How Smart Real Estate Investors Use Data to Predict the Next Market Move

Max Saia (00:00)
So people are going to time out of things. You’re going to spend too much money. And so one of the other things we often think about when we come in and do these bigger enterprise deployments is like, what type of model do we actually need for which use case? do we even, maybe where do we even not need AI? Like people are using AI to shuttle data from here to here or transform it in this way. That’s a very expensive way to do that. We can just build in, you know, basically standard, you know, data engineering and then, know, like a coding layer that’s just going to do

that effectively.

Cody Crabb (02:01)
Hello and welcome back to the Real Estate Pros podcast. My name is Cody Crabb with Investor Fuel. Today I’m joined by Max Saia with Neon Redwood. Max brings deep real estate data and strategy experience. And today we’re going to find out what the latest office demand trends can tell investors about where the market may be heading. Thanks so much for joining us today, Max. This is going to be good.

Max Saia (02:21)
Yeah, awesome. Thanks for having me, Cody. Appreciate it.

Cody Crabb (02:25)
So just to start out, ⁓ let’s get a little bit of info on your background. You mentioned a couple of things that kind of brought you where you are today. I’d love to hear a little bit more about those for our audience to have some context.

Max Saia (02:38)
Yeah, sure. Well, you know, as you mentioned, I work in kind of a catch-all role at a consultancy now, but my background is in commercial real estate. That’s where most of my career has kind of progressed. I broke into commercial real estate with CBRE working on research, ultimately working in their econometric advisors division, which does all their forecasting. And then I also have background on the buy side, working at American Realty’s advisors and Irvine company. But the last five years I was kind of the founder

research member and one of the core architects of the data platform at VTS. And if your viewers aren’t familiar with what VTS is, they’re kind of like the institutional ⁓ software that underpins a lot of commercial real estate, notably ⁓ office in particular. And so, you know, we kind of built a forward-looking data set of, you know, tenant activity as those tenants first enter the market as opposed to when they sign a lease. And so that’s what a lot of our trends are based off of.

Cody Crabb (03:37)
Yeah, so

as someone that’s kind of got this insight into the market right now, what’s the kind of the big takeaway right now, the kind of latest report, what would you say is like a big takeaway people can take from that section as well?

Max Saia (03:46)
Mm-hmm.

Yeah, sure. I mean, obviously, first and foremost, there’s a lot going on in the macro economy, right? ⁓ There’s, you know, ⁓ obviously, as you know, inflation has kind of remained stubbornly elevated and people are obviously more worried about that than ever just with everything going on in the Middle East and the implications of those high oil.

Cody Crabb (03:59)
I’m just kidding. Yeah. Yeah, I hadn’t noticed that that’s weird. Yeah

Max Saia (04:16)
prices may have for the broader economy overall. And obviously, the report came at an interesting time because all of that kind of transpired over the month of March. And we were looking at our Q1 2026 data, surely expecting that to have like a pretty immediate impact on office demand. What we found was actually kind of the opposite. The office market for the major markets across the U.S. that we track was very, very resilient. Demand actually

grew 18 % quarter over quarter and was up 13 % year over year. And one of the really big standout trends was that technology demand in particular was extremely robust, obviously driven by demand from AI tenants in particular. And that was no more acute than in San Francisco where technology demand was actually up 70 % quarter over quarter.

Cody Crabb (05:10)
And so is that, would you say that’s kind of centralized where you are or is that kind of, to what degree is that kind of a nationwide universal trend that you’re seeing?

Max Saia (05:21)
Yeah, it’s a great question. So far, the demand ⁓ for technology and sort of like the impact AI is having on office in particular, ⁓ and driving that need for more space, is very, very, very concentrated in the Bay Area and actually in New York as well. ⁓ We’re not seeing…

Cody Crabb (05:42)
Makes sense.

Max Saia (06:31)
Yeah, definitely. I think we’re not seeing too much of it elsewhere. We see a little bit in Seattle. We actually see a little bit in DC, just given, obviously, a lot of the major labs have been in the news recently alongside ⁓ some of what’s been going on with the government there. You have the Palantirs of the world playing into that. ⁓ But yeah, so those are kind of like the main areas. Elsewhere, we don’t see too much of that sort of like flow through occurring in terms of that tech demand being a real catalyst.

Cody Crabb (06:59)
That’s interesting because I would have thought like my instinct would not be that the demand is growing. My instinct would be that it would be shrinking a little bit. But that’s why do you think that is?

Max Saia (07:03)
Mm-hmm.

Yeah, so it’s interesting because I think there’s a lot of kind of is this time different? Is AI going to take jobs? Like, what does that actually ultimately mean for office demand, et cetera? ⁓ What we’re observing is that

So far, it hasn’t had a material negative impact on any of the traditional industries. think what’s interesting and what’s maybe different this time is like if you look at prior tech cycles, there’s a technology boom. You know, with some sort of lag, it tends to pull all the services demand along with it. You you have M &A, have IPOs, you have all the legal that comes with just launching new companies and products. And that pulls on along professional services, legal, finance, things of that nature. This time, it’s

different because while that is a dynamic, the other side of it is people are like, wait, but all these companies are also adopting this technology and changing the way they work and maybe how many people they actually ultimately hire. ⁓ What we’ve seen so far is there’s some areas where adoption is much faster, some areas where it’s much slower. ⁓ Regardless of kind of like what side of the fence you’re on, ⁓ we’re actually not seeing a material impact on office demand at all. In fact, it’s actually kind of as people race to adopt this and figure it out, we’re

seeing

it pull more people back into the office. So it’s kind of accelerating return to office a little bit. In the areas most disrupted, like coding, we’re actually seeing the biggest increases in job openings. So if you look at Indeed’s ⁓ job posting indexes, software development is actually one of the ones that’s growing the fastest year over year.

Cody Crabb (08:47)
Wow,

that’s not really what we’re kind of hearing about in the kind of general perception of the public. So that’s, think that’s really interesting actually. ⁓ So that’s, ⁓ wow, that’s pretty fascinating actually. ⁓ Let me, let’s kind of, let’s just kind of talk about like, ⁓ for investors that aren’t buying office, because we have plenty of those, probably most of those, I would be curious, like what can they still learn?

Max Saia (09:07)
Yeah.

Cody Crabb (09:15)
from these types of trends? How’s that gonna affect other types of real estate?

Max Saia (09:20)
Yeah, sure. Well, I think, you know, for the better part of all, I’ll it the last cycle, like basically emerging from the GFC up until COVID. We obviously saw a lot of changes in, you know, I’ll use residential as is, you know, the knock on.

We saw a lot of changes there where there was a ton of growth in the Sunbelt in particular. A lot of that was very demographic led and you saw other spillover benefits from what was happening in the office market over that cycle in potentially different markets. Like obviously the tech markets, housing did very well there for a period of time as well as the emerging tech markets of Austin, Charlotte, Raleigh, Atlanta, things of that nature. We kind of saw the tech demand really kind of flow into those later.

in the cycle in terms of hey, Apple’s tapped out in terms of everybody they can hire in the Bay Area for the most part. So great, they’re gonna go to Austin, they’re gonna go to Raleigh and they’re

to start to establish a presence there and those jobs are going to start to migrate to those markets. ⁓ So it was at first very demographic driven that recovery was in terms of where residential was succeeding and thriving. But then secondly, it was that like spillover effect of, we’ve saturated our core labor talent pool. Now we got to go into these other ones. ⁓ What we’re seeing this time

could still unfold in a similar way. I think what’s interesting now is there’s various reasons why housing has kind of stalled a bit. Mortgage rates are still high. The percentage of mortgages that are now over 6 percent is starting to kind of outweigh those really, really low and cheap mortgages. And obviously things on the immigration front have kind of slowed down population growth as well. And employment growth has slowed down over the past year as well. there’s kind of these not super negative but definitely headwinds that residential is facing that’s kind

separate from the technology piece. ⁓ On the technology side, what we’re seeing is these markets where we are

seeing a very clear AI boom.

Those ones are doing really well, both from like a for sale housing standpoint, like prices are really, really going up. San Francisco is now one of the best performing markets in the country, which, ⁓ you know, that’d be very, hard to imagine just four or five years ago. ⁓ New York’s doing quite well as well. ⁓ And we’re seeing similar on the rental side. It’s not a perfect one to one correlation. There’s certainly markets like Chicago.

Cody Crabb (12:12)
Seriously, yeah.

Max Saia (12:23)
where we don’t see a lot of tech or AI demand necessarily. ⁓ But just given the affordability, the lack of supply constraints, and we are seeing pretty positive, not spectacular, but positive ⁓ for sale housing price appreciation and rent growth in that market as well.

Cody Crabb (12:42)
So ⁓ would you call yourself a data guy? I mean, it sounds like it.

Max Saia (12:46)
⁓ Definitely,

yeah, anything daydream about it, for sure.

Cody Crabb (12:50)
Yeah,

so how do you, how does data, this probably seems like a dumb question, but like, I don’t mean exactly how it sounds, like how does data help you be informed, what your, I know what the answer sounds like is, because it’s data and that’s what it does, but like how does data keep you informed about kind of making smart decisions when, in a market? what are important things to look out for?

Max Saia (12:55)
Mm-hmm.

Mm-hmm.

Yeah, totally. So I think the way I like to think about data in particular as it relates to, okay, cool. I’m trying to like understand the economic landscape and how that might better inform me as an investor in terms of skating where the puck is going more so than, you know, trying to chase it where it already was. Especially right now and at this time, at this moment in time, it’s very, very interesting where, you know, our

leading index for office demand data has shown for quite some time that New York and San Francisco were.

going to be very clearly the beneficiaries of this AI tech boom. It definitely took a while for that to transpire into, know, resi demand or like a bigger asset class that maybe more people are invested in. Reason that is, one, you know, they were kind of recovering off a low base, office was hit very hard. But two, let’s say that demand story is very proven, you kind of have to wait for a couple of quarters, you know, in terms of like the surrounding

ecosystem. It’s like, cool, these tenants are doing really, really well. They’ve raised a lot of money. Great. They hired a bunch of people. awesome. Now the revenue is there. Okay. Now that revenue looks sustainable and it’s growing. ⁓ Great. Now all the follow on services can start to benefit from that. And then everybody kind of starts to chase that into a market. And then that’s what really is giving the lift to the ⁓ residential sector in those areas. And so it’s to me using data to inform a decision, it’s about like, okay, like what’s the core driver?

And then what’s the earliest indicator I can find to sort of capture that core driver? And so while our data is imperfect for unpacking every tool, know, the VTS data is, and when there is very clearly like a commercial innovation and trend happening, our data is one of the better ones at capturing it because a lot of times these tend to enter the market at or before they even get fundraising or, you know, anticipation of growth.

And so a lot of times you see these tenants enter the market looking to expand and you see that in our aggregate numbers ⁓ in advance of that actually kind of flowing through and you know, some of the more traditional metrics. And so you can look at that information and say, great, there’s like very, very clear appetite and demand here. What does that mean? Like, you know, what’s causing that and what does that mean downstream? And so you can kind of start to tie that thread through. And, you know, again, so like we’re not going to capture necessarily some of the demographic trends or anything like that. But, but

that’s like kind of what’s telling us that like, hey, these markets have a lot of room to run because the early indicators are still super, super positive. And that kind of gives you a conviction that, some of this price appreciation we’ve seen is very real and very sustainable short term and medium term.

Cody Crabb (16:47)
My takeaway from this is data is less important for what it is and it’s more important for what it means. So like, if you’re looking at data, you should not necessarily think of it as like a here’s where things are, but you should think of it as because of where things are, A, B, and C down the road are going to be the things to keep your eyes on.

Max Saia (16:56)
Mm-hmm.

Yeah, exactly. some things you can take at face value, like all else equal.

higher mortgage rates are going to be worse than lower mortgage rates for stimulating, know, red sea demand. But then backing off from that, it’s like, okay, well, why are they high? Right? Like, what’s the thing before that that’s potentially driving that? And then what could potentially come after that? Like, so I know they’re high today, but I also know the thing that’s making them high. I think this is going to go this direction because here’s what I’m seeing with that, that, you know, with the trends in that data, which means I think that’s going to have the follow on.

effect of this. And so I think it’s not just taking data at face value, but understanding how credible is that data? And from a causal standpoint, and just if A, then B, then C, then D, what does that mean? If I’m looking at A, what does that mean for B, D? And how can I act on that? Yeah.

Cody Crabb (18:06)
Yeah, that makes a lot of sense. Yeah, I’m

so not a numbers guy. So this is actually really kind of helpful for me to kind of work through. ⁓ So tell me a little bit about neon redwood. ⁓ Tell me what you guys do ⁓ for companies and how you kind of help them get ⁓ things where they want to be.

Max Saia (18:27)
Yeah, sure. I think our joke internally is we will figure out how to do whatever you need help with. ⁓ But the reality of it is we have a few sort of lines of business within the company that I think we’re pretty exceptional at and are known for.

Cody Crabb (18:33)
Yeah.

Max Saia (18:43)
One is kind of outsourced finance and operations and accounting and audit support for venture capital and private equity firms in particular. These firms can kind of be all shapes and sizes but you know a common you know

client, will step into someone who is sort of earlier stage in their journey, ⁓ figuring things out, just looking for, you know, an experienced voice in the room to really help them set and drive process the right way. So they can really just go back and focus on the core of their business and raising funds and making the right investments and things of that nature. So we can really come in and take all the pressure and anxiety and like mental load needed ⁓ to kind of get all your financial house

order and we’ll just take that off. ⁓

Same thing on the data side. ⁓ lot of companies over the years have adopted software and tools and they’ve either bought or built certain data sets around their business, but still to this day, even in the age of AI, all that is very disparate. ⁓ And people aren’t necessarily getting the insights they want out of those things. So we can come in, stitch that all together, add AI layers to that to get the right insights faster, but build all the quality assurance and quality control checks along

on

the way so that when you do ask a model, like, hey, tell me about this part of my business, you know it’s right. It’s not going to hallucinate some random number or interject something that’s not actually true about your business. So those are the two core elements. But we also do any manner of economic and data research and consulting. ⁓ So if you have an economic problem or a data problem you want solved, we can take that, analyze it, and help you make an informed strategy decision.

Cody Crabb (20:31)
When

you come in, what’s typically like the broken part? Is it like the tools they’re using? Is it like the processes they’re using? Do the people not get it? Like, what is it that, what are some common problems you see in this kind of pipeline?

Max Saia (20:40)
Mm-hmm. Mm-hmm.

Yeah, a lot of it just comes down to, think, beyond any of the tools or applications of the people, it’s usually companies grow. They have systems and checks and balances in place that were great for maybe a five-person or 10-person company, but they’re a 50-person company now, right? And they’re just trying to…

Everybody has a day job that’s like core to the business and they’re trying to drive it forward and there maybe just hasn’t been time or resources properly allocated to that coordination layer that kind of helps stuff just run faster and more efficiently so they can continue to grow faster and scale better. And that’s really a very common type of scenario where we step into where it’s, cool, like, no worries. We know you need help with this. ⁓

you probably have some stuff that doesn’t need to be fixed. We’re not kind of coming in and saying, let’s rip everything out and replace it and we know exactly what to do and it’s a one size fits all. It’s very much like, okay, cool, what is the current state of the state? What have you thought about doing? What would you like to do? What are everybody’s pain points? If we fix those pain points, what do they do instead that drives the business forward? And so regardless of the way we engage, that’s kind of like the overarching ethos and mindset we have of just like, okay, cool, how can we make

you guys a better place and a better company.

Cody Crabb (22:14)
So ⁓ one last question as we’re kind of winding down here. I’m very interested in the world of AI. think it’s super interesting and fascinating how quickly things are moving. ⁓ So I just love to pick your brain about this. when you have a company ⁓ and there’s nothing more dangerous than a CEO that just went to a seminar, right? He’s like, we’re an AI company now. That’s all we’re doing. That’s what we do now. ⁓ What is it that…

I mean, I’ve seen so many companies do this, even just the ones I personally use. They’re like, we’re an AI company now. We don’t do the thing that we did before. We do AI plus the thing we did before. ⁓ So I would be curious, like, what do you think are some steps that, because we have companies across the spectrum of single person teams to like 50 to 100 to 1000 employees, what would you say would be some steps to take if you are that CEO that wants to implement AI, but you’re not

Max Saia (22:48)
Right.

Yeah, of course.

Mm-hmm.

Mm-hmm.

Cody Crabb (23:12)
Maybe you’re not getting the full use out of it yet and you want to kind of get there. What are some things you should do first to kind of avoid that it’s just chat GPT with more steps is kind of situation.

Max Saia (23:22)
Yeah, exactly.

⁓ I think first and foremost, know, like most companies have data that’s kind of proprietary to them and central to their business. That’s like very secure. ⁓ So, you know, that’s just like a pillar, like sticking the ground. I’ll say, but really, before I come back to that, like two key things always stand out to us. One is, you know, a CEO or executive that like wants to drive AI adoption.

Maybe not the worst thing you can do, but the thing that won’t actually give you the productivity lift is just mandating everybody start using it, right? Because everybody’s going to kind of go down their own thing. They’re going to attach their own data to it. There’s not going to be like a clear process or like centralized, like sort of like knowledge hub that these models can draw from in a credible way and say, cool, these were the locked Q1 2026 numbers. These are good for production. This is the shared data set.

Cody Crabb (23:58)
That’ll show them, yeah.

Yeah.

Max Saia (24:20)
everybody gets to use their enterprise AI off of to drive things. This is the data set we’re going to… ⁓

Cody Crabb (24:25)
Well, I imagine too you run into the security issue too. mean, if you’re just saying

use it however you see fit, people are going to use it however they see fit. There’s all kinds of issues if you’re not kind of centralizing it.

Max Saia (24:33)
Correct.

That’s exactly right. And then the other benefit of that is if you just tell everybody to start using it, like a lot of people have run into this, it can become a very big bill for your business very, very quickly. ⁓

Cody Crabb (24:48)
Yeah, an API an API

token that is there and they’re no joke It is especially if you click click a miss click a model click the wrong model and then you are your toast so Yeah

Max Saia (25:00)
Yeah, exactly. Exactly.

So people are going to time out of things. You’re going to spend too much money. And so one of the other things we often think about when we come in and do these bigger enterprise deployments is like, what type of model do we actually need for which use case? do we even, maybe where do we even not need AI? Like people are using AI to shuttle data from here to here or transform it in this way. That’s a very expensive way to do that. We can just build in, you know, basically standard, you know, data engineering and then, know, like a coding layer that’s just going to do

that effectively.

⁓ Or we can say, hey, for these types of things, that’s so basic, a very, very cheap model can do that. ⁓

like it’s just renaming something automatically. Great. Like you don’t need, you don’t need Claude Opus or ChatGPT 5.4 for that or anything like that. And so, and then you really want to preserve the expensive models for the stuff that really warrants them. And so, you know, kind of having a mindful eye of being like, okay, am I just throwing tokens at like very, very basic stuff? Because I just told everybody to use this and now we’re kind of like a wash and all of these things. And then I think, you know, the last thing when,

Cody Crabb (25:43)
like a local model or something, yeah.

Yeah.

Max Saia (26:10)
people I think push AI very hard. ⁓ Teams kind of start using it very like you know for everything. ⁓

It’s great because it can drive a lot of efficiency, but also if you’re not constantly reviewing, checking, refining, rewriting some of these outputs, ⁓ we’ve seen instances where people just end up throwing a bunch more stuff over the bow, and it kind of ends up being like AI slop. And it’s like, yeah, cool, I AI generate this report, read it, it looks great. But that person didn’t actually take the time to QA it and read it and cull it down to what actually matters. And it can create an asymmetry where the person using the AI

is to produce something is putting a bigger burden on the person consuming it than what was there before. ⁓ And so I think being mindful about that type of thing where it’s like, you’re not gonna get the efficiency you want if you’re not being thoughtful about like the outputs it’s generating is another big thing.

Cody Crabb (27:06)
That

is so true. Wow, this has been, I think it’s been really insightful, I think, ⁓ for people that are kind of trying to use data, trying to use AI in their businesses to kind of make, to grow and to not just grow, but like maybe just make things easier at the same level. I mean, that’s what technology is for. So ⁓ Max, if people wanna get in touch with you, work with you, how can they do that?

Max Saia (27:21)
Mm-hmm.

Yeah, sure. They can go to our website, neonredwood.com and contact us or reach out to me personally. My email is [email protected]. So hopefully that’s simple enough to remember, but yeah, that’s the way to engage us.

Cody Crabb (27:42)
Yeah, I’m pretty sure that’s a pretty good one.

Yeah. Well, thank you so much for your time today. This has been interesting. And thank you listeners for joining us as well. If you feel like you got something out of today’s episode, go ahead and give us a like, subscribe, comment, all the things. And don’t forget to follow us so that you don’t miss another awesome conversation like this one. Max, it’s been a pleasure. Thank you so much.