Jonathan Pong: Yes. Well, just to add to that, when we think about it in a natural hedge, I think it’s more so a lot of lines of lenders that actually matures, because we look at our maturity schedule, we’ve got a decent amount of debt coming to well staggered, but on a nominal basis, still pretty significant, $1.9 billion next year, for instance, or $3.26 billion at almost $3 billion in 2027. And so when you have a corresponding asset that could get cold or will get repaid and it’s hard to replace that coupon in a lower rate environment, chances are we’re much better off as we’re refinancing our liabilities and our cost of capital is obviously much lower. So I think that’s another way to take through the natural hedge element.
Operator: Our next question comes from Harsh Hemnani of Green Street. Please go ahead.
Harsh Hemnani: Thank you. So not a good jump of acquisitions this quarter came from ones that needed the capital and some redemptions. How would you contrast that opportunity set for acquisitions versus maybe the traditional sale-leaseback market when realty income could provide a solution for [indiscernible] financing. It sounds like based on interest rate hopes — hopes of interest going down, more tenants are looking towards the traditional credit market and trying to look for finite sources of capital rather than locking in sale-leaseback capital for a perpetual period of time. Is that something you’re seeing more so in tenant conversations today than compared to a year ago?
Sumit Roy: We are certainly seeing sale-leaseback opportunities. In fact, 13% of what we closed in the first quarter, it was sale-leaseback. But you’re right, Harsh. If you compare it to last year, 46% of everything we did was sale leaseback. The year before that, it was closer to 40%. And we are not seeing that. And I think part of it is because clients are trying to figure out ways to not necessarily lock into 20-year, 25-year leases at these elevated cap rates. And so if there is an alternative available to them, be it through the debt markets, which has much shorter duration, even if it is higher, I think they’re going to be far more inclined to doing that. But I just want to be very clear that we are — again, if it’s a brand-new client that we don’t have a relationship with, we are not going to go and provide them credit if there isn’t a compelling sale-leaseback opportunity with them and our desire to have them as part of our client registry is not there.
We are not going to be pure credit providers like some of the credit funds out there that exist. So, I do see that changing as there is stability in the rate environment, as people start to get much more comfortable about where things are going to sort of play out, I do believe that sale-leaseback will come roaring back. We are in discussions with some names right now. And it really is a disconnect between where they want to transact and what — where we are capable of transacting given our cost of capital. So — but I think it’s a matter of time.
Harsh Hemnani: Thanks for that. I’ll leave it there.
Sumit Roy: Thank you, Harsh.
Operator: And our next question comes from Linda Tsai of Jefferies. Please go ahead.
Linda Tsai: Hi, thank you. Maybe piggybacking off Greg’s earlier question on dispositions, your proprietary predictive analytics platform will be used to help with dispose. Could you just give us some more color on how that works? What are some of the inputs to the analysis?
Sumit Roy: Yes. That’s where the secret sauce is, Linda. But all right. Let me — and I don’t want to get to pedantic, so try to keep it pretty high level. The way our predictive analytics were it is by industry and even at times by client. But largely, the models work by industry. And it tries to identify the key variables, which could be 20, 30, 40 variables that dictate the predictability of a renewal outcome or a leasing outcome. So the pieces around how we created the predictive analytic tool was to figure out, what was our leasing activity going to look like, where will we — the risk was defined as, are we going to be able to maintain the rent that we currently have during a renewal period? Or are we — is it going to go less?
Or is it going to be more — and that’s how we define risk, and that’s how we’ve sort of created these algorithms by industry to identify where does risk lie in our portfolio. And as you can imagine, each industry has its own set of variables that dictate that particular outcome. But the biggest piece of all of this. I think the creation of the algorithms, et cetera, is a fairly simple task. I mean it’s taken us 3.5, four years. So I don’t want to minimize that piece. I’ve got me looking at me strangely here. But it is the data that we have that allowed us to back test these models and continue to refine and calibrate these models to improve their predictability. I think that’s what is whether the true value lies in our platform, having been around for 50 years.
And I think that’s why you see the kind of results that you see when we are posting the release — the re-leasing spreads, when we are trying to get ahead in terms of identification of assets that we should, where we are maximizing the return profile of those assets given what we think will happen some lease renewal. I think that’s where the predictive analytics tools, along with the asset management team was using on-the-ground experience to sort of share their perspective along with the credit view, that group together is what’s dictating how we try to stay ahead with the portfolio. And it’s — it has to be tools driven. It has to be technology driven. When you have 15,400 discrete locations with in 80 different industries with 1,500 different clients I mean it can’t be done manually.
And so that’s the reason why we chose to make this investment Linda, five years ago and several millions in — this is a tool that we are very, very proud of. And it’s now very much part and parcel of every decision we are making. Be it acquisitions, be it disposition, the hold decisions and also, this tool is now being used to dictate the highest and best use for potential vacancies, which may or may not be the old use of that particular asset. And we’ve seen some of the value creation that, that prediction has yielded for us as a platform. So hopefully, I didn’t get too much into the details, but that’s really how the pretty expanded tool works
Linda Tsai: Appreciate the color. And then just in terms of using dispose to get back to that core portfolio you referenced earlier, can you give us some metrics or characteristics of what that looks like. You have more international exposure versus four or five years ago? How does that kind of fit to the core portfolio?
Sumit Roy: Yes. For us, having geographical diversification, the advantages of it played out in the first quarter, right? I mean you saw we were able to find transactions in the UK that had a return profile that far superseded what we are able to find here in the US. And then that will flip, so I think the geographical diversity is a good thing. In terms of the actual composition of the portfolio, we clearly have what we are viewing as an optimal portfolio. And an optimal portfolio, we might like, let’s call it, grocery. But we want grocery to be 13% to 14% of our overall portfolio. If that creeps into the 19% to 20%, that’s not a good thing. And by the way, I’m giving you an example, that’s not the case, grocery happens to be only 10% of our portfolio today.
And then there are other areas like apparel that we may not necessarily want to be exposed to at all. But again, using very broad brushes across particular subsectors is not the right answer either. And that’s the reason why this tool along with credit, et cetera, they help us devise what we believe to be the optimal portfolio. If you think retail and you step back, we like service-oriented businesses. We like low price point businesses. And — that’s sort of the overarching nondiscretionary overarching elements that we look for on the retail side. That doesn’t mean that we won’t deviate from this. But over 90% of our retail portfolio, by the way, has one or more of these characteristics. But that’s, I think, how you think about the composition and geographical diversification is something that sits on top of that.
Operator: This concludes our question-and-answer session. I would like to turn the conference back over to Mr. Sumit Roy for any closing remarks.
Sumit Roy: Thank you all for joining us today, and we look forward to speaking soon and seeing you at the upcoming conferences. Thank you.
Operator: The conference has now concluded. Thank you for attending today’s presentation. You may now disconnect.
