The Airbnb Algorithm: This is Exactly How it Works.

Connection test, my friends, to make sure that this is working and you can hear me well. This video is going to be really data dense. Uh, it's going to be really helpful. If you're an Airbnb host and you're going to be listed this year, 2026, uh, this may be the best work, the most comprehensive work we have on the Airbnb algorithm yet. This is going to be really exciting.

So, um, welcome aboard. My name is Sean Rocky. I've been a host for 11 years. This channel is dedicated to teaching short-term rentals. And while working with my students Saturday, um, everything kind of clicked together. You know, I studied the algorithm a lot and somebody asked me just a really new question and my brain was finally able to visualize in a way that I can share with you exactly how the Airbnb algorithm works, how it ranks in a mental model. So, uh, buckle in. This this is actually going to change your life. This might be some of the best work I've done yet. I'm really excited to share with you. And I'm going to give you two other topics just to make sure that this is super super valuable.

The first thing I do want to walk you through is very quickly if you use a channel manager the 15.5% fee change um some of our students are having a big issue with that. So I just want to show you uh kind of how this error is working um in the world of uh, you know, let me just bring up a screen. Um, I'm not as charming as I should be for this. That's the button. There we go. Thank you guys. Welcome to me learning how to live stream.

So this is a screenshot of hospitalitable. They have something called a listings markup under dynamic pricing. If you guys saw my past video on Guesty, this is in financial settings. What's happening? People are having this three-body problem. They're going from Price Labs through their channel manager into the Airbnb multical. All right? And I brought up one of my accounts to give you an example. Let's say your prices are 200 or $226 and you wanted to manage the 15.5% fee here. Well, what happened is hospitalitable specifically, they had a button that said, "We'll handle everything for you. Just click it. No worries." They didn't tell you what they were going to do. And a lot of my students had no clue. They click the button and it updated listing markups. And then what happens is that $200 in change turns into like $260-something dollars out here. And Price Labs doesn't know that the change exists. So it's actually going to break Price Labs logic and make everybody make Price Labs think that all the market like the market's trending upwards. It's going to see all this higher ADR for no reason and it's going to confuse Price Labs. So, I want to just encourage you guys to update your Price Labs to change your base rate and then delete it in markups here or delete it in financial settings.

Okay, now let's get to the algorithm. And also, I have found a glitch in Airbnb's review removal policy thing that will help you get more reviews removed. I promise this on Instagram, so I'm not leaving you guys behind. It's really cool. So, let me give you some notes. Hope you guys are having fun already. Let me give you some notes on how the Airbnb algorithm works. Okay, my friends. So, the Airbnb algorithm, let me scroll this. I swear I'm way more charming when I when I'm prepared. The Airbnb algorithm weighs five total segments in their algorithm shuffle. Okay? There's trust, there's satisfaction, there's value, there's fit, and then there's policy. And they flow in that exact order and the order is actually very important. Airbnb has shifted their algorithm from being an interest algorithm to being a satisfaction algorithm to um a risk mitigation algorithm is what I really think is going on here. And that means that trust is the very most important part of this whole thing. And so if you have low trust metrics, Airbnb is going to show you less. It's like kind of like a gateway. Um, let me make sure that you if you guys cannot see my screen for um the notes. I'd like to know. No, you can't. Okay, good.

So, first is trust. And trust is the presence of uh guest favorites ultimately is a great way to kind of like round out what trust is. So, trust is the absence of low reviews, adverse claims, and cancels. So, a lot of you want to know how do I get guest favorites? Being super hosted isn't good enough. If you have to give somebody a refund, like a cleaning fee refund, that will actually remove you from guest favorites. If you ever cancel on somebody, if you have to give somebody any money back as part of a resolution or a trip-related issue, um, Airbnb will notate that on your account. So, there's actually negative notations. Even if you have nothing but five stars, that's a negative trust, negative reliability metric. Okay.

Now, trust then leads trust and satisfaction kind of do bundle together. Um, and satisfaction of course is your five-star count in total. This includes the percentage of times people rebook with you, but also the percentage of times people leave you a review. So if somebody leaves a review less than half the time, that means that they were not satisfied enough to leave a review because Airbnb's pol like Airbnb's perspective is if somebody was excited enough to to like if they like their stay enough, they'd be excited enough to leave a review, right? Reciprocity exists when people like you. So, even if you have all five-star reviews, but you have a very low review left percentage, um, that's actually going to mark you down. There's a part where this blends into what's called value and fit. So, your reviews actually um also depend on the circumstance of the review. And I'm going to show you a screenshot or actually a screen example of this. We're going to get into the Airbnb platform here, but you can have five-star reviews for some things and three-star reviews for other things. And that's going to contextually matter down the road when people search for you. And so what'll happen is you got a trust that cascades into satisfaction that then gives you what we call a valuated index score. So, a valuated index score, every listing has one. Okay? And so I'll try to explain this. Airbnb scrapes every listing. They scrape your amenities. They scrape your bed count, your declared number of people that you can stay, like all that stuff. Then they scrape your trust and satisfaction. They put that all together. And then they create what's called a value-weighted index. I named that. This is all theoretical, by the way, guys. Um, so we've reverse-engineered this. Okay. So some of these terms are mine. And so every listing has a value-weighted index which is all your static amenetization and then trust and satisfaction. So imagine peak season. Everybody raises their prices. That's the reason why nobody really suffers in the algorithm. If everybody raises their prices, then everybody's prices are higher versus their value-weighted index. But this is also why if you drop your price 15% seven months out in slow season and you're like one of the lower-priced listings in slow season, you get all this algorithm boost for that date range because your value-weighted index is based on a search-by-search basis. You'll rerank per search. It's very dynamic. So let's say every listing has a $500 value-weighted index. If some listings are 600, they'll go down in search because they're less attractive. If some listings are 480, they'll go up because they're more attractive. Okay, this then creates a uh a risk profile score based on the negative marks that you have. So in like Six Sigma and all the all the stuff that you learn like in business grad school um there is a risk profiling where they give three numbers times a thousand each and they give you like a a bigger number like 990 like 9,900 something out of 10,000 and this is how they measure for risk and I believe Airbnb's modeled this out. It's like their version of a credit score. Um, and I'll we'll bring that full circle, by the way. I promise.

So, up here, your trust and satisfaction would be a number out of a 100. They multiply that by your uh value and fit score out of 100. And we'll have to explain fit a little bit more, but I have to show you, and I'll have to show you on Airbnb. That creates what I would call a true negative. And a true negative then would be, let's say you have 97 and 97 out of 100. Well, they multiply all those together, right? And so now you're like 9,980 something out of 10,000, something like that. My math is probably terribly off. Then what they do is they multiply that against your policy exponent. And let's scroll down and show you fit and policy exponent.

So, fit is search reranks value score um based on specific items. So, a pet-friendly search will rank you higher for relevance if you have five-star uh ratings for pets. Okay? And then um your search for like low local travel. Like for example, I'm going to show you something on the screen for Texas like they when you go quick click home screen, it just shows you nothing but local stuff. But if you search from London and you're in London currently, the home screen will show you places like Seoul or Rio. It gives you common travel destinations. So Airbnb is constantly trying to predict what it is that you want. Um, by the way, let me see. Okay, nice to see you, too, Jose. I'm glad that this this stream is actually working. This is important. Okay. Um, I forgot to check for a second. So, um, so you're fit. If you search for pets and you're 4.7, but you have nothing but five-star reviews for pet-related stays, you'll rank higher because that fit um reranks happen. You have a relevancy rerank. So now uh, yeah, we search from reranks uh levers for fit pets stuff like that, even hot tubs and pools and stuff. We I do believe that any filter clicks actually do matter and if somebody gives you a five-star review and they clicked hot tub and barbecue grill, you get you get indexed for that. Um, and so for example, far future bookings and cancellation policy would be one of those things that people might actually care about.

So, here's how the algorithm actually weighs it as an equation. Screenshot this. Okay, let me make sure that you can see it. Please screenshot um this section right here. Okay, right under trust, satisfaction, value fit policy. Please screenshot this. I want you to think about this a lot. Okay, so trust and safe trust and satisfaction times 100 and they multiply that by your separate value fit times 100. Okay, so you get what's called a true negative. And then they multiply that by your policy exponent to give you your final rank out of 10,000. Okay, so an example, let's say you scored 9600 out of 10,000 from your trust and say trust satisfaction value and fit. You you have a -400 true negative. Okay, now they're going to multiply that negative by your cancellation policy exponent. If you have a flexible cancellation policy, perhaps the number is one. Moderate 35 for firm, for example. So let's say you have a firm cancellation policy and a negative 400 true, you would be negative 2,000. Now when people search online and that's if your cancellation policy matters, right? So super super last-minute bookings. Um, the cancellation policy may be less rated. There could be some temporality there. Okay, this because remember this is hypothetical, but this is what we're seeing with my students. So I've modeled this out from thousands of data points, 500 hours of coaching, like 500 hours of working with my students' listings this year. This is what we've culminated to. Okay, now I want to show you some proof of this and then I want to show you um I want to show you the the hack, the glitch in Airbnb's uh review removal thing. Okay, but screenshot that equation.

So, let's imagine that we're searching in Boston. Okay, if we're searching in Boston, you'll notice that there's a lot of super hosts and a lot of guest favorites. They come to the top, right? And because these guest favorites probably have a very high score and they've got a very low true negative, um, then the multiplier for penalty for cancellation policy isn't as important. So, if we wanted to see some of these listings that are not guest favorites, but they're on the first page, there's going to be a combination of things. There's going to be value, which is their price per night, and see free cancellation policy right there. Right? And so, when we look at these, we're going to see value and we're going to see relevance, free cancellation. Okay? These these ones up here, we're not seeing so much free cancellation for the ones that are guest favorite. This one still does. And this one can charge more money because it has all the trust and satisfaction and it has free cancellation and it can charge more money and be lower rated for value. Okay, so combination of the trust metrics, the value metrics and that exponential policy um is why you will sometimes see listings that are not guest favorites on the first page of search. Um, also for fit because there's some data for my types of travel that Airbnb might know about me and it'll it'll impose uh fit metrics in.

Now I want to show you the fit metrics if we are searching for say a pet. Okay, here's what'll happen. The I want actually want to show you the first page first. Okay, so you see this I want you to see these six change right now. Okay, we're going to rerank for a pet search. That's it. We just pick pet. See the whole six changed. Now let me click the first one and let me show you the reviews. When we go to reviews stayed, actually this one doesn't have it. This one has this one must be pet friendly and highly rated. And then let's check this one. Most relevant. Looks like we don't have any reviews for people staying with pets in Boston one more time. Is anybody staying with a pet? I guess people aren't traveling with with pets to Boston because these are all pet friendly. But we've got stayed with kids. This would be a good example, right? So stayed with kids. If I was going to click, let's redo this to show you. If I was going to click instead of a pet. Oh, actually I didn't click pet. That's why. Look at that. I thought I clicked pet, but instead I clicked kid. That's why. So let's go like this. Two pets. Research. Okay, now these change. You guys can laugh at me. It's okay. This happens. Now stayed with kids is there. Stayed with kids. And actually, let's remove kids from my search just so that way we can see the pet only because I made that mistake. Three adults, two pets. Rerank. Now, let's see if these show stayed with a pet at the top. There we go. Stayed with a pet is now showing as relevant. Sorry that took a took a while, guys. So, this is example of Airbnb's right fitting in the algorithm. They're seeing our search terms and they're going to reposition listings based on what we wanted in our search and they identify that. So now this listing could be not perfect five stars but have five star for pets and if they there's a pet-related search they get a little extra boost for that. They get credit for that. That's an important part of the way that this thing ranks. Okay. Um, the other part that I wanted to give you, I think that was the part that I needed to give you guys for proof. Right. So, um, with that said, uh, the things that you can guys can take away is if you ever dispute on Airbnb, uh, like a guest, um, like try to say that there's a cleanliness issue, instead of just disputing for money, we need to dispute our reputation with Airbnb. Like, we need to battle with Airbnb to make sure that we guard our reputation, to make sure that we can remove negative marks from our record. Negative marks from our record affect our reliability score. And that's a that's a big big deal. Okay.

So, let me get back to then now showing you what's up with Airbnb's remove review removal thing. So, tada. So, I was able to determine how we can re re remove reviews. And um, I just want to let you guys know that part of the cheat code here is my team at Cracking Superhost has built an AI that coaches students on how to re remove, right? Right? So, how like how to make arguments. And when we make an argument to remove a review, we want to structure it like an attorney would. We want to say, "Hey, um, this is our claim." And we've got 10 reasons to remove a review. That's how we would normally like to structure our argument. And normally that would work until Airbnb made their change of policy. And so, what they did with the Airbnb policy is now they force fit you into five different templates. Like there's retaliation, coercion, um, a guest is a competitor, things like that, right? Airbnb says that it's not an AI that goes through this stuff and it's not completely AI, but here's how we here's what we think we see and we've proven this with our students, but it's still theory. We have seen that Airbnb can like in a second just like just say that your claim is no good. How could how could a human like review the claim that fast? It's kind of impossible, right? So, we know that it's a it's like AI-driven to a degree. If you want to increase the chance of a manual review, you should upload photo evidence. And if you want to nearly guarantee the chance of a manual review, we need that photo evidence to not be able to be conclusive through an AI trying to read the photo. So, if all of your photo evidence is just like text stuff, it might be able to interpret the text. But if the photo evidence has people in the shot or if the photo seems a little obfuscated and the like AI can't determine what exactly is going on with your evidence, it would kick it into a manual review. And that's where review disputes can take time. Now, when you do that and have evidence that is obviously not legible to a computer, but only to the human eye, what you're doing is you're now allowing yourself to make your multiple points. I do want to warn you, like Airbnb's really screwed something up. They say like, let's say somebody retaliates. You and I will use the word retaliate right now. Let's say somebody retaliates. They ask for a discount or they ask for a refund and you say no. They retaliate by leaving a bad review. That is actually like semantically the right way to say it. But Airbnb classifies that as coercion because the initial thing was they tried to coerce you. They asked you for a refund. And even if they didn't like pressure you, Airbnb still considers the ask for a refund coercion. So if somebody retaliates when they ask for money or something of the sort, you actually have to check box coercion, not retaliation. Retaliation to Airbnb is if somebody tries to check out late and you say no. If somebody tries to cancel their stay and you say no. If somebody wants to bring a pet and you say no. You enforcing a policy and them leaving you a bad review. That is retaliation, but asking for money's coercion. But let's say you have multiple claims. You can now manually kick this to review for a manual review and you say there was coercion. There was retaliation. The guest didn't stay. Never arrived. Doesn't even know what the property looks like. They said irrelevant stuff in the um, you can attack it for relevancy. You can claim that the person was prejudiced against you or whatever violated terms of service 10 different ways. Airbnb will then be able to see all 10 claims. But if the computer tries to process it, all they do is they do node matching. They say there's a coercion claim, scan evidence for coercion claim validity, and if the evidence exists, then grant it. But if you don't make a good claim for coercion and you had seven other good claims, they won't let you make those claims. And you only get two passes. They told you that you can do one and then do an appeal. So if you have seven reasons to re remove a review, they'll only give you two. And so this is the way to kick the manual review and then you can actually kitchen sink because I've had some of you guys go this the kitchen sink worked. I've had some of you guys go the kitchen sink did not. So you can go watch my past video on the kitchen sink. If you have questions about the algorithm equation that I gave you, um, feel free to ask. I will be doing like a bigger, more fun video where I might even dance and teach you guys the algorithm metaphorically, graphics, and wear a cute outfit, you know, the whole thing. So, um, guys, thank you so much for watching this um, live stream as I report to you something that I think is hyper crucial to your future going into 2026. And, um, oh, we got some Q&A here. One second. Let's not abandon you guys.

U, what about cancellation policies? Think it's a trap. Uh, never received different more reservations. Now you let me try to say this for you. Right? I maybe wasn't completely clear. So let me teach you guys something else. The algorithm ranking sits on a bell curve. All right? Just like I told you how like you get this net negative like true negative score and this final negative score. Every listing has it. Every listing has its own score and it all exists on a bell curve. What percentile do you show up in? Because for you, if you're in Miami, the best listings might have a nearly perfect score, like 99998, right? But in St. Louis, the best listings might have a score of like 8,000 and change. So now you could be first page with a like a true score, like a true final exponential score after your cancellation policy of 9,000, but if you're that same 9,000 in Miami, you get crushed, right? So for you, Jose, you might find that even with your cancellation policy being friendly, your total negative rank might be bad enough that even your cancellation policy can't necessarily save you. Considering that all of your competition also have friendly cancellation policies. What I would like you to do is I'd like you to research your competition and see like the people who do rank on the first page. How good are their reviews? How good are their prices? And how friendly is their cancellation policy? And also how many people do they sleep. So all of these things really do matter. And of course, if if the answer isn't obvious, we also want to look at amenities and stuff that they have too because we have to ask ourselves who's traveling to Columbia and why and what are they asking for? And you may not be giving the things that people are clicking for, by the way. Okay.

Um, another question, do I encourage uh guests to be specific in the reviews to get that algorithm boost with specific amenities? Unfortunately, we cannot have a guest. This is a fun question, guys, right? But you can't have a guest go back and re-click boxes. I am not telling you wink. I'm not telling you to have a friend do a search for every possible checkbox, pets, kids, hot tub, pool, blank, blank, blank, blank, blank, and then book you. Not telling you to do that, but if somebody did do that, um, you'd be well indexed on that five-star review, right? Um, but it has to be something where people clicked it. It proves intention and then they book with you with all of that intention and you get linked. But people can't go back and say, "Oh, I love the pool. I wasn't searching for a pool, but I loved it." It's not Airbnb is not going to index that until they have a large language model AI that goes back and scrubs it.

Um, man, I think I should show you something else crazy that I showed my students. Do you guys want to see something kind of sick? Um, this is a market research thing that we did. Man, I can't believe I'm about to show you this. Oh, this is I I get too excited. So, I'm going to show you something that I should not show you. Chat GPT, what I did to help one of my students do market research is we took um floods. I don't know if you can see it all, but we took floods of reviews. I scraped hundreds of reviews in a city and I asked it a few questions. Here's the questions I asked. Um, I said, "Searching in Spokane, what can we know from these reviews of the guests in their listings?" Right? And it gave me a bunch of insights where people like to go, what they like to do, what they loved, did not love. I asked it a second question. Give us amenities and consumer archetypes ranked in density, how frequent things are happening. 94% of people commented on cleanliness. 82% of people commented on modern or like newly remodeled furniture, mid-century stuff, whatever. 78 talked about beds and pillows. 71 cared about the coffee and snacks, right? Um, 38 talked about pet friendly. Okay. And then the last one I asked was what opportunities or market gaps may exist from the reviews. Isn't that sick? So you can scrape thousands of reviews given to Chat GPT and ask that to analyze data to help you find market gaps. And I probably shouldn't have showed you that because I was probably going to wait like a year and actually uh show you guys that later once my students had a head start. But you showed up for a live stream and I love you guys. So you deserve the nugget. You guys definitely deserve that.

Um, what other Q&A might we have hiding down below here? Um, oh, uh, Mon'nique did ask a question that is kind of sneaky, right? Um, guest, do we just ask the guest if they feel they need a refund to ask us in a message first? Um, two-part answer. If you ever need to refund a guest, do an alteration request, right? Don't actually do a resolution. Do an alteration. So before the reservation is over, change the stay by like going into like change trip dates and price and just change the final price. An alteration doesn't trigger a resolution-based uh thing. But also if a guest was like mad at you after their stay, ask them if they feel like they need a refund in order to um like win a good review for you, you're putting you're actually like baiting them into a coercion trap. And if they say, "Yeah, if you give me uh if you give me money, I won't leave you a bad review." Now you can get the bad review removed. That's good. You could like just trap them in that. Um, uh, Rupchure I, we have about a 90% 90% success rate on review removal between my AI and the stuff that we've learned. So, uh, that's pretty damn good. That's actually really, really good. Um, and Jose, I'm glad you're going to like the dance video. We're going to make it we're going to make it work. Um, yes. Um, the somebody just said the algorithm seems hostile towards price increases. Um, again, let's talk about the let's talk about the waiting. Let's let's bring this back on screen. Okay, let's go bring the notes back on screen. The waiting of reviews here. There are two I believe there are two. This is where I could be wrong. I believe it's a trust and satisfaction compounding score and then a value and fit compounding score. Okay, I think that those are the two totals. They they could be all independent in some other way. Could be crazy, right? But to go back to this, if you have really high trust and satisfaction, you can charge more money, right? And even if your value index isn't as good, your trust and satisfaction can carry you, right? But if you don't have high trust and satisfaction, then this be it's all exponential because they all multiply against each other. So if you want to charge more money, have a flexible cancellation policy and then try to win on fit. Be pet friendly, have a pool, have a hot tub, allow kids, and then on the instances, sorry that just went away. On the instances that people um decide to search for a specific type of stay where you have a pool, your fit score goes up and then you can charge more money. Or if people are searching for pets or kids, your fit score goes up. So, you either have to improve the trust and win five-star reviews and make sure you have no negative claims. You either have to improve your value index, which is the amenity load, and then also future bookings, right? I don't know if I actually put that in the equation here. Let me try to show you guys. It might have been in here and I might have skipped over it. For um for value, your listing has a base score and it's set against comps and market. And when uh it scores for value, it is looking for your amenities. Anything that that can be declared for value. It's measured against your trust and safety and it comps your future bookings and sees how future occupied you are and your trailing bookings how trailing occupied you are. So if you say that you're worth $500, right? And you have no future bookings, then Airbnb has no projected data to say that matches. If you say that you're worth $500 and you have three future bookings at 440, you're a little bit over that, but that's still better than having no bookings. If you say that you're worth $500 and you have three future bookings that are $550, you're actually cheaper. We actually see this guys, right? Let me see if I can actually show you this exact thing that happens. We're going to go to this screen here. I believe it's Chrome. Yes. Okay, cool. So, now let's go to checkout, right? For one of these reviews, these dates are priced lower than usual. You see that right away? See that, guys? So, what happens is Airbnb sees your price action and knows when people book you. And I've said this in an old algorithm video years ago. Airbnb is likely tethering your five-star likelihood percentage versus your average daily rate. It might see that under $500 you're 5.0, but over $600 you're 4.7, right? It might see that there's like a gradient there. So, if you wanted to um do better for value, you would want to get future bookings at the price that you like, right? You have to find a way to get that done, right? Um, if somebody's booked for three days, you could say, "Hey, um, could I alter your reservation to be booked for this is I'm just giving you a hypothetical to make it work. You should not do this probably because it's probably bad. Take a three-day stay, make it a two-day stay at the same price, and now your ADR is higher, right?" Right. So, Airbnb's, they're paying the same price, but they're seeing a higher ADR because it's less total days. That could be like an example of like a a cheapskate way to like manipulate your ADR.

So, let me go back to the Q&A here. Make sure I don't leave you guys behind. Um, people in Salt Lake say it's the best place I've been at by far. I don't quite know what I'm doing as a host, so that's like, you know, maybe the fact that you don't know what you're doing as a host means that you're just being a good person. Um, and I do believe, let's here's another fun hot take on the industry. Being human is the best thing that you can be on Airbnb. Because if you think about every argument that people have to not use Airbnb, hotels have highrises, they've got concierges, they've got baggage check, they've got pools, they've got restaurants, they've got usually pretty good location, they got all this stuff. They have a lot of stuff that hosts just don't have. Hosts, what do we have? We have a kitchen. Full kitchen. That's great. We have more square footage. That's also great. But the biggest thing is that we are like literally a human and the story that comes with that. There's a very romantic notion of home share still where a host actually cares about you, asks about your day. We'll listen to your stories. Tells you all about the local stuff. Like let's say you stay at somebody's house in Boston and that person will tell you all the cool stuff about Boston, places to go, places not to go, like the local drama, the tea if you want it. Like local travelers like they love hosts for the people who book on Airbnb love Airbnb for a reason. And so we need to play the Airbnb game. And so the fact that you don't know how to host might be the reason why you're successful because too too many of us who think we know how to host, we're screwing it up, right? In a lot of ways.

Um, what's up, Kylie? I'm actually using Flexible. I've I've tested it. Um, and let me actually show you a reason why we're using Flexible. It's a big deal. So, let me go in and show you one last thing that's a little bit behind the curtain, right? So, I've got a friend and my friend has this property that got they they took it off the market for two years. 4.92 rating and they took it off the market for two years. They listed it again in end of June, right? And they asked my help to like, you know, like help them with prices and stuff and look what happened. We listed maybe it was early July. We listed early July and the prices were 130 to 150, 96, 83 bucks, 72 bucks and we couldn't get bookings. We could not get bookings. This this review right here this one on a weekend $874 $874 for a weekend and we trudged along $83 bucks, 111 bucks, 180 on a Friday, we couldn't get booked. A mess, right? So then around September, maybe very end of September, early October, I'm I'm like at the Grand Canyon. You guys saw that. And I'm just thinking I'm like, man, I I I don't get it. And I'm just going through his listing and everything seems good. But then I noticed he's super strict 30, like that old school one. And he had an indoor hero photo, which was a beautiful hero photo, is the one that worked before. So I made his hero photo an outside photo because we were in summer season. and I changed him to moderate flexible. And then here's what happened. We started getting bookings. His September made $1,350 with super strict, right? I did the math before. 100 bucks. And this is including cleaning fees. Okay? 674 bucks, 287, and one 351. That is 1350 with four cleanings on the house. He he like net revenueed $500 towards his mortgage. Gross. October immediately did $8,000 because we changed the cancellation policy. He immediately did $8,000. We still couldn't get these weekdays booked to save her life, but we started actually getting real real money for these bookings. Real money, which was good. So, um, I switched and I immediately saw for my listings that had low trust, they're cranking, right? Um, if I if you have a listing that has guest favorite, be firm. Absolutely be firm. But if you have a listing that's not guest favorite and your competitor like competitors are good, then um, be moderate or be flexible. I think that like switching to flexible on a case-by-case basis based on your trust. And one more word that I gave you guys in a previous algorithm video, temporality. Okay, the recency of all this data matters. If you canceled somebody last week, you are high risk. If you gave somebody a cleaning fee refund last week, you're high risk. Just like a credit score, the older the negative claims, the better your reputation gets. Temporality matters. Okay? So, um, if you had a bad thing happened in the last couple weeks, go flexible just for a little while. Just blitz off flexible. And I think it's on a trailing number of reviews. So, if you can grind out a lot of one-day stays and get a lot of reviews and you can quickly get 10 in the bank, I think it resets you. That's another theory, I think. Um, and these are the things that we uh we test. And I I I guess I kind of want to close with this, guys. So much of what I teach you is theory. I went to school for music theory. And maybe that's why I can be so brass to tell you some stuff like like I'm telling you now is because in music theory, Mozart, like look what he did. Look what Beethoven did with theory. Beautiful stuff that we all agree is great with theory. Pricing stuff that I teach you is all theoretical. There's no number that if I tell you you make your number this price two days from now or 14 days from now that you guarantee a booking. I cannot give you an exact number. But I can give you theories, right? And everything that we do with interior design, with pricing strategy, with the algorithm, it's all theoretical. And your ability to take a take like an unsure bet and try something and test it to see if it works for you is going to be the thing that puts you ahead of everybody in 2026. Absolutely ahead of everybody. And uh he switched to flexible, I believe. You want me to double check? You want me to actually confirm if he's flexible or moderate? I can do that for you right now. So let me let me do that. Let me go in. Um, I believe he's flexible. He could be moderate. So, let's see this gent right here. Availability settings. Is it listed here? No. Okay. We got to go to listings. We got to go to him right here. He is flexible. He's flexible right now. And here's the thing. A lot of you guys are like, "Oh, no. I don't want to go flexible." He made $1,350 because he didn't get bookings, but now he made $8,000 while flexible. So, if he had some cancellations, he still made 8,000 versus 1350, right? That's a huge jump on my online all mine being flexible. I had four cancellations on this like this test batch over the last three months. That's not a lot. Okay. Um, I think they say that, you know, you're you're going to make like maybe 13% of people may cancel, but I haven't seen anything near that. Um, and you like the boost. I really think the boost is worth it. Okay.

Um, last thing I would like to tell you guys, I know a lot of you are busy. I know that like you've got a lot of stuff on your plate. Um, business can be hard sometimes and you get a little too stressed. But what I just told you about like theory, I want to bridge I want to bridge into innovation. If you're too stressed to do anything new, then you're not going to get ahead. Your biggest threat in 2026 are new hosts because these new hosts don't know how good it was before, right? I I can tell like these hosts, the reason why they quit for two years is because the thing was making $21,000 to $23,000 a month and it went it dropped down to like 15. And they hated that it dropped down to 15. They're like, "It's not worth it." But they were still making $7,000 or $8,000 net income, but they were making 14 plus before and they thought, "Oh, we used to make 14,000 net a month. We only made seven or eight. It was better. It used to be better. We're just done." I can tell you so many stories about people who have that one foot out the door mentality in this space. But when I used to hire salespeople in the newspaper industry, we'd have sales guys two years and go, "Man, this used to be easier. This was easier. It's getting harder." And then we would hire new sales guys and they would set records. They would crush everybody and actually make new achievements that the company hadn't ever hit in four or five years. And then the sales guys go, "I guess, well, I guess it's not harder. I guess we got to get back to work now." And the same thing is happening with fresh blood in the short-term rental space. A new host is just going to learn what it takes today, right? They're they're learning about going multi-channel sooner. They're learning about direct marketing and Instagram and social media stuff. They're learning about pricing strategy and the algorithm. They're learning this stuff sooner and more eagerly than hosts are today. Like legacy hosts. If you've been a host for four years and you feel like this like this like I don't know this inertia like this like pull this hesitation to get into new stuff and try new stuff and learn new stuff, you're going to get put out of business by the new guy who's willingly learning all this stuff to become comprehensive now, right? And they're going to make a listing that will crush everybody who's not keeping up, right? That's the real risk of this industry. So you guys might be a little like downtrodden sometimes. You might be a little stressed, a little too busy, but I want to remind you this channel is free and there's a lot in this channel. I just dropped a pricing strategy video that is nuts. It's like cutting edge, really cutting edge stuff. I dropped two separate 10 things videos. One is 10 changes you should make immediately and 10 like predictions for 2026 that you should know and you should contemplate the things that I'm saying. And me telling you watch these videos that you've been too busy to watch isn't I'm not an amateur streamer, guys. Hey, thanks for the bits. Like and subscribe. I'm telling you, you will go out of business if you do not innovate and keep pushing forward. That's one of the biggest truths I can give you right now. And I don't want to be hostile or to make any of you feel singled out because actually most of you, the fact that you're still here is a is a big like check mark for you, right? But if you've been too busy to watch my new stuff about the algorithm or about pricing strategy or like predictions for 2026, take the time. Make the time. The best investment you can make is into yourself truly. And I promise you, I've got some new students that man, they are so optimistic. They're so excited. And goddamn, if half of the like if a if a tenth of the people who join Airbnb 2026 are as motivated and optimistic as some of my new students are, you guys are cooked, right? These guys are crushers. Um, and the reason why they're crushers is they don't say no. They don't groan as they do price. They don't groan as a guest issue happens. They don't groan as they've got to spend the money on interior design. So innovation to outpace because you also you have an advantage. Let me circle this back to for one final point. You have the advantage of context and time and wisdom. You're very wise. But if you're wise and slow and cynical, the optimistic person is going to beat you because they don't know that they should be cynical. And so to be cynical and hesitant is actually more damaging than the advantage that you have from being wise. Okay, that's a big thing. So please keep educating yourself and ask me questions. Okay, some of you are doing it here, which is great.

Um, now some of these questions I think we already touched talked on. Um, if a guest cancels for a natural disaster, um, that won't ever hurt you, right? And they can't leave a review, which is great. You can get that removed. Um, so if a guest cancels for like a disaster and tries to leave you a review, that's an instant thing. And if you have a problem getting a review like removed, just DM me on Instagram. If they re if you really if you guys are really getting treated unfairly with a resolution or a review and it's messed up, just send me a DM on Instagram. I'm on your side, guys. I hope you understand by now that I'm on your side. I make you put up with me so much. You guys know I'm on your side. So, I love you guys. Um, thanks for kicking it with me on this live stream. Please ask me some questions, even if you DM me the questions because, you know, this chat is going to switch from being live to being like a forever thing. And I will make you guys more videos. As always, I'll see you on the other side.