DGS 347: Measure, Map, Automate: Smarter Property Management

AI is everywhere, but are property management companies asking the right questions before implementing it? In this episode of the #DoorGrowShow, Jason Hull sits down with Mo Hussein to discuss why successful AI adoption has far less to do with technology and far more to do with operational clarity. Instead of chasing the latest AI tools, Mo introduces his Measure, Map, Automate framework to identify operational bottlenecks, uncover hidden profit leaks, and build workflows that actually improve business performance.

Together, they explore why clean data is the foundation of automation, how undocumented processes create costly inefficiencies, and why AI should enhance human decision-making rather than replace it.

Quotables

“AI value really truly is workflow value.”

“AI depends on trusted operational data.”

“The winners are not gonna be the companies that have the most amount of data, but they’re the ones that can convert data into consistent operating actions.”

Resources

DoorGrow and Scale Mastermind

DoorGrow Academy

DoorGrow on YouTube

DoorGrowClub

DoorGrowLive

Transcript

Jason Hull ([00:00])

welcome everybody. I’m Jason Hull, the founder and CEO of DoorGrow, the world’s leading and most comprehensive coaching and consulting firm for long-term residential property management entrepreneurs. For over a decade and a half, we have brought innovative strategies and optimization to the property management industry.

At DoorGrow, we are on a mission to transform property management business owners and their businesses. We want to transform the industry, eliminate the BS, build awareness, change perception, expand the market, and help the best property management entrepreneurs win. Now let’s get into the show. And my guest today is Mo Hussein, and we’re going to be talking about how property management companies can stop drowning in data and start turning it into real operational growth. In this episode,

Mo is breaking down the measure, map, and automate framework that he has built and approach an approach to uncovering hidden margins, reducing manual oversight, and getting more value out of every door in your portfolio.

right.

is Mo Hussein. Mo, welcome to the show.

Mo Hussein ([01:01])

Hey Jason, happy to be here.

Jason Hull ([01:03])

So today we’re going to be chatting a little bit about how property management companies can stop drowning in data and start turning it into real operational growth. And Mo’s going to break down the measure, map, and automate framework, his approach for uncovering hidden margins, reducing manual oversight, and getting more value out of every door in your portfolio. So cool, measuring is important. We’ll get into that. So before we get into that, Mo,

Can you give people a little bit of background on yourself? How did you get into entrepreneurism? How did you get connected to property management? And help everybody understand who Mo is. Yeah.

Mo Hussein ([01:42])

Yeah.

great question. So I I’ve been in this industry now for probably coming up on 20 years at this point. I I worked at some of the prop tech and software providers that are prevalent in the space. Namely, I worked at both YARTI App Folio, which are both kind of headquartered in in Santa Barbara. and a little bit over ten years ago, I started a consultancy and accounting CPA practice that specifically focuses on

Prop tech and real estate. So we offer consultations with implementations, custom reporting, operationalizing around technology, which is which is now the buzz around kind of AI and automation at this point. and then

we’ve also built products for the space to help with automations, help with you know accounting compliance and bringing visibility and custom reporting capabilities to operators. So kind of leveraging all the experience.

from working as a consultant and also as an accountant and even working as some of these tech providers now being a actual supplier in the industry.

Jason Hull ([02:41])

Very cool. Very cool. So you’re a little bit nerdy.

Mo Hussein ([02:52])

A little bit. Data. I love data. Right.

Jason Hull ([02:53])

Okay, so am I. So am I. All right. So

cool. So let’s talk nerdy to me, Mo. All right. So let’s let’s chat about this. So let’s get into it. So t tell us about this. Wha why is this wh how’d you come up with this framework? Why is this important? I love frameworks because frameworks are usually where we take something that we notice a pattern in, there’s some complexity involved, and we make it simple. So explain to us.

Mo Hussein ([02:59])

Yeah.

Jason Hull ([03:18])

Where does the measure map and automate framework kind of come from?

Mo Hussein ([03:22])

Right, right. And this is this kind of stems from a conversation you probably have with plenty of your your clients and even prospects when you start engaging, you know, the the the very popular question now of how do we use AI? I want to streamline and automate. And it’s a very loaded, it’s a very loaded, fairly ambiguous question, right? How do we use AI? We want to implement AI into our operations, right?

when conversely, like you know, operators and property managers should be starting with a different qu set of questions, right? Like how like where are we losing things like NOI, margin, time, control, or even consistency, right? AI really only matters when it connects and automation really only matters when it connects to a to a revenue lever or some type of a cost lever or productivity gain or or risk reduction, right?

and there’s there’s a couple of key components

in even conversations that you’ve probably even had with with property managers today is that firstly, like, you know, operators today, they already have a lot of data. They probably have access to a lot of different data sets across, you know, operations, but it’s probably, you know, disconnected and disjointed and different reports and disconnected systems hidden in spreadsheets and and dashboards that probably don’t drive much much action, right? and everybody wants to

automate and execute an

operational kind of workflow. But the hard part is not whether, you know, AI can really do something, but the hard part is whether a company even knows where value is leaking and who owns that action and and whether these workflows are even clear enough to to be able to automate. And that’s kind of the premise of this framework is to kind of measure what that pain is, you know, map that workflow, automate that repetitive work and manage

ideally performance through some type of a closed loop accountability. We just put an actual word to it and a framework to it, but I’m sure very

similarly to the conversations that you’re probably having also even with customers.

Jason Hull ([05:15])

Yeah, yeah, got it. Yeah. it’s interesting because we’re now seeing a lot of these tech companies or tech forward companies that are kind of backtracking on AI a little bit. They were giving out basically blank checks to use AI as much as they could. Some were even creating sort of a contest internally, incentivizing like who could use the most tokens.

Mo Hussein ([05:29])

Mm.

Right.

You’re right.

Jason Hull ([05:41])

Which is a little bit insane

to just give people a blank check as if that always the more tokens you burn, the more productivity is being created, right?

Mo Hussein ([05:51])

Right, right, right. And we’re seeing, yeah, and you know, as we’re seeing these newer models that are coming out, whether it’s, you know, through Cloud, Anthropic or even these other these other LLMs, the token utilization is becoming more and more expensive, especially with these newer models. And so now the question of just like, hey, how is that utilization actually translating to actual business value? Right. And this was a question that eventually would have been would have been pushed, right?

Jason Hull ([05:54])

Yeah. Yeah. Yeah. I love it. Like how to use AI. Yeah. Bad question. A better question is how do we actually make sure we’re creating more profit? How do we actually make sure we are lowering costs? Like

And that’s the the idea, they think, well, AI must be so much cheaper than people. And what’s interesting, I’ve also seen some reports lately showing the amount of money these different LLMs are losing right now. They’re spending a massive amount of money to deliver AI to us at a super cheap price right now. And but they’re losing money. Every time we’re chatting, they’re losing money.

Mo Hussein ([06:47])

Mm-hmm.

Right.

Right.

Jason Hull ([07:01])

And that’s that’s a wild business model. They’re obviously hoping to win some sort of AI race. They’re hoping to get us maybe in the future. And there’s a lot of talk lately as well of people thinking we gotta shift to local models. Like we gotta I gotta run this AI stuff on my own computer and not be giving all my money to anthropic or open AI you know, open AI or whatever. So okay.

Mo Hussein ([07:15])

Mm-hmm.

Right, right.

Right.

Jason Hull ([07:26])

Cool. So let’s continue on. Me measure, map and automate. Yeah. Yeah.

Mo Hussein ([07:29])

Yeah. Yeah. And

by the way, going on your point, Jason, it’s you know, you you also, you know, creating automation and leveraging these models locally, it there’s definitely value in that. But you know, now more than ever, f you know, teams are kind of distributed, right? And so ideally, if you’ve built automations and leveraging these L LMs and

And things of that sort.

You probably want to have like some type of an interface that’s like cloud based, right? Or for folks to be able to kind of collaborate in some type of a ideally like a safe environment, right? and so measure, map and and automate. So you know, there’s there’s kind of those three components to be able to actually fully ideally leverage leverage AI. But

There’s there’s a couple of like kind of key core components that I feel like

is very important for folks to to really understand before they can they they can even take advantage of of AI, right? so one is you know AI, AI value really truly is workflow value. And so like the most the biggest opportunities when it comes to automation leveraging AI is things that are repetitive.

you know, judgment heavy, ideally high volume workflows. Think about things like you know, leasing follow-up, d delinquency, terms, maintenance triage, variance explanations. another another key thing to understand is, you know, AI depends on trusted ideally operational data. And so if you don’t have accurate or clean property unit, resident vendor

payment data and it’s and it’s inconsistent, you know, AI just

helps accelerate r the wrong answer, right? And the this notion of like hallucinations also kind of exist. and you know insights without ownership is is just is really just theater. And so although AI may identify a problem and recommend an action, things need to be routed, right? And as I you know action needs to be assigned. There needs to be some accountability that gets created there and then a measurement of of of a

of of a of a result.

and then lastly like humans, humans control still matters, right? Things that have a very high potential opportunity cost. you know, operators should be very careful on how they utilize AI. So, you know, things around fair housing, sensitive sensitive decisions like screenings, evictions, legal communication, you know, employee decisions and maybe even large payment approvals. And so

Once we

have these kind of these table stake table stake items, if you will, kind of address, then you know we can move on to kind of you know the our framework of kind of measure, map, and automate. And so in each of these different components have different purposes, you know. The whole point of the measure step is is to quantify where pain exists and to validate kind of being buying versus buy like building. And so you want to ask things like where

where

time, where margin, where service quality or accountability is lost today, right? Examples can be things like, you know, days vacant, you know, delinquency rate, maintenance response times. These would be kind of like outputs like invoice coding time, reporting hours, renewal conversions, right?

Jason Hull ([10:37])

Yeah. Got it. Yeah, that that makes a lot of sense. So you’ve got to be you have to have good data.

Which the crux of th where their data is all probably housed is inside of their property management software.

Mo Hussein ([11:06])

Right.

Jason Hull ([11:07])

And so hopefully that software is kinda tracking some of this stuff. But, you know, everybody’s had a CRM that the team didn’t put enough notes in. And then it becomes kind of useless, right? So you’re like, what happened with Fred on that call earlier, you know, or previously? I I think I think we talked about this. Can’t remember. Why aren’t you putting in notes? And so then the flaw becomes the human in the loop in a lot of instances. But then you’re saying, you know, also humans matter. Like

Mo Hussein ([11:14])

Right.

Right.

Jason Hull ([11:34])

Related to fair housing. We’ve got to have the human in the loop making decisions. I don’t think it would go fair very well to be standing in front of a judge and say, Well, the AI messed this up. It wasn’t me.

Mo Hussein ([11:43])

Right. Right.

Right. Yeah, that’s very that’s very correct. the the the other thing is is that you know software is a tool, right? So they you know, for like that example that you just gave of like, hey, you know, I had a conversation with Freddie or an owner or what have you, and you know, the notes weren’t captured. And so if there’s if if if if there’s there needs to be also a cultural

shift within the organization to become more performance kind of driven, right? And using, you know, places of truth. You know, I, you know, we use Salesforce in our own internal kind of CRM and you know, there’s this old ad like this old saying of just, you know, hey, if it didn’t happen to Salesforce, it didn’t happen at all. In other words, if your system of record hasn’t been updated and things haven’t been added to it

Jason Hull ([12:06])

Right.

Mo Hussein ([12:29])

to to ensure that it is correct and accurate and up to date, then

the organization sees it as you know as it didn’t happen. And somebody, you know, using anecdotal feedback of like, well I did this, but I just didn’t update this, it means it didn’t happen. And so that’s it’s very important that, you know, whatever system you’re using to kind of measure different KPIs and metrics, that that, you know, that behaviors within the organization are shifting towards that. And it’s it’s some it’s a cultural shift that needs to also cascade

also from from leadership down as well.

Jason Hull ([12:57])

Yeah, the advantage we have nowadays with all the AI stuff that’s come out is now pretty much everything gets transcribed everywhere. So calls get transcribed, notes can be created automatically. You can also go back and ha check the transcription on a call or a zoom call or recording, figure out what happened. So that you know, not leaving notes in the CRM is a little bit less of a problem than it was in the past.

So we’ve so we’ve chatted a bit about measure. What is what’s important about mapping or map?

Mo Hussein ([13:30])

The mapping. Yeah. So this is this goes back to my previous point about like, you know, AI value being it is workflow value. And so, you know, you’ve measured, you’ve identified, you know, your measurements and KPIs, you know, days vacant, delinquency, whatever those KPIs may be. Next, what you need to do is map what the actual what

the actual workflows that are happening, not how leadership or staff thinks it’s happening.

The very key kind of distinction is that, you know, a lot of operators and teams kind of assume, hey, you know, we have a set process, but it may not be happening the way that they are envisioning or the way that they’re assuming that this is happening. And and map that entire workflow end to end.

identify what systems are involved, where handoffs occur, where approvals are required.

where judgment calls are are are are kind of made. And so, you know, every company has, you know, things like experienced managers and accountants and maintenance folks and and they usually know what good looks like versus what bad looks like. And so AI here is to help kind of convert that tribal knowledge ideally into a repeatable operating model. And so examples of how that mapping

would be is, you know, hey, what is the entire lead to lease workflow? You know,

What is the work order to completion? You know? what is our renewal offer to signed and executed actual renewal? And so and actually, and again, documenting that. A a lot of organizations have some notion of what that workflow kind of looks like. but you know, they

haven’t actually done they may not have documented, or if they did, it’s not updated and they have an out of date SOP or a process diagram.

And that’s that’s and that’s that’s that’s a very important kind of key aspect of kind of this process.

Jason Hull ([15:22])

Yeah, yeah. Well I a lot of times I end up talking with clients and I’ve noticed kind of this pattern or trend in the industry of I call it the process myth where everybody thinks if we just had better processes

all of our hopes and dreams would come true when it comes to the off side of the business and we would be more profitable. And especially see this in the two to four hundred door range in single family or small multi-residential property management. And so the challenge there is th that it’s impossible to create enough processes, KPIs, and systems to make mediocre people be great. But they pe that doesn’t stop business owners from trying. They they’re like

Mo Hussein ([15:59])

Right.

Right.

Jason Hull ([16:04])

They they they wake up in the morning, they’re like, I want to play an impossible game today. And they they still try. And I call it the process myth because if you have really great people, even if your processes are garbage, that I’ve seen these businesses still perform well. But the reverse is not true. You have mediocre people, you could have insane amounts of systems and processes and stuff, and the business still has a lot of headaches and problems.

Mo Hussein ([16:16])

Mm-hmm.

Jason Hull ([16:29])

And so I’ve kind of noticed this pattern. I call it the three levels of process. And level one is documentation. It’s just like writing stuff out. But that’s kind of like the owner’s manual in the glove box of the car. Nobody looks at it, it doesn’t get updated. You know, it’s like it’s it’s it’s gathering dust, and people don’t actually, that’s not actually how the processes are run. And over time, things gravitate towards ease or grace or what the flows best for the person doing the job.

Mo Hussein ([16:39])

Mm-hmm, mm-hmm.

Jason Hull ([16:57])

Not for what’s best for the job sometimes. And so it gravitates a little bit towards chaos or being worse. Then there’s this level two, which is checklists. This is where people are using things like Asana or Process Street or Lead Simple or they some sort of checklist space system where now they’re verifying the works getting done in a certain way. But checklist has its own problems in that it’s very linear and not every process is linear.

Mo Hussein ([16:59])

Right, right.

Read simple. Mm-hmm.

Mm-hmm.

Jason Hull ([17:24])

There’s decisions

and splits and merges and sting things happening concurrently in property management. And so the challenge with checklist also it can tend to slow things down. It’s not as efficient. So the next level and the problem I had with checklist, we used to use process street, is that it it if anytime a process got complicated, I had to build logic and you know, if-then sort of situations into it.

And it usually got to the point where I didn’t even understand it. Like a year later, I’m looking at a process. I’m like, I had to retranslate this back into something that made sense to my brain. And I always, and the nerd had to be the one that did all the updates on it because nobody else could understand it. So then we eventually graduated to level three. So level three is visual workflow. This is for humans.

Mo Hussein ([18:01])

Right.

Mm-hmm.

Jason Hull ([18:13])

And so, and with with this, my tip to everybody listening, if you have a system, whether it’s checklist or it’s any of these three levels, you know, documentation, checklist, or visual workflow, that you your first two processes you make as an operator or as a business owner is how to create a process in this system is number one. And number two, how to QA.

Mo Hussein ([18:26])

Did

Jason Hull ([18:37])

A process that is made in the system to know it’s actually a good one. If you just make those two, you don’t have to do any of the other stuff. Everybody else can do it. You just make those two. That’s my tip for all you business owners. And now with AI, you can start adding AI. Once you have things visually mapped out, it’s you’ve got the map like you’re talking about. Now you can figure out all right, where can AI take over some of this stuff? And where do we still need the human in the loop? Right. So yeah.

Mo Hussein ([18:43])

Mm.

And automation. Mm-hmm. Yep.

Yeah.

Right,

Jason Hull ([19:05])

So any tips for those listening to this that are already geeking out with AI, they’re doing a little bit of this measuring and mapping and automating. What are some of the biggest challenges you’ve noticed where this kind of breaks down or people are making mistakes?

Mo Hussein ([19:19])

It’s it’s honestly it’s the it’s the you know, AI value. it’s it’s a lot of the small individual decisions that are made in a in a repetitive fashion and that that are made a lot that really are gonna unlock like true value for for any operator. And so like, you know, having very clean data, standardized, you know, systems of truth by what we mean by that is that, you know, hey, you know.

you know, whatever work order systems that you’re using, for example, has accurate, you know, work order data. People, you know, your maintenance technicians are actually closing out the work order when they complete it. Not just, hey, the end the week, I’m gonna now try to remember what I did earlier in the week.

Close it out. So the data is

the data can’t be trusted, then AI is just going

Jason Hull ([20:04])

and

Mo Hussein ([20:07])

to cause additional kind of confusion. And so having accurate systems of record. And I gave that example of of of a work order when a technician kind of closes that, right? the process map, I think the you know, the three buckets are like three level that you kind of gave, I think is a great, great.

Jason Hull ([20:20])

Yeah, yeah. Yeah, that makes sense.

yeah.

Mo Hussein ([20:29])

anecdote and framing of how processes should be kind of looked at. And and I think one thing that a lot of operators usually tend to overlook or assume is you know how things are being done versus how they actually are being done within the teams, right? It’s an owner, somebody at some point said, okay, hey, this is the process we’re gonna take. And then over time that just kind of got changed. And there

may be, you know, two different property managers

operating in two different regions in the same company that are doing leasing renewal differently, right? That going back to that point that you mentioned about systematizing and having accountability loops and task base or like checklist items and ensuring that those things are actually done in that same quality and that same fashion is very, very key. And so getting data, like getting the right data, accurate data,

and then also like your process mapping and your

Jason Hull ([21:24])

got it. Yeah, I think

Mo Hussein ([21:26])

processes kind of documented. I think I think the visual representation is a great way to have that. Those are the two key things that ninety percent of folks that are trying to leverage AI and automation and even the folks that are starting to try to jump into this space and trying to automate and use AI for these things like usually we’re like we’re where they’re really struggling with.

Jason Hull ([21:50])

I was just on a webinar recently and they were talking about building AI agents and they were talking about if you want to make really effective AI agents, you need to give them a really good job description, just like a human. And what what’s really funny is if you we coach clients on this a lot, but if we tell the clients to to go, we coach clients on

Creating job descriptions. We call our version of them R docs because each section starts with an R, like role, responsibility, et cetera, all the typical stuff. But then we have some additional sections that we found really paramount. So what we’ll tell them to do is go ask your team members, give them this framework, and have them create their own R Doc. And then you take a look at this and see if that’s what you would have created. Because it’s never like what they think their job is. It’s usually very different than what the business owner thinks their job is.

Mo Hussein ([22:25])

Mm-hmm.

Right.

Jason Hull ([22:36])

And maybe even different what the manager, the ops person thinks the job is, but then you can actually literally get on the same page with them. You can be like negotiate this and be like, this is what we think your priorities should be, and what your outcomes should be, and what we want you to be able to accomplish. And this is helpful for them to know what they’re aiming for so that they can please you because your team members want to please you if they’re good. But usually there’s a big disconnect, like you’re saying, between what

Mo Hussein ([23:00])

Mm-hmm. Mm-hmm.

Jason Hull ([23:05])

the the employee thinks their j role and job is versus what their manager thinks they should be doing versus what the business owner thinks everybody should be doing. And so nobody’s on the same page. And then you’re everybody’s roles are a little messy. And then you’re going, let’s give them processes now to work on. And they’re not even clear on what their job is or what their role is. Yeah. And so same thing if you were going to build an AI agent and you were like, I want you to try and be good at everything. And then suddenly it’s like really

Mo Hussein ([23:29])

Right.

Jason Hull ([23:34])

Hallucinating a lot and it’s messing everything up and yeah. And it’s not a realistic creature, you know, just like some people give create job descriptions that are for like four different personality types. Right. And then they hire somebody that maybe can actually do all four things, and we call those really highly adaptable, weird creatures entrepreneurs. And then they wonder why that property manager left and stole all their clients.

Mo Hussein ([23:34])

Horrible.

Right.

Yeah.

Jason Hull ([23:57])

Instead of finding somebody that’s like really good at being one thing.

Mo Hussein ([24:00])

Right. That that that that role clarity is very, very, very important, right? And that’s how you should see agents as well, is that

like, hey, it’s like a trained employee. And so you should exp you should expect the same level of, you know, investment involvement, if you will, and trying to and try to help them be the best of like, you know, whether it’s a leasing agent, a maintenance coordinator, or whatever that their role may be. And I I think another aspect is and I’m curious how like how

you know, when you guys having conversations with clients around role descriptions and stuff, it’s the concept of ownership. Like, hey, what, you know, how to how to align ownership to and lining that up to hopefully an eventual business outcome or KPI

or something so that, you know, their performance drives also the business performance, right? How have you guys had this conversation or how do you talk about kind of that concept? I can kind of allude to it without kind of explicitly calling it out.

Jason Hull ([24:42])

Yeah, I think well, sometimes I’ll just totally call a business owner out on things. But I think what I think will be interesting is people are building starting to build agents. I think that they should.

They should have an understanding of personality types. I think they should have an understanding maybe or a conversation with AI about what Myers Briggs type might be good for this agentic role. And because like somebody that’s really good at like strategy and the strategist role, which would be like an INTJ in Myers Briggs, might be good at some operational stuff, but they would be really terrible at customer service.

Mo Hussein ([25:19])

Mm-hmm.

Jason Hull ([25:33])

Because a lot of INTJs don’t even like humans, right? And so they’re logical thinkers and they’re really judging and they’re practical and they’re in you know introverted and they’re really bad at understanding how the other person feels or even expressing that. And so you’re you you don’t want to create these try and create AI AI agents that are multiple split personality types, because I don’t think they’re gonna be as effective. And you can’t also, just like you wouldn’t want somebody building the process.

QE QA QA of the process. You don’t want them both. You don’t want AI to be checking itself. Right. Right. The the brain that had problems doing the messing things up, maybe, or didn’t do it totally right. You don’t want them checking their own work. Right. And so, yeah, so I think this is going to be interesting that people are going to be building agents and they usually think just logically here’s the context it needs, here’s the role, whatever. But I think also maybe give it the personality that it.

Mo Hussein ([26:08])

Right, right.

Right.

Jason Hull ([26:29])

What’s the disc assessment for this person, this agent? What’s the Myers Briggs type for this agent? And then if especially if they’re communicating with humans or doing a task that you want them to be somewhat human like, they’re going to be much better at doing this if you give it you create them in the right way. Just an idea.

the other thing to know as a business owner, you need to know who you are so that you can build your dream team around you. So your advisors, whether they’re agentic or human, your advisors, your team members, it should be built ultimately around you thriving and being healthy in your own business so that you’ve got the tea the tools and the resources that fit you. But most business owners make the mistake.

Of trying to build the business around the business and then wonder why they’re miserable and why they’re kind of a slave to their own business. Right.

Mo Hussein ([27:16])

Right. Right. Right.

Jason Hull ([27:20])

So anyway, Mo, measure, map, automate, MMA. Doesn’t involve fighting too much. You know, like mixed martial arts. It’s a little bit on the, you know, less physical side of things. fun chatting about.

Mo Hussein ([27:26])

No.

Jason Hull ([27:34])

all the the AI stuff that’s going. How can people anything else that you want to add to our conversation here about yeah this model? And then could you tell us a little bit about what you do and how maybe you help property managers with this stuff?

Mo Hussein ([27:48])

Yeah. Yeah. so I guess just to put it succinctly, kind of a a a sandwich kind of takeaway. So, yeah, operators don’t need to wait for a perfect AI strategy.

Start by identifying, measuring where value exists, where things are leaking, then mapping workflows and then deciding what can be safely automated and measuring whether those actions improve performance. and so

like you know, over time we’ll see that you know the winners are not gonna be the companies that have the most amount of most amount of data, but they’re the ones that can convert data into consistent operating actions across how they’ve operated every door. if you we help clients with you know putting together SOPs, also mapping their technology needs, where where they’re where they’re having operational leaks in the business can be

Optimized further using

technology and automation. We have a platform that we’ve built, Prop Strata, to actually connect and and help with that automation kind of efforts. then we’re also we also do a lot of accounting and and CPA work. you can reach us at www.balanceasset solutions.com and and then my email is mo at at propstrata.com or you can reach out to our team at info at balanceasset solutions.com.

Jason Hull ([29:03])

Cool. So they could take a look at this at propstrata.com.

Mo Hussein ([29:07])

Correct. W dot propstrata.com.

Jason Hull ([29:11])

Okay, cool. Very cool. All right. yeah, check that out, everybody. It sounds interesting. All right. Well, Mo, I appreciate you coming out and hanging out with me here on the DoorGrow show and sharing everything.

All right.

So if

If you have ever felt stuck or stagnant in your property management business and you want to take it to the next level, reach out to us at doorgrow.com. We are the world’s best at creating high-growth property management companies in the single-family residential space or the small multi-space. And if for a free training or how to get unlimited leads for free, text the word leads to 512-648-4608. That’s 512-648-4608.

Also, join our free community just for property management business owners at doorgrowclub.com. And if you want tips, tricks, and ideas to learn about our offers, subscribe to our newsletter by going to doorgrow.com slash subscribe. And if you found this even a little bit helpful, don’t forget to subscribe and leave us a review on whatever channel you saw or heard this on. We’d really appreciate it. And until next time, remember the slowest path to growth.

is to do it alone. So let’s grow together. Bye everyone.