AI agents may reshape the global hotel booking market! Morgan Stanley is bullish on industry giants such as Marriott (MAR.US) and Hilton (HLT.US), and has raised price targets on multiple related stocks.

On Monday, Morgan Stanley noted in its latest research report that as AI agents gradually enter the travel search and hotel booking space, the global hotel industry's distribution model is expected to undergo a major transformation. AI may not only help hotels reduce their reliance on online travel agencies (OTAs), but also increase the proportion of consumers booking directly through hotel official websites through more personalized recommendations, thereby lowering the industry's overall marketing and distribution costs. The bank believes that international hotel groups with massive loyalty programs will be the main beneficiaries, and has therefore raised its earnings expectations and price targets for multiple hotel companies including Marriott International (MAR.US) and Hilton Worldwide Holdings Inc (HLT.US). Morgan Stanley estimates that global annual travel booking transaction volume exceeds $1.6 trillion, of which hotel room revenue is approximately $640 billion. OTAs contribute about $220 billion in hotel booking transaction volume and earn approximately $30 billion in related fee revenue from it. At the same time, the global hotel industry spends approximately $22 billion to $55 billion annually on marketing, of which about 40% to 50% flows to search channels such as Alphabet Inc. Class C (GOOGL.US). Combining OTA commissions and hotel marketing expenses, Morgan Stanley estimates that AI agents could influence approximately $55 billion to $85 billion in related annual spending in the global hotel industry. The bank noted that the traditional hotel booking process typically begins with Alphabet Inc. Class C search, OTA platforms, or hotel official websites, while the rise of AI agents could change the way consumers find and book hotels. In the future, consumers may directly describe their travel purpose, budget, accommodation preferences, and loyalty benefits to AI, which will then screen hotels and recommend the most suitable booking channel. This change is expected to reduce hotel customer acquisition costs, particularly benefiting large hotel groups with mature loyalty programs and high brand recognition. Morgan Stanley's latest research found that compared with traditional search engines, large language models (LLMs) clearly tend to display branded hotels and their official websites when recommending hotels. According to tests conducted by the bank's AlphaWise team across multiple U.S. travel destinations, in traditional Alphabet Inc. Class C search results, branded hotels accounted for approximately 41% and independent hotels approximately 59%; while in recommendations generated by large language models, branded hotels accounted for 87% and independent hotels dropped to 13%. The difference in booking links is even more pronounced. In U.S. destination tests, hotel official website links accounted for only about 13% of Alphabet Inc. Class C search results, while OTA platform links accounted for about 78%; in contrast, in large language model recommendation results, hotel official website links accounted for as much as 97%. Similar trends have emerged in the European market. Morgan Stanley's tests in London, Nice, and Florence showed that in large language model recommendation results, hotel official website links accounted for 74%, higher than approximately 8% in Alphabet Inc. Class C search results. However, the bank emphasized that these results reflect current AI recommendation mechanisms and do not mean that all future hotel bookings will shift to direct channels. Morgan Stanley further found that hotel loyalty programs may become an important factor determining AI recommendation results. When consumers explicitly state in AI searches that they hold membership in a certain hotel group, AI recommendation results will clearly favor the corresponding brand and its official website. This means that hotel groups' long-accumulated member data, points rewards, exclusive room rates, and membership tier benefits may stronger competitive advantages in the AI era. Morgan Stanley believes that large chain hotel groups such as Marriott International, Hilton Worldwide Holdings Inc, and Hyatt Hotels Corporation Class A (H.US) are best positioned to leverage massive loyalty programs, proprietary customer data, and technology partnerships to drive more consumers to book directly. In contrast, although smaller hotel groups are also expected to benefit from lower distribution costs, their actual gains may fall short of larger competitors due to relatively limited loyalty program scale and brand premium. The rise in hotel direct booking proportions will also bring significant financial benefits. Morgan Stanley pointed out that OTA platforms typically charge hotels commissions of about 10% to 20%, while the related cost of completing bookings through branded hotel official websites is about 5%. Therefore, even if only a small number of bookings shift from OTA platforms to hotel official websites, it could improve hotel operating profit margins. The bank estimates that for every 1 percentage point increase in the proportion of direct hotel bookings, hotel owners' EBITDA is expected to grow by approximately 0.1% to 0.5%, while hotel brand operators' EBITDA is expected to grow by approximately 0.25% to 0.45%. In addition, for every 1 percentage point decrease in OTA commission rates, hotel owners' and brand operators' EBITDA could also gain approximately 0.15% to 0.65% and 0.25% to 0.65% of growth space, respectively. Based on the above analysis, Morgan Stanley expects that the distribution cost reductions brought by AI agents will gradually materialize starting in the second half of 2027 and further drive hotel company earnings improvement in 2028. The bank estimates that by 2028, the average profit margin of the hotel CareTrust REIT Inc (REITs) it covers will increase by approximately 0.2 percentage points compared with previous forecasts, while the average franchise and related fee rates of large hotel brand operators are expected to increase by approximately 0.1 percentage points. In terms of valuation, Morgan Stanley raised its price targets for multiple U.S. hotel companies. Among them, Marriott International's price target was raised from $380 to $384, Hilton Worldwide Holdings Inc from $332 to $336, Hyatt Hotels Corporation Class A from $209 to $211, Wyndham Hotels & Resorts (WH.US) from $91 to $92, and Choice Hotels International, Inc. (CHH.US) from $91 to $92. For hotel REITs, Morgan Stanley's price target adjustments were even more pronounced. The bank raised Park Hotels & Resorts' (PK.US) price target from $11 to $13, an increase of about 18%; DiamondRock Hospitality (DRH.US) from $11 to $12; Host Hotels & Resorts, Inc. (HST.US) from $22 to $24; and Pebblebrook Hotel Trust (PEB.US) from $13 to $14. Morgan Stanley believes that hotel REITs are expected to benefit directly from lower distribution costs, so their valuation improvement potential may be higher than that of brand operators. The bank maintained its overweight ratings on Marriott International and Hilton Worldwide Holdings Inc. However, the rise of AI agents does not mean that online travel platforms such as Booking Holdings (BKNG.US) and Expedia (EXPE.US) will lose competitiveness. Morgan Stanley pointed out that OTA platforms have extensive hotel inventory, mature loyalty programs, payment capabilities, and order fulfillment infrastructure, and these advantages remain difficult for AI agents to fully replace. At the same time, AI may mainly change the way consumers discover hotels rather than completely disrupt the final transaction stage. For consumers looking for distinctive accommodations or independent boutique hotels, OTA platforms' rich hotel resources may instead play a greater role in AI recommendations. In addition, OTA platforms may also reduce their own marketing spending due to intensified competition in search channels, thereby offsetting part of the impact of declining commission revenue. Therefore, Morgan Stanley's overall assessment of Booking Holdings and Expedia is relatively neutral, and it maintained the price targets for both companies unchanged. However, considering that large chain hotels may attract more consumers to book directly through AI, the bank lowered its 2027 and 2028 EBITDA forecasts for Expedia by 1% and 6%, respectively, but maintained its $235 price target. Booking Holdings' earnings forecasts remained unchanged. For Alphabet Inc. Class C, Morgan Stanley believes that AI agents may pose certain challenges to its traditional travel search advertising business. The bank estimates that a considerable portion of hotel-related marketing spending flows to search engines, and AI agents will become a new entry point for consumers seeking accommodation information. As travel search traffic shifts toward large language models, Alphabet Inc. Class C may face more intense competition. However, Morgan Stanley noted that travel-related business accounts for approximately 5% of Alphabet Inc. Class C's revenue, and even if some revenue is affected, its overall AI business development opportunities may still far outweigh the risks facing the travel search market. It is worth noting that the bank also reminded investors that the long-term impact of AI agents on the hotel industry remains uncertain. If AI platforms strengthen commercialization in the future through advertising, sponsored recommendations, or commission models, hotel distribution costs may rise again. At the same time, if a small number of large AI platforms ultimately control the main travel search traffic, their bargaining power may even exceed that of traditional search engines and OTA platforms. In addition, AI agents may also directly assume booking and transaction functions in the future, further changing the distribution of interests among hotel groups, OTA platforms, and technology companies. Morgan Stanley believes that although the above risks are worth watching, current research results already indicate that AI agents are expected to help large hotel groups increase direct booking proportions and reduce the hotel industry's persistently high distribution costs. The bank expects that as AI technology becomes further integrated with hotel loyalty programs and booking systems, hotel groups' brand influence and member loyalty will become more important, and companies with massive member bases and strong direct sales capabilities are expected to occupy a more favorable position in this round of industry transformation.