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Senior Housing Investors
Harnessing the Power of AI for Strategic Senior Housing Decisions with Kyle Gardner
Welcome to today's episode where we dive deep into the transformative impact of artificial intelligence on the senior housing investment landscape. Joining us is Kyle Gardner, the COO of NIC MAP Vision, who brings a wealth of expertise to our discussion. Today, we'll uncover how AI is revolutionizing the field, turning intricate market analysis into a streamlined and efficient process essential for investors, operators, and developers across the spectrum—from quaint small towns to bustling metropolitan areas.
With the help of NIC MAP Vision's cutting-edge technology, we'll explore how what once took days of financial analysis can now be accomplished in mere minutes, enhancing the precision and efficiency of investment teams. We’ll also delve into some fascinating niche applications of AI, such as fall prevention in senior housing, illustrating how these technologies are not only improving operational efficiencies but also boosting customer engagement.
Amid a backdrop of rising occupancies and lagging development, we'll discuss how the baby boomer generation is fueling a surge in demand, creating a market teeming with opportunities. Kyle will help us navigate through the latest data trends that suggest we are on the cusp of a golden age for investment in this sector.
It's an exhilarating time to be involved in senior housing investments, and with Kyle's insights, you'll be equipped to lead the way in this dynamic field. So, tune in as we explore the critical role AI is playing in shaping the future of senior housing investments. Join us for a compelling conversation on the cutting edge of senior housing real estate technology.
so when you go and ask the model to do something, say, hey, chat, gbt, give me a recipe for turkey dinner. It goes into its memory to say great, I've seen thousands or hundreds of thousands of recipes and let me find ones that I understand are connected to cooking turkey or something like that, and I'll give you an output. Well, for senior housing it might be. Hey, nai, go and read the regulations for assisted living in the state of North Carolina and then tell me what are the licensing requirements or the staffing requirements for medical professionals. And so it'll go into its memory banks or go into a reference document, consume that information and then produce you an answer.
Speaker 2:Welcome to the Senior Housing Investors Podcast. If you are an owner operator, investor, developer or buyer of senior housing, you've come to the right place. The best way to stay connected with us is to sign up for our weekly newsletter at havenseniorinvestmentscom. This podcast doesn't exist without you, our community. Thank you for listening and reach out to us anytime.
Speaker 1:Welcome back everyone. Today, john Haber is speaking with Kyle Gardner, the Chief Operating Officer at NICMAP Vision. They have an interesting conversation about data and decision-making and how AI is playing a role in that.
Speaker 2:John. Thanks, Kelsey. Today, I have the pleasure of sitting down with Kyle Gardner, the Chief Operating Officer of NICMAP Vision. He was also Chief Operating officer of Vision LTC, which was acquired by Nick in 2021. His current responsibility is providing market analysis tools and comprehensive data to clients in the senior housing sector. Welcome to the show, Kyle.
Speaker 1:Yeah, thanks for having me on John Nice to be with you.
Speaker 2:Yeah, absolutely, man. You and I go back four years when you were with Vision LTC and I called out of the blue and said my understanding is you have an amazing tool, and so tell us a little bit about the origins of both Vision LTC and then, as you got acquired by Nick, tell us that story.
Speaker 1:Yeah, absolutely, nick tell us that story? Yeah, absolutely. So I joined Vision LTC back in 2017, when it was a small but promising startup and our goal was to provide kind of market analytics tools for investors and operators and developers focused on the senior housing space. On the senior housing space, there was another product in the market that was much bigger and more well-known than us, known as NICMAP, which provided market fundamentals, inventory rate, occupancy absorption metrics and, on the Vision LTC side of the space, what we found is a lot of our customers were also NICMAP customers, so it was a really nice compliment to each other. You know the ketchup and hot dogs kind of combination, and so we were growing the Vision LTC business and then had an opportunity to join forces with Nick, and so we created a deal where Nick purchased Vision LTC and spun us out into a separate entity that's now known as NickMap Vision, where the product suite of what was formerly Vision LTC and formerly NickMap is all under one roof, and we've spent hundreds or maybe even thousands of man hours and a lot of money combining the products into one platform psychographics, inventory rate, occupancy migration patterns of seniors, healthcare utilization within senior housing communities, the relationship between construction activity and changes in market demand and a lot more position ourselves to serve any stakeholder who has an interest in the real estate of senior housing, whether you're investing in it, building it or operating it, or even selling to it In some cases.
Speaker 1:We have data to help you and recently we've started expanding into AI as a data provider. It makes sense and we'll talk about that, I think, here in a few minutes. But we've also got some tools around what I call a value add for our customers. We have a listings platform where customers can buy, sell and trade senior housing assets. That's no cost to them, no cost to the broker, no cost to the seller or the buyer. You just have to be a client of the platform to get access. We've got some healthcare data to help operators become aware of who the healthcare providers are in the space to artificial intelligence, specifically through OpenAI's GPT models, to bring AI-powered automations to senior housing investors and operators. So we have a very wide product footprint, but we focus solely on the senior housing space and our customers have proven with their wallets and their feedback that we're on the right track.
Speaker 2:So Well, I can say that when we started with Vision LTC as a company at Haven Senior Investments, we were just blown away. And then doing the combination of both Nick and Vision's data sets combined has given us the ability to really advise our clients wisely with data and also on the brokerage side of our business, being able to understand the markets all the way down to the smallest markets in the United States, even the small towns. It is a beautiful package that you all have, and I'm very excited to learn more about the AI side of the business. And so you know how does AI play a role in allowing operators and investors to access data for decision-making, and I'd love to understand what the challenges are currently and how this AI addresses those challenges.
Speaker 1:Yeah, yeah, absolutely. So to answer that question, let me start first with kind of the challenges at large that our data and our tools solve, because AI is really an extension of that and a continuation of that Mission's the wrong word, but kind of the service that we play. So if you're a real estate investor whether it be a first-time fund entering senior housing or you're a REIT purpose-built for this industry you need information in order to make investment decisions right. So 20 years ago the way to get that information was to hire a consultant or a broker to go and put boots on the ground and maybe live in the market for a few days and do some recon. You'd probably spend some time there as well, look at Census Bureau data and so on, and then they'd bring you a large report and you'd kind of debrief with them over time and kind of fast-forwarding to now.
Speaker 1:The capability of analytics tools and demography and other analytics sets like Knickknack Vision and like GIS technologies have enabled kind of desktop studies where you don't have to leave the office to get at least like a fingertip feel of the population size and what's in the market, and Google Maps gives you some pile of visuals. You're still putting boots on the ground before you write the check. But the time it takes to access that information has gone from maybe weeks and months to days, and now with AI and the amount of data that we have in our tool, it can go down to minutes. And so the challenges that we're helping solve is really helping investors make informed decisions with more confidence and in less time, like that's the motto that we're kind of telling ourselves here at Nickmap Visions. That's the motto that we're kind of telling ourselves here at Nickmap Visions how do we help people execute faster to either get to an informed no and walk away from a bad deal, get to an informed yes and take advantage of an opportunity for their competitors and maybe even help them define what a good deal or a bad deal is for them. That's going to change depending on the shop and risk preferences and things of that nature.
Speaker 1:But information and intelligence is kind of at the core of all we do. We've been servicing customers for 20 years on that theory, going back to the early days of NICMAP, and now that AI is here, it just allows us to accelerate that process even faster, but it also lets us go considerably deeper into detail, right? So I think everyone's familiar with Census Bureau data or Bureau of Labor Statistics data. You know tables on tables of numbers and you know CSV spreadsheets nothing really sexy, but it's the source of truth that we have. People are probably familiar with like Claritas and Esri those are two data partners that we use in our systems and that data is available.
Speaker 1:It's available very quickly, especially in systems like NICMAP Vision, but it requires you as the analyst to make an opinion. You know, read the data and then figure out is this good, bad, is this normal, is it an outlier? Right? With AI combined with kind of industry experience, we can help the customer go from information to opinion or go from information to recommendation very quickly, and so AI is kind of an extension of the you know data as a service. Our CEO likes to use the phrase you know, ai is the arms and legs and the data is the body. You know, so it helps make it makes it more actionable Awesome makes it more actionable Awesome.
Speaker 2:And to tell our audience a little bit about just overall artificial intelligence I'm sure many know what it means or what it is or just give a high level overview of artificial intelligence using OpenAI, chatgpt 4, and here shortly ChatGPT5 is coming out. So pretty cool stuff. So if you could describe kind of in a layman's term what you mean by AI, yeah, absolutely so.
Speaker 1:Well, sam Altman, ceo of OpenAI, if you're listening, please release GPT-5 sooner than later. But what is AI? What is a large language model? I think the average person's heard about ChatGPT, at least in passing, since it was released a few years ago. At its core, ai, or large language AI, is a type of technology. Under that umbrella or under that segment of technology, there's a couple of different applications. There's large language models, machine learning, natural language processing and a few other technology applications, and I am not on the technical side of the business so I won't try to explain the nuances of all of those. But the way that I understand it, the way that I bring to my clients and working with our AI engineering team they're the true geniuses here is large language models is basically a statistical model that you've trained an application to read and consume information. So whether that's a press release, a blog article, the content on a webpage, if you were to take a picture of a menu you know at a restaurant and you could convert that picture to text, you know the language model could read that text and consume it. And then there's kind of a method where the technology is reading the text that's on the screen or on the page and putting looking at, you know, open AIs GPT-4 is currently, you know, in April of 24, when we're recording this is currently considered kind of the market leader in model performance. There's other models from Anthropic and Meta and Google, but what they're all doing is they're being trained on a certain type of data set and then those models take what they've learned and, much like a human, try to apply that to complete certain practices or complete certain outcomes or create certain outcomes.
Speaker 1:And I know this is probably a little confusing here, so let me tie this back together to something that real-world example that makes sense. So OpenAI goes and trains these models on billions of data points webpages, pictures, videos. Just think about the content on the internet, and OpenAI has probably seen it or touched it in some capacity newspapers, blogs, so on and so forth. As it's reading all this content, it's gathering information in terms of knowledge or facts, but it's also gathering information in terms of how the human language is transcribed and written. So when you go and ask the model to do something, say written. So when you go and ask the model to do something, say, hey, chatgpt, give me a recipe for a turkey dinner. It goes into its memory to say great, I've seen thousands or hundreds of thousands of recipes, and let me find ones that I understand are connected to cooking turkey or something like that, and I'll give you an output.
Speaker 1:Well, for senior housing and approach you know where it becomes applicable for investors and operators, it might be. Hey, nai, go and read the regulations for assisted living in the state of North Carolina and then tell me what are the licensing requirements or the staffing requirements for medical professionals in the state, what's the ratio I need of a med tech or an RN or an LPN to certain residents or any of those positions even require, so on and so forth? And so it'll go into its memory banks or go into a reference document, consume that information and then produce you an answer. It's incredibly powerful, it's incredibly complex, but at the end of the day, it's a type of technology, albeit it's a cutting edge one. So it feels new, it feels, you know, in some cases kind of scary, but it's code at the end of the day.
Speaker 2:Well, it's interesting that you spoke about. You know, tell ChatGPT for a question and it'll go into its memory and bring out output. Well, I did that before the show. I wanted to quote on what ChatGPT for would come up with in regards to AI itself, and they came up with an optimistic vision, ethical consideration, transformational change but I like this one the best, and it's a quote from Stephen Hawkins, and AI will be the best or worst thing ever for humanity, so let's make it the best. And it's so true. It's really the individuals that are training these AI models. Are you going to train it to be the best it can be and be the best for humanity, or are you going to create it in a fashion that is detrimental to us? I believe in many other beliefs that it's one of the most transformational changes that we're going to encounter in the history of the world, and so let's get back to where AI can add value to the senior housing ecosystem today. Can you riff on that a little bit?
Speaker 1:Yeah, absolutely. If you look at the jobs and I don't mean like job titles or the positions that people have, I literally mean like the work to be done by people who work for a private equity company or an investment manager or an operators management company Look at the job content that they're doing every day. Yeah, if you had to boil it down to really simple concepts, are they reading, writing, doing math? A lot of work that is done in the investment workflow and the asset management workflow comes down to reading something, reading it once and committing it to memory, or coming back to it multiple times, applying critical thinking and then creating an action plan and going from there. Or it's read something, go and build a model in Excel to represent the world and do some scenario analysis of what could or might happen, and then create an action plan from there, and that's a very oversimplified way of thinking about the world. But when you look at the jobs to be done or the work to be done by professionals in this industry and you realize we spend a lot of time reading management contracts, regulations, laws. We spend a lot of time looking at offering them randoms, broker opinions of value, pitch books and pitch decks and things of that nature, and then multiple parties are asked to make in most cases million dollars, sometimes tens of million dollars decisions on that information. But what is AI really really good at? It is exceptional at reading, consuming information and applying a kind of a critical thinking to that information, with some caveats. There has been research over the last year that, like Chat, gbt or gbt4 those are the same models, by the way that gbt4 can pass the bar exam that practicing attorneys have to take in the us at a level higher than the average lawyer. Yeah right, because it's your, it's reading and critical thinking and and analysis, but it's really bad at math. Llms are terrible at math. So it's your, it's reading and critical thinking and and analysis, but it's really bad at math. Llms are terrible at math, so it's kind of funny that it can't do third grade math properly, but it can pass the bar exam, which is, in certain circles, right Considered kind of one of the highest white collar jobs or most prestigiouscollar jobs that we have in the US.
Speaker 1:And so if we play to AI strengths, it can help us accelerate our decision-making time in certain areas. It can help us dig deeper into subject knowledge. It can help us do scenario analysis in many different ways, and if I was going to break those out into a couple of different examples, let's look at an investment analyst or a VP of investment at a firm. They're reading OMs, they're reading contracts, they're drafting OMs, they're reviewing BOVs all to make a decision. Well, with NICMAT Vision, we've taken AI and our deep industry expertise and created automations that use a mixture of LLMs, like an open AI, not a chat GBT or, sorry, a GBT4. Combine that with our kind of own proprietary technology is where, like our engineering team, our developers have created code-based workflows that are what I'll call rigid, meaning that they're not llm based, that one plus one is always two, uh. So we've combined kind of the power of that custom technology with the creativity of the LLMs to focus on specific automations.
Speaker 1:And so one of the automations that our investors have been using and love is take a offering memorandum, just upload it in any readable format so the PDF has to be readable or it can be a Word document. We'll use the LLM to read the offering memorandum, pull out the information that's interesting and relevant, based on some customizable prompts that our team's created, and then draft kind of a two page, three page executive summary and as part of that summary that the automation's creating, we're injecting NICMAP vision data into it. So your broker sends you an OM, you load it into the tool. You answer a couple of basic questions what's the name of the subject property? What do you want to ask the offering memorandum? The same way, you kind of enter a prompt in the chat GPT, and then a couple of other basic parameters. I think there's maybe five questions total. You hit run and in five to 10 minutes you know the time it takes to go refill your coffee. You come back and you've got a three-page executive summary of the offering memorandum. Looking at the subject property, it's going to analyze any of the financial information that's in there. It's going to add market comps from our NICBAT Vision market fundamentals data set. It's going to pull in demographics specific to your market area. It'll even give you questions that you should send back to the broker or to the seller so you can dig deeper into the product or, sorry, into the investment opportunity and kind of go from there.
Speaker 1:Is that automation going to replace your investment team? No, absolutely, absolutely not. It's going to replace your investment team. No, absolutely, absolutely not. It's going to make them a whole hell of a lot faster at what they do, though, because now, instead of waiting, a well-staffed analyst team who's putting in long hours maybe charge that same analysis around in two business days. Maybe say, an average team maybe takes a whole week. I'm talking about like a true, like deep dive into the, the OM, the underlying data, uh, doing their own third party analysis. You know they maybe takes them a week, and you're getting that same kind of V1 draft in 10 minutes. So imagine what your team could. You know that same team who is pumping out reports in two to five days. You've just given them that whole. You know, let's call it 48 hours back minimum, to then go even deeper or to go look at more deals, and what could that do for your, your process? What could that do for your process? What could that do for your competitiveness? I think it can do a lot.
Speaker 1:There's other things we're doing as well, just at a high level. We've created automations to help with reviewing income statements and reading financials, providing summary analyses of those, basically helping you stay ahead of market trends. We've got some tools built on the regulations, obviously senior housings, regulated at the state level by a large, specifically for assisted living and memory care. So the regional and national providers have a lot of rule books that they have to keep handy and, fortunately, ai is quite good at analyzing those, and we've created some workflows that, if every building is getting surveyed and if you're getting deficiencies, back at some point, you're going to get asked to create a plan of corrections. Back at some point, you're going to get asked to create a plan of corrections.
Speaker 1:Well, why not have AI do your first draft and then have your legal experts spend their time vetting the information and updating the workflow to make it absolute and applicable to the reuse case? But instead of spending their time drafting what is basically a template form, let's put AI to work there. Or, if you've got questions of the regulations, instead of spending hours and hours going through these terrible websites, the hundreds of thousands of click-through links that the states love to utilize, why not just ask a question of a chat bot and get an answer back in less than a minute? It's quite powerful. So everything we're doing with AI really ties back to accelerating your decision-making process and giving you quick, strategic market insights. Ai is not going to replace your job today, and if someone's telling you that they have not spent enough time using AI, but if your team is using AI, you will outperform a team who is not plain and simple.
Speaker 2:Agreed Bottom line is I always felt that AI was going to be a companion to us as human beings, and that just makes everything more powerful our ability to how much they're walking or how much they aren't walking. Maybe that decision making through AI can alert us in the future to areas that we have no idea we would have uncovered if we didn't have AI next to us. So tell us about the future of the senior housing ecosystem when it comes to AI. What's the future thinking that's going on behind the scenes at NICMAT Vision?
Speaker 1:Yeah, absolutely. I'll give you a two-part answer on this. I'll talk a little bit about what we're working on and how we're thinking about things. But I feel like I'd be doing the pot of disservice, john, if I didn't share a little bit about what I'm already seeing in the market from other providers in the AI space and kind of where the industry is going at large. I feel kind of unique and privileged to get to see quite a lot from the different NIC conferences and stuff that I've been to and it's quite impressive and stuff that I've been to and it's quite impressive. So for NICMEM Vision, we see AI as being a I like your word like a partner. We see it as being an enabler for team members to accelerate their workflow.
Speaker 1:The most value to be had right now in 2024 is for research and customer service and labor management, asset management, investment management workflows. So whether that might be someone in the FP&A department, that might be someone on the acquisitions team at a REIT, it might be the asset manager of an investment fund, it could be the VP of HR, vp of people at an operator or something of that nature. Those are the areas that we see it kind of living in right now and it kind of goes back to what I was talking about before with. It's very good at reading and critical thinking and doing some scenario analysis. And doing some scenario analysis when it's going, is it kind of leading us to think that there's potential to help with, maybe, labor management or labor assistance at the community level. At the management level, that we're probably going to see that in the shape of staffing optimization. We'll probably see that in terms of embedded features in current applications, whether you're EHR, erp. I think there's some very clear opportunity there. The ecosystem at large that's not something NCHMAP Vision's likely to want to touch on, but we see that as kind of an opportunity for the investment folks. I think portfolio management, business intelligence, reporting there's a really clear opportunity there. That's something we are investing in. In terms of where's power, of my ability performing now relative to my peers, what's likely to happen over the next three to six months? I think that's a question every asset manager asks themselves right now, and there's every REIT, every PE fund has an AM function and they're spending their days kind of mulling over the data and trying to get ahead of their next competitor, the new entrant into the market.
Speaker 1:Well, ai doesn't sleep, doesn't need to eat, and once you've told it how to think about the world in terms of you know, you've given it a prompt and you said this is the problem I need you to solve. Here's how I want you to approach solving it. Then you've armed it with contextual data, whether that's the NICMAP vision data set. Maybe you're giving it access to your financials, maybe you're giving it access to some other information you've prepared, like you would see at an internal BI dashboard. Well, now you can just have your AI set up alerts and have it ping you when it sees changes in the data.
Speaker 1:Oh, this building's staffing expense has been increasing over the last few days. Looks like overtime budget will miss the overtime budget this month. Is there, you know, kind of ping you. It's not going to know every answer. It can give you some, you know, based on how you've trained it or what you've prompted it to do, it could say hey, here's three things you might want to go look into as possible source of the problem. The human is still going to have to go do that. So fortunately, john, we still have jobs. Uh, but it's a, it's a great companion, it's a great early warning system there and it just helps with the ethos of data-driven decision making.
Speaker 1:Now we're seeing some like we're seeing you brought up using ai to maybe prevent falls or kind of monitor how people are, how they're moving around, like there is some tech out there now that's using more machine learning and video analysis to do that A company called Safely you. They're increasingly well-known in the space for that exact use case. There's customer success tools or kind of customer engagement tools that listen to a conversation between a sales leader at a community and a prospective resident and make opinions or recommendations on how those conversations are happening. So there's a lot of really interesting and highly specific use cases coming out with AI in this space and it gives me a lot of excitement and hope for where we're going as an industry.
Speaker 1:I will caveat that and say I also see AI providers out there who are trying to be everything to everyone and I think those are the ones most likely to fail and I think those are the ones most likely to fail. Llms specifically. They have their constraints and they seem to be most effective and most valuable when applied in a very narrow use case, and so that's the thesis that Enigma Vision is taking with AI. It's how do we help investment professionals, how do we help operator professionals with specific jobs within those companies? And again, jobs referring to investment analysis, regulatory compliance, talent acquisition, financial analysis, not trying to replace the VP of sales or the VP of investment, because that's just unrealistic. We're seeing, and we're we're getting feedback, that this approach is is working.
Speaker 2:Awesome. So when? When did you guys release your AI module and you? When did that come about?
Speaker 1:Yeah, absolutely so. We had a couple of early testers who were using it in 2023. It was a Definitely a work in progress at that time, as we were kind of ironing out everything. But January 1st 2024 was our kind of official release and then we did a push, kind of really started pushing it to the market at large in March of this year. So it is hot off the presses, but we've got a nice mix of investors, operators and REITs using it today.
Speaker 2:Awesome. Well, I went through one of your demonstrations and I thought it was fantastic what you guys have done, so congrats on really shifting real quickly to what's needed in the marketplace and using AI to do that. That's awesome, kyle. Yeah, thank you. Let's talk about the latest data trends from NICMAP, vision Data and what's going on in the marketplace today and where you see the trends.
Speaker 1:Yeah, absolutely. So it's fun to talk about the data side, ai. I enjoy the products growing. We're doing a lot of cool things, but the data is reality. The data is the truth. You know it's a measure, it's truth and fortunately, you know well, my job when it comes to the data is just tell the story. You know what do we see and fortunately the story recently has been a very good one and a very positive one, so it makes it that much easier and that much more fun to tell.
Speaker 1:Last week we just released our first quarter 2024 market fundamentals data which showed occupancies improving in our primary markets, which is the 31 largest metros in the US, and that marks 11 straight quarters of occupancy improvement since the COVID pandemic, which feels really nice to see in the industry rebounding, to see some customers making money and maybe more importantly than that is a lot of businesses feel good, they're performing well. But the optimism in the industry of we survived the pandemic, we've made it through an incredibly tough series of years and there's light at the end of the tunnel we're kind of approaching that piece where it's like, hey guys, it's not only at the end of the tunnel, but it's here. We're enjoying that. So at a high, high level, occupancies have been rising. That's being driven by three quarters of just massive absorption. When we look at the data, the trough of COVID, following that trough, when there were sometimes forced closures at a state level or some states were preventing move-ins or new admissions and so that obviously put a huge dent on the occupancy and the performance of communities. And following that there was this wave of pent-up demand where we had a few quarters in 2021 and 2022 just absolutely rip on a demand basis as people were moving back in that they had been forced to stay at home longer than originally planned. That COVID rebound slowed down a little bit in early 24, but starting in third quarter of 23 and carrying all the way through first quarter of 24, we've seen another big surge in absorption come back to the market. Our hypothesis right now is that is actually the early wave of the baby boomers coming into the space, or maybe the last part of the wave of the silent generation coming in and moving in. And when we look out the next few years, we think that that demographic wave is going to continue and maybe only accelerate in terms of its adoption of senior housing.
Speaker 1:A couple more specific data points I can share with you is we have seen total occupied units so talking about numbers and not percentages here hit record highs in the last few quarters. So even though we're not back to pre-pandemic occupancy rates for every market, a good number of our primary markets are at or above their pre-pandemic occupancy rate. Our aggregate occupied units or stock is out or near an all-time high right now and I think that's a really important thing to note because it's telling us that the industry is growing. We're just adding more supply, as we should be, given the expected demand in the future, but I think it's a promising sign future, but I think it's a promising sign.
Speaker 1:Additionally, we're seeing kind of two different stories between the primary markets, which would be the 31 largest metros in the US, kind of sorted by population, and what we would call our secondary markets, which would be the 32nd through the 99th largest metros in the US. Those secondary markets are actually recovering faster than their primary counterparts, largely driven by what we call majority assisted living communities. So those are campuses or communities that have assisted living on site and they could have IL or skilled nursing or memory care on the camps as well. But if you look at their primary unit count. It's coming from assisted living that's really driving the recovery as opposed to independent living. So hinting at that needs-based driven demand maybe not surprising, but just interesting to note. Not surprising, but just interesting to note. Additionally, we're seeing construction starts kind of remained near all-time lows on a percentage basis. Looking at last quarter's data specifically so this would be for fourth quarter 2023, year-over-year inventory in our primary markets only grew by 1.4%, which is near the recorded lows and the smallest increase we posted since 2012. So we're at this kind of interesting pivot in the market right now where the aging boom of, you know, the baby boomers or the aging wave, as I've seen it be referenced a couple of times is started. I suspect it's starting to break now, where we're kind of seeing the early parts of it enter the space.
Speaker 1:I think at large, everyone agrees 2026, 2027 is when the baby boomer generations maybe officially entering the senior housing industry as a potential consumer and while that's literally weeks away, months away, we are delivering an insufficient amount of supply, of new supply, to meet that demand.
Speaker 1:So we're doing some analysis right now on what the next 10 to 30 years is going to look like for the industry on a market fundamentals basis, and one of the things our CEO, eric Morton, has talked about publicly is if you just hold penetration rates constant from 23 to 2050, and you look at the best year of construction activity that we've ever delivered as an industry, as an industry advocate, if we were to deliver our best year of construction every year until 2050 and our industry penetration rate was remaining constant, we would be short something like $800 billion worth of development between now and 2050.
Speaker 1:So we're still delivering hundreds of billions of dollars of assets in that timeline, but we would be very, very short of what's expected to be needed by the time. The aging wave has kind of impacted the industry at large. So there's a lot of work that we need to do collectively and I think if you're considering entering the senior housing market, you should take a good hard look at it right now, because we're entering an opportune time for medding. It's not going to be an easy path. I don't want to lead you astray on that, but there's a lot of upside that we see in this space and I'm sure you're seeing the same thing, john.
Speaker 2:We've had a number of individuals on the podcast. I've stated the number that you all stated was 775,000 units are needed by 2030. And this is six years. How are we going to do that? I mean, the current operators and owners are happy because they are getting their occupancy up, because there's no new product coming up on the marketplace. So our others that are doing construction currently or constructing larger buildings or smaller buildings, whatever it may be how are they doing it today? How are they getting units out of the ground?
Speaker 1:Yeah, it's a good question. I don't have a firm answer for you, unfortunately. It's depending on the market you're in. I'm based in North Carolina. The development game here is a little bit different than California or New York, and if you go to Kansas City, if you go to Minneapolis, if you go to Dallas, texas, it's a different story in each place. So this business is definitely one that thrives under kind of regional focus. I would say. I think you know local and regional focus tends to work best. Not only do we have this large need with very like different market behaviors across the US, but we're also going to need more players in order to meet that need too. So it'll be interesting to see how existing constituents respond, kind of take on the challenge. I would expect to see an influx of new market participants over the next decade as well.
Speaker 2:Yeah, both your firm and our firm are there to support them, and so we want to make sure that individuals listening to this podcast reach out to Kyle and his team, look into nickmattvisioncom and look at and see their product set. It's absolutely the best in the industry, and so you know. Let's recap real quickly the importance of having a trusted AI partner who understands the industry.
Speaker 1:Yeah, absolutely so. If you're going to come and invest in senior housing, whether you're going to develop a property or you're going to acquire an existing portfolio, whether you're spending 5 million or 500 million, you have to have confidence that what you're buying or building is the right fit, it's in the right market and that it's going to be successful. And why wouldn't you want the best data in the industry to help you make that decision and utilize AI to make your process go faster and smoother? Have decades of senior housing operating, investing, development and management experience. We have a best-in-class technology tool and team. We have a purpose-built analytics system for helping you with your financial analysis, your site selection, your asset management and, in some cases, even your sales and marketing workflows Makes your life easier.
Speaker 1:I had a data partner. I don't want to talk my book my own book too much, but at a minimum, I recommend asking a lot of questions and doing your diligence, whether you use a tool like mine or you use a partner or, like John, a tool like mine or you use a partner or, like John, just be thoughtful in your approach.
Speaker 2:Well, it is quite amazing how, number one, you have the best tool on the marketplace. Not only that, you're improving that product every day and it really tells how much you all have a center of excellence within your company. So it has really helped us be able to save individuals that we're consulting with a ton of money Anyone from your individual that is looking to build or develop or acquire a 12-bed all the way up to hundreds and hundreds of beds. I mean, it's just the data set is just so robust. So we never debate within Haven whether or not we're going to not use NICMAP Vision as our software provider. There is no debate at all. It is the best of the best. So how do people get in touch with your company or you, or how do they start the process of understanding how they can acquire your software?
Speaker 1:The fastest way would be to go to our website at nickmapvisioncom N-I-C-M-A-P-V-I-S-I-O-Ncom. If you want to learn about AI nickmapvisioncom slash AI. If you want to learn about AI nickmattvisioncom slash AI. I'm also available on LinkedIn and happy to connect there and talk more. But we've got a great team of product experts who can sit down and kind of learn from you what you're trying to achieve, where you're at right now in your technology lifecycle and what you want to accomplish over the next six to 24 months. And you know, if we're a good fit, we'll make some recommendations on how to get started. But we'll be transparent with you too if it's not a good time and we'll kind of go from there.
Speaker 2:Awesome. Well, it's been great to have you on. I've wanted to do this for the last three years. What a great time for you to be on the show is when AI is coming out in your product. So thank you, thank you. Thank you so much for being with me today, kyle, and let's see where things go over 2024 and 2025. It's going to be a wild ride, so have a great day, kyle, thank you.
Speaker 1:Thanks, john Cheers.
