How we define trade areas, quantify demand, and build the market evidence that underpins every feasibility conclusion
The market analysis is the analytical foundation of every feasibility study we produce. If the market analysis is wrong, the financial projections built on top of it are wrong, the DSCR is wrong, and the credit decision is compromised. This is why we invest more analytical time in the market analysis section than in any other component of the study. A 60-page feasibility report may contain 15 to 20 pages of market analysis, representing 30% to 40% of the total document and 40% to 50% of the total analytical effort.
Our market analysis methodology operates on three principles. First, every demand assertion must be quantified, not described. "Strong demand" is not a finding; "14,520 addressable customers within a 5-mile radius, of which 2,520 are currently unserved by existing supply" is a finding. Second, every quantified assertion must be traceable to a named data source. Third, every projection must be validated through at least two independent methodologies before it enters the financial model.
Our market analysis draws from both primary research (data we collect directly through field work) and secondary research (data from published sources and commercial databases). The two research types serve different purposes and complement each other.
This market analysis methodology is applied asset by asset: competitive-set and RevPAR penetration work in hotel feasibility studies (see the US hotel market report), traffic counts and wash-club saturation in car wash feasibility studies (see the car wash industry outlook), and rent comparables plus absorption in multifamily feasibility studies.
| Dimension | Primary Research | Secondary Research |
|---|---|---|
| Definition | Data collected firsthand through site visits, competitor surveys, phone interviews, and direct observation | Data from published sources, government databases, and commercial data providers |
| Examples | Competitor pricing (observed at site), occupancy verification (phone calls, lock checks), site condition photos, traffic pattern observation, waitlist inquiries | Census demographics, CoStar rent comps, STR hotel data, NACS c-store benchmarks, BLS employment data, DOT traffic counts |
| Strengths | Current, specific to the trade area, captures qualitative factors databases miss (condition, visibility, customer experience) | Statistically robust, standardized methodology, covers large geographies, longitudinal trend data |
| Weaknesses | Time-intensive, limited sample size, observer bias possible | May be 6 to 18 months old, may not cover micro-market conditions, may not reflect recent changes |
| When it dominates | Competitive analysis, site assessment, niche property types with limited database coverage (glamping, agritourism, breweries) | Demographic analysis, industry trends, large-market hotel and multifamily analysis where database coverage is strong |
| Our typical allocation | 30% to 50% of market analysis effort | 50% to 70% of market analysis effort |
The balance shifts by property type. A hotel feasibility study in a major metro relies heavily on secondary research (STR data provides comprehensive performance benchmarking). A glamping feasibility study in a rural market relies heavily on primary research because no database tracks glamping tent occupancy in rural Wisconsin. The analyst must know which data type to prioritize for each engagement, and the methodology section of the report must document the approach used.
The trade area is the geographic boundary within which the subject property draws the majority of its customers. Defining it correctly is critical because an oversized trade area overstates demand and an undersized one understates it. We use three approaches and select the one most appropriate for the property type.
| Method | How It Works | Best For | Limitation |
|---|---|---|---|
| Radius-based | Concentric rings (1, 3, 5 miles) from the subject site | Self-storage, laundromats, daycare, convenience stores, fitness centers | Ignores natural barriers (rivers, highways, mountains) and assumes uniform accessibility |
| Drive-time based | Isochrone contours (5, 10, 15 minute drive) from the subject site | Restaurants, medical offices, car washes, general retail | Accounts for road network and speed limits; more accurate in areas with variable access |
| Corridor-based | Follows traffic corridors rather than concentric shapes | Gas stations, highway-oriented hotels, truck stops, QSR with drive-through | The trade area follows the road, not a radius; captures corridor dynamics that radius-based misses |
For destination-oriented properties (wedding venues, glamping resorts, destination hotels), the trade area extends to a regional or even national draw. A wedding venue draws couples from a 30 to 60 minute drive radius. A destination glamping resort may draw 70% of guests from a 3 to 5 hour drive radius. These larger trade areas require different analytical frameworks than neighborhood-serving businesses, and the demand model must account for the broader competitive set that comes with a wider geography.
We document the trade area definition, the rationale for the boundary chosen, and any adjustments made for natural or artificial barriers. A 3-mile radius that crosses a major interstate with no convenient interchange effectively excludes the population on the other side. The study must acknowledge and adjust for these real-world constraints.
Once the trade area is defined, we profile its demographic and economic characteristics. The specific indicators analyzed vary by property type, but the framework follows a consistent structure.
| Demographic Category | Key Metrics | Data Source | Property Types Most Affected |
|---|---|---|---|
| Population and growth | Total population, 5-year growth rate, projected growth, net migration | Census Bureau, ESRI, state demographer | All property types (foundational metric) |
| Age distribution | Median age, cohort breakdowns (under 5, 18-34, 35-54, 55-64, 65+) | Census ACS, ESRI | Daycare (under 5), fitness (18-44), medical (55+), senior housing (65+) |
| Household income | Median HHI, income distribution brackets, per capita income | Census ACS, ESRI | Restaurants (average check), hotels (rate tolerance), glamping (willingness to pay premium) |
| Housing characteristics | Renter %, multi-family density, median home value, housing units without in-unit laundry | Census ACS | Laundromats (renter %), multifamily (supply context), self-storage (mover rate) |
| Employment | Unemployment rate, major employers, industry concentration, daytime population | BLS, Census LEHD/OnTheMap, state workforce agencies | Restaurants (daytime traffic), medical (employer-sponsored insurance), retail (spending power) |
| Vehicle ownership | Vehicles per household, commute patterns, public transit usage | Census ACS | Gas stations (fuel demand), auto repair (service demand), c-stores (walk-in vs drive-in) |
| Consumer spending | Retail spending per capita, food-away-from-home expenditure, entertainment spending | BLS Consumer Expenditure Survey, ESRI Tapestry | Restaurants, entertainment venues, retail |
| Education | Bachelor's degree %, school enrollment, college/university proximity | Census ACS | Daycare (educated parents = higher enrollment rates), co-working, boutique fitness |
The demographic analysis is not a data dump. We do not present 30 demographic tables and leave the reader to interpret them. Each demographic finding is connected to a demand implication. "The 1-mile trade area has a 52% renter population" is data. "The 52% renter population implies approximately 4,200 households without in-unit laundry access, representing the core demand base for the subject laundromat" is analysis. The feasibility study must bridge the gap between data and demand.
The demand model converts demographic data and competitive supply into a projected capture for the subject property. We use property-type-specific methodologies, each validated against real-world performance data.
| Property Type | Demand Model | Key Calculation |
|---|---|---|
| Hotel | Room night demand analysis | Total room demand in market (from STR) minus existing supply at projected occupancy = unaccommodated demand + competitive displacement = subject capture |
| Self-storage | SF per capita saturation | Total rentable SF in radius / population = current SF/capita. Compare to equilibrium (7-9 SF/capita). Deficit x population = absorbable new SF |
| Gas station | Traffic capture model | Daily traffic count x capture rate (1-4%) x avg fill-up volume = daily gallon projection. Cross-reference with market share and comparable benchmarks |
| Restaurant | Dining expenditure model | Trade area population x dining-out frequency x avg check = total dining expenditure. Subject capture = expenditure share based on competitive positioning |
| Multifamily | Absorption-based model | Historical quarterly absorption in submarket (from CoStar) x subject share = projected lease-up rate. Validated against vacancy rate trends |
| Medical office | Provider-to-population ratio | Population / existing providers per specialty = patients per provider. Compare to national benchmarks (HRSA data). Gap = unmet demand |
| Fitness center | Penetration rate model | Trade area adult population x gym membership rate (20-22%) = total gym members. Minus existing capacity = unmet demand. Subject capture rate applied |
| Daycare | Childcare need model | Children under 5 x dual-income household rate x center-based care preference = addressable demand. Minus existing licensed capacity = unmet demand |
| Car wash | Traffic-based + membership model | Drive-by traffic x capture rate = transient wash count. Membership ramp model (target members x washes/month) layered on top |
| Laundromat | Renter household model | Renter households without in-unit laundry x laundromat usage rate x visits/month x revenue/visit = addressable market. Subject capture based on proximity and quality |
The dual-validation principle. We never rely on a single demand model. Every projection is cross-referenced against at least one alternative methodology. The gas station fuel volume projection from the traffic capture model is validated against the market share model and comparable station benchmarks. If all three converge on a similar range, confidence is high. If they diverge, the study documents the discrepancy, investigates the cause, and uses the most conservative estimate for the base case DSCR calculation.
The quality of a feasibility study is directly tied to the quality and specificity of its data sources. We maintain subscriptions and access to the following commercial and government databases, selected for each engagement based on the property type and market.
| Data Source | What It Provides | Property Types |
|---|---|---|
| CoStar | Commercial real estate comps: rent, occupancy, absorption, cap rates, transaction data, submarket analytics | Multifamily, office, retail, industrial, self-storage |
| STR (Smith Travel Research) | Hotel performance: occupancy, ADR, RevPAR by comp set, market, and submarket | Hotels, resorts, B&Bs |
| ESRI / ArcGIS | Demographic data, consumer spending, Tapestry segmentation, drive-time analysis, heat maps | All property types (demographic foundation) |
| Census Bureau (ACS, LEHD) | Population, income, housing, employment, commute patterns at tract/block group level | All property types (baseline demographics) |
| Bureau of Labor Statistics | Employment, wages, CPI, consumer expenditure, occupational data | Restaurants (wage rates), medical (provider compensation), all (labor market) |
| NACS (National Association of Convenience Stores) | C-store performance benchmarks: sales/SF, category margins, operating metrics | Gas stations, convenience stores |
| ICA (International Carwash Association) | Car wash performance data: wash counts, RPC, membership metrics | Car washes |
| MGMA (Medical Group Management Association) | Provider productivity, revenue per provider, operating benchmarks by specialty | Medical offices, dental practices |
| NAA (National Apartment Association) | Multifamily operating expense benchmarks by region, size, and property class | Multifamily, apartment |
| State DOT / Municipal Traffic | Average daily traffic counts by road segment | Gas stations, car washes, QSR, any traffic-dependent property |
| Placer.ai | Foot traffic analytics: visitor counts, visit duration, trade area origin, cross-shopping patterns | Retail, restaurants, fitness, any consumer-facing property |
| HRSA (Health Resources & Services Administration) | Provider shortage designations (HPSA), provider-to-population ratios, facility databases | Medical offices, dental, behavioral health |
| OPIS (Oil Price Information Service) | Fuel pricing: rack prices, retail margins, regional benchmarks | Gas stations |
| FRED / Federal Reserve | Interest rates, economic indicators, commercial lending benchmarks | All property types (financial modeling inputs) |
The data source selection is not static. Each engagement requires a customized data stack based on the property type, market, and analytical questions. A hotel study in Nashville uses STR, CoStar, and Placer.ai. A daycare study in rural Georgia uses Census, HRSA, and primary research (competitor phone surveys). A gas station study uses DOT traffic counts, OPIS, NACS, and Placer.ai. The feasibility study cites every data source used, including access dates, to ensure the analysis is replicable and verifiable.
The competitive supply analysis is the counterpart to the demand analysis. Demand without supply context is meaningless. A trade area with 50,000 potential customers sounds strong until you discover that 8 competitors already serve that population.
We inventory every competing facility within the defined trade area using a combination of database searches (CoStar, Google Maps, Yelp, industry-specific platforms), site visits (for properties within driving distance), and phone surveys (for competitor occupancy, pricing, and availability verification). Each competitor is documented with name, address, capacity, pricing, age/condition, competitive positioning, and any known expansion plans.
Pipeline supply is non-negotiable. We identify planned, permitted, and under-construction competitive supply through local planning department records (building permits, conditional use applications), commercial real estate listing services (CoStar, LoopNet), physical site observation (construction activity visible during the site visit), and conversations with local planning staff. A market analysis that only counts existing supply and ignores the pipeline is incomplete and potentially dangerous. We have seen markets flip from undersupplied to oversupplied within 12 months based on pipeline additions alone.
The market analysis is not a standalone section. It is the evidentiary foundation that feeds directly into the financial projections. Every revenue assumption in the pro forma traces back to a specific market finding. Projected occupancy derives from the demand model. Projected rate derives from the competitive benchmarking. Projected absorption timeline derives from the historical absorption data and competitive supply analysis.
This traceability is what separates an institutional-quality feasibility study from a template-based report. A credit officer reviewing our study can follow the logical chain from demographic data to demand quantification to competitive positioning to revenue projection to DSCR. Every link in that chain is documented, sourced, and verifiable. If any link is weak, the study acknowledges the limitation rather than papering over it with optimistic assumptions.
Request a complimentary assessment and we will explain which demand model, data sources, and trade area methodology apply to your specific property type and market. No cost, no obligation.
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