Sizing Up a Market: Does a Franchise Fit?

The Bottom Line: How Reno Scores for a Jet's

Scoring the Reno–Sparks metro against the key metrics, using the verified figures from the Market Analysis and Competition pages. Demographic and growth figures are verified; pizzeria counts are triangulated estimates and marked est.

Metric	Reno–Sparks reading	vs benchmark	Score
Pizzerias per 10k est	~100–120 pizzerias over Washoe ~513k = ~2.0–2.3/10k	~10–25% BELOW U.S. ~2.2–2.6 — modestly under-served	4
Median household income	Metro ~$87k; target zips $114k–$127k (89521 / 89511 / 89436)	Metro ~8% above U.S. ~$80.6k; target suburbs 40–58% above	5
Population growth	Metro ~1.3–1.5%/yr (corrected down from a 2.2% claim)	~2–3× the ~0.5% national rate	4
Detroit-style style-void	No national Detroit chain in N. NV (nearest Jet's ~440 mi); but R Town owns S. Virginia & Longboards sells it in 2 nodes	Real but NOT virgin — cleanest gap = Spanish Springs / Kiley Ranch	4
Trade-area population	Metro ~513k (Washoe) / ~545k MSA; Kiley Ranch cites a >130k-person trade area >$100k HHI	Clears any single-unit QSR floor (~25–50k) with margin	5
Occupancy cost / rent	Metro rent ~$1.57/sf/mo NNN; 2nd-gen box ~$1.75–$2.25; new-construction $3.00–$4.50; vacancy ~3.4–4.1%	Workable via 2nd-gen churn; new-construction premium tightens the ratio	3
Real-estate availability	Sub-4% vacancy, landlord-favorable; South Reno effectively full (~1.8%)	Tight but executable via 2nd-gen or 2026–27 deliveries	3

Verdict

Reno–Sparks reads as a favorable, not slam-dunk, fit: modestly under-served on pizzeria density, incomes well above national (especially in the target suburbs), growing ~2–3× the national rate, with a real — but not virgin — Detroit-style void concentrated in the newest affluent rooftops (Spanish Springs / Kiley Ranch strongest). The screens that decide it from here are the ones the metrics can't settle from a desk: the real Item 19 AUV, a physically-fit site at a healthy occupancy ratio (the 2nd-gen box vs the Kiley Ranch endcap trade), and protected territory that actually covers delivery.

How we got there — the metrics and method behind that read:

Why fit is the whole game

Market and site selection is the single strongest predictor of a franchise unit's success. A great operator on a starved site loses; an average operator on the right rooftops wins. Demographics, competition, and real estate set a ceiling on volume that no amount of operating skill can lift — which is why franchisors screen sites against hard criteria before they ever sign a franchisee. The point of this page is to make that screen explicit and repeatable.

Everything below is organized around a five-step screening framework. Run a candidate market through it in order; a failure at any step is usually fatal, so there is no reason to reach the later, more expensive steps if an early one fails.

Step	Screen	The question	Kills the deal when…
1	Trade area	How do you draw the geography whose demand you count?	You count demand a customer can't actually reach (rings across a freeway/river).
2	Demand	Are there enough of the right people, with the right money and taste?	Too few rooftops, wrong income band, or the category under-indexes.
3	Competition / void	Is the market over- or under-stored for your format?	Saturated on your style, or a dominant incumbent owns the share.
4	Financial	Does the rent, at realistic volume, leave a profit?	Occupancy cost blows past ~10% of sales; break-even isn't clearable.
5	Real estate / site	Can you actually secure a physically-fit box on terms?	No available prototype-fit space, or unaffordable/unavailable.

National context used as the pizza baseline throughout: the U.S. has roughly 75,700 pizzerias (IBISWorld / PMQ Pizza Power Report 2026, 2025 figure) on ~$49.6B revenue — about 1 pizzeria per ~4,500 residents, or ~2.2–2.3 per 10,000. Chain and independent unit counts are near even (~36k chain / ~40k independent), so competitors should always be counted by format, not lumped.

The metrics that matter, grouped

Each card gives the metric, what it is, why it matters, how to measure it, and the good benchmark. Confidence tags reflect how well-sourced the benchmark is; where a widely-repeated number was wrong, the corrected figure is shown.

GROUP 1 Demand & Demographics

Are there enough of the right people, with the right money and the right taste for the category?

Trade-area population & rooftops (households)high

WhatPeople and occupied households ("mailable rooftops") inside the trade area — the base denominator for all demand math.

WhyA pizza unit needs a minimum captive base; too few rooftops is the most common cause of unit underperformance.

HowPull population + household counts for a 1–3 mi ring or 5–10 min drive from Census/ACS, Esri BAO, or Placer.ai; compare to brand criteria.

GoodDomino's screens for 3,000–15,000 households in a 3-mile ring; food/QSR generally underwrites at ~15,000–50,000 residents per unit depending on format.

Population / household growth (5-yr projection)high

WhatProjected forward change in population/households, the standard 5-year field in demographic reports.

WhyA 7–10 yr lease erodes on a declining base and rides a tailwind on a growing one; growth also flags under-built, less-entrenched markets.

HowRead the 5-yr projection in Esri BAO; compare trade-area rate to metro and national.

GoodFloor = positive. Strong markets beat the ~0.5%/yr national rate (Census; CBO's forward projection is lower, ~0.3–0.4%). A ~2–3%/yr metro is a genuine tailwind.

Trade-area definition: ring vs drive-timehigh

WhatThe method for drawing the geography you count: concentric mile rings vs drive-time isochrones that follow the road network.

WhyThe delineation IS the demand answer. Rings overstate demand where freeways/rivers block access; drive-time reflects how customers actually arrive.

HowBuild both in Esri BAO / SiteSeer / GrowthFactor; use drive-time as primary, rings as a cross-market sanity check.

GoodConvenience QSR/pizza: ~1–2 mi ring or 5–7 min drive. (Keep the demographic trade area separate from the pizza delivery radius of ~2–5 mi / 15–20 min quality window.)

Median household income vs brand's bandmed

WhatTrade-area median household income measured against the income band of the brand's core customer.

WhyValue QSR pizza skews to moderate income + density; premium/fast-casual needs a higher base. A mismatch either way suppresses volume.

HowPull median HHI from ACS/Esri; benchmark to the franchisor's stated band (FDD), not a universal number; layer with a category-spend index.

GoodBrand-specific. QSR overall indexes best ~$40k–$80k; "higher income" is NOT universally better (value pizza deliberately targets moderate-income density). Even Little Caesars has shifted upmarket as delivery grew.

Daytime / employment populationhigh

WhatPeople present during business hours (workers + visitors), distinct from residents; often office/employee count within a 5-min drive.

WhyWorkers drive lunch dayparts; residents drive dinner/weekend. A pizza unit skews residential/dinner but still benefits from lunch worker density.

HowCensus LEHD/OnTheMap, Esri daytime fields, or Placer.ai mobility for the ~11am–2pm window.

GoodConcentration > raw volume: want high counts on both daytime and residential. No universal cutoff (the "8,000-worker office park" is illustrative, not a threshold).

Market Potential Index (MPI) — Esrihigh

WhatEsri index of local demand for a category (e.g. "went to a pizza restaurant") vs the U.S. average. MPI = local rate / U.S. rate × 100.

WhyNormalizes propensity so markets compare apples-to-apples: does this area over- or under-index on pizza demand, independent of raw size?

HowRun an Esri BAO Market Potential report on the relevant variables; read the MPI. Pair with absolute population (high MPI in a tiny market is still small).

Good100 = U.S. average; favor >100 (120 = ~20% above). Practitioner target ~115–120+ (a heuristic, not an Esri-published cutoff).

Spending Potential Index (SPI) & category $/HHmed

WhatEsri index of household spend on a category vs U.S. (100 = avg), paired with the absolute annual $/household figure.

WhyConverts propensity into dollars: $/HH × trade-area households = total available spend to size a unit and estimate capture.

HowRead SPI + the dollar average from an Esri Consumer Spending report (BLS CE Survey source); multiply by households.

GoodSPI >100. Anchors: food away from home ~$3,945/household (2024); pizza ~$314–$357/household (≈ ~$123/person — the often-cited "$318/person" is a per-household figure mislabeled).

Psychographic segmentation (Esri Tapestry)high

WhatClassifies trade-area neighborhoods into Esri Tapestry segments sharing demographic + consumer-behavior traits.

WhyTwo areas with identical income can behave differently; Tapestry shows whether residents match the brand's proven best-customer segments.

HowRun a Tapestry profile in Esri BAO; compare dominant segments to the brand's top-quartile-store customer mix.

GoodNo universal cutoff: "good" = the area over-indexes (index >100) on your best-store segments. Use the current 60-segment / 12-LifeMode taxonomy (the legacy 67/14 system was retired).

Traffic count / vehicles-per-day (ADT)high

WhatAverage daily traffic on the fronting road — a proxy for pass-by exposure and impulse/carryout demand.

WhyVisibility on a busy corridor converts pass-by trips to orders and supports the delivery-hub model; franchisors treat a minimum ADT as a gate.

HowPull ADT from state/municipal DOT counts; confirm near-side / correct direction. Prefer day-parted, directional counts over one 24-hr number.

GoodDomino's criterion 20,000 ADT (variable by market); broader QSR seeks 20,000–40,000 vehicles/day.

GROUP 2 Competition & Saturation

Is the market over- or under-stored for your format — and how much of the demand can you actually win?

Outlets per capita (pizzerias per 10k)high

WhatCompeting units of a category ÷ population, expressed per 10,000. The fastest read on over- vs under-storing. (Detailed in the spotlight below.)

WhyThe denominator check that stops you opening the 6th pizzeria on a block that supports 3 — or reveals genuine white space.

HowDraw the real drive-time trade area, count same-format competitors (Placer.ai / Esri / SafeGraph / Google Places), divide by population, compare to national.

GoodU.S. baseline ~2.2–2.3 per 10k. Above ~2.5–3.0/10k skews saturated; below ~1.5–2.0/10k (with income + no style reason) skews under-served.

Why per-capita alone misleadshigh

WhatA correction layer: units-per-capita only means something after adjusting for income, product-style/price band, and daypart/occasion.

WhyA "low per-capita" market can be bad (poor, wrong taste, daytime-only) and a "high per-capita" one can have room (all cheap delivery chains vs your premium style-void).

HowOverlay three adjusters: income/spend (SPI/MPI), style-gap (per-capita only within your band), and demand-side (IRS + daytime vs residential + delivery overlap).

GoodMethod, not a number: SPI/MPI >110–120 = a tailwind that justifies tolerating higher per-capita competition; <90 flags that even a "low-competition" ratio may not convert.

Index of Retail Saturation (IRS)high

WhatDemand-and-supply ratio: (households × annual category spend/HH) ÷ competing selling capacity. Demand dollars backing each unit of supply.

WhyPuts demand dollars in the numerator, so it captures both how much the market spends AND how much capacity chases it — the antidote to per-capita.

HowHouseholds × per-HH spend ÷ competitor sq ft (or unit count proxy); compare the supportable sales-per-sq-ft to your break-even.

GoodComparative, not absolute: rank candidate markets and favor the higher index (higher = more unmet demand). Sales/sf and sq-ft/capita are the two most-cited yardsticks.

People-per-unit threshold (density rule)med

WhatResidents required to support one unit: trade-area population ÷ existing same-category units, vs the concept's minimum.

WhyThe number development teams underwrite against; translates the abstract ratio into a go/no-go unit count and "headroom units."

HowPopulation ÷ existing units = current people-per-unit; compare to the concept minimum; headroom = (pop / required) − existing.

GoodCross-check pizza against ~4,500 residents per existing pizzeria (all formats). Single-concept QSR minimums vary widely (~20,000–50,000+); pull the concept's own FDD figure, not a generic band.

Category void / white-space analysishigh

WhatGap detection: formats/price-bands under-represented relative to the area's demand — the "white space" where demand exists but supply doesn't.

WhyReframes "is there a pizzeria here?" to "is there a pizzeria of my type, and does demand exceed the supply that exists?" — the affirmative case to enter.

HowEsri Retail MarketPlace Leakage/Surplus; positive leakage = demand escaping = void. Layer format/price to find style-level voids. Tools: Tango, Buxton, SiteZeus, Placer.ai.

GoodFavor positive leakage for your format. (Caveat: a surplus can mean a strong destination cluster worth co-locating near, not "avoid" — pair with qualitative read.)

Cannibalization & trade-area overlapmed

WhatShare of a new unit's sales transferred from your own nearby unit(s) rather than incremental — driven by overlapping drive-time areas.

WhyA market that "looks open" is a trap if the new site just steals from your store next door. Determines whether it adds incremental system profit.

HowModel drive-time overlap; estimate demand drawn from the overlap zone; post-open, measure sales decline at existing stores.

GoodHeuristic band ~15–30%; >20% is the soft danger line. Peer-reviewed fast-food evidence shows only ~13% cannibalized on average — the real test is positive ROI net of transfer, not the %.

Protected / exclusive territory (FDD Item 12)high

WhatThe contractual buffer the franchisor grants: an area it won't open another same-brand unit in — exclusive, non-exclusive, or "protected."

WhyCaps same-brand saturation and defines how much demand you can own. A weak/non-exclusive zone lets a sibling unit next door.

HowRead Item 12 for the definition method, exclusivity, and reserved rights; convert the radius/population to a drive-time polygon over your real trade area.

GoodUrban food concepts seek ~3–5 km / ~3-mi protected zones. Red flag: the FTC requires disclosure, not size, and most zones carve out online/app/delivery orders — the fastest-growing pizza channel.

Competitor quality & share-of-demandmed

WhatWeight competitors by strength (brand, ratings, traffic, format overlap), then estimate the share of demand your unit can realistically capture.

WhyTen weak indies ≠ one high-traffic Domino's + a beloved 4.7-star local. Counting units misses this; share-of-demand becomes a real revenue forecast.

HowScore each competitor on foot-traffic/ratings/format; run a Huff/gravity model (Placer.ai, Buxton, SiteZeus, Esri) for capture % × demand dollars.

GoodNo fixed cutoff: proceed when modeled capturable demand clears break-even AUV after cannibalization; be wary when one dominant incumbent already owns the share.

GROUP 3 Financial & Real Estate

At realistic volume, does the rent leave a profit — and can you secure a physically-fit box?

Occupancy-cost ratiohigh

WhatTotal real-estate carry (base rent + CAM + tax + insurance) ÷ gross annual sales. The single most important lease-viability filter.

WhyOccupancy is mostly fixed; if it eats too much of the sales dollar, the unit can't profit however well it operates. Most often kills a good site at lease.

HowCompute at break-even AND conservative projected sales; if it blows past 10% at conservative volume, walk. Negotiate percentage rent.

GoodQSR/pizza healthy 6–8% of sales; ideal 5–7%; 10% is the danger line where profit is seriously impaired.

Sales per square footmed

WhatAnnual gross sales ÷ total square footage — a productivity yardstick for whether the box is right-sized.

WhyRent is priced per sf, so sales/sf decides whether the box carries its occupancy. A too-large box in a modest market drags the ratio down.

HowSales ÷ interior sf; compare to segment actuals and to break-even sales/sf. Standard QSR box ~1,800 sf.

GoodSegment actual: QSR ~$460/sf, fast-casual ~$505/sf. Break-even floor ~$200–$300/sf; strong target $750–$850/sf. Use break-even as the hard floor.

Break-even sales volumehigh

WhatThe annual sales level where contribution margin exactly covers fixed costs — the minimum the site must produce to avoid losing money.

WhyConverts a lease + cost structure into the one number the trade area MUST support. Below realistic capture, the site fails regardless of operator.

HowBreak-even $ = fixed costs ÷ contribution-margin ratio; convert to break-even sales/sf; stress-test against trade-area demand.

GoodLimited-service break-even ~$200–$300/sf. Site passes only if conservative volume clears break-even with cushion — target sales ~1.3–1.5× break-even.

AUV vs investment (sales-to-investment)high

WhatAverage Unit Volume (FDD Item 19) ÷ total initial investment — revenue dollars returned per invested dollar per year.

WhyBenchmarks the candidate market against real system performance and drives payback + franchisee ROI. Separates strong concepts from cash traps.

HowPull median (not mean) AUV from Item 19; sales-to-investment = AUV ÷ Item 7 investment; payback ≈ investment ÷ annual unit cash flow.

GoodSales-to-investment: 2× = pass line, 3×+ excellent. Pizza AUV: chains ~$656k avg, top systems $1.35–$1.4M, indies ~$385k. Payback 4–6 yrs typical.

Traffic, visibility & accesshigh

WhatAADT on the frontage combined with sign visibility, sightline distance, and ingress/egress quality.

WhyPassing traffic is the funnel, but a driver must SEE the site, decide, and safely turn in. Poor access wastes high counts.

HowDay-parted directional AADT from DOT/INRIX; verify unobstructed signage at road speed and a signalized/protected turn-in.

GoodQSR target 20,000–40,000 vehicles/day; signage readable at ~500 ft in both directions; at least one direction with a protected turn.

Co-tenancy & anchor synergymed

WhatThe mix and pull of neighboring tenants/anchors, and whether they draw the same target customer as your concept.

WhyStrong anchors (grocery, big-box, fitness) create shared trip generation; a mismatched or dying center drags a site down even with good traffic.

HowInventory anchors/co-tenants, assess demographic overlap, quantify anchor pull + cross-shopping with Placer.ai; secure a co-tenancy clause.

GoodGrocery/big-box anchor + on-profile neighbors (e.g. fitness + fast-casual), validated by Placer.ai visitation overlap. Qualitative, not a scalar.

Parking & drive-thru stackingmed

WhatOn-site spaces per 1,000 sf (ICSC standard) plus vehicle stacking capacity in any drive-thru queue.

WhyRestaurants peak far higher than general retail; thin parking chokes throughput at the rush, and short stacking causes drive-offs.

HowSpaces ÷ (sf/1,000) vs ICSC restaurant standards; count cars that queue without blocking the lot; check zoning minimums.

GoodRestaurant-heavy centers 8–12 spaces/1,000 sf (vs ~4–4.5 general); drive-thru stacking ≥8 cars; parcel ~0.5–1 acre.

Labor-market availability & wageshigh

WhatLocal hourly-worker availability and prevailing/minimum wage, which set the unit's labor-cost %.

WhyLabor is a top-two variable cost and is geographically set — an identical concept can run 3–5 pts higher in a high-min-wage market, raising break-even.

HowModel labor cost at the LOCAL minimum wage, not federal; check unemployment/turnover; stress-test the P&L at the market's wage floor.

GoodQSR labor target 25–28% of sales; prime cost (food+labor) ≤60–65%. High-min-wage markets (CA $20/hr fast-food, WA, NY) run 3–5 pts higher.

Site footprint & physical fithigh

WhatWhether the physical space (sf, frontage, venting for ovens, lot geometry) fits the brand's prototype.

WhyA too-small box can't fit the make-line/ovens; an oversized one pays rent on dead space. Build-out feasibility (hood/gas/grease) can kill a good site.

HowCompare against the franchisor prototype spec; verify venting, power, gas, grease-trap feasibility with the construction team on a site visit.

GoodPizza QSR prototype ~1,200–1,600 sf (Jet's carryout/delivery footprint sits here); a 2nd-gen restaurant box that inherits a code kitchen cuts build-out sharply.

GROUP 4 Data & Tools

Where the numbers above actually come from — the free public sources and the paid platforms that run the screen.

Census / ACS — the free base layerhigh

GivesPopulation, households, household size, median income, and (via LEHD/OnTheMap) block-level jobs / worker inflow for daytime population.

Use forSteps 1–2: trade-area population, income band, daytime workers. The free ground truth every paid tool builds on.

Wheredata.census.gov (ACS); onthemap.ces.census.gov (LEHD/LODES employment). No cost.

Esri ArcGIS Business Analysthigh

GivesRing + drive-time trade areas, 5-yr projections, MPI, SPI, Tapestry segments, and Retail MarketPlace leakage/surplus (the void engine).

Use forNearly every metric above — the one platform that ties demand, propensity, spend, and category void together.

WhereEsri BAO (subscription). Index convention: 100 = U.S. average throughout.

Placer.ai — mobility & foot-traffichigh

Gives"True Trade Area" from actual visitor origins, daypart visitation, competitor foot-traffic, cross-shopping/co-tenancy, and white-space modules.

Use forCompetitor quality, daypart demand, cannibalization overlap, and validating anchor pull — observed behavior, not assumed radius.

WherePlacer.ai (subscription). SafeGraph / Azira are alternative POI + mobility sources.

Buxton / SiteZeus / eSite — forecastingmed

GivesCustom best-customer models (Buxton scores 115M+ households) and Huff/gravity + AI regression sales forecasts, cannibalization, and void scoring.

Use forTurning demand + competition into a modeled AUV and capture rate — the number that actually decides viability.

WhereBuxton, SiteZeus, eSite, Kalibrate, GrowthFactor (subscription). Output = forecast sales vs your break-even/target AUV.

DOT traffic counts + INRIXhigh

GivesAverage annual daily traffic on the frontage road; INRIX adds day-parted, bi-directional counts.

Use forThe traffic/visibility screen — confirm the count is on your frontage, correct direction, and strong in your operating daypart.

WhereState/county DOT maps (free AADT); INRIX (paid, day-parted). Verify against a site visit + Street View.

The FDD — the franchisor's own numbershigh

GivesItem 7 (investment), Item 12 (territory), Item 19 (AUV / performance), Item 20 (unit counts + franchisee contact list).

Use forThe brand-specific benchmarks the generic tools can't supply: income band, per-unit threshold, protected radius, real AUV.

WhereThe current dated FDD + validation calls to Item 20 franchisees. Always beats a blog aggregate.

Spotlight: "outlets per capita," pizza edition

Pizzerias per 10,000 residents — the fastest saturation read, and its trap

The most common question about a new pizza market is the simplest: is it already full of pizzerias? Outlets-per-capita answers it — count the pizzerias in the real (drive-time) trade area, divide by population, express per 10,000. It normalizes a dense city and a small town onto one scale.

~75,700

U.S. pizzerias, 2025 (IBISWorld / PMQ Pizza Power Report 2026), on ~$49.6B revenue.¹

~2.2–2.3

Pizzerias per 10,000 residents nationally (~1 per ~4,500 people; ~22 per 100,000).

~36k / ~40k

Chain vs independent units — near even, so count competitors by format, never lumped.³

The real national benchmark is ~2.2–2.3 pizzerias per 10,000 residents. A widely-cited figure of ~2.6 per 10,000 (26.1 per 100,000) is real but comes from a broader ~87,000-establishment POI universe (Maptitude/Caliper, HERE data) that tags any venue serving pizza — use the tighter IBISWorld count for underwriting. Treat ~22–26 per 100,000 as a range driven by what you count as a "pizzeria."

The trap: per-capita alone lies in both directions. Three adjusters have to ride on top of the raw ratio:

Income & spend. A "low per-capita" market with weak incomes or low pizza-spend propensity (SPI/MPI <90) may never convert the apparent opening. Weight the ratio by an Esri spend index.
Style-gap. A market can look saturated on count while every unit is cheap delivery chain and yours is a differentiated Detroit-style dine-in/carryout — compute per-capita only within the format/price band you'd actually enter.
Daypart & occasion. Ten pizzerias that all serve daytime workers who leave at 6pm don't compete with a dinner/delivery unit. Pair the ratio with the Index of Retail Saturation so demand dollars, not headcount, drive the call.

Bottom line: outlets-per-capita is the right first screen and the wrong only screen. A number materially below ~2.0/10k is a reason to look harder — then income, style-void, and daypart decide whether it's real.

The scorecard — run it on any market

A reusable checklist a franchisee can score for any market and any brand. Rate each metric 1–5 against "what good looks like," weight by importance, and pre-commit the pass threshold before scoring so the number — not the excitement — decides.

Metric	What good looks like	How to score 1–5
1. Pizzerias per 10k (saturation)	Below ~2.2/10k with adequate income; not above ~2.6/10k	5 = ≤1.8/10k under-served · 3 = ~2.2 at national · 1 = ≥3.0/10k over-stored
2. Trade-area population	≥ franchisor per-unit threshold (3-mi ring / drive-time) with positive growth	5 = well above threshold · 3 = meets floor · 1 = below floor
3. Population growth (5-yr)	Positive, ideally >1%/yr (national ~0.5%)	5 = >2%/yr · 3 = ~0.5–1% · 1 = flat/declining
4. Median income vs brand band	Squarely inside the brand's target income band	5 = center of band · 3 = edge of band · 1 = far outside
5. Category / style void	Positive leakage; your format/price band under-supplied	5 = clean style-void · 3 = format present but thin · 1 = surplus/over-supplied
6. Demand propensity (MPI/SPI)	Index >100, ideally 115–120+	5 = ≥120 · 3 = ~100 · 1 = <90
7. Competitor quality & share	Modeled capturable demand > break-even AUV after cannibalization	5 = clear white-space · 3 = winnable · 1 = dominant incumbent owns share
8. Traffic / visibility / access	20–40k VPD, endcap, protected turn-in, signage ~500 ft	5 = all four · 3 = decent count, average access · 1 = hidden, low count
9. Occupancy-cost ratio	≤8% of sales at conservative volume (danger at 10%)	5 = ≤6% · 3 = 8% · 1 = >10%
10. Co-tenancy / anchor	Grocery/big-box + on-profile neighbors, cross-shopping	5 = strong on-profile anchor · 3 = mixed · 1 = dead/mismatched center
11. Territory protection (Item 12)	Protected zone covering your real trade area incl. digital/delivery	5 = strong exclusive · 3 = protected w/ carve-outs · 1 = non-exclusive
12. Labor availability & wages	Staffable at 25–28% labor; wages modeled at the local floor	5 = ample labor, moderate wage · 3 = workable · 1 = thin labor / high-wage squeeze

Decision rule

Weight the twelve to taste (returns-driving items — saturation, population, income, occupancy, territory — carry the most). A GO needs both a strong weighted total and no single must-pass metric (saturation, population, occupancy, territory) scoring a 1. One deal-killer overrides a high average. Whitespace is a reason to look, not a reason to sign.

How to tell if a market is right for a franchise