Hotel feasibility study is wrong when ADR and occupancy are treated as fixed certainties instead of uncertain variables with downside risk. If your model says “75% stabilized occupancy at USD 250 ADR” without showing probability distributions, supply pressure, and shock scenarios, it is not investment analysis. It is a point-estimate narrative dressed up as a spreadsheet.
Most investors don’t lose money because someone “got the ADR wrong by $10.” They lose money because the model never priced the probability of a bad year—or a bad three years—where ADR compresses, occupancy softens, acquisition costs rise, and the asset is forced into discounting to maintain volume.
Executive summary (for investors)
- Single-number ADR/occupancy assumptions systematically overvalue hotels in volatile markets.
- What matters is the distribution of outcomes: base, downside, tail risk, and probability of failure.
- Decision-grade feasibility combines: probability ranges + scenario planning + Monte Carlo simulation + supply/policy risk modules.
- If your feasibility cannot show P10 / P50 / P90 outcomes and downside probability, it is incomplete.
If this resonates, it connects directly to Zenith’s broader critique of simplistic returns modeling: Hospitality ROI Miscalculations.
Why hotel feasibility study is wrong in practice?
Because many feasibility studies still follow a legacy habit: forecast demand with a single “most likely” ADR and occupancy path, then compute returns as if reality will cooperate. But hotel markets do not behave like that. They operate under:
- demand volatility and seasonality
- competitive rate pressure from new supply
- channel mix shifts (and distribution costs)
- staffing constraints and service consistency risk
- policy and compliance uncertainty
This is why investor-grade underwriting increasingly needs probabilistic thinking (not only point estimates). The academic and practitioner logic behind Monte Carlo approaches is well established; for example, see the research on Monte Carlo simulation in hotel forecasting: Springer – Monte Carlo simulation in hotel forecasting.
PAA answer block: What does it mean when a hotel feasibility study is wrong?
A hotel feasibility study is wrong when it presents one confident outcome (ADR, occupancy, ROI) but does not quantify uncertainty. Investors need probability ranges, downside scenarios, and the likelihood of breaching debt service or equity targets. If the report cannot show percentiles (P10/P50/P90) and downside probability, it is not decision-grade.
Hotel feasibility study is wrong: the ADR and occupancy fantasy?
The fantasy is the clean stabilization story:
- Occupancy fantasy: “We stabilize at 70–80% because tourism is growing.”
- ADR fantasy: “We price at USD 200–350 because comps do.”
- Timing fantasy: “We ramp smoothly in 24–36 months.”
Reality is messier. Stabilization is not a straight line; it is a fight against competition, seasonality, distribution costs, staffing quality, and product-market fit. In supply-sensitive submarkets, the more common pattern is:
ramp → rate pressure → channel dilution → delayed stabilization
This is exactly the mechanism behind “bubble” dynamics and oversupply-driven underperformance that we’ve addressed elsewhere: Bali’s Real-Estate Bubble: Oversupply Threatens Long-Term ROI.
Where point estimates break first
| Model assumption | What happens in real operations | Why investors get hurt |
|---|---|---|
| Single ADR target | ADR is a distribution across seasons, channels, LOS, and room types. | Base-case ADR gets “averaged” upward, hiding weak months and discounting. |
| Single occupancy target | Occupancy is volatile; supply shocks can shift the curve quickly. | Debt service and cash flow fail in the downside tail, not in the average month. |
| Linear ramp-up | Ramp depends on brand clarity, distribution, reviews, staffing, opening readiness. | Underestimated ramp time destroys IRR and triggers liquidity events. |
| No policy module | Permits, enforcement, restrictions, taxes can shift quickly. | You price risk at zero, then pay for it later. |
Why Bali makes this problem obvious (even when performance is strong)
Investors often confuse “strong demand right now” with “stable risk profile.” Bali can show strong performance signals while simultaneously facing supply additions and policy uncertainty—exactly the combination that breaks point-estimate feasibility thinking.
If you want credible, destination-specific context, use a triangulation of:
- macro/official demand indicators (e.g., periodic tourism development statistics like this BPS Bali release: BPS Bali – Tourism Development (June 2024))
- market performance and pipeline analysis from reputable hospitality analysts (e.g., Horwath HTL – Bali hotel / branded residences publications and the report PDF: Horwath HTL – Bali Hotel and Branded Residences (March 2024 PDF))
- comparable third-party market reporting (e.g., C9 Hotelworks – Bali Hotel & Branded Residences Report (March 2025 PDF))
- policy signalling (e.g., Reuters – Bali development restriction / moratorium reporting (Sept 2024))
The underwriting point is simple: even in a “hot market,” your feasibility must quantify what happens when supply grows faster than demand in your specific micro-location and category.
This links closely to the structural shift away from standalone product and into resilience through integrated ecosystems: Integrated Hospitality in Bali.
What does a decision-grade feasibility study look like in 2026?
A decision-grade feasibility does not “predict” one future. It maps a range of futures and quantifies how often each future occurs. The outputs should look like underwriting:
- ADR and occupancy as distributions (not single values)
- percentiles (P10 / P50 / P90)
- probability of breaching thresholds (IRR hurdle, DSCR covenant, cash burn)
- explicit risk drivers (which assumptions cause downside)
If hotel feasibility study is wrong at underwriting, it will be wrong in capital structure, ramp timing, and exit expectations.
PAA answer block: What occupancy should I use in a hotel feasibility study?
Use a range, not a single number. Start with a conservative base band informed by comparable assets, then model a downside band that reflects supply additions, rate compression, and seasonality. The right question is not “what is stabilized occupancy?”—it is “what is the probability occupancy sits below my debt-safe threshold for 6–12 months?”
PAA answer block: How do I know my ADR assumption is credible?
ADR is credible when it is built from a rate architecture (segments + seasons + channels), validated against comps and your positioning, and stress-tested for compression. If your ADR is simply “market average + optimism,” the model is fragile—especially once new supply enters the set.
If you want a broader investor framing of why simplistic ROI narratives keep repeating in SEA, see: Hospitality ROI in Southeast Asia.
How to stress-test when hotel feasibility study is wrong (Zenith method)
This is the step-by-step section most feasibility reports avoid—because it removes the illusion of certainty. But it is exactly what investors and asset managers need.
- Define the non-negotiable thresholds
Examples: minimum DSCR, minimum cash-on-cash, maximum payback, minimum IRR hurdle, and a “survivability occupancy” line. - Convert ADR and occupancy into distributions (not single values)
Use realistic ranges (e.g., triangular/lognormal). Build ranges with seasonality, comp set, and execution risk. - Add a supply and compression module
Model new room supply by micro-market and apply price/occupancy pressure rules (elasticity assumptions, channel mix shifts). - Add a policy and compliance module
Include permit timing risk, enforcement risk, development restriction scenarios (probability-weighted). A policy signal reference point in this context: Reuters – Bali development restriction / moratorium reporting (Sept 2024). - Run Monte Carlo simulations (1,000–10,000 iterations)
Each run randomly draws ADR + occupancy (and key costs), then calculates NOI, DSCR, and IRR. For a research baseline on why this technique is used in hotel forecasting, see: Springer – Monte Carlo simulation in hotel forecasting. - Report percentiles, not averages
Minimum set: P10, P50, P90 for IRR and DSCR, plus probability of breaching thresholds. - Decide using downside probability
The decision question becomes: “Can this project survive the downside tail without rescue capital or forced discounting?”
Investor rule: If the model cannot quantify the probability of failure, it cannot price risk.
What should the Monte Carlo output look like?
| Metric | P10 (downside) | P50 (base) | P90 (upside) | Decision meaning |
|---|---|---|---|---|
| Project IRR | Below hurdle | Near hurdle | Above hurdle | Do we survive downside without recapitalization? |
| DSCR | Potential breach | Stable | Strong | How often do we miss debt safety? |
| Cash burn risk | High | Manageable | Low | How much liquidity must we hold? |
This format forces clarity: not “what’s the return,” but “what’s the probability we fail.”
So what should investors do differently?
- Stop buying point estimates. Demand distributions and downside probability.
- Underwrite the product, not only the spreadsheet. Concept clarity and operating readiness are value drivers.
- Model supply explicitly. Pipeline is not noise; it is competitive reality (see pipeline/performance reporting such as Horwath HTL – Bali Hotel and Branded Residences (March 2024 PDF) and C9 Hotelworks (March 2025 PDF)).
- Model policy risk. If the market has visible restriction/moratorium signaling, feasibility needs a policy module (e.g., Reuters – Sept 2024).
- Plan liquidity. Most failures are not “bad projects,” but undercapitalized downside tails.
FAQ: ADR, occupancy, and feasibility risk
1) Why is ADR the most dangerous number in a feasibility study?
Because ADR is the easiest number to “round up” without immediate detection. A small ADR uplift across a full year inflates NOI, which inflates valuation and IRR—especially in low-density luxury or lifestyle concepts. Decision-grade ADR must be built from rate architecture, validated against comps, and stress-tested for compression and channel cost.
2) What is a realistic way to model occupancy in Bali or Indonesia?
Model occupancy as a distribution with seasonality bands and a downside case tied to supply and rate pressure. Use official tourism statistics to understand variability (for example: BPS Bali – Tourism Development (June 2024)). Translate occupancy volatility into survivability: how often you fall below your debt-safe line, and for how long.
3) Does Monte Carlo replace scenario planning?
No. Monte Carlo quantifies probability across thousands of combinations, while scenario planning captures structural shifts (supply surges, policy changes, demand shocks). The best approach is both: scenarios define the worlds; Monte Carlo quantifies outcomes and threshold breaches inside each world.
4) What is the operator’s role in making feasibility targets achievable?
Operators convert assumptions into reality through opening readiness, SOP execution, staffing quality, guest journey consistency, and review performance. If operations are weak, ADR and occupancy targets become marketing claims rather than outcomes, delaying stabilization and compressing margins.
If you want a practical operations lens on why feasibility assumptions die at opening, start here: Pre-Opening SOP Checklist.
5) What should I ask before accepting a feasibility report?
Ask for: (1) P10/P50/P90 outputs, (2) probability of DSCR breach, (3) explicit supply and policy modules, and (4) clear downside drivers. If the consultant cannot quantify failure probability or show percentile outcomes, you are not seeing investment-grade analysis.
Summary takeaways
- Hotel feasibility study is wrong when it sells certainty instead of pricing risk.
- ADR and occupancy must be treated as distributions, not single numbers.
- Supply pipeline and policy uncertainty are first-order underwriting variables in Bali/Indonesia (see, for example, Horwath HTL and Reuters).
- Monte Carlo + scenario planning turns feasibility into decision-grade investment analysis (see: Springer Monte Carlo paper).
- If you cannot see percentiles and downside probability, you cannot protect capital.
Call to action
If you want a second-opinion review of your feasibility (ADR logic, occupancy logic, supply risk, downside probability), Zenith can audit your model and convert it into a risk-assessed decision brief—built for investors, not marketing decks.
If your project is approaching opening, pair feasibility stress-testing with operational readiness audits: 42-Point Pre-Opening Handover Audit.
About the author
André Priebs is the CEO of Zenith Hospitality Global, an operator-first hospitality advisory and management platform focused on luxury boutique hotels, lifestyle retreats, and wellness/longevity assets across Bali and Southeast Asia. André works with owners, developers, and family offices to translate “concept” into measurable performance—through product DNA, feasibility and underwriting, pre-opening governance, operating systems (SOPs, org design, training), and commercial strategy.
His core viewpoint is simple: most hotel pro formas are not wrong because analysts can’t model—most are wrong because they underprice uncertainty. Zenith’s work replaces point-estimate feasibility with risk-assessed underwriting (probability distributions, scenario planning, and Monte Carlo simulation), so capital decisions are based on downside survivability, not optimistic averages.
Connect with André on LinkedIn: https://www.linkedin.com/in/priebs/
Learn more about Zenith Hospitality Global: https://zenith-hospitality.com/
