Today’s guest is Arunabh Dastidar.
As an ex-asset owner and manager of projects worth over $5B, Arunabh Dastidar has first-hand experienced the flaws that hinder growth in the real estate industry. His mission is to revolutionize the future of rental management.
Show summary:
In this podcast episode, Arnab Naskar, discusses how his platform simplifies and accelerates data analysis in the real estate industry. He explains the streamlined integration process and emphasizes positive customer feedback. Arnab shares his background and how he got involved in real estate, highlighting the need for a solution to improve decision-making. Sam delves into Arnab’s entrepreneurial journey, and Arun Doster discusses the incorporation of AI in real estate. They emphasize the importance of data-driven decision-making and storytelling.
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Intro [00:00:00]
Revolutionizing the Future of Rental Management [00:00:42]
Arnab Naskar’s Background and Journey [00:01:09]
Predictive Pricing and Decision Making in Real Estate [00:04:59]
The power of language models with AI [00:12:57]
Detecting ongoing issues and tenant satisfaction [00:14:17]
Real Grow: Using external data for portfolio growth [00:21:12]
The future of real estate decision-making [00:22:10]
Getting in touch with the guest [00:22:33]
Closing [00:23:05]
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Connect with Arunabh:
Linkedin: https://www.linkedin.com/in/arunabhadastidar/
Connect with Sam:
I love helping others place money outside of traditional investments that both diversify a strategy and provide solid predictable returns.
Facebook: https://www.facebook.com/HowtoscaleCRE/
LinkedIn: https://www.linkedin.com/in/samwilsonhowtoscalecre/
Email me → sam@brickeninvestmentgroup.com
SUBSCRIBE and LEAVE A RATING. Listen to How To Scale Commercial Real Estate Investing with Sam Wilson
Apple Podcasts: https://podcasts.apple.com/us/podcast/how-to-scale-commercial-real-estate/id1539979234
Spotify: https://open.spotify.com/show/4m0NWYzSvznEIjRBFtCgEL?si=e10d8e039b99475f
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Want to read the full show notes of the episode? Check it out below:
Arunabh Dastidar ([00:00:00]) – They can literally drag drop their CSV or Excel files to bring their internal data into the system. And then our AI actually maps those with and cleans it up to a greater extent so that it becomes immediately usable because as you mentioned, this is a daunting task of ingesting all that data. We have made it so simple and we are one of the fastest integration the industry has ever seen. And this is not what I am saying is what like our customers say.
Intro ([00:00:29]) – Welcome to the How to scale commercial real estate show. Whether you are an active or passive investor, we’ll teach you how to scale your real estate investing business into something big.
Sam Wilson ([00:00:42]) – As an asset owner and manager of projects worth over $5 billion, Arun Doster has firsthand experience the flaws that hinder growth in the real estate industry. His mission is revolutionized the future of rental management. Arnab Welcome to the show.
Arunabh Dastidar ([00:00:58]) – Thanks for having me.
Sam Wilson ([00:00:59]) – Absolutely. The pleasure is mine. Arnab There are three questions I ask every guest who comes on the show in 90s or less.
Sam Wilson ([00:01:05]) – Can you tell me where did you start? Where are you now and how did you get there?
Arunabh Dastidar ([00:01:09]) – Oh that’s amazing. Where did I start? I’m an engineer myself. I sold my first company when I was 25 and then travel 20 countries before ending up in real estate private equity world. And one evening I was taking a call on $130 million deal and my Excel was crashing. And that cuts gets you to where I’m at now. We are solving that problem of decision making across real estate by bringing AI and data models into the industry. So where we are heading towards is revolutionizing the way how real estate makes decisions using advanced data models and making it more efficient, productive and more accurate so that everyone of us can make more money.
Sam Wilson ([00:01:56]) – I love it. I love it. I mean, those are those are high ideals, but I’d love to get into the weeds and really find out what that means. But means but before we get there, you sold your first company at the age of 25 that you just grow up in an entrepreneurial family that just said, hey, you know what? Go out and carve your own way.
Sam Wilson ([00:02:14]) – How did that happen?
Arunabh Dastidar ([00:02:16]) – Oh, that’s that’s interesting. I did not grow up. My my family was, you know, like very humble beginnings. We had mostly people working in sales. So I had the sales knack from the very beginning, but I did engineering and ended up in a very clear cross. So I started coding when I was 12, and by the time I was in engineering I was like, I know a lot about computers. Maybe I should select more build worlds. So I ended up doing civil engineering. So that’s how the whole infrastructure and technology comes into play. And then at the very early age, I was involved in bigger projects with public private partnership the world has ever seen. Because of this, you need mix of technology and infrastructure and found a solution for hyperlocal deliveries for renovation materials across Asia. And then that was my first company, which I coded from scratch with a couple of other buddies. We we were in 22 different cities, huge operations, and then we went out racing and ended up getting acquired by one of the top companies.
Arunabh Dastidar ([00:03:26]) – And then, you know, rest is history.
Sam Wilson ([00:03:28]) – Wow. Wow. That’s a lot of experience that you’ve wrapped up long before many of us even figure out who we want to be when we grow up, which I’m not sure I know the answer to that question. But, you know, I think we’ll spend the rest of our life figuring that out. So you sold that company and then what did you do to how did you get involved in real estate when you said, hey, you know what, I think real estate is the next move.
Arunabh Dastidar ([00:03:52]) – Yeah. So I was always interested in pre-development investments, walled off real estate. So, you know, during my voyage to like, travel around the world, Abed was like exploring what to do next. And I was drawn towards, you know, drawn towards learning more. And that’s how I pursued my MBA. And that’s where I specialized in real estate investments and ended up working with real estate private equity for a couple of years to handling around $1 billion of project assets in North America.
Arunabh Dastidar ([00:04:29]) – And that’s what like drawn me towards the new problem, which now we are solving about. Everyone talks about real estate moves slow. Nobody knows why. I’m going to tell you it is the decision making which makes it slow because there are so many different things you need to look at before you say a thumbs up or thumbs down to a deal or thumbs or thumbs down to a to a capital expenditure. And that’s the process which we’ll say is definitely helping to bridge the gap in.
Sam Wilson ([00:04:59]) – Well, tell me tell me, what are some of those decisions that you feel like you’ve been able to accelerate the decision making process on?
Arunabh Dastidar ([00:05:08]) – Yeah. So we have different data models. One of the most used data models of ours is predictive pricing. So before going to market, you have major asset classes which are prone to seasonality, market changes, neighborhood changes and things like that. You can bring your internal and external data sets on our platform and our platform can predict and prescribe how much rentals you can should charge in each of these neighborhoods preemptively and also can tell you three months from now you’re going to see there’s a high probability that you’re going to see this week and see you should use this marketing channel versus this marketing channel to nail it.
Arunabh Dastidar ([00:05:49]) – So those are a few of the use cases which are most used. Apart from that, we have use cases around how to reduce your expenditure or improve your tenant satisfaction. So we correlate data points on work orders then in satisfaction online ratings as well as your actual cost basis and give you predictions about if you change a in this building, this will improve your in your tenant satisfaction, which is some of the decisions asset managers are regularly making. And you can see that in a very nice looking dashboard and by which is associated with it.
Sam Wilson ([00:06:29]) – This this year above my pay grade by a long shot here. So I’m going to attempt to ask intelligent questions and they’ll probably come out as unintelligent ones along the way. But that’s that’s okay. I’m here to learn right along with our listeners today because this is truly fascinating to me, aggregating this data in a meaningful way, like acquiring like you were talking. Let’s go back to predictive pricing to me, that just sounds like a daunting task where you’re going, okay, you’ve got to get all the historical data.
Sam Wilson ([00:06:59]) – You then have to collect even the hyperlocal data and then and then and then aggregate that and then synthesize it into some sort of meaningful output. How in the world are you doing that?
Arunabh Dastidar ([00:07:12]) – Yeah. So thanks to our data plug partners, that’s one of the major things which we have worked on. We partner with a variety of data providers within the platform. If you’re already subscribed to some of them, you can bring that data through into the platform and do on demand analysis on that or the beauty of how what where we spend most time is making it so simple and no code for asset owners that they can literally drag drop their CSV or Excel files to bring their internal data into the system. And then our AI actually maps those with and cleans it up to a greater extent. So that it becomes immediately usable because as you mentioned, this is a daunting task of ingesting all that data. We have made it so simple and we are one of the fastest integration the industry has ever seen. And this is not what I am saying is what like our customers say.
Sam Wilson ([00:08:12]) – That’s really, really wild. So you guys partner with a whole bunch of others? I mean, let’s face it, there’s a million data sources out there. Exactly right. And so you obviously, you’re not out there, you know, picking up the phone and calling all the local apartments and saying, okay, what’s your rental rates? What’s this and that? You’re aggregating this from other data sources. But I think the key there is what you’ve said is that you’re able to get that in, ingest it, and then have a meaningful output in mere seconds. How long did you work on this before you felt like you had had the template refined to where you could then take this to the public?
Arunabh Dastidar ([00:08:47]) – It’s it’s a team, actually. We work with PhDs from some of the top universities across North America who have built these models with us for the last two years. We did a commercial launch last year and now we’re being used by 300 plus buildings and now like have tons and tons of more interest in the models because the what it can do is just give superpowers to asset managers, because now they can do like ten x more work without spending that much of like grind and like legwork time.
Sam Wilson ([00:09:24]) – Right? Yeah. They’re not, like you said, you know, digging through an Excel model at [10:00] at night going, okay, did I, did I map these fields correctly for the output that I got? And I think that’s correct. And then you sleep on on the next morning, you find out you made an error.
Arunabh Dastidar ([00:09:38]) – Oh, yeah. Yes. Yeah. It used to happen to me so much. So many times the next morning. Oh, I missed that assumption.
Sam Wilson ([00:09:46]) – Right, right, right. This assumption. I was off by a factor of two on this assumption. And that really made which I mean, I still find myself doing that on occasion. I’m like, okay, I’m going to sleep on this and come back tomorrow and see if this makes sense still. So yeah, that’s really, really cool. Predictive pricing. Let’s get let’s get back to that a little bit. When you say that, are you are you talking about resale pricing? Are you talking about predictive pricing on what you should be charging on a per unit basis? Like what how many things wrap into predictive pricing?
Arunabh Dastidar ([00:10:18]) – So predictably we can tell rental pricing.
Arunabh Dastidar ([00:10:21]) – So how much you should charge for your corner unit in a particular neighborhood in the summer month which is coming up. Or you can also based on. So now there are two elements on predictive pricing. Of course, go to market. The second is renewals, which are very, very important, right? So if you’re having a lease renewal coming up, you want to know the tenant satisfaction data, you want to know how many work orders they have released, and then you want to know what’s their alternative to before proposing a price to for that renewal as well. Right? So our technology can actually bring these three data sets and can synthesize a price which they cannot like. They would have high probability to accept and not get you into a vacancy, which you have to gain market and spend money on. So that is what we do. We are building on models on resale as well. So it’s something in our product pipeline. Currently we do more on asset management where you have to constantly deliver results on the bottom line of your assets, right?
Sam Wilson ([00:11:30]) – No, I think that’s cool.
Sam Wilson ([00:11:32]) – So so if I’m just to recap what you said, you have the resale data, things like that. That’s part of the in the in the pipeline for the predictive pricing side of things. Yeah, that’s cool. I love that, man. This is really, really wild. I think the incorporating artificial intelligence into this, I mean, we’ve seen just the what even the the I’m going to call it simple, but it’s not even what ChatGPT is doing right now. I mean, that’s what most of us who aren’t in the AI world, at least that’s what I think of. I’m like, okay, chat GPT. I even used it last week, which I’m just astounded by because I was writing a newsletter and it was, you know, if anybody’s written anything ever, you just hammer out a whole 100 different thoughts and like, yeah, because you don’t want to stop the flow. And I’m like, I was going to read, synthesize it or reorder it and kind of make it into a meaningful newsletter.
Sam Wilson ([00:12:22]) – I’m like, You know what? I’m going to stick all of this in chat GPT and see what it can do to turn this into one cohesive thought, like organize this thoughts and make it a logical, a logical paragraph. And seconds later I had a 15 newsletter and of course I edited because there was some things that were not perfect, but I mean, it saved me two hours of time at least. That’s right. Rewriting something, I’m like, Oh my gosh, this is. This is amazing. So you guys are doing kind of the same thing with incorporating AI and how that’s, you know, that’s shaping the future of real estate. But tell us tell us what your thoughts are on that.
Arunabh Dastidar ([00:12:57]) – You gave a great example. Now imagine that power of language models with descriptive and productive AI. So which works on math, right? ChatGPT is still bad at math, right? Okay. Math is tough, right? So it’s still bad in math, but it’s still a great at language.
Arunabh Dastidar ([00:13:15]) – So how we do these two things together is we have built a proprietary, specific industry, specific data models that feeds the language models to give you the responses with math. So now you are you’re actually accelerating not only the process of like data driven decision making, but also the storytelling aspect of it. So when you see look at our our dashboard, so you can see all the data points. But then when you look at our insights, you can see that explained in a human language format, which gives you more confidence to take that action or nudge you towards the right direction to look at it. So it’s it’s really great language which helps you like make more human sense, right? Instead of like spreadsheets. And then it’s great data models that give you that data. And combining these two is what changes decision making, what accelerates that decision making part.
Sam Wilson ([00:14:17]) – Wow, that’s really awesome. So let’s let’s cover some more of those decisions that your platform helps people make. What, outside of predictive pricing? Give me some other examples if you can.
Arunabh Dastidar ([00:14:30]) – Yeah. So one of the very interesting things which were platform was able to detect is the kind of work orders you generate and preemptively telling you that these are the ongoing issues or regular issues which comes up and which impacts your tenant satisfaction. So there are a few maintenance modules which tells you the ongoing system but cannot correlated with external factors or few things which are outside of that particular niche. Right? So what our system can tell you that, you know, maybe this is an ongoing issue, but this is a smaller issue. But this every time this particular issue comes in, maybe it is water leakage or it is like an issue comes in, it impacts your tenant sentiment much more because we connected with the tenant sentiment data, which is another software as well. So think of us at that layer where synthesizing multiple different, you know, the ERP as well as external data points and giving you what is the most relevant decision to make without like without having a tunnel vision, right? So if you go currently to each of these systems, they give you a tunnel vision on, okay, this is the most repeated work order, that’s great.
Arunabh Dastidar ([00:15:53]) – But what with that right, if you if you get the data but you don’t know the how, you are actually missing 80% of the picture and our system helps you get to the how connecting from these two areas.
Sam Wilson ([00:16:06]) – That and doing that again on the fly is, I think, the most powerful part of this. Yes. Because it’s one thing like and I like the way you put that where you said, hey, it’s you know, it’s one thing to see, okay, this might be the most repeated work order, but it might be that it really doesn’t impact tenant satisfaction any. That’s right. Might not impact the bottom line in a way that maybe there’s one work order that’s only 5% of the total work orders that come in, but it really honks every tenet off when it happens and they end up leaving because. Well, and so and that can drive, you know, an outsized and outsized return or lack thereof, you know, without and being able to aggregate that data in a meaningful way.
Sam Wilson ([00:16:47]) – I think one of the and this again, I’m not a techie. I’m just going to tell you that right now, I can send an email, I can get on a Zoom call. And outside of that, I’m pretty well stuck. But I’m just thinking about stuff like even our investor portals where it’s like we upload all these documents and it just maps it all to the right, you know? Okay, this is your k one, your k one, and reads the names on it and plugs it without us having to go through and select who the 100 investors are in that deal and like and that’s that’s a small idea of what technology can do that really makes our lives easier. But you guys are kind of doing that same thing at scale. And I think that’s that’s a powerful part of what we haven’t even mentioned the name of it here. Can you tell us the name of your company so our listeners can actually go out and find this?
Arunabh Dastidar ([00:17:30]) – It’s called Real Sage. Yeah. So real sage.
Arunabh Dastidar ([00:17:35]) – Sage is the wise. Yes. Real sage. Sage is the wise person. Real comes from real estate. So we’re bringing wisdom to real estate. And that’s how it is. Real sage.
Sam Wilson ([00:17:45]) – That’s really fantastic. How did you sell this idea? Let’s get into the nuts and bolts of how you built your company just for a couple of minutes here, because I think this is always, always a fun conversation as we talk about how to scale commercial real estate. You had this great idea. You saw a problem. You have the intelligence in the background to figure out how to solve that. But then how did you incorporate all these other PhDs, universities and people like that into what you were doing and sell them on the idea that this is a problem that we can solve.
Arunabh Dastidar ([00:18:13]) – It’s first of all, it’s believing in the problem, right, as a team. So I have two other co-founders. The other guy went to Columbia for his master’s, comes from very deep real estate background work with private equity.
Arunabh Dastidar ([00:18:28]) – We are the people who come from industry. There is no one else who can be passionate, more passionate than us to actually solve the problem. And you know, when you go with the passion and like show what you’re trying, what you’re trying to do as a mission driven company ourselves, people will tag along. You know, that’s I have seen like for us and I so I advise a couple of companies in San Francisco and other places. One thing which I recommend, anyone who is in entrepreneurship, it’s a hard journey. If you’re not passionate about it, you can’t do it. You have to have that really great mission. And that’s what differentiates us as a team and product to have that mission, to change it for the industry. And that’s how the first nuts and bolts got together. We spoke to our clients, we heard them, we resonated with their problem and each fact of our product, when you look at it, it will tell you, it will scream that this is a customer problem, which we are solving.
Arunabh Dastidar ([00:19:36]) – Right? And a lot of technical products, right? Like do this mistake of making it too technical on the behind. So you need lots of analyst or external consultant to like actually bridge it together. So which is a big pull for an asset manager or anyone from the industry to take. We have made it so simple that it’s a no code solution for anyone who can get on a zoom and know how to open an Excel file can start using our system. Why? Because we come from the industry and we understand that. And that’s how, like the first few pieces started talking, talking to a lot of customers, always helpful. And now where we are at is also like given regular feedback, regular touch with the people. And I personally do onboarding. I personally sit on onboarding calls as well.
Sam Wilson ([00:20:23]) – So that’s really, really cool. Yeah. You’ve lowered or eliminated as much as you can the barriers to adoption because like you’re saying, you know, if all of a sudden you have to learn to code or you know, you mentioned the word APIs and this and that and the other, and it’s like, I mean, you call a guy like me and if I needed your product, I’d be like, Yeah, that’s just that’s too hard.
Sam Wilson ([00:20:42]) – I don’t I don’t have three weeks to learn how to adopt what it is that you’re not. I see that. I think you’ve seen it probably too, in the tech space. And it’s like, this just doesn’t work simply because the, the interface with a common user just isn’t there. So I think that’s really cool. You guys have done some awesome stuff. I wish we had more time to dig into some of the other things that that your current product does. But I got one last question here for you, and we talked about this briefly about things that are in the pipeline. What are some other major problems you guys are looking to solve in the near term?
Arunabh Dastidar ([00:21:12]) – Yeah, one of the big launches of which we are looking to do is real grow. We know there are so many commercial real estate investors who are going into transaction and actually building our growing their portfolio. Our technology would be helping them to bring more external data sets and do disposition and acquisition analysis based on the trends on those markets.
Arunabh Dastidar ([00:21:38]) – So imagine you want to buy something in Texas and you don’t know anything about Texas. Market partner with one of our data plug partners. Bring the Texas demographic data our system can do on demand, No code analysis on where the market is going and then can suggest you spots where you can then go and scout for deals that can help you grow in the next five years and six years horizon. So that’s one of the big things which we are working on and we’re looking forward to it.
Sam Wilson ([00:22:10]) – Wow, that’s really, really cool. I have thoroughly enjoyed having you on the show today. Thank you for taking the time to come on and share with our listeners just the future of real estate and how decisions are going to be made. Because I think this is this is you’re ahead of the curve on this. And I think this is a really, really fun topic to to discuss. So thank you again for your time today. If our listeners want to get in touch with you and learn more about you. What is the best way to do that?
Arunabh Dastidar ([00:22:33]) – Yes, you can go to real estate and hit on request.
Arunabh Dastidar ([00:22:40]) – Our team would be love to like chat with you, understand your problem and go from there. You can follow us on our LinkedIn page at Real Sage or reach me out on LinkedIn at the Star or Twitter as well at T.
Sam Wilson ([00:22:56]) – Fantastic. We’ll make sure we include all of that there in the show notes. Arnab Thank you again for your time today. I certainly appreciate it.
Arunabh Dastidar ([00:23:02]) – Thanks a lot, Sam, for having me. Take care.
Sam Wilson ([00:23:05]) – Hey, thanks for listening to the How to Scale Commercial Real Estate podcast. If you can do me a favor and subscribe and leave us a review on Apple Podcasts, Spotify, Google Podcasts, whatever platform it is you use to listen. If you can do that for us, that would be a fantastic help to the show. It helps us both attract new listeners as well as rank higher on those directories. So appreciate you listening. Thanks so much and hope to catch you on the next episode.
