reflect the author's judgment from appraisal practice. No point estimates or percentage ranges are given because none can be supported by published evidence. An analyst applying a numerical adjustment for any factor listed here should derive it from comparables in the subject market and document that derivation, not import a figure from a framework.

This table is deliberately weaker than the version a practitioner might prefer. A table of percentage premiums would be more convenient and would survive less scrutiny. In a valuation subject to review, an adjustment that cannot be traced to a comparable is a liability regardless of how confidently it was stated.

7. What Has Actually Been Measured

Section 6 gives the evidence from one multi-airport operator. This section gives the rest, because state aviation agencies publish hangar rates broken out by airport classification and the classifications are themselves built from the variables this paper argues matter. That evidence has not been assembled before, and it changes a claim made earlier in this paper.

7.1 Rent by Airport Classification, Georgia

Georgia surveyed and interviewed all 102 of its publicly owned public-use airports for a statewide hangar inventory and demand analysis (Georgia Department of Transportation 2024), with 101 responding to the rate survey. The state system plan assigns airports to Levels I, II and III, with all commercial service airports and the most developed general aviation airports at Level III. The study reports average monthly rents by that level.

Table 6. Average monthly hangar rent by Georgia state system plan airport role.

Source: Georgia Department of Transportation, Statewide Hangar Inventory and Demand Analysis, Table 6-4. Percentage row is this paper’s arithmetic.

The state’s own reading of its data matches the argument here: rents are highest at Level III airports and lowest at Level I, which “tend to have the lowest volumes of activity in terms of based aircraft and annual operations.”

Two features of the table deserve attention. The corporate hangar spread of 281 percent is an order of magnitude larger than the T-hangar spread of 31 percent, which is what the gating argument in Section 3.1 predicts: a T-hangar holds a light single that can operate from almost any paved field, so its rent has little airport component, while a corporate hangar holds an aircraft that can only operate from some of them. And the community parking space spread sits between the two, which is the ordering the argument predicts as well.

7.2 Rent Against a Runway Length Threshold, Minnesota

Minnesota supplies the cleanest available test of the gating variable, because its classification draws an explicit line at a runway length. The 2022 state system plan divides its Intermediate category by paved runway length (Minnesota Airport Technical Assistance Program 2023): Intermediate Large is 4,900 feet or more, Intermediate Small is less than 4,900 feet. A statewide rates survey then reports average T-hangar rent by class.

Table 7. Average monthly T-hangar rent by Minnesota state system plan class.

Source: Minnesota Airport Technical Assistance Program, Rates and Charges Study, 2023 survey results.

Holding the state, the survey instrument and the year constant, a paved runway of 4,900 feet or more is associated with an average T-hangar rent 18.7 percent above otherwise comparable airports below that threshold. Key general aviation airports average 74.8 percent above Intermediate Small.

The same survey reports box hangar rents of $1,282 a month at Key General Aviation airports against $315 at Intermediate Small, a spread of 307 percent, again far wider than the T-hangar spread at the same airports. That is the second independent state observation of the pattern in Section 7.1.

These are cross-sectional averages rather than controlled estimates. They do not hold building age, size or condition constant, and no state survey collects those. What they establish is that the association exists, that it runs in the predicted direction, that it is large, and that it is larger for aircraft classes with fewer usable fields.

7.3 A Correction to the Evidence Claim

Elsewhere this paper states that no published dataset of hangar rents by airport characteristic exists. That is too broad and is corrected here. Published rent data broken out by airport classification exists in at least Georgia, Minnesota, Wisconsin and Oregon, and Georgia’s own comparison table compiles statewide rate studies from nine further states.

The narrower statement is the accurate one. No published dataset of hangar rents at the building level, with airport characteristics as covariates and building characteristics controlled, exists. The state surveys give class averages, not observations, and a class average cannot separate the airport effect from the composition of the buildings at those airports. That is the study that has not been done, and Section 10 sets out what it would take.

8. Illustrative Valuation Case

The following comparison shows how the two channels interact. Both properties are hypothetical. The figures are chosen to fall within the ranges discussed above and are used to demonstrate the arithmetic of the mechanism, not to report a measured premium.

Table 8. Illustrative comparison of two physically identical hangars at differing airports.

Simplified illustration assuming a net operating income ratio of 86 percent for Property A and 85 percent for Property B. Both properties are hypothetical. Rents and capitalization rates are assumptions selected to fall within observed ranges, not measured values. An actual valuation requires detailed expense analysis and market-derived rates.

The $4.45 million difference between two physically identical buildings arises from the interaction of the two channels rather than from either alone. Property A commands $7 per square foot more in rent, which is $280,000 in additional gross revenue, while also capitalizing at 150 basis points lower. Had the capitalization rates been equal, the value difference would have been roughly $3.5 million rather than $4.45 million. The capitalization rate channel contributes close to a fifth of the total gap in this illustration, which is the point the arithmetic is meant to make.

An appraiser who does not assess airport characteristics rigorously risks material error in either direction. Applying generic regional capitalization rates to a metropolitan-airport hangar ignores the scarcity and institutional demand that compress yields there. Applying aspirational rents to a regional-airport hangar, unsupported by the local demand pool and fleet composition, produces an error of the same size in the opposite direction.

9. Implications for Practice

9.1 For Appraisers

The framework has direct implications under the Uniform Standards of Professional Appraisal Practice. Standard 1 requires that an appraiser not commit a substantial error of omission or commission. Failing to evaluate airport operational characteristics, service infrastructure, and competitive supply geography in a hangar assignment is precisely that kind of omission. A structured location analysis, documented in the report, improves both the rigor of the conclusion and its defensibility on review.

The sales comparison approach requires particular care. Adjustments for airport characteristics routinely exceed the magnitude of typical commercial location adjustments, which means that an unadjusted comparison across airport tiers is not conservative, it is wrong. A database drawn from small-field T-hangars is not a substitute for corporate hangar comparables at a metropolitan field regardless of geographic proximity (Carter 2026b).

One further discipline follows from Section 5.3. Where an index score is reported, the underlying Runway Design Code and the lowest published approach minimum should be reported with it, because those are the facts a reviewer can verify.

9.2 For Investors and Lenders

Institutional capital entering aviation real estate has concentrated development at metropolitan fields, which is an implicit endorsement of the airport quality hierarchy even where the hierarchy has never been formalized. A structured framework gives investors a basis for screening acquisition and development opportunities against a consistent standard and for benchmarking targets against existing holdings.

For lenders, airport quality should inform underwriting parameters. A property at a field with a deep tenant pool and active institutional interest supports different loan-to-value and coverage requirements than one at a field where demand is thin and the exit is uncertain. The relevant question at underwriting is not only what the property earns today but who the second tenant would be, and airport characteristics determine whether that question has an answer (Carter 2026a).

9.3 For Airport Authorities

An authority that understands the relationship between airport characteristics and hangar values is better positioned to set ground lease rates reflecting the value-generating capacity of its facility, and to defend those rates when challenged. An airport that invests in runway extension, approach improvement, or service infrastructure can justify higher ground rent and attract different tenants than one that allows capability to stagnate. Where capital is constrained, the framework suggests prioritizing the operational tier, because those investments change which tenants can use the field at all rather than adjusting the experience of tenants already there.

10. Limitations

Four limitations bear directly on how this framework should be used.

The weights are judgment. The 40/30/30 allocation in Table 3 reflects the author's experience and is not estimated from transaction data. No published dataset permits estimation. A different analyst with comparable experience could defend 50/25/25 or 35/35/30, and nothing in this paper establishes that one is better than another.

The index has not been validated. Uncertainty and sensitivity analysis of the kind prescribed by Saisana, Saltelli, and Tarantola (2005) has not been performed, so the gap between the nominal weights and their effective influence on rankings is unmeasured. Paruolo, Saisana, and Saltelli (2013) show that gap is routinely large. Until it is measured, the index ranks airports in a way that is transparent but not tested.

The rent and value figures are illustrative. The ranges in Table 4 and the figures in Table 8 are not measurements. They fall within ranges the author has observed, which is a different claim from being derived from a sample. The only figures in this paper drawn from a published document are those in Section 6.

The evidence base is one metropolitan system. The Metropolitan Airports Commission schedule is useful because it holds the operator and policy environment constant, but it is a single system in a single region with a single governance model. Whether the spreads observed there generalize is an open question, and the six fields differ across several characteristics at once, so the spread cannot be attributed to any one of them.

11. Conclusion

This paper has organized the airport-level characteristics that drive commercial hangar values and set out the mechanism through which each operates.

Runway length is the dominant driver because it gates the tenant pool. The thresholds are set by aircraft performance and are sharper than general discussion suggests: a field under 4,000 feet excludes essentially the entire business jet fleet other than the lightest types, and does so before any adjustment for elevation, temperature, or operator margin. Airport characteristics reach value through both rent and capitalization rate, and because those channels move together the resulting differences are larger than either would produce alone. Published rate schedules from a single multi-airport authority show land rent varying by 60 percent for storage use and 120 percent for commercial use across fields under common ownership and policy, which establishes the magnitude of the effect without resolving its composition.

The Airport Value Index provides a structured method for recording and comparing these characteristics. It is a checklist with arithmetic attached rather than a measurement instrument, and it is offered on those terms.

Three research directions follow. A hedonic analysis of hangar rents against airport characteristics, using ground lease rates from multi-airport authorities where the operator and policy environment are held constant, would convert the weights in this framework from judgment into estimates. Uncertainty and sensitivity analysis of the resulting index, following the established method for composite indicators, would establish which rankings are robust to weighting choices. And longitudinal study of how hangar rents respond to discrete capability changes, such as a runway extension or a new approach procedure, would give practitioners the forward-looking adjustment that current income-based methods cannot support.

The broader point is that aviation real estate needs analytical tools the conventional commercial framework does not supply. The hangar inventory across more than 5,000 public-use airports deserves the same rigor of location analysis that office, retail, and industrial property have long received, adapted to the operational realities of the airport. Building that case honestly means being explicit about which parts of the framework rest on evidence and which rest on judgment. This paper has tried to mark the line rather than blur it.

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This paper is research and general information for professionals evaluating aviation real estate. It is not appraisal, legal, or tax advice, and it does not create an engagement.