1 00:00:05,800 --> 00:00:08,720 Speaker 1: Welcome to the Bloomberg p m L Podcast. I'm Pim Fox. 2 00:00:08,760 --> 00:00:11,520 Speaker 1: Along with my co host Lisa Bramowitz. Each day we 3 00:00:11,640 --> 00:00:15,120 Speaker 1: bring you the most important, noteworthy, and useful interviews for 4 00:00:15,200 --> 00:00:17,840 Speaker 1: you and your money, whether you're at the grocery store 5 00:00:17,960 --> 00:00:20,720 Speaker 1: or the trading floor. Find the Bloomberg p m L 6 00:00:20,840 --> 00:00:32,480 Speaker 1: Podcast on Apple Podcasts, SoundCloud, and Bloomberg dot Com. You know, Lisa, 7 00:00:32,560 --> 00:00:33,879 Speaker 1: one of the things I like to do when we 8 00:00:33,920 --> 00:00:37,080 Speaker 1: talk about interest rates, for example, is always think of context, right, 9 00:00:37,080 --> 00:00:39,760 Speaker 1: because we're worried about whether the federal rates interest rates 10 00:00:39,760 --> 00:00:42,560 Speaker 1: a quarter? You know, how many interest rate increases will 11 00:00:42,560 --> 00:00:44,239 Speaker 1: we get this year? Do you know the back in 12 00:00:45,120 --> 00:00:49,960 Speaker 1: seventy nine, um FED funds rate was eleven percent and 13 00:00:50,000 --> 00:00:54,000 Speaker 1: it wasn't even done then because in nineteen one, I remember, 14 00:00:54,040 --> 00:00:56,680 Speaker 1: I think it was in that summer, the June that summer, 15 00:00:57,480 --> 00:01:01,640 Speaker 1: they were twenty percent believed. Do you think that would 16 00:01:01,640 --> 00:01:04,160 Speaker 1: be a good time to buy a bank, Well, I 17 00:01:04,160 --> 00:01:06,800 Speaker 1: think that it was. For George Gleeson, he's our next guest. 18 00:01:07,000 --> 00:01:09,600 Speaker 1: She's a chief executive officer and chairman of the Bank 19 00:01:09,760 --> 00:01:13,920 Speaker 1: of the Ozarks based in Little Rocks. We did that, 20 00:01:14,360 --> 00:01:18,440 Speaker 1: thanks for us, You know it seems like a very 21 00:01:18,440 --> 00:01:20,720 Speaker 1: different era from back then. Well, that's what I want 22 00:01:20,720 --> 00:01:23,320 Speaker 1: to Maybe we could just start off. I'd like maybe 23 00:01:23,360 --> 00:01:25,160 Speaker 1: just for you to tell a little bit of the 24 00:01:25,200 --> 00:01:28,440 Speaker 1: story of the bank. And as I said, nineteen seventy 25 00:01:28,560 --> 00:01:32,720 Speaker 1: nine eleven percent FED funds and we're worried about quarter 26 00:01:33,280 --> 00:01:35,959 Speaker 1: in crease. Well, that's true, and of course Paul Volker 27 00:01:36,360 --> 00:01:40,200 Speaker 1: was at the FED and was pursuing the quest to 28 00:01:40,319 --> 00:01:45,320 Speaker 1: slay inflation by all possible means. So within my first 29 00:01:45,360 --> 00:01:49,360 Speaker 1: two years as chairman, CEO and majority owner of Bank 30 00:01:49,400 --> 00:01:51,720 Speaker 1: of the Ozarks, the FED funds, right, did go from 31 00:01:51,760 --> 00:01:56,280 Speaker 1: ten to twenty percent. And the interesting thing about that 32 00:01:56,560 --> 00:01:59,640 Speaker 1: was that we were in a state that had a 33 00:02:00,040 --> 00:02:03,880 Speaker 1: institutional usery law then that limited our interest rate we 34 00:02:03,920 --> 00:02:07,240 Speaker 1: could charge on loans to ten percent. Needless to say, 35 00:02:07,280 --> 00:02:09,280 Speaker 1: we were doing a lot of investments and not much 36 00:02:09,360 --> 00:02:12,240 Speaker 1: lending in those days and still managed to have record 37 00:02:12,280 --> 00:02:16,040 Speaker 1: profits that year, both those years actually well, and partly 38 00:02:16,120 --> 00:02:18,359 Speaker 1: probably because of net interest margin, right, I mean, you 39 00:02:18,360 --> 00:02:21,040 Speaker 1: could just generate so much from your loans from just 40 00:02:21,120 --> 00:02:23,840 Speaker 1: client activity, and I have to wonder how that contrasts 41 00:02:23,880 --> 00:02:28,760 Speaker 1: with today when we're facing pretty low rates for the 42 00:02:28,840 --> 00:02:33,600 Speaker 1: foreseeable future regardless of h to minimus interest rate tikes. Uh, 43 00:02:33,680 --> 00:02:38,279 Speaker 1: you know how optimistic are you? Well, we're very optimistic 44 00:02:38,320 --> 00:02:41,680 Speaker 1: about our situation. Of course, our net interest margin is 45 00:02:42,040 --> 00:02:44,680 Speaker 1: close to five percent. The quarter just ended, our net 46 00:02:44,680 --> 00:02:49,760 Speaker 1: interest margin was four point nine So we've very carefully 47 00:02:50,000 --> 00:02:53,600 Speaker 1: built all of our lines of business over the thirty 48 00:02:53,639 --> 00:02:56,359 Speaker 1: eight years that I've been chairman, chief executive officer to 49 00:02:57,040 --> 00:03:02,560 Speaker 1: to generate really good return better than average returns, while 50 00:03:02,600 --> 00:03:06,040 Speaker 1: we have much better than average industry credit quality. Well, 51 00:03:06,080 --> 00:03:08,520 Speaker 1: I have to wonder how you're doing. And I do 52 00:03:08,720 --> 00:03:13,680 Speaker 1: know that there was some controversy over your bank at 53 00:03:13,680 --> 00:03:17,359 Speaker 1: this own conference when Carson Flock of Muddy Waters UH 54 00:03:17,520 --> 00:03:20,680 Speaker 1: labeled your firm is the big short, They're big short, 55 00:03:20,720 --> 00:03:24,440 Speaker 1: they're big idea, which failed spectacularly, I should say, with 56 00:03:24,480 --> 00:03:28,760 Speaker 1: your firm gaining about thirty the shares in the following year. 57 00:03:28,880 --> 00:03:31,800 Speaker 1: So people who followed that advice would have been badly 58 00:03:31,880 --> 00:03:34,760 Speaker 1: burned if they had tried to go against the momentum 59 00:03:34,760 --> 00:03:38,040 Speaker 1: in your shares. But but part of the rational was that, Uh, 60 00:03:38,080 --> 00:03:40,960 Speaker 1: you were investing a lot in bigger cities and real estate, 61 00:03:41,080 --> 00:03:44,160 Speaker 1: and that this could potentially be a problem. Are you 62 00:03:44,200 --> 00:03:47,200 Speaker 1: still investing in that and what's your rational? Well, absolutely, 63 00:03:47,240 --> 00:03:51,720 Speaker 1: we're commercial real estate lender first and foremost, and we've 64 00:03:51,760 --> 00:03:56,440 Speaker 1: done that all of my career, and our clients include 65 00:03:56,720 --> 00:04:00,000 Speaker 1: probably eighty five of the hundred largest real estate develop 66 00:04:00,000 --> 00:04:05,080 Speaker 1: oppers in the country. Uh. We do very high quality 67 00:04:05,160 --> 00:04:09,240 Speaker 1: projects with very sophisticated, high quality sponsors, but we do 68 00:04:09,320 --> 00:04:13,040 Speaker 1: those transactions at very low leverage. The Real Estate Specialties 69 00:04:13,080 --> 00:04:17,400 Speaker 1: Group which handles all of our large national commercial real 70 00:04:17,520 --> 00:04:20,680 Speaker 1: estate leaning and accounts for about sixty eight percent of 71 00:04:20,760 --> 00:04:24,520 Speaker 1: our non purchase loans loans other than those loans we've 72 00:04:24,520 --> 00:04:30,360 Speaker 1: acquired in acquisitions, the weighted average loan to cost if 73 00:04:30,400 --> 00:04:32,839 Speaker 1: we fully advance every one of those loans is forty 74 00:04:32,960 --> 00:04:36,559 Speaker 1: nine percent, and the weighted average loan to appraise value 75 00:04:36,640 --> 00:04:40,760 Speaker 1: is forty two percent. Obviously, a commercial real estate loan 76 00:04:40,839 --> 00:04:43,720 Speaker 1: that you're at forty two percent loan to value and 77 00:04:43,800 --> 00:04:46,799 Speaker 1: forty nine percent loan to cost has an incredibly different 78 00:04:46,880 --> 00:04:50,080 Speaker 1: risk profile than a commercial real estate loan at eighty 79 00:04:50,160 --> 00:04:52,760 Speaker 1: or eighty five percent loan to value and loan to cost. 80 00:04:52,839 --> 00:04:58,280 Speaker 1: So we're very conservative. Um, and I think uh, when 81 00:04:58,520 --> 00:05:02,640 Speaker 1: UM that the sis was introduced at the SOUND conference 82 00:05:02,640 --> 00:05:07,200 Speaker 1: early in two six the guys had probably screened us 83 00:05:07,400 --> 00:05:10,800 Speaker 1: and knew, wow, look at their commercial real estate concentration, 84 00:05:10,960 --> 00:05:13,880 Speaker 1: look at their growth rates, they must be doing something 85 00:05:14,000 --> 00:05:17,640 Speaker 1: very risky. But in fact, we've probably got the most 86 00:05:17,640 --> 00:05:22,440 Speaker 1: conservative commercial real estate loan portfolio in the US banking industry. 87 00:05:22,760 --> 00:05:27,039 Speaker 1: The way that you described how Carson Block may or 88 00:05:27,080 --> 00:05:31,760 Speaker 1: may not have arrived at what this conclusion the very 89 00:05:31,880 --> 00:05:35,840 Speaker 1: nature that's screening and the very nature of how markets 90 00:05:35,839 --> 00:05:39,359 Speaker 1: have evolved. I'm wondering if you could just offer, as 91 00:05:39,640 --> 00:05:44,040 Speaker 1: I think, you're the fourth largest shareholder in the bank. Uh, 92 00:05:44,279 --> 00:05:47,920 Speaker 1: the bank makes no bones about your participation in its 93 00:05:47,960 --> 00:05:51,240 Speaker 1: founding and running. I mean that's so there's a level 94 00:05:51,320 --> 00:05:55,480 Speaker 1: of personal responsibility. And I'm wondering how that maybe even 95 00:05:55,520 --> 00:05:58,640 Speaker 1: ties in with the map of where you locate. Well, 96 00:05:58,680 --> 00:06:02,920 Speaker 1: there there is a great of personal responsibility. And and um, 97 00:06:03,279 --> 00:06:06,800 Speaker 1: I see all of the loans in the company above 98 00:06:06,880 --> 00:06:10,600 Speaker 1: ten million dollars. Of course, Dan Thomas, who runs our 99 00:06:10,600 --> 00:06:14,240 Speaker 1: real estate specialties group for US, sees all of those loans, 100 00:06:14,240 --> 00:06:18,599 Speaker 1: and all those loans are approved by committee. Um. But 101 00:06:19,920 --> 00:06:23,520 Speaker 1: the culture that we've built over thirty eight years is 102 00:06:23,560 --> 00:06:28,840 Speaker 1: far more important than individuals. We have created a culture 103 00:06:29,640 --> 00:06:34,680 Speaker 1: um that is very conservative, very customer centric, and very 104 00:06:34,760 --> 00:06:38,719 Speaker 1: focused on excellence in all we do. Uh for example, 105 00:06:39,480 --> 00:06:42,960 Speaker 1: on asset quality. In the twenty years we just celebrated 106 00:06:43,000 --> 00:06:47,040 Speaker 1: today at at NASDAC ringing the bell, they're celebrated our 107 00:06:47,120 --> 00:06:51,440 Speaker 1: twentieth anniversary as a public company. And in that twenty years, 108 00:06:51,480 --> 00:06:55,040 Speaker 1: there's not been a single year where our net charge 109 00:06:55,080 --> 00:06:59,160 Speaker 1: offer ratio has equalled or exceeded the industry's average net 110 00:06:59,240 --> 00:07:02,440 Speaker 1: charge offeration. We beat the industry every single year for 111 00:07:02,480 --> 00:07:06,160 Speaker 1: twenty years and beat them on average by sixty So 112 00:07:06,200 --> 00:07:09,680 Speaker 1: we're very conservative and and to do that, it's not 113 00:07:09,800 --> 00:07:12,680 Speaker 1: one person or a loan committee or two or three people. 114 00:07:12,760 --> 00:07:16,840 Speaker 1: It is a culture that invades and and pervades the 115 00:07:17,000 --> 00:07:19,960 Speaker 1: entire company. So given the fact that you do have 116 00:07:20,200 --> 00:07:24,640 Speaker 1: very high standards, you're expecting to increase your assets from 117 00:07:24,680 --> 00:07:27,760 Speaker 1: about twenty billion dollars to fifty billion dollars of us 118 00:07:27,760 --> 00:07:31,920 Speaker 1: it's probably over the next three years, well, I would 119 00:07:31,960 --> 00:07:35,240 Speaker 1: I would say that's an overstatement. And of course we've 120 00:07:35,280 --> 00:07:39,000 Speaker 1: made fifteen acquisitions in the last eight years, so the 121 00:07:39,040 --> 00:07:42,520 Speaker 1: pace of acquisitions one can never predict. You don't know 122 00:07:42,560 --> 00:07:44,920 Speaker 1: whether you're going to do an acquisition or not, because 123 00:07:44,960 --> 00:07:47,120 Speaker 1: you have to find a transaction that makes sense and 124 00:07:47,840 --> 00:07:50,440 Speaker 1: reach agreement between a willing buyer and a willing seller. 125 00:07:51,120 --> 00:07:54,320 Speaker 1: So if you exclude acquisitions, I would say it's more 126 00:07:54,440 --> 00:07:57,360 Speaker 1: realistic to assume that we would grow from twenty bill 127 00:07:57,400 --> 00:07:59,840 Speaker 1: and to fifty b and over a five, six or 128 00:08:00,040 --> 00:08:03,600 Speaker 1: seven year time frame apart from acquisitions. All right, So 129 00:08:03,680 --> 00:08:05,840 Speaker 1: I imagine it's it's an interesting time right now, and 130 00:08:05,880 --> 00:08:08,960 Speaker 1: I wish we could speak for the next hour because frankly, 131 00:08:09,000 --> 00:08:11,760 Speaker 1: I have so many questions about running a bank in 132 00:08:11,760 --> 00:08:13,600 Speaker 1: this current environment. And we didn't even get into the 133 00:08:13,600 --> 00:08:16,120 Speaker 1: regulatory rollbacks and what that might do. I mean, there's 134 00:08:16,160 --> 00:08:19,680 Speaker 1: so much. Unfortunately we have to leave it there, but 135 00:08:19,760 --> 00:08:22,520 Speaker 1: I promise back he'll give you the phone number, please do. 136 00:08:22,760 --> 00:08:25,080 Speaker 1: George Gleeson, thank you so much for joining us. Truly 137 00:08:25,480 --> 00:08:29,840 Speaker 1: fascinating uh story and a fascinating bank. Really, Chief executive 138 00:08:29,840 --> 00:08:32,439 Speaker 1: Officer and Chairman of the Bank of the Ozarks in 139 00:08:32,520 --> 00:08:35,960 Speaker 1: Little Rock, Arkansas. Since that zone meeting, the shares gained 140 00:08:36,040 --> 00:08:51,360 Speaker 1: more than thirty percent in the following year. Well, there 141 00:08:51,520 --> 00:08:54,839 Speaker 1: is a new sub prime boom, and here to tell 142 00:08:54,880 --> 00:08:59,000 Speaker 1: us about it is Gabrielle Coppola and Gabrielle, you know 143 00:08:59,040 --> 00:09:01,160 Speaker 1: you are I always I didn't think of you necessarily 144 00:09:01,200 --> 00:09:03,679 Speaker 1: just as someone who's interested in the credit but also 145 00:09:03,720 --> 00:09:07,600 Speaker 1: in the underlying manufacture of vehicles autumn, you know, around 146 00:09:07,600 --> 00:09:10,120 Speaker 1: the world. Can you just describe what you're trying to 147 00:09:10,120 --> 00:09:12,160 Speaker 1: get at here in terms of the US and the 148 00:09:12,200 --> 00:09:15,360 Speaker 1: way we buy cars and what's happened, because a lot 149 00:09:15,440 --> 00:09:18,960 Speaker 1: of this has to do with the way people finance 150 00:09:19,000 --> 00:09:22,839 Speaker 1: their automobiles. Yeah, um, I think that. You know, after 151 00:09:22,920 --> 00:09:26,679 Speaker 1: the mortgage crisis, Um, the housing sector wasn't a very 152 00:09:26,679 --> 00:09:29,400 Speaker 1: healthy place for banks to play. But then there was 153 00:09:29,440 --> 00:09:32,720 Speaker 1: also the rebound, the revival of the auto industry, helped 154 00:09:32,760 --> 00:09:35,280 Speaker 1: in course by the you know, bailout by the US government. 155 00:09:35,679 --> 00:09:39,480 Speaker 1: Car sales came booming back, and um, people needed banks 156 00:09:39,520 --> 00:09:42,360 Speaker 1: to finance that, and that created a huge opportunity. There 157 00:09:42,400 --> 00:09:45,280 Speaker 1: was a huge credit expansion, and we saw a huge 158 00:09:45,760 --> 00:09:50,240 Speaker 1: credit expansion for people with lower credit scores buying cars. Right. 159 00:09:50,280 --> 00:09:51,960 Speaker 1: And we've heard a lot about has some of the 160 00:09:52,520 --> 00:09:55,400 Speaker 1: subprime auto loans that have been originated since the crisis 161 00:09:55,440 --> 00:09:59,320 Speaker 1: have been souring at a faster pace than many have expected. 162 00:09:59,440 --> 00:10:02,640 Speaker 1: That it's kind have created this spiraling effect where you 163 00:10:02,679 --> 00:10:05,400 Speaker 1: had a lot of not only subprime auto loans, but 164 00:10:05,440 --> 00:10:07,240 Speaker 1: you also had a lot of auto leases. So then 165 00:10:07,280 --> 00:10:10,080 Speaker 1: the resale values are going down as the leases come up, 166 00:10:10,120 --> 00:10:12,560 Speaker 1: and you know, there's been a big issue here. But 167 00:10:12,640 --> 00:10:14,680 Speaker 1: I really want to home in on two companies in 168 00:10:14,720 --> 00:10:19,359 Speaker 1: particular Santander, which is the leading subprime auto loan originator, 169 00:10:19,480 --> 00:10:21,640 Speaker 1: as well as Fiat Chrysler, which is thought of as 170 00:10:21,640 --> 00:10:26,520 Speaker 1: sort of the third in the triumvirate of vehicle manufacturers 171 00:10:26,520 --> 00:10:28,959 Speaker 1: in the US, And how do they come to kind 172 00:10:29,000 --> 00:10:33,000 Speaker 1: of join forces and how it significantly it has their 173 00:10:33,000 --> 00:10:35,640 Speaker 1: partnership kind of created a lot of the subprime auto 174 00:10:35,679 --> 00:10:39,200 Speaker 1: loans that weren't looking at right now. Well again, when 175 00:10:39,280 --> 00:10:41,360 Speaker 1: Chrysler was sort of coming back from the dead, you know, 176 00:10:41,400 --> 00:10:43,960 Speaker 1: in the wake of the financial crisis, they needed they 177 00:10:43,960 --> 00:10:47,160 Speaker 1: didn't have a captive finance arm anymore, and they needed one, 178 00:10:47,200 --> 00:10:49,520 Speaker 1: just like you have Foreign Motor Credit or GM financial. 179 00:10:49,640 --> 00:10:53,240 Speaker 1: Chrysler needed a partner to finance its car sales instead 180 00:10:53,280 --> 00:10:55,000 Speaker 1: of I think they didn't have the money to buy one, 181 00:10:55,000 --> 00:10:57,880 Speaker 1: so they made this partnership with Sintender, And I think 182 00:10:57,960 --> 00:10:59,959 Speaker 1: one of the reasons the head of sales for cry 183 00:11:00,000 --> 00:11:02,040 Speaker 1: sort of at the time said he chose Santanair because 184 00:11:02,200 --> 00:11:06,000 Speaker 1: their expertise in automated decision NG and big data. So 185 00:11:06,040 --> 00:11:08,440 Speaker 1: the idea that they could really um use big data 186 00:11:08,440 --> 00:11:10,840 Speaker 1: to kind of harness get it on this boom um 187 00:11:11,080 --> 00:11:13,520 Speaker 1: speed up the process of buying cars, like big data 188 00:11:13,559 --> 00:11:17,040 Speaker 1: meaning that they could they could use less information from 189 00:11:17,040 --> 00:11:20,800 Speaker 1: each borrower to more quickly originate loans, or big data 190 00:11:20,840 --> 00:11:23,679 Speaker 1: to basically identify the credit quality. I think that more 191 00:11:23,800 --> 00:11:26,400 Speaker 1: that's the idea that analytics data mining being able to 192 00:11:26,520 --> 00:11:30,520 Speaker 1: kind of assess people's credit quality in a more efficient way. 193 00:11:31,000 --> 00:11:33,520 Speaker 1: And by the reason why Ascess in particular is because 194 00:11:33,520 --> 00:11:37,200 Speaker 1: Santandair in particular has been pinpointed is not verifying the 195 00:11:37,240 --> 00:11:40,880 Speaker 1: incomes of a lot of the uh people who uh 196 00:11:41,000 --> 00:11:43,680 Speaker 1: it lends to right, yes, And that is actually in 197 00:11:43,720 --> 00:11:45,760 Speaker 1: my story I talked about this settlement they had with 198 00:11:45,800 --> 00:11:49,160 Speaker 1: attorneys general in Massachusetts and and and Delaware where they 199 00:11:49,160 --> 00:11:51,720 Speaker 1: paid twenty six million dollars. They you know, they didn't 200 00:11:52,160 --> 00:11:54,240 Speaker 1: neither admit nor deny any wrongdoing, but if you read 201 00:11:54,280 --> 00:11:56,720 Speaker 1: that settlement, it talks about what they're basically, what they're 202 00:11:56,760 --> 00:11:58,880 Speaker 1: accused of by the prosecutors is kind of looking the 203 00:11:58,920 --> 00:12:02,240 Speaker 1: other way when there's very obvious signs of fraud, you know, 204 00:12:02,360 --> 00:12:04,640 Speaker 1: when when you get there's They had a group they 205 00:12:04,679 --> 00:12:07,280 Speaker 1: called them the fraud dealers, their internal audit system. There 206 00:12:07,280 --> 00:12:10,440 Speaker 1: were dealers who were um sending applications where someone would 207 00:12:10,559 --> 00:12:13,240 Speaker 1: um default you know on the very first payment or 208 00:12:13,600 --> 00:12:15,520 Speaker 1: you find out that the car they said the certain 209 00:12:15,520 --> 00:12:18,080 Speaker 1: trim levels were a certain level that made the collateral 210 00:12:18,120 --> 00:12:20,480 Speaker 1: bigger so that they could justify getting a bigger loan 211 00:12:20,520 --> 00:12:22,240 Speaker 1: and put that person in that car. And turned out 212 00:12:22,280 --> 00:12:25,839 Speaker 1: that those were not true. So that's happening at the 213 00:12:25,920 --> 00:12:28,480 Speaker 1: at the real originator is the car dealer who's getting 214 00:12:28,480 --> 00:12:30,760 Speaker 1: you in that car, right, But they're saying, Santander, you 215 00:12:30,760 --> 00:12:33,040 Speaker 1: did not do your job in terms of having your 216 00:12:33,080 --> 00:12:35,560 Speaker 1: due diligence to check that and that issue came up again. 217 00:12:35,640 --> 00:12:37,839 Speaker 1: This is separate, but when you know, Moody's did a 218 00:12:37,880 --> 00:12:40,040 Speaker 1: report early this year looking at the A B S 219 00:12:40,120 --> 00:12:45,520 Speaker 1: that Santender originates. They said they only chat securities and 220 00:12:45,600 --> 00:12:48,320 Speaker 1: thank you. Um they said, oh we did. We had 221 00:12:48,360 --> 00:12:50,360 Speaker 1: this new data that came out under some new rule, 222 00:12:50,480 --> 00:12:52,080 Speaker 1: you know, they were able to see more data and said, uh, 223 00:12:52,120 --> 00:12:55,160 Speaker 1: Santenda only checks eight percent of the incomes in this 224 00:12:55,400 --> 00:12:59,439 Speaker 1: subprime this's a billion dollars of of subprime auto auto loans. 225 00:13:00,040 --> 00:13:02,320 Speaker 1: Santandera will say that they have other criteria that they're 226 00:13:02,440 --> 00:13:04,560 Speaker 1: using to check and it's not just that that's basically 227 00:13:04,559 --> 00:13:07,240 Speaker 1: one of the things that they say. But that's the issue, 228 00:13:07,240 --> 00:13:09,960 Speaker 1: and I think that's what people are uh scrutinizing. What's 229 00:13:10,000 --> 00:13:14,199 Speaker 1: regulators are looking at eight percent and that compares with ally, 230 00:13:14,240 --> 00:13:19,720 Speaker 1: which is another big old GM. Yes, now they're quite 231 00:13:19,720 --> 00:13:22,199 Speaker 1: separate from GM, but they said, hey, you know, by 232 00:13:22,200 --> 00:13:24,520 Speaker 1: the way, we check sixty five percent of our incomes, 233 00:13:24,880 --> 00:13:28,160 Speaker 1: and GM Financial also said we check about sixty So 234 00:13:28,200 --> 00:13:30,480 Speaker 1: how many vehicles do you have any sense of how 235 00:13:30,520 --> 00:13:33,960 Speaker 1: many or the proportion of Chrysler vehicles sold that relied 236 00:13:34,160 --> 00:13:38,480 Speaker 1: on santan Dair for financing. Well, that's actually so the 237 00:13:38,480 --> 00:13:40,760 Speaker 1: actual it's kind of so they have this partnership and 238 00:13:40,840 --> 00:13:43,280 Speaker 1: it's been very hard for them to meet the pennant. 239 00:13:43,320 --> 00:13:45,920 Speaker 1: They wanted Santender to be doing like sixty of their 240 00:13:46,400 --> 00:13:49,480 Speaker 1: you know, Chrysler capital, that's the brand of it. They 241 00:13:49,480 --> 00:13:51,120 Speaker 1: wanted it by this time, but this year it should 242 00:13:51,160 --> 00:13:54,800 Speaker 1: have been sixty. It's so it's actually very low. So 243 00:13:54,840 --> 00:13:58,079 Speaker 1: even though Sintenda is a very low proportion of Chrysler financing, 244 00:13:58,480 --> 00:14:01,200 Speaker 1: christ would makes up a very more than half of 245 00:14:01,240 --> 00:14:06,319 Speaker 1: the auto financing that Santander does. Well. That goes back 246 00:14:06,360 --> 00:14:09,320 Speaker 1: to that agreement that you described earlier, saying that maybe 247 00:14:09,400 --> 00:14:13,160 Speaker 1: Fiat was not large enough to buy or uh, Fiat 248 00:14:13,240 --> 00:14:16,160 Speaker 1: Chrysler was not large enough to buy its own credit company, 249 00:14:16,240 --> 00:14:18,160 Speaker 1: so they had to make where they did make this 250 00:14:18,200 --> 00:14:20,760 Speaker 1: alliance with Santander, right, and this is supposed to be 251 00:14:20,800 --> 00:14:23,000 Speaker 1: and it still is. I think Santander still wants it 252 00:14:23,040 --> 00:14:26,240 Speaker 1: to be a big avenue for expansion. Growth is Chrysler, 253 00:14:26,560 --> 00:14:28,280 Speaker 1: and they're actually doing a lot of things. You know, 254 00:14:28,320 --> 00:14:31,320 Speaker 1: they have a program to clean up, They're trying the 255 00:14:31,320 --> 00:14:34,360 Speaker 1: new CEO, Scott Powell. They've actually fired eight hundred dealers 256 00:14:34,400 --> 00:14:37,880 Speaker 1: across their network to clean They're trying, they are doing things, 257 00:14:37,920 --> 00:14:40,360 Speaker 1: but this is still out there. Gabrielle Coppola, thank you 258 00:14:40,440 --> 00:14:42,760 Speaker 1: so much for joining us, and uh, congratulations on an 259 00:14:42,760 --> 00:14:45,400 Speaker 1: awesome story. It definitely takes a look at a deeper 260 00:14:45,480 --> 00:14:47,320 Speaker 1: look at an issue that a lot of people have 261 00:14:47,440 --> 00:14:51,040 Speaker 1: been homing in on as a possible problem. Gabrielle Coppola 262 00:14:51,080 --> 00:15:05,880 Speaker 1: is an autos writer for Bloomberg US. Well, to learn 263 00:15:05,880 --> 00:15:08,120 Speaker 1: more about what's going on with Blue Apron, we have 264 00:15:08,200 --> 00:15:11,600 Speaker 1: an expert, Selena Wang is joining us now she is 265 00:15:11,600 --> 00:15:15,440 Speaker 1: are Bloomberg. I gotta say, you're like the meal You 266 00:15:15,520 --> 00:15:20,000 Speaker 1: cover meal companies and uh delivery companies, but they're really 267 00:15:20,040 --> 00:15:23,240 Speaker 1: tech companies. Right, this is Blue Apron. What is going on? 268 00:15:23,280 --> 00:15:25,800 Speaker 1: The stock was down as much as twelve right. I 269 00:15:25,800 --> 00:15:27,320 Speaker 1: think you just hit on something that a lot of 270 00:15:27,320 --> 00:15:30,120 Speaker 1: people say. You know, just because they sell orders digitally 271 00:15:30,160 --> 00:15:32,480 Speaker 1: doesn't make it a tech company necessarily. This is a 272 00:15:32,520 --> 00:15:35,680 Speaker 1: super cost intensive business. They have to source all the ingredients, 273 00:15:35,920 --> 00:15:37,840 Speaker 1: they have to put it all together. These are really 274 00:15:37,880 --> 00:15:41,240 Speaker 1: expensive ingredients, put them to fulfillment centers, delivered to people. 275 00:15:41,760 --> 00:15:46,520 Speaker 1: And this Amazon trademark filing is just another kind of 276 00:15:46,600 --> 00:15:48,960 Speaker 1: nail on the coffin for Blue Apron, as some investors 277 00:15:48,960 --> 00:15:51,240 Speaker 1: would see it. I mean, Blue Apron already has all 278 00:15:51,240 --> 00:15:53,960 Speaker 1: of these financial issues, and now you have a behemoth 279 00:15:54,040 --> 00:15:56,800 Speaker 1: that may be getting into the same business that it's in. Okay, 280 00:15:56,840 --> 00:15:59,240 Speaker 1: so that maybe getting into the same business, right, I 281 00:15:59,280 --> 00:16:03,280 Speaker 1: mean they file a trademark Apple hard food kits. This 282 00:16:03,360 --> 00:16:07,160 Speaker 1: is Amazon dot Com? How soon? How quickly could they 283 00:16:07,160 --> 00:16:11,680 Speaker 1: develop an actual business based on this filing? And second 284 00:16:11,680 --> 00:16:13,400 Speaker 1: of all, do we have a sense of whether it 285 00:16:13,680 --> 00:16:16,080 Speaker 1: really does directly overlap with bluavor? I mean it does. 286 00:16:16,120 --> 00:16:18,240 Speaker 1: It definitely sounds like right right. So, in the world 287 00:16:18,320 --> 00:16:21,200 Speaker 1: of these types of applications, there's kind of two categories. 288 00:16:21,200 --> 00:16:24,000 Speaker 1: So there's one which is, we're already using this trademark 289 00:16:24,040 --> 00:16:26,920 Speaker 1: for products, so you should consider application. And the second 290 00:16:26,960 --> 00:16:29,240 Speaker 1: one is we're planning to use this trademark for products, 291 00:16:29,280 --> 00:16:32,840 Speaker 1: so you should uh is we're planning to use this 292 00:16:32,880 --> 00:16:36,400 Speaker 1: trademark for products, so you should consider application. Amazon filed 293 00:16:36,520 --> 00:16:38,840 Speaker 1: under the ladder, which means they're planning to use it, 294 00:16:38,840 --> 00:16:41,160 Speaker 1: doesn't necessarily mean this is all going to go through 295 00:16:41,280 --> 00:16:44,000 Speaker 1: that is going to get approved. So that means that 296 00:16:44,040 --> 00:16:48,160 Speaker 1: they're definitely looking at the business and they're considering it. Uh. 297 00:16:48,240 --> 00:16:51,160 Speaker 1: No idea when they would actually start doing this. Why 298 00:16:51,240 --> 00:16:53,360 Speaker 1: why would they get into it? It's a crowded business 299 00:16:53,440 --> 00:16:56,400 Speaker 1: that is of dubious lucrative nous. Well, if we look 300 00:16:56,440 --> 00:16:59,400 Speaker 1: at this Amazon Whole Foods deal, they were willing to 301 00:16:59,440 --> 00:17:02,680 Speaker 1: put in PLoP down billions of dollars to buy a 302 00:17:02,800 --> 00:17:06,800 Speaker 1: grocery store business. Now, uh, food is incredibly important. It's 303 00:17:06,840 --> 00:17:10,000 Speaker 1: a huge, huge market. Amazon clearly wants to get into it. 304 00:17:10,119 --> 00:17:12,520 Speaker 1: They have the brand now, they have the distribution, they 305 00:17:12,600 --> 00:17:15,639 Speaker 1: have the fulfillment centers with the Whole Food's acquisition. So 306 00:17:15,680 --> 00:17:18,400 Speaker 1: you can think of all sorts of integrations to make 307 00:17:18,480 --> 00:17:23,959 Speaker 1: something like meal delivery actually pretty efficient for somebody like 308 00:17:24,040 --> 00:17:27,480 Speaker 1: Amazon when they have so much backing and support on 309 00:17:27,520 --> 00:17:29,959 Speaker 1: the back end and in terms of the physical locations. 310 00:17:30,880 --> 00:17:34,840 Speaker 1: Have you heard anybody saying that, uh, Blue Apron is 311 00:17:34,880 --> 00:17:38,040 Speaker 1: now a buy at six? I mean, if you're talking 312 00:17:38,040 --> 00:17:41,479 Speaker 1: about it right, because nothing the deal was done right 313 00:17:41,560 --> 00:17:46,240 Speaker 1: June Stockho's public at ten. They sold thirty million shares 314 00:17:47,040 --> 00:17:53,160 Speaker 1: and stock is what trading at six and change what 315 00:17:53,280 --> 00:17:55,760 Speaker 1: That doesn't necessarily mean they're, you know, throwing away all 316 00:17:55,760 --> 00:17:58,600 Speaker 1: their strategic plans. They already had the money, They've got 317 00:17:58,760 --> 00:18:02,960 Speaker 1: big cash flow. Um, they have a brand name. I'm 318 00:18:03,000 --> 00:18:08,120 Speaker 1: just wondering, they've built infrastructure that is theirs, and I'm wondering, 319 00:18:08,200 --> 00:18:10,720 Speaker 1: you know, you hear anybody saying anything like, well, no, 320 00:18:10,880 --> 00:18:13,920 Speaker 1: they're a player. Remember what happened to Facebook. It came 321 00:18:13,920 --> 00:18:15,840 Speaker 1: out I think at thirty five, and was that whole 322 00:18:15,840 --> 00:18:19,320 Speaker 1: thing with the Nastac and you know, people were furious, 323 00:18:19,359 --> 00:18:21,320 Speaker 1: but the turn out that if you had held on 324 00:18:21,359 --> 00:18:23,000 Speaker 1: it was a good bed, right. I mean, I don't 325 00:18:23,040 --> 00:18:25,400 Speaker 1: think we can say for sure right now whether it's 326 00:18:25,440 --> 00:18:27,200 Speaker 1: going to be a total flop or if it's going 327 00:18:27,200 --> 00:18:29,919 Speaker 1: to succeed and get to facebooks. I mean, it's not 328 00:18:29,960 --> 00:18:32,359 Speaker 1: like Amazon was buying, you know, the sharpest hack in 329 00:18:32,400 --> 00:18:34,879 Speaker 1: the box in the sense that the Whole Foods was 330 00:18:34,920 --> 00:18:37,560 Speaker 1: facing competition, when I mean in a way, this was 331 00:18:37,880 --> 00:18:40,560 Speaker 1: great for Whole Foods to get. Asking whether Amazon would 332 00:18:40,560 --> 00:18:44,439 Speaker 1: consider buying Blue Apron, I don't think it would make 333 00:18:44,480 --> 00:18:46,040 Speaker 1: a lot of sense for them at this point. I mean, 334 00:18:46,080 --> 00:18:48,480 Speaker 1: they have Whole Foods, they could build their own Blue 335 00:18:48,480 --> 00:18:51,240 Speaker 1: Apron esque sort of business. But like as you did say, 336 00:18:51,280 --> 00:18:53,840 Speaker 1: it is quite cheap right now. I mean Blue Apron 337 00:18:53,960 --> 00:18:56,760 Speaker 1: does have the brand, that's true, they have you know, 338 00:18:56,800 --> 00:18:59,399 Speaker 1: people know that it's a quality product they're getting when 339 00:18:59,400 --> 00:19:00,840 Speaker 1: they go to Blue Brint. But this is not a 340 00:19:00,920 --> 00:19:03,359 Speaker 1: cheap product. And the type of customer that's going to 341 00:19:03,440 --> 00:19:05,720 Speaker 1: Whole Foods is a very similar demographic as a type 342 00:19:05,720 --> 00:19:07,960 Speaker 1: of customer that is going to Blue Apron. Kind of 343 00:19:07,960 --> 00:19:11,359 Speaker 1: this top ten percent of income in households, it's not cheap. 344 00:19:11,840 --> 00:19:14,160 Speaker 1: At what point does Amazon just have so much momentum 345 00:19:14,200 --> 00:19:16,960 Speaker 1: because they already have the brand name, They're already associated 346 00:19:17,000 --> 00:19:20,679 Speaker 1: with something that many people view as quality and reliable 347 00:19:20,800 --> 00:19:22,840 Speaker 1: and consistent, so they don't have to spend all this 348 00:19:22,920 --> 00:19:26,360 Speaker 1: money on advertising like Blue Apron has, which has been 349 00:19:26,480 --> 00:19:30,440 Speaker 1: the bulk of their revenue right exactly, I mean marketing 350 00:19:30,520 --> 00:19:33,640 Speaker 1: and fulfillment centers had has been a huge reason why 351 00:19:33,640 --> 00:19:36,640 Speaker 1: they've had net losses even as revenue has been growing. 352 00:19:37,080 --> 00:19:39,919 Speaker 1: And you're right, I mean Amazon with Amazon Prime, they 353 00:19:39,920 --> 00:19:42,240 Speaker 1: already have kind of like an existing customer base and 354 00:19:42,280 --> 00:19:44,000 Speaker 1: you can think of all sorts of ways where you know, 355 00:19:44,040 --> 00:19:46,920 Speaker 1: you order you know, Amazon sort of meal delivery kit 356 00:19:47,040 --> 00:19:49,200 Speaker 1: and shop at Whole Foods and you get extra Prime 357 00:19:49,240 --> 00:19:52,080 Speaker 1: points and this all kind of works together. An ecosystem 358 00:19:52,119 --> 00:19:54,680 Speaker 1: makes it very easy to sign up and and get 359 00:19:54,680 --> 00:19:56,960 Speaker 1: people to use whatever service they may come up with 360 00:19:57,000 --> 00:20:00,720 Speaker 1: in the future. Zappos, that was what was thinking of. 361 00:20:00,800 --> 00:20:04,480 Speaker 1: Zap Posts was the shoe business, and they've let it 362 00:20:04,640 --> 00:20:07,040 Speaker 1: have its own brand name, and everyone knows it's owned 363 00:20:07,040 --> 00:20:09,520 Speaker 1: by Amazon, but it's let you know, it's doing its 364 00:20:09,560 --> 00:20:13,440 Speaker 1: own thing. Who news I wouldn't put anything past Amazon, right, Well, 365 00:20:13,520 --> 00:20:17,000 Speaker 1: I'm clearly a strategic vision that is not just confined 366 00:20:17,080 --> 00:20:20,840 Speaker 1: to uh, you know what has happened, more like making 367 00:20:20,840 --> 00:20:23,000 Speaker 1: it up into the future. I have to wonder how 368 00:20:23,080 --> 00:20:26,560 Speaker 1: much this is related to the Whole Foods purchase. Well, 369 00:20:26,760 --> 00:20:29,960 Speaker 1: you know this did come around the same time, this 370 00:20:30,040 --> 00:20:32,920 Speaker 1: patent application, this trademark application as a Whole Foods purchase. 371 00:20:32,960 --> 00:20:35,080 Speaker 1: And I think that now that they have Whole Foods, 372 00:20:35,600 --> 00:20:38,800 Speaker 1: they have so many a whole world of opportunities in 373 00:20:38,840 --> 00:20:40,919 Speaker 1: the space of food um to go forth with now 374 00:20:40,960 --> 00:20:43,320 Speaker 1: that they have these physical locations in this brand. So 375 00:20:43,359 --> 00:20:46,000 Speaker 1: retailers of all types are just cowering in the corners 376 00:20:46,040 --> 00:20:49,240 Speaker 1: hoping that Amazon won't come after them. Selena Wayne, thank 377 00:20:49,240 --> 00:20:51,440 Speaker 1: you so much for joining us and for illuminating what's 378 00:20:51,440 --> 00:20:53,719 Speaker 1: going on today with Blue Apron shares, which have dropped 379 00:20:53,880 --> 00:20:57,840 Speaker 1: as much as twelve percent following this trademark application for 380 00:20:58,280 --> 00:21:02,359 Speaker 1: something that Amazon just consider at some point in the future. 381 00:21:02,480 --> 00:21:05,640 Speaker 1: Selena Wayan is a Bloomberg Tech reporter and she joins 382 00:21:05,720 --> 00:21:20,160 Speaker 1: us here at our Bloomberg eleven three oh studios. Well 383 00:21:20,200 --> 00:21:23,320 Speaker 1: here to help us define the polls about President Donald 384 00:21:23,359 --> 00:21:26,400 Speaker 1: Trump is and Selzer, the president of Selzer and Company 385 00:21:26,400 --> 00:21:30,560 Speaker 1: are based in the Mourne Right Public opinion Research firm. 386 00:21:30,680 --> 00:21:34,879 Speaker 1: And uh, and thank you very much for being with us. Um, 387 00:21:34,920 --> 00:21:39,320 Speaker 1: just according to the new poll, just of Americans approve 388 00:21:39,440 --> 00:21:43,000 Speaker 1: of the job that President Donald Trump is doing. Now 389 00:21:43,280 --> 00:21:46,400 Speaker 1: you are this is the polling number that you've been 390 00:21:46,440 --> 00:21:49,719 Speaker 1: able to put together and analyze. Correct, you're doing this 391 00:21:49,760 --> 00:21:52,360 Speaker 1: for for Bloomberg, for us, that's right? Where the proposter 392 00:21:52,440 --> 00:21:55,480 Speaker 1: for Bloomberg nots right? And maybe just explain how do 393 00:21:55,560 --> 00:21:59,000 Speaker 1: you actually go about this polling and how does it 394 00:21:59,160 --> 00:22:03,600 Speaker 1: vary so much between polls. Well, if what we do 395 00:22:03,640 --> 00:22:07,359 Speaker 1: is do a scientific survey of a randomly selected sample 396 00:22:07,400 --> 00:22:10,320 Speaker 1: of a thousand people across the country, uh, and make 397 00:22:10,359 --> 00:22:13,159 Speaker 1: sure that that's a good cross section of residents of 398 00:22:13,160 --> 00:22:16,360 Speaker 1: the United States. And we asked them a battery of questions. 399 00:22:16,359 --> 00:22:19,520 Speaker 1: Some of these questions we've been asking since the inception 400 00:22:19,600 --> 00:22:22,920 Speaker 1: of the poll in two thousand nine. And UM, that 401 00:22:23,000 --> 00:22:25,480 Speaker 1: gives us a way to kind of track how things 402 00:22:25,480 --> 00:22:28,720 Speaker 1: have changed and how things have stayed the same over time. 403 00:22:28,800 --> 00:22:31,000 Speaker 1: So you know, two thousand nine was the beginning of 404 00:22:31,000 --> 00:22:33,359 Speaker 1: the Obama administration. So we have the highs and the 405 00:22:33,440 --> 00:22:39,199 Speaker 1: lows of that presidency, and early in Obama's presidency his 406 00:22:39,640 --> 00:22:43,200 Speaker 1: numbers were in the fifty percent and mid fifty percent range. 407 00:22:43,480 --> 00:22:47,440 Speaker 1: So Donald Trump is beginning his presidency at a historic low. 408 00:22:47,480 --> 00:22:49,679 Speaker 1: And there's some other polls out right now which are 409 00:22:50,160 --> 00:22:52,600 Speaker 1: UM have a longer history, that are saying this is 410 00:22:52,640 --> 00:22:57,240 Speaker 1: the lowest for any president this early in his presidency. So, 411 00:22:57,400 --> 00:22:59,840 Speaker 1: and let's talk about the latest survey that you just 412 00:23:00,040 --> 00:23:03,960 Speaker 1: conducted on behalf of Bloomberg and what it says about 413 00:23:04,520 --> 00:23:07,280 Speaker 1: the state of the American consumer. There's some good news 414 00:23:07,320 --> 00:23:11,320 Speaker 1: and there's some bad news. Yes, there there is good news. 415 00:23:11,320 --> 00:23:15,239 Speaker 1: There's optimism, Uh, there's good feeling. Is people take a 416 00:23:15,240 --> 00:23:19,000 Speaker 1: look at job security very specifically, and people take a 417 00:23:19,000 --> 00:23:21,920 Speaker 1: look at the value of their home. Uh. It's the 418 00:23:22,000 --> 00:23:25,160 Speaker 1: highest numbers that we've seen for both of those since 419 00:23:25,160 --> 00:23:27,680 Speaker 1: we've been asking those questions. So that's a very personal 420 00:23:27,760 --> 00:23:31,920 Speaker 1: look at what's happening economically. But we all going back 421 00:23:31,960 --> 00:23:35,600 Speaker 1: to before the crisis or since the crisis. UM. We 422 00:23:35,640 --> 00:23:39,880 Speaker 1: started asking those questions in December of two thousand twelves, 423 00:23:40,119 --> 00:23:44,440 Speaker 1: so since the crisis exactly. UM. But and and then 424 00:23:44,480 --> 00:23:48,119 Speaker 1: a more general question about whether the people feel they 425 00:23:48,119 --> 00:23:50,879 Speaker 1: are moving closer to their hopes for their career, and 426 00:23:50,960 --> 00:23:56,760 Speaker 1: finances are moving farther away from their dreams. Now say 427 00:23:56,800 --> 00:23:59,680 Speaker 1: that they're moving closer. That's as high as we've ever 428 00:23:59,720 --> 00:24:04,159 Speaker 1: seen it. So you have this juxtaposition of feeling good 429 00:24:04,480 --> 00:24:08,120 Speaker 1: about things and what's happening with them personally, and and 430 00:24:08,480 --> 00:24:13,199 Speaker 1: yet awarding a low approval rating for the president. And 431 00:24:13,240 --> 00:24:16,320 Speaker 1: so I kind of think about Jim Carvel, who was 432 00:24:16,400 --> 00:24:20,000 Speaker 1: an advisor to President Clinton, who said, you know, when 433 00:24:20,000 --> 00:24:22,480 Speaker 1: you see a turtle on a on a sense post, 434 00:24:22,680 --> 00:24:25,520 Speaker 1: it didn't get there by themselves by itself. So you 435 00:24:25,680 --> 00:24:27,480 Speaker 1: kind of think, well, what is that that's happening in 436 00:24:27,520 --> 00:24:30,520 Speaker 1: the economy, and did it happen by itself? Or is 437 00:24:30,560 --> 00:24:34,439 Speaker 1: Donald Trump having any leadership impact. It's just there's a 438 00:24:34,480 --> 00:24:36,560 Speaker 1: little bit of a disconnect. There's a little bit of 439 00:24:36,560 --> 00:24:40,359 Speaker 1: a dissonance between the goodness that people are feeling and 440 00:24:40,480 --> 00:24:44,080 Speaker 1: not getting any attribution for the president who's leading this country. 441 00:24:44,200 --> 00:24:46,600 Speaker 1: You know, And just to play devil's advocate, what if 442 00:24:46,640 --> 00:24:51,600 Speaker 1: somebody said, look, polls have been radically inaccurate for the 443 00:24:51,680 --> 00:24:55,960 Speaker 1: past few elections, and you know, maybe this dissonance reflects 444 00:24:56,000 --> 00:24:59,440 Speaker 1: the way that questions are being asked to get an 445 00:24:59,480 --> 00:25:03,800 Speaker 1: unfavored opinion. Well, I would I would take issue with 446 00:25:03,840 --> 00:25:06,840 Speaker 1: the radically inaccurate. In fact, our polls have been highly accurate. 447 00:25:06,840 --> 00:25:10,600 Speaker 1: Were rated among the most accurate polling companies in of 448 00:25:10,640 --> 00:25:14,320 Speaker 1: the three hundred and fifty polling companies that have been ranked. 449 00:25:14,400 --> 00:25:16,919 Speaker 1: And the fact of the matter is that we we 450 00:25:17,000 --> 00:25:21,280 Speaker 1: see remarkable consistencies. We see things only fluctuating by a 451 00:25:21,280 --> 00:25:24,400 Speaker 1: little bit. So it's not as though we suddenly got 452 00:25:24,480 --> 00:25:29,159 Speaker 1: something very different. Um. You know, I think there is 453 00:25:29,200 --> 00:25:32,080 Speaker 1: no way, given that we asked these questions the same 454 00:25:32,119 --> 00:25:35,800 Speaker 1: way that they were asked of Barack Obama, to say 455 00:25:35,800 --> 00:25:38,800 Speaker 1: that it's the wording of the question that is leading 456 00:25:38,880 --> 00:25:42,520 Speaker 1: us to say that that, but Donald Trump's numbers are lower, 457 00:25:42,640 --> 00:25:47,040 Speaker 1: that there's just no evidence that would support that claim. 458 00:25:47,080 --> 00:25:50,920 Speaker 1: And do the poll results indicate to you a polarized 459 00:25:51,000 --> 00:25:54,840 Speaker 1: nation the way it is often described anecdotally, Well, what 460 00:25:54,880 --> 00:25:57,399 Speaker 1: the poll results do tell us is that there's a 461 00:25:57,520 --> 00:26:01,479 Speaker 1: there's a there's a market difference in how Trump Trump 462 00:26:01,560 --> 00:26:05,320 Speaker 1: voters are rating their president, which is they are sticking 463 00:26:05,320 --> 00:26:08,320 Speaker 1: with him. Uh, there is a very strong majority who 464 00:26:08,400 --> 00:26:11,520 Speaker 1: approve of the job that he's doing. Um, So that 465 00:26:11,640 --> 00:26:14,840 Speaker 1: overall low rating is the people who did not vote 466 00:26:14,880 --> 00:26:17,600 Speaker 1: for him, whether they didn't vote in that election at all, 467 00:26:18,000 --> 00:26:20,600 Speaker 1: or whether they voted for Hillary Clinton or for somebody else. 468 00:26:20,760 --> 00:26:23,520 Speaker 1: What kind of number was What was that coming up 469 00:26:23,560 --> 00:26:26,679 Speaker 1: as that you describe. The Trump voter number is in 470 00:26:26,760 --> 00:26:30,840 Speaker 1: the high eighties. So there that base that he has 471 00:26:31,119 --> 00:26:35,280 Speaker 1: created is still saying that what they see him doing 472 00:26:35,440 --> 00:26:39,280 Speaker 1: is what they hired him to do. And unrelated to Bloomberg, 473 00:26:39,320 --> 00:26:41,520 Speaker 1: we did some focus groups earlier this year to ask 474 00:26:41,720 --> 00:26:45,920 Speaker 1: exactly that question, focus groups with exclusively Trump voters who said, look, 475 00:26:46,240 --> 00:26:49,880 Speaker 1: we hired him so that he would create some havoc, 476 00:26:50,000 --> 00:26:53,000 Speaker 1: and he's creating some havoc, and we're not going to 477 00:26:53,119 --> 00:26:55,879 Speaker 1: like all of it, but it's that general approach that 478 00:26:55,960 --> 00:26:59,400 Speaker 1: they were looking for. They were looking to shake things up. 479 00:26:59,800 --> 00:27:02,640 Speaker 1: So while they might not agree with all of the specifics, 480 00:27:02,920 --> 00:27:06,160 Speaker 1: the general idea of changing the way that the nation 481 00:27:06,320 --> 00:27:10,960 Speaker 1: is governed they were supportive of. And Seltzer really fascinating. 482 00:27:11,000 --> 00:27:13,000 Speaker 1: Thank you so much for joining us and for conducting 483 00:27:13,000 --> 00:27:15,880 Speaker 1: the poll, which is really illuminating and a good look 484 00:27:15,880 --> 00:27:18,840 Speaker 1: into some of the polarization within people's own opinions about 485 00:27:18,880 --> 00:27:22,000 Speaker 1: what's going on right now and Seltzer, President of Seltzer 486 00:27:22,280 --> 00:27:25,439 Speaker 1: and Company based in De Moyne, Iowa. She's sharing the 487 00:27:25,440 --> 00:27:28,960 Speaker 1: results of this broad based poll on President Trump conducted 488 00:27:29,000 --> 00:27:31,440 Speaker 1: for Bloomberg. It was a telephone poll of more than 489 00:27:31,480 --> 00:27:37,840 Speaker 1: a thousand American adults. Thanks for listening to the Bloomberg 490 00:27:37,880 --> 00:27:40,520 Speaker 1: P and L podcast. You can subscribe and listen to 491 00:27:40,560 --> 00:27:45,080 Speaker 1: interviews at Apple Podcasts, SoundCloud, or whatever podcast platform you prefer. 492 00:27:45,520 --> 00:27:49,080 Speaker 1: I'm pim Fox. I'm on Twitter at pim Fox. I'm 493 00:27:49,080 --> 00:27:52,520 Speaker 1: on Twitter at Lisa abramoids one Before the podcast, you 494 00:27:52,520 --> 00:28:02,040 Speaker 1: can always catch us worldwide on Bloomberg Radio.