Smarter Multifamily Decisions Start Here: Meet HelloData

Hello, everyone. Welcome to today's webinar. We will begin in just a moment. We're gonna allow everyone to join, and then we'll be right with you. Alright. The numbers are climbing, which is a wonderful thing. So hello, and welcome, everyone. We're so glad you could join us for today's webinar, Hello Data, a Smarter Approach to Multifamily Performance and Analytics. I'm Stephanie Anderson, and on behalf of the Gracehill team, I wanna thank you for spending part of your day with us today. Whether you're here to streamline your reporting, uncover hidden insights, or you really just want a better way to make data driven decisions, you're really in the right place. So today, we are going to show you how Hello Data is changing the game when it comes to multifamily analytics. Now I'm thrilled to introduce the person who is leading the charge behind this innovation. Mark Rutzen is the EVP here at Grace Hill, but most importantly, he is a cofounder of Hello Data. Mark is a recognized leader in real estate tech with a passion for using data to solve real world challenges in multifamily. So today, he's gonna walk us through a live demo, show off some of the most powerful features, and explain how this tool is already helping teams drive results across their entire portfolio. So without further ado, Mark, take it away. Thank you. Thank you. And thanks everyone for joining. So high level on the platform, I'm I'm just gonna share my screen, and I'm gonna jump right in and and kinda show you how it works. And then I'll I'll talk through it kinda as I go here. So what we do you know, the the platform is, designed to automate market surveys and deal analysis for for multifamily deals specifically. So, we go to well over a quarter million individual property websites straight to that leasing page every single day, as well as, several listing sites so so that we capture the entirety of the market. And we update every single day so that we catch the last price and the concession that was active when each and every unit is is taken down when it leaves the market. We found that this price, the the last price on the day the listing is removed, more than ninety six percent of the time is the exact number that ends up on the rent roll. So by tracking, this data for about five million assets, thirty five million units nationwide, we're able to, tabulate that data in real time, serve up with just a property name or address, your most relevant rent comes. We'll survey their rents, concessions, and amenities in very granular detail down to the unit level, and then we'll recommend, where you should price your units based on real time market activity. So show you how it works with a property that I know in Chicago. It's one eleven property. It's, from one of a a client of ours, Buzzuto. Really high end class a, downtown property here. Now when you first pull it up, any property in the platform, like you saw, I just type the property name. You could also type the address. It's gonna show some high level average rents over the last thirty days, number of stories, units, year built. And we'll show since it's all public data, right, straight from property websites and listing sites, we can't see behind the scenes. Like, if you have some offline units or something and and they're not advertised on the site, we're gonna assume they're occupied. So when we calculate lease percent and exposure, we're we're comparing that to the number of units. We're comparing the number of units that are currently being advertised to the unit count of the building. So I like to point that out because sometimes people people think that we have, like, their true occupancy data. Can't get that from public data that would potentially run a foul of antitrust. So we we don't do that, all public data. But the the big, thing that we do, to the data and the the AI part of HelloData dot ai is that we're actually using artificial intelligence to extract some really detailed information, on the the data we collect and the properties we we analyze. One of the things we do here is this quality score that we've developed to analyze the listing photos. As we're collecting rent and availability data, we're also analyzing listing photos, assessing the condition and quality of each room and common area on an objective one to ten scale, and using that to recommend comps that are similar, not just in year built unit count and proximity, but that actually have a similar look and feel, similar conditioning quality to the subject too. So here you can see, we'll recommend ten comps by default, and these comps were were recommending based on several criteria. So we're looking at the submarket down to the census track level, making sure that the people and properties around each asset are also similar, not just the property itself. Physical distance, which will vary based on the the market type and density. So if you're in a dense downtown market like this, you shouldn't go, you know, ten miles to pick your next comp. Everyone kinda understands this. But in the suburbs, you can go several miles, and it will still be comparable. So we scale that distance penalty based on the market density. Number of units, vintage story is kinda table stakes for comps. We'll also gauge the overlap of the unit mix, so the types, the the square footage, the proportion of units of each type in the building. But the quality that we extract from the photos, and the overlap of about two hundred different amenities that we extract from both the photos and the listing descriptions, that, that gets us that resident side of the equation. Right? Similar look and feel, similar product offering overall. And that's why we have a very high degree of overlap with what our users select in the platform. Nine out of ten times, we're gonna recommend the exact comps that our users will will leverage in their reports. But they are just recommendation. So if you see a a property that you're like, you know, North Water, that doesn't feel like a good comp. I know this one. I've walked it. It's it's not comparable. You can always take it out. And if you wanna add a comp, you just type it in like you did with the subject property. And, you know, if you pick a twenties vintage deal that's six miles away in in a dense downtown market, we're just gonna tell you it's probably not that similar. Might not be your best comp. But you're free to add whatever you want, and a lot of people will just type in the comps that they want. Now two things I'll I'll point out before I go ahead and generate this report. There are settings on the comps, So you can constrain the radius, the unit count that you're built, and we will recommend comps within the parameters you set. But because we have this computer vision model analyzing these listing photos every day, we're also able to source your value add comps. We basically turn that model in reverse and look for the next level up in finished quality. So these are are nice pretty new assets, class a high end, properties. But if you're looking at something that's a bit older and you wanna see what would my rents look like if I were to raise the level of finish to to my post renovation comps, this feature will allow you to pull that post renovation comp set in and really see where rents will will be if you were to renovate to that finish level and what what you'd need to do to compete with those assets. And then finally, a really convenient feature, the email notifications. This will just send you updated rent availability and concessions data to everyone on your team without you having to create a new report every time or or really do anything. You create this report one time, and it will send you email updates until you tell it to stop every week right in your inbox. So it's a a nice way to not have to log in to yet another platform, every day. Now, as you scroll down the page, once the report's generated here, I think what people really value about the platform is the level of detail that you can reach in just a few clicks. So here, we're looking at the rents by unit type table, and this is gonna include data on each property. So, your builds, number of leased units over the time frame selected, and you can change the time frame to last two weeks, the last week, today, last year, all the way back to June of twenty three very easily. Let's say we're looking at the last thirty days here. This is gonna show you for the property the the number of units leased over that time frame, current active listings on the property website, average time on market, square footage, rent, and effective rent. But here, it's by unit type. So if you drop this down, you'll be able to see the breakdown of bed and bath count on the left and detailed data for each of those different unit types. Now these are filterable up top just like the the time frame was. If I come up here, I can look at only the the one beds across the report, only the two beds, only specifically the two bed, two bath units if I want to. But where we really shine is here is by unit type. A lot of platforms provide by unit type. We take it a step further, and we'll actually show the individual floor plans within that unit type. So what did leasing activity look like for each and every floor plan within the one bed, one bath unit type in Mila? And for each of them, if I want to see, like, okay, they they did two of the junior one a's, and they have eighteen active right now. What's the deal with that? Why do they have so many active? Why do they only lease two? I can click on this and see the specific two units that left the property website during that time frame. So these two units, we see that they were on the market for ninety six days and and seventy six respectively. The the eighteen twelve unit was on, was vacant for sixty nine days, meaning the availability date where someone could move in had passed, so it was sitting vacant. The prices changed negative three point two percent during the time they were on the market in terms of effective rent. So they they started at a higher price and ended lower. And if I click into these, it will show me the full data on this floor plan, when it became available, when at least, time on market, even applications and cancellations. So looking at times where the unit disappeared from the website temporarily and then quickly returned. We'll we'll count that as a application, and then we we don't know why something fell through. We just know they didn't end up signing a lease because it's back on the site a week later. So we'll count that as a application and then cancel, and then we'll track the price trend the entire time this unit is on the market all the way through to the last price on the day the listing was removed. And it looks like this one was seven thirteen was the last time we saw it at a price of twenty seven ninety five. And during that time, in the last week there before it left the market, they put a concession in place. So that banner on the property website, as you guys are going in in surveying data from from property websites and and calling around for data, you know, you're you're probably looking at that banner to see what specials they're offering. We're doing the same thing. So we'll we'll take the banner from the the front of the website. We'll analyze it with AI, and we'll apply it only to the specific unit types that it's supposed to apply to and just that, that discount. Right? And that's how we calculate that effective rent number that you see. So we do this for each and every property, every single unit in the platform. That's why you can drill down into such detail and see all this information on each individual floor plan. Now for a lot of them, if they're advertising a lot of unit level amenities, we'll be able to pick that up, from the the leasing page of the property website or from the floor plan image itself. So AI is amazing. You know, if you see that someone's tagged it as being a corner unit, having a balcony or patio, you know, that that's all well and good, but a lot of times people don't mention those things in the listing description or the unit level amenities. So in those cases, we'll actually still pick it up because we'll analyze the floor plan. And just like you or I can see, you know, there's a a rectangle jutting out from one side of the floor plan. That's probably a balcony. Or, you know, there's glazing on two sides of the unit that is probably a corner unit. We can pick that up up too from the floor plan image using AI, and we populate those amenities here. And finally here, very important, lease terms and respective pricing. So for any units that are advertising different lease terms and different pricing associated with them, we're gonna track the best priced lease term option. In this case, it's the twelve to fifteen. They're all priced the same, and that is the option we're gonna track in the chart below. And we allow you not just to see it for one individual unit, but if you want to, you can go filter by this across the report. So all the rents you're looking at could be the eighteen month or the nineteen month. If you if you want a true apples to apples comparison, this offers a really effective way to get to it, and this is data that you don't usually see in in other market analysis platforms. So, I covered the rents by unit type. Scrolling down from there, we've got a nice property ranking table that we just recently added that will show where you sit relative to your comps in terms of rent on a host of different, dimensions. So you can do asking, can do effective, you can do per square foot. And for each of these, it will vary over the time frame you select. Right? So you can kinda gauge how you're performing relative to the market over time, and it will vary based on, the lease terms, the listing status, the amenities that are present. One thing to point out here, because we're doing that unlocking of amenities from the floor plan images, from listing descriptions, we can actually sort or filter, I should say, the data by only the corner units or only the ones that have a balcony or patio. And see, apples to apples, for all the comps that have those amenities being advertised, how are you performing relative to them? This really comes into play with renovated units. I'll take a a quick undo that. I'll take a quick, detour over here to the unit level insights and show you that if you're looking at this, you know, say you you widen the time frame to, like, three months back. You're doing a three month look back. Over that time, we've seen over a thousand listings across this comp set. I can come in here and say, only wanna look at renovated units, those that are tagged as renovated on the the property website. And this will show me my average rents for all the comps across the entire comp set, including the subject property for only the units that are tagged as renovated that were on the market during that time frame. So you can see how this could be really helpful in honing, amenity premiums. You know, if I wanna look at just the ones with the balcony or patio and compare the subject to the comps, just the ones that are a corner unit, just those that have been renovated, I can easily do that. I I can even, look at ones that have not been renovated and tag just those units. So it's a really robust system, and all that we unlock from the the unit level data really helps in in driving those decisions on how to price individual units or how much you could potentially earn in in a value add. I'll jump back over to the rent comps tab here, scroll down a bit more. So we showed the property rankings. One thing that we do with the data that's really interesting and and helpful for for acquisitions, you know, when you're you're doing a potential acquisition, you get a rent roll for the deal that you're analyzing, but you don't get it for every other deal. Right? Like, you don't get the same level of detail on your comp set as you get on the subject property. Makes perfect sense. Why would they give you their their rent rolls? We have found that we can actually create a synthetic rent roll with a very high degree of accuracy, ninety six percent match with the effective rents on actual rent rolls. By just tabulating what is leaving the websites, these property websites every single day. Now how we do this? We'll look at how many listings have we seen associated with the three bed, three bath floor plan out of all three hundred thirty listings we've seen for this property? And we'll take that ratio, the sixty six over three thirty multiplied by five zero five, and then we'll get to the the distribution of units in that property. Right? The listing distribution, very representative of the the unit distribution. So we craft this unit mix at a high level and show exposure, renewal rates by floor plan, looking at units that, left the market last year and and have not reappeared this year, implying they renewed. We'll look at time on market apps versus cancellations, again, looking at units that left the market and returned shortly after, implying, there was a cancel, and then their rent and effective rent, as well as, over to the right, the lease trade out per floor plan. Right? So here, we're looking at the the pricing trend overall, and here, we're looking at the trade out same units that left the market last year when they reappear this year. We're comparing the rents to get kinda same store sales, for each of these properties. Now while we call it the rent roll, when you drop this down, we'll actually show the lease dates of each unit, the last price that we observed on that day when the listing was taken down, and given the concessions that were active at the time, what was the effective rent of that unit? And you can drill down into any one of them and see that full price trend. And this one, it looks like, actually declined quite a bit during the time it was on the market and ended up at twenty twenty eight here with the con concessions tabulated below and then the effective rent calculated for you. So, for acquisitions teams, being able to see this same level of detail for every unit or every property that, you consider a comp, super valuable. And, all this can be downloaded, into Excel to to do more in-depth analysis. Last thing I'll I'll cover here, I think, is really important for, for property managers and asset managers who are tracking, you know, how the market is performing, what does leasing activity look like. In this chart, what we're showing here is how many units have left the property website implying they lease. And you could look at new units that that enter the site each week, and you could look at units that left the site each week. But how many units are have left the property website each week by type up top. So implying that, you know, eleven two beds left the market last week across this comp set. So we assume those units leased, and we give you a sense of that distribution by unit type with the ability to click into any of these and see the underlying unit data. If you scroll down here, you'll see the exact units that left the market. And then below that, we'll show it broken down by property. So showing over the last get it. I picked a a three month look back period here. Over the last three months, this is how many units have been leaving one eleven's website each week. On average, four point two. In total, fifty nine over that period. And here's how that compares to all the other comps in my my analysis. So just a really good way to see their leasing activity day over day, week over week, and then be able to click on these units and and see the specific units that leased in that week. It's a level of transparency that that people aren't used to. You know, if you do, like, call arounds, you're getting, oh, we signed, five leases this week. You don't get much of a breakdown. This gives you a full breakdown, full transparency into what's happening in real time. So we have a bunch of different charts and graphs. We got nice historical rent trends, exposure charts that are useful for lease ups. But I will point out that, we we do show the data in a side by side view, the the more traditional market survey view. And this is customizable, so if you want to, go in here and add or remove parameters, you can easily do that. We'll show it broken down by unit type below this. So, if you wanna get very detailed, we have a ton of great metrics you can add for each individual unit type. And below that, one of the things we do with the data to just make it so that there there's no guesswork. When you're having these conversations on, you know, is the market going down or is the market going up in in our how are we performing relative to the market? Is it a performance issue with our property, or is it the general market that's that's in decline? A lot of these conversations can be pretty tense between owners and managers. So what we do is we we put together this market alerts panel that will look at key metrics by unit type and compare the last thirty days subject property versus market and last seven days. And we'll note any significant changes between the the subject property and the comp set. Like, if the the subject really increases rent over the last few weeks and the market stays flat, well, that's something to know. Right? Or if the the whole market goes down and you go down at the same time, well, then it's not really likely a performance issue at the property. It's the entire market declined. And so what we do here is we put in plain text. The average asking rent for the one beds increased slightly over the last seven days while the overall market was stable over that period. Or the average number of days on the market for one beds decreased considerably. Right? So here, we're seeing a subject property where they're flying off the shelves. Average time on market is decreasing while the comp set increased, and they're increasing their price at the same time. So I I'd say, you know, I I don't know much about this deal in particular, but first glance, they're doing a great job leasing their one beds. You can probably imagine how this could be very helpful when you're having those pricing conversations, like where should we set price to know real time how you're comparing relative to the market. It takes out the guesswork. It's not we might be performing worse here. We might be performing better here. It's here's what the market's doing. Here's what we're doing, and here are the areas to dig into further. Another area where you can gauge performance like that is, looking at the review analysis. Now what we do here, this is another way we use machine learning and artificial intelligence. We will analyze all the the public reviews for these assets, each and every one of them. As reviews hit Google or, listing sites or other public sources, we're tabulating those reviews and not just showing the overall rating, which you can see here. These guys are four point seven out of five, but we're digging in deeper. What is mentioned in those reviews? So here, there are a hundred six mentions of location, ninety seven point two percent positive. So people saying a positive thing about proximity to amenities, local shops, restaurants. I mean, it's smack dab in the middle of the city. It it should do pretty well there. Staff and management, ninety four point seven percent positive, but there were eleven negative mentions. Right? So that could be an opportunity for improvement. So these are ways to to qualify. You know, it's not just a number of reviews and an average score. Where are there opportunities for improvement? And I could use this and easily dig in to the detail behind each category. So all of this detail that we go into is really in the service of producing pricing recommendations. There are a lot of good charts and graphs and visualizations all downloadable, but what we're really using it for is to recommend where should you price based on real time market activity. So if I look at this pricing recommendations chart, I've got a recommended rent over to the right. I've got the per square foot value, which is useful when people are running a new development analysis. I could show you that in a a minute. We can model a ground up development, based on parameters you put in and actually generate a full market survey and year one financial analysis for it. So that's pretty cool. I'll show you that in a sec. But looking at the recommendations here, we really approach this very similar to how an appraiser would look at valuing an asset. But instead of looking at similar properties that sold recently, we're looking at similar units that leased recently. So high similarity at the unit level recently left the market, were actively listed today, and capturing their last rent and rent per square foot. And then we just do a blended average of these three columns to come up with today's market rent. So it's not, as you can imagine, a a full revenue management solution. It's not taking into account your exposure and your your, your lease expirations. But what it's doing is it's benchmarking in real time to the market as new listings hit the market, as new listings leave the market. It's picking up the most similar. It's picking up their price, and it's computing an average price across those supporting units to tell you what their real market rent is at any given point in time. So a lot of people, our our biggest clients use this alongside LRO or YieldStar or whatever system they use for that market context in their pricing calls. It gives them a lot of transparent data to be able to dig in and say, hey. You know what? I think that recommendation's a little bit too high based on what's going on in the market. Hello Data is saying it's a little bit lower. That's where things are actually moving. I think we're gonna set the price maybe somewhere in the middle between the the too high, output and and what we're saying and and see what happens there. There's really good ammo for those, pricing calls. Now, last couple tabs here, we do capture, data on fees and amenities. So here, we'll show application, admin, storage, etcetera. Just give you a benchmark relative to your your comp set and see where there's potential upside. A lot of demos I've done, I I've seen people are actually undercharging for application or admin fees. And post demo, they're like, yeah. I was able to increase it because that's what their their comps are doing. We'll we'll chart historical concessions, so this will be based on the time frame you select. You could go back, all all the way to June of twenty three when we start collecting data if you wanna expand that. And then we'll analyze the finishes. This is the output of our computer vision model, analyzing the the unit interiors from the photos, as well as two hundred different amenities at the building and unit level that you could compare side by side in a grid. So people do, like, quarterly amenity surveys to see how their their, position relative to to their comps. This is a a much more streamlined way to do it. You don't have to manually tabulate that data. You could just download it straight from the platform. And the last, big thing I'll show you here, if you head to the financial analysis tab, what we do here is we we capture public financial reporting data, so from, like, CMBS, from HUD, from GSE finance loan data. It's it's hard to to, cobble together and map charts of accounts and remove outliers and really clean the data. But once we do that, we get a really powerful training set. We combine that set with our, property and rent data, and we train a machine learning model to underwrite this property just like a human being would. Controlling for the fact that, you know, this is a high rise deal. It's five hundred five units. It's gonna have a different expense profile from a property that is garden style out in the suburbs. And so, for each line item, we'll show, a lower estimate, a upper estimate, and the average, and then we'll break down the most important factors that the model considered in, delivering this particular prediction, the the why behind it. Ultimately, we get to NOI with under ten percent median error with only a property name or address. All I did was type in one eleven, and I have this analysis instantly produced for me. So for acquisitions teams, who are trying to to evaluate deals faster, this really accelerates deal screening times. You know, I never tell people, oh, it's perfect. We got a prediction. Just slap that in a LOI, like, a cap rate on it, send your LOI out. You're gonna underwrite the deal. You're gonna do a full analysis, but we make that a lot easier to do by coupling it with a really robust Excel export. And, I'll I'll show you the export here as it loads in a sec, but I'll note that we also do have a customizable PDF export. So if you do wanna share this, out to to collaborators, owners, managers, whoever you work with, you can pick and choose what's included, and you could send them a detailed report mirroring the interface. So grabbing that, that export Mhmm. Always opens on my other tab or my other, screen here. I'll, I'll pull that over. In this export, we're gonna show weekly market updates. So this is similar to the email that goes out each week, with updates on the comp set. It'll show how many units leased, their average rents, new listings, and their average rents, and then active listings and exposure across the board. We'll show thirty day trend analysis, comparing the the subject property to the average of the comps and then breaking it down by unit type in these charts below. The data analysis tab really robust. This is gonna include everything that is leased over the entire time frame we've we've analyzed back to June of twenty three. So, if I want to, say, look at the entire, year of twenty twenty four, this will show me asking an effective rent trends, leasing velocity, time on market, the the breakdown across that time frame, whatever time frame I select, on asking versus effective rent averages, and then a full breakdown below sorting by property, unit type, and floor plan. So this data that, you know, if you were tabulating this yourself, this would take a long time to put together. You could do it. It just takes a long time. Leasing trends will show month over month, week over week, how how these things are leasing. Rent caps will show raw data over the thirty, sixty, and ninety day time frames and a comparison of the subject property to the market on each dimension. Side by side view of the same data, the the rank comparison tab table. All the raw unit level data, and and this is data on, at this point, thirty four hundred listings. Right? This is what powers most of the report, and we we use that data in this unit mix tab to show you for every property so that we'll have the property, the unit type, or the floor plan, I should say, the the unit type info, the lease rents. So this is synonymous with that estimated rent rule in our platform looking at all, rents, for all unit types across time versus the active listings that are are right now being advertised on the website. So you can kinda see, you know, your your loss to lease if if you compare active to the to the rent roll. Then you have thirty, sixty, and ninety day look backs, and these are editable. So, you know, if you wanna look over a broader time frame, you could. This will show you what has left the website over whatever time frame, you're analyzing. You could break this down by chunk rent or per square foot so you can get pretty detailed. What's great about this, because we're controlling for the property, the floor plan, the unit type, it's it's true apples to apples comparison. If you go over here to the value add amenity analysis and say you wanna look at just corner units, only the ones that are corner units across the the concept. This is gonna show you what has been tagged with a core with corner unit as a attribute or we detect it from the floor plan image versus what has not been tagged with that attribute. And you could see the delta in terms of the rents they generate over to the right as well as a percentage difference. If you're trying to understand amenity premiums, like, what's the premium for being a corner unit versus not or having a balcony versus not or, you know, being, renovated versus classic unit, this will show you the differentials for each and every one of those units and give you a sense of what those premiums are for each amenity. We do take the reviews analysis over to this tab as well so you can see that full breakdown First positive versus negative mentions, for each, of the the attributes you saw on the platform. So location, amenities, staff, maintenance, and value. This is how people are actually rating, these properties in each category on Google, on listing sites. We're just compiling all that data so you can see, you know, where are we doing well versus the comps and where, are the comps doing well, on particular attributes. Where's your opportunity for improvement? There's always something you can improve. This makes it a lot easier to drill down to that information. We'll also tabulate the fees and amenities. So, just like you saw on the platform, this is all included here. Specials, you get all the historical data. And then we wrap it together with this financial analysis, which which mirrors that, year one analysis you saw on the platform and a full pro form a model that will export this in such a way that you can play around with the assumptions. Say you wanna do a eight year hold or you wanna do cash out refi after forty eight months versus thirty six, it'll flow through the whole model and show you detailed breakdown of the NOI, the cash flow, levered and unlevered, and the full investor waterfall structure where you can play around with, returns, play around with catch ups. You know, I had to dig deep back to my my analyst days to to remember how to do a proper waterfall structure. I think we've come up with a really clean and and usable model here. So, acquisitions teams really love this. Property managers love it too because they they'll take this to owners, and the owner's like, wow. That's a really sophisticated analysis you put together. And they do it in minutes. Right? They just log in, type in a property, export, and they've got this full analysis built for them. So, that is, essentially the the template in the platform. The last thing I'll show, because I I mentioned it before, is this new development analysis. So the way this works, if I put in a property name or address, well, in this case, an address because it's using, Google search there. Say I wanna do a new development on this site. I can type in, you know, a plot of land. I could type in, like, the the a single family home next to a plot that I wanna develop. Anything with an address, I can just type in here. Say I wanna do three hundred units. I wanna deliver this in, twenty twenty seven, and, it's gonna be, you know, a sixteen story sixteen story building here. What this will do is it's actually going out and finding new construction properties around this location that are similar in terms of number of units, year built, and stories. So similar new construction properties. It's finding the most, similar fifty properties and surveying all of their units, and then it's recommending a unit mix based on the the typical distribution of units in these new construction properties. With that, it's it's calculating an average rent across those or an average square footage, I should say, and an average rent across those, fifty properties pulled in as as kinda high level comps such that you can use our recommended unit mix to get a quick sense of what should be built on that piece of land. You can also it's pretty flexible. So if you already have, like, a development planned out and you have your unit mix, you can upload that template here, and we'll parse out all the units and and build the full analysis for you. So if, you know, you you can always type in floor plans here, add different floor plans. But, you know, to do that for some of these developments, maybe it's thirty floor plans. You you don't wanna sit here and type it in all day. You can use the CSV import to to do that automatically. But with that, we'll recommend the amenities that are most frequent in the market for for similar properties. You could use our recommendations. You can get more detailed on it and exclude or or include as as needed. When you save this, it will do a full ground up development analysis, full market survey, recommend your most relevant comps, survey their rents, and then recommend pricing for this, for every unit in this property. So here you'll see over to the right, the recommended rent for the studios. If I scroll over, recommended rent for the ones and recommended rent for the the twos. And just like we did before, if I drop this down, it's gonna pull supporting units that are similar in terms of amenities, square footage, similar, competing for similar demand, essentially, in the market. It's gonna pull, their most recent lease date, the last asking rent, and rent per square foot, and come up with that true market rent today. So when you're deciding, what should I do? Should we develop a property in this piece of land or or is this a better investment area versus this? You can easily generate reports for each and view them in the platform and get a a real sense of if this property existed today, what would rents look like? And that's pulled through all the way to this, financial analysis tab where you can get a sense of year one operations as if this property existed and was stabilized today. So that's that's pretty much the the platform. I know there were some questions that came in, which I'm happy to answer here. I saw a little bit in the chats. If the Grayscale team has been answering them, I haven't seen it yet, but I'm happy to answer if anyone's able to to raise their hand and ask questions at this point. Alright. Well, maybe it's a a one way. Maybe people can ask questions. But oh, there we go. I see the the chat here. Hold on. Let me open this up. I got plenty here. So, okay. Let me go back to the beginning. Thanks everyone for joining. Let's see. Looking for some questions here. Lot of lot of good vibes. Can you just look at market rate properties? Yes. So, you you can filter. We we detect affordable units and affordable properties by looking at unit level amenities. So a lot of times if they tag, like, the arch program in Seattle or, the ARO program in Chicago or they they mentioned, sixty percent AMI in the floor plan name or listing description, we'll detect that as an affordable, unit. And even if they don't mention it, we reference the the HUD LITEQ database to see, pure, built purpose built affordable housing, and we'll tag that in the platform. And you can filter those out either through the editing of the report to select comps. So here, let me show that setting here. If you want only affordable or you wanna exclude affordable, you can do that here. And if you want to filter by amenity, a lot of times, if there are affordable units in the the mix here, we'll we'll include an amenity for you to filter in or out those affordable units. In this case, it's brand new construction hypothetical development here, so we don't have much affordable in the mix. But if you, do have affordable units across your comp set, they'll they'll be filterable here. See what else do we have. Data does go back to, twenty twenty three to to answer Alex's question. So it's June seventeenth of twenty three was when we launched the platform, so it goes back to them. Do we work with any student housing operators? Yes. We have several of the largest student housing developers, owners, investors in the country on our platform. We have the largest, second largest, and third largest, using it across their portfolios. Many more that that are are smaller are using it. It it may be that they they use it on, the via API because we have all the data is available, to bring into Power BI, your data warehouse. Not all of them use the interface because it's, it's more geared toward conventional multifamily. Like, for student, you're not gonna see the breakdowns of, like, actual leased units because they they typically advertise at the floor plan level. So the interface is not as helpful for student, but the data itself on what their rents are every single day and their their, concessions, Very valuable. And a lot of these groups will pull it in via API to Power BI or their data warehouse. Let's see. Yep. Okay. I think, and will it be accessible through PerformanceHQ? We're we're brainstorming on it. So so someone asked, will Hello Data become accessible through PerformanceHQ? We're going through the process right now of seeing how we could integrate the data. So so, yes, coming soon. It may be that there's, some high level data there, and then, you know, you can dig in deeper if you need to in HelloData, but we're we're looking at ways to to better combine Grayscale and and HelloData offerings. Since I mentioned the API thing, I'll share, one other thing real quick. We do have all this data is available to pull in wherever you want via API or or, or the query builder here. And the the query builder, what this will do is, you know, I can go say, I want to, pull average rents by ZIP code across the country. I can actually build a report, you know, adding custom columns and filters here. So say it's gonna, by default, apply to, like, just one city, but I can come in here and and build essentially a report from scratch, adding whatever columns I want, whatever data that I wanna display. And, you know, say I wanna add, like, some market level stuff. I want a city, MSA, property level stuff. I want the number of units, building name. Maybe I wanna plot it on a lap a map, so I'll add lat long coordinates, you know, detailed information, all the same stuff you see in the the general platform here. And then, here, you know, maybe I wanna do, like, monthly average rents. Well, if I generate this report, this is actually gonna query all the properties that that meet the parameters that I I specified, just in the city of Chicago here, but I can open this up to anywhere. And I could download a CSV with the average rents week over week or month over month for the entire set of properties that that meet my my filters and criteria here. I could download it as CSV, or I can create a report that will send this to me once a week, once a month, whatever time frame I want, right into my inbox so I could download that or pull it in via API. It makes it really, really easy to analyze, data at a portfolio level or to to screen which markets you wanna go into to do investment analysis at scale. A lot of groups will are now using this. It's something we released pretty recently, but a lot of groups are using it to prospect for acquisition deals, or to analyze portfolio level performance across all of their assets. So, this all available via API as well. It's just a different product called the query builder here. We encourage people to to check it out. Okay. That really is all, I've got for today. So, really appreciate everyone jumping on. I see one other question. Is the data still vast with smaller cities? I'll I'll answer. Yes. It is. We've we've tested this quite a bit. So you go into tertiary markets. You go into rural areas. As long as they have rental housing, we go all the way down to single family rental. Five million properties. You know, there aren't five million institutional hundred plus unit properties in in the market. But if you go all the way down to single family rental, we're tracking that too. And a lot of times, that's gonna be the the bulk of what's shown in a in a tertiary market. So, I encourage everyone to check out the free trial. Go to our our website. You see it right here, hello data dot a I, in my background. You can see see it right up there. Grace Hill solution, hello data dot a I. Head to the website, check out the free trial. Or if you're a Grace Hill or a Hello Data customer and you you wanna dig in on any of these things in more detail, I'm happy to set up a call. Feel free to reach out. Thanks, everybody. Really, really appreciate your time, and, I look forward to working with you. Take care.