Improve Resident Outcomes with Data-Driven Application Decisions

Yolanda Muchnik:
Hello, multifamily pros, welcome back or if you’re a new listener, welcome aboard. Today I’m talking with Peter Lohmann, co-founder and CEO of RL Property Management, a residential property management company in Columbus, Ohio.

Peter’s also the host of the Owner Occupied podcast, and he co-founded Crane, a private paid community for experienced property management company owners. Peter, I’m so happy to have you on here, and welcome to the show.

Peter Lohmann:
Hey, thanks for having me. I’m excited to be here.

Yolanda Muchnik:
Awesome. So, Peter, you have had an impressive journey from starting in electrical engineering to property management. So please share a little bit with our audience about your journey and what led you to this industry and all the innovative initiatives you’re involved in.

Peter Lohmann:
Yeah, happy to do that, and thanks again. This is, this is really great to be on your show. I appreciate it.

So my background, like you said, electrical engineering. So I have a degree in electrical engineering, and I worked as a control systems engineer for about five years right out of college. At that time, I had no real estate experience or property management experience. The only thing you could count as experience in that area was that my eventual business partner and I, at the time, he was just a friend, he and I started buying single family rentals. This was around, I think we bought our first one in 2008, which was like my second year, right out of school, we started buying those and just renting them out ourselves. He’s also an engineer, so we would buy like one a year and self manage them. And over the next few years, I started to have a passion for entrepreneurship. I wanted to start my own business, and so did he.

And so we started looking around at what opportunities there were to start a company and eventually decided there could be a great opportunity to start a property management business here in Columbus, focused on single family residential and small multifamily. So in 2013, I took the leap. I quit my engineering job and I started RL Property Management. My co-founder, Adam Rich, stayed at his engineering job and worked nights and weekends on the business.

And over that first year, we got our first handful of clients. We were able to get an office and really kind of got things moving and so he was able to come on board after that first year, and I’ve been doing it ever since. So it’s been eleven years now that we’ve been managing property professionally. We’re up to about 650 units under management here in central Ohio. And again, that’s a mixture of single family and small multifamily.

And in the last couple years, I’ve started to get involved in a few other things. My business partner and I bought a small engineering firm that he now runs, and we bought some more real estate. I also started a podcast and a newsletter for residential property managers and property management business owners and creating the private community that you mentioned. So that’s kind of the short story.

Yolanda Muchnik:
Awesome. Wow. Sounds like a very serendipitous career turn and that you’re very much enjoying it.

Peter Lohmann:
Yeah, absolutely.

Yolanda Muchnik:
So let’s dive straight into our topic today. Your blind application screening process is quite unique. So for our listeners, can you explain a little bit about what does this process entail and why you decided to implement it? What was that aha moment that convinced you this was the right direction to head?

Peter Lohmann:
Yeah. So the whole idea, and I think why I became interested in this, is I’m a very data oriented person. Coming from my engineering background, I’m huge on systems and processes. And like I mentioned, when I started the company, I had no property management experience. I never worked in another real estate or property management office. I didn’t. We have no family businesses or anything like that.

So when I started, I had no network, I had no experience, and I started just inventing everything from scratch. So when my partner and I started to get applications for our properties that we were managing, we immediately got to thinking, okay, what’s the right process for handling a rental application? How do we collect this information? What information do we need? What do we not need? What should be the minimum requirements to live at a property that we manage?

What should we charge for a deposit? When do we collect the deposit versus when is the lease signed? Like all of these little details, I really wanted to figure out the right thing to do, kind of from first principles, not just what other people had done, because I didn’t know what other people had done. And then once I figured all that out, I wanted to put it into a process that would be run the same exact way every time.

And that over time, we could improve and streamline that process. And eventually this was kind of like a fantasy at the time, maybe one day I could hand this off to somebody else and I wouldn’t have to be the one doing it. At the time, it seemed like this far away thing that could just only happen in my dreams so that’s sort of like how I first started thinking about this problem from a systems perspective.

And of course, the outcome that you’re trying to optimize for when you are screening tenants is a long, successful residency from a great resident, meaning they pay the rent on time, in full every month, they don’t damage the property, they don’t bother the neighbors, and they hopefully stay there for three, four, or five years, ideally, especially in single family. So everything, again, I’m coming at it from that lens of, like, what’s the outcome or the output that I’m trying to optimize for my control systems engineering background. You’re all about controlling the output.

And so I started at the time reading and researching, like, what are best practices for tenant screening? Like, what do we know about credit scores and the impact on residencies? What do we know about income? What do we know about rental history? And I found that there were some rules of thumb that folks in the industry use, and it kind of depends on the type of property that you’re managing and, and, where you’re located in the country and things like that.

And as I started to think about this, I realized that the way that most property managers handle this is there’s some property manager that posts the listing and then shows the unit to prospective tenants, receives the application. And then I think usually that same person will sort of do an initial pass and sort of give a recommendation on approve, deny, or conditional approval that will then usually go to, like, a manager of some kind for like, a final sign off.

And as I was sort of maturing, you know, I started the company. I was in my twenties. As I started to mature as an individual and become exposed to a broader array of society, of people of all different ages and incomes and different areas that they lived in. Because we manage, at the time, we managed property anywhere and everywhere in central Ohio.

What I realized is that there were a lot of biases that could make their way into this process of tenant screening. There’s a lot of different ways this can happen, some of which we don’t have control over, like, someone’s credit score. You know, I can’t control what their credit score is, but there are factors that could affect that, what kind of job they’re able to get or the circumstances of, like, their previous residencies and things like that. But what I realized is what we could control is any internal or inherent unconscious bias that myself or my team may have when reviewing these applications, and that can sneak in.

I’ve watched some really compelling sort of training videos on this, and I’ve dug into the research, and you would be depressed and amazed if you realized how powerful your sort of unconscious bias really is when you are meeting someone for the first time. How old they are, what they look like, the color of their skin, their accent, what car they drive, how they’re dressed. All these things can affect your perception of that individual, how likely they are to be successful, how likely they are to pay their bills.

I mean, this is well documented, going back many decades. Everything from when you’re screening applicants for a job to when you’re applying for a mortgage. Like this is, this is kind of a known thing. So that was sort of the approach that I was bringing to this problem, is, like, I want to be a very systems oriented person. I want to deliver the best possible outcomes for my property owners, which is great residents who stay there a long time, and we’ve got this problem of bias that could cost us.

So what does it really mean, this problem of bias? Well, there’s two bad things that can happen here. One of them is you may inadvertently decline a tenant who would actually be great because of this bias. Or could be the opposite. You could inadvertently approve somebody who turns out to be a big problem because they presented all the right, you know, this is John Smith and, like, a white male, and he’s got the right, but. And so you overlook the low credit score or something like that.

So there’s two bad outcomes that can happen. So it’s a really important problem. So I don’t want to do a big monologue here. Let me pause and see if you want me to talk more specifically about any of that before I go on about what we did next.

Yolanda Muchnik:
Yeah, I mean, you talked a lot. I was curious to understand what some of those specific biases that you guys aim to eliminate, what those are, but certainly want to dive a little bit further to understand what changes have you observed after implementing it. And also, I mean, can you share any specific instances where you noticed a significant difference in resident selection to this blind process?

Peter Lohmann:
Unfortunately, I don’t think I have, like, specifics I can speak to. You’ve got this problem of, like, you know, if I said, well, yeah, we approved this resident when we otherwise would have. I don’t know that that’s really true. Or, like, the residents we declined that we might have otherwise approved. I don’t know how those residents. How those occupancies would have turned out. But, yeah, I’ll kind of keep going here, which is like, the next step is I realized, all right, how do we deal with this problem of unconscious bias?

How can we separate and make sure that we’re not making either of these two errors the sort of false positive or false negative? What I realized is that if you want to take a really data driven sort of engineering approach, what you want to have is a set of objective minimum standards or minimum criteria for a resident. And this is everything from rental history, income, credit score, eviction history, criminal history, all the stuff we normally look at as property managers.

What I wanted to do is set the minimum standards for all these things. And then I wanted every applicant, I wanted to compare the data on that applicant against this minimum standard and make it like a pass fail. I wanted to be first in, like first in, first out. So it’s like we go in the order of applications received, and if somebody meets the minimum standard, they’re approved. And if they don’t, they’re declined.

Take the human judgment out of the equation and let the data make the decision. That seemed to me to be the most fair way to do this. Now, the situation is that, there’s no matter how objective you try to get with this stuff, there’s always, like, these little edge cases where you are relying on a human to make a decision. And so I’ll give you an example. Like, let’s say the credit score minimum is 620, and you get an outstanding applicant who comes through with a 618, right? And now you go to your manager, and you’re like, can we just, like, they’re so close. They told me they cleaned up their credit. Like, I’ve met. They’re so great.

Or it could be the other way, where on paper, it seems like they really, they, technically meet the requirements, but they’re, like, barely meeting them across all the criteria. And so, anyway, I wanted to make sure that the data was making the decisions, and I wanted to make sure that there was no unconscious bias creeping in. So the way to do this is you want to make the person who’s making the go / no go decision.

You want to strip away and make sure they have no personal information about the applicant or the applicant group. That means they never meet them. They never hear a voicemail from them. They never see their names, even, because a person’s name can even be a source of sort of unconscious bias here. So what we did was we set up our property managers and our leasing agents to be the ones taking in all this information about the resident making sure, or the applicant making sure the application was fully completed.

And they fill out a Google sheet with just data, and it won’t even have the name of the group or of the applicant. It just has data. There’s no picture, there’s no anything. So they put all the data into the spreadsheet, and then the spreadsheet goes over to a manager, and the manager then reviews the spreadsheet, make sure everything is in there, make sure they either meet the minimums or don’t.

And then that person makes the approve or deny or conditional approval decision, having never met or knowing nothing personally about these individuals. And so that seemed to me to be the most fair way to do this. We could never be accused of, like, a fair housing violation if the person making the decision never even met. The person doesn’t even know their name. We’re just going off the data. So that’s sort of what I call our blind approach.

Yolanda Muchnik:
Yeah and I imagine there’s also probably considerable time savings there, too, if you don’t actually have to meet the person and have that conversation. It’s fine if you feel like you’ve touched on this enough, but I was just curious.

I mean, you emphasize so much the importance of using data in analytics and setting criteria appropriately. It’d be great to hear some insights on the data types that maybe you guys have found most valuable when evaluating applicants and how those data points go into making final decisions. Can you share some of that?

Peter Lohmann:
Yep. So I’ve dug around and found several white papers on this topic of tenant screening and evictions and credit scores, and like these factors. I’ve got a lot of opinions on this. The first thing is, the data is really clear on this. And I know some people are going to want to fight me after I say this. Credit scores are highly predictive of resident outcomes. They just are. And you can look it up. I can send you, if you email me, I’ll send you all the white papers I’ve read.

The major credit bureaus have created custom credit scores. Most people don’t know this, but you don’t have one credit score. There’s actually like 50 credit scoring models, and like, your bank is going to use a different credit scoring model than your car, etc. And so here, us as professional property managers, the scoring models that we use from TransUnion and Equifax and all these groups, they are custom made for our industry and they are more tailored to predicting rental outcomes than say, when you go to get a bank loan.

So these custom credit scores that we’re using to make applicant decisions are very powerful and they’re very predictive. Does that mean that someone with a great credit score can never be evicted? Of course not. We’re talking about statistics and, like, normal distribution probabilities here and vice versa as well. There can be great applicants with a bad credit score who would be fine. But if you’re trying to make decisions about thousands and thousands of applicants over multiple properties over many years, you need to be using credit score in that decision making process.

Beyond credit score, I don’t know what is predictive of resident outcomes because I’ve never seen any data on it. There’s a lot of strong opinions on this matter about rental history, income, eviction history, & criminal history. There’s a lot of property managers with strong opinions on the ability of those things to predict resident outcomes, but those are anecdotal. I have not seen comprehensive studies using that data that tells me whether or not we should be using them when we’re making decisions, nor telling me how strongly predictive they are relative to credit scores. And so what do we do? Well, we do what everyone does, which is you just kind of go with what everyone seems to go with. Like, so for us, that means no evictions or eviction filings in the last five years.

We look to see three times the rent in income. Like, a lot of the same things that a lot of property managers are using. The reason I know that this is kind of make believe is if you ask property managers, like, if you ask ten property managers, what’s the minimum income to be approved for your property? Most of them are going to say, like, three times the rent in income, right? And it’s like, oh, really? 3 times the rent? It’s not 3.1, it’s not 2.95. It just happens to be exactly 3. It’s ridiculous, right? If this was actually being done scientifically, we would have some data that shows it should be 3.295 times. Right. We would have had a more specific number, and instead we’re just going, like, back of the envelope, just gut feel.

I actually hate that. I wish we had more data and more studies here that we could use to make better decisions about applications because of those same two problems I mentioned earlier right now, today across the country, at your company and everyone else’s, we have great applicants who are getting denied, and we have bad applicants who are getting approved because we don’t have more data on how to make these decisions. And that, to me, is really unfortunate.

Yolanda Muchnik:
And I think one of the benefits of having all these data points upfront is down the line. They may help influence or inform how you build longer term relationships with those residents. And so I’m curious, do you have any examples of how a data driven decision upfront has ultimately led to a successful resident onboarding or some experience later down the road?

Peter Lohmann:
Man, yeah, the specifics are hard. For one, I’ve been out of the loop of this process for many years. I will say that since we implemented this process, and we’re big on tracking things like lease renewal rates and eviction rates and things like that, I know that those data points have all improved. And so our property owners, they are the one, because we’re a third party property manager, so we manage, for a bunch of property owners, they are the ones that benefit from this sort of data driven approach in the form of reduced turnover for one, more frequent and consistent on time rent payments, less damage and wear and tear on the property.

So when I think about people who are specifically benefiting from this, it’s not only the residents who may have been turned away because of the way they look or the way they talk or their name from another rental agency and they’re getting approved. And that’s, I mean, that nothing makes me feel better about what we do than that kind of example. But it’s also the rental owner who now has a great resident in the property for three to four years, where otherwise it may have sit vacant for an extra two weeks.

That’s two weeks now that we could have had a break in. We could have had like a furnace failure here in Ohio when it’s cold. Now you’re talking about a burst pipe. So anything we can do to reduce those vacancies and give residents a fair shot. I mean, we’re facing a housing crisis in America. To me, this seems like a great area where we could focus on is like, let’s give people who deserve it a better chance of getting a great home to live in.

And let’s, let’s make sure that folks who are going to tear up the property and not pay the rent frankly don’t get approved. I don’t want those people living in properties that I manage. Let them figure, figure it out. Right?

Yolanda Muchnik:
Exactly. And I mean, this all sounds, setting up the system, getting it going, getting that bind it all sounds like quite the undertaking. And I’m curious, you know, what challenges did you guys face in adopting this blind screening process, both logistically and culturally within your organization?

Peter Lohmann:
Yeah, so logistically, it is a little bit more work because you have this sort of intermediate step of entering the data onto a spreadsheet because the way it normally works, of course, is like that manager is going to go right into the application data itself and look at the screening reports and look at the name of the person and maybe see something on the credit report that they don’t like, even though it’s not necessarily relevant to the minimum criteria that we have.

So there’s this extra step of, for every single applicant group who makes it through the process, the data has to be entered on a spreadsheet. Then the manager reviews the spreadsheet. I do think there’s some time savings, as you mentioned, that helps to make up for that little bit extra work. So that’s, you know, logistically what we’re looking at.

One thing I do like about that logistics step, though, is it provides a great record for us. If we were to ever have a fair housing violation or a complaint, which, which we haven’t, we could go back and say, look, hey, here’s the sheet we use to make the decision. Here’s the exact reason why they weren’t approved, rather than just like, oh, yeah, I think Cindy looked at the file and she didn’t like one of the evictions. Like, yeah, there’s no record of what actually happened.

And then culturally, I think at the time we rolled this out, the company was still pretty small. And so as sort of the owner / founder, it was easy for me to just say, boom, this is what we’re doing now. And at the time, I don’t know that we encountered a lot of pushback. But as I’ve discussed this with other folks in our industry and as we’ve brought other more experienced property managers onto our team, since, there’s definitely an element of like, ooh, I don’t know about that.

Yolanda Muchnik:
Right.

Peter Lohmann:
There’s the feeling of like, I don’t know about you, but I want to meet someone before I approve them and I want to shake their hand and look them in the eye. Yeah, that’s a powerful human instinct. It’s a powerful drive to feel like I need to meet this person. I need to shake their hand and see what they look like and see what I’ve even heard some property managers talk about, like, why? Like, look in their car to see if their car is messy. That tells me if they’re going to be a good resident or not. And that’s nonsense.

I mean, that’s just, that’s the old wives tale type type stuff that’s just not appropriate in a modern. In a modern rental agency, I think. And so overcoming some of the skepticism is going to be a big challenge if you’re looking to roll this out at your company. But again, I just got to fall back on the data, and it’s like we either have a belief that people should be given a chance to rent a home or an apartment, irrespective of some of these factors that are, that are subject to unconscious bias, or we don’t and I think we do.

So the only real way to do that is through some type of a process similar to what I’ve talked about. If there’s another way, I’m all ears but this is, I think, works for us.

Yolanda Muchnik:
Yeah, and again, okay, if you don’t have anything further to add here, but I actually had a question for you around this, like, you know, component of human nature, this, like, discomfort with not knowing or not having ultimate control or being met with change and ultimately being skeptical or resistant to it. How have you overcome this with those who are showing that discomfort?

Peter Lohmann:
I think the first thing is you have to acknowledge what they’re feeling. You can’t dismiss it as stupid or archaic or inappropriate. I think it’s very natural to have this desire of, when you’re making a big decision like this and it involves another human, you want to meet the human. So I don’t fault people for having that instinct. I totally empathize. This was hard for me at first, too. I think the way to talk about this is just, so, like the objections that come up, it’s like, well, I really want to just meet the person.

And what comes up for me when someone says that is, let’s put you in a courtroom in front of a jury, under cross examination, and the opposing counsel is like, so let me get this straight. This person applied for your property on this date. Is that correct? Yes. And you received their application in full, and they paid the rental, the application fee in full. Is that correct? Yes. And you reviewed their application, and it met all the minimum criteria to rent this apartment. Is that true? Yes. Then you decided you needed to meet them. Why is that? Oh, well, it’s just like, what could you possibly say?

Yolanda Muchnik:
Excellent way to change their minds. The courtroom, courtroom example, I think will be highly effective.

Peter Lohmann:
Yeah. It’s like, why do you want to meet them? What are you going to find out that’s not already captured in your criteria? It’s just an opportunity for, if you were to, then decline them after they met all the criteria, but then you met them. Like, what is that telling any person, any third party is going to look at that and be like, this doesn’t look good. It does not look good. The optics are good.
So, you know, the message here is, like, help me save you from making a horrible, fair housing mistake. Let the company bear this risk. Let’s take your personal decision making out of the loop here.

Yolanda Muchnik:
That was really helpful. So, going back to when you started to implement this, I’m curious, did you guys have specific metrics that you used to track the success of the screening process, or you pretty much trusted that this was the better way?

Peter Lohmann:
Yeah. So for years and years, we had this massive spreadsheet where we recorded every single approved applicant, all the data about them, like, their credit score and, like, all this stuff. And then again, this went many years, and then when they eventually moved out, we would record, like, the outcome, like, you know, moved out, got full security deposit back, moved out, got partial security deposit back, moved out, got no security deposit back. Or eviction or skip. I think it was like, those are the different options that we were tracking.

And so we had this big spreadsheet, and it was all this work to maintain it and then track the outcomes. And I had this vision that once we got, like, hundreds and hundreds of records in here, that we could then, like, do some data analysis on this and start to build our own model. Unfortunately, there’s some issues with that. One, me, we just, we’re not big enough to produce the quantity of data that would really be needed for, like, a rigorous analysis here.

The other problem is we’re not getting a true and complete picture, because what we’re not having, what we’re not capturing data on is all the applicants that we didn’t approve. And so the real way to do this, if you truly wanted to, like, track and improve the criteria that you use to approve residence. The actual way to do this would be to just have everyone apply, and then everyone’s approved, like, there’s no minimum. Okay?

And then, so, everyone’s approved, and you have to do this for, like, five years. And then, then you track the outcomes, and then you would be able to tie the applicant data to the outcomes and build a new model. But my guess is most people aren’t going to be up for that.

Yolanda Muchnik:
I’m going to guess that, too.

Peter Lohmann:
Yeah, so, but that would actually be the purest way to capture this data, would just be like, anyone who applies is approved, and then, like, we capture the data on the back end and see what happens. And, like, who knows? Maybe it turns out that, like, experimental reasons, I think it would be really interesting, right?

Maybe it turns out that, like, eviction history has no relevance at all to outcomes. Or maybe it turns out that, like, criminal history is, like, positively correlated with great rental outcomes, or, like, who knows? It could be, like, really weird findings from that. But, yeah, I don’t think that’s gonna be happening anytime soon.

Yolanda Muchnik:
So you talked a little bit about, like, the legal benefits of implementing something like this. I’m curious, are there legal or ethical considerations that property managers to consider when implementing the blind screening process?

Peter Lohmann:
I mean, ethically, I think it’s the best thing you could possibly do. I mean, I sleep great at night knowing that the folks making decisions are just making a data driven decision. Legally, I’m not a fair housing attorney. I haven’t, like, studied fair housing law. There’s probably some, you know, I read through the recent fair housing guidance that was released by HUD just a few weeks ago, actually.

I don’t know if you’ve looked at that document, but it has a lot of wacky things that it says, and basically, if you read it, you get the sense that HUD wants you to be making a case by case decision on whether or not you approve applicants or not. Pretty much the opposite of what I think is fair. And they say in the document, they say some crazy things, like, if the applicant has an eviction on their record, but the eviction was due to a job loss, that is an unusual circumstance that should not be counted against them when applying for rental housing.

It’s like, what are you talking about? That is completely ridiculous. You know, I don’t know. I guess if I were to go talk to the lawyers at HUD, they would probably say that this is too objective and does not allow for, like, individual case by case leniency. The problem I have with that is how do you decide what, how do you decide who should be getting leniency and why? Now all of a sudden you’re opening yourself back up to this bias? Problem is, like, well, I’m giving, you know, let’s say you did that. Let’s say you started, okay, well, we’re going to do this blind thing, but then we’re also going to go case by case.

Years later, HUD takes a look at your, at your, like, record, and they’re like, well, it turns out, Mr. property manager, that you were lenient with white applicants twice as often as you were with black applicants. So we’re finding you in violation of fair housing law. It’s like you were the ones that told me to go case by case and I was, you know, it’s just, it’s crazy. It’s crazy. You know, as property managers, it’s really unfair, the position that we’re put in with this, this guidance.

So talk to your own lawyer before you roll out a policy like this. I’m just sharing what’s been working for us, I guess.

Yolanda Muchnik:
Well, thank you for sharing it. I had not seen that report or known that. That’s really interesting to know. You know, I’m betting there are some property managers listening to this who might actually want to consider implementing a blind process like this at their properties. And I’m curious, what should they consider before making the shift to a more data driven, unbiased screening process like this?

Any other considerations other than, obviously, this HUD piece?

Peter Lohmann:
Yeah, well, I just think you gotta, you know, you’ll need to speak to everyone at your company or at your property and let them know sort of what’s going to be happening and why, especially ownership, like, if you’re in third party management, you know, sharing this somehow with the owners of those properties or the owner of the building that you’re managing, just so everyone is up to speed on what’s going on, because I do think this is a little outside the norm.

Other than that, I think you should be prepared for outcomes to be slightly different than what you would have previously done. And so you need to be comfortable with moving some folks that maybe wouldn’t have made it through prior and declining some folks that maybe you would have found a way to approve them. When you start to remove that human element, it’s going to naturally deviate a little bit from how you may have been making decisions prior because you’re not a computer.

So I just sort of like, if you’re mentally ready for that in advance, that will help. It will make the process smoother. And the other thing I’ll say is, you know, the first time that someone moves in that you may not have otherwise approved, you can’t like, draw all your conclusions off that one applicant, right? Or that one lease. So you need to let the system work for a couple years or over a few hundred leases before you can really draw conclusions, I think.

Yolanda Muchnik:
Right, great point. So this blind application process, obviously a small but critical step in the resident journey. I’m curious, looking ahead more broadly, is there any innovation or trend that you are keeping your eye on to continue improving your property management practices?

Peter Lohmann:
Yeah, I mean, I’m always looking ahead toward what’s coming down the road. I think the natural thing that comes up here as what could be down the road for this part of our industry is AI. And I know that, again, part of that HUD guidance was basically like, we don’t want AI making decisions about occupancies, about approving or declining residents. And they go out of their way to specify that the model that’s making decisions needs to be understandable.

So if the model declines or approves somebody like some sophisticated AI model, you as the property manager or you as the service that provides this need to be able to explain the factors that went into that decision. So kind of, like credit score, where if you get declined for, like a loan, you get something in the mail saying, these are the factors that went into it. I think something like that. So I actually think AI would be really, it would be very powerful to apply AI to this domain space.

I was talking about earlier, this problem of we don’t have good data on what leads to great resident outcomes or poor resident outcomes. This is like what AI was made for, right? Just dump all this data on an AI and let it run some analysis. Unfortunately, I don’t think that will be happening due to HUD guidance. And also, you know, if you put yourself in the resident’s shoes, it would seem a little bit unfair.

If you get denied for housing and you ask why? And they say, I couldn’t tell you. The AI model said no. It’s like, that doesn’t feel great. So I hear that. And that probably is the right move, is to keep this a little more tethered to factors that we can explain and control. So, yeah, our industry, just like every industry, it’s changing fast and it’s almost unrecognizable from ten years ago. And I really look forward to kind of seeing what’s next. I do think that our industry would do well to bear in mind that we’re talking about housing.

The most important, the largest, first of all, it’s the largest monthly bill for every human on earth is their housing. And it’s also one of the most important things that they interact with is, like, literally their shelter. And so when we’re talking about the housing crisis and tenant screening, these are areas that our industry has the opportunity to come out ahead of and be a leader and a pioneer in taking, like a human approach to the technology that we’re using and to the problems that we’re solving.

Or we could go the other way and come across as greedy jerks and a bunch of landlords who don’t care anything about humans and just want to make more money and so I think we have an opportunity with technology like AI and housing crisis solutions to maybe start to reshape the way we’re perceived in the broader landscape of businesses and humans. I think we should take advantage of that.

Yolanda Muchnik:
Such a great point and a great way to tie a bow on this discussion.
Peter, thank you so much for sharing your insights with us today. Where can listeners find more information on any of this if they’re interested in pursuing it or stay updated on your work?

Peter Lohmann:
Yeah, the best way to do that is going to be on my weekly newsletter. So I write a newsletter for property management professionals. Comes out every Friday morning. You can sign up for that on my website, peterlohmann.com

Yolanda Muchnik:
And thank you so much again for joining us Peter, and folks, hold on one more moment for some Good News.

Good News

Amber Halteman:
Hey multifamily pros. I’m Amber, your behind the scenes podcast producer and I’m stepping into the spotlight to share some listener Good News with you.

Now what the heck is Good News? Well, it can be anything. A successful work initiative, a fantastic resident review, shoutout to a work colleague, a friend, heck, go and shout out yourself. Love self promotion. This is your time to shine. There’s enough stress and anxiety in multifamily, and heck really just life, so help us shine a brighter light on what’s going right.

How do you submit your Good News? Well, luckily it’s super simple to do so. Simply go to the show bio on any platform you’re listening on or go to our podcast page gozego.com/podcasts/ and click the contact the show link. It’s huge. It’s in pink. Super simple to see. When you click on that link, you’re taken to a page where you can leave a text or even a voice message of your Good News and we’re going to highlight it on the following show. All right, let’s get to the good stuff.

Our first bit of Good News is from Anonymous. They say after a long year of studying hard, I was able to obtain my broker license. This is a huge achievement for me and will only assist me more with my current job. Big congrats Anonymous. That’s a big thing. Studying, working, working while you’re studying is hard. So congratulations on getting that license.

From another Anonymous is, uh this is super fun, becoming a dungeon master, woo woo! For those of you who might not be into the geeky gaming area, that is from Dungeons and Dragons. So congrats Anonymous. Love to hear it and go whip those people into shape.

Our next good news is from Shivani S.. Shivani says the best thing that’s happened to me this year is meeting and making new friends who became a huge part of my life and journey. I love when I’m having a hard time. My friends are there to help me get through life. It’s nice to know that there are still good people around who are willing to do anything for me. I’m extremely grateful to have some incredible people in my life. I also have some of the best people I work with for the past six years in the same company. My coworkers are like my family and everyone is treated with so much love and respect. Shivani, I love that. Nothing is better than having supportive people in your life. Thank you for sharing. I’m so glad that you’re feeling that love and support from your friends, co-workers, and community in general.

Next good news from Anonymous. I was promoted to Accounting Manager with my job which led to a higher pay. Woo, congrats! I also graduated college with my second degree, a BBA, and I’m enrolled in college to get my MACC. Oh my, Anonymous, I wish you put your name on there so I could shout you out individually. Huge congrats. Not only do you have a second degree, you’re getting your Master’s. I love it. I love all of it. Keep on trucking and I can’t wait to hear an update.

And our final good news today is from Dean J.. Dean says, I was asked last week to join the education committee for the Mississippi Apartment Association. Huge congrats Dean. I love hearing people get involved at the local level. I know your fellow committee members and fellow property management professionals in Mississippi are going to be lucky to have you on board.

Alright folks, that’s the end for today’s Good News. Thank you to everyone who has written in and I can’t wait to hear what our listeners have to say next week. Make sure you write in. Talk to you later.