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Every Friday, join us as we dive into the latest in real estate multifamily with David Moghavem, Head of East Coast Acquisitions at Trion Properties. David invites top experts who know the ins, outs, and trends shaping the real estate multifamily market across the nation!
Whether you’re a seasoned investor or just curious about where the next big opportunity might be, Deal Flow Friday brings you the weekly inside scoop on what’s hot, what’s not, and what to watch for in today’s ever-evolving real estate scene.
David Moghavem (01:36)
All right. Welcome to another episode of Deal Flow Friday. I'm your host, David Mogavam. And today we got Alex Slocum, founder and CEO of Apartment IQ. Apartment IQ is completely transforming the way people are pulling data, pulling rent comps, market surveys. This is a product that when AI first came out, even I was like, man, it would be great if there was a way to scrape apartment data quickly as we underwrite hundreds, thousands of deals a day.
And Alec, you've done it and you've done it incredibly. Your team's also amazing. And I'm glad that we were able to set this up together and let the audience hear about the product you've created and just the momentum of pulling RenComp data, market surveys and ⁓ apartment data in our industry. So thanks for hopping on.
Alec Slocum (02:28)
Thanks for having me, this should be fun. And I'm blushing a little bit. That was very nice.
David Moghavem (02:30)
Yeah, definitely. And my analyst
wants to thank you. My AM team wants to thank you. Everyone out here really wants to thank you and the product you've created. So really appreciate it.
Alec Slocum (02:42)
Well, thank you guys.
Yeah, thanks for thanks for using the product and helping us build it.
David Moghavem (02:47)
Of course, of course. Why don't you go a little bit about your background and how you created the product, what it does. ⁓ I probably don't even know half the bells and whistles of it, so you could probably explain it better than me.
Alec Slocum (03:00)
So so I started with my two co founders started the our umbrella company is a company called rentable and apartment IQ is our flagship product. And we actually started as a ILS that was focused on college campuses. So ⁓ we got going started the company a few months after I graduated from from college.
UW Madison. ⁓ the reason that we started investing a lot in crawling the web, collecting data on properties was actually because we were seeding this, this ILS that was our business up until 2021 or so. And so a lot of what we found out was ⁓ we had customers call us, our advertising customers, and they'd say, listen, ⁓ you guys have the best data on all my competitors, but it's on your ILS. Like, can we package this thing up and, ⁓ and make it little bit more interesting? And so
That's when we started. Yeah, yeah, sorry. ILS, it's internet listing services. So like it's apartments.com, Zillow, et cetera. Yeah. And so that, yeah, that was our focus. That was our business. And so we, the way, one of the ways that we seeded our marketplace, that we put properties on our marketplace was we'd go out and crawl the web and collect information. And it helped us create a significant amount more supply than we're on legacy providers at that time. And so.
David Moghavem (03:54)
What's an ILS? Sorry, just to back up a bit. I don't even know what that
Okay. Okay, okay, okay.
Alec Slocum (04:23)
Yeah, right around 2021, we had a number of our customers had been calling us and we said, there might be something here. And so that's when we learned about the world of market surveys and rent comps and the pain that you all have been experiencing for a very long time. And so we realized that a lot of the data that we'd been collecting that we were very good at was quite useful to you all in automating your process. And so that was when we created this new category of the daily automated market survey.
David Moghavem (04:38)
yeah.
Alec Slocum (04:53)
⁓ And that's it originally started and it's gotten a lot of traction ⁓ with operations teams. So revenue management operations, ⁓ client services ⁓ and really throughout the org and then of course to ownership groups because they want to be able to monitor their comps. so Apartment IQ became this way to take a market survey process, which is very historically very manual process. There's a lot of people looking at property websites.
A lot of people were calling each other. Sometimes they did share data on Google Docs, things like that. And it took that process. And instead of seeing data that's 30 days out of date, 10 days out of date, 14 days out of date, and that's pretty high level, we could go get unit level availability for every property in the country and report it on a daily basis for people. And it took this manual, expensive process with low data quality and turned it into a process that we think has been a lot more seamless for folks.
David Moghavem (05:49)
Yeah. And I'm sure anyone in the industry can agree on it. I think the debate that I'd love to get your take on, you know, I always saw Rencoms as more of an art than a science, right? And I think it still is, but you've eliminated some of the tedious work. But do you think this product is inching towards either replacing or taking out even like the art of calling or walking or, or talk to the manager? What's your take on that?
Alec Slocum (06:18)
Yeah, I think our goal, we follow our customers lead. So everybody, they have their own processes. It's not our business to tell you how to run your business and what that looks like. I think our goal is to, I think it's really been a art forever and that's about all there's been. And so I think our goal has been to make it a bit more of a, know, boost up the science side of things. ⁓ And so we think about there are, you some shops are really on the same
David Moghavem (06:41)
Mm-hmm.
Alec Slocum (06:47)
the same page of what's a good comp, how do you define it? Everybody's on the same page. But we also have found that within some of our customers, we've got two thirds of the NMHC top 50 now using department IQ every day. And sometimes now everybody agrees on what a comp is. And so at that point, it can be better to use the data to try ⁓ and rectify that. And we've seen it be very effective there. And I think what we've found is everybody can put their own rule-based system of what a comp is together.
What distance counts what class counts what price counts? How should they be weighted against each other? It's gonna be hard to really Figure that out other than just arguing about it ⁓ on its own so what we try to do is we try to use a new definition of what a comp actually is and I we were somewhat inspired actually by the way that co-star initially took an approach at this where they were like Let's look at you know renters that look at this property and apartments calm You know what other properties they look at and I I think the execution of that
There's a it can be hard because ⁓ properties are on different tiers. So you end up with one property that's everybody's comp. But I think the concept of it is really interesting, which is to me, a competitor of mine is somebody who wins when I lose and vice versa. And that's what a real competitor is. So it's who's who you actually competing for business and how do you relate to that property? And so what we tried to resolve this on is not just giving you the ability to say, yes, you can wait distance at 25 percent of the comp score, etc.
we tried to give our customers insight into when you lose leases, where do they go and vice versa. And what are those relations? And then what we also try to do is use all of our competitor or all of our customers data to train our algorithms on. So we can look at the comps that you decided are your actual comps. So we can do that. We could pull that across the 5 million units using a partner IQ. And so when we're training a data set, we actually have a truth set. That is here's what our customers say is actually a true comp.
And so you can start to sort of crowdsource the true definition of a club. ⁓ And so we've just tried to push the ball forward on what can we give you in terms of insights, ⁓ no matter where you stand on the art versus science debate.
David Moghavem (08:47)
Right.
Right. And the good thing about the product itself is it gives your recommended comps and then you can add or delete. And I've done that too, where I'm like, all no, this isn't a real comp. And maybe I'm just being biased, or maybe I know something that the algorithm in Apartment IQ doesn't see. And so you can play around, you can mend it, and then you create that report. And then it gives you daily pricing all the time on how these comps are moving and moving about across each other.
Alec Slocum (09:07)
for sure.
David Moghavem (09:26)
⁓ I just came across, I just came through two property tours and walked a couple of comps as well. And I think what's interesting is it is, I don't think it replaces talking to the manager. I think the manager gives you some incredible insight, but sometimes the manager also doesn't know the true daily pricing of that day. Maybe they're giving you stale information and their boots on the ground. so.
At one point, you start to see how maybe there's human error even in gathering comps that Apartment IQ is able to correct.
Alec Slocum (10:04)
Yeah, we've seen that even like when we'll go through a QA check with some of our customers and they are a property manager might swear the system's wrong. And then we just realized that actually the system's more up to date than the information they're seeing ⁓ themselves. So that does happen. ⁓ I think there's some data sets that, you know, can be pretty much automated with department IQ. ⁓ But, you know, in your world,
These are pretty big transactions. ⁓ And so our job is to make more and more of this data available in an automated way for you to manage. So one of the use cases we still hear a lot about is, can I truly get an idea of every unit in a property that's renovated? ⁓ And so we're doing a lot of work right now to give you what we think is the best answer on that. We've got a data science team that tries to solve for that. But in the meantime,
It's probably tough to get if you're going to make a massive ⁓ acquisition. You probably want to make sure that you understand every single unit that's renovated, the exact amenities that are in there. ⁓ And so we can give you a pretty good idea, but not that exhaustive idea quite yet.
David Moghavem (11:11)
Yeah, and you guys, you, right,
right. And you guys have done a really good job actually delivering accurate information. I think that's probably one of the biggest hurdles that when you were first starting this is like, all right, how can we get real time data? How do we know if this unit's renovated or not? And you guys have figured out a way, I guess, without, if, know, however much you're willing to discuss without spilling the secret sauce, like how have you been able to get,
the right occupancies, the the rents and what's leased and how are you able to scrap that data for it to be as accurate as it is?
Alec Slocum (11:50)
A lot of time, effort and resources, I guess, is the sort of simple answer. ⁓ We have ⁓ a team inside the company made up of data scientists and their only job is to work on accuracy. And so they'll go from metric to metric to metric and any metric that we release, we have a pretty high bar of accuracy before it goes into the product. ⁓ And so we it's
It's something that we've always felt pretty passionate about. mean, it sounds pretty obvious, but one of the things that we've found is this is still one of the major pain points that our customers run into every day is they might be used to using, you know, a product that gets its data from call centers or from other properties, kind of self submitting the data. And the automation component of our product is, is great. And they love that, but the accuracy, frequency of the data, but also the granularity of the data where they can see every single unit every day.
⁓ That becomes a major selling point. I can tell you, I personally spend four hours a week. I have a meeting with the team, the team that runs our accuracy. And I personally spend four hours a week with that team going through where are we seeing our customers can tell us where, Hey, I think your occupancy is off by 2 % here. And we go through every single one of those customer ⁓ requests and we look at what's what do we need to adjust about our algorithms to make sure we get that right?
And it's just one of those things where you just got to tediously go through and make it work and make it a priority.
David Moghavem (13:23)
Yeah.
And it's our job to still verify, right? Like this is a great tool, but you can't just, you got to verify, you got to still call and you got to see if there's discrepancies and why, as you said, these are huge transactions. I think one of the tools that we've been using on asset management side is we're at a time where we're starting to see operational distress and operational turmoil and some softening and
It's good to use this tool to spot check whether it's on us or if it's on the market or if it's on both and holding regionals accounted for on sites as well. And I think this is a tool where you can finally get real time data on is this market is something happening? Is there a turn or is this something that, we need to do better as an operator.
Alec Slocum (14:19)
Yeah. Is it me or is it the market? That's what we hear. Yeah, a lot. Yeah. ⁓
David Moghavem (14:21)
Yeah, exactly. And also for
our investors too, right? It's like, hey, look at what's happening here. Like we're giving concessions, but so are all of our comps right now. Look at this. And I also like how you get to also look at the exact unit. You know, usually when you pull comps and you're doing this on a Google doc, you just say like one bedroom is asking this, but it's like, right, which one bedroom and how did that one bedroom unit 502 change?
from last week and did they just put on the concession? they not? Like that's on in a in a upmarket that that one unit doing that and saying like we're testing this out and we got it is what separates you from your competitor and buying a deal and in a down market that's how you can find yourself catching under a fallen knife before it actually comes down. So it's it's those like granularities that really make a world of a difference.
Alec Slocum (15:22)
Yeah. Yeah, I should. One of the things we found early that I think is pretty related to to that idea as well is, ⁓ you know, most most companies when they first start thinking about where are we going to go get availability and our competitors from they go to ILS's. And we pretty quickly found out that that's not a great source of good, reliable availability data and pricing data and being able to get insights like how many.
leases have been signed in a property, what is that property's occupancy? It's just tough to do through ILSs. So we invested a ton into making sure that we were getting data directly from property websites. And even from multiple sources on a property website, we're constantly checking ⁓ accuracy against that. So hopefully it gives you that ability to be able to go in and actually get those insights out of a unit. We're pretty excited about being able to layer AI on top of that as well to be able to make that analysis a bit easier for you too. ⁓
all in all. It's big endeavor, but we think it's worth it.
David Moghavem (16:22)
Yeah, so how are you guys leveraging AI ⁓ in both apartment IQ and also I know you guys you're running Maven AI. Maybe you could explain a little bit about that, but how are you leveraging AI heading into this AI era of PropTech?
Alec Slocum (16:38)
Yeah. we actually, ⁓ one of the key uses of ⁓ AI within Apartment IQ is taking concession information from property websites. usually you'll get pop-ups. You might see it in different parts of ⁓ a website of what the concession is. And it's actually usually, yeah, the banners. So taking that from ⁓ whatever format we captured in and turning it into structured data where we can ⁓ get a net effective rent out of it. We can track that concession history.
David Moghavem (16:55)
Banners, yeah, exactly.
Alec Slocum (17:08)
That's a ⁓ great use ⁓ of artificial intelligence. And we've been doing this for long enough that the first version ⁓ that we used was GPT-2 to do that. So it's been great as these frontier models have gotten better. It's really helped us be able to do that. We also use artificial intelligence to do things like parse out ⁓ whether a property or a unit is renovated by looking at the coding and the floor plan name or the unit level amenities associated with that unit.
David Moghavem (17:21)
Mm-hmm.
Alec Slocum (17:35)
It's very good at being able to grab that information, synthesize it, and then tag whether it's...
David Moghavem (17:39)
Yeah, like when you
when you click into a website, you click into the unit and then it has different prices and one is $300 higher than another and then you click on details and it says Platinum, Platinum, this that so it'll know it's renovated. That's kind of like how you guys are. Yeah.
Alec Slocum (17:53)
Sure. It will.
Yeah. Yeah. So we use it there. And then also, like, there'll be a floor plan image and there's information in that floor plan image we can also use to infer is this a corner unit? Is it a balcony? ⁓ We can even look at, the site map and see the location of the unit. And AI can piece out, does this thing actually have a pool view or is it looking at the highway? And so that those type of insights are pretty straightforward. I think we've got
David Moghavem (18:02)
Mm-hmm.
Alec Slocum (18:20)
⁓ We've been doing a fair amount of work internally on a version of the product which we're planning to launch over the next few weeks here, which will enable you to use more natural language to query the product as well. ⁓ It means like the same, you can ⁓ imagine that instead that chat GBT was hooked up to your apartment IQ data. ⁓ So...
David Moghavem (18:33)
What does that mean?
Alec Slocum (18:46)
We want people to not only be able to come in, see the reports, drill in, use the interface, but we want you to be able to just talk to your data as well.
David Moghavem (18:51)
Yeah,
and have it synthesized or you can just start asking questions.
Alec Slocum (18:56)
For sure, it could create emails, reports for you. ⁓ You can do anything you want with it basically. ⁓ And it's gonna be able to drive those insights. And so we're excited to get that out into the world as well.
David Moghavem (19:08)
And yeah, you know, one of the things that we do too is like when we're calling these comps, just seeing their path, the path of a resident going in and who's answering like that also tells a story of like, hey, their rents are low because I called them 10 times and they didn't pick up, know, like, you know what I mean? Like, it like, so it would be cool if there was a way to kind of
Alec Slocum (19:27)
Yeah.
David Moghavem (19:35)
also leverage AI in that regard where it actually starts spot checking and testing some of these comps and seeing like how is their team on site? How are they, you know, and maybe even going and asking the questions that I would normally ask. Hey, like we're buying a property nearby. How's the crime? How's this? It would be kind of the next level, right? Do you think, yeah.
Alec Slocum (19:59)
Yeah. Like an automated secret
show. Functionally. Yeah. Yeah.
David Moghavem (20:04)
Yeah, exactly. Like an automated secret job. Exactly. Do you think
we're on that path where we're just completely going to replace that type of work?
Alec Slocum (20:16)
⁓ I don't know. I I think there's some jobs that are going to be pretty exposed. know, like a lot of the calls, like we've probably already seen some impact in like call centers that the industry uses to manage leads coming in. ⁓ Generally speaking, I think my view is like what we've like our internal philosophy and how we use the tools and how I think it's actually going to apply. I don't think multifamily is wildly exposed to like major job loss from AI.
I do think it's going to enable us to do a lot more. So I think the baseline level of how much you can know about a competitor or how much you can know about your own property, how many deals you can look at in a day or a week, I think that baseline is going to go up. I have a hard time believing you're going to see major reductions with the current forms of the models as they are. And they do seem to be plateauing a little bit. So I think the primary thing that we need to do now is
Yeah, I view this as like we're in kind of like 1999, 2000 of the internet. Like we know it's there, but we haven't really installed it into our industry across everything. You're seeing some really interesting good signs, like with companies like Elise that they've been able to take an initial use case and like verticalize AI to be really useful. But man, it's going to touch everything. And I think we're at less than 1 % of where that's going to be. And I think it's mostly just going to be an augmentation force more so than a replacement force.
David Moghavem (21:28)
Mm-hmm.
Yeah. And how do you see multifamily operators? You know, you kind of touched on it how we're kind of like the laggard, right? Of the industry and embracing technology. How do you see us embracing this new wave and what's kind of advice you have for multifamily operators on navigating through this new world of multifamily?
Alec Slocum (22:04)
Yeah. Yeah, I, you know, I, I've seen, you know, I, I understand the, the consensus view that like technology takes a little longer to percolate into multifamily. I've also seen the industry move really fast. ⁓ you know, we went apartment IQ two and a half years ago, maybe had a hundred thousand units on it and we got 5 million now. ⁓ and so when the industry decides to do something, it moves. It has been my experience.
And the key is you've got to actually solve a pain point that's valuable to their businesses. And that's what I've been starting to see. You know, I was talking to some operators about, they are using AI to automate some of their operations. And like it's, one of the first integrations of AI into my business where I'm actually making money. I'm making money using this technology, which is the goal of all of this. And so, you know, you mentioned Maven AI, which is another product offering we have.
And Maven's thesis, know, Apartment IQ's thesis is largely, we think there's a generational shift in the way that data is going to be sourced and used in multifamily. We think this for who knows how long multifamily has been based on private data and exchanging private data. And in the last two years, that's totally changed. And I think there's major advantages to public data that's available now on the web and using that. so Apartment IQ is all about taking that shift and enabling operators to make that shift faster.
David Moghavem (23:13)
Right.
Alec Slocum (23:27)
So yes, it's automated market surveys, but it's also revenue management. ⁓ We think there's major shifts happening there and we're also ⁓ doing more on the market research side as well. So we're announcing actually a product tomorrow, which by the time this thing goes live, we'll be live. And ⁓ that is going to be more focused on a use case that you're going to be more familiar with is underwriting, researching assets, et cetera. And so we want it to live in that entire data stack. We want to be powered there.
On the AI side, Maven's bet is that ⁓ AI has been integrating itself into leasing and operations, but we think that marketing has really not been impacted so far by our official intelligence. I even it was interesting. was looking, I was watching one of the big conferences that one of the legacy PMS has had and they were outlining the agents they had, you know, and they had an operations agent, they had a leasing agent, they had a maintenance agent and there was nothing for marketing. ⁓
David Moghavem (24:25)
Interesting.
Alec Slocum (24:26)
And
so it's one of the largest line items in a multifamily operator's budget and nobody's focused on it. so Maven is an approach where we believe marketing is being left behind by artificial intelligence. And so how do we take marketing and take this what has historically been a very inefficient spend ⁓ line item? ⁓ You know, the amount of money that's being spent on agencies to maintain Google business pages, to post to social media accounts.
to audit whether your prices match what's on your website and what's on ILS. It's it's insane. so agents, either you're paying agencies $1,000 a month per property to do that, or you've got property managers that should be leasing apartments and instead they're having to post on social. so Maven is, Maven's whole focus is how do we take AI, verticalize it for marketing and multifamily? That's what we've seen. And so we started with Google Business Profiles. That's gone very well.
We just recently announced that we can automate social for folks and we're going to launch two or three additional products over the next 12 months or so that's related to that. And so that to me is what the future is going to look like. It's going to take these use cases and these workflows in multifamily that are specific to the industry, specific to the people that know it, and take this new technology and customize it for that workflow. And that's what's exciting to me about it.
David Moghavem (25:50)
Yeah. And I think, you know, focusing on top of the funnel marketing, bringing on prospects. The first thing they're checking is the Google ads, the reviews, and that's part of the value-add strategies, rebranding and creating a new type of vibe and feel. so to leverage AI in order to kind of optimize that top of the funnel is incredible. And I agree, it has been overlooked. think there's
a lot of PropTech tools that are focusing on the operations side and the tenants that are once they're already on board or from ⁓ the asset management side, but marketing itself, I think there's a huge component to that for sure. so is Maven AI going to have bots that are kind of like doing outreach follow-up, hey, I saw you were browsing on our site ⁓ or I saw you toured, there's a look and lease special.
Talk me through maybe like a case example of how that would play with your product.
Alec Slocum (26:52)
Yeah.
So right now what Maven's going to do is it takes ⁓ two functions that we think ⁓ generally properties are spending hundreds of dollars a month to do. And we take it down and we reduce the cost by about 90 % to do these things. So the first is Google Business Profiles. So Google has said for a decade now, the most important part of your local SEO strategy is your Google Business Profile. The answer that the age that
the industry has been given up until Maven was, okay, you need to have somebody go in, update your pricing and your products, post to that page so it's active, manually do this or pay an agency to do this. And AI is incredible at this. I mean, it's really great at it and it can do it at a ton of scale. And so you can be posting to your Google business profile every day. You can update your pricing and availability on your Google business page the same time you do it on your ILS. ⁓ And so it automates that.
David Moghavem (27:26)
manually.
Alec Slocum (27:47)
first and now it also is creating content using your property specific data that we get out of the PMS and creating content that's specific to you and your property and what you want to talk about for your social pages. So it's automating that. ⁓ In terms of what's next, mean, basically we are looking for any part of the marketing funnel that has friction involved where maybe it's going to a property manager's plate and it shouldn't be or maybe
It's something that's being mined out to an agency that's using human labor to do something that AI could do much more effectively and much more cost effectively. ⁓ And so there's going to be more to come there. ⁓ But yes, you could definitely see a world where lead nurturing ⁓ is a part of
David Moghavem (28:35)
Amazing, super exciting. ⁓ Alec, I wanted to pick your brain a bit. there, from all this data, I know you're super focused on the product side and you're creating some incredible tools. Are you looking, or is your team looking at all on the data that you're aggregating at this point and seeing trends in the market that are sticking out like a sore thumb that, you know, it's like, hey, I'm in the product side, I'm a little too busy, but like.
look what I'm seeing right now, multifamily. Is there anything that sticks out? If there isn't, that's fine. If you're like, hey, we're leaving that to you, I understand, but I just got to ask you, because you have a lot of data at this point, and maybe are you creating tools that are kind of maybe synthesizing that type of data?
Alec Slocum (29:20)
Yeah. I mean, there's so many ways you can go with this. ⁓ One of the things that is, I think, talked about a fair amount, but we have seen just rapidly move is the amount of information coming online around fees and deposits and the sophistication around it. So I think in six months, the industry has gone from thinking a rent price is a rent price to a rent price is a base price. And then you need to see the TMLP ⁓ of this to actually understand what your competitors are getting done. And
David Moghavem (29:49)
Mm-hmm.
Alec Slocum (29:50)
And also understanding like who is utilizing, who are utilizing fees well, who are not doing it. How do I understand how that fits into the entire ⁓ portfolio? So we have seen a rapid, I mean, it's been months and it went from maybe there were one or two of the top 50 that had their fees and deposits on their website to everybody. ⁓ So that's been very interesting to watch play out. It's an interesting kind of product sector as well. If you kind of dive into who's powering it and ⁓
I think the property management software providers have been a little slow to get there, which is interesting. We've seen a ton of that. We've seen this sort of staggered recovery throughout the country. So we've seen the AI boom in our San Francisco rent prices. I mean, it's been fascinating to watch that. But even then you see markets like Chicago. mean, Chicago is going crazy right now and has been for the last few months.
David Moghavem (30:23)
Mm-hmm.
yeah.
Alec Slocum (30:47)
There's plenty of dynamics there, but it's interesting to watch that play out at the same time that Phoenix isn't even through its supply peak yet. A lot of areas in the Southeast aren't either. And so we've been doing more as a part of getting into more market research and macro level insights, we've been doing more rent growth forecasting, occupancy forecasting. And it's been fascinating to watch how jagged the recovery has been as we've kind of been working our way out of this ⁓ boom in new supply.
David Moghavem (31:17)
Yeah. So I want to, I want to go back to your first point and then I want to touch back on, your second point. So the fees thing is very interesting because I know you're sitting in Boulder, right? Colorado just went through this whole legislation on junk fees and now they're eliminating billbacks and it's causing some pain for landlords. And so it's actually, I think, I think there's a movement towards being transparent with fees just from a legality perspective. And now
Tenants are also, they know what's behind the curtain, where they're like, all right, I'm not falling for this again. What's the fees? Like, that's just a base right. So I definitely hear your point on that. And I think it's interesting to see how other markets are reacting to that. ⁓ To your second point, I think you're seeing what many people in the industry are just starting to see right now, which is like every market is now getting priced completely different.
Alec Slocum (32:02)
This is
David Moghavem (32:16)
now that there's a rate hike and the supply side stories in some of these blue states or blue cities are starting to pan out for the better in terms of rent growth where like you have a San Francisco where there's no supply, there's a return to office, there's the AI boom, which is so, so clear and Chicago, which I'm not personally tracking as much, but similar story in that regard and a little bit of a cleanup within the city and people wanting to live there.
it's you're starting to see the benefits. Meanwhile, all the the reds, red states or red cities that Sunbelt that had a lot of demand and still has good demand top of the funnel is just getting muted by the glad to supply that hit it and it's still getting absorbed. And so you're like seeing a tale of two tapes right now.
Alec Slocum (33:02)
Yep. Yeah.
Yeah, it is fascinating when you start getting into the forecasting ⁓ and we've got a forecasting team that's been in the industry for a decade or so. And when you cruise around MSAs, what you find though is generally speaking, it looks pretty good. ⁓ The difference between these markets is more of timing. It looks like, okay, they're pretty much all coming back. We're going to get through this supply boom and then the rent growth is going to go
back into the black, it's gonna look good, and the difference is a few quarters at this point. So that is ⁓ always great to see for the health of the industry in general, but yes, very different scenarios today.
David Moghavem (33:49)
Yeah, so I guess with that, what market are you bullish on right now? She had to choose.
Alec Slocum (33:57)
This is like the I like the stock part of this part of this podcast. Man, I don't know. I don't even know what I should. I mean, I can tell you right now what we're focused on. So one of the things we're focused on is our data is updated every day. And so we see shifts in the market faster than data that's updated, call center data that's updated every 30 to 60 days.
David Moghavem (34:01)
Yeah, yeah, exactly.
Alec Slocum (34:25)
And even our revenue management offering, it has access to our public market data. And so most of what we're trying to do is make sure that folks are seeing as these markets move, because they all are moving in one way or another, we're pretty focused on how do we make sure they're out ahead of that and seeing that move as it takes as it happens in real time. ⁓ Yeah, think generally like I think I guess generally I'm pretty ⁓ I know California has its wrinkles in terms of where you would
where you might invest, but I'm pretty optimistic about where this AI thing's going and the heart of it's in San Francisco. man, it's real and it's probably just get started. Yeah.
David Moghavem (35:02)
on the bay.
I agree. I
agree. think that was more contrarian beginning of the year, but I think if you're, if you're paying attention, you could, you could see exactly why that's the case for sure. I agree. I agree. we've
Alec Slocum (35:21)
It seems like the
local politics are getting a little more friendly too to development. Yeah.
David Moghavem (35:25)
yeah. And, and they
have a new mayor, ⁓ Daniel Laurie. And then in California, they're, they're, they're trying to get rid of the NIMBYism and, ⁓ the return to office, the AI boom. I mean, you're seeing it on the office side, you're seeing it on the retail side. And it's just a matter of time where it comes back on the multi-side, which you're seeing, ⁓ already coming back on the fundamentals. You're already seeing it for sure. So.
Alec Slocum (35:50)
Yeah,
it looks positive. We just gotta make it through.
David Moghavem (35:54)
Yep, exactly, exactly. Well, Alec, it was really nice having you on. Glad we got to get also some multi-takes from the source behind all the data. And thanks again for your product. Looking forward to seeing what Apartment IQ and Maven AI do next. So great for having you on.
Alec Slocum (36:06)
I do.
Thanks, Dave. This is great. Appreciate it.