ReSolve Riffs: Oliver Rust of Truflation on Real-time Inflation Forecasting with Blockchain & Oracles

In this episode, the ReSolve team talks with Oliver Rust, Head of Product at Truflation, a blockchain-based project that aims to provide real-time inflation forecasting. We discuss a range of topics, including:

  • The current challenges in measuring inflation and the potential benefits of utilizing blockchain technology in inflation forecasting
  • An introduction to Truflation and its mission to provide more accurate, real-time inflation data
  • How decentralized oracles can help improve the accuracy and reliability of inflation forecasting
  • The potential impact of recent central bank actions on inflation and the global economy
  • Jerome Powell’s tough position as the head of the Federal Reserve and his focus on his legacy
  • The complexity of managing inflation, employment, and financial stability simultaneously
  • The role of the Fed and the government in supporting local and regional banks during turbulent economic times
  • The importance of new inflation measurement tools like Truflation in helping investors make better allocation decisions
  • The long-term implications of current economic policies on inflation and financial markets
  • How to get involved with and follow the developments of Truflation and its data

This episode is a must-listen for anyone interested in understanding the complexities of inflation management, the potential benefits of blockchain technology in forecasting, and the role of central banks in a rapidly changing global economy. Discover insights and strategies to navigate these intricate markets and stay informed on the latest developments in real-time inflation forecasting.

This is “ReSolve’s Riffs” – live on YouTube every Friday afternoon to debate the most relevant investment topics of the day, hosted by Adam Butler, Mike Philbrick and Rodrigo Gordillo of ReSolve Global* and Richard Laterman of ReSolve Asset Management Inc.

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Oliver Rust
Product Lead, Truflation 

Oliver Rust is the current product lead for Truflation, an independent economic and financial data business in real-time. He brings over 20 years of experience leading business intelligence companies in Asia Pacific, Europe, and the United States. Prior to his appointment with Truflation,  Oliver was the CEO of ENGINE Insights and Data, including numerous global and regional leadership roles with Nielsen, Taylor Nelson Sofres, Tesco, and Bluebell. Oliver is a recognized innovator in customer experience and engagement strategies.

About Truflation

Truflation is an economic data aggregator serving independent, unbiased, real-time data on-chain and off-chain. Truflation’s goal is to help individuals, investors, companies, and institutions make more informed decisions by having access to independent and unbiased economic information. Truflation also enables developers to create tools to help people maintain their purchasing power, navigate their portfolios through a challenging macroeconomic landscape, and propel the DeFi space into the new era of an inflation-proof and blockchain-powered economy.





Richard: 01:48

Happy Thursday.

Mike: 01:50

Happy Thursday. It feels – I’m ready for a Friday too. So we got a long weekend coming. I’ll take…

Richard: 01:59

It’s exactly what this one feels like.

Mike: 02:02

Yeah, I will take it. And it’ll give something for people to contemplate on their long weekend, too, as we dive in. So before we start, we’re going to have a wide ranging conversation. It’s not advice. If you need advice, go get it from someone who’s qualified, not three guys on the internet on a Thursday, before the long weekend. Anyway, with that said, we’ve got an amazing guest today, Oliver Rust, and we’re going to talk Trueflation, which is super cool. I can’t wait to dig into this.


Mike: 02:22

So maybe to get us started, Oliver, you want to just give everybody a sort of background on who you are, where you came from and how you got to this particular situation and then we’ll dig into moving that forward.

Oliver: 02:46

Sounds great. And first of all, thanks a lot for having me, Richard and Mike, on the show. It’s fantastic to talk to you about inflation, which obviously is really topical lately.

So, yeah, a bit of background of myself. I’ve been in the data world for virtually my whole career, actually. I started off working at a retailer, actually in the UK called Tesco’s. It was fantastic fun to work in that, working in the retail shelves, working in the store, and then working on their loyalty card programs. And then I progressed from there, working with data. The interesting thing in retailing, in particular, what makes data come alive, is that you can see if you change a price of a product today, you can see the reaction of how consumers buy that product tomorrow. And that quick speed turnaround was  phenomenal. And so Tesco launched the Tesco Club Card, which is their loyalty program, and we started to play with that and run promotional activities, which really got me hooked on the utilization of data.

And I became a firm believer since then, and ever since then, that companies, organizations and businesses, investors need to make decisions based on real information rather than gut reaction. And that got me hooked in the data world, and I’ve been there ever since. So I worked at a company called TNS, and then from there I joined Nielsen, I worked on the TV ratings business, worked on retail measurement businesses, and then I moved from there to work at CRM business called ORC, and then from ORC, I went to go and join the Engine Group, and then eventually sold parts of the Engine Group business to outside investors. And then, that got me into this and more into Trueflation, which was more about how do we set up a more accurate measurement or a more comprehensive measurement for that way, of inflation, right? I don’t think inflation measurement systems have been updated or evolved dramatically, and this was a big opportunity, and people were asking to build some sort of inflationary protected products, whether it was on Stable coin, whether it was on the bonds, on equities, on mutual funds.

And they were looking at saying, how is inflation affecting equities? And we started looking into this and started saying, actually, the founder, actually my brother Stefan Rust, and he was asking me, like, Oliver, you got all this data background, can you set this up for me? I’ve been working with him and the team ever since. It’s been a great ride so far.


Richard: 05:18

So how long has Truflation been around? Have you been with the team and the company since the get go, and if you can just describe what Truflation actually is, what the project entails, and what problem you guys are really trying to solve.

Oliver: 05:36

Yeah. So Truflation isn’t a new company, isn’t an old company, sorry. We’ve only started about, the company started about a year and a half ago, say in the summer. This summer would be our second year anniversary, so to speak. I joined in late, early last year, and the product is designed, Truflation is designed to provide a comprehensive and accurate measurement tool of inflation, but not only inflation, but also financial and other economic data sets. So we go beyond inflation for bespoke clients who want us to track other data sets.

So, for an example, we want to track movements of inflation. Why is the housing prices going down or going up? Is it because there’s more supply in the market? Is it because they’ve been discounted, ,or example? Is it because renters vacancy rates are lower? In the sort of food categories, what’s happening to commodity prices, what’s happening to production yields? Is wheat product, for example, wheat production being imported, is it being produced locally? Is it affected by weather conditions? How much sun, precipitation? All that affects the productions, and therefore, in the long run, prices.

And also, how do we look at things like PCE, like personal consumption expenditures, how do we look at manufacturing prices, right, and all these other aspects. We’re trying to get more into and expand the portfolio that way. So that’s really what Truflation does, is providing a new form of comprehensive measurement of financial and economic data.
And inflation, what is the primary element in the beginning. And since then, we’ve been evolving that.

As for Truflation, what makes it unique? I think we built a system on three fundamental approaches, to measure any product, not only inflationary measurement, but any inflationary product or any product that we release. It’s based on having a comprehensive data partner list. So, I don’t believe and we don’t believe in having one methodology or one approach to track prices in the marketplace. We have multiple different approaches, and we have multiple different data partners as a result of that.

So we’re operating with more than nearly up to 40 different data partners that we’re collaborating with to measure the US. inflationary market. So that’s one thing. You have a breadth of data, and that’s important because every data source or every data application has an inherent flaw in it, right? Data collection and data measurement is great if it’s done comprehensively, but there’s always a flaw in anything, right? Nothing’s perfect. And so we’re trying to minimize the imperfections with having multiple different data sets in there and different methodologies, different providers. And that gives us that breadth.

And then also multiple different data sources. We’re tracking, so let’s say in the food category, we have about five different or six different data sources that we’re working with in the food category. And that makes a massive difference because then we can see trends from different data sets. So that’s the first element.

I think the second element that makes Truflation unique is this ability for us to have more comprehensiveness of data, the depths of data. So we are tracking about, not far off, somewhere between, about 18 million items. We’re tracking items of goods and services and their price movements over time. And that is a massive comprehensive detail.

So we don’t need to model certain additional things. So if you look at the food category, for example, we’re tracking over in the food category, I think we’re nearly up at now nine or 8 million different food items, that we’re tracking on a daily basis. And so what does that mean, is that we’re, our measurement is reflective of consumer behavior. So imagine you’re going into a supermarket and you go and buy, or in a grocery store, and you buy, I don’t know, a can of Coke today, and tomorrow you might be like, well, actually, I’m going to buy, instead of buying a 500 ml bottle of can of Coke, I’m going to buy a 330 ml bottle. So I use metric terms. So I apologize. And we don’t need to adjust for those type of things because it’s an evolution of consumer behavior. You might be buying a filet steak, and now you jump it down to a flank steak, for example.

We’re reflective of those price movements in the marketplace.

Mike: 10:15

So you’re aware of the substitution effect?

Oliver: 10:20

Correct, and the substitution effect, the downgrading effect, and…

Richard: 10:25

Also, how hedonic adjustments, do you capture those as well?

Oliver: 10:33

Yeah, 100%. So we don’t need to do those hedonic adjustments as the BLS does, or other organizations do, because you have this wide comprehensiveness of data sets.

Richard: 10:44

Could you please explain to folks what those hedonic adjustments actually are. Maybe you want to explain what that is, Oliver, and how you guys go about, why you guys don’t actually have to address it explicitly.

Oliver: 10:56

Yeah. So hedonic adjustments are normally done to reflect substitution changes, whether it’s in the manufacturing of the product or in the price of the product itself.
So in apparel, if you think of it this way, if you go to an apparel retailer and you go into any store, and they might have changed the material of the product, that would generally reflect a change of price of that product. Not always. We can get back into that later on, but that adjustment then would affect a price movement. And if you are not tracking, and this is where you go for selective items when you do price checking, you go and check, select a very bespoke product, and if that product has had a change of of material, for example, they have to model out what that change is over time. So they don’t have that same price, that product a year ago, but they have the product today, and they have to manually, they have to create a model to adjust for that price, that substitution effect.

When you’re dealing with the data volume that we’re dealing with, we don’t have to deal with that substitution modeling, because we’re seeing people evolve and buying other prices, normally. So it’s already built into into our data set, and that removes an additional variability into the data, right? It’s an additional factor that could… now, in certain categories, that factor is minimal, but in other categories, it can be quite significant.

Mike: 12:26

You were talking about depth and breadth, and we did kind of sidetrack you there, but I wondered if you wanted to sort of finish that. That was our fault. Bad hosts. But no, if you had a little continuation, because I’m not sure you were done there.

Oliver: 12:40

No, just the third one is just like we wanted to get this thing out daily, right? And that’s what we do. If you go and look at the web page,, you can have a look at it. We report the stuff daily. And for us, that means we’re about 30 days, if not 45 days ahead of where, for example, the BLS reports. And on certain categories, like on housing, we’re almost, like nine months ahead of where they are, just because the nature of how they create the housing data.

So, yeah, it’s the recency of the data and the frequency of updates. It’s the depth and then the breadth of the data set that gives us a pretty unique proposition.

Mike: 13:15

It’s pretty amazing. I don’t know if you want to, Richard, pop up the actual website, because just the chart itself is pretty cool. I don’t know if we want to share that for a minute and just, kind of show it? Let’s do it. I don’t think a lot of people would be very familiar with it, but yeah, I find it interesting.

Oliver: 13:40

No, it’s a fun site. I mean, look, if anyone’s got any feedback on the website and any ideas, suggestions, funnel them through, right? We’re trying to adjust the website on a far more frequently basis. You’ve probably seen already a change what we do. You’re going to start seeing minor changes every coming out every week or so, and that’s going to be as a result of people’s feedback and interaction. So bring it on.

So yeah, this is where you see the, this is our home page and you can go into seeing what the US Truflation rate is. You can toggle between the markets there, and you’re looking at the long term trend of what Trueflation measures, the inflationary market there. And of course if you go down into the web page you can look at the different categories that we have.

So to make up Truflation we have these twelve categories, and each of these categories represent a portion of household expenditure, right? And so I think you got it there on housing. And so if you go down in housing you’re like, okay, well housing represents now 15% of the total household expenditure, and then you can look at the trend line of housing. So you can look at housing and then you’re like, well actually, I would love to, that’s why, that was food you’re looking at. But if you click on the housing tab exactly, you can see now, if you go down the chart, you’re starting to see, well not only seeing housing, but you’re also seeing, well, what’s the impact of rented housing and owned housing, right? And you can see the variability of those two.

And then the next evolution, which is coming online in the next couple of weeks, you’re going to look at the drivers of that, and what causes the inflation of the housing movement? Is it supply? Is it vacancy rates in the rental market? What’s population migration movement around the US? So normally, typically the migration movement has opened up space for housing prices to evolve.

So all this data set that we have, we’re just trying to get it in a nice easy graphical format to allow people to digest. But yeah, we look at this stuff obviously every day and it gets updated every day. And that’s why this daily need and the speed and recency is a critical factor for us.

Mike: 15:39

It does for a moment – go ahead.

Richard: 15:45

Yeah, no, I was just curious because one of the criticisms that have recently been leveled at CPI and the way that inflation is calculated, particularly in the US, has been around housing and owners equivalent rent and a lot of the aspects of CPI that are not so much measured as they are inferred or estimated by different sources of information. So how does your particular way of looking at housing differ from CPI on that specifically? And then I’d like to ask more broadly about CPI itself, but just to dwell on housing for a moment.

Oliver: 16:23

Yeah, it’s an interesting question. I much appreciate it because I think that’s a big debate, right? And I’ll be very transparent and clear on this. Measuring housing, if you’re measuring food products in a grocery store, it’s actually relatively easy to measure. Measuring housing prices and the movement of prices is a bit more complex, significantly more complex. So it’s a lot harder to do.

And I guess the way the BLS does it is, what they do is they interview 16,000 households over six months. So work that out roughly around three, and 2000 – 3000 households every month. And within that they’re then taking what they call the renters’ equivalent. So they say the renters’, what do you pay for rent in your property? And then they ask them if you’re owning the property, what other features exist in that property, and where is your property located?

And so based on that information, they model out the owner’s equivalent. So they say, okay, because your place is, you own in this location, and we have a renter person living in this location, the price differential should be an increase of 20%. But because it’s your own, you probably have it in a better condition. So we’ll add another 5%. Because it’s got four bedrooms and not one bedroom or two bedrooms, we’ll add this percentage. So they model out the trends and the price points.

Now, that’s great. That’s one way of doing that, is it will highlight to you the level differences between rented and owned, but it doesn’t necessarily reflect owned property and the prices of owned property.

Now, you could also then argue, well, if you own a property, relying on housing prices is not the most ideal scenario either. And so this is where, I was mentioning earlier the breadth of data sets and different methodologies, matters. Because if you’re paying a mortgage on a property and you’ve taken a 30 year loan out, and you only have two years left on your mortgage, the amount that you’re paying back is significantly less than what a rent equivalent would be, right?

And I think that opens up a whole lot of additional income to a household to spend on other things. For us, we look at, not only do we look at the housing prices, we also look at mortgage rates and how much the mortgage rates are evolving, in terms of the price movement of mortgage rates, and what is the total mortgage outstanding. In other words, household debt for mortgages. And we also look at the actual rent, actual, we add on top of that surveys where we plug into that and say, how much do you pay on your mortgage and how much is remaining?

And on top of that, we also have tax information, right? So how much are you paying in your, you know, in tax for owning a property and how much is your tax in your environment that you’re living in. And then finally, there’s also other elements that include into maintaining your property, whether it’s household content insurance, whether it’s upgrades you’ve done, all this type of stuff.
So we have that as our comprehensive measurement for owners, or owned property, rather than using, modeling out the pricing trends of rented on owned. And if you model out the pricing trends of rented on owned, they become very consistent. And it’s eerie how aligned the BLS numbers are in terms of trend lines, and the level isn’t too much different either. And so that’s where for us, at Truflation, we’re seeing alignment of trends, yes, but the levels are slightly different, but they’re not 100% aligned, right? They do vary quite a bit, and timings of issues varies. And what’s worrying us right now is now the property market, the pricing markets of owned property is going up, right? We’re starting to start seeing an increase again, versus the BLS is around nine months behind us. And so they’re going to probably start seeing a decline in their numbers now. But we’re starting to see an increase now again.

Mike: 20:46

Sorry. Yeah, the inflation was transitory. Oh, it’s not transitory. Oh. Now it’s abating. But it seems like they’re about six to nine months behind.

Richard: 20:59

And whose inflation are we talking about here? Right. Really? Which brings me to sort of another question following up on this. So the BLS surveys a certain fixed set of cities that they believe are representative of the broader basket of consumptions of the main or the median individual in the US, or however they state it. Do you guys focus on the same cities? Do you have more cities, do you have different cities. How does your approach to data gathering, in addition to triangulating across multiple different sources for a single data point, how does your approach to that data gathering differentiate itself from BLS?

Oliver: 21:39

Yeah, so the BLS track, I think it’s about 60,000, memory serves me correct, somewhere around 60 to 80,000 items, they track on of goods and services on a monthly basis. I don’t have the geographical spread of how that 80,000 works, but the way we do it is if we’re tracking, let’s say, food prices, we’re working with data aggregators as well as companies like Nielsen, like IRI, also retailers themselves that are nationally representative. And so when we receive the price points from all their stores, we are representing that pricing in our numbers. So wherever there is a store, for example, wherever there’s a Walmart, whether there’s a Safeway, wherever there’s a Kroger, those prices are reflected, wherever those stores are in our data.

So we’re not looking at geographical spreads. We’re looking at much more representing the country at large and making sure that the data that we absorb into the system is reflective of that. Now, there is in certain categories where logic, there is inherently if you look at, for example, the car market, if you’re looking at the used and new car purchase markets, there are differences of the types of cars people buy in certain areas, right? And funny enough, when we look at purchasing behavior, and this can get quite an interesting discussion, so I apologize for being really nerdy and boring about this, but it’s actually interesting is that, there’s a lot more consistency and alignment in the expenditure by income levels, than by geographical location. So if you happen to live in, I’m going to take the polar opposites. Let’s say you live in San Francisco and you’re on this income and you have a person living in New York on the same level of income, their purchasing behaviors are relatively similar.

And so we need to reflect, ensure that we’re representing all spectrums of income across the system. And that was a bit of an eye opener when we started looking at the data across the country, especially when we started building up our weighting allocations for each of the household, each of the household categories.

Richard: 23:48

And so when you think about the people that are actually using Truflation right now, businesses, or maybe even asset managers, maybe you can describe some of the different profiles, some of the different use cases, real world use cases, and where you expect you guys are going to have the largest impact or some of the sectors and industries that would have the highest or the largest interest in sourcing data from you guys.

Oliver: 24:22

Yeah, interesting question. I think we were shocked, to be honest with you, about the variability and the application of our data sets. Was I personally, maybe my brother or others in the company maybe were different, but I certainly was shocked at the depth of potential different users.

So users, we’ve sort of bulked into three types of users that are interested in it. First of all is a typical retail investor that’s looking to look at the market movements and trying to predict that. You then have macro hedge funds. You’ve got financial institutions that are looking to either measure the macro levels and the movements of the macro levels, or the implications it has on equities, on products and prices, whether it’s commodity prices, whether that’s equities of manufacturing companies, or even when people are looking to create other products, based on our data sets.

And then there was a third set of audiences that we’ve come into, which is in audiences in the DFI, in the DFI world, in the crypto world about trying to create new products, are based on our data, right? So they’re looking at crypto coins that are using inflation as a protected measurement tool of the value of those dollars, of those coins coming through as well. So the variability of this has just been phenomenal. And that’s what’s been really interesting. The most interesting aspect now is we’re getting conversations where people are like, well, actually, can you tell me what the precipitation levels are going to be in certain counties of Arizona where they’re producing wheat production, that will give me an indication of how much production is going to be done for wheat, how it’s going to affect the wheat commodity prices in the US, versus import versus export. That’s then going to determine the prices of food, let’s say bread, rice on the grocery shelves, or when you go out for a meal, thinking the application of this data goes forward. That was mind blowing and it was really exciting to be working in that environment.

Richard: 26:45

So you mentioned an application of Truflation being something, I guess, of an Oracle within the blockchain space. Are you guys also used as an Oracle for those applications?

Oliver: 26:58

Yes, that’s correct, we are. We also use connect into Chainlink, and Chainlink uses the feeds. We feed our data through Chainlink and we use that decentralized network to distribute our data set, so to speak. And we’re increasingly driving more of that because the applications are significant in that environment, but not negating where the opportunity for us is across all our user bases, and trying to find the optimal distribution ways to get our information out there.

Richard: 27:36

Yeah, interesting. I guess it begs the question, how much disruption can there actually come from this? I mean, obviously with the inflation problem, not just in the US, but in much of the world, having manifested into this full-blown banking crisis right. The downstream effects of that, there’s definitely room for a new way of thinking about inflation, especially when you think about different baskets of consumptions and how CPI understates the cost of inflation in so many different areas, I guess. What are some of the larger findings that you guys have had in your measurements of inflation, particularly when it comes to, I guess, sectors like education or healthcare, which have been famous for being way ahead of the actual CPI itself. So have you guys had interesting revelations or discoveries as you triangulate the price increases in those sectors?

Oliver: 28:41

Yeah, I think that to be frankly, the biggest revelations that we always get questioned about and dug into, are the categories that are the most important to households in the US, or in any market, right. And so there’s the big household categories, which tend to revolve around transportation, ah, utility bills, or energy bills, as the, as the BLS calls it. For Truflation, we, we look at utilities and then it’s obviously the housing impact and then obviously food, right? And it’s funny, when we started looking at the, we started reporting the seasonal effects of food products, we noticed on food alone, we started noticing spikes of premiumization happening at certain times of the year, and especially like over Christmas and New Year.

You saw alcohol just going through the roof, right? People was like, okay, I got to go and buy the premium products and then go buy some champagne, or whatever it was. And then suddenly you see January come into play and they’re like, I’m going on a diet, I’m giving up, I’m giving up alcohol for a month and going dry.

So that part is reassuringly fun to look at and entertaining. And I think it’s a reflection of our lives, right? We do want to make those adjustments. But on some of the other categories, look, I think gasoline prices, how the impact of Federal Reserves back in, what was it, back in June or June of last year, or I think it was before that, May of last year, petrol or gas prices were at an all time high and that came down because the Federal Reserves got released. And now obviously the crude oil price has come down. You can argue now it’s going to happen with the latest releases of supply restrictions by OPEC, but you can really start to see the impact it generates and that started to reduce prices and then you start to see people use their cars more frequently.

This notion of going back home after the pandemic and people working back in offices again, all that’s starting to change the trend lines that we’re seeing. But overall, like, inflation is, I believe inflation is, that it is here to stay. It’s not a transitory moment that we’re going through. I think the last decade that we’ve been in, we’ve had, haven’t had, really had inflation, and now we have. And I don’t necessarily think the inflation levels that we’re at the moment are high, but I think having a 1%, 2% inflation is also not such a great thing.

And I think what we’re trying to do is, is say, trying to be reflective of, you’re seeing right now manufacturers or organizations posting quite significant profits in their businesses. Like, you look at ConAgra, you look at Coke, you look at, these guys have produced actually massive profit margins and they’ve been taking price hikes. The big question is, is that price hikes too much, right? Are they taking advantage of what’s happened in the past where they haven’t been able to increase price and taking advantage of the inflationary environment, to increase prices further? I don’t know. And I think this raises questions like that, which I’m all up for. And if you’re the Fed, you’re looking at interest rate deployments, what data set are you using? Are you using the BLS/CPI measurement that’s potentially outdated, to predict what’s going to happen with the inflation rate, the interest rates? And therefore, how does that impact, as we see in the banking infrastructure, in the banking world? So I think there’s a lot of consequential impacts. And I’m not saying that our data has to be the be all and end all, but I certainly think Truflation has a role to play in modernizing that and being an input factor in some of those decision making processes that were not there in the past.

Richard: 32:49

As your methodology evolves, and as we were talking offline earlier, that you guys are somewhat converging, a little bit closer to what the Fed’s measurement for inflation currently sits at. There’s an argument to be made that maybe we want the true value of inflation, the Truflation, whatever measurement you guys are coming up with, to remain somewhat independent of the Fed’s measure, to perhaps offer an alternative viewpoint.

So how do you deal with that? The evolution of the methodology, the integrity of the data, and to what extent not allowing there to be this reflexive loop of, while they’re trying to address the problem of potential CPI mismeasurement being caught by that loop of perhaps converging onto their mis-measurements? I mean, I don’t know if that made sense, but trying to retain some independence of your methodology while still serving as a valid counterpoint to official statistics?

Oliver: 33:56

Yes, I think, in my view, there are three points to answer that question. The first one is we’re not a government funded organization. So inherently we have that independence, right? We are an individual company that’s trying to provide a measurement tool that’s more accurate in the marketplace. So we’re not influenced, whether if you have an extreme belief that some of these things are influencing government decisions, it can be. But we are not owned or not biased by any governmental viewpoints on our data set.
So I think that’s for us, that certainly is a significant proportion.

And I think second of all is our data and our measurement, is that going to be updated, and will that evolve over time? 100%. We’re adding in new data partners every month.
We’re looking at ways to improve our methodology. So that’s always going to happen and that’s going to affect, every time we do an update, we communicate what we’re updating, why we’re updating it, and what the impact is on the data set and the historical data sets.
And I think the third thing is, we’re transparent as much as we possibly can be, right, in every angle. People come in and ask us for questions, we’re happy to divulge it and they’re like, okay, we’ve got to divulge more transparency. We got to go deeper into the data so people can play with it themselves and they can make their own decisions on it. My view, as long as we stay firm to our belief that we’re connected with our community that ask us questions, and we’re happy to answer any questions that come in and we’re transparent, we’re independent, and we are constantly finding ways to upgrade and keeping our data modern, I think we will have that independent viewpoint every time.

And engaging with people like the Fed as an alternative data provider, for them to make their decisions on, rather than relying only on the BLS or on the PC, on the personal consumption expenditure data sets, that’s important. And so that’s what we’re trying to do, trying to be part of that decision making processes.

Richard: 36:19

Right. A seat at the table, part of the conversation offering a different viewpoint. It’s curious. You guys posted something today on your Twitter feed, a comparison of CPI data in the US versus the UK, and one is the true order of magnitude of the other, right? You guys are measuring US CPI, currently at 4.23, whereas the US government is reporting it around 6%, whereas the UK Truflation measurement right now sits at 16.23, well above the already high 10.4 that the UK government has reported.

So what accounts for that? Obviously, the delta between the two countries, but also the delta between how each government is measuring their data, that one also was quite astonishing.

Oliver: 37:20

Yeah, I love these data comparisons, when you compare each individual market. You’re like, oh my God, how does that market work? So, every time we’re looking at a new market, so actually we’re exploring a number of new markets right now, and it’s fun to look at some of these data sets. So anyway, looking at the UK and the comparison with the US, let me explain it this way first. Rather than looking at the numbers, our viewpoint has always been is, a lot of our clients want to use our data, especially in the financial institutions world. They want to use our data to look at ways of how the market is going to move. And therefore it’s predicting what the BLS does, or the equivalent of the BLS in the UK, which is the ONS. And we believe, because the way the BLS works and the government statistics work, we believe our data is a far more comprehensive and far more realistic and timely measurement tool, versus what they’re reporting.

And it’s not to say what they’re reporting is wrong, but it’s just a different level and so therefore the levels don’t necessarily bother me. I mean, it’s a question about what is included in them and what the adjustments are. So, for example, in the UK, we have a full measurement tool that includes everything, so we’re including interest rates. As the interest rates are going up, the effect, that has on mortgage repayments.

It’s not like in the US where you can get a 15 year, 30 year fixed mortgages. In the UK you’re looking at far shorter periods of time, where it’s three years fixed terms and then the variable rate comes in. And with the interest rates right now, the effect of variable rates on mortgage repayments is significant. And that’s where a lot of the differences happen in the UK.

I think in the UK there’s also an additional factor that comes into play. Food prices are significantly higher than in the US or have been increasing faster than the US, and that’s because the UK has gone under Brexit, right? And that Brexit impact on importation of food, the cost of energy costs, in this case, gas costs in Europe have increased because it’s not part of the EU anymore and a lot more is imported. So all these cost factors were not expected. And then if the UK has shortages in vegetables, in eggs, and a lot of that is now being imported, and so therefore that adds additional cost because it has import duties, right? And so all these impacts are different from what represents, for example, in the US where it doesn’t have those factors, or to a lesser extent, let me put it that way.

So that’s the sort of differences in the levels in some of the market, between those two markets and then versus the government rates, it’s more of a question of the nuances in how they measure it, right? And so we’re looking always at having the most comprehensive measurement and in the UK we also include equivalent of what we call poll tax or council tax, and that that’s not measured in the ONS results, and so forth.
So, it’s a very comprehensive measurement tool that exists and that allows us to have a different reading versus what the government trends are.

Mike: 40:35

The infrastructure…

Richard: 40:37

Sorry, go ahead.

Mike: 40:40

No, it’s interesting to me the structural differences of US mortgages and housing and the duration of them, and how you can lock in and what you might do. Trading up versus, or trading down versus that whole infrastructure that you gave in the UK, would have a dramatically different input on both what the weight is from that particular sector, how it would function, how quickly it’s going to impact the number.

And I’m sure housing is the one that’s kind of obvious, but I’m sure there’s just a number of other ones that we haven’t even contemplated, which are in the millions of data points, which I think is the idea that you’ve got a lot of ensembling of a lot of data, and thus sourcing a better signal to noise ratio as you’re pulling this information from the data on each specific area.

Oliver: 41:28

Yeah, no, 100%. I think what’s actually really interesting is if you start looking at the different behaviors and the expenditure pattern. So in the UK, the amount that people spend on health, for example, is significantly lower than what people pay in the US. And that’s because they have a National Health Service proposition, right. And it’s for everybody. And they don’t really do privatized health there, or Medicaid is for a certain income level and then there’s Medi-aid. And so those variable levels means in the UK, there are different expenditure levels, right? So I think, if memory serves me correct, health in the UK represents less than around 1% of total expenditure of a household versus in the US it’s something like 6%, I think.

So that comparison is one, and then the other one, which was really flipped the opposite way, which was this idea of recreation and culture. In the UK, it’s so high because everything is… in the US, that you can go and have recreation and cultural events that are sponsored by the government, sponsored by museums, sponsored by organizations, the National Park Surveys, you can go into national parks. In the UK, it’s a different mode, and it’s fascinating to see those differences that exist and consequently those price increases, right?

And you can imagine the increase of services, of entertainment, the ticket prices of those things going skyrocket high right now. Yeah, it’s actually quite interesting. I mean, I love this stuff, but you look at things like, what’s the impact of Netflix and Disney channels, that they’ve now started reducing their prices to allow for advertising to come in? But you saw that during the last two years, they had a surge in volume of people buying their products, which probably wasn’t there as much as it was there in the past. And in the UK, they have other ones, the equivalent of seeing those price surges go across the board.

So the local dynamics of a market does have a significant role to play when we look at each of these future markets. And we’ve been very, at Truflation,, we’ve been very focused on launching a market that we have some, at least starting off, where we have some sort of knowledge base of how these markets operate. But, yeah, working across Europe, we’re now looking at expanding into other countries in Europe, like the bigger economies, like France, Germany.

But then people are also asking us to go, hey, can you go across into Japan? Can you measure Australia? Can you measure Turkey? Can you measure, you know, Argentina? And so some of these markets we know, some of these markets we don’t know that well. And so we’re reliant on other people to help us and guide us in that process, which is what we’re working through right now.

Mike: 44:25

What are some of the criteria that go into, is it a highly cash oriented society where you’re not able to source sort of data formats that would be easily ingested? What are the markers of places where you would get much better indications versus where you would struggle? Maybe that’s a better question.

Oliver: 44:47

Yeah. First of all, the decisions of which markets we look into and explore further into is a result of the community, right, whether people on Twitter, people reach us out on our contact page and they’re asking us constantly saying, can you explore this market? So we go and look into them. So that’s obviously a key factor in our decision making process. The second one is a factor around, can we deliver something that’s useful to the market, right?

And if, for example, I’ll take an extreme case, in the case of Australia, inflation is only updated once a quarter, right? You can imagine everyone’s talking about once a month, and our recency is 30 days, we’re like, in Australia, it would be like 90 days, if not further, right? So that has another factor, right. And then, of course, what is inflation happening in those respective markets? If inflation in the market, let’s just say in Switzerland, where it’s hovering around 2%, now two to 3%, it’s probably less of an interest for the market to launch that, whereas in other markets, in Germany and Turkey, in Australia, Argentina, the inflation rates are quite high.

So you assume that as a market assessment of where interest is, and then there’s a secondary set of criteria that we look at is saying, how can we set up in that market? Do we have enough data sources? What are the opportunities there, now? Thankfully, in some of our categories, we have global partners, that allow us to launch. So, for example, in the food category, one of our data sources are Nielsen and they operate across the world, so we’re able to tap into their measurement systems.

But in other categories, that doesn’t exist, right? So therefore, we have to look at what, how can we source those data across? And and, you know, it’s not the big categories that are the the harder ones to find, it’s the smaller ones, right? So it’s like recreation, it’s like education, it’s health. Trying to find price movements in those categories, even in the most developed economies, is not easy. And that becomes a barrier then for what we look at. But we have a pretty good now, a pretty decent assessment sheets that we build up, and it’s a process protocol that we go through. And so we’ll know if we’re able to launch into a market within normally within two weeks, we know if we’re able to do it, right?

And then that gives us a quick gut reaction of whether we go, or no go.

Richard: 47:15

But to pull a thread on Mike’s earlier question about economies that are more based on cash transactions versus more digital transactions, I’m from Brazil originally, and so I know that there’s a huge informal economy in Brazil that is all cashless, that flies under the radar of government tax collection and other forms of statistics. So have you encountered, as you’re looking at different countries, to provide services for some of these hurdles and some of these difficulties in dealing with countries with large informal economies?

Oliver: 47:57

Yeah, let me phrase it this way. We have not launched a market yet where we faced those issues. So if I’m blunt, the answer is no. But am I aware of those circumstances, having worked in industries and across those markets, what the impact is? 100%. And actually, that’s what becomes the harder measurement, actually, for us to reflect in the marketplaces. When you start looking at Brazil, Argentina, Turkey, parts of Asia-Pacific, where there is this undercurrent, and people are afraid to report pricing because they don’t want to pay taxes on it, right?

But there is a real price movement in those environments and we’ve got to find a way how to protect it. And I remember, I did a lot of work in the past in Latin America and I’ll travel to Colombia and I’d go to some of these areas where people were forced to increase prices significantly, but it wasn’t being reported. And so, am I consciously aware of those circumstances? 100%. Do I have experience in it? Yes. But have we launched those markets in Truflation, not yet. And I would hesitate, I don’t want to get ahead of myself, but that is an additional complication in those markets that we have to deal with.

Richard: 49:26

At the risk of getting too deep into the weeds of the methodology, I’m just curious to circle back on the earlier question about US inflation, UK inflation, the way that you were comparing the two. I would imagine that in order to provide a CPI comparison, that you’re keeping those categories within CPI somewhat static, or proportional to what official CPI is, and then you’re triangulating on the different data points for those categories and subcategories independently. Is that accurate?

Oliver: 49:59

Yes. So we keep the category definition, the category definitions and the naming. And each of the twelve categories that we have, they are consistent in all our markets. So we do an annual update of tracking how much household expenditure has evolved in each of those twelve categories, every year, right? And we have that now, in I think. I would hazard a guess, if I were to say something like 40 different markets around the world, right? So we have that data, right? And it’s a big adjustment for us to have that, to access that data set, and then yes, within that data set, so the category definition does not change. Well, it might change over time, but very minuscule amounts. What you spend on housing is what you spend on housing, but we might sub-define it as more granular as we go along. But how much you spend on housing evolves year on year, right? And that’s what we look to. And then we look for data sources within each of those categories and that changes far more frequently. That changes, like every month we’re adding a new data partner into our systems, roughly.

Richard: 51:12

Yeah. I guess what I was trying to get at is the official US CPI, as measured by the BLS, will have a static or semi-static weighting for the different categories, that will somewhat shift over time. Do you guys do a comparison, holding their weights constant, and then have your own weights? How does it – changing between the weights of all? Because I assume there might be a criticism to the actual weights that they afford to the different categories, because they’re trying to represent a median household, whereas the variability between the different consumers is so large that some people will, housing will represent a third or maybe 40% of their expenditure, whereas for other people it might be much lower than that.

So that’s what I’m trying to get at. Are you guys perhaps providing an alternative vantage point as well when it comes to the weighting of the categories?

Oliver: 52:02

Interesting topic. Love it. You probably know, earlier this year the BLS in the case of the US, have updated their weighting methodology of each of the individual categories. And if you didn’t know what they’ve done is they’ve taken –  historically, they used to take, so let’s say we’re now 2023. They would take 2021 and 2020 data and then take the average of 2020 and 2021, and then apply that weighting into 2023. That’s roughly what they would do. And now they remove the averages of the two, and now they say, okay, in 2021, we’re applying those household expenditure categories onto the 2023 data set.

We’ve gone a bit more the opposite. We say, okay, we’re taking last year’s data and we update that into the system. So we’re now using 2022 household expenditure data onto 2023. So that’s one different area. We’re trying to get more recent and actually at Trueflation, we just launched our personal inflation calculator, where people can type in your own household expenditure and then seeing what impact inflation has on your household. And that helps us to predict a bit to see if we can get more real time on those weighting categories and those weighting elements. So we’re exploring that, but we’re trying to give something back to individuals so they can track inflation monitors for themselves. So, yes, that’s one element.

I think the second element is then the category definitions as well, right? And I think that’s where it becomes the challenge. In particular, when we look at the BLS, the way they merge and define housing and energy costs is slightly different the way we do it.
We’ve checked initially how our trend lines are going against the BLS, but at some point we’re like, well, why are we trying to measure ourselves against the BLS? The BLS should be measuring ourselves against us, right? Because we’re far more real time than they are.

Now, that’s obviously a biased viewpoint, but it is a question saying, we ask our clients, they’re saying, well, why do you need to base our most up to date and accurate information against a system and a measurement tool that’s potentially not as modern as the way we measure? I’m not saying it’s incorrect, I’m just saying it’s just not as modern and a way of operating, right? If you think about the BLS methodology, the way they do it, was predated, was all the way back to the First World War when they started tracking the system. And yes, have they updated it and created new categories –  yes. But the principles, the basic formed principles of how they do it, is the same.

And since then, we’ve had all these cars have been involved, we know how to drive around, we’ve now had an iPhone, we have the internet. All these things have changed. So we’re just like saying, well, let’s modernize the way we check it, so that adds an additional factor. So we have looked at applying our data against and trying to merge it back into the BLS category definitions and there is very strong alignment. And that’s where we know, for example, in the housing category, we’re nine months ahead of them. That’s how we get to those type of metrics, because we’re applying our data back into their systems as much as we possibly can with what we know and what we don’t know.

And then we use that data to predict where the BLS is going and where they’re going to announce it, at, what our clients want to do with that data. And then we have other companies that come in with us and say, hey, actually, can I use your Truflation data to predict what the BLS? Can I run my model? And you’re like, yeah, sure, go ahead. And so we have people using our data for those predictions as well, which is quite fun.

Richard: 55:55

Very interesting. What are some of the main criticisms that get leveled against you guys? I mean, if there is anything what have been some of the pushbacks or drawbacks? Obviously show me the incentives and I’ll show you the outcomes I can expect. There’s going to be some groups that are going to necessarily be pushing back against a new disruptor. But in general, what have been some of the valid criticisms that you have faced and what are some of your responses to them?

Oliver: 56:20

Yeah, we’re not perfect, right? And I want to state that as much as I’m a profuse and profound advocate of what Truflation does and obviously I’m biased, but I’m acknowledging that we’re not perfect and we never will be, right? And as long as we have that mindset, means that we’re open to feedback and we’re open to criticism, because we have to be. And it’s a question more about how we adjust to that criticism. So I think there are three buckets where we have received criticism and where we’re focused on.

One is the granularity of data, right? So we’re like, okay, you’re looking at food. Okay, great, I want more data, right? I can’t go deeper. Tell me what grocery food prices are like versus food at restaurants. Within grocery stores, tell me what’s happening with dairy, what’s happening with fresh food, what’s happening with eggs, what’s happening with all these other categories. And that depth of the data set has been a criticism. So the way we adjust with that is adding more and more data partners in, which allows us to go more and more granular. And that’s what we are, one of our key focus areas is to continuously do, and hence why that notion where we showed earlier on the website, where you’re looking at housing, now you’re seeing owned versus rented, and soon you’re going to see what is owned, the breakdown of owned, right? And that those are the types of things that we’re looking at.

I think the other feedback and criticism that we get is, well, it’s great to have that data set, but can you help explain me why the market is moving like this, right? On occasions, you have different trends, what the BLS shows. So how do I know that your data is a much more accurate measurement of what the BLS is showing? Just showing me the numbers is not enough, right? And that’s where we’ve been collecting other data sets.

So in the housing market, we’re looking at now like, well, what’s the supply in the housing market, coming up? What’s labor movement would cause adjustments to housing prices? What’s vacancy rates? What is the prices of houses and the discounts that people are offering, so what we call our secondary or inflationary driver data sets. And that then says, okay, well, that’s aligning to your data set. So, okay, that explains the movement. And so we come more comfortable with that.

And I think then the third aspect of it is the transparency. So who are data partners, right? So it’s one thing saying, okay, well, you have, let’s say, in the food category, it’s trending this, but who’s behind that? What’s behind that, so to speak, that black box and iron curtain? So we’re becoming much more transparent in that process and divulging all that stuff. And so we have a methodology section on the web page now that people can go in there and look at the detailed contributors of each of our data streams and what the value is and so forth.

So those have been the big ones. I mean, are there other ones? Like our website? We need to upgrade it. We want more historical viewpoints. And we’re like, well, yeah, you can, but the website gives you one year. If you want more, you can come and talk to us, or you can purchase more on the website itself. So there are smaller ones, but I’d say those are the big ones. It’s more transparency, it’s getting more data partners, and it’s finding, is more explanation of the data sets.

Mike: 59:48

That sounds like opportunities more than anything.

Richard: 59:55

Yeah. Data analytics on top of data gathering and data aggregation. You guys are actually going one level, two levels deeper, and actually providing some analytics as to, what are some of the other categories or other sub-items that we should be investigating to understand the trends, to see where the macro picture aligns.

Oliver: 01:00:10

Yeah. And also, I think, imagine, well, we’re already starting to put all the sort of automated analytics, sort of AI on top of our data set to be able to predict everything. And, you know, you know, if you imagine you put a full powered ChatGPT equivalent on our data set and you’d go nuts, right? Kind of scary for me. So a much more controlled approach is where we are at the moment. We’re not as advanced as that, but that’s where we’re going, right, and that’s adding a lot of value to start seeing permutations of seeing what if models that people want to run.

Mike: 01:00:44

Yeah. Now, you said something at the very outset of when we started chatting. I’m wondering if you can elaborate on it. You mentioned inflation is here and it feels like it’s here more to stay, rather than moving back to this sort of 2% land of perfection. And so is there anything that you can point to that feeds that intuition that you’re having? I mean, you’re in this all the time. You get your fingers in a whole bunch of different data sets, a whole bunch of different countries. So, I’m biased in the way you say that. So I’m interested in what is it that your intuition is that you can articulate, maybe that gives you that feeling that, kind of this is going to be stickier inflation for a longer term? This is not a cyclical thing, this is a secular thing.

Oliver: 01:01:33

You, so you guys, you have an opinion on this? One of the big questions that we’re saying is, what’s driving the price increases at the moment in the past two years, right.
And you could argue there’s been supply driven price increases, across the whole sectors. Have those been alleviated recently? To a certain degree, yes, not in all categories but there’s certainly a supply driven price increases and you’re seeing that when you look at PPI, when you look at producers’ price index increases, you’re seeing that on the cost of goods that are sold out.

Now since then, some of the commodity prices have come down, especially on precious metals. If you look at transportation, what is copper prices, what’s steel prices, all this type of stuff, what’s crude oil prices?

So that is one aspect, but I think then there’s a secondary aspect. Inflation, what a consumer pays for goods and services, also includes labor costs, it includes transportation, includes production costs and those have not abated yet, right? And so if you look at wage increases in the US, they’re still hovering around 6%. You look at petrol prices, gas prices, they’ve still come down a bit, but not enough to offset that. And you also have other factors in particular in markets, US to maybe to a lesser degree, but there’s tariffs, import duty, import tariffs that exists, so from our point of view, if you look at certain product categories, you can sort of sense that pricing is not going to go away.

So yes, we might have diminished the supply side element of it, but the demand side and the economic outfactors of labor, transportation costs, they are still very much prevalent in our daily life. So as a result of that, I don’t think it’s going to go away as much as what people think it is and I think it’s going to hover around this 3% to 4%, right and food prices, with the war we have in Ukraine, that’s impacted commodity prices significantly. Has that been alleviated? Will that continue? OPEC prices to maintain supply. Recently, that announcement is going to affect energy costs. What’s that consequential impact going to be on gas prices, on logistical costs? All these type of things I think just go and reinforce the need for, reinforce the desire that inflation is here to stay.

And I think also then you look at corporate, right? You look at the number of profits that people are paying, how much labor shortages are, how much their talent that people are paying for talent. Now, we’ve obviously got the issues of recent announcements with unemployments and layoffs. But the bigger question now, in my view, is like saying, well, I think prices are here to stay. Are we going to see a soft landing or is this going to turn into recession? Is the 2% inflation target by the Fed, this arbitrary 2% number, is that the best number or should there be a higher number? Is 4% a good number. Is 3% a good number? I’m not saying I know what the answer is, but this arbitrary 2% target, I don’t know where that comes from, and how do you justify that is the most optimal inflation number to have? Because higher inflation means people get paid more, purchasing power becomes stronger. If you can manage the gap between wage inflation with that of their expenditure behaviors.

Richard: 01:05

Not to mention the axiom of Goodheart’s Law, that when a measurement becomes a target, it ceases to be a good measurement. Right. So the reflexivity that exists and people starting to anchor toward certain numbers and trying to re engineer certain outcomes and re-jig the weights of basket consumptions, there’s so many angles to this inflation problem. I mean, the realization that this is the easiest way out of a debt overhang, right? It’s a soft debt jubilee, right? You get to inflate the nominal value of your debt away over time.

We obviously have some version of that same bias, this perception that things are different than what they were in the previous decade, not least of which, it’s not just monetary policy expanding QE, once you start to understand that quantitative easing keeps the money trapped into the financial system, and that’s not high powered money, and that’s not money in your pocket, right? That’s money that stays in to inflate financial assets. But we’ve had a tsunami of fiscal policies, fiscal expansion that becomes hard cash in people’s pockets that then actually expands the monetary base that chases these scarce goods at a time when we have some degree of deglobalization, right?

The realization that cooperation is becoming more competition, and we’ve all but removed perhaps the single largest producer of commodities on the planet from that global supply chain, or at least for the west. Not that they aren’t guilty of every charge that has been leveled against them, but the fact of the matter is now, there’s going to be this fracturing of the global order to some degree, and some countries are going to be able to benefit from those commodities, others are not.

So there’s bound to be localized inflation and different trends within that inflationary process, and it’s not going to be homogeneous across different countries.

Oliver: 01:07:24

Interesting, the other flip side of it is you also got the welfare benefit. Who’s going to pay for this welfare bill that people, that the governments have got to fund for, right? In the US, in the case, you’ve not only got to pay for the welfare benefits, you also got to start also paying for Medicaid. And the outstanding amount that’s needed to pay for that, given the population structure, is going to add another complexity on top of this. We’ve gotta have, I don’t know what the what the outcome is going to be, but I think that’s something we got to start looking into.

Mike: 01:07:59

You’ve provided a tool on the site, too. If you’ve got a different consumption basket, an individual can go in and sort of think of their consumption basket and understand where they stand with respect to that. Because these items that you refer to are obviously affecting a, more so affecting the aging population and the structure of the population pyramid. It presents some very interesting implications for longer term persistent inflation.

It’s interesting. I think wage inflation would be great generally, if we can get some wage inflation going, people can pay for the other stuff, anyway.

Oliver: 01:08:35

Yeah, I agree. The question really, of course, is how much is wage inflation? What’s a good wage inflation number, right?

Mike: 01:08:44

That’s the amount that keeps the revolt from occurring. The amount that prevents civil war. That’s probably something like that.

Richard: -1:08:55

It’s actually probably why it’s even moving in the first place, because for arguably four decades, the pendulum swung so far towards capital and away from labor that this is somewhat of a torches and pitchfork movement, dragging that pendulum kicking and screaming a little bit farther towards the middle. I wouldn’t say it’s necessarily swung to the other end of the spectrum, necessarily, but it’s definitely moved us slightly away from, or at least the zeitgeist seems to be shifting in that direction, right? That Overton window of policies which is kind of made visible when you think about Democrats and Republicans, or Republicans are no longer the party of fiscal prudence and tight purse. It’s, one of the few bipartisan things or topics in the US, aside from antagonism towards China, is fiscal populism. Right. It’s the fact that both parties seem really to have a strong appetite to spend right now because they can feel that palpable populist discontent, and then it’s only a matter of time. I guess it was only a matter of time until that became a factor again.

Richard: 01:10:01

I’d be curious, Oliver, to learn a little bit more about, aside from the public stuff that you guys divulge and the tool that you can your own basket of consumptions, what are some of the other bespoke mandates, insofar as you can share with us that some of your clients ask you guys to track? I’d be curious, what are some of the more obscure or esoteric baskets that you guys are tracking?

Oliver: 01:10:41

Yeah, I don’t think it’s necessarily any, I think it’s more the types of indexes that we generate for clients, right? I think that’s more the differences. We’ve had a recent one which has looked at if people are trying to look at wind power, and the number of wind impact on renewable energy sources and the effect, what that impact has on those equities and stocks, right? And so they’re looking at wind energy or wind supply off the coastal, off the coastal regions of certain geographical areas right. Whether it’s in Europe or it’s North America. And they’re like, okay, and these companies have wind power generators, and how much can they store and how much will that face them into the grid in these local markets? So that’s certainly been one area that we’ve been looking into and trying to help people to, I’ll look into that.

But yeah, a lot of stuff that you don’t see on the website that we do, is a lot of prediction analytics on the data set, which clients are interested in. We also doa  one-on-one webinar where we look through people, who take people through the data and what the impact is on the Fed increases. For example, what’s the impact, our viewpoints on long- term versus short-term inflationary pressures? What’s the impact that might have on the BLS? We write out reports on a monthly basis. We take deep dives into certain categories, and that adds a lot of value to our clients that we funnel through there.

Now, again, different sets of clients use the data differently. So, some clients say, can I just have the data, and I’ll run my own models? Other people want the information. Other people use the information to create products that they want to build for themselves.

So it’s been a pretty interesting ride. But those are the differences of services. So you can either look at the data itself and then amalgamating the data. where we’re going to, hopefully I alluded to it a bit, but the future direction is to say, well, how do we start creating? Allowing individuals to create their own indexes on our data. So create like, what I like to call a studio, what we call a studio, right? Where you can go in there and start merging different data sets into indexes together. And that certainly is of interest, right? And that’s where we’re going, adding more data sets, partnering up with, for example, with a company called Hyphen who provide us with greenhouse gas emission data. And so people are like, oh, I want to know what methane is. Methane production is around the world, and what’s that impacting on this and this and this? So those type of stuff is where we’re going.

We’re looking at imagery data. And if you think about image data, it’s no longer a quadrant of data set, time based series data sets. You’re now looking at image data sets, and how do you use that image data and dissect that to allow for how many cars are turning up in a shopping mall, or how many boats are turning up at the harbor ports in LA versus New York and stuff like that, for import duty products?

So that’s certainly where we’re looking at doing and combining those data sets and those services that people are asking for, right?

Richard: 01:13:57

And maybe a final question to you is how has your thinking on inflation evolved since you’ve come across the methodologies that underpin true inflation? How has that made your thinking around inflation itself evolve? And your decisions for your own investment allocation, has they materially changed as you think about perhaps what true inflation actually is by the way you guys measure it?

Oliver: 01:14:26
Yeah, we’ve got to believe in our product, right? And so if we believe in our product, which we do, we make investment decisions on that as well, right? But it’s personal. We stand by where we are, and I think some of where do we see long term investments going and we see alignment on those on certain investment decisions, I think the Fed, we believe that the Fed is going to potentially go look at one more 25 basis hike increase maybe, and that would be it. I think we will not see drop, which the market seems to be predicting in at the moment, that the interest rates are going to drop quite significantly before the end of the year. I just don’t see that happening unless, of course, we get into a recession and then therefore they need to reverse their trends dramatically. But, if inflation is here to stay, I think interest rates will slowly come down.

I don’t think it’s going to be a dramatic U-turn. If they need to do a dramatic U-turn, they can. But that then affects what equities you build into and which stocks you buy. And so for investment decision, I’m not going to give out investment advice, not what we do, but we firmly believe the direction of what we’re doing and what the impact it has. But I think inflation is here to stay, and I think then the impact on interest rates is going to be quite an interesting viewpoint for the next two or three Fed meetings or the FOMC meetings that they have in one in May and then the next one in June. I think they’re going to be quite interesting outcomes. But a lot of that’s going to be determined again, what the BLS is going to predict. We believe we need to have that seat at that table to be able to give them a trend data set that they’re all not only relying on the BLS, but not only relying on the personal consumption expenditures, but also on Truflation to help them make the most or the most effective decision that they possibly can. Because the consequences are significant, right? I mean, you’ve seen the impact of interest rates and if interest rates go further, how many more banks, regional or local banks, are going to be under pressure?

Mike: 01:16:50

This is the tough spot. So as you’re observing sort of more real time inflation numbers, you’re getting a sense to what’s coming down the pipe, and then you’re saying, okay, well, maybe they’ll stop raising, but they’re not going to cut. So right now the market is suggesting they’re going to cut, which then is, okay, well what are the implications of that with respect to the investments that are anticipating that out there in the world? Would they go up or down on that? Difference of opinion, if you will, sort of the long-term, more persistent type of inflation. Yeah, I think that we all agree that there’s sort of a structural nature to it, whether it’s near-shoring offshoring, changes in the structure of employment, all of these things just lead to it’s not cheap and cheerful, just in time, everything from everywhere else at the lowest cost possible. And the inputs from which we make all of these things are also going up in price because of certain global dynamics.

So it’s going to be interesting to see what the Fed’s flexibility even is, even in this run on fixed income prices, meaning a decline in the rates. We’ve seen that have a surge effect in housing, as you mentioned. Well, that’s not really what the Fed wants at this point in time. And so it’s this really interesting, some sort of sine wave yin and yang, where, yeah, it’ll be interesting to see what assets perform in this type of environment. I think it wasn’t speculate on your portfolio, though. We won’t, but I think I can figure it out. Yeah, exactly.

Oliver: 01:18:40

I think it’s interesting. Right. How much further are they going to go? Right? And what are the consequences? He listened to Jamie Dimon. Jamie came out and said he believes that more banks are going to start to wave if they go further, right? And so how much more further is Jerome Powell willing to go to get  inflation under control? And at what cost? Is he going to support those banks in the background? If it’s a smaller local, regional bank, is he going to support them? Um. Is the Fed going to sit there and the government going to support that, then offsets that so you can get inflation under control.
Those dynamics of the financial stability, the employment market or the labor market combined with inflation, it’s in a tough spot. Right. And the implications that’s coming out are going to be significant.

Richard: 01:19:35

One of the major problems, I think, with trying to anticipate the Fed’s next moves is the fact that we’re dealing with a chairman that is optimizing for legacy, as opposed to some of the other variables that they would probably or they should probably optimize for. So, there’s this big argument between him wanting to go down in history as the new Volcker and not Arthur Burns, right? There’s that dynamic. He also very much dislikes the moniker of having pivoted in 2018 after the two main events that happened there. So there’s this flip flopping. He doesn’t want that to be his legacy. So there’s a little bit of that component to what is already a very complex and dynamic system. So this is definitely going to be interesting to watch.

And if we can lean on new ways to measure inflation, like Truflation, that’s definitely going to help investors around the world to make better allocation decisions. So, Mike, any final thoughts here?

Mike: 01:20:39

No, I think you summarized it well. I’ll add one point. I mean, Jay Powell has probably the hardest job that any head of the central bank has had in the last 40 years. So I feel for the guy, but…

Richard: 01:20:47

Fair enough.

Mike: 01:20:50

I wish him luck. Wouldn’t want to be in his shoes – none of us would. Absolutely. He’s got a tough road to hoe. But anyway no, it’s been great. Thank you so much, Oliver, for taking the time with us today. And it’s been a great conversation.

Mike: 01:21:07

I certainly enjoyed it. And maybe give everybody one more time where they can reach you. See you and the Truflation gang.

Oliver: 01:21:20

Yeah. So, first of all, before I do that, massive thanks to both of you and for having us on, allowing us to talk about inflation. I think it’s such a topical subject and I don’t think it’s going to go away. So it’s exciting to talk about this and if there’s anything we ever can do to support you guys, let us know. But thanks a lot for having me on the show.

For Truflation, yeah, have a look at the website. It’s You can reach out to us there. You can reach out to us on Twitter. It’s @ truflation. You can also reach us on Telegram if you’re interested. That’s @ truflation as well. So reach out to us through those contact channels. We do have a miniscule YouTube channel that’s starting to grow, but our base community is either through the website or through Twitter or Telegram. So reach out to us any way there you can, but if you want the interest in the data, have a look at it. It’s all for free on the website and any feedback anyone has, let us know.

Richard: 01:22:10


Mike: 01:22:12

Amazing. This is great.

Richard: 01:22:14

Thank you, Mike. And just to give you guys a teaser for next week, we have Vlad Aldia and Ross Fortune.

Mike: 01:22:22

That’s going to be a good one. Yeah. This is the power markets. This is getting electrons from one place in the United States to the other place in the United States, and that is going to be a fun one. We did a pre interview with them. That was shocking. Man, you see your alarm clock sitting by your bed and the electrons that get there every day so reliably? It’s incredible, electrons. Have a great long weekend.

Richard: 01:22:52

Long weekend. Thank you, everyone, for watching and Share, Like and Subscribe, and we’ll see you next time. Thanks a lot, everybody.

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*ReSolve Global refers to ReSolve Asset Management SEZC (Cayman) which is registered with the Commodity Futures Trading Commission as a commodity trading advisor and commodity pool operator. This registration is administered through the National Futures Association (“NFA”). Further, ReSolve Global is a registered person with the Cayman Islands Monetary Authority.