Hotel real estate professionals, such as developers and lenders, often depend on independent market studies to evaluate the feasibility of hotel assets. Traditionally, these studies are prepared by third-party consultants or in-house analysts who assess supply and demand dynamics, project operating performance, and provide financial forecasts. As artificial intelligence continues to evolve, the industry is asking an important question: Can AI replace traditional consulting methods in conducting hotel market studies?
This article explores the current role of AI in market studies, its limitations and where human expertise remains essential.
Traditional approach to hotel market studies
Before evaluating the role of AI, it’s helpful to understand the key steps in a traditional hotel market study. With more than 2,000 hotel market studies prepared across a 35-year career — and having reviewed hundreds of studies by other professionals — I have found the hotel market study process to be generally consistent, even as each site location and hotel asset presents unique variables.
A comprehensive hotel market study typically includes the following six steps:
- Site inspection: A physical inspection of the subject site to assess attributes such as visibility, accessibility, surrounding demand generators and other factors critical to hotel performance. A comparison of the subject site to the attributes of its competition.
- Area review: Analyzing local and regional economic indicators that influence lodging demand. This step involves both the use of national databases and direct interviews with local sources. For example, collecting office market data such as including vacancy and absorption statistics (secondary research) followed up with interviews (primary research) with major employers to discuss work from home rules, lodging needs and/or travel policies that would directly impact future lodging demand.
- Competitive supply and demand analysis: Primary research through interviews with hotel general managers, brand representatives, developers and other market participants. Secondary data sources such as STR, CoStar and AirDNA are also reviewed to support findings.
- Market projections: Forecasting changes in market demand and supply, along with occupancy and average daily rate trends, for the local competitive set. These forecasts must be supported by both primary and secondary research.
- Subject property analysis: Recommending facility assumptions, estimating market share through a penetration analysis or build up analysis, and preparing detailed financial projections (income and expense statements). As with market projections, these forecasts require an experienced consultant to interpret both primary and secondary research.
- Report preparation: Drafting a report tailored to client needs — ranging from internal-use summaries to comprehensive documents required by lenders.
These six steps rely not only on data and facts, but also on the judgment of experienced professionals. Many components of a market study are subjective: assessing site suitability, defining the competitive set, interpreting new supply data and forecasting demand growth. All require insight gained through experience.
What AI can and can’t do
To assess AI’s readiness to conduct a market study, I posed the following questions to a generative AI platform:
1. Is it feasible to build a hotel in Newburyport, Massachusetts?
AI answered, “It appears to be a feasible endeavor,” citing the city’s popularity as a tourist destination — but without analyzing hotel-specific factors.
2. What is the occupancy and ADR projections for a proposed hotel in Newburyport, Massachusetts?
AI returned projections of 63% to 70% and $230 to $260 without any knowledge of the hotel’s location, size, class or positioning.
3. Can you generate a cash flow projection and determine feasibility for a hotel in Newburyport, Massachusetts?
AI provided estimated construction costs, loan terms, and a basic income and expense statement. Once again, AI returned projections without any key project details. It then concluded that the project may not be feasible.
These examples clearly show that AI is not yet capable of independently conducting reliable hotel market studies. However, AI offers several promising applications that can enhance the work of human consultants.
Where AI adds value
While AI cannot replace traditional methods, it can improve efficiency and add value in specific areas:
- Area review: AI can quickly gather and summarize economic and demographic data from multiple databases. When directed properly, it can provide useful context for lodging demand analysis.
- Secondary research: AI can assist in compiling data from many reliable, secondary sources. However, it cannot replace primary research — such as site visits or interviews with market participants — which are essential to understanding project-specific nuances.
- Report writing: Writing a comprehensive hotel market study can take up to 40 hours. AI can streamline this process by drafting area descriptions, demographic summaries, hotel overviews, and formatting maps and tables. I have found that AI can reduce writing time by up to 25%. However, human review remains critical to ensure context is preserved and facts are accurate.
The limits of AI in market feasibility
Despite its benefits, AI has notable limitations:
- No primary research capability: AI cannot conduct interviews, inspect sites or assess local nuances —activities that provide insights into brand changes, upcoming renovations, development timelines or unannounced pipeline projects.
- Data reliability: AI pulls from a wide range of sources, which may be outdated, inaccurate or irrelevant. Consultants must verify data sources to ensure accuracy.
- Lack of contextual understanding: Hotel feasibility is inherently subjective. AI cannot evaluate whether a proposed brand fits local demand generators, nor can it assess competitive positioning or the importance of design elements like level of finish.
Final thoughts: The best of both worlds
AI is not a substitute for experience, intuition and on-the-ground knowledge. However, it can be a powerful tool when used to complement traditional methods. The most effective approach today is a hybrid model: combining AI's speed and access to data with the critical thinking, local insight and professional judgment of experienced consultants.
In short, AI can support, but not at all supplant, the hotel market study process. When paired with “boots on the ground,” AI becomes a valuable assistant — not a replacement — for thoughtful, well-substantiated feasibility analysis.
Rachel J. Roginsky, ISHC, is a principal, owner, and founder of Pinnacle Advisory Group, a premier hotel consulting firm with offices in Boston, New York, Providence, DC and Tampa.
This column is part of ISHC Global Insights, a partnership between CoStar News and the International Society of Hospitality Consultants.
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