1 00:00:10,800 --> 00:00:14,920 Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast. 2 00:00:15,000 --> 00:00:19,480 Speaker 1: I'm Joe Wisenthal and I'm Tracy Halloway. Tracy, do you 3 00:00:19,520 --> 00:00:22,960 Speaker 1: remember I think it was probably like last June or 4 00:00:23,079 --> 00:00:26,160 Speaker 1: last July, like over a year ago. Now, I think 5 00:00:26,160 --> 00:00:30,720 Speaker 1: we're talking to Vitour Constantio, and I think you asked 6 00:00:30,760 --> 00:00:33,680 Speaker 1: like one of the most important questions, like at the 7 00:00:33,720 --> 00:00:36,960 Speaker 1: time and maybe still like that we've had on this podcast. 8 00:00:37,040 --> 00:00:39,600 Speaker 1: I struggle to remember things that happened a month ago, 9 00:00:39,840 --> 00:00:42,080 Speaker 1: um let alone twelve months ago. Everything is sort of 10 00:00:42,120 --> 00:00:47,440 Speaker 1: blurring into this one long stretch. But um, yeah, that's fair. 11 00:00:47,720 --> 00:00:53,000 Speaker 1: What did I ask, Well, you asked him whether economists 12 00:00:53,120 --> 00:00:57,120 Speaker 1: or anyone these days had a cogent theory of inflation. 13 00:00:58,000 --> 00:01:00,200 Speaker 1: Do you remember that? And at the time, like you know, 14 00:01:00,240 --> 00:01:02,360 Speaker 1: there wasn't much inflation going on. I think we're probably 15 00:01:02,360 --> 00:01:06,000 Speaker 1: still in something resembling deflation or disinflation not long after 16 00:01:06,000 --> 00:01:09,760 Speaker 1: the initial shock. But now, you know, fast forward to 17 00:01:10,000 --> 00:01:15,840 Speaker 1: August and official inflation measurements are pretty elevated relative to 18 00:01:15,880 --> 00:01:19,480 Speaker 1: recent history. And I think that question and I think 19 00:01:19,520 --> 00:01:21,440 Speaker 1: it's basic into us. No, although I don't want to 20 00:01:21,520 --> 00:01:24,760 Speaker 1: quote him on that, that question of like whether economists 21 00:01:24,800 --> 00:01:28,440 Speaker 1: understand inflation has probably never been is extremely top of 22 00:01:28,480 --> 00:01:31,000 Speaker 1: mind these days totally, And I mean, I think I 23 00:01:31,040 --> 00:01:34,080 Speaker 1: would agree with you on the no part. But of 24 00:01:34,080 --> 00:01:36,319 Speaker 1: course there's a really big irony right now, which is 25 00:01:37,040 --> 00:01:41,160 Speaker 1: right when the FED changed to the flexible average inflation 26 00:01:41,200 --> 00:01:44,440 Speaker 1: targeting regime, after you know, more than a decade of 27 00:01:45,000 --> 00:01:47,880 Speaker 1: undershooting the two percent inflation target. As soon as they 28 00:01:47,920 --> 00:01:53,320 Speaker 1: did that, now we seem to have possibly um transitory inflation, 29 00:01:53,600 --> 00:01:57,040 Speaker 1: but like certainly more than they would have expected a 30 00:01:57,080 --> 00:02:01,080 Speaker 1: year ago. And and yeah, like it just illustrates that 31 00:02:01,120 --> 00:02:03,760 Speaker 1: no one seems to have a very good handle on 32 00:02:04,040 --> 00:02:09,840 Speaker 1: what exactly causes price increases or decreases. Right, So, even 33 00:02:09,919 --> 00:02:14,040 Speaker 1: prior to the new framework, there's always been this idea that, okay, 34 00:02:14,080 --> 00:02:16,320 Speaker 1: we'll use and this came up in our recent episode 35 00:02:16,320 --> 00:02:19,919 Speaker 1: of Neil Cary of the Minneapolis Fed, that okay, we'll 36 00:02:20,040 --> 00:02:23,320 Speaker 1: use inflation as our gauge, as our speed limit to 37 00:02:23,360 --> 00:02:26,360 Speaker 1: know when we say hit maximum employment or full employment. 38 00:02:26,639 --> 00:02:28,520 Speaker 1: That's true under the old FED, that's true under the 39 00:02:28,520 --> 00:02:32,480 Speaker 1: current Fed. Now we have elevated inflation, but it's like, oh, 40 00:02:32,560 --> 00:02:35,440 Speaker 1: this doesn't really count because it's transitory and it's related 41 00:02:35,480 --> 00:02:37,000 Speaker 1: to shipping, and we know that doesn't have anything to 42 00:02:37,040 --> 00:02:39,480 Speaker 1: do with employment, but there's always probably gonna be a 43 00:02:39,560 --> 00:02:42,640 Speaker 1: story to tell which sort of calls to mind whether 44 00:02:42,720 --> 00:02:45,400 Speaker 1: these are sort of like useful things. Of course, no 45 00:02:45,440 --> 00:02:49,600 Speaker 1: one predicted this sort of like very few people accurately 46 00:02:49,600 --> 00:02:51,880 Speaker 1: predicted the timing and the degree of elevation of the 47 00:02:51,919 --> 00:02:55,520 Speaker 1: current inflation. So I think we're still sort of like 48 00:02:55,639 --> 00:02:58,080 Speaker 1: back to square one. We don't know how long this 49 00:02:58,120 --> 00:03:00,720 Speaker 1: will last. I don't even think there's a agreement of 50 00:03:00,800 --> 00:03:04,040 Speaker 1: like what really would be transitory, of what would be worrisome, 51 00:03:04,120 --> 00:03:06,240 Speaker 1: of what would spur the FED to act sooner than 52 00:03:06,280 --> 00:03:09,919 Speaker 1: maybe markets effect, And so I think, um, that question 53 00:03:10,120 --> 00:03:14,480 Speaker 1: that you asked Constancio several months ago remains really the 54 00:03:14,520 --> 00:03:18,520 Speaker 1: sort of the key thing right now. Yeah, alright, So 55 00:03:18,560 --> 00:03:21,880 Speaker 1: today we're going to be talking about inflation and really 56 00:03:21,919 --> 00:03:24,840 Speaker 1: what it is and how it's measured, and whether it's 57 00:03:24,840 --> 00:03:28,520 Speaker 1: even possible to forecast it accurately or how we should 58 00:03:28,560 --> 00:03:31,720 Speaker 1: be thinking about it right now, And we have literally 59 00:03:31,760 --> 00:03:35,720 Speaker 1: the perfect guest. We're gonna be speaking with Omar Sharif. 60 00:03:35,760 --> 00:03:38,320 Speaker 1: He's currently uh the founder and president of a new 61 00:03:38,360 --> 00:03:41,000 Speaker 1: shop that he's set up called appropriately enough for this 62 00:03:41,040 --> 00:03:45,160 Speaker 1: episode Inflation Insights, but he has a long track record. 63 00:03:45,240 --> 00:03:48,440 Speaker 1: He's prior to that a by side strategist at the 64 00:03:48,480 --> 00:03:51,840 Speaker 1: asset management for Millennium. He's been an economist at various 65 00:03:51,880 --> 00:03:56,000 Speaker 1: shops including a Soak, Gen, RBS, and so forth. So 66 00:03:56,280 --> 00:03:58,800 Speaker 1: we're going to talk about whether there is a a 67 00:03:58,880 --> 00:04:01,280 Speaker 1: cogent theory of ation and how to think about it 68 00:04:01,360 --> 00:04:03,760 Speaker 1: right now. So Amara, thank you so much for joining us. 69 00:04:04,280 --> 00:04:07,640 Speaker 1: Thank you having me. Well, what's your answer to Tracy's question? 70 00:04:08,200 --> 00:04:12,800 Speaker 1: Do do economists have a useful or cogent theory of 71 00:04:12,840 --> 00:04:17,960 Speaker 1: inflation that that works in practice? I think the short 72 00:04:18,000 --> 00:04:20,159 Speaker 1: answer to that is, now, you know, all you have 73 00:04:20,200 --> 00:04:22,440 Speaker 1: to do is sort of look at the fact that 74 00:04:22,480 --> 00:04:26,520 Speaker 1: there's a massive academic literature that's basically just devoted to 75 00:04:26,839 --> 00:04:30,280 Speaker 1: forecasting inflation and you know, coming up with variou types 76 00:04:30,279 --> 00:04:33,800 Speaker 1: of models to figure out what the inflation process is. 77 00:04:34,320 --> 00:04:37,560 Speaker 1: And there's an equally large literature talking about why we're 78 00:04:37,600 --> 00:04:40,960 Speaker 1: so bad and forecasting inflation. So I don't know that 79 00:04:41,000 --> 00:04:44,599 Speaker 1: there's a coaching theory. It seems to sort of change 80 00:04:45,000 --> 00:04:48,160 Speaker 1: based on where we are kind of in the cycle, 81 00:04:48,839 --> 00:04:51,039 Speaker 1: and there's different ways of approaching you know, how you 82 00:04:51,040 --> 00:04:53,320 Speaker 1: want to forecast inflation. They're sort of the top down 83 00:04:53,400 --> 00:04:57,360 Speaker 1: modeling approach that a lot of academics use. And I 84 00:04:57,400 --> 00:04:59,880 Speaker 1: think sort of what's more kind of in favor and 85 00:05:00,000 --> 00:05:01,920 Speaker 1: out something that I started doing, you know, well over 86 00:05:01,960 --> 00:05:03,440 Speaker 1: ten years ago, which was kind of more of the 87 00:05:03,480 --> 00:05:08,120 Speaker 1: bottom up approach UM to forecasting inflation. But ultimately, I 88 00:05:08,120 --> 00:05:11,400 Speaker 1: don't know that there's a coaching theory that really can 89 00:05:11,520 --> 00:05:14,039 Speaker 1: explain the inflation process over you know, let's say the 90 00:05:14,080 --> 00:05:17,400 Speaker 1: last several decades. UM. You sort of try to understand 91 00:05:17,440 --> 00:05:19,800 Speaker 1: it based on where you are, I think in the cycle. 92 00:05:21,040 --> 00:05:25,440 Speaker 1: So what is it that makes inflation UM so difficult 93 00:05:25,480 --> 00:05:28,560 Speaker 1: to grasp? Is it the idea of UM that you 94 00:05:28,600 --> 00:05:30,800 Speaker 1: just pointed out that it basically depends on where you 95 00:05:30,839 --> 00:05:33,960 Speaker 1: are in the cycle, and so your framework or the 96 00:05:34,040 --> 00:05:39,400 Speaker 1: dynamics that are underpinning prices are are kind of changing constantly. Yeah, 97 00:05:39,440 --> 00:05:41,600 Speaker 1: I think that's that's exactly what it is. It's just 98 00:05:42,160 --> 00:05:45,840 Speaker 1: it's a constantly evolving process UM. And you know, one 99 00:05:45,839 --> 00:05:47,440 Speaker 1: way to think about it is that if we're trying 100 00:05:47,480 --> 00:05:49,280 Speaker 1: to forecast it, and let's say we just want to 101 00:05:49,279 --> 00:05:51,000 Speaker 1: think about where it's going to go over the course 102 00:05:51,040 --> 00:05:55,000 Speaker 1: of the next year. You know, there are all sorts 103 00:05:55,000 --> 00:05:57,479 Speaker 1: of approaches you can take in terms of modeling, but 104 00:05:57,640 --> 00:06:00,479 Speaker 1: some of the simplest approaches actually worked the which is 105 00:06:00,480 --> 00:06:02,599 Speaker 1: simply to say that, you know, if I want to 106 00:06:02,600 --> 00:06:04,880 Speaker 1: forecast inflation over the course of the next four quarters, 107 00:06:05,720 --> 00:06:08,200 Speaker 1: I might just use the average of the last four quarters, 108 00:06:08,600 --> 00:06:11,120 Speaker 1: and most often than not, that will actually perform better 109 00:06:11,720 --> 00:06:14,000 Speaker 1: than trying to come up with some models that you know, 110 00:06:14,360 --> 00:06:18,360 Speaker 1: Phillips curve type models for example, because inflation persistence is 111 00:06:18,600 --> 00:06:21,960 Speaker 1: actually a pretty big key I think in trying to 112 00:06:22,040 --> 00:06:25,600 Speaker 1: understand the dynamics with an inflation um and that persistence 113 00:06:25,720 --> 00:06:29,000 Speaker 1: is is it varies across time and and that is 114 00:06:29,040 --> 00:06:31,320 Speaker 1: one of the keys really and trying to think about 115 00:06:31,360 --> 00:06:33,240 Speaker 1: it is you know, if I if I were to 116 00:06:33,279 --> 00:06:35,840 Speaker 1: tell you that the core CPI has been between one 117 00:06:35,880 --> 00:06:37,240 Speaker 1: and a half to two and a half percent for 118 00:06:37,279 --> 00:06:41,160 Speaker 1: the last twenty years, those types of models that are 119 00:06:41,360 --> 00:06:44,159 Speaker 1: what we call nightive models work quite well because if 120 00:06:44,200 --> 00:06:47,080 Speaker 1: inflation has been around that for the last twenty years, 121 00:06:47,120 --> 00:06:48,880 Speaker 1: pretty good odds that you know for the next year 122 00:06:48,920 --> 00:06:52,560 Speaker 1: will be somewhere in that range. Uh. But that persistence 123 00:06:52,640 --> 00:06:55,200 Speaker 1: varies in the short run. It varies, you know, even 124 00:06:55,240 --> 00:06:58,040 Speaker 1: within a decade, and so trying to capture that kind 125 00:06:58,040 --> 00:07:01,520 Speaker 1: of time varying nature of inflation for sistance is really 126 00:07:01,520 --> 00:07:05,279 Speaker 1: what everyone's striving to do. And that's why certain models perform, 127 00:07:05,480 --> 00:07:08,599 Speaker 1: you know, really well in certain decades and they completely 128 00:07:08,640 --> 00:07:12,440 Speaker 1: collapse in the next decade. Well, so this gets to 129 00:07:12,520 --> 00:07:15,640 Speaker 1: something that we um talked about in the beginning that 130 00:07:15,680 --> 00:07:18,240 Speaker 1: whether we're talking about the FEDS new framework, and we 131 00:07:18,240 --> 00:07:22,040 Speaker 1: should point out recording this auguste by the time people 132 00:07:22,040 --> 00:07:23,600 Speaker 1: listen to this, it will have been the one year 133 00:07:23,640 --> 00:07:26,760 Speaker 1: anniversary of Jackson Hole where they laid out and so 134 00:07:26,960 --> 00:07:28,640 Speaker 1: we make it some new speeches on this, but where 135 00:07:28,640 --> 00:07:32,040 Speaker 1: they laid out the new framework last year. So whether 136 00:07:32,080 --> 00:07:35,840 Speaker 1: it's the new framework, the Flexible Average Inflation Targeting Framework, 137 00:07:35,880 --> 00:07:38,120 Speaker 1: or the old framework, I'm not even sure what that was, 138 00:07:38,360 --> 00:07:41,080 Speaker 1: both are premised on this sort of like Phillips curve, 139 00:07:41,160 --> 00:07:44,800 Speaker 1: thinking that there is some inherent trade off, that there 140 00:07:44,880 --> 00:07:48,400 Speaker 1: is some inherent speed limit or maximum employment, and we'll 141 00:07:48,400 --> 00:07:51,240 Speaker 1: know we'll get there not by some number of employment level, 142 00:07:51,320 --> 00:07:55,400 Speaker 1: but by inflation readings. And if the nature of inflation 143 00:07:55,440 --> 00:07:58,720 Speaker 1: sort of changes all the time, maybe decade by decade 144 00:07:58,800 --> 00:08:01,640 Speaker 1: or some other time interval. Is that going to be 145 00:08:01,680 --> 00:08:04,680 Speaker 1: a folly for the FED to think that inflation or like, 146 00:08:04,800 --> 00:08:08,000 Speaker 1: is there any reason to think that Philip's curve thinking 147 00:08:08,080 --> 00:08:12,080 Speaker 1: or Philips curve framework will be a useful guide post 148 00:08:12,600 --> 00:08:14,840 Speaker 1: for the FED? It's hard to say. I think that 149 00:08:14,960 --> 00:08:17,040 Speaker 1: the thing is that the Feds, you know, their time 150 00:08:17,040 --> 00:08:20,000 Speaker 1: horizons really if you think about it is essentially about 151 00:08:20,040 --> 00:08:22,200 Speaker 1: three years, right. We we get about three years of 152 00:08:22,200 --> 00:08:25,320 Speaker 1: forecast from the FRET FED within the SCP, and so 153 00:08:25,560 --> 00:08:28,000 Speaker 1: you know, if you're thinking about inflation changing over ten 154 00:08:28,080 --> 00:08:30,200 Speaker 1: years or fifteen years, that's less of an issue for 155 00:08:30,240 --> 00:08:32,920 Speaker 1: the FED. So that can the Phillips curve work accurately 156 00:08:32,960 --> 00:08:35,800 Speaker 1: for them? And then a three to five year horizon sure, 157 00:08:36,040 --> 00:08:37,400 Speaker 1: And and so you know there are times that it 158 00:08:37,440 --> 00:08:40,960 Speaker 1: has actually performed relatively well. So for example, kind of 159 00:08:40,800 --> 00:08:44,240 Speaker 1: the late seventies when you ran aggressions sort of these 160 00:08:44,480 --> 00:08:47,200 Speaker 1: you know, using output on employment gaps, Phillips curves actually 161 00:08:47,240 --> 00:08:49,960 Speaker 1: turned out to to be kind of useful. But you know, 162 00:08:50,040 --> 00:08:52,520 Speaker 1: the FED will always tell you that they have a 163 00:08:52,559 --> 00:08:54,760 Speaker 1: suite of models that they look at. They will look 164 00:08:54,800 --> 00:08:58,720 Speaker 1: at everything from core inflation to you know, trim means 165 00:08:58,760 --> 00:09:01,280 Speaker 1: and medium c P I S. So I don't know 166 00:09:01,360 --> 00:09:05,000 Speaker 1: that they're as reliant on the Phillips curve as they 167 00:09:05,120 --> 00:09:06,800 Speaker 1: used to be, and I think they've kind of spelled 168 00:09:06,800 --> 00:09:09,440 Speaker 1: that out for us over the course of you know, 169 00:09:09,480 --> 00:09:12,120 Speaker 1: the last year. It's not clear that the Philip's curve 170 00:09:12,160 --> 00:09:14,600 Speaker 1: really works, and I think Powell's kind of ditched that approach, 171 00:09:15,000 --> 00:09:16,240 Speaker 1: and it's you know, it's kind of back to the 172 00:09:16,280 --> 00:09:18,760 Speaker 1: old adage of saying, you know, we'll we'll know it 173 00:09:18,800 --> 00:09:20,720 Speaker 1: when we see it, and we'll kind of wait for 174 00:09:20,760 --> 00:09:23,040 Speaker 1: the whites of the eyes of inflation before we decide 175 00:09:23,080 --> 00:09:26,920 Speaker 1: to move on on policy. MHM. So you mentioned all 176 00:09:26,960 --> 00:09:32,520 Speaker 1: the different inflation stats or figures that the FED can 177 00:09:32,600 --> 00:09:35,200 Speaker 1: look at, and of course there are, I mean, there's 178 00:09:35,240 --> 00:09:38,920 Speaker 1: probably dozens of different measures of inflation um that are 179 00:09:38,960 --> 00:09:43,559 Speaker 1: based on hundreds, if not thousands, of different baskets. I 180 00:09:43,600 --> 00:09:47,959 Speaker 1: guess my question is how much does your interpretation or 181 00:09:48,040 --> 00:09:52,679 Speaker 1: does one's interpretation or thinking around inflation actually depend on 182 00:09:53,000 --> 00:09:55,760 Speaker 1: the measure that they're looking at, And how do people 183 00:09:55,800 --> 00:10:00,120 Speaker 1: go about choosing which measure is most relevant m in 184 00:10:00,120 --> 00:10:05,400 Speaker 1: a particular time. Well, I think, you know, we there's 185 00:10:05,400 --> 00:10:07,720 Speaker 1: basically two main measures. Is the way that I really 186 00:10:07,920 --> 00:10:09,640 Speaker 1: look at it. You know, there's obviously the c p 187 00:10:09,800 --> 00:10:11,840 Speaker 1: I and then there's the there's the Feens preferred measure 188 00:10:11,920 --> 00:10:14,559 Speaker 1: that the core PC. Are there other measures that you 189 00:10:14,600 --> 00:10:17,040 Speaker 1: can look at, absolutely, but typically they tend to be 190 00:10:17,120 --> 00:10:20,040 Speaker 1: kind of variations on those two. So you know, the 191 00:10:20,600 --> 00:10:23,880 Speaker 1: trim mean and and the median c p I and 192 00:10:24,080 --> 00:10:27,640 Speaker 1: the median PC are just variations and different ways to 193 00:10:27,720 --> 00:10:30,920 Speaker 1: kind of approach those same baskets. And you know, maybe 194 00:10:30,920 --> 00:10:33,280 Speaker 1: you're taking some of the volatility out from the top 195 00:10:33,280 --> 00:10:36,440 Speaker 1: and the bottom, but you're really sticking with those two 196 00:10:36,480 --> 00:10:39,800 Speaker 1: main baskets, which one you want to really focus on? 197 00:10:40,160 --> 00:10:42,320 Speaker 1: You know, I think it depends. Um, if you're the Fed, 198 00:10:42,360 --> 00:10:44,880 Speaker 1: they've already made that decision for us. You know, it 199 00:10:44,920 --> 00:10:47,199 Speaker 1: kind of makes it easy. Um, we're gonna be focusing 200 00:10:47,200 --> 00:10:49,520 Speaker 1: on the core PC if you're thinking about monetary policy. 201 00:10:49,840 --> 00:10:52,360 Speaker 1: But obviously for the markets, what matters is the c 202 00:10:52,480 --> 00:10:54,880 Speaker 1: p I and what matters is the CPI because that's 203 00:10:54,920 --> 00:10:58,199 Speaker 1: what it goes into pricing tips. So it sort of 204 00:10:58,240 --> 00:11:00,040 Speaker 1: depends on who you are. You know, if you're the A, 205 00:11:00,160 --> 00:11:02,360 Speaker 1: obviously you're going to be concerned and obviously the two 206 00:11:02,360 --> 00:11:05,600 Speaker 1: are related about you know, roughly something like seventies seventy 207 00:11:06,120 --> 00:11:09,559 Speaker 1: of the core PC is actually just passed through from 208 00:11:09,640 --> 00:11:12,560 Speaker 1: the course cp I. So those to me are really 209 00:11:12,600 --> 00:11:15,080 Speaker 1: the at least in the US, are the two main 210 00:11:15,200 --> 00:11:18,280 Speaker 1: metrics that you want to focus on. Well, let me 211 00:11:18,320 --> 00:11:22,400 Speaker 1: ask you. You know, if economists, you know, don't have 212 00:11:22,440 --> 00:11:24,960 Speaker 1: a great track record of forecasting inflation, they don't have 213 00:11:24,960 --> 00:11:28,400 Speaker 1: great models um for it. So we have these things 214 00:11:28,480 --> 00:11:30,520 Speaker 1: that Okay, if I talked to Tracy, I say, let's 215 00:11:30,559 --> 00:11:32,959 Speaker 1: talk about inflation, and we want to know where inflation is. 216 00:11:33,000 --> 00:11:36,120 Speaker 1: We'll look up some indicries on the terminal, might look 217 00:11:36,160 --> 00:11:39,280 Speaker 1: up to c p I, course, CPI, core PC, etcetera. 218 00:11:39,800 --> 00:11:44,760 Speaker 1: Are the concepts of inflation indices themselves cogent? In other words, 219 00:11:45,240 --> 00:11:48,400 Speaker 1: we're put we're aggregating all these different prices and trying 220 00:11:48,440 --> 00:11:50,720 Speaker 1: to arrive at one number. And right now I think 221 00:11:50,720 --> 00:11:53,000 Speaker 1: the number, you know, cp I, it's a little bit 222 00:11:53,000 --> 00:11:56,120 Speaker 1: over five percent, maybe five point four percent, I don't 223 00:11:56,160 --> 00:11:58,880 Speaker 1: remember exactly. That's down from a recent high. But is 224 00:11:58,920 --> 00:12:04,320 Speaker 1: there some true information contained in that headline number or 225 00:12:04,440 --> 00:12:07,400 Speaker 1: is it just a number that gets spit out when 226 00:12:07,679 --> 00:12:09,960 Speaker 1: you go through the work of adding up all of 227 00:12:10,000 --> 00:12:13,480 Speaker 1: the thousands of prices that go into it. Yeah. Look, 228 00:12:13,480 --> 00:12:15,560 Speaker 1: I mean at the end of the day, Um, all 229 00:12:15,559 --> 00:12:18,760 Speaker 1: of this stuff is is a construct, right, and and 230 00:12:18,840 --> 00:12:21,480 Speaker 1: there's certain things in there which I think do a 231 00:12:21,559 --> 00:12:25,920 Speaker 1: great job of reflecting reality. And if we think about energy, 232 00:12:26,080 --> 00:12:28,320 Speaker 1: that's a very simple one. You know, everybody knows what 233 00:12:28,360 --> 00:12:32,160 Speaker 1: they're paying at the pumper for gasoline, and within the 234 00:12:32,200 --> 00:12:35,280 Speaker 1: c p I, you know you're you're capturing these movements 235 00:12:35,320 --> 00:12:38,200 Speaker 1: and energy prices, those are pretty straightforward. The same thing 236 00:12:38,320 --> 00:12:40,760 Speaker 1: is true when you think about you know, car prices. 237 00:12:41,320 --> 00:12:43,559 Speaker 1: These are items that are pretty easy, easy to capture, 238 00:12:44,000 --> 00:12:46,080 Speaker 1: and you know they do a pretty good job sort 239 00:12:46,080 --> 00:12:49,760 Speaker 1: of representing reality. Where you start to get we start 240 00:12:49,800 --> 00:12:52,079 Speaker 1: to lose people, frankly, is when you kind of get 241 00:12:52,120 --> 00:12:56,200 Speaker 1: into some of the sort of the price index theory 242 00:12:56,320 --> 00:12:59,360 Speaker 1: and sort of the number theory where you start to 243 00:12:59,400 --> 00:13:02,400 Speaker 1: talk about imputations and you know how to handle missing 244 00:13:02,440 --> 00:13:05,600 Speaker 1: prices and you know, owner's equivalent RAN is a great example. 245 00:13:06,360 --> 00:13:08,480 Speaker 1: There's been a debate about this for you know, pretty 246 00:13:08,559 --> 00:13:10,679 Speaker 1: much the last forty fifty years about how should we 247 00:13:10,679 --> 00:13:14,720 Speaker 1: really capture house prices? What is the role in an index? 248 00:13:15,200 --> 00:13:17,160 Speaker 1: And the CPI is very clear, we don't want to 249 00:13:17,240 --> 00:13:19,520 Speaker 1: We don't want anything to do with house prices. Why, 250 00:13:19,600 --> 00:13:22,440 Speaker 1: because we consider it to be an asset. We're trying 251 00:13:22,480 --> 00:13:25,000 Speaker 1: to measure what you pay out of pocket as a consumer, 252 00:13:26,080 --> 00:13:28,800 Speaker 1: And so these are the kinds of debates that I think, 253 00:13:28,840 --> 00:13:31,480 Speaker 1: you know, when you're paying, when you're seeing you know, 254 00:13:31,760 --> 00:13:35,760 Speaker 1: home prices go up, you care less about what satistations 255 00:13:35,760 --> 00:13:38,239 Speaker 1: are arguing about when it comes to you know, imputation 256 00:13:38,280 --> 00:13:39,920 Speaker 1: and so on. You just know that if you want 257 00:13:39,920 --> 00:13:42,240 Speaker 1: to buy a house, you've got to pay more than 258 00:13:42,280 --> 00:13:44,520 Speaker 1: maybe you did last year. And so you tend to 259 00:13:44,559 --> 00:13:47,120 Speaker 1: kind of lose the public when you're getting into those 260 00:13:47,120 --> 00:13:49,480 Speaker 1: sorts of weeds in the inflation data. But I think 261 00:13:49,480 --> 00:13:52,319 Speaker 1: for the most part, these indexes do a pretty good 262 00:13:52,400 --> 00:13:55,000 Speaker 1: job of capturing what's going on in terms of the 263 00:13:55,000 --> 00:14:14,360 Speaker 1: pricing environment around US. UM. Can we spend a little 264 00:14:14,360 --> 00:14:18,240 Speaker 1: bit of time on owners equivalent rent or o E ER, 265 00:14:18,360 --> 00:14:20,920 Speaker 1: because as you mentioned, this is a source of big controversy. 266 00:14:21,360 --> 00:14:24,520 Speaker 1: Anyone looking at the housing market, you know, over the 267 00:14:24,560 --> 00:14:27,760 Speaker 1: past few decades will say that house prices have gone up, 268 00:14:28,160 --> 00:14:32,600 Speaker 1: rents have gone up, um, certainly relative to one's income. 269 00:14:33,280 --> 00:14:37,400 Speaker 1: So why is it that it seems difficult for the 270 00:14:37,440 --> 00:14:41,160 Speaker 1: inflation indices to capture that, is it just is it 271 00:14:41,240 --> 00:14:45,600 Speaker 1: just that they've made a conscious decision not to include 272 00:14:45,600 --> 00:14:47,840 Speaker 1: it or to include it in this very specific way, 273 00:14:47,960 --> 00:14:51,560 Speaker 1: or is it something else? So we actually used to 274 00:14:51,560 --> 00:14:55,520 Speaker 1: include it, um so so prior to the CPI, for example, 275 00:14:56,080 --> 00:14:58,320 Speaker 1: um they took what was called the asset approach, and 276 00:14:58,360 --> 00:15:01,280 Speaker 1: so that included, uh, you know, the price of a house, 277 00:15:01,600 --> 00:15:06,400 Speaker 1: included everything related to your mortgage, interest costs, your property taxes, 278 00:15:06,440 --> 00:15:09,080 Speaker 1: and so on. And even though from about the late 279 00:15:09,080 --> 00:15:13,040 Speaker 1: fifties to about three they they did it in this manner, 280 00:15:13,560 --> 00:15:15,880 Speaker 1: they knew from really the early sixties that this was 281 00:15:15,960 --> 00:15:18,560 Speaker 1: not the right approach and this was just a conceptual 282 00:15:18,680 --> 00:15:20,840 Speaker 1: issue with the cp I. All this had to do 283 00:15:20,960 --> 00:15:24,240 Speaker 1: was was to say, what we're really doing here is 284 00:15:24,520 --> 00:15:27,880 Speaker 1: we're capturing an investment piece of the house, and we're 285 00:15:27,880 --> 00:15:30,680 Speaker 1: also we really want to capture is just the consumption 286 00:15:30,720 --> 00:15:32,840 Speaker 1: aspect of it. And so they knew for you know, 287 00:15:32,960 --> 00:15:36,480 Speaker 1: twenty plus years um that even though they were doing 288 00:15:36,520 --> 00:15:38,480 Speaker 1: in this manner, this was not what they wanted to 289 00:15:38,520 --> 00:15:42,120 Speaker 1: actually be doing in terms of the CPI. And finally 290 00:15:42,160 --> 00:15:45,120 Speaker 1: around the late seventies they started doing more detailed work 291 00:15:45,120 --> 00:15:47,800 Speaker 1: on how to get around, you know, eliminating the investment 292 00:15:47,840 --> 00:15:51,040 Speaker 1: piece of it and focusing just on sort of the 293 00:15:51,080 --> 00:15:53,680 Speaker 1: shelter part of it UM. And they made that change 294 00:15:53,680 --> 00:15:57,560 Speaker 1: in three and you know, another part of the reason 295 00:15:57,600 --> 00:15:59,560 Speaker 1: for making that change was, you know, as you recall, 296 00:15:59,640 --> 00:16:01,720 Speaker 1: interest rates were kind of all over the map in 297 00:16:01,800 --> 00:16:05,920 Speaker 1: the late seventies and the early eighties, and that introduced 298 00:16:05,920 --> 00:16:08,760 Speaker 1: a tremendous amount of volatility in the CPI to the 299 00:16:08,800 --> 00:16:12,200 Speaker 1: point where it sort of became, I don't want to 300 00:16:12,200 --> 00:16:15,120 Speaker 1: say useless, but so volatile that it was really hard 301 00:16:15,160 --> 00:16:16,760 Speaker 1: to get any kind of sense of what was going 302 00:16:16,800 --> 00:16:20,720 Speaker 1: on with underlying inflation. And you had everything tied to it, 303 00:16:20,760 --> 00:16:24,800 Speaker 1: you know, cost of living adjustments UM, wage negotiations were 304 00:16:24,800 --> 00:16:26,400 Speaker 1: tied to it. And one year it was up you know, 305 00:16:26,440 --> 00:16:28,800 Speaker 1: ten eleven, the next year might be back down to three. 306 00:16:29,560 --> 00:16:31,960 Speaker 1: So it was sort of losing its significance, and so 307 00:16:32,120 --> 00:16:35,360 Speaker 1: this decision was made to work on trying to implement 308 00:16:35,720 --> 00:16:40,120 Speaker 1: this owner's equivalent rent index, which finally came into playe UM. 309 00:16:40,240 --> 00:16:44,040 Speaker 1: So that is a consensual issue. It's it's just underlies 310 00:16:44,080 --> 00:16:46,240 Speaker 1: sort of what the CPI is really meant to do. 311 00:16:47,280 --> 00:16:50,360 Speaker 1: The second part of this, I think, is that you know, 312 00:16:50,400 --> 00:16:53,280 Speaker 1: when you look at for example, Zello core logic and 313 00:16:53,280 --> 00:16:57,160 Speaker 1: you're seeing rents up, where you're seeing case Shiller saying 314 00:16:57,200 --> 00:17:01,000 Speaker 1: house prices are one of the you don't see it 315 00:17:01,040 --> 00:17:04,560 Speaker 1: in the CPI indexes. To that magnitude is because it 316 00:17:04,680 --> 00:17:07,920 Speaker 1: is there's just very little turnover in the CPI sample. 317 00:17:08,800 --> 00:17:12,720 Speaker 1: So every month, only about ten to fift pent the 318 00:17:12,760 --> 00:17:17,640 Speaker 1: sample actually represents new renters, and so what you see 319 00:17:17,640 --> 00:17:20,560 Speaker 1: in list prices um you know that might be moving 320 00:17:20,640 --> 00:17:23,920 Speaker 1: very quickly when market conditions are changing, that shows up 321 00:17:23,960 --> 00:17:26,960 Speaker 1: over the course of about twelve to eighteen months in 322 00:17:26,960 --> 00:17:30,360 Speaker 1: the CPI because you just don't have that same kind 323 00:17:30,359 --> 00:17:33,360 Speaker 1: of turnover that you see in these indexes that look 324 00:17:33,359 --> 00:17:35,879 Speaker 1: at just for example, listed rents. So part of its 325 00:17:35,920 --> 00:17:39,040 Speaker 1: conceptual issue, but the other part is just the methodology 326 00:17:39,080 --> 00:17:41,840 Speaker 1: and the way that the sample works. You're just never 327 00:17:41,880 --> 00:17:44,320 Speaker 1: going to have the magnitude of the changes that you 328 00:17:44,359 --> 00:17:47,240 Speaker 1: see in all these sort of you know, private private 329 00:17:47,320 --> 00:17:50,040 Speaker 1: sector indexes. So like if we're talking about something like 330 00:17:50,080 --> 00:17:54,879 Speaker 1: buying apples or buying milk or buying gasoline, everybody buys 331 00:17:54,960 --> 00:17:57,520 Speaker 1: those things every day or every week all the time 332 00:17:58,000 --> 00:18:02,760 Speaker 1: with rant, not only do very few people actually signed 333 00:18:02,760 --> 00:18:06,280 Speaker 1: a new lease every every month, but also many of 334 00:18:06,320 --> 00:18:08,680 Speaker 1: the new leases that people actually sign or with their 335 00:18:08,680 --> 00:18:12,240 Speaker 1: current landlord, and so they probably uh don't get the 336 00:18:12,240 --> 00:18:15,280 Speaker 1: full market rent because those are those don't adjust as fast. 337 00:18:15,760 --> 00:18:18,520 Speaker 1: And so you know, looking at this, Okay, this is 338 00:18:18,560 --> 00:18:20,119 Speaker 1: one of the big questions right now. We know that 339 00:18:20,200 --> 00:18:23,080 Speaker 1: headline inflation has come down a little bit. Used cars 340 00:18:23,080 --> 00:18:25,240 Speaker 1: seem to have stopped going up. That's a big factor. 341 00:18:25,520 --> 00:18:28,240 Speaker 1: But everyone's like, okay, ore is coming, ohr is coming. 342 00:18:28,520 --> 00:18:31,159 Speaker 1: We see the market rent some places like Zillo and 343 00:18:31,200 --> 00:18:33,920 Speaker 1: so forth, those are shooting up. Why don't you give 344 00:18:34,000 --> 00:18:36,439 Speaker 1: us what you know? Sort of you just explained the theory. 345 00:18:36,720 --> 00:18:38,720 Speaker 1: What are you actually seeing in practice when you look 346 00:18:38,720 --> 00:18:42,000 Speaker 1: at the data and how much is rent and other 347 00:18:42,359 --> 00:18:44,960 Speaker 1: attempts that the CPI or that these in disease used 348 00:18:45,000 --> 00:18:48,160 Speaker 1: to capture shelter. How much upward pressure are they going 349 00:18:48,200 --> 00:18:51,800 Speaker 1: to put on the measures in the months and years ahead. Yeah, 350 00:18:51,800 --> 00:18:54,439 Speaker 1: so we you know, we have seen both of these measures, 351 00:18:54,480 --> 00:18:56,280 Speaker 1: both rent and now we are actually bottom out over 352 00:18:56,280 --> 00:18:58,760 Speaker 1: the course of the last several months, and you're starting 353 00:18:58,760 --> 00:19:02,680 Speaker 1: to see price increases. Is in the major metro areas, 354 00:19:02,880 --> 00:19:06,640 Speaker 1: and so even you know, places like New York, Los Angeles, Chicago, 355 00:19:06,680 --> 00:19:09,600 Speaker 1: San Francisco, which are still down pretty sharply year over year, 356 00:19:09,920 --> 00:19:12,199 Speaker 1: it looks like on a monthly basis they've they've finally 357 00:19:12,240 --> 00:19:14,919 Speaker 1: kind of begun to stabilize a little bit. And you know, 358 00:19:14,960 --> 00:19:16,840 Speaker 1: it's important to kind of understand when you talk about 359 00:19:16,840 --> 00:19:19,919 Speaker 1: the c p I that a lot of these rent indicries, 360 00:19:20,160 --> 00:19:22,639 Speaker 1: what really matters is where these you know, so called 361 00:19:22,800 --> 00:19:25,320 Speaker 1: Class A cities, which are you know, the big ones 362 00:19:25,320 --> 00:19:27,960 Speaker 1: with populations over two and a half million, what they're 363 00:19:28,000 --> 00:19:32,280 Speaker 1: doing because the weights on these cities um is incredibly large. 364 00:19:32,560 --> 00:19:34,320 Speaker 1: So if you looked at just three of them, New York, 365 00:19:35,000 --> 00:19:40,240 Speaker 1: Los Angeles, Chicago, that is of the entire rent inducts 366 00:19:40,240 --> 00:19:43,679 Speaker 1: from just those three metro areas. So where they go 367 00:19:43,880 --> 00:19:46,280 Speaker 1: matters quite a lot for the overall index. But we 368 00:19:46,320 --> 00:19:49,199 Speaker 1: are starting to see these places stabilize and move up. 369 00:19:49,240 --> 00:19:51,120 Speaker 1: But I think there's something to keep in mind here 370 00:19:51,160 --> 00:19:53,879 Speaker 1: about you know, this whole story about shelters commenting and 371 00:19:53,880 --> 00:19:55,360 Speaker 1: O we are is going to go up and so on. 372 00:19:56,080 --> 00:19:59,520 Speaker 1: Number One, people again are looking at these private market 373 00:19:59,560 --> 00:20:02,760 Speaker 1: rent data and they're seeing seven percent eight percent growth. 374 00:20:03,400 --> 00:20:05,480 Speaker 1: We've never seen anything like that in the c p I. 375 00:20:05,480 --> 00:20:08,119 Speaker 1: I'm hard pressed to think, you know, we'll see anything 376 00:20:08,160 --> 00:20:09,919 Speaker 1: like that over Let's say that the course of the 377 00:20:09,960 --> 00:20:13,679 Speaker 1: next year, um will rent go up, yes, but you know, 378 00:20:13,760 --> 00:20:16,480 Speaker 1: don't forget before the pandemic we were running around three 379 00:20:16,480 --> 00:20:19,320 Speaker 1: and a half percent. We're about two and a half 380 00:20:19,320 --> 00:20:22,320 Speaker 1: percent right now, and rent and we are combined. So 381 00:20:22,480 --> 00:20:24,320 Speaker 1: you know, even if you move up a full percentage 382 00:20:24,320 --> 00:20:26,000 Speaker 1: point over the course of the next year, you'll be 383 00:20:26,080 --> 00:20:28,760 Speaker 1: kind of right back where you started in early two 384 00:20:28,760 --> 00:20:31,400 Speaker 1: thousand and twenty. But let's assume for a moment that 385 00:20:31,640 --> 00:20:33,520 Speaker 1: you know, we move up to four and a half percent, 386 00:20:34,320 --> 00:20:36,879 Speaker 1: so another two percentage points from where we are today. 387 00:20:37,280 --> 00:20:40,040 Speaker 1: What that basically means is you're looking at overall core 388 00:20:40,080 --> 00:20:44,480 Speaker 1: inflation rising by roughly about another eighty basis points. You know, 389 00:20:44,480 --> 00:20:47,760 Speaker 1: rents got about a weight, you go up two percentage points. 390 00:20:47,920 --> 00:20:50,240 Speaker 1: That's about eighty bits on on the core cp I. 391 00:20:50,280 --> 00:20:52,840 Speaker 1: Now that sounds like a lot, but you mentioned used 392 00:20:52,840 --> 00:20:56,119 Speaker 1: cars earlier. They're adding over a hundred and thirty basis 393 00:20:56,160 --> 00:20:58,520 Speaker 1: points to the core CPI right now in the year. 394 00:20:58,600 --> 00:21:01,720 Speaker 1: Your basis that it's almost certainly going to come off. 395 00:21:02,119 --> 00:21:05,120 Speaker 1: So even if we are goes up and rent goes up, 396 00:21:05,200 --> 00:21:06,920 Speaker 1: you know, a couple of percentage points over the course 397 00:21:06,920 --> 00:21:09,800 Speaker 1: of let's say, the next year eighteen months, that's almost 398 00:21:09,880 --> 00:21:12,120 Speaker 1: certainly going to be offset to a great extent by 399 00:21:12,440 --> 00:21:14,200 Speaker 1: you know, a lot of these things that we're seeing 400 00:21:14,240 --> 00:21:17,600 Speaker 1: now that we you know, continue to think of transitory. 401 00:21:17,920 --> 00:21:19,639 Speaker 1: That's going to upset a lot of the upward pressure 402 00:21:19,640 --> 00:21:21,600 Speaker 1: you're gonna get from from shelter, I think, over the 403 00:21:21,600 --> 00:21:23,560 Speaker 1: course of the next eighteen months. So it's kind of 404 00:21:23,600 --> 00:21:27,359 Speaker 1: important to keep that in perspective because again, four and 405 00:21:27,400 --> 00:21:30,080 Speaker 1: a half percent is really where we've peaked in the past, 406 00:21:30,080 --> 00:21:31,520 Speaker 1: and even if we get a bit higher than that, 407 00:21:32,040 --> 00:21:35,240 Speaker 1: you know, year a year on use cars is not 408 00:21:35,280 --> 00:21:37,399 Speaker 1: going to stick. You know, that's going to potentially more 409 00:21:37,440 --> 00:21:41,320 Speaker 1: than offset what we see out of shelter. So since 410 00:21:41,359 --> 00:21:45,359 Speaker 1: we're talking about the current environment in terms of inflation, 411 00:21:45,880 --> 00:21:48,680 Speaker 1: maybe it's worth asking, you know, when you hear the 412 00:21:48,800 --> 00:21:53,600 Speaker 1: term transitory inflation, what does it actually mean to you? 413 00:21:53,760 --> 00:21:57,960 Speaker 1: Because we've had um at least one feed official come 414 00:21:58,000 --> 00:22:00,680 Speaker 1: on and talk about how they sort of regret using 415 00:22:00,720 --> 00:22:03,800 Speaker 1: the term transitory inflation, and other people have said you know, 416 00:22:03,840 --> 00:22:05,480 Speaker 1: maybe it would have been better if the FED was 417 00:22:05,520 --> 00:22:10,960 Speaker 1: talking about narrow inflation versus more broader inflation, or um 418 00:22:11,040 --> 00:22:16,480 Speaker 1: manageable inflation versus unmanageable inflation. So what does that term 419 00:22:16,520 --> 00:22:20,600 Speaker 1: actually mean to you? Transitory inflation? So to me, it's 420 00:22:20,600 --> 00:22:24,639 Speaker 1: just about, you know, how long that rate of change 421 00:22:24,680 --> 00:22:29,160 Speaker 1: continues to sort of accelerate. So you know, use cards 422 00:22:29,200 --> 00:22:32,440 Speaker 1: going up yar over a year from zero basically, which 423 00:22:32,480 --> 00:22:36,399 Speaker 1: is where they were pre pandemic too. Now the question 424 00:22:36,520 --> 00:22:39,520 Speaker 1: is how long can we sort of not just how 425 00:22:39,560 --> 00:22:41,760 Speaker 1: long will that persists, but can it continually go up 426 00:22:41,760 --> 00:22:44,080 Speaker 1: at sort of the rates we've seen over over the 427 00:22:44,160 --> 00:22:46,560 Speaker 1: last six months. That's kind of the way I think 428 00:22:46,560 --> 00:22:49,400 Speaker 1: about it. That's I think the way that share Powell 429 00:22:49,480 --> 00:22:53,800 Speaker 1: has sort of explained inflation as well, is it's this 430 00:22:53,880 --> 00:22:57,000 Speaker 1: process of can we see continually this rate of growth 431 00:22:57,359 --> 00:23:00,480 Speaker 1: sort of accelerate year after year after year, And that's 432 00:23:00,480 --> 00:23:02,480 Speaker 1: what we're sort of looking at for these components, but 433 00:23:02,560 --> 00:23:05,399 Speaker 1: obviously much more sort of in the short term. But 434 00:23:05,440 --> 00:23:07,080 Speaker 1: I do think there's a couple of ways to think 435 00:23:07,119 --> 00:23:10,359 Speaker 1: about this transitory question. And if you're trying to figure 436 00:23:10,359 --> 00:23:13,919 Speaker 1: out is is the inflation I'm saying that transitory or 437 00:23:13,960 --> 00:23:17,040 Speaker 1: is it gonna continue will spread out to other components. 438 00:23:17,800 --> 00:23:19,280 Speaker 1: There's I think a few ways to kind of think 439 00:23:19,320 --> 00:23:22,600 Speaker 1: about that um. One, for example, is just simply look 440 00:23:22,640 --> 00:23:27,080 Speaker 1: at the dispersion within the cp I, so you know 441 00:23:27,160 --> 00:23:31,320 Speaker 1: what share of components are seeing price increases today versus 442 00:23:31,359 --> 00:23:33,920 Speaker 1: price decreases, And also you know what does that look 443 00:23:34,000 --> 00:23:37,159 Speaker 1: like on a weighted average basis versus history. So you 444 00:23:37,200 --> 00:23:40,240 Speaker 1: could have a lot of components, for example, rising, but 445 00:23:40,280 --> 00:23:42,920 Speaker 1: if combined the weight of those components is not that 446 00:23:42,920 --> 00:23:46,280 Speaker 1: that high, it really may not matter for kind of 447 00:23:46,320 --> 00:23:49,399 Speaker 1: the longer term inflation picture. Another thing that I like 448 00:23:49,440 --> 00:23:52,240 Speaker 1: to look at is momentum within the core cp I. 449 00:23:52,880 --> 00:23:55,280 Speaker 1: So here what I want to look at is, you know, 450 00:23:55,400 --> 00:23:58,560 Speaker 1: what is the share of components that are either accelerating 451 00:23:59,359 --> 00:24:02,000 Speaker 1: or de cel a rating within the corese c p I. 452 00:24:02,480 --> 00:24:04,040 Speaker 1: And here you can sort of you know, normally you 453 00:24:04,040 --> 00:24:07,080 Speaker 1: would look at, for example, the twelve month change in 454 00:24:07,160 --> 00:24:09,679 Speaker 1: the year of a year rate of specific components and 455 00:24:09,720 --> 00:24:12,800 Speaker 1: see if that's picking up steam or losing steam, and 456 00:24:12,880 --> 00:24:15,720 Speaker 1: that once you wait those those sort of changes gives 457 00:24:15,720 --> 00:24:18,640 Speaker 1: you a sense of kind of the underlying momentum that's 458 00:24:18,680 --> 00:24:21,800 Speaker 1: really sort of driving the you know, the aggregate core number. 459 00:24:22,240 --> 00:24:23,880 Speaker 1: And then one final thing, which I think is pretty 460 00:24:23,880 --> 00:24:26,600 Speaker 1: important right now, especially since we're sort of comparing everything 461 00:24:26,640 --> 00:24:29,800 Speaker 1: to kind of the pre pandemic time, is you've got 462 00:24:29,840 --> 00:24:32,280 Speaker 1: to kind of keep a close eye on where the 463 00:24:32,320 --> 00:24:37,159 Speaker 1: price level is today for certain components versus not just 464 00:24:37,200 --> 00:24:40,760 Speaker 1: where it was let's say in but where would you 465 00:24:40,800 --> 00:24:43,679 Speaker 1: expect it to be today, you know, given the pre 466 00:24:43,760 --> 00:24:46,920 Speaker 1: pandemic trend. So are we overshooting that or are we 467 00:24:47,000 --> 00:24:49,240 Speaker 1: undershooting that? And I think a good example here is 468 00:24:49,240 --> 00:24:52,399 Speaker 1: something like air fares. Right there's still about ten percent 469 00:24:52,480 --> 00:24:56,119 Speaker 1: above excuse me below their fable, but they're more like 470 00:24:56,840 --> 00:24:59,560 Speaker 1: pent below where you would expect them to be if 471 00:24:59,600 --> 00:25:02,760 Speaker 1: they had just continued on their pre pandemic trend. So 472 00:25:03,400 --> 00:25:06,240 Speaker 1: that kind of tells you, you know, things normalize. That's 473 00:25:06,240 --> 00:25:08,320 Speaker 1: an area where you might start to see some upward 474 00:25:08,320 --> 00:25:10,640 Speaker 1: pressure come, as in airfares. And on the flip side, 475 00:25:10,880 --> 00:25:13,360 Speaker 1: hotel rates are running about eight percent above where you'd 476 00:25:13,359 --> 00:25:15,560 Speaker 1: expect them to be right now, and so that's the 477 00:25:15,560 --> 00:25:17,360 Speaker 1: place where you might get a little bit of get back. 478 00:25:17,400 --> 00:25:20,680 Speaker 1: And if you don't, then then you you know, potentially 479 00:25:20,680 --> 00:25:22,440 Speaker 1: start to get a little bit concerned that this might 480 00:25:22,520 --> 00:25:25,840 Speaker 1: stick a bit longer than you would have expected. Big picture, 481 00:25:25,880 --> 00:25:28,040 Speaker 1: I mean, we you know, we we fixate on a 482 00:25:28,080 --> 00:25:32,320 Speaker 1: few of these so called reopening categories, and used cars 483 00:25:32,640 --> 00:25:35,520 Speaker 1: we know the story there, and rental cars we know 484 00:25:35,600 --> 00:25:39,680 Speaker 1: the story there. And with airplanes and hotels we understand 485 00:25:39,720 --> 00:25:42,080 Speaker 1: some of this also. You know, we talk a lot 486 00:25:42,160 --> 00:25:46,400 Speaker 1: about certain goods related that relate to shipping and logistics, 487 00:25:46,440 --> 00:25:49,080 Speaker 1: which we know is supply chains jammed. When you look 488 00:25:49,119 --> 00:25:52,359 Speaker 1: at some of these measures, like you do the breadth 489 00:25:52,400 --> 00:25:57,159 Speaker 1: of the inflation, general inflation momentum and so forth, what 490 00:25:57,240 --> 00:26:00,119 Speaker 1: are you seeing right now? Is their process happening. It 491 00:26:00,200 --> 00:26:04,440 Speaker 1: appears to be broadening out, and momentum is gathering steam 492 00:26:04,600 --> 00:26:07,480 Speaker 1: or or is it something else? You know, let's let's 493 00:26:08,040 --> 00:26:09,639 Speaker 1: we go back to its kind of the FATS preferred 494 00:26:09,680 --> 00:26:12,320 Speaker 1: measure of the PC. The San Francisco FAT actually does 495 00:26:12,359 --> 00:26:14,280 Speaker 1: a nice job keeping track of some of these dispersion 496 00:26:14,320 --> 00:26:17,080 Speaker 1: measures and so on. And what you look at now 497 00:26:17,240 --> 00:26:20,240 Speaker 1: is that about roughly, you know, if you sort of 498 00:26:20,240 --> 00:26:24,439 Speaker 1: look at all the components in the PC, about eight 499 00:26:24,720 --> 00:26:29,080 Speaker 1: percent of them currently are showing price games. And you 500 00:26:29,080 --> 00:26:31,480 Speaker 1: know that sounds like a pretty big number. That most 501 00:26:31,480 --> 00:26:34,359 Speaker 1: of the most of you know, the components arising, but 502 00:26:34,440 --> 00:26:36,600 Speaker 1: in fact it's it's only a couple of percentage points 503 00:26:36,680 --> 00:26:39,639 Speaker 1: more than what we were seeing sort of you know, 504 00:26:39,680 --> 00:26:42,720 Speaker 1: pre pandemic. So it doesn't it's not clear to me 505 00:26:42,760 --> 00:26:47,240 Speaker 1: that we've seen a big broadening out of of price pressures. 506 00:26:47,760 --> 00:26:52,040 Speaker 1: We've seen, as you mentioned, just really concentrated increases in 507 00:26:52,080 --> 00:26:55,280 Speaker 1: pressures in some components. So I'm still very much in 508 00:26:55,320 --> 00:26:57,600 Speaker 1: the camp that you know, as we sort of get 509 00:26:57,600 --> 00:27:01,920 Speaker 1: through the spring of two, we're essentially going to see 510 00:27:02,160 --> 00:27:04,160 Speaker 1: a lot of slow down I think in the core 511 00:27:04,200 --> 00:27:06,760 Speaker 1: and much more I think in the core PC for example. 512 00:27:07,000 --> 00:27:08,560 Speaker 1: Like I wouldn't be surprised if by the middle of 513 00:27:08,600 --> 00:27:11,600 Speaker 1: twenty two we're talking about core PC being closer to 514 00:27:11,680 --> 00:27:16,119 Speaker 1: around two whereas the core CPI potentially is is still 515 00:27:16,320 --> 00:27:18,639 Speaker 1: you know, punching along and around two and a half percent. 516 00:27:19,000 --> 00:27:21,119 Speaker 1: And one thing I just want to mention is, you know, 517 00:27:21,119 --> 00:27:22,560 Speaker 1: you talked a little bit about some of these macro 518 00:27:22,680 --> 00:27:25,560 Speaker 1: stories with you know, chip shortages and so on. You know, 519 00:27:25,560 --> 00:27:27,800 Speaker 1: these are important, right we we we we like to 520 00:27:27,840 --> 00:27:31,159 Speaker 1: have narratives to try to explain something. But one of 521 00:27:31,200 --> 00:27:33,240 Speaker 1: the things with when you are really in the weeds 522 00:27:33,240 --> 00:27:36,240 Speaker 1: of this inflation data. Is that as important as those 523 00:27:36,320 --> 00:27:40,000 Speaker 1: narratives are to kind of understand the picture, most people 524 00:27:40,000 --> 00:27:41,840 Speaker 1: don't really pay attention to the fact that a lot 525 00:27:41,920 --> 00:27:44,040 Speaker 1: of price movements that you tend to see have nothing 526 00:27:44,040 --> 00:27:46,720 Speaker 1: to do with the macro story or a micro story. 527 00:27:46,760 --> 00:27:50,919 Speaker 1: It's literally just about the way the methodology works in 528 00:27:51,000 --> 00:27:54,879 Speaker 1: the index. It's about the seasonality of the index. You know, 529 00:27:54,960 --> 00:27:59,400 Speaker 1: it's about changes in the way we actually compute and 530 00:27:59,440 --> 00:28:02,560 Speaker 1: construct the data, and it has less to do with 531 00:28:02,760 --> 00:28:05,880 Speaker 1: you know, these broader stories that we're trying to explain. 532 00:28:06,160 --> 00:28:08,480 Speaker 1: We're trying to use explain the inflation number. Sometimes it's 533 00:28:08,520 --> 00:28:12,880 Speaker 1: just about understanding how this thing is built and sort 534 00:28:12,920 --> 00:28:16,000 Speaker 1: of really getting into the weeds of, you know, understanding 535 00:28:16,040 --> 00:28:17,800 Speaker 1: the parts that sort of make up the sum. And 536 00:28:17,840 --> 00:28:20,399 Speaker 1: that good example is motor vehicle insurance. This is an 537 00:28:20,400 --> 00:28:22,080 Speaker 1: index that you know a lot of people don't pay 538 00:28:22,080 --> 00:28:24,840 Speaker 1: attention to. Last month was down about two and a 539 00:28:24,840 --> 00:28:26,840 Speaker 1: half percent, you know, a little bit less than a 540 00:28:26,840 --> 00:28:29,680 Speaker 1: full tenth off the core CPI, which is a lot 541 00:28:29,680 --> 00:28:32,679 Speaker 1: when the core is around point three only and it 542 00:28:32,720 --> 00:28:34,800 Speaker 1: has nothing to do with a big story. You know, 543 00:28:35,080 --> 00:28:38,120 Speaker 1: UM insurers are not cutting your your rates. It just 544 00:28:38,200 --> 00:28:40,880 Speaker 1: has to do with the way the seasonality is working 545 00:28:40,880 --> 00:28:43,520 Speaker 1: out this year for this particular index, and it's going 546 00:28:43,560 --> 00:28:47,040 Speaker 1: to be a very similar story when the next point 547 00:28:47,080 --> 00:28:49,920 Speaker 1: comes out insurance motor vehicle insurance should be down around 548 00:28:49,920 --> 00:28:52,360 Speaker 1: two and a half three percent. Again, do you just 549 00:28:52,640 --> 00:28:56,440 Speaker 1: to the seasonal factor. So, no big story, but if 550 00:28:56,440 --> 00:28:59,120 Speaker 1: you understand the seasonality and understand how this thing is constructed, 551 00:28:59,800 --> 00:29:02,920 Speaker 1: it gives you an edge in terms of forecasting. Uh, 552 00:29:03,040 --> 00:29:06,600 Speaker 1: this number just to play Devil's advocate for a second. 553 00:29:06,680 --> 00:29:09,160 Speaker 1: So um, when it comes to those macro stories that 554 00:29:09,200 --> 00:29:11,560 Speaker 1: you just mentioned, one of the things that Joe and 555 00:29:11,600 --> 00:29:13,720 Speaker 1: I have been discussing a lot over the past year 556 00:29:13,800 --> 00:29:16,760 Speaker 1: or so is this idea of the bullwidth effect and 557 00:29:16,800 --> 00:29:20,400 Speaker 1: that you end up seeing a massive amount of volatility 558 00:29:21,120 --> 00:29:26,240 Speaker 1: in orders and stockpiling because of the uncertain environment. So 559 00:29:26,360 --> 00:29:28,120 Speaker 1: you know, you get a shortage one month and then 560 00:29:28,160 --> 00:29:30,400 Speaker 1: everyone ramps up their orders because they don't want to 561 00:29:30,400 --> 00:29:33,360 Speaker 1: be caught short again, and suddenly they're oversupplied and you 562 00:29:33,400 --> 00:29:37,440 Speaker 1: get these sort of intense um price increases and decreases. 563 00:29:37,840 --> 00:29:40,840 Speaker 1: So I guess my question is, like, is that a 564 00:29:41,040 --> 00:29:46,360 Speaker 1: risk to inflation Actually proving transitory. Is that something that 565 00:29:46,400 --> 00:29:49,960 Speaker 1: could start to come into play. Yeah, that's a that's 566 00:29:49,960 --> 00:29:53,479 Speaker 1: a good point. It's it's possible. But I think you know, 567 00:29:53,520 --> 00:29:56,280 Speaker 1: where you would see that obviously would be a ramping 568 00:29:56,360 --> 00:29:59,600 Speaker 1: up in in the inventory numbers for some of the 569 00:30:00,040 --> 00:30:02,120 Speaker 1: you know, the places where we're seeing where we are 570 00:30:02,160 --> 00:30:05,760 Speaker 1: seeing shortages now, where we are seeing orders pick up steam, 571 00:30:05,800 --> 00:30:09,080 Speaker 1: you know, I mean, honestly, to some extent to your point, 572 00:30:09,480 --> 00:30:11,920 Speaker 1: we're kind of seeing this. We use cars now, so 573 00:30:12,120 --> 00:30:14,800 Speaker 1: wholesale prices have been coming off the last couple of months, 574 00:30:15,440 --> 00:30:17,720 Speaker 1: and again where you really want to look as on 575 00:30:17,760 --> 00:30:21,640 Speaker 1: the inventory side, so when you look at use cars, 576 00:30:21,800 --> 00:30:24,200 Speaker 1: when you look at the wholesale piece of that, we're 577 00:30:24,200 --> 00:30:26,880 Speaker 1: only about a couple of days below a normal level 578 00:30:26,880 --> 00:30:31,200 Speaker 1: of inventory for wholesale used vehicles, and on the retail 579 00:30:31,240 --> 00:30:34,680 Speaker 1: side is actually pretty similar as well. So you know, 580 00:30:34,720 --> 00:30:37,600 Speaker 1: we were catching up on the inventory side and getting 581 00:30:37,600 --> 00:30:41,280 Speaker 1: to something that actually really resembles normality in the wholesale 582 00:30:41,360 --> 00:30:45,200 Speaker 1: used vehicle market. And you know, low and Behold you 583 00:30:45,520 --> 00:30:48,040 Speaker 1: were down about three or four and wholesale prices over 584 00:30:48,080 --> 00:30:51,000 Speaker 1: the last couple of months. So I think, sort of 585 00:30:51,040 --> 00:30:52,520 Speaker 1: to your point, I think where we are starting to 586 00:30:52,560 --> 00:30:54,920 Speaker 1: see some of that happen in some of these components. 587 00:31:12,000 --> 00:31:13,600 Speaker 1: So I'm thinking, you know, I kind of want to 588 00:31:13,720 --> 00:31:16,400 Speaker 1: zoom out a little bit and talk about the relationship 589 00:31:16,440 --> 00:31:19,080 Speaker 1: between some of your work and you know, how investors 590 00:31:19,160 --> 00:31:22,160 Speaker 1: use it. You've been on the cell side, You've been 591 00:31:22,160 --> 00:31:24,520 Speaker 1: on the BI side, now you have your own shop. 592 00:31:24,880 --> 00:31:27,280 Speaker 1: As Tracy and I have talked about the past episodes, 593 00:31:27,360 --> 00:31:30,680 Speaker 1: inflation gets people going. It gets consumers going emotionally, but 594 00:31:30,720 --> 00:31:33,320 Speaker 1: it also gets traders and investors going, and people are 595 00:31:33,400 --> 00:31:36,840 Speaker 1: very strong views about the FED and so forth. I'm 596 00:31:36,840 --> 00:31:41,520 Speaker 1: curious about like receptiveness to your way of thinking, because 597 00:31:41,560 --> 00:31:44,160 Speaker 1: you obviously clearly take this bottoms up approach where it 598 00:31:44,200 --> 00:31:48,360 Speaker 1: looked not only at individual categories, but individual category construction. 599 00:31:49,360 --> 00:31:51,720 Speaker 1: How do like, you know, traders investors who want to 600 00:31:51,800 --> 00:31:55,320 Speaker 1: use this, are they receptive to it? Are they do 601 00:31:55,400 --> 00:31:58,440 Speaker 1: they get you know, are they angry at the ideas like, oh, 602 00:31:58,560 --> 00:32:01,160 Speaker 1: you know, this is all the FED money printing, etcetera. 603 00:32:01,200 --> 00:32:02,840 Speaker 1: Which is kind of seems to be the opposite of 604 00:32:02,880 --> 00:32:04,800 Speaker 1: how you think about these questions. What do you talk 605 00:32:04,840 --> 00:32:07,280 Speaker 1: a little bit more about your work and how how 606 00:32:07,320 --> 00:32:09,960 Speaker 1: investors use it? I think, but the most part, people 607 00:32:09,960 --> 00:32:12,040 Speaker 1: are incredibly receptive to it. I mean, when I started 608 00:32:12,080 --> 00:32:14,800 Speaker 1: doing this, um, I don't know of many shops or 609 00:32:14,800 --> 00:32:17,600 Speaker 1: many individuals who were taking this kind of bottom approach 610 00:32:17,600 --> 00:32:20,480 Speaker 1: and sort of doing you know, detailed analysis of the 611 00:32:20,520 --> 00:32:23,720 Speaker 1: components and index construction and so on. And I think 612 00:32:23,800 --> 00:32:27,360 Speaker 1: people are especially periods like this, they want to understand 613 00:32:27,400 --> 00:32:31,160 Speaker 1: what is moving the print, Is it a one off print, 614 00:32:31,280 --> 00:32:33,760 Speaker 1: is there you know, was there something driving it this 615 00:32:33,800 --> 00:32:36,200 Speaker 1: month that could be more persistent? And how do I 616 00:32:36,240 --> 00:32:38,920 Speaker 1: think about that for the following month. Because if you're 617 00:32:38,920 --> 00:32:41,200 Speaker 1: an investor and you're you know, in the tips market, 618 00:32:41,280 --> 00:32:45,160 Speaker 1: or you're you're, you know, interested in the fixings, one 619 00:32:45,200 --> 00:32:48,040 Speaker 1: month obviously influences everything, and so you really want to 620 00:32:48,080 --> 00:32:51,000 Speaker 1: understand what is what's going on kind of beneath the 621 00:32:51,000 --> 00:32:53,160 Speaker 1: hood of the data. So I think people are incredibly receptive. 622 00:32:53,640 --> 00:32:55,760 Speaker 1: And you know, in terms of getting pushed back from 623 00:32:55,760 --> 00:32:57,480 Speaker 1: folks who are like, hey, this is just the FEDS 624 00:32:57,480 --> 00:33:01,880 Speaker 1: money printing, there's there's always element of that, but I 625 00:33:01,920 --> 00:33:04,160 Speaker 1: find that those are the folks who were, you know, 626 00:33:04,400 --> 00:33:09,680 Speaker 1: potentially removed from actually trading or managing money. Um, you know, 627 00:33:09,800 --> 00:33:11,760 Speaker 1: the folks who are managing the money. They are into 628 00:33:11,800 --> 00:33:14,680 Speaker 1: the weeds of it. And you know, it's funny because, 629 00:33:15,080 --> 00:33:16,960 Speaker 1: as I mentioned, when I was on the cell side, 630 00:33:17,360 --> 00:33:19,080 Speaker 1: when I did this, very few people did it. Now, 631 00:33:19,280 --> 00:33:20,720 Speaker 1: being on the buy side of the last two years, 632 00:33:20,760 --> 00:33:23,120 Speaker 1: I was a consumer of all the cell side research, 633 00:33:23,600 --> 00:33:25,560 Speaker 1: so I got all the research from the banks on 634 00:33:25,640 --> 00:33:28,400 Speaker 1: inflation and how they forecast it and so on. And 635 00:33:28,560 --> 00:33:31,200 Speaker 1: it's it's funny to me because now a lot of 636 00:33:31,240 --> 00:33:33,800 Speaker 1: people take this bottom of approach on on the cell 637 00:33:33,840 --> 00:33:36,640 Speaker 1: side as well, some more so than others, but it's 638 00:33:36,720 --> 00:33:38,760 Speaker 1: kind of the way everyone on the cell side is 639 00:33:38,800 --> 00:33:41,640 Speaker 1: doing it now because I think there's a value add 640 00:33:41,720 --> 00:33:45,560 Speaker 1: in understanding the weeds of you know, what's driving shelter 641 00:33:45,640 --> 00:33:49,520 Speaker 1: inflation and what's driving apparel prices and so on, because 642 00:33:49,560 --> 00:33:51,720 Speaker 1: it really does give you a window into where the 643 00:33:52,000 --> 00:33:55,280 Speaker 1: kind of headline numbers going and importantly, is it going 644 00:33:55,320 --> 00:33:56,760 Speaker 1: to stick or is it just you know, kind of 645 00:33:56,760 --> 00:34:00,160 Speaker 1: a one off. So in a bottom up approach like 646 00:34:00,200 --> 00:34:02,840 Speaker 1: the one, you just describe what role, if any, does 647 00:34:02,880 --> 00:34:06,200 Speaker 1: monetary policy actually play. And you know, I'm thinking of 648 00:34:06,240 --> 00:34:09,640 Speaker 1: that famous Milton Friedman quote about inflation is always in 649 00:34:09,719 --> 00:34:14,600 Speaker 1: everywhere a monetary phenomenon like is that incorporated anywhere in 650 00:34:14,640 --> 00:34:16,719 Speaker 1: the type of work that you do, or is it 651 00:34:16,840 --> 00:34:19,520 Speaker 1: irrelevant the way when you're when you're doing the sort 652 00:34:19,520 --> 00:34:22,560 Speaker 1: of approach, it is almost by design, it's a very 653 00:34:22,600 --> 00:34:25,000 Speaker 1: short term approach. You know, I'm not going to sit 654 00:34:25,080 --> 00:34:27,759 Speaker 1: here and say, hey, I'm in a forecast inflation for 655 00:34:27,800 --> 00:34:31,439 Speaker 1: the next five years, um doing this approach because it's 656 00:34:31,440 --> 00:34:34,319 Speaker 1: it's just not designed to do anything like that. When 657 00:34:34,320 --> 00:34:36,719 Speaker 1: you're doing something like this, it's much more looking at 658 00:34:36,840 --> 00:34:41,640 Speaker 1: let's say the twelve next, twelve to eighteen months UM. 659 00:34:41,719 --> 00:34:43,720 Speaker 1: And really, if you think about policy in the lags, 660 00:34:43,760 --> 00:34:47,799 Speaker 1: you know, to some extent, you know, maybe it's impacting, uh, 661 00:34:47,920 --> 00:34:50,319 Speaker 1: some of these components in that time frame, let's say, 662 00:34:50,400 --> 00:34:54,240 Speaker 1: especially housing, but in the twelve eighteen months, it doesn't 663 00:34:54,280 --> 00:34:56,799 Speaker 1: play that big of a role. And honestly, if you 664 00:34:56,800 --> 00:34:59,840 Speaker 1: even think about the way that these indexes work. The 665 00:35:00,000 --> 00:35:01,640 Speaker 1: San Francisco Found a couple of years ago had a 666 00:35:01,640 --> 00:35:04,600 Speaker 1: great paper where they applied kind of the Phillips curve 667 00:35:04,680 --> 00:35:09,960 Speaker 1: methodology to individual components of the PC and what they 668 00:35:09,960 --> 00:35:12,719 Speaker 1: found was a roughly sixty percent of these components or 669 00:35:12,760 --> 00:35:17,040 Speaker 1: what they would term a cyclical and essentially, you know, 670 00:35:17,120 --> 00:35:20,120 Speaker 1: policy can't really impact them. So stuff like medical care, 671 00:35:20,160 --> 00:35:23,879 Speaker 1: for example, whatever you're doing with policy is probably not 672 00:35:23,960 --> 00:35:27,279 Speaker 1: really going to impact physicians prices um or you know, 673 00:35:27,320 --> 00:35:31,600 Speaker 1: hospital prices, and so six the index just doesn't really 674 00:35:31,760 --> 00:35:36,239 Speaker 1: react to policy. And even that does, you know, it's 675 00:35:36,239 --> 00:35:37,880 Speaker 1: going to be a bit of time. That's twelve to 676 00:35:37,920 --> 00:35:42,960 Speaker 1: eighteen months, And this approach is pretty narrowly focused on 677 00:35:43,040 --> 00:35:45,000 Speaker 1: kind of you know, just that kind of window. So 678 00:35:45,640 --> 00:35:48,640 Speaker 1: I would say that if it does, it's kind of 679 00:35:48,719 --> 00:35:51,920 Speaker 1: hard to really it's hard to really incorporated into this 680 00:35:52,000 --> 00:35:55,239 Speaker 1: sort of a framework when you're thinking about forecasting. Should 681 00:35:55,239 --> 00:35:58,279 Speaker 1: we sort of established that there isn't that much of 682 00:35:58,320 --> 00:36:01,520 Speaker 1: a sort of cogent theory of an inflation from sort 683 00:36:01,520 --> 00:36:05,480 Speaker 1: of like pure macro standpoint. What is your pitch then? 684 00:36:05,800 --> 00:36:08,520 Speaker 1: Is it just that you're going to help explain what 685 00:36:08,600 --> 00:36:11,640 Speaker 1: you a little bit more about your pitch to potential 686 00:36:11,719 --> 00:36:15,880 Speaker 1: clients to help them understand what's going on. Like what 687 00:36:16,000 --> 00:36:17,759 Speaker 1: is it that you say it's like, okay, you do 688 00:36:18,400 --> 00:36:21,840 Speaker 1: at your new shop appropriately enough called inflation insights? What 689 00:36:22,040 --> 00:36:25,040 Speaker 1: is like the basic sales pitch of what you can 690 00:36:25,040 --> 00:36:28,799 Speaker 1: bring to the table. Sure, so you know, for for me, 691 00:36:28,880 --> 00:36:33,560 Speaker 1: my target audience, is mostly um, you know institutional clients, right, Um, 692 00:36:33,600 --> 00:36:35,839 Speaker 1: the folks who actually are are trading tips and who 693 00:36:35,840 --> 00:36:39,080 Speaker 1: are trading to fix things. Um, So in that respect 694 00:36:39,239 --> 00:36:41,680 Speaker 1: that the pitch is really sort of uh. I would 695 00:36:41,680 --> 00:36:44,319 Speaker 1: say there's kind of three main elements. One is the 696 00:36:44,320 --> 00:36:47,040 Speaker 1: actual forecast. You know, for me, luckily, I've been doing 697 00:36:47,040 --> 00:36:50,080 Speaker 1: this long enough where I've got a reputation, I've got 698 00:36:50,120 --> 00:36:52,000 Speaker 1: a hit track record in history that I can present 699 00:36:52,040 --> 00:36:54,560 Speaker 1: to clients and say, look what I'm trying to build. 700 00:36:54,560 --> 00:36:56,880 Speaker 1: Here is the best in class forecasts, Um that you 701 00:36:56,920 --> 00:36:58,960 Speaker 1: will get on the c P I S N s 702 00:36:59,040 --> 00:37:02,279 Speaker 1: A Index, which is what matters for tips, and kind 703 00:37:02,280 --> 00:37:05,080 Speaker 1: of here's my my history of that, and that's you know, 704 00:37:05,440 --> 00:37:07,640 Speaker 1: the goal for is to have that be the best 705 00:37:07,640 --> 00:37:10,440 Speaker 1: in class moving forward. The second is just the detailed 706 00:37:10,440 --> 00:37:14,640 Speaker 1: analysis making sure that everyone understands what's going on. And 707 00:37:14,719 --> 00:37:17,560 Speaker 1: the timeliness I think also matters quite a lot. So 708 00:37:18,040 --> 00:37:20,120 Speaker 1: you know, the stuff that I put out typically will 709 00:37:20,160 --> 00:37:22,640 Speaker 1: be well in advance of anything you're gonna get from 710 00:37:22,640 --> 00:37:26,440 Speaker 1: the south side, and it will give you an opportunity. Um, 711 00:37:26,520 --> 00:37:28,080 Speaker 1: you know, if you agree with my view, for example, 712 00:37:28,360 --> 00:37:30,960 Speaker 1: that it will give you opportunity to actually traded in 713 00:37:31,000 --> 00:37:33,640 Speaker 1: the market before the CPI comes out, whereas right now 714 00:37:33,680 --> 00:37:35,919 Speaker 1: a lot of seal side research, you know, it's coming 715 00:37:35,920 --> 00:37:38,840 Speaker 1: out forty eight hours before the number prints, and that's 716 00:37:38,880 --> 00:37:41,840 Speaker 1: really not much of an edge, but it's the detail analysis, 717 00:37:41,840 --> 00:37:45,600 Speaker 1: the timeliness, and then finally, you know, I would say 718 00:37:45,600 --> 00:37:48,520 Speaker 1: I'm probably on the horn with the BLS, if not daily, 719 00:37:48,760 --> 00:37:50,920 Speaker 1: you know, at least once a week. Even though I've 720 00:37:50,960 --> 00:37:52,880 Speaker 1: been doing this for a long time. It is honestly 721 00:37:52,960 --> 00:37:56,440 Speaker 1: just a constant kind of learning process. I mean, there's 722 00:37:57,440 --> 00:38:01,600 Speaker 1: there's about two eleven indicators that go into the cp I. 723 00:38:02,200 --> 00:38:06,360 Speaker 1: There's over seven thousand basic item in area indexes that 724 00:38:06,440 --> 00:38:09,440 Speaker 1: you could look at, and so it's just kind of 725 00:38:09,440 --> 00:38:12,680 Speaker 1: a constant learning process. And it's you know, for me, 726 00:38:12,719 --> 00:38:16,000 Speaker 1: I've always had this likely good rapport with with the 727 00:38:16,000 --> 00:38:18,280 Speaker 1: folks there, who I think are incredibly helpful in terms 728 00:38:18,280 --> 00:38:21,680 Speaker 1: of learning about the components and so on, And that's 729 00:38:21,840 --> 00:38:23,880 Speaker 1: sort of the kind of I think, you know, the 730 00:38:23,960 --> 00:38:25,759 Speaker 1: kind of knowledge you're not really going to be able 731 00:38:25,800 --> 00:38:29,080 Speaker 1: to get most other places. So now I have to 732 00:38:29,120 --> 00:38:32,280 Speaker 1: ask how specific you can actually get when it comes 733 00:38:32,320 --> 00:38:36,640 Speaker 1: to the inflation baskets. So this is a really weird question. 734 00:38:36,680 --> 00:38:39,520 Speaker 1: But I went on like a massive tangent a couple 735 00:38:39,560 --> 00:38:41,879 Speaker 1: of weeks ago because there was a restaurant in North 736 00:38:41,920 --> 00:38:45,200 Speaker 1: Carolina that that a guy was quoted as saying that 737 00:38:45,239 --> 00:38:47,960 Speaker 1: he was spending two hundred dollars more per week in 738 00:38:48,120 --> 00:38:52,040 Speaker 1: mayonnaise because of inflation, and so of course everyone started 739 00:38:52,080 --> 00:38:55,960 Speaker 1: calculating like, well, how much mayonnaise is this restaurant actually 740 00:38:56,000 --> 00:38:59,080 Speaker 1: buying based on CPI, And then I started going on 741 00:38:59,120 --> 00:39:02,520 Speaker 1: Bloomberg and looking at the components and c p I, 742 00:39:02,680 --> 00:39:07,240 Speaker 1: and it turns out mayonnaise comes under the salad dressing 743 00:39:07,480 --> 00:39:12,120 Speaker 1: um and spreads basket. And so I guess I'm just curious, like, 744 00:39:13,120 --> 00:39:15,840 Speaker 1: how in the weeds do you go? And can you 745 00:39:15,920 --> 00:39:20,120 Speaker 1: give me like a quick, uh quick read on what's 746 00:39:20,120 --> 00:39:24,319 Speaker 1: going on with with salad dressings? Yeah, so I don't 747 00:39:24,360 --> 00:39:26,560 Speaker 1: know if I can pull up the salad dressing forecast 748 00:39:26,719 --> 00:39:30,560 Speaker 1: just now, but you know I would stick. When you 749 00:39:30,600 --> 00:39:33,319 Speaker 1: get to kind of that level, Um, you have to 750 00:39:33,320 --> 00:39:35,399 Speaker 1: make choices, right, I mean, like I said, there's there's 751 00:39:35,440 --> 00:39:38,200 Speaker 1: over seven thousand I had to marry indexes. Uh, there's 752 00:39:38,239 --> 00:39:41,880 Speaker 1: over two sort of broad components in the c p I. 753 00:39:41,960 --> 00:39:44,200 Speaker 1: Most likely when you can kind of get something like food, 754 00:39:44,520 --> 00:39:48,239 Speaker 1: which has dozens of indexes, you kind of have to 755 00:39:48,280 --> 00:39:50,960 Speaker 1: make a choice in terms of how far are you 756 00:39:51,040 --> 00:39:53,680 Speaker 1: going to drill down. So I might follow all of these, 757 00:39:53,960 --> 00:39:55,879 Speaker 1: you know, I've got them in my spreadsheets and so on, 758 00:39:56,280 --> 00:39:58,400 Speaker 1: but when it kind of comes to forecasting, you're probably 759 00:39:58,440 --> 00:40:00,759 Speaker 1: gonna want to stick, for example, with looking at the 760 00:40:00,800 --> 00:40:03,160 Speaker 1: broader too, which is the food at home index, which 761 00:40:03,239 --> 00:40:06,520 Speaker 1: kind of encompasses the entire grocery basket and the food 762 00:40:06,520 --> 00:40:09,359 Speaker 1: away from home with restaurant prices and there with within 763 00:40:09,440 --> 00:40:11,960 Speaker 1: food at home. You know, you would if you want 764 00:40:11,960 --> 00:40:13,839 Speaker 1: to drill down, you would break break it down into 765 00:40:13,840 --> 00:40:16,800 Speaker 1: some of these components, so cereals, you know, the various 766 00:40:16,800 --> 00:40:19,880 Speaker 1: types of meats, eggs, food, vegetables and so on. But 767 00:40:20,840 --> 00:40:22,680 Speaker 1: it doesn't mean that you're necessarily going to go in 768 00:40:22,800 --> 00:40:26,680 Speaker 1: and you know, forecast uncooked beef steaks for example, right, 769 00:40:26,719 --> 00:40:29,680 Speaker 1: I mean you could, you could, but it would take 770 00:40:29,719 --> 00:40:32,000 Speaker 1: you a month or longer to just come up with 771 00:40:32,520 --> 00:40:35,120 Speaker 1: a simple forecast. I mean I used to spend probably 772 00:40:35,160 --> 00:40:38,360 Speaker 1: two days just doing the food forecast. So you have 773 00:40:38,440 --> 00:40:41,239 Speaker 1: to kind of make some choices about the timeliness of 774 00:40:41,239 --> 00:40:44,680 Speaker 1: your forecast and how uh, into the weeds you're going 775 00:40:44,719 --> 00:40:46,759 Speaker 1: to be able to go in order to produce something 776 00:40:46,800 --> 00:40:50,279 Speaker 1: that's actually you know, actionable. So this reminds me of 777 00:40:50,320 --> 00:40:52,600 Speaker 1: something I wanted to ask you. Um So, when I 778 00:40:52,640 --> 00:40:56,759 Speaker 1: was in my Mayo analysis adventure, one of the things 779 00:40:56,840 --> 00:40:59,000 Speaker 1: I was trying to do because I couldn't find an 780 00:40:59,000 --> 00:41:02,799 Speaker 1: inflation pick up in the official CPI basket, but I 781 00:41:02,880 --> 00:41:06,400 Speaker 1: tried to look at um an Amazon tracking website to 782 00:41:06,480 --> 00:41:10,400 Speaker 1: see if prices had gone up on Amazon. So I'm curious, 783 00:41:10,440 --> 00:41:13,400 Speaker 1: do you ever look at alternate data in order to 784 00:41:13,440 --> 00:41:15,880 Speaker 1: make your forecast? Yea, So there's a couple of things, 785 00:41:16,080 --> 00:41:19,839 Speaker 1: UM I do for certain components that where I will 786 00:41:19,880 --> 00:41:23,279 Speaker 1: look at you know, uh, non BLS data sets to 787 00:41:23,400 --> 00:41:25,239 Speaker 1: try to get a sense of what's going on. And 788 00:41:25,280 --> 00:41:28,040 Speaker 1: one of those, for example, is just airfares. Air Fares 789 00:41:28,120 --> 00:41:29,480 Speaker 1: is only worth you know, a little bit less on 790 00:41:29,440 --> 00:41:32,120 Speaker 1: one percent of the core, but it's it's basically been 791 00:41:32,120 --> 00:41:34,400 Speaker 1: the bane of my existence and forecasting for the last 792 00:41:34,440 --> 00:41:39,120 Speaker 1: you know, fifteen years, because it's incredibly convoluted the way 793 00:41:39,120 --> 00:41:42,080 Speaker 1: that it's done. But um, you know, at the end 794 00:41:42,080 --> 00:41:44,680 Speaker 1: of the day, what they're really pricing is you know, 795 00:41:45,360 --> 00:41:47,640 Speaker 1: they're going to the websites of you know, Delta American 796 00:41:47,719 --> 00:41:50,560 Speaker 1: Southwest so on UM, and they're pricing flights out, so 797 00:41:50,640 --> 00:41:53,800 Speaker 1: you can try and come up with an index yourself 798 00:41:54,239 --> 00:41:56,319 Speaker 1: where you just you know, go onto these websites and 799 00:41:56,400 --> 00:41:59,440 Speaker 1: try to say, hey, what's the flight, um, you know, 800 00:41:59,520 --> 00:42:01,239 Speaker 1: from your to l A going to cost me or 801 00:42:01,440 --> 00:42:04,600 Speaker 1: new or to Miami or whatever. And so you can 802 00:42:04,600 --> 00:42:06,600 Speaker 1: sort of look at those sorts of data to try 803 00:42:06,600 --> 00:42:09,839 Speaker 1: to help you forecast, um the airfare sundas. You can 804 00:42:09,840 --> 00:42:12,440 Speaker 1: look at you know, Black Book and JD Power and 805 00:42:12,480 --> 00:42:14,560 Speaker 1: so on to get a sense of what use card 806 00:42:14,600 --> 00:42:18,240 Speaker 1: prices might do. And then there's of course the Billion 807 00:42:18,280 --> 00:42:20,400 Speaker 1: Prices project there. I think you just have to be 808 00:42:20,440 --> 00:42:22,399 Speaker 1: a little bit careful because a lot of what they 809 00:42:22,440 --> 00:42:26,040 Speaker 1: are capturing is much more UM has much more to 810 00:42:26,080 --> 00:42:28,600 Speaker 1: do with goods prices and much less to do with 811 00:42:28,640 --> 00:42:31,719 Speaker 1: services prices. But for goods prices, you know, that does 812 00:42:31,760 --> 00:42:34,919 Speaker 1: a pretty decent job from time to time. So there 813 00:42:34,920 --> 00:42:37,280 Speaker 1: are other things that you can certainly look at gas Buddy, 814 00:42:37,280 --> 00:42:39,880 Speaker 1: which is actually now being used directly in the c 815 00:42:40,040 --> 00:42:42,719 Speaker 1: p I UM. So they've gone from having you know, 816 00:42:42,960 --> 00:42:46,200 Speaker 1: one thousand quotes on gasoline each month to having millions 817 00:42:46,200 --> 00:42:50,320 Speaker 1: of quotes because essentially they've crowdsourced the data is something 818 00:42:50,360 --> 00:42:51,960 Speaker 1: else that you can also look at. So there definitely 819 00:42:52,040 --> 00:42:54,960 Speaker 1: are alternative data sets that you can try to to 820 00:42:55,040 --> 00:42:58,600 Speaker 1: work into into your forecast. I just want to say, Tracy, 821 00:42:58,719 --> 00:43:01,279 Speaker 1: you you stole the question right. That was literally the 822 00:43:01,280 --> 00:43:03,360 Speaker 1: next thing I was gonna ask you. No, no, no, 823 00:43:03,440 --> 00:43:05,040 Speaker 1: that was great, You asked it. Great. But I just 824 00:43:05,120 --> 00:43:08,719 Speaker 1: weirdly continue to be perfect blade length. You know, I'm 825 00:43:09,400 --> 00:43:12,399 Speaker 1: bigger picture, or I guess sort of like medium term. 826 00:43:12,440 --> 00:43:15,279 Speaker 1: You know, you present, as you said a little bit ago, 827 00:43:15,320 --> 00:43:17,719 Speaker 1: when you look at some of the broader metrics, you 828 00:43:17,800 --> 00:43:23,319 Speaker 1: don't necessarily see a sustained upward move in inflation that 829 00:43:23,400 --> 00:43:25,839 Speaker 1: there isn't necessarily this kind of momentum that even if 830 00:43:25,880 --> 00:43:29,160 Speaker 1: rent were to go above um grow at a pace 831 00:43:29,280 --> 00:43:32,560 Speaker 1: that's well above historical averages, they might be offset. What 832 00:43:32,680 --> 00:43:35,240 Speaker 1: would make you worried or what would make you think, Okay, 833 00:43:35,280 --> 00:43:38,920 Speaker 1: this is going to be a type of elevated inflation 834 00:43:38,960 --> 00:43:44,320 Speaker 1: that persists. And maybe monetary policy doesn't really affect inflation 835 00:43:44,400 --> 00:43:47,719 Speaker 1: at least in the medium term, but inflation could certainly 836 00:43:47,960 --> 00:43:51,160 Speaker 1: affect monetary policy if the FED gets spooped or so forth. 837 00:43:51,280 --> 00:43:55,359 Speaker 1: So I assume that's important information for investors. What would 838 00:43:55,400 --> 00:43:57,840 Speaker 1: you be looking for saying through the rest of this 839 00:43:57,960 --> 00:44:00,279 Speaker 1: year or early next year to say, oh, this is 840 00:44:00,600 --> 00:44:02,960 Speaker 1: going to be higher and more persistent than I would 841 00:44:02,960 --> 00:44:05,040 Speaker 1: have gived. Yeah. So one of the things that's you know, 842 00:44:05,120 --> 00:44:07,600 Speaker 1: kind of come up in the last print or two, um, 843 00:44:07,640 --> 00:44:10,120 Speaker 1: that I'm going to be keeping a pretty close eye 844 00:44:10,120 --> 00:44:13,920 Speaker 1: on going forward, is this, you know, idea of whether 845 00:44:13,960 --> 00:44:16,680 Speaker 1: some of the pickup we've seen recently in wages begins 846 00:44:16,680 --> 00:44:19,959 Speaker 1: to pass through more persistently into the inflation data. And 847 00:44:20,280 --> 00:44:22,520 Speaker 1: you know, we saw this actually last month in the 848 00:44:22,520 --> 00:44:25,680 Speaker 1: food away from Home index. Um. You know, there was 849 00:44:25,880 --> 00:44:29,040 Speaker 1: a pretty big increase in what are called you know, 850 00:44:29,239 --> 00:44:33,160 Speaker 1: limited service restaurants so fast food, and it was for 851 00:44:33,160 --> 00:44:36,399 Speaker 1: for that index was was a huge huge jump. And 852 00:44:37,360 --> 00:44:39,759 Speaker 1: we know that wages are going up in lena leisure 853 00:44:39,800 --> 00:44:42,360 Speaker 1: and hospitality for example, and so the idea that some 854 00:44:42,440 --> 00:44:45,240 Speaker 1: of this might be feeding through, for example, into hotel 855 00:44:45,320 --> 00:44:48,919 Speaker 1: rates or limited service restaurants and things like that, those 856 00:44:48,920 --> 00:44:52,239 Speaker 1: are areas where potentially you start to say, okay, you know, 857 00:44:52,320 --> 00:44:54,360 Speaker 1: we've keep seeing wages move up at these sorts of 858 00:44:54,440 --> 00:44:57,440 Speaker 1: rates if this is what, if this really is that 859 00:44:57,520 --> 00:44:59,520 Speaker 1: kind of a path through, then this is potentially something 860 00:44:59,560 --> 00:45:02,680 Speaker 1: that is more persistent that will last into next year. 861 00:45:02,719 --> 00:45:04,719 Speaker 1: And it's not going to be something you know, sort 862 00:45:04,719 --> 00:45:06,600 Speaker 1: of a one off shock like let's say, you know, 863 00:45:06,640 --> 00:45:08,960 Speaker 1: oil price shock or something of that nature. You know, 864 00:45:09,040 --> 00:45:11,719 Speaker 1: this is something that is potentially more persistent. And I 865 00:45:11,760 --> 00:45:13,719 Speaker 1: have to say, I think even with something like use cars, 866 00:45:13,840 --> 00:45:15,880 Speaker 1: we know they're starting to come off, I am a 867 00:45:15,920 --> 00:45:18,480 Speaker 1: little bit wary of just kind of having a repeat 868 00:45:18,520 --> 00:45:21,120 Speaker 1: of what we had, which is, you know, last summer 869 00:45:21,120 --> 00:45:23,920 Speaker 1: we had a huge jumping used car prices, it completely 870 00:45:24,000 --> 00:45:27,520 Speaker 1: tailed off, they declined throughout the fall and winter until 871 00:45:27,640 --> 00:45:30,800 Speaker 1: we had another huge burst over the last several months. 872 00:45:31,640 --> 00:45:33,560 Speaker 1: And whether that's you know, mostly a function of sort 873 00:45:33,560 --> 00:45:35,520 Speaker 1: of the demand side or the supplies I'm definitely more 874 00:45:35,520 --> 00:45:38,440 Speaker 1: on the supply side part of that story. But you know, 875 00:45:38,560 --> 00:45:40,719 Speaker 1: it's we still have kind of to deal with this 876 00:45:40,800 --> 00:45:42,759 Speaker 1: idea of the delta variant and what's what is that 877 00:45:42,800 --> 00:45:45,560 Speaker 1: going to do to activity going forward? What's that going 878 00:45:45,600 --> 00:45:47,520 Speaker 1: to do for the demand for use cars and new 879 00:45:47,640 --> 00:45:50,799 Speaker 1: vehicles you know later into this year and into next year. 880 00:45:52,000 --> 00:45:54,120 Speaker 1: I don't know that anyone's got a good answer for that. 881 00:45:54,200 --> 00:45:56,760 Speaker 1: But that is something that I think kind of remains 882 00:45:56,800 --> 00:45:59,840 Speaker 1: or upward risk on the in the inflation story. Is 883 00:46:00,600 --> 00:46:02,640 Speaker 1: you know, we sort of see a repeat of some 884 00:46:02,719 --> 00:46:06,600 Speaker 1: of these um some of these upward pressures from from 885 00:46:06,640 --> 00:46:09,239 Speaker 1: you know, something like the delta variant going forward, but 886 00:46:09,280 --> 00:46:11,600 Speaker 1: more persistently, it would definitely be some of this wage 887 00:46:11,600 --> 00:46:14,080 Speaker 1: passed through into some of these components that I mentioned earlier. 888 00:46:15,000 --> 00:46:17,600 Speaker 1: So on a related note, is there you know, in 889 00:46:17,680 --> 00:46:23,680 Speaker 1: your very long career analyzing inflation, is there any particular 890 00:46:24,280 --> 00:46:28,399 Speaker 1: component that has just remained an absolute mystery to you? 891 00:46:28,520 --> 00:46:31,920 Speaker 1: And that is sort of like I guess, immune to 892 00:46:32,360 --> 00:46:36,399 Speaker 1: the bottom up analytical approach, like something that really flaw 893 00:46:36,480 --> 00:46:41,160 Speaker 1: makes you Yeah, I think apparel. Apparel is the is 894 00:46:41,200 --> 00:46:44,080 Speaker 1: one where in you know, years of doing this, I've 895 00:46:44,200 --> 00:46:49,320 Speaker 1: literally just never found anything that works at forecasting apparel 896 00:46:50,360 --> 00:46:53,000 Speaker 1: other than you know, one of the approaches I mentioned earlier, 897 00:46:53,040 --> 00:46:56,560 Speaker 1: which is just this naive approach of saying, Okay, you know, 898 00:46:56,600 --> 00:46:59,200 Speaker 1: looking at these things on an unadjusted basis, you get 899 00:46:59,239 --> 00:47:01,919 Speaker 1: a sense for a sort of seasonal patterns, and uh, 900 00:47:02,000 --> 00:47:05,200 Speaker 1: you know, let's say apparel every March tends to decline 901 00:47:05,200 --> 00:47:08,600 Speaker 1: by about two tents. You're going to be probably um, 902 00:47:09,239 --> 00:47:11,040 Speaker 1: doing a decent job if you put in you know, 903 00:47:11,320 --> 00:47:14,040 Speaker 1: a drop of two tents, because there's very little to 904 00:47:14,080 --> 00:47:16,719 Speaker 1: go on when it comes to two apparel prices. You know, 905 00:47:16,760 --> 00:47:20,920 Speaker 1: I've tried using everything from import prices, um two you know, 906 00:47:21,000 --> 00:47:23,919 Speaker 1: different data sets, retail sales and so on, and there's 907 00:47:23,920 --> 00:47:27,520 Speaker 1: just nothing that is gives you a good lead into 908 00:47:27,520 --> 00:47:30,600 Speaker 1: what apparel is doing. And that's been you know, that's 909 00:47:30,600 --> 00:47:34,160 Speaker 1: been another tough one to do. UM, not as difficult 910 00:47:34,200 --> 00:47:37,120 Speaker 1: as as air fares. Air fares at least you know 911 00:47:37,160 --> 00:47:41,319 Speaker 1: what they're doing, UM, you just can't replicate it exactly. 912 00:47:41,760 --> 00:47:44,200 Speaker 1: But Apparel's yeah, apparel is one that has just really 913 00:47:44,800 --> 00:47:46,600 Speaker 1: you sort of just have to lick the patterns and 914 00:47:46,600 --> 00:47:49,400 Speaker 1: and honestly, it's it's it's much more about kind of 915 00:47:49,400 --> 00:47:51,719 Speaker 1: looking at these patterns and thinking about, you know, to 916 00:47:51,760 --> 00:47:54,239 Speaker 1: what extent you're following the same trajectory as you did 917 00:47:54,280 --> 00:47:57,440 Speaker 1: in the past for something like apparel. So one of 918 00:47:57,480 --> 00:48:02,080 Speaker 1: the sources of like constant controversy and you know CPI inflation, 919 00:48:02,160 --> 00:48:04,319 Speaker 1: truth thors and so forth always like to talk about 920 00:48:04,360 --> 00:48:07,680 Speaker 1: it is like the so called hedonic adjustments, and they're 921 00:48:07,680 --> 00:48:10,000 Speaker 1: always like, oh, this is not you know, we all 922 00:48:10,080 --> 00:48:14,480 Speaker 1: we've all heard the conspiracy theories right now. For example, though, 923 00:48:14,719 --> 00:48:17,839 Speaker 1: if if everyone is complaining about the service they get 924 00:48:17,840 --> 00:48:21,600 Speaker 1: at restaurants because of the so called worker shortage, people 925 00:48:21,640 --> 00:48:24,279 Speaker 1: are complaining about the service they get at hotels, and 926 00:48:24,360 --> 00:48:27,319 Speaker 1: we know, for example that hotels in some cases have 927 00:48:27,440 --> 00:48:31,319 Speaker 1: degraded service or not picking up towels is often or 928 00:48:31,400 --> 00:48:35,120 Speaker 1: whatever it is. Are these sorts of things captured to 929 00:48:35,320 --> 00:48:37,040 Speaker 1: the BLS. I mean, as you said, you talk to 930 00:48:37,040 --> 00:48:39,600 Speaker 1: them all the time and you're trying to learn their approach. 931 00:48:39,960 --> 00:48:43,319 Speaker 1: Are they trying to capture these types of things such 932 00:48:43,440 --> 00:48:46,600 Speaker 1: that maybe the experience at a restaurant or a hotel 933 00:48:46,800 --> 00:48:52,120 Speaker 1: isn't what it was in no, so hedonic adjustments are 934 00:48:52,200 --> 00:48:57,960 Speaker 1: just applied um two goods. So yeah, and so you know, 935 00:48:58,040 --> 00:49:01,839 Speaker 1: if if I'm sure you kind of remember this whole 936 00:49:01,880 --> 00:49:04,960 Speaker 1: you know, wireless thing in Martial two seventeen. Yeah, so 937 00:49:05,040 --> 00:49:07,560 Speaker 1: things like that is where you see hedonics. Apparel is 938 00:49:07,600 --> 00:49:10,200 Speaker 1: one where you see hedonics um and it so it 939 00:49:10,200 --> 00:49:12,040 Speaker 1: really is just limited to goods. And also when you 940 00:49:12,040 --> 00:49:14,879 Speaker 1: think about hedonic adjustments, for the most part, I think 941 00:49:14,880 --> 00:49:17,200 Speaker 1: it's about only about four or five percent of the 942 00:49:17,239 --> 00:49:21,000 Speaker 1: c p I is actually subject to those sorts of 943 00:49:21,080 --> 00:49:25,120 Speaker 1: kind of quality adjustments. Um, you know, other indexes are 944 00:49:25,160 --> 00:49:27,759 Speaker 1: subject to other types of adjustments, but they tend to 945 00:49:27,800 --> 00:49:31,000 Speaker 1: be much smaller. So for example, with rent, you know, rent, 946 00:49:31,480 --> 00:49:35,160 Speaker 1: there's something called an age bias adjustment because you're let's say, 947 00:49:35,200 --> 00:49:37,319 Speaker 1: let's say you've got an apartment and you happen to 948 00:49:37,360 --> 00:49:39,960 Speaker 1: make it into the BLS survey and in January they 949 00:49:40,000 --> 00:49:41,560 Speaker 1: come to you and they say, you know, what do 950 00:49:41,600 --> 00:49:43,879 Speaker 1: you pay for rent? You give them a number. They 951 00:49:43,920 --> 00:49:46,799 Speaker 1: come back to you in July six months later, and 952 00:49:46,800 --> 00:49:48,480 Speaker 1: of course you let's say you've got your least your 953 00:49:48,480 --> 00:49:52,120 Speaker 1: rent hasn't changed, but your apartment six months older, and 954 00:49:52,160 --> 00:49:55,239 Speaker 1: so they apply what's called an age bias adjustment to 955 00:49:55,239 --> 00:49:58,279 Speaker 1: to your apartment. It doesn't really change, you know, very much. 956 00:49:58,360 --> 00:50:01,600 Speaker 1: But those are the other sort of types of adjustments 957 00:50:01,600 --> 00:50:04,120 Speaker 1: that the BLS will make. But for hedonics, it's it's 958 00:50:04,160 --> 00:50:07,239 Speaker 1: just not a big fraction of the index that is 959 00:50:07,280 --> 00:50:09,360 Speaker 1: really kind of getting that kind of a treatment. And 960 00:50:09,360 --> 00:50:11,279 Speaker 1: it's really only you know, it only really comes to 961 00:50:11,360 --> 00:50:13,359 Speaker 1: light when you have these huge moves like you did 962 00:50:13,360 --> 00:50:16,239 Speaker 1: with wireless a couple of years ago. Those sorts of 963 00:50:16,280 --> 00:50:19,280 Speaker 1: quality adjustments. UM. You know, they get a lot of press, 964 00:50:19,520 --> 00:50:22,280 Speaker 1: but they don't really remind people what happened. I remember 965 00:50:22,280 --> 00:50:24,319 Speaker 1: the wirelessing happening, but I don't remember what it was. 966 00:50:24,400 --> 00:50:26,239 Speaker 1: Can you just remind me? Yeah, I think we went 967 00:50:26,280 --> 00:50:29,920 Speaker 1: to um, we went to unlimited wireless plans, and I 968 00:50:29,960 --> 00:50:33,160 Speaker 1: think it's February March in two seventeen. So you know, 969 00:50:33,160 --> 00:50:36,080 Speaker 1: when we switched over from let's say you paid however much, 970 00:50:36,280 --> 00:50:39,040 Speaker 1: however much your bill was for a certain amount for 971 00:50:39,080 --> 00:50:41,879 Speaker 1: your phone. When we went to unlimited plans and you had, 972 00:50:41,960 --> 00:50:45,359 Speaker 1: you know, instead of having you know, time gigs and 973 00:50:45,400 --> 00:50:47,920 Speaker 1: you maybe you had thirty or whatever it was, they 974 00:50:47,960 --> 00:50:51,200 Speaker 1: had to find a way to price that out. And 975 00:50:51,440 --> 00:50:53,759 Speaker 1: that's where kind of the hedonic regressions came in, was 976 00:50:53,840 --> 00:50:57,440 Speaker 1: to say, you know, how much is this extra speed worth, 977 00:50:57,520 --> 00:51:00,359 Speaker 1: or how much is this extra memory worth? Um? How 978 00:51:00,440 --> 00:51:04,160 Speaker 1: much is extra data plan worth to the consumer. And 979 00:51:04,280 --> 00:51:06,640 Speaker 1: once they came up with those measurements, they applied them 980 00:51:06,640 --> 00:51:09,319 Speaker 1: and what it ended up leading to was about a 981 00:51:09,320 --> 00:51:13,040 Speaker 1: seven percent decline in the wireless index in a one month, 982 00:51:13,040 --> 00:51:17,560 Speaker 1: which is a record decline. And it's subtracted about almost 983 00:51:17,560 --> 00:51:19,440 Speaker 1: close to a tent just a bit over a tent 984 00:51:19,560 --> 00:51:23,120 Speaker 1: off of the monthly changing the core CPI, and you know, 985 00:51:23,200 --> 00:51:26,640 Speaker 1: that's a huge, huge number, got a lot of attention, 986 00:51:26,719 --> 00:51:29,239 Speaker 1: and so people start talking about hedonics again, and you 987 00:51:29,280 --> 00:51:31,319 Speaker 1: know that's where you know, the so called sort of 988 00:51:31,320 --> 00:51:33,560 Speaker 1: inflation truths come in. It's like, well, this is just 989 00:51:33,600 --> 00:51:36,799 Speaker 1: this arbitrary adjustment that they're making and so on. But 990 00:51:37,120 --> 00:51:38,600 Speaker 1: you know, this is the kind of stuff that goes 991 00:51:38,640 --> 00:51:41,359 Speaker 1: on in a pretty regular basis. Typically you just don't 992 00:51:41,400 --> 00:51:43,399 Speaker 1: see those kinds of moves. But for the most part, 993 00:51:43,440 --> 00:51:45,480 Speaker 1: these things are are pretty standard and not just for 994 00:51:45,560 --> 00:51:47,440 Speaker 1: by the way, not just for the BLS, but almost 995 00:51:47,480 --> 00:51:50,239 Speaker 1: for every statistical agency that does a cp I. At 996 00:51:50,239 --> 00:51:51,480 Speaker 1: the end of the day, the c p I is 997 00:51:51,800 --> 00:51:54,439 Speaker 1: you always want a price between you know, one month 998 00:51:54,440 --> 00:51:58,040 Speaker 1: and the prior month the same good, and if it's 999 00:51:58,120 --> 00:52:00,200 Speaker 1: changing in quality, you have to try to control all 1000 00:52:00,640 --> 00:52:03,239 Speaker 1: for that quality. So these adjustments are happening, you know 1001 00:52:03,400 --> 00:52:07,960 Speaker 1: Canadian cp I and your status so on. So it's 1002 00:52:07,960 --> 00:52:12,600 Speaker 1: a pretty sort of time tested methodology that everyone uses 1003 00:52:13,160 --> 00:52:16,719 Speaker 1: in all kinds of m consumer price and nexes. So 1004 00:52:16,800 --> 00:52:18,880 Speaker 1: I just have one last question and it's sort of 1005 00:52:18,960 --> 00:52:20,759 Speaker 1: big picture. But you know, in the very beginning you 1006 00:52:20,840 --> 00:52:25,759 Speaker 1: talked about, Okay, different regimes, different times, different relationships might work. 1007 00:52:25,840 --> 00:52:29,279 Speaker 1: Philip's curve thinking sometimes seems to be robust, sometimes not 1008 00:52:29,480 --> 00:52:32,560 Speaker 1: so much. One of the things in the post crisis 1009 00:52:32,640 --> 00:52:34,799 Speaker 1: period is people are asking, well, is this like a 1010 00:52:34,800 --> 00:52:37,719 Speaker 1: new regime, like as this is the economy now just 1011 00:52:37,760 --> 00:52:40,080 Speaker 1: going to be fundamentally different, maybe because of some sort 1012 00:52:40,080 --> 00:52:43,279 Speaker 1: of change to international trade or something like that. Is 1013 00:52:43,320 --> 00:52:45,640 Speaker 1: that something that you're on the lookout for or thinking 1014 00:52:45,680 --> 00:52:48,840 Speaker 1: that maybe like even post virus, maybe we'll get something 1015 00:52:49,040 --> 00:52:53,080 Speaker 1: resembling normalization, but that's something structurally might be a different 1016 00:52:53,120 --> 00:52:57,160 Speaker 1: economy than we had pre crisis, thus forcing you know, 1017 00:52:57,200 --> 00:52:59,800 Speaker 1: thus causing a sort of different way to think about 1018 00:53:00,080 --> 00:53:04,120 Speaker 1: what might uh manifest inflation. Yes, I think let me 1019 00:53:04,200 --> 00:53:05,799 Speaker 1: preface this by saying, you know, for the most part, 1020 00:53:05,880 --> 00:53:08,520 Speaker 1: economists are really terrible at picking up like turning points 1021 00:53:08,560 --> 00:53:11,520 Speaker 1: and you know, paradigm shifts and things of that nature, 1022 00:53:12,120 --> 00:53:14,040 Speaker 1: which is why there's such a large literature on how 1023 00:53:14,040 --> 00:53:16,319 Speaker 1: to forecast inflation. But yeah, I think one of the 1024 00:53:16,360 --> 00:53:18,440 Speaker 1: things that you know, at least I'm on the lookout 1025 00:53:18,440 --> 00:53:19,880 Speaker 1: for it, and I think others are as well. Is 1026 00:53:20,200 --> 00:53:23,200 Speaker 1: to think about the idea of how all this sort 1027 00:53:23,200 --> 00:53:25,759 Speaker 1: of disturbce and supply chains is potentially going to lead to, 1028 00:53:25,800 --> 00:53:28,400 Speaker 1: let's say, on shoring. You know, we're talking about building 1029 00:53:28,400 --> 00:53:32,680 Speaker 1: more semiconductor factories here in the US. We're talking about 1030 00:53:32,680 --> 00:53:35,480 Speaker 1: having sort of you know, more of you know, manufacturing 1031 00:53:36,000 --> 00:53:38,160 Speaker 1: in the US, And you know, what does that mean 1032 00:53:38,239 --> 00:53:42,160 Speaker 1: for for inflation going forward? So that's that's potentially a 1033 00:53:42,160 --> 00:53:43,960 Speaker 1: big paradigm shift that I think we need to be 1034 00:53:44,000 --> 00:53:46,799 Speaker 1: on the lookout for. But is that going to be 1035 00:53:46,840 --> 00:53:50,080 Speaker 1: a six, twelve, eighteen month thing. Uh, you know, I'm 1036 00:53:50,080 --> 00:53:53,880 Speaker 1: pretty skeptical of that. To meet that is a much broader, 1037 00:53:53,960 --> 00:53:56,160 Speaker 1: much sort of you know, longer tenure type of topic 1038 00:53:56,640 --> 00:54:00,880 Speaker 1: um to think about, and you know, probably not something 1039 00:54:00,920 --> 00:54:02,520 Speaker 1: that you're really going to be able to capture kind 1040 00:54:02,520 --> 00:54:06,399 Speaker 1: of doing a bottom up type forecast. Omar Sharif, thank 1041 00:54:06,400 --> 00:54:08,799 Speaker 1: you so much for coming on odd lots really appreciate it. 1042 00:54:09,560 --> 00:54:12,920 Speaker 1: Thank you appreciate it. Thanks O Mary. That was fantastic. 1043 00:54:13,560 --> 00:54:28,399 Speaker 1: Thanks Sorry, I really like that was great. I really 1044 00:54:28,440 --> 00:54:31,879 Speaker 1: like talking to Amara. I feel like, at least right now, 1045 00:54:32,440 --> 00:54:35,600 Speaker 1: it really feels like if you're not doing some sort 1046 00:54:35,600 --> 00:54:39,080 Speaker 1: of bottoms up analysis where you're actually looking at the component. 1047 00:54:39,400 --> 00:54:42,200 Speaker 1: There's probably like no hope to understanding what's going on 1048 00:54:42,280 --> 00:54:45,400 Speaker 1: with inflation. Yeah, totally, especially since so much of it 1049 00:54:45,400 --> 00:54:48,680 Speaker 1: seems to be driven by the reopening like not just 1050 00:54:49,120 --> 00:54:52,520 Speaker 1: the reopening categories, but literally one or two or maybe 1051 00:54:52,520 --> 00:54:55,640 Speaker 1: three reopening categories like a big chunk down to use 1052 00:54:55,760 --> 00:55:01,040 Speaker 1: car prices, air affairs, um and hotels. I think. Yeah. Uh. 1053 00:55:00,840 --> 00:55:03,400 Speaker 1: I also thought it was super interesting that, you know, 1054 00:55:03,480 --> 00:55:05,719 Speaker 1: it's like that he sort of pushed back a little 1055 00:55:05,719 --> 00:55:08,320 Speaker 1: bit about the so called like stories we tell about 1056 00:55:08,360 --> 00:55:11,800 Speaker 1: even those categories, and so even though like, okay, we 1057 00:55:11,800 --> 00:55:15,600 Speaker 1: can talk about semi conductors are shipping containers, but that actually, 1058 00:55:15,719 --> 00:55:18,399 Speaker 1: per his view, you have to go even deeper and 1059 00:55:18,480 --> 00:55:22,480 Speaker 1: just like really get to know index construction and really 1060 00:55:22,480 --> 00:55:27,760 Speaker 1: no methodology and seasonality and to actually sort of do it. 1061 00:55:27,800 --> 00:55:29,439 Speaker 1: Is not enough to just be able to like sort 1062 00:55:29,440 --> 00:55:32,360 Speaker 1: of like tell some like bigger stories about the categories 1063 00:55:32,360 --> 00:55:35,400 Speaker 1: that are really moving. Yeah. I'm very curious about the 1064 00:55:35,400 --> 00:55:38,720 Speaker 1: seasonality portion of it. And I guess, like if everything 1065 00:55:38,800 --> 00:55:42,279 Speaker 1: is so seasonal and predictable, why do people still get 1066 00:55:42,280 --> 00:55:45,799 Speaker 1: it wrong occasionally? I guess it goes back to what 1067 00:55:45,840 --> 00:55:48,719 Speaker 1: we started um the episode talking about, which is this 1068 00:55:48,800 --> 00:55:53,440 Speaker 1: idea that you know, despite decades and decades of studying inflation, 1069 00:55:53,680 --> 00:55:56,960 Speaker 1: it does feel like economists certainly struggle to look at 1070 00:55:57,000 --> 00:56:00,799 Speaker 1: it as a whole. It's also interesting, Uh, by the way, 1071 00:56:00,840 --> 00:56:03,560 Speaker 1: I really liked your question about mayonnaise inflation or the 1072 00:56:03,840 --> 00:56:06,839 Speaker 1: sailor addressing category. But it is interesting that there were 1073 00:56:06,840 --> 00:56:09,880 Speaker 1: like these categories that he like, you know, he he 1074 00:56:09,920 --> 00:56:12,640 Speaker 1: expressed sort of like confidence about his ability to make 1075 00:56:12,680 --> 00:56:14,640 Speaker 1: a forecast and then other runs. And I think he 1076 00:56:14,680 --> 00:56:17,120 Speaker 1: said like air fairs, and you wouldn't necessarily think of 1077 00:56:17,239 --> 00:56:20,680 Speaker 1: with air fares, because again, it seems like the numbers 1078 00:56:20,680 --> 00:56:23,760 Speaker 1: are kind of transparent or there's like dozens of websites 1079 00:56:23,840 --> 00:56:26,600 Speaker 1: that track airfairs. But it's interesting that they're like these 1080 00:56:26,640 --> 00:56:29,520 Speaker 1: categories that like, you just can't quite crack. I have 1081 00:56:29,560 --> 00:56:32,600 Speaker 1: a great book idea, So what if you, um, what 1082 00:56:32,680 --> 00:56:36,440 Speaker 1: if you went through the like two hundred CPI components 1083 00:56:36,800 --> 00:56:38,840 Speaker 1: and like for each one kind of told the story 1084 00:56:38,840 --> 00:56:41,560 Speaker 1: of the industry and how prices are actually made and 1085 00:56:41,600 --> 00:56:46,400 Speaker 1: how the BLS incorporates them. Yeah, best seller New York Times. No, 1086 00:56:46,480 --> 00:56:48,719 Speaker 1: I think it's interesting. I've read a book one through 1087 00:56:48,880 --> 00:56:53,080 Speaker 1: every ingredient of twinkies, and and that was that was fascinating. 1088 00:56:53,160 --> 00:56:55,600 Speaker 1: There's something like a hundred ingredients in there. You could 1089 00:56:55,600 --> 00:56:59,160 Speaker 1: do the same for CPI. No, Actually, on ironical, maybe 1090 00:56:59,160 --> 00:57:02,360 Speaker 1: what about a a coffee table book like each picture 1091 00:57:02,440 --> 00:57:04,719 Speaker 1: is like sort of like a really glossy photo, beautiful 1092 00:57:04,760 --> 00:57:09,360 Speaker 1: photo of like mayonnaise picture or something like that, and 1093 00:57:09,400 --> 00:57:12,360 Speaker 1: then a page on the left talking about how the 1094 00:57:12,400 --> 00:57:16,960 Speaker 1: prices derived. Yeah, okay, literary agents and publishers hit us up, 1095 00:57:17,040 --> 00:57:20,160 Speaker 1: We're ready to write it. Reach out. Let's leave it there. 1096 00:57:20,280 --> 00:57:23,440 Speaker 1: All right, let's leave it there. This has been another 1097 00:57:23,480 --> 00:57:26,400 Speaker 1: episode of the All Thoughts podcast. I'm Tracy Alloway. You 1098 00:57:26,440 --> 00:57:29,400 Speaker 1: can follow me on Twitter at Tracy Alloway and I'm 1099 00:57:29,480 --> 00:57:32,200 Speaker 1: Joe wisn't thought you could follow me on Twitter at 1100 00:57:32,200 --> 00:57:35,760 Speaker 1: the Stalwart. Follow our guest Omar Sharif. His handle is 1101 00:57:35,880 --> 00:57:40,360 Speaker 1: at f Cast of the Month. Follow our producer Laura Carlson. 1102 00:57:40,480 --> 00:57:43,920 Speaker 1: She's at Laura M. Carlson. Follow the Bloomberg head of 1103 00:57:43,960 --> 00:57:47,840 Speaker 1: podcast Francesca Levi at francesco Today, and check out all 1104 00:57:47,880 --> 00:57:51,520 Speaker 1: of our podcasts at Bloomberg under the handle at podcasts. 1105 00:57:51,680 --> 00:58:00,720 Speaker 1: Thanks for listening to