Wert-Berater, Inc. is an independent agritourism feasibility study consultant preparing lender-grade analyses for farm attractions, U-pick operations, corn mazes and fall festivals, farm stores, on-farm food service, agritourism lodging, and diversified farm venues. Our studies evaluate the resident and visitor market area, attendance and visitor capture, seasonality and operating days, admission and per-capita spending, retail and food-service revenue, weather exposure, staffing and site capacity, capital cost, debt-service coverage, and downside sensitivity. 4,000+ engagements since 1998 covering $41.2 billion in evaluated project value.
Agritourism is a visitor-attraction business operated on a farm, and it must be underwritten as one. Its revenue is attendance multiplied by spend per visitor, both of which have to be built from the population within driving distance rather than from the farm’s acreage or production. The distinguishing risk is compression: most agritourism operations earn the majority of their annual revenue in a small number of weekends, which means a handful of rained-out days can decide the year.
Methodology draws on U.S. Census Bureau and American Community Survey population, household income, and age distribution for the drive-time market area, state and regional tourism office visitation data where the operation targets travellers, drive-time analysis of the actual road network, a direct inventory of competing and complementary attractions with their pricing and operating calendars, NOAA climate records for the operating season, and the operation’s own gate counts, point-of-sale records, and admission history where one exists.
Every Wert-Berater financial model is fully linked with no hardcoded values, so any reviewer can stress any input. Deliverables comprise a complete narrative report and the linked Excel model, with ten-year pro forma, sensitivity analysis at ±5, 10, and 15 percent, interest-rate stress from +0.5 to +3.0 percent, and ratio analysis benchmarked against comparable visitor-attraction and diversified-farm operating data.
Agritourism sits at a boundary between agricultural and commercial lending, and the applicable programme depends on how the enterprise is structured. USDA Rural Development programmes reach rural tourism and small business enterprises in defined circumstances; SBA programmes may apply where the venture qualifies as a small business; Farm Service Agency and Farm Credit System lending is directed at the farming operation itself; and conventional lenders set their own coverage standard, commonly but not universally 1.20x. Where an agritourism enterprise is added to a working farm, lenders commonly examine the two as separate cash-flow centres, and the study presents them that way. Eligibility is determined by the lender and the agency on the applicant’s facts, and we do not assume a programme applies.
Wert-Berater completed a $10,066,000 winery and event venue feasibility study in Temecula Valley, California, an engagement combining agricultural production with a visitor and hospitality component and requiring exactly the attendance, capture, and per-visitor spend analysis an agritourism credit demands. We state its scope precisely: it was a winery and event venue rather than a U-pick, farm attraction, or seasonal festival operation, and we do not present it as one. We hold no published completed engagement for a farm-attraction or U-pick enterprise. That work sits within 4,000+ engagements since 1998 covering $41.2 billion in evaluated project value, including a substantial visitor-attraction and hospitality practice. Independence is non-negotiable: determinations follow the evidence and are not revised under pressure.
An agritourism feasibility study establishes how many people will visit, how often, how much they will spend, and whether the resulting revenue — earned across a compressed and weather-exposed season — services proposed debt. It is a visitor-attraction analysis conducted on agricultural land, and the farming operation and the attraction are modeled as separate cash-flow centres.
Attendance is built from the market rather than asserted from a target. The analysis first defines the market area by drive time along the actual road network — not by radius, since rural travel times vary widely with road quality and terrain — and typically distinguishes a primary catchment of frequent local visitors from a secondary catchment that will travel for a destination-quality experience once a season.
Within each ring, the relevant population is the segment that actually visits this kind of attraction. For most farm attractions that is households with children, and the analysis uses Census and American Community Survey data on household composition, age distribution, and income rather than raw population, because a market area’s total headcount overstates the addressable audience substantially.
Capture rate — the share of the addressable population that visits in a season — is the study’s central judgement and the assumption most often inflated. It is derived from the competitive position: how many comparable attractions already serve the same catchment, what they charge, how established they are, and what the subject operation offers that they do not. A market already served by several established farm attractions supports a materially lower capture rate for a new entrant than an unserved one. Repeat visitation is modeled separately from first visits, since an operation with a seasonal-pass or multi-event model earns several visits from one household while a single-event attraction earns one. Where the operation is established, actual gate counts govern; where it is new, the projection is anchored to documented attendance at comparable attractions in comparable markets and ramped over a realistic period rather than opening at stabilized volume.
Agritourism revenue concentration is extreme and the model reflects it at the level of individual days rather than as an annual total. A great many operations earn most of their revenue in a fall season of six to eight weekends, and within that season the peak weekends may account for a disproportionate share again. Spreading annual attendance evenly across an operating calendar produces a projection that bears no relationship to how the business actually works.
The model therefore builds attendance by weekend and by day type, distinguishing peak weekends, shoulder weekends, weekdays, and any school or group programme operating midweek. That structure is what makes the two most important questions answerable: whether site capacity can physically handle the peak days the projection requires, and what happens when a peak weekend is lost. Off-season revenue — spring events, summer U-pick, school tours, or a farm store operating year-round — is modeled where it genuinely exists and not credited where it does not, since fixed costs continue through months that may generate almost nothing.
Revenue per visitor is built line by line rather than as a single blended figure, because the components behave differently and carry different margins. Admission or gate revenue is the most predictable and is modeled at the pricing structure actually proposed, including any group, child, senior, or season-pass differential, with the resulting effective average admission calculated rather than the headline rate applied to every visitor.
U-pick revenue is a function of the crop, not just of visitor count. Volume picked per visitor, the price per unit or per container, and above all the length of the picking window govern it, and that window is agronomically fixed: a crop ripens when it ripens, and a season shortened by weather or advanced by heat cannot be extended by demand. The model ties U-pick revenue to the crop’s realistic harvest window and to the yield actually available for picking, and it recognises that unpicked fruit in a short season is lost revenue that cannot be recovered later.
Retail and food service are modeled at realistic capture and spend. Not every visitor buys, and the model applies a purchase rate rather than assuming universal participation. Food service in particular is capacity-constrained on peak days: an operation with limited service points will lose sales to queue length regardless of demand, and the model reflects throughput capacity rather than crediting unlimited spend. Where established, point-of-sale data governs; where new, per-capita assumptions are anchored to comparable operations and stated as such rather than presented as certainties.
Ancillary enterprises are each modeled as a business with its own cost structure rather than as incremental margin. A farm store carries inventory, staffing, and shrink, and its margin differs sharply between farm-produced goods and purchased resale merchandise; the model separates the two. Food service carries food cost, labour, equipment capital, and licensing obligations, and where it operates only on event days its fixed cost is spread across a small number of trading days.
Where an operation proposes lodging, group facilities, or private bookings, each is modeled on its own occupancy and rate assumptions rather than folded into a visitor average. We draw a clear line around wedding and formal event business: a dedicated wedding and event venue is a different enterprise with a different market, a different sales cycle, different facility requirements, and a different underwriting profile, and it is addressed in our event venue and winery feasibility study practice. Where a farm proposes both an agritourism attraction and a wedding venue, the two are modeled separately, because a projection that blends high-value event bookings into a per-visitor average will misstate both.
Weather is the defining operating risk of this asset class and it is modeled explicitly rather than absorbed into a general contingency. Most agritourism activity is outdoors, and a rained-out Saturday in October is not deferred demand — those visitors largely do not come the following week, because the season is short and their plans move on.
The analysis uses NOAA climate records for the operating season to establish the historical frequency of adverse days, then models the revenue consequence of losing peak days specifically rather than average days. The headline result is stated plainly: how many lost peak weekends the operation can absorb before coverage fails. That figure is more informative to a lender than a base-case ratio, because in this sector a single poor season is a realistic annual outcome rather than a remote scenario.
Mitigation is assessed for what it actually delivers. Covered or indoor space, advance ticketing with non-refundable terms, and rain-date policies each reduce exposure to a degree, and each carries its own cost or demand consequence. Where the operation relies on advance sales, the model reflects the realistic take-up rather than assuming the whole gate is pre-sold.
Site capacity sets a hard ceiling that an attendance projection must respect. Parking is usually the binding constraint: the model calculates spaces required at peak-day attendance using realistic vehicle occupancy and dwell time, compares that against the parking actually available including any overflow field and its usability in wet conditions, and identifies where the projection exceeds it. An attendance figure the site cannot park is not achievable.
Restroom capacity, queue space at admission and food service, circulation width on paths and in the attraction itself, and any occupancy limit imposed by local authorities are each assessed against peak-day flow. Where capital investment is required to reach the projected attendance safely, that cost is in the pro forma rather than deferred.
Staffing is modeled as the sharply peaked seasonal requirement it is. A farm attraction may need a large crew on a dozen days and almost none for the rest of the year, and that pattern must be sourced from a rural labour market, trained each season, and supervised. The model prices the peak-day crew, the training and recruitment cost of assembling it annually, and the year-round core staff, and identifies availability risk where the peak requirement is large relative to the local workforce. Owner and family labour is costed rather than treated as free.
The financial conclusion is a coverage determination under stress. The model produces a ten-year pro forma with sensitivity at ±5, 10, and 15 percent applied to attendance, per-capita spend, and operating cost independently and in combination, plus interest-rate stress from +0.5 to +3.0 percent in half-point increments.
Two cases govern. The first is a poor-weather season in which several peak weekends are lost, which the model expresses as the number of peak days the operation can absorb before coverage fails. The second is a capture rate below projection, which for a new attraction is the most common cause of shortfall and which the model tests by holding spend constant while attendance falls. The study also reports break-even attendance — the visitor count required to meet debt service at the modeled spend — and where the farming operation and the attraction are financed together, it shows each as a separate cash-flow centre so a lender can see which one is carrying the credit. An explicit statement of conditions identifies the information relied upon and the assumptions that would change the finding.
This is an economic and financial feasibility analysis. It does not replace land-use or zoning counsel, traffic engineering, life-safety or building-code review, or food-service licensing work. Where a project requires a zoning determination, a traffic impact study, an occupancy or life-safety assessment, or permitting, those are professional inputs to the economic analysis and the study identifies where it has relied on them. Agritourism is subject to state and local land-use rules that vary widely, and confirming what a given jurisdiction permits on agricultural land is a matter for counsel and the local authority rather than for this study.
Related engagements: for weddings and formal events see event venue and winery feasibility studies. For the production side of a diversified farm see vineyard and orchard and crop farming feasibility studies, and where a producer proposes to process or brand farm production for sale through the farm store or beyond, a USDA Value-Added Producer Grant feasibility study addresses that programme directly.
An agritourism feasibility study consultant establishes how many people will visit, how often, how much they will spend, and whether revenue earned across a compressed and weather-exposed season services proposed debt. It is a visitor-attraction analysis conducted on agricultural land.
The work covers drive-time market area and addressable population, visitor capture and attendance, seasonality by day type, admission and per-capita spend, U-pick and retail and food-service revenue, weather exposure, parking and site capacity, seasonal staffing, and capital cost. Where an attraction is added to a working farm, the two are modeled as separate cash-flow centres.
From the market rather than from a target. We define the catchment by drive time along the actual road network rather than by radius, since rural travel times vary widely, and distinguish a primary ring of frequent local visitors from a secondary ring that travels once a season for a destination-quality experience.
Within each ring we identify the addressable segment — for most farm attractions, households with children — using Census and American Community Survey data on household composition, age, and income, because total headcount substantially overstates the audience.
By applying a capture rate to the addressable population in each drive-time ring, then modeling repeat visitation separately from first visits. An operation with a seasonal pass or multiple events earns several visits per household; a single-event attraction earns one.
Where the operation is established, actual gate counts govern. Where it is new, the projection is anchored to documented attendance at comparable attractions in comparable markets and ramped over a realistic period rather than opening at stabilized volume. The result is then checked against site capacity, since an attendance figure the site cannot park is not achievable.
Capture rate is the share of the addressable population that visits in a season, and it is the study’s central judgement and the assumption most often inflated. We derive it from competitive position: how many comparable attractions already serve the same catchment, what they charge, how established they are, and what the subject operation offers that they do not.
A market already served by several established farm attractions supports a materially lower capture rate for a new entrant than an unserved one. We do not apply a universal capture percentage.
At the level of individual days rather than as an annual total, because revenue concentration in this sector is extreme — many operations earn most of their revenue in a fall season of six to eight weekends, with peak weekends accounting for a disproportionate share again.
The model builds attendance by weekend and day type, distinguishing peak weekends, shoulder weekends, weekdays, and midweek school or group programmes. That structure is what makes the two decisive questions answerable: whether the site can physically handle the peak days, and what happens when a peak weekend is lost.
U-pick revenue is a function of the crop as much as of visitor count. Volume picked per visitor, price per unit or container, and above all the length of the picking window govern it, and that window is agronomically fixed — a crop ripens when it ripens, and a season shortened by weather cannot be extended by demand.
The model ties U-pick revenue to the realistic harvest window and to the yield actually available for picking, and recognises that unpicked fruit in a short season is lost revenue that cannot be recovered later.
Line by line rather than as a blended figure, because the components carry different margins and behave differently. Admission is modeled at the pricing structure actually proposed — including group, child, senior, and season-pass differentials — with an effective average admission calculated rather than the headline rate applied to every visitor.
Retail and food service are modeled at realistic capture: not every visitor buys, so a purchase rate is applied rather than universal participation assumed. Food service is capacity-constrained on peak days, since an operation with limited service points loses sales to queue length regardless of demand.
Explicitly, rather than absorbed into a general contingency. Most agritourism activity is outdoors, and a rained-out Saturday in October is not deferred demand — those visitors largely do not return the following week, because the season is short and their plans move on.
We use NOAA climate records for the operating season to establish the historical frequency of adverse days, then model the loss of peak days specifically rather than average days. The headline result is how many lost peak weekends the operation can absorb before coverage fails, which is more informative to a lender than a base-case ratio.
They are different businesses and we model them separately. An agritourism attraction sells many low-value admissions concentrated into a short season, and its economics are attendance, capture, and per-capita spend. A wedding and event venue sells few high-value bookings with a long sales cycle, a different facility standard, different service and staffing requirements, and a different competitive set.
Blending high-value event bookings into a per-visitor average misstates both. Where a farm proposes both, each is modeled on its own terms. Dedicated wedding and event work is addressed in our event venue and winery feasibility study practice rather than here.
The fee is fixed and quoted within one business day of the initial inquiry. It does not vary with the finding and is never contingent on loan approval. Because scope varies with the number of revenue components, whether lodging or food service is included, and the lending programme involved, we quote after a brief intake conversation rather than publishing a schedule.
Standard delivery is ten to fifteen business days from receipt of a complete data room, with rush delivery available. An engagement typically requires the site plan with parking and visitor infrastructure, the proposed pricing schedule and operating calendar, gate counts and point-of-sale history where the operation is established, crop acreage and harvest windows for any U-pick component, construction bids, three years of tax returns and a current balance sheet for the farm, any zoning determination or use permit, and the proposed loan term sheet.
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Wert-Berater, Inc. is an independent provider of feasibility studies and other related services. The firm does not provide financing or equity investment advice, and does not arrange, broker, or place debt or equity capital of any kind.
All appraisal assignments are performed by Bruce E. Jones, MAI, ASA-GC, BCA, CMEA, a member of the Appraisal Institute since 2006, a staff member of Wert-Berater, Inc. and owner of Special Purpose Realty Valuation.