Right-Sizing Market Demand: TAM/SAM/SOM for Location-Bound Projects in Hospitality, Retail & Healthcare

For lenders and developers of location-bound projects – whether a new hotel, a retail center, or a healthcare facility – an honest appraisal of market demand can make or break the venture. A project’s success hinges on aligning its scale with realistic demand. Overestimating the market can lead to underperformance and financial shortfalls, while underestimating it could mean leaving opportunity on the table. In feasibility consulting, right-sizing demand means using data-driven analysis to determine how much of the market a specific project can capture in its location. To achieve this, top-tier feasibility studies often employ the TAM/SAM/SOM framework: Total Addressable Market, Serviceable Available (or Addressable) Market, and Serviceable Obtainable Market. This framework provides a structured way to funnel from the broadest possible market down to the portion that a particular project can realistically attract. By clearly defining these tiers – and adjusting them for geography, demographics, and competition – consultants ensure that demand projections are grounded in reality. In this article, we’ll explain the TAM/SAM/SOM framework in the context of U.S. hospitality, retail, and healthcare projects, illustrate how to adjust each layer for local conditions, and demonstrate why right-sizing demand is critical for financial feasibility, risk mitigation, and capital planning. Real-world examples in each sector will highlight the methodology in action, common pitfalls to avoid, and the importance of integrating local data and primary research into market sizing.

TAM, SAM, SOM: A Market Sizing Framework for Feasibility

Total Addressable Market (TAM) – This represents the entire potential market demand for your product or service in a broad sense, assuming no limitations. It’s essentially the revenue opportunity if one could capture 100% market share of all possible customers in the relevant market category. Crucially, TAM is unconstrained – it does not factor in geographic limits, competition, capacity, or other real-world frictions. For example, in hospitality, the TAM for a new hotel might be the total annual spending on lodging by all travelers to the region (or even nationally, if we’re being very broad). In retail, TAM could be the total consumer expenditure in your retail category (e.g. all grocery spending in the U.S., or all apparel sales, etc.). In healthcare, TAM might be the total need for a certain type of care (say all orthopedic surgeries in the state). TAM is a “big universe” number – it tells us the theoretical maximum market size, a starting point for understanding the opportunity. As one hospitality consultant explains, TAM is “the entire potential market size without any constraints like geography, budget, sociographics, or operational limits”. Its value in feasibility studies is mainly to indicate the upper bound of opportunity and to validate that a broad demand exists for the project concept.

Serviceable Addressable Market (SAM) – The SAM is the portion of the TAM that your specific project can target given real-world constraints. We refine the TAM to account for factors like geographic limitations, demographics, and operational scope. In other words, SAM is the market you can actually reach or serve. For a location-bound project, geography is often the biggest filter: only people within a certain radius or drive-time are realistic customers. Demographic and socioeconomic factors further narrow it: e.g. focusing on the income brackets, age groups, or customer segments that align with your offering. SAM also excludes portions of TAM that are not feasible for you to serve – due to regulatory barriers, your capacity, or the subset of demand that prefers something you don’t provide. For example, if TAM for a new retail store is “all grocery shoppers in the city,” the SAM might be those within, say, a 5-mile trade area of the store who match the target customer profile (e.g. families with certain income levels). If TAM for a hospital is the entire region’s healthcare needs, the SAM might focus on the service area for that facility – perhaps a particular county or set of zip codes – and possibly on the services the hospital will offer (excluding specialties it won’t have). Essentially, SAM asks: “Of the big pie (TAM), what slice could we reasonably go after given who and where we are?” As a hospitality example, for a new bar, “SAM would represent all the people within a certain radius who are willing to visit a bar with a similar concept, who can afford your menu, and who are within your reach given your physical location”. This concept of reach is especially critical for location-specific projects, as we will explore with trade area analysis.

Serviceable Obtainable Market (SOM) – Finally, SOM is the share of the SAM that your project can realistically capture. No project captures 100% of its addressable market; competitors and practical limits on market share must be considered. SOM accounts for the competitive landscape and your own capacity/strategy to estimate what percentage of the serviceable market will actually choose your project over others. In effect, SOM is your projected market sharewithin the SAM. This will depend on how many similar offerings exist, how strong your value proposition is, and any operational constraints (like number of rooms or beds, opening hours, etc.). For instance, if your SAM is $50 million in annual demand (say, all potential customers within reach), you might estimate your SOM as perhaps 5–15% of that, depending on competition and your advantages. In feasibility terms, SOM translates directly to forecast revenues or usage – it’s the demand you expect to actually tap into. As one source succinctly puts it, “SOM is the segment of the SAM that you can capture… taking into account competition, unique value, and market positioning”. The SOM gives a realistic goal for your project’s performance, anchored in market realities.

Each layer – TAM, SAM, SOM – adds realism to the demand picture. TAM is broad and theoretical; SAM is narrower and more practical; SOM is targeted and achievable. Think of it as a funnel: “These metrics provide a progressively more realistic picture of your business’s revenue potential”, moving from the broad universe to what you can obtain. In feasibility studies, using all three helps avoid the trap of overly rosy projections. By calculating TAM, SAM, and SOM, consultants ground their market projections in defensible assumptions at each step, which is vital for investor and lender confidence. A feasibility plan that simply says “we target all adults in this city” (i.e. uses TAM only) is far less credible than one that demonstrates how the target market was narrowed and what share is reasonable to expect.

Adjusting for Geography, Demographics, and Competition

In location-based projects, geography is destiny when it comes to market sizing. Unlike an online business with national reach, a hotel, store, or hospital draws most of its clientele from a defined catchment area. Thus, a critical part of moving from TAM to SAM is determining where your customers will come from. This often means defining a trade area – the geographic zone that provides the bulk of your customers. Studies show that for everyday businesses, consumers are not willing to travel very far. For example, about “93% of consumers travel no more than 20 minutes for everyday purchases”, underscoring that a retail store’s market is largely local. In practical terms, a feasibility analysis for a retail project will delineate a primary trade area (perhaps the 5- or 10-mile radius, or a 10–15 minute drive, that contains, say, 70–80% of expected customers), and maybe secondary and tertiary areas for the few customers coming from farther afield. The trade area defines the geographic scope of your SAM – essentially drawing a boundary around the portion of total demand that is serviceable by virtue of proximity. A convenience-oriented business (like a grocery store or urgent care clinic) will have a small, tight trade area, whereas a “destination” business (a unique regional mall or specialized hospital) might have a wider draw. Understanding this radius is crucial; as one location analytics expert notes, “the further people are from your store, the less likely they are to visit”, so modeling must weight customers by distance and accessibility.

Demographics and spending power are the next filters. Not all members of the population within your geography are equally likely to be customers, and their economic profiles vary. A common mistake is to assume every warm body in the area counts toward demand, which can inflate your SAM beyond credibility. Instead, robust feasibility studies segment the local population: by age, income, preferences, etc., to identify the true target subset. For instance, if you plan an upscale boutique hotel, you might focus on travelers or locals in a certain income bracket or lifestyle segment who prefer boutique accommodations, rather than all travelers. The KRG Hospitality example showed this clearly: in a medium city with 1,000,000 annual visitors (TAM for hotel rooms), they estimated that only 20% of those visitors prefer boutique hotels and fit the age profile of the concept – yielding a SAM of 200,000 visitors (and $200M revenue) relevant to the boutique hotel. By narrowing focus to the right demographic slice, they dramatically shrank the market size to a realistic scope. Similarly in retail, a luxury retail developer in a metro area must account for the fact that, say, “a rural household in the Midwest won’t spend like an affluent Manhattan household”, so counting them the same in a TAM would be misleading. The solution is to weight and adjust the market by local spending power and consumer profiles: “focus on the top 50 metro areas where your ideal customer actually lives” (if that applies), or within a city, focus on the neighborhoods that match your target demo. In other words, refine your SAM to the people in your area who have the need and means for your offering. This might reduce the numerical market size on paper, but it “dramatically increases your credibility” with stakeholders, as it shows you’re realistic about who your customers will be.

Finally, to get to SOM, we incorporate the competitive landscape and market share assumptions. Even within your serviceable market, you will rarely be alone in serving those customers. A thorough feasibility study will map out existing and potential competitors – for example, other hotels in the city (for a hotel project), other shopping centers or stores in the trade area (for retail), or existing hospitals/clinics in the region (for a healthcare project). This competitive analysis helps determine a reasonable capture rate. If your concept is strongly differentiated or the first of its kind in the area, you might capture a higher share of the SAM; if entering a crowded field, your share might be modest. Analysts often use market share benchmarks or analogs: e.g., looking at similar projects in comparable markets to see what portion of local demand they captured. They also consider capacity – if you’re building a 100-bed hospital in a region with 1,000 patients needing care (SAM), in theory it could serve 10% if the patients choose it, but operational realities (staffing, referral networks) might mean you effectively capture a smaller fraction initially. As a retail startup guide notes, projecting SOM is about applying “realistic market share assumptions” based on competition and your own capabilities. It could be as simple as saying: “We aim to get 10% of the local market in our first few years,” or more detailed using gravity models or share-of-market calculations. The key is that SOM should be grounded in evidence – either through pilot data, competitor performance, or a bottom-up build of how many customers you can win. For example, one retail analysis suggests that in a crowded sector, an initial SOM might be on the order of only 0.5–2% of the SAM for a new entrant – a sober reminder not to overestimate.

To illustrate adjusting these tiers, consider a new grocery store in a suburban community. The TAM might start as “all grocery spending in the county,” but the SAM is trimmed to the store’s primary trade area (perhaps a 3-mile radius) and the households within that area. Say there are 50,000 people in that radius, and the average household spends $5,000/year on groceries. That could imply, very roughly, ~$200 million annual grocery demand in the trade area. But if our store is a specialty organic grocer targeting health-conscious consumers, maybe only 50% of that demand is relevant (those who buy organic or premium groceries) – now SAM might be $100 million. Finally, with two established supermarkets in town, a realistic goal might be to capture 10% of the serviceable market by offering differentiated products and experience. That yields a SOM of $10 million in annual sales for our store (equating to roughly that 10% share). Such a calculation is necessarily approximate, but it shows how geography (3-mile radius), demographics (health-conscious shoppers), and competition (existing supermarkets) all constrain the raw TAM into a feasible opportunity. This disciplined approach prevents overly optimistic sales forecasts and ensures the store’s size and cost structure are aligned with the true local market potential.

Why Right-Sizing Demand is Critical: Feasibility, Risk & Capital Planning

Getting the market size right – neither too high nor too low – is foundational for a project’s feasibility analysis. Lenders and equity investors rely on these demand forecasts to gauge whether a project will generate sufficient revenue to cover costs and debt service. Overestimating demand is one of the most dangerous mistakes in development feasibility. If a project’s pro forma is predicated on capturing a market that isn’t truly there, the result can be chronic underperformance, cash flow shortfalls, and ultimately loan defaults or project failures. Unfortunately, it’s not uncommon for optimistic entrepreneurs or less rigorous studies to overstate TAM or assume an unrealistically high SOM. In fact, research on failed business ventures shows that poor market sizing is a major factor: “42% of startups shut down because they built products with no market need”, often due to overestimating the total market, targeting unreachable segments, or projecting unrealistic share. While real estate projects differ from tech startups, the core lesson applies: if you overinflate the market size or your capture of it, the venture is set up for trouble from the start.

Right-sizing demand is thus a form of risk mitigation. By using TAM/SAM/SOM diligently, a feasibility study imposes a conservative, evidence-based filter on revenue projections. It forces the question: “What must be true in the market for our project to hit these numbers?” For example, if the only way to justify a hotel’s financials is to assume it achieves an unprecedented 90% market share in its city, that’s a red flag. Lenders will much prefer a study that shows, say, a 10% market share assumption backed by data on comparable hotels – a conservative scenario that the project can reasonably achieve even if conditions are lukewarm. One hospitality feasibility article emphasizes that TAM/SAM/SOM analysis can help avoid “common mistakes such as overestimating your market potential or failing to identify target demographics accurately.” By narrowing the target market realistically, you reduce the chance of a nasty surprise post-opening when revenues fall short. It’s essentially a stress test on paper for the business case. As Qubit Capital notes in the retail context, using honest, data-driven market sizing builds trust: “Conservative, data-backed estimates build trust with investors and stakeholders. Overstated market numbers can undermine credibility”. Lenders in particular are quick to discount hockey-stick projections; they want to see that projections are grounded in real demand, not wishful thinking.

Right-sized demand projections are also crucial for capital planning and design. Knowing the true market size informs how big to build, how much to invest, and how to phase the project. For instance, suppose a feasibility study finds that the serviceable market for a new hospital in a community would only fill about 50 beds at steady-state. If developers were planning a 100-bed facility hoping to attract all regional patients (TAM thinking), the study might save them from overbuilding – or identify that additional demand would need to be stimulated or attracted through partnerships. Building too large for the market leads to overcapacity, which is inefficient and costly to carry. On the other hand, undershooting the size could mean turning away business or not fully addressing community needs (under-capacity). The TAM/SAM/SOM framework helps strike the balance by aligning the project’s scale with the realistic demand. In healthcare, one guidance noted that analyzing local demographics and health needs ensures the hospital is “designed to meet actual community needs, avoiding overcapacity or service gaps”. For a retail developer, right-sizing might mean planning the square footage of a shopping center such that the projected sales per square foot (based on SOM) are healthy, rather than diluting sales across too much space. For a hotel, it might dictate how many rooms to build or what size event space is justified by the market.

From a financial feasibility standpoint, accurate demand sizing underpins revenue forecasts, which drive all the financial metrics – ROI, debt service coverage, etc. Feasibility consultants often present multiple scenarios (base case, upside, downside) varying the capture rate or growth of demand. If the project still “pencils out” under conservative SOM assumptions, that gives financiers confidence. As KRG Hospitality puts it, “when preparing a feasibility study, using TAM, SAM, and SOM ensures that your projections are grounded in reality, giving potential investors or stakeholders confidence in your plan.” Lenders reviewing such a study will recognize the discipline: the analysis isn’t just selling a dream, it’s mapping out how that dream connects to tangible market facts. Moreover, identifying the SAM and SOM explicitly helps in identifying risks – you can discuss, for example, what if competition increases and you only get half of the expected SOM? Is there still enough demand to be viable? By quantifying the obtainable market, you also clarify your dependence on certain assumptions (like winning X% of customers), which can then be monitored or addressed with contingency plans.

In short, right-sizing demand via TAM/SAM/SOM is not an academic exercise – it directly impacts project design and finance. It keeps the project team honest and aligned with reality, which is essential for securing funding. No bank wants to lend on a project premised on capturing a fantasy market. They will, however, respond to a thoughtful analysis that shows exactly how the project’s revenues have been sized to the local market’s depth, with buffers for competition and uncertainty. It’s about aligning vision with viability, to borrow a phrase. A post-pandemic hotel development article noted that feasibility studies must bridge the gap between community “wish lists” and developer ROI demands. Often local stakeholders might insist there is huge “need” for a project (based on anecdotal evidence), but a professional study reframes that into quantifiable demand and ensures that year-round viability is considered, not just peak moments. This way, both the public and private interests can see what the real market picture looks like, and make informed decisions.

Real-World Application Examples

To make these concepts concrete, let’s look at how a feasibility consultancy would apply TAM/SAM/SOM in three sectors – hospitality, retail, and healthcare – highlighting key decision points and insights in each case.

Hospitality Example: Boutique Hotel Feasibility

Imagine a developer is considering a new boutique hotel in a medium-sized U.S. city. The first step in the feasibility study is to assess the overall lodging market in that city (TAM). Analysts gather data on how many visitors the city attracts annually and how much they spend on hotels. Suppose the city welcomes 1,000,000 visitor nights per year and the average spend per visitor on accommodation is about $1,000 (for simplicity). That suggests a TAM of $1 billion in annual hotel revenue potential in the city (1,000,000 × $1,000). This TAM includes all types of travelers (business, leisure, group events) and all classes of hotels. On its face, a billion-dollar market sounds encouraging – but our boutique hotel will only go after a segment of this.

The study then identifies the Serviceable Market (SAM) for the boutique concept. Perhaps market research and the hotel’s positioning indicate that it will appeal to a certain niche: travelers aged 22–42 who prefer independent or boutique accommodations over chain hotels. Through visitor surveys or industry data, we estimate that this segment might represent around 20% of the total market. That would give a SAM of $200 million (which corresponds to about 200,000 of the 1,000,000 visitors) that are in the target demographic and preference group. We also factor geography into SAM: if the hotel is in downtown, it will primarily compete for visitors who want to stay in that area of the city (versus airport or suburban lodging demand). So we might refine the SAM further to “the boutique-preferring visitors who specifically want to stay in the downtown submarket,” which could be a fraction of that $200M. For this example, let’s stick to the $200M for simplicity, acknowledging it’s already a narrowed slice of the pie.

Now, for the Obtainable Market (SOM), we examine how much of that $200M our boutique hotel could capture. We look at the supply of existing hotels, their occupancy rates, and how a new entrant might fare. Suppose downtown has a few boutique or lifestyle hotels already, plus many chain hotels. If our proposed hotel has, say, 100 rooms, what occupancy and average daily rate (ADR) could it reasonably achieve? Perhaps by year 3 it aims for a stabilized occupancy of 70% at an ADR equal to market average. That might equate to serving ~25,000 room nights a year. As a share of the 200,000 boutique-inclined visitors (which is ~200k room nights demand), that’s about 5% market share. Indeed, our consultants might have initially assumed a 5% capture of the SAM given the competition and the hotel’s capacity. Calculating 5% of the $200M SAM yields a projected $10 million annual revenue for the hotel (i.e. SOM = $10M). We’d cross-check that $10M against the hotel’s 100 rooms and rate – does it make sense? $10M/year for 100 rooms implies $100k per room per year, which is roughly $274 per room per day. If ADR is around $200, that implies ~74% occupancy – in the ballpark of our target. These sanity checks ensure the SOM is coherent with both market share and operational capacity.

By right-sizing the demand in this way, the feasibility study sets realistic expectations. The TAM of $1B might have seduced an uninformed observer into thinking “there’s plenty of market for another hotel,” but our analysis shows the project should bank on about $10M of that – just 1% of the broad TAM, or 5% of the relevant niche. This disciplined approach is critical. It prevents the developer from pro formaing the deal with inflated revenue (which could lead to building too large or taking on too much debt). Instead, the project is scoped such that capturing 5% of the niche market yields a viable business. It also highlights the importance of the hotel’s strategy – to achieve that SOM, the hotel must compete effectively. This is where the feasibility study’s insight guides decisions: maybe the hotel needs a distinctive food & beverage offering or boutique design to pull those guests from competitors. The TAM/SAM/SOM analysis thus informs not just how much demand to expect, but what kind of demand to target and how to position the property.

It’s worth noting common misconceptions we’ve encountered with hospitality clients. City officials or local investors sometimes say, “We need another hotel; every hotel was sold out during the big festival last summer!” Indeed, during peak events the unconstrained demand (TAM) might exceed supply. But one must consider the whole year. A seasoned hotel consultant will caution that anecdotal peak shortages don’t equal a viable year-round market. As HVS Hospitality reports, community stakeholders often focus on special event peaks, “not considering lodging demand during off-peak periods, such as a Tuesday night in January”, whereas developers care about consistent demand that supports profitability year-round. In other words, true feasibility requires looking at average and off-peak demand, not just TAM at peak moments. Our TAM/SAM analysis inherently does that – it looks at total annual visitors and typical behavior, smoothing out the seasonal highs and lows. So, by right-sizing demand, we avoid the pitfall of building for the peak and being half-empty the rest of the time. In this boutique hotel example, the result might be a go/no-go recommendation or perhaps a resizing of the project (fewer rooms or phased development) to ensure that the capturable demand will be sufficient to achieve the necessary occupancy and rates for financial success.

Retail Example: Trade Area Market Sizing for a New Store

Consider a plan to develop a new open-air retail center on the edge of a growing metropolitan suburb. The anchor of this center is a specialty grocery store, accompanied by some smaller shops and eateries. From a market sizing perspective, the development team needs to know: is there enough spending power in this location to support the new retail, and how much of that spending can our project capture?

We’d start by defining the trade area for the center – essentially the geographic polygon from which it will draw the majority of customers. Using tools like drive-time analysis and population mapping, suppose we determine the primary trade area includes three ZIP codes with a total population of 80,000. These residents currently have to drive further to reach certain specialty grocery offerings, indicating a potential gap. We compile data on consumer expenditures in the area: let’s say the average household in these suburbs spends $10,000 annually on retail goods and food (groceries + dining). With roughly 30,000 households (80k population at ~2.7 per household), that’s a TAM of about $300 million in annual relevant consumer spending within the trade area. That TAM represents the total wallet for retail categories our center might cover (grocery, dining, etc.) among people who live nearby.

Now we refine to Serviceable Market (SAM). Not all that $300M is up for grabs – there are existing competitors and certain categories we might not have. Perhaps out of the $300M, grocery and food-away-from-home constitute $150M. If our project is focused on those categories, we zero in there. Moreover, maybe only a certain segment of consumers will be attracted to our specialty grocer (for example, health-conscious or upscale shoppers) – say 50% of the households, based on income and preferences, are target customers. That might reduce the serviceable segment to ~$75M in grocery/food spending that aligns with our concept and is within reach geographically. Also, consider that some of the spending by local residents will inevitably continue at competitors (people won’t exclusively shop at the new center). The SAM, however, is just identifying what is potentially addressable if we captured all target customers in the area. We might also include some inflow from secondary trade area (nearby towns) but keep it conservative if those folks are less likely to drive here regularly.

Finally, we estimate the Obtainable Market (SOM) – what share of that ~$75M can our center realistically capture? We look at competition: if there’s one other large grocery store serving part of this area, and a few smaller markets, how will the pie split? For illustration, we might forecast that the new specialty grocer could capture around 20% of the grocery spending by local target customers, especially if it’s offering something unique (this could be higher or lower depending on differentiation; a truly unique store might capture more). That would be about $15 million in annual grocery sales (20% of $75M) flowing to our store. The ancillary shops and dining in the center might capture another, say, $5M from the local dining/retail budgets. So the total center SOM could be on the order of $20M/year initially. We’d then evaluate if $20M in sales is enough to make the project feasible given rents, development costs, etc. If not, perhaps the capture rate can grow over time as the area population increases or as the center proves its draw. We might also test scenarios: if the store only captures 10% ($7.5M in sales), does the investment still work? This ensures we understand the risk if uptake is slower.

During this retail market sizing, a few key insights emerge. One is the role of access and convenience – because the project is location-bound, ease of access via roads and the distance people are willing to travel will limit the market. If our trade area analysis shows that a highway or river cuts off part of the would-be market (acting as a barrier), we adjust the geography accordingly. Another insight is not to over-generalize population. We leveraged demographic specifics (like income levels) to refine SAM, which is crucial. If we had simply taken the 80,000 people and multiplied by an average spend, we might have overestimated if, for instance, some of those neighborhoods are lower-income with less spending power. We applied the advice from market analysts: “Weight your SAM calculations by population density and spending power, not just raw population numbers.” This gives a more accurate picture of what portion of local wallets we can target.

A real-world style vignette: Let’s say the feasibility study found that within a 10-minute drive, there is currently $50M of unmet grocery demand (perhaps residents are driving out of the area to specialty stores elsewhere). This indicates an opportunity. The study might cite an example like Texas grocery trends: in one region, grocery store visits rose 3.2% in early 2025 as new stores opened to meet local needs – illustrating how capturing unmet local demand can immediately boost usage. By presenting such data, the study shows lenders that the demand is not just theoretical, but evidenced by consumer behavior patterns.

From a developer’s perspective, this right-sizing of demand informs how large to make the center and what tenant mix to pursue. If the SOM for the grocery anchor is $15M, the grocer can size their store appropriately (maybe a 25,000 sq ft store might do that volume, rather than a 50,000 sq ft store that would overshoot demand). The other shops can be calibrated in number and size to match the remaining capture. This avoids the common mistake of overbuilding retail space in hopes that “people will come.” Feasibility studies remind us that people come if the demand is there – and we can quantify that demand. By integrating TAM/SAM/SOM, we aim to calibrate the project to the market. The retail world is littered with failed centers that overestimated their draw (TAM) and ended up half-vacant. Our approach is designed to prevent that, giving confidence to investors that even under realistic market share, the project’s revenue can hit the necessary thresholds.

Healthcare Example: Hospital Service Area Demand Right-Sizing

Let’s turn to a healthcare scenario: a regional health system is considering building a new community hospital (or expanding an existing facility) in an outlying suburb. This is a big capital project – say a $70 million investment – so a thorough feasibility study is essential before proceeding (and indeed, lenders like the USDA or municipal bond investors will demand it).

In healthcare, TAM could be interpreted as the total need or utilization of healthcare services in a broad region. For example, one could look at the entire county or multi-county area and sum up all hospital admissions or all surgeries performed annually – that’s the “universe” of demand for hospital services. However, not all of that is addressable by our specific project. We need to define the service lines and service area for the new hospital to get to SAM. Suppose the new hospital will be a 65-bed general hospital focusing on common inpatient services (medical/surgical, obstetrics, etc.) in the southern end of the county. The Serviceable Market (SAM) will be determined by the service area populationthat the hospital can reasonably serve. If the south end of the county is booming with population while the north end is served by the existing main hospital, it makes sense to define the southern suburbs (maybe including a portion of a neighboring county) as the target service area. We gather data: population size, age distribution, and health utilization rates in that area. If there are 100,000 people in the service area and the average hospitalization rate is X per 1,000 people per year, we can estimate how many hospital admissions originate from that area per year. Let’s say our analysis finds there are 5,000 admissions per year of residents in the service area (across all hospitals). That might be our starting TAM for hospital admissions in that locale.

Next, we refine by service lines and scope: perhaps the new hospital will not have complex tertiary services (no cardiac surgery or neurosurgery, for example), so we exclude the portion of admissions that are for those specialized services (assuming those patients will still go to a major urban hospital). We also account for health trends (if the population is aging, admissions might rise; if outpatient care is shifting some procedures out of hospitals, effective demand might drop for inpatient). After this, we might determine the serviceable admissions (SAM) that the hospital could capture if it drew everyone in area needing general hospital care. Maybe out of the 5,000 area admissions, 4,000 are in scope (the rest being tertiary cases) – that’s our SAM in terms of volume.

However, in the status quo, those 4,000 admissions are going somewhere – likely split between the existing main hospital in the county and a large tertiary hospital in a nearby city (since currently the south county lacks a hospital). So now we consider the competitive dynamic to find our SOM. How many of those admissions can the new hospital attract? Initially, it might not be all 4,000, because habits and referral patterns take time to change. But convenience is a big factor – as seen in a real case where a new suburban outpatient campus “attained 30% market share immediately after opening, indicating that residents preferred the convenience of local healthcare”, even though a larger urban hospital was an alternative. If we assume a similar effect for inpatient care, the new hospital might capture, say, 50-60% of the service area’s in-scope admissions within a few years, especially if it’s the only hospital in that part of the county (loyalty and convenience are on its side). Let’s say the target SOM is 50% of 4,000 = 2,000 admissions per year ultimately. We would then ensure the hospital is sized appropriately (65 beds might handle ~2,000 admissions, depending on length of stay and occupancy rates). Financial models would use those 2,000 admissions (plus associated outpatient services) to project revenues.

The feasibility study would detail how we arrived at those numbers. It likely involves a deep analysis of seven key factors: “patient origin data and service area definition; forecasted population in the service area; socioeconomic characteristics; competitive environment; medical staff (availability and support); service area use-rate trends; and market share trends.” This comprehensive approach mirrors TAM/SAM/SOM logic: define where patients come from (geography), how many there are and how they use services (demographic and utilization TAM), narrow to the subset of services we’ll provide (service line SAM), and assess market share given competition and physician support (SOM). In one case study, the initial feasibility done by a general accounting firm had failed to do this kind of market analysis – they simply took management’s volume projections (which were overly optimistic) at face value. The result? The hospital expansion underperformed because the projected demand wasn’t there. As the healthcare consultants later noted, “without a deep understanding of healthcare markets, [the previous analysts] were in no position to evaluate projected volumes, which proved to be inflated.” This underscores how critical proper market sizing is in healthcare, where millions of dollars and community health outcomes are at stake. Our rigorous approach, by contrast, is meant to produce a debt-ready feasibility report that lenders (like the USDA in a rural hospital loan) can trust because every assumption is documented and grounded in local data.

One insightful twist from that case: even after right-sizing demand and building the new hospital, there was an unexpected issue – the hospital didn’t initially capture as many patients as projected, not due to lack of demand, but due to operational execution. Physicians had promised to refer and be present at the new facility, but some did not follow through, so patients kept going to the main hospital by default. This was quickly corrected by adjusting staffing, and volumes rose to expected levels. The lesson for feasibility: even if you size the market right, you must also align the project’s operations (doctors, services, quality) to actually capture that market. In planning, we assume if we build it and run it well, X% will come – that assumption itself needs buy-in from stakeholders like physicians or retailers (in retail context) who drive the traffic. Therefore, a good feasibility study doesn’t just toss out numbers; it also identifies what needs to go right for SOM to be achieved (e.g., physician recruitment, competitive positioning, marketing efforts) and flags those for the development team to implement.

In summary, the healthcare example demonstrates the TAM/SAM/SOM framework in a complex setting: defining the community need (TAM), the portion we can serve (SAM), and the share we expect to win (SOM). It shows how right-sizing demand leads to tangible decisions – the number of beds, the services to offer, the financing needed – and how it highlights critical success factors (like physician alignment). From a lender’s viewpoint, seeing that a hospital’s projections are built up from local population health stats, competitor analysis, and conservative share capture provides confidence that the project is viable and that risks (like slower ramp-up) have been contemplated. It’s far more reassuring than a blanket statement like “this county needs a hospital because people are leaving the county for care” – we quantify how many people, for what services, and how we’ll attract them back.

Common Mistakes and Misconceptions in Market Sizing

Even with a solid framework, there are several common pitfalls in TAM/SAM/SOM analysis for location-bound projects. Being aware of these can help lenders and developers recognize whether a feasibility study is robust or flimsy:

• Confusing TAM for the actual market: Some plans implicitly treat the TAM as if it were the achievable market, leading to wildly optimistic projections. This is the classic “if we get just 5% of a billion-dollar market, we’ll be rich!” fallacy in pitch decks, which may ignore that the project has no realistic path to that 5%. In local projects, a variant is using a city’s total population or tourist count as the target market without narrowing by relevance or reach. Always ask: has the study trimmed the TAM down to a serviceable scope, or are they assuming a broad-brush market that overestimates real potential? Overestimating TAM is cited as a top reason ventures fail, because it often means there was “no market need” in the slice the business could actually serve.

• Neglecting Geographic Limits: This is a major issue for site-based projects. If a report claims a new shopping center will capture dollars from an entire metropolitan region, but shoppers won’t actually drive that far, the SAM is overstated. Similarly, for a new hospital to assume it will serve the whole county when the far half of the county already goes to another hospital is an oversight. Feasibility studies must delineate a realistic trade area or service area. A rule of thumb: most customers come from nearby, especially for frequent services. Ignoring this is a mistake. Modern retail analysis emphasizes that population is not the same as accessible market – e.g., dense urban populations might be close by but if transport or barriers impede them, they might as well be distant. A good study uses maps, drive times, and perhaps cell phone mobility data to avoid mis-defining the geography.

• Target Market Too Vague: A poorly done analysis might define the target as “everyone” or in overly broad strokes (e.g. “our target market is all adults 25-54 in the city”). This is insufficient. A feasibility study should integrate a detailed target profile, factoring in demographics and psychographics. As one hospitality expert noted, “Your business plan cannot be ‘targeting males and females between 25 to 45 years old.’ You have to go much deeper.”. If the study doesn’t specify who the likely customers are (beyond age/gender to things like income, preferences, and behavior), it likely hasn’t truly drilled down from TAM to SAM. This can lead to mis-sizing because not all in that broad group are actually prospects.

• Unrealistic Market Share (SOM) Assumptions: This mistake is subtle but very important. After narrowing to a SAM, one might still overestimate how much the project can capture. Watch out for excessive SOM percentageswithout strong justification. For example, if a new hotel proforma quietly assumes it will achieve higher occupancy and rate than all its competitors (hence capturing outsized share of the market), that’s a red flag. In the startup world, investors joke about slides that claim “we only need a 10% market share” – they want to see why you think you can get that 10%. In our context, a study should base SOM on competitive gaps or unique selling points. Claiming 50% of the available market in Year 1 with no evidence is a sign of hubris. A safer approach might be a ramp-up: maybe 5% in Year 1, 10% by Year 5, etc., with reasoning. Essentially, SOM must reflect the competitive reality and the project’s capacity. As Qubit’s analysis highlighted, many failed businesses were overconfident in targeting segments they “can’t actually reach” or grabbing market share that wasn’t realistic.

• Ignoring Seasonality and Peaking: Especially in hospitality and tourism, demand isn’t uniform year-round. If TAM is calculated on an annual basis, a study must ensure the project can survive the troughs as well as the peaks. Sometimes a mistake is to use peak month data and annualize it, which overstates yearly demand. A robust study uses seasonally adjusted figures or at least recognizes variability. This ties into the earlier point about anecdotal “need” during special events – you don’t build a business just for the peak unless the business model supports low utilization outside of it.

• Static Data / No Updates: Markets change. If a feasibility study relies on a static TAM from, say, a few years ago and doesn’t update for recent trends (population growth, new competitors, shifts in consumer behavior), it can mis-size the opportunity. Good analyses will note the trend – is TAM growing or shrinking? – and possibly adjust SAM/SOM projections accordingly. They also may do scenario analysis (what if population grows faster, or a new competitor enters?). A common oversight is failing to anticipate known developments (like another project in planning that could cut into the SOM). The TAM/SAM/SOM framework itself isn’t static; consultants update these metrics as conditions evolve. If an old report is used for a decision a few years later without refresh, that’s risky.

• Lack of Primary Research: Numbers from databases and industry reports are great for TAM and high-level SAM, but local nuances often require primary research. A mistake is leaning solely on secondary data and not validating on the ground. For example, traffic counts, intercept surveys, or stakeholder interviews might reveal patterns that raw data missed (e.g., maybe people say they would travel farther for a unique entertainment venue – extending the practical trade area in that case). Primary research also helps refine assumptions: a survey of local physicians might gauge their willingness to refer to a new hospital (feeding into SOM assumptions), or a focus group of consumers could test how appealing the new retail concept is versus incumbents. As one startup guide advises, combining “primary research (surveys, interviews) to refine estimates” with secondary research for broader context produces the best results. If a study has no evidence of primary data (like zero interviews or local data collection), it may miss critical demand drivers or constraints.

• Overlooking Operator Constraints: Sometimes the market is there, but the project cannot fully capture it due to internal limitations. One classic example is a restaurant that has huge demand (lines out the door) but a small kitchen that limits throughput – their SOM is capped by capacity. In a hotel, if management or staffing issues prevent delivering quality service, market share could lag even if TAM/SAM were ample. In healthcare, as we saw, if doctors aren’t available, patients won’t come. A feasibility study should match the operational plan to the market plan. Assuming 100% capture of SAM while investing in only minimal resources to actually serve customers is a disconnect (we’ve seen unrealistic cases where a project assumed high utilization but budgeted too low for marketing or talent – essentially hoping demand falls in its lap). The misconception is “if demand exists, it automatically flows to us” – not true; one must compete for it.

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By recognizing these common pitfalls, developers and lenders can critically evaluate market studies. If a feasibility report avoids these mistakes – by carefully delineating trade areas, defining target customers, justifying market share, incorporating local intel, and stress-testing assumptions – it’s likely a solid, credible document. If it falls prey to one or more of these, further scrutiny or a second opinion may be warranted before green-lighting the project.

Integrating Primary Research, Local Data & Assumptions

A theme running through successful demand right-sizing is the integration of data with real-world insight. TAM/SAM/SOM shouldn’t be abstract exercises done in an ivory tower; they must be informed by the local context and stakeholder knowledge. Top-tier feasibility consultants blend secondary data (industry reports, demographic databases, published statistics) with primary research (firsthand data collection and validation) to get the clearest picture.

Primary research can include: stakeholder interviews, surveys, focus groups, site visits, and direct observations. For example, in the healthcare case, the consulting team “conducted extensive interviews” with physicians to gauge their support for the new hospital. This revealed whether doctors would shift their practice to the new facility, which is crucial for capturing the patient volume (SOM). In a retail project, primary research might involve surveying residents on their shopping habits or even running a pop-up store trial to see how much interest there is – providing hard data to plug into market share estimates. For a hotel, interviewing local corporate travel planners or event organizers might indicate how much business could be steered to the new hotel. These insights help fine-tune SAM (by confirming who the serviceable audience really is and their preferences) and SOM (by indicating how readily the project can win them over). As StartupNV notes, primary research is “valuable for refining estimates and understanding customer needs,” ensuring your bottom-up calculations aren’t off-base.

Local data is equally important. Generic ratios or national averages can mislead if applied blindly. If average household spend on dining is $3,000, that might not hold in a specific locale where incomes are higher or lower. Thus, consultants pull local economic data – often from sources like the U.S. Census, Bureau of Labor Statistics, or specialized market data providers – to ground TAM and SAM in the reality of that location. For instance, instead of using a national hospitalization rate, you’d look at the county’s actual hospital discharge rates (which might differ due to demographics or health conditions in the community). Instead of assuming a standard retail sales per square foot, you’d look at what comparable centers in the region achieve. Geospatial data can map where customers for existing facilities are coming from, revealing patterns to inform our project’s assumed trade area. In recent years, even anonymized mobile phone data is used to see how far people travel for similar services. All this local data feeds into more precise TAM and SAM calculations.

Developer and operator assumptions must be integrated but also vetted. Often, the project proponents have certain expectations – e.g., a hotel operator might assume they can achieve a certain occupancy by leveraging their loyalty program, or a retailer might project higher sales based on their brand recognition. These assumptions are valuable because they come from experience, but the role of the feasibility analyst is to test and justify them in the context of the local market data. For instance, if a developer assumes a new mall will draw customers from 30 miles away because it’s an upscale outlet center, the analyst would check: is there precedent for that in this region? Perhaps they’d cite data from a similar outlet mall’s study. If an operator plans to price higher than competitors, does the demographic data support that customers can afford it? Sometimes, primary research is used to validate developer assumptions – e.g., a quick survey might confirm that X% of local customers say they’d pay premium for convenience, supporting an assumption that a local micro-hospital could charge slightly higher rates for immediate access.

It’s critical that a feasibility study documents these assumptions and their basis. In the Ascendient case, the final report was 60 pages with every assumption and calc laid out for USDA underwriters. That level of transparency is what lenders/developers should expect. If an assumption is solely “Management believes we will get 30% market share,” it should be backed with reasoning (maybe management’s track record, plus evidence of unmet demand). Otherwise, it’s just a hopeful guess.

The interplay of primary vs. secondary research can be illustrated in an example: say we’re sizing the market for an assisted living facility (healthcare/hospitality hybrid). Secondary data can tell us how many seniors live within 10 miles (TAM in population terms) and what percentage typically need assisted living (perhaps 5% of seniors 75+). That gives a TAM of X potential residents. We refine to SAM by focusing on seniors with income or assets to afford the facility and who don’t already have a solution – maybe that cuts it to Y. Now, primary research can validate these numbers: interviews with local senior centers might reveal a waiting list at existing facilities (implying demand > supply), or conversely, maybe many seniors plan to move away to retire (reducing local demand). We could also survey adult children in the area (who often help decide on assisted living for parents) to gauge interest. If local sentiment is very positive (e.g., 60% say they’d consider the new facility for their parents), we might adjust our capture rate upward; if there’s skepticism, we adjust downward. By integrating these insights, the final SOM projection is not just a number from a formula, but a richly informed estimate.

Finally, it’s important to emphasize iteration. The feasibility process might cycle through TAM/SAM/SOM calculations multiple times as new data comes in or as project plans change. If initial analysis shows too low a SOM to be viable, the team might tweak the project concept (e.g., add a service line, expand the trade area via a shuttle service, or target a different customer segment) and recalc. This agility – combining data analysis with strategic adjustments – is part of what a top-tier consultancy offers. The end product is a demand forecast that all stakeholders have pressure-tested: it incorporates the developer’s on-the-ground knowledge, the consultant’s analytical rigor, and feedback from potential customers or partners. Such a forecast is far more likely to hold true once the project is operational.

Conclusion

In development and financing of hospitality, retail, and healthcare projects, right-sizing the market demand is a critical discipline that separates successful ventures from flops. The TAM/SAM/SOM framework provides a clear, logical way to achieve this, funneling our view from the grand total market (TAM) down to the slice we can feasibly win (SOM) after accounting for location, target demographics, and competition. When applied with rigor, this framework ensures that a project’s ambitions are matched to real opportunities and constraints of its environment.

From the boutique hotel that must thrive beyond the convention weekends, to the suburban retail center that can only draw from so far, to the community hospital that needs sufficient patients to sustain operations – every case shows the importance of aligning supply with true demand. Right-sizing demand underpins financial feasibility: revenue projections derived this way inspire confidence because they are backed by data and realistic assumptions, not wishful thinking. This in turn helps in risk mitigation – stakeholders can identify where assumptions (like capture rate or growth) have uncertainty and plan accordingly. It also guides capital planning – informing how big to build and where to invest resources for the best return.

In practice, using TAM, SAM, SOM is both an art and a science. The science is in the data gathering and quantitative analysis – crunching population stats, spending figures, utilization rates, etc. The art is in understanding human behavior, local quirks, and competitive nuances – something that comes from local research and experience. A top-tier feasibility consultancy leverages both, often uncovering insights that raw data alone would not reveal. For example, learning why residents drive 30 minutes for a certain restaurant (maybe there’s a social aspect or a unique product) can inform whether a new local entrant could capture that business.

We also saw how common misconceptions (like conflating anecdotal “need” with sustained demand, or assuming one can capture the whole market) can be dispelled by a proper TAM/SAM/SOM analysis. It brings a dose of realism that, while it may temper the initial excitement, ultimately leads to more robust and bankable projects. Integrating primary research and stakeholder input further grounds the analysis in reality – it’s no longer just numbers, but a story about this community, this location, and how the project fits into it.

For lenders and developers reading feasibility reports, looking for a clear TAM/SAM/SOM breakdown can be a helpful gauge of quality. It indicates the study went through the right thought process: starting broad, then narrowing focus with justifications at each stage. As one hospitality advisor noted, “calculating TAM, SAM, and SOM is not just about understanding your market; it’s about maximizing your opportunity to stabilize and then scale within it.” In other words, it’s a tool for strategic planning, not just number-crunching. It helps the development team prioritize efforts (e.g., marketing will focus on that SOM segment) and set realistic goals for ramp-up and growth.

In conclusion, right-sizing market demand using TAM/SAM/SOM is a best practice that adds analytical rigor to feasibility studies. It aligns visionary development ideas with the economic reality on the ground. By doing so, it creates a win-win: lenders get confidence that the project can deliver the numbers it promises, and developers get a roadmap for how to capture the market they need. In an era where markets can shift quickly and capital is cautious, this insight-driven approach is not just advisable – it’s essential for turning ambitious projects into thriving assets that serve their communities and meet their financial objectives.

Sources:

1. Klemt, D. (2024). "Unlocking Growth Potential: Understanding TAM, SAM, and SOM." KRG Hospitality – Definitions of TAM/SAM/SOM and their role in hospitality feasibility studies.

2. Radkey, D. (2024). KRG Hospitality Blog – Sample calculations for a boutique hotel TAM/SAM/SOM. – Illustrative example of TAM $1B, SAM $200M, SOM $10M for a boutique hotel.

3. Qubit Capital (2025). "Market Sizing Your Retail Startup: TAM/SAM/SOM." – Discussion of common market sizing mistakes (overestimating TAM, unreachable SAM, unrealistic SOM) and importance of geographic nuances in market sizing.

4. HVS (2025). "Hotel Market & Feasibility Studies: Connecting Vision with Viability in Untapped Markets." – On stakeholders’ perceived “need” vs actual demand, highlighting importance of evaluating year-round demand for hotel projects.

5. Ascendient Healthcare (n.d.). "Healthcare Financial Feasibility: What Is Your CPA Firm Missing?" – Case study of a hospital expansion: initial feasibility with inflated assumptions vs revised study with detailed local market analysis; factors considered in forecasting (service area, population, competition, etc.).

6. Aninver Development Partners (2025). "Feasibility study of healthcare centers and hospitals: key considerations."– Emphasizes aligning hospital size with community needs to avoid overcapacity or gaps.

7. GrowthFactor (2025). "All About Trade Area." – Defines trade area layers and consumer travel behavior (93% won’t go beyond 20 minutes), underlining the geographic limits of retail demand.

8. StartupNV (2023). "TAM SAM SOM Examples." – Highlights the need for primary vs secondary research in refining market size estimates.

9. Logrocket Blog (2025). "How to conduct a feasibility study: Step-by-step guide." – Recommends evaluating TAM, SAM, SOM as part of market analysis in feasibility studies