fbpx

Land Investing Online

Revitalizing Vacant Office Buildings for Residential Use

As we navigate the ever-evolving demands of modern living, cities worldwide are witnessing a remarkable shift in how unused commercial properties are being repurposed to meet the needs of their communities.

As we move into the second quarter of 2024, this trend continues to gain momentum, reshaping skylines and revitalizing neighborhoods in its wake.

Evolution

The rise of remote work, accelerated by global events and technological advancements, has catalyzed a reevaluation of traditional office spaces. Companies, now more than ever, are embracing flexible work arrangements, leading to a surplus of vacant office buildings in urban centers.

Discord Logo

Join our free Discord channel!

Engage & network with thousands of new and experienced investors, participate in weekly Deal Reviews, and more!

However, rather than succumbing to disuse, these structures are being viewed through a new lens — one that sees opportunity in adaptability.

A Sustainable Solution

Enter adaptive reuse, a sustainable approach to urban revitalization that repurposes existing buildings for new functions. In 2024, vacant office buildings have become prime candidates for adaptive reuse projects, presenting an opportunity to breathe new life into dormant spaces. By repurposing these structures for residential use or condominiums, developers are not only addressing the surplus of office space but also promoting sustainable growth and community revitalization.

Environmentally-Friendly-Home-Features

Addressing Housing Needs

In an era marked by housing shortages and affordability challenges, the revitalization of vacant office buildings for residential use offers a solution. By adding new housing stock to the market, these projects contribute to greater housing affordability and diversity.

Whether catering to young professionals seeking urban living or families in search of spacious accommodations, converted office buildings provide an array of housing options to meet the needs of diverse communities.

Investor Opportunity

Residential properties, particularly in desirable urban locations, often command higher rental rates or sales prices compared to commercial office space. Investors stand to benefit from potentially higher rental yields or property appreciation, leading to attractive returns on investment over time.
 
Investors can capitalize on the growing demand for eco-friendly and socially responsible real estate projects, appealing to environmentally conscious tenants or buyers.
who-should-invest-in-NNN-Lease-Properties

The Future of Real Estate

The revitalization of vacant office buildings for residential use or condominiums represents a promising chapter in urban development. In 2024 and beyond, as cities continue to evolve and adapt to changing dynamics, the transformation of these once-dormant structures into vibrant, livable spaces is poised to reshape the urban landscape for generations to come.

Curious about buying land but don’t have the capital?
We offer deal funding where we finance a deal for you!
Fill out the form HERE.
We will review and get back to you about your deal within 24 hours!

Listen to the Latest Podcast

View Transcript here

Ron: As you guys are pricing your first, second, third mailer, make sure you are looking in detail at the data. There is a lack of data with land in general in rural America. What is the most challenging aspect of pricing counties?

Haider: One of the biggest hurdles I would say is having no data, but it comes to smaller counties.

The county is with no comps. That’s when I’m set to face hurdles.

Ron: The advice you have for someone who might be, let’s just say someone who’s mailing their first mailer, someone who’s getting ready to price their first mailer

Haider: One of the advice is I would give them is to get a county, which has more. I believe that data drives business nowadays, and it doesn’t really matter what sort of company you get into.

It’s always your belief to strive to make data, get insights for you.

Ron: Hey everybody. Welcome back to the real estate investing podcast. I’m your host, Ron Apke today, joined by one of our employees, Heider Jan. He’s been our data analyst here for what a year you going on a year?

Haider: More than a year now.,

Ron: More than a year now. Okay. Um, so yeah, Hyder’s been with us.

He’s priced hundreds and hundreds of thousands of pieces of mail. He’s looked at so many parcels. Hyder, welcome to the show.

Haider: Thank you so much, Ron. It’s a pleasure to be here.

Ron: Let’s just go Hyder first, kind of into your background pre working for land investing online, pre doing anything with the land.

What were you doing, uh, school wise? What’s kind of your background?

Haider: So, uh, long story short, I have a master’s degree in, uh, robotics and ai. Uh, I got my master’s from the University of Cincinnati. I got to, got into the US in 2021. So, uh, before that I was in Pakistan. Uh, I have, I’m a Pakistani by, by birth.

Um, so, yep, I, I was very much into coding. I got. interested in robotics and artificial intelligence. That’s what I decided to pursue my degree in. And I got a master’s eventually and that, yep. And then I saw the job posting data analyst, um, on, uh, on indeed. com. That’s when I applied.

Ron: So what, uh, I guess you never thought you were going to be working for a land investment company.

That’s kind of out of the blue, I guess.

Haider: Never, never. Uh, I always thought I’ll be working for, uh, Uh, something related to companies like robotics, companies like, uh, Nvidia or, or, uh, companies of that, of such sort. But yeah, uh, I believe that data drives business nowadays and it doesn’t really matter, uh, what sort of company you go, you get into.

It’s always, uh, it’s always your belief to strive to make data. Yeah. And I feel like, yep.

Ron: So like I said at the beginning, like you’ve priced hundreds of thousands of pieces of mail and pricing is a big, not a hundred percent your role, not your full role. Let’s let me say, um, you do a ton of data stuff for us as well, but what kind of, what are the hurdles when you’re pricing a set of mailers?

Because every county is different, right? Every county has its own unique characteristics, its own unique, whatever it is. The land is so different from county to county. Prices are so different. But what makes, what is the most challenging aspect of pricing counties, pricing mail typically?

Haider: Well, um, I have about a year experience of pricing mail now.

Um, the learning journey has been incredible, uh, from you teaching me. How to price counties with no comps and then counties with a lot, with a lot of comps. Uh, there’s, of course, there’s a huge difference, uh, when it comes to smaller counties, or counties with no comps. That’s when, that’s when I’m set to face hurdles.

Uh, it always, it always depends. I, I would say like one of the biggest factors, uh, that plays a role in pricing a county very well is having a lot of comps. No matter whether the comps are good or not, when you have a lot of comps, you generally get a very good idea of, uh, of what the pricing point should be for a certain part of the county or for, or for a county as a whole.

Um, one of the biggest hurdles I would say is having no data.

Ron: Mm hmm.

Haider: When you have, uh, you know, about, about like 15 to 20 comps for, For a county that is large in size and has cities around, um, you’re kind of faced with this disadvantage of not knowing what to price where. And so I think one of the ways, uh, that I cope with this is looking at comps of the surrounding counties or just filtering out the best comps that I can from the county that I’m working in.

Um, and just kind of going by, uh, the average prices from there on.

Ron: Yup. So I think one thing when you’re doing this, and I don’t think you’re necessarily a perfectionist by trade or anything like that, but it’s hard because there are so much unknown when you’re, so I want to also give you guys a little background for those of you who are new listening.

Um, what he does essentially is. Prices land at scale. So if you are mailing Hamilton County, Ohio, he’s going to price all the land on the entire data set for all the owners, for all the different acreages from two to a hundred acres, whatever the acreage range is. Um, but what I was getting at, like how difficult you can’t be perfect in this, like you cannot be perfect.

And that’s what I’ve always kind of taught you is like, it’s not going to be perfect, but we need to price 80, 90 percent of the mail correctly. What has, is that a hurdle for you or is that something that you’ve just kind of over time? It’s like, okay, I understand this isn’t going to be a perfect data set.

Haider: You touched up quite nicely on one of the points, uh, which was pricing for smaller acreages. That is the hardest when you, when you have, when you want to mail, uh, when you want, when you want to mail a county that has a lot, a lot of, uh, landlots with, with smaller acreage, uh, sizes. The problem with that is that smaller acreages are harder to price in general.

They’re more likely to be used for residential projects. Larger landlords are more likely to be used for commercial projects, and there may be factors affecting that too. But I guess, I guess my, my aim and my target is to, to make the, to make us or to make any of our customers who buy done for you with us.

to get as many leads as they can. Um, it may always not be perfect and that depends on the scale of data we have to price in the, in the first place. So, uh, it’s not perfect, but we strive to get our customers and us as much as many leads as we can, especially in the higher acreages, um, when it comes to data.

When it comes to county, where we have like, where we are hamstrung by having no data.

Ron: Yeah, so like when you have smaller acreages, like you said, like if you guys think about it from a location perspective, in your neighborhood that you live in, like Smaller acreages have so much location dependence because that is where someone is going to live.

So like they might not want to live on one street and then a mile down the road, like they love that spot. And that’s why everyone loves that spot. And that’s why smaller acreages can be very, very. Difficult because they are extremely location dependent where if someone’s trying to buy 10 acres, they don’t really care if it’s exactly where it is necessarily because 10 acres, a ton of land, even if it isn’t an area you might not like, or you have neighbor, like they’re not really neighbors when you have that much land.

Um, but going from there, Hyder, like, where do you think the future of this is? And you obviously have. Experience in machine learning and, uh, and AI, all that stuff is so, so big right now. Is it possible, is it feasible to price land without human, uh, interaction or I guess you want to say or some kind of human aspect?

Haider: I believe it is possible. Uh, it’s been, it’s been in, it’s been in place and the work has been done, uh, by using machine learning when it comes to house pricing. Thank you. Uh, there are companies like, uh, Zillow and Redfin who, who are already doing this. Uh, not, not as, not as good, not as good as they’re doing it with, uh, with land, but they’re doing a pretty good, pretty good work with pricing, um, with pricing houses, uh, house properties.

I mean, their ML models are doing really good. Um, I feel like it is feasible. Uh, the amount of data we have. Uh, I feel like it adds to the, adds to our, uh, benefit in a, in a way that as much, when you have a lot of data and the data is good, I feel like, uh, your data model can only be as good as the data you have.

And right. So the attribute of the data, which in our case is the retail price or the offer price, which you want to predict will be as good as the data you get. Um, There are some attributes in, in the data we get from data tree or from the land portal. The attributes are just floodplains, uh, buildable area, um, and other attributes like lot acreage size, like latitudes and longitudes.

I feel like there’s a huge potential to, there’s huge potential for machine learning to be playing its role in pricing.

Ron: Yeah. I think it’s just the lack of data. Like we’ve talked about a lot in this episode, the lack of data. Is what makes Zillow’s Zestimate. Zillow doesn’t care about their Zestimates for land being accurate at the end of the day, like at this point, like maybe they do eventually, but the lack of data in rural America, um, the unknown with the actual land, I think is huge.

Like you said, like there are different attributes, but the accuracy of it, like if you, if it’s a, if it’s a three bedroom, two bedroom, Two, two bath house, 1500 square feet. Zillow can very accurate, like, okay, we have comps down the road exactly like this, but like every piece of land is different and that’s why it makes it so, so complex.

And I don’t know, I, I’m not pessimistic with this, I don’t think, but it’s just hard to imagine. Accuracy as accurate as what I want in general, but you can definitely see that down the road Going into that hider like we obviously have a lot of new people joining land investing online joining a community Everything like that and they’re pricing their first second third fourth mailer What kind of advice would you give them as they’re going through?

Uh, like what are key things that they shouldn’t be missing? What are key steps you think? uh anything like that any advice you have for someone who might be let’s just say someone who’s mailing their first mailer someone who’s getting Ready to price their first mailer

Haider: Well, uh, one of the advices I would give them is to scrub their data properly.

Uh, that is, that is one of the major pieces of advice. More sometimes what would happen is they would get data from DataTree or, or from the LAN portal, and then maybe scrub it the wrong way, scrub it down the wrong way, not providing one of the most essential, uh, attributes like latitude and longitude, and that would basically affect me pricing their mail for them.

Uh, one of the other.

Ron: What if they’re pricing it themselves though?

Haider: What if they’re pricing it themselves? Um, I would say when you’re, when you’re pricing land uh, first of all you have to you have to recognize what acreages you have to price, or you want to price, or you want to mail and acquire. Um, I feel like I feel like when you’re when you’re pricing pricing land from 5 to 50 acres, let’s say so bigger acreages bigger acres.

Five onwards you get a pretty good idea of what the price point should be and this goes for counties Which have comps this doesn’t go for counties which don’t have comps this goes for county Which has a large number of comps one of the best things to do is to first filter out the county you want to price And go for a more restricted not essentially a county where you can’t get more profit from But, go for a more restrictive, um, county with respect to, uh, your, uh, pri with respect to your business strategy, which means that, get a county which has more comps.

So that you can, you can get a good idea of what the pricing point should be.

Ron: That’s a good point. And, I’ll add to that, I think one, we go down the line, so that’s choosing a county, all that stuff. And a huge thing when pricing and you might just be so natural to you. It might not even clicked with you and it’s just, you’re so used to it is getting rid of bad comps.

Um, because with land, there are so many. Buy for five acres or five acres sold for 12, 500 acres, 12, 500. And if you just use an average and then you have a bunch of outliers from a data perspective, it doesn’t make sense. Like if we have five acres is selling for 45, 000 for 47, 000 for 42, 000, and then you have a five for 12, 500 and you keep that in your data set.

You’re going to severely underpriced things because that is a clear outlier. Uh, so getting rid of bad comps, I think your first, second, third time pricing is the number one thing. Like I think it is so key in getting an accurate or somewhat accurate market value where you’re not, you’re not ever going to be perfect with it, but.

Getting rid of bad data. Do not just export all the data and then run an average. Because that’s what some people do. They do it, they have all their lists of comps, they run an average, and then there’s outliers, and they just like, they’re just like, that’s just part of their data set. Those outliers cannot be part of your data set.

Um, and then also adding on to that is looking into the actual land like you do for everyone, like looking into the comps, making sure your upper end prices, like let’s say all those five for 40, 45 thousands have a structure on it. Like that’s not a good comp either. So you need to find average land and then price based on that.

Essentially. Don’t, don’t find the best land in the county. Don’t find the worst land in the county. Average land, right?

Haider: Correct. I would say, uh, outliers. can really skew your pricing. Not only will you, will you not find good deals, you’ll find bad deals. Um, you’ll, you’ll, you’ll get, you’ll get people who want to sell their land, which is completely on a floodplain and you’ve, you’ve overpriced it.

They’ve gotten mail and you’ve overpriced the land. Now they’re, they’re really happy with the, with the offer you’re, you’re bringing up to them. And so you’ll get bad deals. Um, and so yeah, outliers really, really skew your data, really skew your pricing. Yeah, I feel like, uh, removing, removing the bad comps and recognizing bad comps.

A comp can essentially be good. It, there may be land which looks average, but has a structure on it. It has a fully, full structure on it with, with a proper sewage system with, with electricity on it. And you may, you may feel like you, you’ve, you’ve, uh, just recognized a really good comp to base your pricing on.

But, uh, what you fail to recognize is that there has already been work done on that property, which raises its value.

Ron: Sometimes you got to go into it too. Like sometimes the first picture isn’t what should be the first picture. And it’s like, you don’t know that there’s a structure until you click into that Zillow link.

Like, Oh crap, this has a pull barn on it. It has a mobile home on it. Then they’ll say in the description, they might have some pictures down there as well. And those are huge, huge add ons in terms of prices. And that’s not an average piece of land. Um, but yeah, go on. I’m sorry about that.

Haider: Um, yeah. Something clicked into my mind and, uh, this is, uh, I want to, uh, put it on, put it forward is that Zillow Zillow basically always has a lot of data as compared to Redfin when it comes to counties.

And when you recognize when you’re looking at comps, Zillow, you’ll find Zillow to always have more data. And that this is because Zillow is a bigger data company. Uh, it, it gives users the power to add their own, uh, MLS listings. And it also pulls all the other MLS listings and, uh, public record data. What this does is that it accumulates a lot of, uh, a lot of comps.

necessarily. And this gives you more work to remove the outliers. Uh, this is one of the reasons we use. Well, I use predominantly Redfin to, um, make our tabular visualization, which you guys may have seen in the done for you, uh, product. Uh, I feel like Redfin as compared to Zillow doesn’t have a lot of comms with public record data.

And we found that a 90 to 80 to 90 percent of public record data is not good comps. Um, yep.

Ron: That’s a good point. So going into it guys, like going down the line, so you guys are pricing your, whatever it is, wherever you are in this process, maybe you don’t plan on sending mail. Like understanding the data behind land is so, so valuable and understanding good comps versus bad comps.

When you are sending 2000 piece of mail, 2, 500 piece of mail, the price you have on your offer is a huge, huge component. I don’t want to act like it’s all like there are a lot of variables that go into a successful mailer. the letter you’re sending, how you’re sending it. Obviously the County you select the price you put on there is a huge component of the number of leads you’re going to get.

You don’t want to overprice. You don’t want to underprice cause you don’t want to get deals back that you can’t do because you price too high. Um, but going into that, like that, that’s really it guys. Like as you guys are pricing your first, second, third mailer, Make sure, and that’s what I want you to get out of this.

Make sure you are looking in detail at the data. You cannot look at the data from a, uh, far out lens or anything like that. Like you need to zoom into the data and figure out what’s actually going on with the data points. Because like you said, Hyder is Hyder, there is a lack of data with land in general in rural America where we’re doing deals.

So if you go to places where you don’t have that data, it can be very, very, Difficult if you don’t go in to each data point and figure out what’s going on. Other than that, guys, thank you, Hyder, so much for joining us today. We’ll probably get you back on if you guys have future more detailed information you want from Hyder, I’d be more than happy to get him back on the podcast.

Other than that, thank you so much. If you guys are watching on YouTube, please hit that subscribe button. Thank you so much. We’ll see you next time. As always, for joining. Please do us a huge favor and like and subscribe our YouTube channel and share this with a friend. It really means the world to Ron and I, but more importantly, it could help change the life of someone else.

Thanks for joining and we’ll see you next episode.

Watch the Full Episode Here