AI agents may reshape the global hotel reservation market! Damo is optimistic that industry giants such as MAR.US (MAR.US) and Hilton (HLT.US) will raise the target prices of many related stocks

The Zhitong Finance App learned that on Monday, Morgan Stanley pointed out in its latest research report that as AI agents gradually enter the field of travel search and hotel reservations, the distribution model of the global hotel industry is expected to usher in major changes. AI may not only help hotels reduce their reliance on online travel platforms (OTA), but will also increase the proportion of consumers who book directly through the hotel's official website through more personalized recommendations, thereby reducing overall marketing and distribution costs in the industry. The bank believes that international hotel groups with a huge membership system will be the main beneficiaries, and as a result, they have raised the profit expectations and target prices of many hotel companies such as Marriott International (MAR.US) and Hilton Hotels (HLT.US).

Morgan Stanley estimates that global travel booking transactions exceed $1.6 trillion a year, with hotel room revenue of around $640 billion. The online travel platform contributed approximately US$220 billion in hotel reservation transactions and received approximately US$30 billion in related fee revenue from it. At the same time, the global hotel industry spends about 22 billion to 55 billion US dollars in marketing each year, of which about 40% to 50% goes to search channels such as Google (GOOGL.US). Combining OTA commissions and hotel marketing expenses, Morgan Stanley predicts that AI smart devices are expected to affect the global hotel industry's annual related expenses of about 55 billion to 85 billion US dollars.

The bank pointed out that traditional hotel reservation processes usually begin with Google searches, OTA platforms, or official websites, and the rise of AI agents may change the way consumers find and book hotels. In the future, consumers can directly describe travel purposes, budgets, accommodation preferences and membership benefits to AI, and AI will screen hotels and recommend the most suitable reservation channels. This change is expected to reduce hotel customer acquisition costs, particularly for large hotel groups with mature membership systems and high brand awareness.

A recent study by Morgan Stanley found that compared to traditional search engines, the Big Language Model (LLM) clearly favors showing branded hotels and their official websites when recommending hotels.

According to tests by the bank's AlphaWise team on several travel destinations in the US, in traditional Google search results, branded hotels accounted for about 41% and independent hotels accounted for about 59%; in the recommendation results generated by the big language model, brand hotels accounted for 87%, and the proportion of independent hotels dropped to 13%. The difference in booking links is even more pronounced. In the US destination test, the proportion of links to the hotel's official website in Google search results was only about 13%, and OTA platform links accounted for about 78%; in comparison, in the big language model recommendation results, the proportion of links to the hotel's official website was as high as 97%.

European markets are also showing similar trends. Morgan Stanley's tests in London, Nice, and Florence showed that in the big language model recommendation results, the proportion of links to the hotel's official website reached 74%, which is higher than about 8% of Google search results.

However, the bank emphasized that these results reflect the current AI recommendation mechanism and do not mean that all future hotel reservations will switch to direct sales channels.

Morgan Stanley further discovered that hotel membership loyalty programs may be an important factor in determining AI recommendation results. When consumers clearly indicate in an AI search that they have a membership in a certain hotel group, the AI recommendation results will clearly be skewed towards the corresponding brand and its official website. This means that the membership data, point rewards, exclusive room rates, and membership level benefits accumulated by hotel groups over a long period of time may be transformed into a stronger competitive advantage in the AI era.

Morgan Stanley believes that large hotel chains such as Marriott International, Hilton Hotels, and Hyatt Hotels (H.US) are best able to use huge membership systems, proprietary customer data, and technical cooperation relationships to drive more direct consumer bookings.

In contrast, although smaller hotel groups are also expected to benefit from lower distribution costs, actual benefits may fall short of larger competitors due to the relatively limited size of the membership system and brand premiums.

The increase in direct hotel bookings will also bring significant financial benefits. According to Morgan Stanley, OTA platforms usually charge hotels a commission of about 10% to 20%, while the costs associated with completing a reservation through the official website of the brand hotel are about 5%. Therefore, even if only a small number of bookings are transferred from an OTA platform to the hotel's official website, it may improve the hotel's operating profit margin.

The bank estimates that for every 1 percentage point increase in the share of direct hotel bookings, the profit before tax, depreciation and amortization (EBITDA) of hotel owners is expected to increase by about 0.1% to 0.5%, while the EBITDA of hotel brand operators is expected to increase by about 0.25% to 0.45%. Furthermore, for every 1 percentage point reduction in the OTA commission rate, the EBITDA of hotel owners and brand operators may also gain room for growth of about 0.15% to 0.65% and 0.25% to 0.65%, respectively.

Based on the above analysis, Morgan Stanley predicts that the reduction in distribution costs brought about by AI smart devices will gradually be reflected starting in the second half of 2027, and will further promote the improvement of hotel companies' profits in 2028.

The bank expects that by 2028, the average profit margin of hotel real estate investment trusts (REITs) covered by it will increase by about 0.2 percentage points compared to the previous forecast, while the average franchising and related fees of major hotel brand operators is expected to increase by about 0.1 percentage points.

In terms of valuation, Morgan Stanley raised the target prices of several US hotel companies. Among them, the target price for Marriott International was raised from $380 to $384, Hilton Hotels was raised from $332 to $336, Hyatt Hotels & Resorts (WH.US) was raised from $209 to $211, Wyndham Hotels & Resorts (WH.US) was raised from $91 to $92, and Select International Hotels (CHH.US) was raised from $91 to $92.

In terms of hotel REITs, Morgan Stanley's target price adjustments are even more obvious. The bank raised the target price of Park Hotels & Resorts (PK.US) from $11 to $13, an increase of about 18%; DiamondRock Hospitality (DRH.US) from $11 to $12; HST.US (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 directly benefit from falling distribution costs, so their valuation improvement room may be higher than that of brand operators. The bank maintained its incremental ratings for Marriott International and Hilton Hotels. 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 their competitiveness.

Morgan Stanley pointed out that OTA platforms have extensive hotel inventory, mature membership systems, payment capabilities and order fulfillment infrastructure, and these advantages are still difficult to be completely replaced by AI agents. At the same time, AI may mainly change the way consumers discover hotels rather than completely disrupt the final transaction process.

For consumers looking for specialty accommodations or independent boutique hotels, the OTA platform's rich hotel resources may instead play a greater role in AI recommendations. In addition, OTA platforms may also reduce their own marketing expenses due to increased competition on search channels, thereby offsetting the impact of some of the decline in commission revenue. As a result, Morgan Stanley's overall judgment on Booking Holdings and Expedia was relatively neutral, and the target prices of the two companies remained unchanged.

However, considering that large hotel chains may use AI to attract more direct consumer bookings, the bank lowered Expedia's 2027 and 2028 eBITDA forecasts by 1% and 6%, respectively, but maintained its target price of $235. Booking Holdings' earnings forecast remained unchanged.

As far as Google is concerned, Morgan Stanley believes that AI agents may pose a challenge to its traditional travel search advertising business. The bank estimates that a significant portion of hotel-related marketing expenses goes to search engines, and AI agents will become a new entry point for consumers to find accommodation information. As travel search traffic shifts to big language models, Google is likely to face more intense competition.

However, Morgan Stanley pointed out that the travel-related business accounts for about 5% of Google's revenue, and even if some revenue is impacted, the overall AI business development opportunities may still far outweigh the risks faced by the travel search market.

Notably, the bank also reminded investors that there is still uncertainty about the long-term impact of AI agents on the hotel industry.

If AI platforms strengthen commercialization through advertising, sponsorship recommendations, or commission models in the future, hotel distribution costs may rise again. At the same time, if a few large AI platforms eventually seize the main travel search traffic, their bargaining power may even surpass traditional search engines and OTA platforms.

In addition, AI agents may also directly assume reservation and transaction functions in the future, further changing the distribution of benefits among hotel groups, OTA platforms, and technology companies.

Morgan Stanley believes that although these risks are worth paying attention to, current research results have shown that AI intelligence can help large hotel groups increase the proportion of direct bookings and reduce the distribution costs that have remained high in the hotel industry for a long time. The bank expects that with the further integration of AI technology with the hotel membership system and reservation system, the hotel group's brand influence and member loyalty will become more important, and companies with a large membership base and strong direct sales capabilities are expected to occupy a more favorable position in this round of industry transformation.