1 00:00:03,000 --> 00:00:12,160 Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. This is Master's in 2 00:00:12,280 --> 00:00:16,800 Speaker 1: Business with Barry rid Hoolds on Bloomberg Radio. 3 00:00:18,800 --> 00:00:22,360 Speaker 2: This week on the podcast, I have an extra special guest. 4 00:00:22,920 --> 00:00:27,160 Speaker 2: Sean Dobson has really had a fascinating career as a 5 00:00:27,200 --> 00:00:31,040 Speaker 2: real estate investor, starring pretty much at the bottom and 6 00:00:31,120 --> 00:00:35,080 Speaker 2: working his way up to becoming a investor in a 7 00:00:35,159 --> 00:00:39,960 Speaker 2: variety of mortgage backed securities, individual homes, commercial real estate, 8 00:00:40,360 --> 00:00:46,599 Speaker 2: really all aspects of the finding, buying, and investing in 9 00:00:46,640 --> 00:00:50,000 Speaker 2: real estate. And on top of that, he's pretty much 10 00:00:50,200 --> 00:00:53,440 Speaker 2: a quantitative geek, so he is looking at this not 11 00:00:53,800 --> 00:00:58,600 Speaker 2: simply from the typical real estate investment perspective, but from 12 00:00:58,640 --> 00:01:03,880 Speaker 2: a deep quantitative analytical basis. If you're interested in any 13 00:01:04,000 --> 00:01:09,920 Speaker 2: aspect of commercial, residential, mortgage back real estate, then you 14 00:01:09,959 --> 00:01:12,959 Speaker 2: should absolutely listen to this. It's fascinating and there are 15 00:01:13,000 --> 00:01:16,800 Speaker 2: a few people in the industry who not only have 16 00:01:16,920 --> 00:01:21,479 Speaker 2: been successful as investors, but also very clearly saw and 17 00:01:21,560 --> 00:01:25,120 Speaker 2: warned about the Great financial crisis coming because it was 18 00:01:25,200 --> 00:01:26,840 Speaker 2: all there in the data if you were looking in 19 00:01:26,840 --> 00:01:30,520 Speaker 2: the right place. And continues to build and expand the 20 00:01:30,600 --> 00:01:34,360 Speaker 2: Amherst Group into a real estate powerhouse. I found this 21 00:01:34,400 --> 00:01:37,560 Speaker 2: conversation to be absolutely fascinating, and I think you will 22 00:01:37,600 --> 00:01:41,880 Speaker 2: also with no further ado my discussion with Amherst groups 23 00:01:42,000 --> 00:01:42,800 Speaker 2: Sean Dobson. 24 00:01:43,160 --> 00:01:44,720 Speaker 1: Thank you very much. It's great to be here. 25 00:01:44,959 --> 00:01:47,640 Speaker 2: So let's talk a little bit about your career in 26 00:01:47,680 --> 00:01:49,760 Speaker 2: real estate. But before we get to that, I just 27 00:01:49,800 --> 00:01:53,960 Speaker 2: got to ask on your LinkedIn under education, it says 28 00:01:54,520 --> 00:01:58,040 Speaker 2: didn't graduate none, working for a living. What does that mean? 29 00:01:58,520 --> 00:02:02,400 Speaker 1: I think I answered questions of of when did you graduate? 30 00:02:02,440 --> 00:02:04,400 Speaker 1: And so I said I didn't graduate, and then that 31 00:02:04,560 --> 00:02:07,000 Speaker 1: was your what degrees did you achieve? And I said none? 32 00:02:07,720 --> 00:02:09,840 Speaker 1: And then I think the question was what were you 33 00:02:09,880 --> 00:02:11,280 Speaker 1: doing or what were your interested in? So I was 34 00:02:11,320 --> 00:02:13,560 Speaker 1: working for a living, but I didn't. 35 00:02:13,360 --> 00:02:17,200 Speaker 2: Go to college, did not go to college. So that 36 00:02:17,280 --> 00:02:19,760 Speaker 2: leads to the next question, what got you interested in 37 00:02:19,800 --> 00:02:20,320 Speaker 2: real estate? 38 00:02:21,040 --> 00:02:24,680 Speaker 1: It was it was happenstance. I took a temporary job 39 00:02:25,200 --> 00:02:28,760 Speaker 1: at a brokerage firm in Houston, Texas the summer after 40 00:02:28,840 --> 00:02:32,080 Speaker 1: high school, between high school and college. Really as the 41 00:02:32,160 --> 00:02:36,040 Speaker 1: office runner, run around picking up people's drying cleaning, grabbing lunch, 42 00:02:36,400 --> 00:02:39,160 Speaker 1: opening the mail, that sort of thing. And I took 43 00:02:39,200 --> 00:02:42,119 Speaker 1: the job really because a friend of ours a friend 44 00:02:42,160 --> 00:02:44,120 Speaker 1: of the families had worked there and just said, what 45 00:02:44,160 --> 00:02:47,600 Speaker 1: an interesting sort of industry it was. This is back 46 00:02:47,600 --> 00:02:49,800 Speaker 1: when mortgages were sort of a backwater of the fixed 47 00:02:49,880 --> 00:02:51,440 Speaker 1: income market, so they were traded a little bit like 48 00:02:51,480 --> 00:02:55,000 Speaker 1: UNI bonds or not really well understood, not. 49 00:02:55,000 --> 00:02:58,720 Speaker 2: Well followed nineteen nineties or eighty. 50 00:02:58,480 --> 00:03:01,720 Speaker 1: Seven, wow, nineteen eighty seven. So after that it was 51 00:03:02,280 --> 00:03:06,080 Speaker 1: I later was given some opportunities to join the research team, 52 00:03:06,960 --> 00:03:08,639 Speaker 1: and then took over the research team, and then took 53 00:03:08,680 --> 00:03:11,480 Speaker 1: over the eventually took over the trading platform, and then 54 00:03:11,520 --> 00:03:14,080 Speaker 1: by nineteen ninety four a group of us had started 55 00:03:14,080 --> 00:03:17,800 Speaker 1: our own business, and that's the predecessor to Amherst, which 56 00:03:17,840 --> 00:03:20,440 Speaker 1: we bought in two thousand and have been running it 57 00:03:20,800 --> 00:03:21,200 Speaker 1: since then. 58 00:03:22,440 --> 00:03:24,320 Speaker 2: So when you say you were running the trading desk, 59 00:03:24,360 --> 00:03:29,440 Speaker 2: you're running primarily mortgage backed securities. That's mortgageback exactly. Anything 60 00:03:29,440 --> 00:03:31,040 Speaker 2: else swaps, derivatives and anything wrong. 61 00:03:31,080 --> 00:03:32,920 Speaker 1: So back then it was really just mortgage back securities 62 00:03:32,960 --> 00:03:35,600 Speaker 1: and structured products that were derivatives of mortgage backed securities. 63 00:03:35,680 --> 00:03:37,480 Speaker 1: We sort of carved out a name for ourselves in 64 00:03:38,240 --> 00:03:41,720 Speaker 1: quant analytics around mortgage risk, and that's still a big 65 00:03:41,960 --> 00:03:45,160 Speaker 1: core competency of Amherst is understanding the risks of mortgages 66 00:03:45,160 --> 00:03:47,440 Speaker 1: are kind of boring, but they're also very complicated. The 67 00:03:47,480 --> 00:03:50,160 Speaker 1: borrower has so many options around when the refinance, how 68 00:03:50,160 --> 00:03:53,760 Speaker 1: to repay, if the repay. It takes quite a lot 69 00:03:53,760 --> 00:03:56,960 Speaker 1: of research, quite a lot of modeling, quite a lot 70 00:03:57,000 --> 00:03:59,119 Speaker 1: of data to actually keep up with the mortgage market. 71 00:03:59,200 --> 00:04:02,440 Speaker 1: It's really forty million individual contracts, forty fifty million individual 72 00:04:02,480 --> 00:04:05,480 Speaker 1: contracts and a million different securities, so it takes quale. 73 00:04:06,080 --> 00:04:08,440 Speaker 1: We've built an interesting system to allow to sort of 74 00:04:08,480 --> 00:04:10,120 Speaker 1: monitor all that and price it in real time. 75 00:04:10,320 --> 00:04:14,720 Speaker 2: So if you're running a desk in the two thousands 76 00:04:14,800 --> 00:04:16,840 Speaker 2: and you're looking at mortgage backed then you're looking at 77 00:04:16,839 --> 00:04:21,239 Speaker 2: securitized product. One would think, especially from Texas as opposed 78 00:04:21,279 --> 00:04:24,760 Speaker 2: to being in the thick of Wall Street, you might 79 00:04:24,800 --> 00:04:29,240 Speaker 2: have seen some signs that perhaps the wheels are coming 80 00:04:29,279 --> 00:04:31,680 Speaker 2: off the bus. Tell us about your experience in the 81 00:04:31,720 --> 00:04:33,440 Speaker 2: two thousands, what did you see? Comment? 82 00:04:33,560 --> 00:04:37,279 Speaker 1: Yeah, So from the late eighties until the really the 83 00:04:37,400 --> 00:04:41,560 Speaker 1: late nineties, we were focused primarily on prepayment related risk 84 00:04:41,640 --> 00:04:44,320 Speaker 1: in agency mortgs backed securities. By the time you get 85 00:04:44,320 --> 00:04:46,919 Speaker 1: to the early two thousands, Freddie mc fanny age and 86 00:04:46,960 --> 00:04:49,440 Speaker 1: may were losing market share. A lot of mortgages were 87 00:04:49,440 --> 00:04:52,400 Speaker 1: coming straight from originators and going and being packaged into 88 00:04:52,440 --> 00:04:55,600 Speaker 1: what later became the private label securities market. So as 89 00:04:55,640 --> 00:04:58,760 Speaker 1: part of our just growth, we attack that market. And 90 00:04:58,920 --> 00:05:01,400 Speaker 1: up until that moment in time, we didn't spend a 91 00:05:01,400 --> 00:05:03,720 Speaker 1: lot of time on credit risk in mortgages. We didn't 92 00:05:03,720 --> 00:05:05,920 Speaker 1: really have the model credit risk because that risk was 93 00:05:05,960 --> 00:05:09,720 Speaker 1: taken by the agencies. But in these private labels you had, 94 00:05:09,760 --> 00:05:11,839 Speaker 1: the market was taking the credit risk. So we took 95 00:05:11,880 --> 00:05:14,799 Speaker 1: the exact same modeling approach, which is loan level detail, 96 00:05:15,279 --> 00:05:19,920 Speaker 1: borrower behavior, stochastic processes, options based modeling, and we said, 97 00:05:19,960 --> 00:05:21,640 Speaker 1: let's just take a little detour here and make sure 98 00:05:21,640 --> 00:05:24,240 Speaker 1: we understand the credit risk of these things before we 99 00:05:24,279 --> 00:05:28,159 Speaker 1: sort of travel start making markets and banking and making 100 00:05:28,160 --> 00:05:29,839 Speaker 1: these a core part of our business. At that time, 101 00:05:29,960 --> 00:05:32,600 Speaker 1: this market was about a third of all mortgages, were 102 00:05:32,640 --> 00:05:34,440 Speaker 1: the ones where the credit risk was going into the 103 00:05:34,440 --> 00:05:37,320 Speaker 1: capital markets. So that little detour was in two thousand 104 00:05:37,440 --> 00:05:41,360 Speaker 1: and three and we found a couple of things we modeled. 105 00:05:42,160 --> 00:05:45,120 Speaker 1: We modeled defaults the same way with model prepayments, which 106 00:05:45,160 --> 00:05:49,719 Speaker 1: is an option for the consumer to not pay most. 107 00:05:48,880 --> 00:05:50,719 Speaker 2: Of it rarely here it described that way. 108 00:05:50,800 --> 00:05:53,640 Speaker 1: Well, it's a unique approach, right, and it was unique 109 00:05:53,680 --> 00:05:56,520 Speaker 1: at the time, and so we thought there were conditions 110 00:05:56,600 --> 00:05:59,640 Speaker 1: under which the option probably should be exercised. If you 111 00:05:59,680 --> 00:06:01,760 Speaker 1: have a two hours set out, if you have a 112 00:06:01,800 --> 00:06:04,080 Speaker 1: two hundred thousand dollars home and one hundred thousand dollars mortgage, 113 00:06:04,120 --> 00:06:07,000 Speaker 1: and the consequence for not paying is ding on your 114 00:06:07,040 --> 00:06:09,440 Speaker 1: credit report, you're probably not supposed to pay, is the 115 00:06:09,480 --> 00:06:12,120 Speaker 1: position we took. So through that lens, we said, okay, 116 00:06:12,200 --> 00:06:14,040 Speaker 1: let's price these securities, and we found a bunch of 117 00:06:14,240 --> 00:06:17,880 Speaker 1: interesting things. For example, we found that the follow on 118 00:06:18,080 --> 00:06:22,160 Speaker 1: rating surveillance for mortgage backed securities doesn't follow the same 119 00:06:22,279 --> 00:06:25,760 Speaker 1: ratings methodology that the initial rating does. So over time 120 00:06:26,440 --> 00:06:28,880 Speaker 1: the risk composition of the pool which would change dramatically. 121 00:06:28,920 --> 00:06:30,680 Speaker 1: So think about two thousand and three. Home price has 122 00:06:30,760 --> 00:06:33,400 Speaker 1: gone up a lot from two thousand, So mortgage position 123 00:06:33,480 --> 00:06:35,520 Speaker 1: in two thousand were way more valuable in two thousand 124 00:06:35,520 --> 00:06:37,440 Speaker 1: and three than they were they originated because they weigh 125 00:06:37,480 --> 00:06:40,960 Speaker 1: less credit risk. Not the same thing couldn't be true 126 00:06:41,000 --> 00:06:42,280 Speaker 1: as you went forward. 127 00:06:42,080 --> 00:06:44,800 Speaker 2: In time, each subsequent vintage became risk, and risk became 128 00:06:44,880 --> 00:06:47,760 Speaker 2: risking risk as prices went up because rates had gone 129 00:06:47,800 --> 00:06:48,320 Speaker 2: lower and lower. 130 00:06:48,400 --> 00:06:49,520 Speaker 1: And that's the way we thought about it. The way 131 00:06:49,560 --> 00:06:51,000 Speaker 1: we think about it when you make someone alone. This 132 00:06:51,440 --> 00:06:53,800 Speaker 1: is sort of the credit oas world. So we think 133 00:06:53,800 --> 00:06:55,880 Speaker 1: about when you make someone alone on a building, whether 134 00:06:55,920 --> 00:06:59,440 Speaker 1: it's this building or or a home, you're implicitly United States, 135 00:06:59,480 --> 00:07:02,320 Speaker 1: you're implicit giving them the option to send you the keys. 136 00:07:03,360 --> 00:07:06,640 Speaker 1: So jingle mail is exactly and so we thought it 137 00:07:06,680 --> 00:07:09,720 Speaker 1: was least Okay, we've been pricing complicated options our whole career, 138 00:07:09,920 --> 00:07:12,040 Speaker 1: so let's just price the option to default as if 139 00:07:12,080 --> 00:07:14,920 Speaker 1: it is a financial option. When you do that, and 140 00:07:15,040 --> 00:07:17,400 Speaker 1: then you looked at the types of loans or being originated, 141 00:07:17,720 --> 00:07:19,480 Speaker 1: and this is where Amher's story is a little different 142 00:07:19,520 --> 00:07:21,160 Speaker 1: than some of the stories you've seen are on the 143 00:07:21,160 --> 00:07:24,040 Speaker 1: financial crisis. What we figured out was that the premium 144 00:07:24,120 --> 00:07:27,560 Speaker 1: that you were being paid as this option seller was 145 00:07:27,680 --> 00:07:30,320 Speaker 1: way below the fair market price of their premium, meaning 146 00:07:30,360 --> 00:07:32,840 Speaker 1: that the default risk you were taking was way higher 147 00:07:32,880 --> 00:07:35,240 Speaker 1: than the market had appreciated. So they were underpricing the 148 00:07:35,320 --> 00:07:38,560 Speaker 1: fault risk dramatically. Then, as we dug in and dug 149 00:07:38,600 --> 00:07:40,640 Speaker 1: in and dug in, we realized that there were a 150 00:07:40,680 --> 00:07:42,840 Speaker 1: lot of loans that were really experiments. There were financial 151 00:07:42,880 --> 00:07:45,960 Speaker 1: experiments where the bar hadn't been through dal diligence, the 152 00:07:46,840 --> 00:07:50,080 Speaker 1: LTV was very high, the underlying risk of the home 153 00:07:50,120 --> 00:07:51,080 Speaker 1: market was very high. 154 00:07:51,920 --> 00:07:54,600 Speaker 2: Other of these were the no dock or Ninja. 155 00:07:54,360 --> 00:07:57,560 Speaker 1: Loans were limited DOC no dock, no. 156 00:07:57,680 --> 00:08:03,680 Speaker 2: Income, no no assets exactly, Ninja no pulse seem seems 157 00:08:03,720 --> 00:08:04,720 Speaker 2: reasonable exactly. 158 00:08:04,840 --> 00:08:06,280 Speaker 1: So you look back at these things, you're like, how 159 00:08:06,360 --> 00:08:09,880 Speaker 1: could it happen? But we're low level people, right, so 160 00:08:09,960 --> 00:08:12,640 Speaker 1: we don't see the mortgage backed securities market as a market. 161 00:08:12,720 --> 00:08:15,400 Speaker 1: We see it as like I said, about fifty million assets, 162 00:08:15,440 --> 00:08:17,280 Speaker 1: and we're modeling up the value of every home in 163 00:08:17,320 --> 00:08:19,960 Speaker 1: the country every every week basically, and we're modeling with 164 00:08:20,040 --> 00:08:21,960 Speaker 1: the value of every mortgage in the country, and we're 165 00:08:22,000 --> 00:08:24,480 Speaker 1: modeling with the value every derivative of that mortgage, of 166 00:08:24,480 --> 00:08:26,720 Speaker 1: the structure, products, and so forth. So through our lens, 167 00:08:26,800 --> 00:08:29,120 Speaker 1: it was like, Okay, we've made these financial experiments. The 168 00:08:29,240 --> 00:08:32,360 Speaker 1: underlying real estate has become very volatile, so we could 169 00:08:32,440 --> 00:08:36,160 Speaker 1: construct trades that had very very low premiums to sell 170 00:08:36,240 --> 00:08:39,520 Speaker 1: this volatility, to basically join the consumer on their side 171 00:08:39,520 --> 00:08:42,480 Speaker 1: of the trade, which is in essense buying insurance on 172 00:08:43,000 --> 00:08:45,720 Speaker 1: the bonds that were exposed of these great risks. Soult. 173 00:08:45,720 --> 00:08:47,719 Speaker 1: We did that for a lot of the market, so 174 00:08:47,760 --> 00:08:49,480 Speaker 1: a lot of the headline names you see, a lot 175 00:08:49,559 --> 00:08:52,839 Speaker 1: of the stories you see about the financial crisis, a 176 00:08:52,920 --> 00:08:58,079 Speaker 1: significant number of those investors we were helping in security selection, 177 00:08:58,240 --> 00:09:01,160 Speaker 1: modeling and analytics. That sort of put Amherst on a 178 00:09:01,240 --> 00:09:04,280 Speaker 1: different pack because prior to that, our core business model 179 00:09:04,400 --> 00:09:08,120 Speaker 1: was investment, banking, brokerage, market making and underwriting. By the 180 00:09:08,160 --> 00:09:10,640 Speaker 1: time we got to two thousand and five and figured 181 00:09:10,679 --> 00:09:13,240 Speaker 1: out that there was such a large sector that was 182 00:09:13,320 --> 00:09:16,319 Speaker 1: so mispriced, we started hedge funds, opportunity funds. We took 183 00:09:16,400 --> 00:09:19,240 Speaker 1: sub mandates from the big global macro hedge funds, and 184 00:09:19,360 --> 00:09:22,760 Speaker 1: we started to build our model around investing in our research. 185 00:09:23,200 --> 00:09:25,960 Speaker 1: Co investing our research and earning carried interest in sort 186 00:09:26,000 --> 00:09:28,679 Speaker 1: of big complicated trades that we thought we had figured 187 00:09:28,679 --> 00:09:31,400 Speaker 1: out the market, maybe the market had imprised something properly. 188 00:09:31,920 --> 00:09:33,319 Speaker 2: How did that end up working out? 189 00:09:34,000 --> 00:09:35,080 Speaker 1: It was a wild ride. 190 00:09:35,240 --> 00:09:36,240 Speaker 2: It was a wild ride. 191 00:09:36,080 --> 00:09:38,280 Speaker 1: Because by the time you got so in two thousand 192 00:09:38,280 --> 00:09:41,000 Speaker 1: and five, we went on a road show trying to 193 00:09:41,080 --> 00:09:43,760 Speaker 1: tell people we had learned and there wasn't a lot 194 00:09:43,800 --> 00:09:44,240 Speaker 1: of reception. 195 00:09:44,480 --> 00:09:46,760 Speaker 2: We literally, let me, let me interrupt you and ask you. 196 00:09:47,160 --> 00:09:48,520 Speaker 2: Did people laugh at you? 197 00:09:49,679 --> 00:09:52,200 Speaker 1: They were more polite than that, but they didn't invest. 198 00:09:53,520 --> 00:09:57,040 Speaker 1: So there were very few people that thought because at 199 00:09:57,080 --> 00:10:00,360 Speaker 1: that time the triling credit performers, for you, single thing 200 00:10:00,440 --> 00:10:02,480 Speaker 1: mortgagees great, impeccable, peckable. 201 00:10:02,800 --> 00:10:05,319 Speaker 2: I want to say five was where we peaked in 202 00:10:06,559 --> 00:10:08,880 Speaker 2: price and six's volume or am I getting that? 203 00:10:09,080 --> 00:10:10,920 Speaker 1: So six O five was six? It started to turn 204 00:10:11,000 --> 00:10:13,240 Speaker 1: over and our thesis on a lot of these mortgages 205 00:10:13,520 --> 00:10:17,200 Speaker 1: and the very very exposed securities within these structured products 206 00:10:17,320 --> 00:10:19,520 Speaker 1: wasn't that home prices needed to go down. It was 207 00:10:19,640 --> 00:10:21,439 Speaker 1: that the only way that the loan was going to 208 00:10:21,480 --> 00:10:24,600 Speaker 1: perform is that the consumer could refinance out of it quickly. Right, 209 00:10:25,160 --> 00:10:27,439 Speaker 1: So you really just wanted the music to stop, right 210 00:10:27,800 --> 00:10:29,360 Speaker 1: or if you mean, this whole thing was going to 211 00:10:29,400 --> 00:10:31,800 Speaker 1: come down if the music stopped. So by the time 212 00:10:31,840 --> 00:10:33,800 Speaker 1: the music stopped, it was pretty apparent, but we had it. 213 00:10:34,080 --> 00:10:37,679 Speaker 1: There's a there's a big industry conference called AFS that 214 00:10:37,800 --> 00:10:39,880 Speaker 1: happens twice a year, and in the two thousand, the 215 00:10:39,880 --> 00:10:41,920 Speaker 1: two thousand and five conference, it's kind of wild. So 216 00:10:41,960 --> 00:10:44,320 Speaker 1: these big brokens frooms get together and they set up 217 00:10:44,320 --> 00:10:47,000 Speaker 1: a convention like like plumbers, and they also give out 218 00:10:47,080 --> 00:10:49,120 Speaker 1: choski's and they have a and then they give presentations 219 00:10:49,120 --> 00:10:51,120 Speaker 1: in their business. And so we participated in this. Our 220 00:10:51,200 --> 00:10:53,679 Speaker 1: choshki that year was a hard helmet, was a was 221 00:10:53,720 --> 00:10:55,920 Speaker 1: an orange hard hat, and they said beware of falling 222 00:10:55,960 --> 00:10:58,520 Speaker 1: home prices. And our whole thesis was that was what 223 00:10:58,559 --> 00:10:59,760 Speaker 1: I'm trying to describe. 224 00:10:59,400 --> 00:11:01,280 Speaker 2: Which is that's a great swag. Do you do you 225 00:11:01,360 --> 00:11:01,640 Speaker 2: still have? 226 00:11:01,840 --> 00:11:03,959 Speaker 1: I have one in my office now. I have a 227 00:11:04,040 --> 00:11:06,640 Speaker 1: helmet from Beware of Falling home Prices, and I have 228 00:11:06,800 --> 00:11:09,559 Speaker 1: one for our new construction division where we build entire neighborhoods. 229 00:11:10,960 --> 00:11:13,559 Speaker 1: And that's really to sort of bring it all together 230 00:11:14,240 --> 00:11:18,120 Speaker 1: with this core competency and analytics, and we're probably the only, 231 00:11:18,280 --> 00:11:20,839 Speaker 1: maybe not the only, but but I don't know of 232 00:11:20,920 --> 00:11:23,559 Speaker 1: a competitor. We're the quant shop in real estate and 233 00:11:23,600 --> 00:11:27,360 Speaker 1: the quant shop in physic glasses. So with that core competency, 234 00:11:27,480 --> 00:11:29,480 Speaker 1: that's the reason we're in the single play rental business. 235 00:11:29,559 --> 00:11:31,520 Speaker 1: You followed that all the way through. There were amazing 236 00:11:31,640 --> 00:11:34,480 Speaker 1: trades to do, amazing opportunities, wild scary things to do. 237 00:11:35,040 --> 00:11:36,480 Speaker 1: I got to spend a lot of time in DC 238 00:11:36,640 --> 00:11:39,400 Speaker 1: consulting on the response to the financial crisis and trying 239 00:11:39,400 --> 00:11:41,480 Speaker 1: to sort out sort of what was really going on. 240 00:11:42,000 --> 00:11:43,880 Speaker 1: And what we figured out in two thousand and nine 241 00:11:43,920 --> 00:11:46,640 Speaker 1: really when we started buying homes is that we made 242 00:11:46,679 --> 00:11:49,839 Speaker 1: the bet that I mean, it wasn't a very exotic bet, 243 00:11:49,920 --> 00:11:51,840 Speaker 1: but we made the bet that the sub primorgage market 244 00:11:51,920 --> 00:11:53,120 Speaker 1: wasn't coming back at all. 245 00:11:53,280 --> 00:11:55,480 Speaker 2: So wait, let me unpack some of that. Christ there's 246 00:11:55,480 --> 00:11:58,600 Speaker 2: a lot of really interesting things. When you mentioned DC, 247 00:11:59,200 --> 00:12:02,559 Speaker 2: I'm aware of the Act that you briefed Congress, the 248 00:12:02,640 --> 00:12:06,320 Speaker 2: Federal Reserve, the White House. Who else did you speak 249 00:12:06,360 --> 00:12:08,560 Speaker 2: to when you were there? Well, what was that experience? 250 00:12:08,679 --> 00:12:10,840 Speaker 1: So I lived in Washington, d C. For five years, 251 00:12:10,920 --> 00:12:13,600 Speaker 1: my family and I moved to McLain, Virginia in two 252 00:12:13,640 --> 00:12:16,599 Speaker 1: thousand and eight. So we were down the street, and 253 00:12:16,720 --> 00:12:19,160 Speaker 1: we were in a pretty interesting situation because we were 254 00:12:19,559 --> 00:12:21,480 Speaker 1: we were one of the biggest, if not the only, 255 00:12:22,320 --> 00:12:25,600 Speaker 1: investment banks specializing in the core risk that the nation 256 00:12:25,800 --> 00:12:28,760 Speaker 1: was facing. And we didn't need any help, right, So 257 00:12:28,840 --> 00:12:31,440 Speaker 1: we weren't there looking for changing of a reg cap, 258 00:12:31,600 --> 00:12:34,000 Speaker 1: you know, of anything. We weren't looking for a bailout. 259 00:12:34,040 --> 00:12:36,280 Speaker 1: We were looking for recapitalization or anything. We were just 260 00:12:36,960 --> 00:12:39,800 Speaker 1: there as a source of information. So we met a 261 00:12:39,840 --> 00:12:41,760 Speaker 1: lot of interesting people in DC, and it was the 262 00:12:41,840 --> 00:12:45,000 Speaker 1: whole gamut. We were consulted on the recapitalization in Freddie 263 00:12:45,080 --> 00:12:47,760 Speaker 1: mckin family. We participated in that with Treasury and FHFA 264 00:12:47,960 --> 00:12:51,240 Speaker 1: and the regulators of the White House. And I would 265 00:12:51,240 --> 00:12:53,680 Speaker 1: say that Washington was pretty interesting because we had gone 266 00:12:53,920 --> 00:12:55,560 Speaker 1: and spoken to people in two thousand and five, two 267 00:12:55,600 --> 00:12:58,160 Speaker 1: thousand and six and to kind of let people know 268 00:12:58,240 --> 00:13:01,000 Speaker 1: that there was something these this is a trillion dollars 269 00:13:01,000 --> 00:13:02,120 Speaker 1: worth miss price risk right. 270 00:13:02,400 --> 00:13:08,319 Speaker 2: And I very vividly recall six even o seven. People were, hey, 271 00:13:08,400 --> 00:13:10,559 Speaker 2: we're in the middle of a giant boom. Why do 272 00:13:10,679 --> 00:13:13,360 Speaker 2: you have to come, you know, rain on our purrass? 273 00:13:13,520 --> 00:13:14,800 Speaker 2: But what was your experience? 274 00:13:15,120 --> 00:13:17,840 Speaker 1: It was lonely, I tell you. The analogy was something 275 00:13:17,960 --> 00:13:20,640 Speaker 1: like this, is that we had seen what had happened, 276 00:13:20,920 --> 00:13:22,760 Speaker 1: and by two thousand and six it was over right. 277 00:13:22,840 --> 00:13:26,040 Speaker 1: The mortgages were defaulting. People were taking out mortgages and 278 00:13:26,120 --> 00:13:28,439 Speaker 1: defaulting in the third payment, the fourth payment. 279 00:13:28,679 --> 00:13:33,640 Speaker 2: Ninety day warranty on those non conforming non Fanny May 280 00:13:34,200 --> 00:13:37,599 Speaker 2: mortgages from those private contractors, like a toaster comes with 281 00:13:37,679 --> 00:13:39,160 Speaker 2: along a warranty, it's amazing. 282 00:13:39,160 --> 00:13:41,200 Speaker 1: So eventually even that was gone, even that they wouldn't 283 00:13:41,200 --> 00:13:44,240 Speaker 1: provide nine day warranty. Eventually it was take it at 284 00:13:44,320 --> 00:13:47,200 Speaker 1: cash for keys or cash and carry. So like for us, 285 00:13:47,320 --> 00:13:49,079 Speaker 1: it was weird though, because the analogy I give is 286 00:13:49,120 --> 00:13:50,559 Speaker 1: it in two thousand and six it happened. It was 287 00:13:50,600 --> 00:13:52,200 Speaker 1: over first four to two thousand and six, the market 288 00:13:52,240 --> 00:13:56,079 Speaker 1: was over. The market kept issuing securities, And I think 289 00:13:56,080 --> 00:13:57,600 Speaker 1: the analogy that we think about it is that if 290 00:13:57,640 --> 00:13:59,120 Speaker 1: you're standing, if you're sitting in front of a bank, 291 00:13:59,720 --> 00:14:01,880 Speaker 1: and you know, a van rolls up and people with 292 00:14:02,000 --> 00:14:04,360 Speaker 1: masks run in and they empty out the bank and 293 00:14:04,400 --> 00:14:06,400 Speaker 1: they leave with all the money, and you see it, 294 00:14:07,200 --> 00:14:09,040 Speaker 1: and then people keep coming and going from the bank 295 00:14:09,080 --> 00:14:12,000 Speaker 1: for another year. You're like, yo, there's there's no money 296 00:14:12,000 --> 00:14:14,079 Speaker 1: in that bank, right, And so so we sort of 297 00:14:14,120 --> 00:14:15,920 Speaker 1: felt pretty stupid for a while because we did a 298 00:14:15,960 --> 00:14:18,079 Speaker 1: lot of losing trades in two thousand and six that 299 00:14:18,200 --> 00:14:20,240 Speaker 1: were the you know, the obviously didn't come to fruition 300 00:14:20,400 --> 00:14:23,680 Speaker 1: until the actual people could see the losses. So in mortgages, 301 00:14:23,920 --> 00:14:27,360 Speaker 1: the barber can stop paying maybe a year to two 302 00:14:27,440 --> 00:14:31,480 Speaker 1: years before the lenders actually book a loss. So there's 303 00:14:31,560 --> 00:14:35,200 Speaker 1: this great lag in housing that is affecting the market, 304 00:14:35,600 --> 00:14:39,120 Speaker 1: affecting today's CPI numbers that the market doesn't do a 305 00:14:39,200 --> 00:14:41,800 Speaker 1: great job of adjusting the real time for information that 306 00:14:41,800 --> 00:14:43,560 Speaker 1: they already have. So when the barber hasn't paid in 307 00:14:43,600 --> 00:14:46,880 Speaker 1: twelve months, probably not going to get back the loan, 308 00:14:47,040 --> 00:14:49,280 Speaker 1: probably not going to start paying again. And then you 309 00:14:49,320 --> 00:14:51,160 Speaker 1: can model up what happens, like what's the home going 310 00:14:51,200 --> 00:14:52,880 Speaker 1: to sell for, what are my expenses to sell, awa, 311 00:14:52,920 --> 00:14:54,160 Speaker 1: how long it's going to take, And all of a sudden, 312 00:14:54,200 --> 00:14:56,360 Speaker 1: you have a loan that was worth, you know, one 313 00:14:56,400 --> 00:14:58,040 Speaker 1: hundred cents on the dollar, and now it's worth thirty 314 00:14:58,080 --> 00:15:00,640 Speaker 1: cents on the dollar. And you knew that eight months 315 00:15:00,680 --> 00:15:02,200 Speaker 1: into the loan or eight or maybe a year ago 316 00:15:02,280 --> 00:15:03,920 Speaker 1: or two years ago, But it takes that long to 317 00:15:04,040 --> 00:15:05,800 Speaker 1: it takes that long for the losses to get through 318 00:15:05,800 --> 00:15:07,360 Speaker 1: to the securities. And so I don't know if it's 319 00:15:07,400 --> 00:15:10,320 Speaker 1: sort of just the fact that we're so myopic into 320 00:15:10,400 --> 00:15:13,640 Speaker 1: the minutia of each little detail, or if it's the 321 00:15:13,720 --> 00:15:15,560 Speaker 1: fact that the market kind of doesn't want to buy 322 00:15:15,640 --> 00:15:17,080 Speaker 1: an umbrella until it starts raining. 323 00:15:18,800 --> 00:15:23,560 Speaker 2: Really very fascinating. So so coming out of this in nine, 324 00:15:24,160 --> 00:15:28,560 Speaker 2: home prices on average across the country down over thirty percent, 325 00:15:28,720 --> 00:15:32,040 Speaker 2: but really in the worst areas like Las Vegas and 326 00:15:32,160 --> 00:15:35,760 Speaker 2: South Florida and you know, parts of California and parts 327 00:15:35,760 --> 00:15:36,880 Speaker 2: of Arizona, Phoenix. 328 00:15:37,320 --> 00:15:38,320 Speaker 1: Two thirds in Phoenix. 329 00:15:38,520 --> 00:15:41,960 Speaker 2: Unbelievable. So you say, I have an idea, Let's buy 330 00:15:42,000 --> 00:15:44,120 Speaker 2: all these distressed real estate and rent them out. 331 00:15:44,280 --> 00:15:45,960 Speaker 1: Yeah, I had. I had a very good idea. So 332 00:15:45,960 --> 00:15:47,520 Speaker 1: I have very good partners, are very patient with me, 333 00:15:48,080 --> 00:15:50,440 Speaker 1: and I said, Okay, we don't think the subprime market 334 00:15:50,440 --> 00:15:52,400 Speaker 1: to mark is coming back, which was a non consensus 335 00:15:52,480 --> 00:15:54,880 Speaker 1: view at the time. People were buying up mortgage originators 336 00:15:54,920 --> 00:15:56,600 Speaker 1: and things waiting for the machines to sort of get 337 00:15:56,600 --> 00:15:59,960 Speaker 1: turned back on. We were thinking, this is investors are 338 00:16:00,120 --> 00:16:01,920 Speaker 1: never going to buy these loans again at any price. 339 00:16:02,760 --> 00:16:04,240 Speaker 1: So what's going to happen, what's going to happen to 340 00:16:04,240 --> 00:16:06,480 Speaker 1: the homes, and what's going to happen to the people 341 00:16:06,520 --> 00:16:09,000 Speaker 1: that were living these homes. And what a lot of 342 00:16:09,040 --> 00:16:11,480 Speaker 1: people I think didn't follow is that there was a 343 00:16:11,560 --> 00:16:14,760 Speaker 1: concept that job loss is called mortgage caused to mortgage defaults. 344 00:16:15,120 --> 00:16:17,840 Speaker 1: But in the Amir's view, a mortgage default can be rational, 345 00:16:18,080 --> 00:16:20,600 Speaker 1: as distasteful as it may sound. And when I give 346 00:16:20,640 --> 00:16:23,520 Speaker 1: this presentation in Europe or the EU or the UK, 347 00:16:23,640 --> 00:16:25,360 Speaker 1: they look at me like you're crazy. Or in Australia 348 00:16:25,440 --> 00:16:27,480 Speaker 1: or in Canada they're like, what do you mean? Mortgage 349 00:16:27,520 --> 00:16:29,400 Speaker 1: is a recourse and so like, well not in the US. 350 00:16:29,560 --> 00:16:31,240 Speaker 2: Well, actually some states are recourse. 351 00:16:31,280 --> 00:16:32,960 Speaker 1: In some states I try to tell people, is that 352 00:16:33,120 --> 00:16:35,960 Speaker 1: one person's default you have, you can handle. But when 353 00:16:36,000 --> 00:16:38,960 Speaker 1: seven or eight million people default, we don't have debtors' 354 00:16:38,960 --> 00:16:41,880 Speaker 1: prisons right their recourse, I mean, they're not recourse. So 355 00:16:42,400 --> 00:16:45,760 Speaker 1: in this in this context of a mortgage now being 356 00:16:45,840 --> 00:16:48,840 Speaker 1: clear to everyone that this default risk is present, it's real, 357 00:16:49,400 --> 00:16:53,200 Speaker 1: and it's hard to price because following the borrowers economic profile, 358 00:16:53,520 --> 00:16:56,480 Speaker 1: there are defaults that are related to just life events, 359 00:16:56,520 --> 00:16:59,560 Speaker 1: but there's also defaults related to a macroeconomic event. So 360 00:17:00,200 --> 00:17:02,160 Speaker 1: the position, you know what, investors are not going to 361 00:17:02,200 --> 00:17:06,399 Speaker 1: buy these loans anymore. The homes are here, and the 362 00:17:06,920 --> 00:17:09,760 Speaker 1: job loss wasn't as big as the mortgage defaults were, right, 363 00:17:10,160 --> 00:17:12,720 Speaker 1: so the people still had jobs, they slid revenue, and 364 00:17:12,800 --> 00:17:15,080 Speaker 1: the homes were very affordable now because the prices have 365 00:17:15,160 --> 00:17:18,480 Speaker 1: been reset. So we asked ourselves, Okay, we've seen this 366 00:17:18,600 --> 00:17:23,520 Speaker 1: movie before. Can we at Amherst make a three hundred 367 00:17:23,560 --> 00:17:27,160 Speaker 1: thousand dollars home investible to a global financial investor? Which 368 00:17:27,520 --> 00:17:30,120 Speaker 1: we spent our whole careers turning a three hundred thousand 369 00:17:30,119 --> 00:17:34,040 Speaker 1: dollars mortgage investible in the global capital markets. So we said, okay, 370 00:17:34,080 --> 00:17:36,000 Speaker 1: this is probably not a long put for us because 371 00:17:36,040 --> 00:17:38,440 Speaker 1: we've been following the mortgage with all this minutia for 372 00:17:38,520 --> 00:17:40,680 Speaker 1: thirty years. Now we're just going to follow the house 373 00:17:40,720 --> 00:17:42,680 Speaker 1: the same way. So we took our same analytic and 374 00:17:42,760 --> 00:17:45,400 Speaker 1: modeling team and we said, let's press down one more 375 00:17:45,480 --> 00:17:48,000 Speaker 1: level so we can actually price the home instead of 376 00:17:48,359 --> 00:17:51,399 Speaker 1: the mortgage with precision. And then let's set up an 377 00:17:51,400 --> 00:17:55,240 Speaker 1: operating capability that allows us to acquire the homes, renovate 378 00:17:55,280 --> 00:17:59,359 Speaker 1: the homes, manage the homes, and then more importantly, scale 379 00:17:59,400 --> 00:18:02,439 Speaker 1: the homes in to an investible pool. So we created 380 00:18:02,480 --> 00:18:04,919 Speaker 1: pools of homes just the same way we created pools 381 00:18:04,920 --> 00:18:06,480 Speaker 1: and mortgages in nineteen eighty nine. 382 00:18:06,760 --> 00:18:09,080 Speaker 2: So are you keeping these homes and leasing them out 383 00:18:09,200 --> 00:18:10,560 Speaker 2: or are they flips? 384 00:18:11,359 --> 00:18:13,440 Speaker 1: So they're kept and leased out, So we're starting in 385 00:18:13,480 --> 00:18:16,080 Speaker 1: two thousand and nine. There was no flip market. There 386 00:18:16,160 --> 00:18:18,679 Speaker 1: was no one to sew them to because the mortgage 387 00:18:18,720 --> 00:18:22,040 Speaker 1: market had basically closed on a large section of the 388 00:18:22,040 --> 00:18:22,640 Speaker 1: consumer base. 389 00:18:22,680 --> 00:18:26,000 Speaker 2: So think about and that credit market was frozen pretty. 390 00:18:25,800 --> 00:18:28,440 Speaker 1: Much, and it's still frozen for most people. So really 391 00:18:28,600 --> 00:18:32,119 Speaker 1: still today, still today. Basically the barrier to entry to 392 00:18:32,160 --> 00:18:35,879 Speaker 1: getting a mortgage became irreversibly higher, and we spent a 393 00:18:35,920 --> 00:18:37,960 Speaker 1: lot of times. You mentioned my time in DC. I 394 00:18:38,040 --> 00:18:40,399 Speaker 1: got to go and brief the Fetal Reserve, which is 395 00:18:40,480 --> 00:18:41,919 Speaker 1: kind of cool. I got to go into the FMC 396 00:18:42,119 --> 00:18:45,040 Speaker 1: room and I got to sit with with Yelling and 397 00:18:45,119 --> 00:18:48,000 Speaker 1: Vernaki and walk them through kind of in our view, 398 00:18:48,119 --> 00:18:50,600 Speaker 1: how we got here and the best way out. And 399 00:18:50,720 --> 00:18:52,800 Speaker 1: I asked them not to shut down the sub prime 400 00:18:52,840 --> 00:18:56,240 Speaker 1: mortgage market because it does serve a large swath of 401 00:18:56,280 --> 00:18:59,840 Speaker 1: the American public who has a slightly higher rent in 402 00:19:00,160 --> 00:19:03,320 Speaker 1: or debt income ratio, or has defaulted on a credit 403 00:19:03,400 --> 00:19:05,880 Speaker 1: card in the past or something, but they can pay, 404 00:19:06,119 --> 00:19:08,600 Speaker 1: they've had a problem in the past, they've cured it. Well, 405 00:19:08,640 --> 00:19:10,440 Speaker 1: those people now are pretty much blocked out of the 406 00:19:10,440 --> 00:19:13,119 Speaker 1: mortgage market. So I was unsuccessful in talking people and 407 00:19:13,200 --> 00:19:15,720 Speaker 1: still to this day unsuccessful into talking to people to 408 00:19:15,760 --> 00:19:20,080 Speaker 1: get back into lending to lower credit quality consumers. Because 409 00:19:20,119 --> 00:19:22,440 Speaker 1: you can do it, you can risk base prizing. So 410 00:19:22,520 --> 00:19:24,280 Speaker 1: we took we took the view like, hey, that market's 411 00:19:24,280 --> 00:19:25,960 Speaker 1: not coming back. People are not going to listen to us. 412 00:19:25,960 --> 00:19:28,080 Speaker 1: They're not going to say there's some good subprime loans 413 00:19:28,119 --> 00:19:30,840 Speaker 1: and some bad sub loans. They're just gonna they're just 414 00:19:30,920 --> 00:19:33,000 Speaker 1: going to draw a line and say you have to 415 00:19:33,080 --> 00:19:35,280 Speaker 1: have a credit score above a certain level, you have 416 00:19:35,320 --> 00:19:37,119 Speaker 1: to have income above a certain level, you have to 417 00:19:37,200 --> 00:19:39,440 Speaker 1: have a debt load below a certain level, or the 418 00:19:39,520 --> 00:19:41,639 Speaker 1: price for you is zero. You just get the answer is. 419 00:19:41,680 --> 00:19:42,879 Speaker 2: No, you're out of the market. 420 00:19:43,119 --> 00:19:44,640 Speaker 1: Used to you would say you would pay one percent 421 00:19:44,720 --> 00:19:48,400 Speaker 1: more or two percent right now. He said no, that's 422 00:19:48,400 --> 00:19:49,800 Speaker 1: so that's how we So we said, okay, well, how's 423 00:19:49,800 --> 00:19:51,080 Speaker 1: it's going to work. And we had seen this movie 424 00:19:51,160 --> 00:19:55,919 Speaker 1: before aggregating mortgages, strapping services on them, getting them rated, 425 00:19:56,200 --> 00:19:59,080 Speaker 1: getting them available to the global capital markets. So we 426 00:19:59,720 --> 00:20:03,840 Speaker 1: also so saw the conflicts and the frictions of the 427 00:20:03,880 --> 00:20:07,440 Speaker 1: mortgage market when it went under duress, the problems with 428 00:20:07,560 --> 00:20:10,120 Speaker 1: getting service to the consumers, the problem with getting service 429 00:20:10,200 --> 00:20:12,720 Speaker 1: to investors. The litigation. A lot of people don't know it, 430 00:20:12,800 --> 00:20:15,880 Speaker 1: but were We represented a large swath of the US 431 00:20:15,960 --> 00:20:19,520 Speaker 1: investor base and their litigation for buying these bus and securities. 432 00:20:19,880 --> 00:20:22,120 Speaker 1: So we said, you know what, let's just build under 433 00:20:22,200 --> 00:20:27,800 Speaker 1: one platform everything you need to originate, managed service, aggregate 434 00:20:28,400 --> 00:20:31,040 Speaker 1: and the long term service. These homes on behalf of 435 00:20:31,119 --> 00:20:34,840 Speaker 1: the residents and the investors. So that's the single platform 436 00:20:34,880 --> 00:20:35,200 Speaker 1: we built. 437 00:20:35,400 --> 00:20:38,480 Speaker 2: H absolutely fascinating. So let's talk a little bit about 438 00:20:38,920 --> 00:20:42,560 Speaker 2: who the clients are for Amherst. I'm assuming it's primarily 439 00:20:42,680 --> 00:20:46,359 Speaker 2: institutional and not retail. Tell us who your clients are 440 00:20:46,520 --> 00:20:48,800 Speaker 2: and what they want to invest in. Sure. 441 00:20:49,119 --> 00:20:52,960 Speaker 1: Over the years, we've migrated really to what I would 442 00:20:52,960 --> 00:20:54,960 Speaker 1: say is the largest customer base in the world, the 443 00:20:55,080 --> 00:21:00,160 Speaker 1: largest single investors. So we do business with most most 444 00:21:00,200 --> 00:21:03,200 Speaker 1: of the sovereign wealth funds, most of the big US 445 00:21:03,359 --> 00:21:08,159 Speaker 1: national insurers, global insurers, the largest pension funds, and we 446 00:21:08,880 --> 00:21:12,480 Speaker 1: try to position ourselves as an extension of their capabilities. 447 00:21:12,720 --> 00:21:15,520 Speaker 1: And since we're smaller, more nimble, we can kind of 448 00:21:15,560 --> 00:21:17,160 Speaker 1: get in there and do some of the gritty things, 449 00:21:17,200 --> 00:21:20,359 Speaker 1: the smaller things. Imagine setting up a platform with in 450 00:21:20,480 --> 00:21:23,040 Speaker 1: thirty two markets that has to buy each individual home 451 00:21:23,119 --> 00:21:26,600 Speaker 1: and execute a CAPEX plan on a thirty forty thousand 452 00:21:26,640 --> 00:21:29,120 Speaker 1: dollars CAPEX plan on a home. So these large investors 453 00:21:29,160 --> 00:21:31,320 Speaker 1: need someone like us to kind of make things investable 454 00:21:31,359 --> 00:21:34,159 Speaker 1: in scale, and so that's where we've been. So it's 455 00:21:34,160 --> 00:21:38,320 Speaker 1: all institutional investors. It's the call it five hundred largest 456 00:21:38,320 --> 00:21:39,160 Speaker 1: investors in the world. 457 00:21:39,320 --> 00:21:43,320 Speaker 2: Is that patient capital? Did they have the bandwidth to hey, 458 00:21:43,600 --> 00:21:44,560 Speaker 2: we're in this for decades. 459 00:21:44,640 --> 00:21:49,000 Speaker 1: Yeah, it's super patient, it's super sophisticated. Their asset allocation 460 00:21:49,240 --> 00:21:53,800 Speaker 1: model driven folks. The bulk of our investors are investing 461 00:21:53,880 --> 00:21:57,800 Speaker 1: on behalf of consumers, on behalf of taxpayers. So we're 462 00:21:57,840 --> 00:21:59,800 Speaker 1: partners with the State of Texas. The actual state of 463 00:21:59,800 --> 00:22:01,600 Speaker 1: time is not one of the pension funds, but the 464 00:22:01,640 --> 00:22:04,440 Speaker 1: state itself. So we have a lot of sovereign wealth 465 00:22:04,520 --> 00:22:06,359 Speaker 1: on types that are investing on behalf of tax payers. 466 00:22:06,359 --> 00:22:10,840 Speaker 1: So it's very long dated capital. They're lower risk tolerance, 467 00:22:10,880 --> 00:22:14,520 Speaker 1: I would say, very high standards on quality of service 468 00:22:14,600 --> 00:22:17,840 Speaker 1: and quality of infrastructure decision making. So we're very proud 469 00:22:17,920 --> 00:22:20,679 Speaker 1: that we're a partner to that type of capital. 470 00:22:21,040 --> 00:22:23,880 Speaker 2: So let's talk a little bit about the residential side. 471 00:22:23,920 --> 00:22:27,520 Speaker 2: Before we look at the commercial side. You mentioned you 472 00:22:27,840 --> 00:22:31,159 Speaker 2: are in thirty two markets buying single family homes. How 473 00:22:31,240 --> 00:22:32,160 Speaker 2: many homes have you, guys? 474 00:22:32,200 --> 00:22:34,560 Speaker 1: So their platform service is about fifty thousand units now. 475 00:22:34,600 --> 00:22:37,119 Speaker 1: So we've purchased and most of the homes were purchased 476 00:22:37,119 --> 00:22:40,680 Speaker 1: one at a time, independent due diligence, independent construction management 477 00:22:40,760 --> 00:22:42,880 Speaker 1: to get the home back up to current market standards, 478 00:22:43,280 --> 00:22:45,280 Speaker 1: and we manage each home independently. 479 00:22:45,720 --> 00:22:48,680 Speaker 2: So that implies that some of the homes you're buying 480 00:22:48,760 --> 00:22:52,919 Speaker 2: are kind of project homes. A wrecked or otherwise neglected 481 00:22:53,200 --> 00:22:56,159 Speaker 2: doesn't even have to be a willf elected destruction, just 482 00:22:56,280 --> 00:22:57,159 Speaker 2: time and tide. 483 00:22:57,400 --> 00:22:59,880 Speaker 1: Just what we like to say is it's deferred cappex. 484 00:23:00,119 --> 00:23:01,880 Speaker 1: So you'll find that owners that have owned the home 485 00:23:01,920 --> 00:23:05,280 Speaker 1: for ten, fifteen, twenty years become pretty comfortable with a 486 00:23:05,359 --> 00:23:09,200 Speaker 1: smudge paint or a stained floor, or old countertops, or 487 00:23:09,280 --> 00:23:13,440 Speaker 1: appliances that may make noises at night or that or 488 00:23:13,560 --> 00:23:15,960 Speaker 1: that you know, that bathroom set that leaks and whatever, 489 00:23:16,000 --> 00:23:17,520 Speaker 1: and so people just get comfortable in their homes and 490 00:23:17,600 --> 00:23:19,960 Speaker 1: they they tend not to reinvest in real time on 491 00:23:20,119 --> 00:23:22,760 Speaker 1: keeping that home up to current market standards. So we 492 00:23:22,840 --> 00:23:25,080 Speaker 1: buy those homes that haven't really been touched in fifteen 493 00:23:25,119 --> 00:23:28,080 Speaker 1: or twenty years. They've still got the original builder interior. 494 00:23:29,119 --> 00:23:31,240 Speaker 1: We make sure that, of course that the bones of 495 00:23:31,280 --> 00:23:33,240 Speaker 1: the house are good, the foundation and the walls and 496 00:23:33,280 --> 00:23:35,600 Speaker 1: so forth, but then we pretty much trip them down 497 00:23:35,680 --> 00:23:37,520 Speaker 1: to I wouldn't say down to the studs, but down 498 00:23:37,560 --> 00:23:39,840 Speaker 1: to the sheet rock and put a brand new interier 499 00:23:39,920 --> 00:23:42,560 Speaker 1: in on. We oftentimes people don't buy a roof, they'll 500 00:23:42,960 --> 00:23:45,679 Speaker 1: let the roof go a longer than maybe the staple 501 00:23:46,359 --> 00:23:48,359 Speaker 1: exact or a third one, or bought a lot of 502 00:23:48,480 --> 00:23:50,520 Speaker 1: roofs and buy a lot of hvacs. We take out 503 00:23:50,560 --> 00:23:52,760 Speaker 1: a lot of compressors that are still running on those 504 00:23:52,800 --> 00:23:55,480 Speaker 1: old toxic gases. So we basically bring the home up 505 00:23:55,480 --> 00:23:57,119 Speaker 1: to a current monitor standard and there's a there's a 506 00:23:57,200 --> 00:23:59,359 Speaker 1: profit in that that the home you get paid to 507 00:23:59,440 --> 00:24:00,600 Speaker 1: go and improve PA's real estate. 508 00:24:01,880 --> 00:24:04,080 Speaker 2: And then how do you figure out what to lease 509 00:24:04,160 --> 00:24:06,280 Speaker 2: these for? And do you ever sell any of these homes? 510 00:24:06,400 --> 00:24:08,560 Speaker 1: We do sell we do. The platform is pretty nimble. 511 00:24:08,640 --> 00:24:11,080 Speaker 1: So if for example, we were talking before the show, 512 00:24:11,119 --> 00:24:13,760 Speaker 1: we were talking about how some markets have really benefited 513 00:24:13,800 --> 00:24:17,840 Speaker 1: from the post COVID migration and it's changed their customer 514 00:24:17,880 --> 00:24:21,120 Speaker 1: based dramatically. So think about Naples, Florida and clear Water 515 00:24:21,240 --> 00:24:23,680 Speaker 1: and those types of places. So in those places, home 516 00:24:23,800 --> 00:24:28,359 Speaker 1: prices since pre COVID are up maybe forty fifty percent 517 00:24:28,800 --> 00:24:31,159 Speaker 1: and rents are up twenty twenty five percent, so they 518 00:24:31,200 --> 00:24:33,520 Speaker 1: really don't really make much sense more as a as 519 00:24:33,560 --> 00:24:36,320 Speaker 1: a rental investment. So we're cleaning those homes back up 520 00:24:36,359 --> 00:24:38,520 Speaker 1: and selling them back to the consumers. So that's an 521 00:24:38,520 --> 00:24:42,080 Speaker 1: active part of portfolio trimming and optimization, and it's cool 522 00:24:42,119 --> 00:24:45,159 Speaker 1: to have the capability to sort of execute in both markets. 523 00:24:45,440 --> 00:24:48,200 Speaker 2: So it's funny you mentioned Naples and Clearwater. A few 524 00:24:48,280 --> 00:24:51,760 Speaker 2: of the areas adjacent to those really got to lacked 525 00:24:51,800 --> 00:24:54,879 Speaker 2: by that last hurricane that came through last year. What 526 00:24:55,000 --> 00:24:57,119 Speaker 2: do you do when you have a natural disaster? Is 527 00:24:57,200 --> 00:25:00,920 Speaker 2: that does that create any interest or is it just 528 00:25:01,560 --> 00:25:02,280 Speaker 2: just too much may. 529 00:25:02,240 --> 00:25:04,960 Speaker 1: Have It's well, we've been hit by hurricanes several times, 530 00:25:05,119 --> 00:25:08,560 Speaker 1: floods several times, tornadoes several times. Given that the homes 531 00:25:08,600 --> 00:25:11,080 Speaker 1: are in thirty markets, the good news is no one 532 00:25:11,200 --> 00:25:13,560 Speaker 1: event has a big impact on the portfolio. The bad 533 00:25:13,640 --> 00:25:16,200 Speaker 1: news is all events you get to experience. 534 00:25:15,840 --> 00:25:19,040 Speaker 2: Right, So diversified, which means you're embracing every natural right. 535 00:25:19,280 --> 00:25:21,399 Speaker 1: So in Houston one year, we got hit in Houston 536 00:25:21,600 --> 00:25:24,680 Speaker 1: and in Florida at the same time, two different hurricanes. 537 00:25:25,160 --> 00:25:27,840 Speaker 1: So what's interesting is that now we have a natural 538 00:25:27,920 --> 00:25:31,200 Speaker 1: disaster of team and response unit and a playbook which 539 00:25:31,240 --> 00:25:32,720 Speaker 1: is a little bit unfortunately you have to have that, 540 00:25:32,800 --> 00:25:34,080 Speaker 1: but we use it every couple of years. 541 00:25:34,119 --> 00:25:34,280 Speaker 2: Now. 542 00:25:35,240 --> 00:25:38,240 Speaker 1: We tend not to invest when those markets are busted. 543 00:25:39,160 --> 00:25:41,600 Speaker 1: We do see a lot of demand for our rentals 544 00:25:41,640 --> 00:25:43,920 Speaker 1: because when you know, a few percent of the housing 545 00:25:43,920 --> 00:25:46,760 Speaker 1: stock gets taken offline for a storm, it creates pressure 546 00:25:46,840 --> 00:25:49,000 Speaker 1: on demand. But now our job is just to go 547 00:25:49,080 --> 00:25:50,960 Speaker 1: in there and get the homes fixed as fast as 548 00:25:50,960 --> 00:25:52,200 Speaker 1: we can and get them back into service. 549 00:25:52,400 --> 00:25:54,520 Speaker 2: So fifty thousand homes, I'm going to assume you're a 550 00:25:54,600 --> 00:25:56,320 Speaker 2: self insurer on all those homes. 551 00:25:56,400 --> 00:25:58,879 Speaker 1: We do so Amerus is completely verted integrated. We own 552 00:25:58,920 --> 00:26:02,240 Speaker 1: our own insurance platform, so we're the we're you know, 553 00:26:02,320 --> 00:26:05,560 Speaker 1: we basically access our coverage through the reinsurance markets. At 554 00:26:05,600 --> 00:26:08,159 Speaker 1: our scale, it's hard to go get insurance through the 555 00:26:08,200 --> 00:26:10,000 Speaker 1: normal channels, and so we set up our own insurance 556 00:26:10,040 --> 00:26:13,639 Speaker 1: brokerage and risk attention platform and now we we insuran 557 00:26:13,960 --> 00:26:15,600 Speaker 1: through the resurance markets. 558 00:26:16,040 --> 00:26:19,600 Speaker 2: Huh really very very intriguing. Uh So let's let's talk 559 00:26:19,600 --> 00:26:22,720 Speaker 2: a little bit about some data and technology you use. Sure, 560 00:26:22,880 --> 00:26:25,320 Speaker 2: you guys created your own platform. Tell us a little 561 00:26:25,359 --> 00:26:28,320 Speaker 2: bit about what it was like developing that and what 562 00:26:28,560 --> 00:26:31,600 Speaker 2: makes it specific and unique to Amhurst. 563 00:26:31,840 --> 00:26:34,240 Speaker 1: It's interesting because you know, today we talk about AI 564 00:26:34,440 --> 00:26:37,680 Speaker 1: and and uh, you know, high speed computing, and what 565 00:26:38,119 --> 00:26:40,119 Speaker 1: I look at, what we do is being comically you know, 566 00:26:40,359 --> 00:26:42,080 Speaker 1: simple compared to what we talked what we're talking about 567 00:26:42,080 --> 00:26:44,600 Speaker 1: today with generative AI. But when we started this in 568 00:26:44,680 --> 00:26:46,240 Speaker 1: the late eighties, so that was the job I was 569 00:26:46,280 --> 00:26:49,119 Speaker 1: pronted into, which was, hey, let's figure out how to 570 00:26:49,240 --> 00:26:53,080 Speaker 1: differentiate pricing from one mortgage pool to the next. They've 571 00:26:53,119 --> 00:26:55,720 Speaker 1: got different interest rates, they've got different LTVs, they've got 572 00:26:55,760 --> 00:26:58,320 Speaker 1: different credit scores, they must have different values. So I 573 00:26:58,520 --> 00:27:00,879 Speaker 1: was part of a small or the our team was 574 00:27:00,960 --> 00:27:03,560 Speaker 1: part of a small group of people tackling this problem 575 00:27:03,600 --> 00:27:05,800 Speaker 1: in the late eighties early nineties, and what we do 576 00:27:05,880 --> 00:27:08,240 Speaker 1: today is just now growth of that original project. So 577 00:27:08,320 --> 00:27:11,320 Speaker 1: it's a quantitative analytics approach. It's highly data driven, but 578 00:27:11,400 --> 00:27:13,600 Speaker 1: we need to know the price history for us, that's 579 00:27:13,640 --> 00:27:16,520 Speaker 1: the correlation to to what drives price, and then we 580 00:27:16,600 --> 00:27:19,639 Speaker 1: have a big consumer behavior modeling infrastructure because we have 581 00:27:19,800 --> 00:27:22,560 Speaker 1: what's nice is that over the thirty years of our 582 00:27:22,920 --> 00:27:25,440 Speaker 1: history and then we purchase data that was probably twenty 583 00:27:25,480 --> 00:27:28,960 Speaker 1: five years old at the time, we can measure how 584 00:27:29,080 --> 00:27:32,240 Speaker 1: consumers behave to changes in their economic environment, and that 585 00:27:32,320 --> 00:27:35,160 Speaker 1: consumer behavior will affect home prices and will affect performance 586 00:27:35,240 --> 00:27:38,960 Speaker 1: on credit. It's that So that's the core competency, and 587 00:27:39,000 --> 00:27:42,320 Speaker 1: it's just leveraged into if it's a loan, if it's 588 00:27:42,359 --> 00:27:44,320 Speaker 1: a security back by a loan, if it's the actual 589 00:27:44,440 --> 00:27:46,960 Speaker 1: estate itself. So from a data perspective, think about it 590 00:27:47,000 --> 00:27:48,800 Speaker 1: this way. So obviously the S and P five hundred 591 00:27:48,840 --> 00:27:51,080 Speaker 1: is five hundred names and they report four times a year, 592 00:27:51,720 --> 00:27:53,600 Speaker 1: and God loved the analysts that have to figure out 593 00:27:53,600 --> 00:27:56,159 Speaker 1: how to price these things with so little information. We 594 00:27:56,280 --> 00:27:58,240 Speaker 1: have one hundred million items that we're following. Is one 595 00:27:58,280 --> 00:28:00,520 Speaker 1: hundred million piece of real estate in the country. We've 596 00:28:00,600 --> 00:28:02,639 Speaker 1: gathered up all the information you would need to do 597 00:28:02,680 --> 00:28:05,200 Speaker 1: an appraisal, and we keep that information current in real time, 598 00:28:05,440 --> 00:28:09,600 Speaker 1: and we've automated the appraisal process for evaluation both intrinsic value, 599 00:28:10,160 --> 00:28:11,639 Speaker 1: meaning like where would we pay it, where would we 600 00:28:12,200 --> 00:28:15,560 Speaker 1: buy it, and where is the fair market price that asset? 601 00:28:15,840 --> 00:28:18,760 Speaker 1: From that level, from price and from consumer behavior. Now, 602 00:28:18,800 --> 00:28:21,000 Speaker 1: so now we're watching the payments on every mortgage in 603 00:28:21,040 --> 00:28:24,440 Speaker 1: the country, so you can see who paid, Did Maryland 604 00:28:24,480 --> 00:28:27,480 Speaker 1: do better than Texas last month? And more importantly, versus 605 00:28:27,600 --> 00:28:31,280 Speaker 1: the model, who outperforms, who underperformed. Because there's a schedule, 606 00:28:31,520 --> 00:28:34,280 Speaker 1: there's an expectation for not everyone to pay every month. 607 00:28:34,440 --> 00:28:36,800 Speaker 2: So when you're trying to put a value on a home, 608 00:28:37,040 --> 00:28:40,000 Speaker 2: you're not just sending a third party appraiser oute to 609 00:28:40,120 --> 00:28:42,080 Speaker 2: do a drive buy and go. Yeah, that's about two 610 00:28:42,160 --> 00:28:45,560 Speaker 2: seventy five. You're actually crunching a lot of numbers. And 611 00:28:45,640 --> 00:28:47,200 Speaker 2: this is proprietary you're running. 612 00:28:47,360 --> 00:28:51,080 Speaker 1: We're running a ten year Monte Carlo. That's probably twenty thousand, 613 00:28:51,200 --> 00:28:54,040 Speaker 1: ten thousand paths of outcomes on that asset. It includes 614 00:28:54,080 --> 00:28:57,920 Speaker 1: all of its changes in its property taxes, it's depreciable 615 00:28:58,000 --> 00:29:00,360 Speaker 1: life for the improvements of the assets, and course it's 616 00:29:00,400 --> 00:29:02,000 Speaker 1: revenue stream from rental demand. 617 00:29:02,840 --> 00:29:06,840 Speaker 2: So it's interesting that you started this after the financial crisis, 618 00:29:07,560 --> 00:29:11,880 Speaker 2: given your technological expertise and your unique way to value 619 00:29:11,960 --> 00:29:15,880 Speaker 2: these things. I'm curious how much of this is a 620 00:29:16,000 --> 00:29:20,240 Speaker 2: legacy of your experiences during the Great Financial Crisis. How 621 00:29:20,320 --> 00:29:23,560 Speaker 2: did that couple of years affect how you look at 622 00:29:23,680 --> 00:29:26,120 Speaker 2: risk and pricing of real estate process? 623 00:29:26,320 --> 00:29:30,560 Speaker 1: It's infecting, I would say. So the problem, the problem 624 00:29:30,680 --> 00:29:33,320 Speaker 1: for me, I'll speak for myself personally in the financial 625 00:29:33,440 --> 00:29:36,840 Speaker 1: crisis is that once you find something like that, because 626 00:29:36,920 --> 00:29:39,880 Speaker 1: literally we were saying to people these loans aren't going 627 00:29:39,920 --> 00:29:41,920 Speaker 1: to pay off in two thousand and five, two thousand 628 00:29:41,920 --> 00:29:44,120 Speaker 1: and six, and they were like, Sean, in the worst 629 00:29:44,160 --> 00:29:48,440 Speaker 1: of fault rate, it's been geographically focused. Rather it was 630 00:29:48,480 --> 00:29:51,120 Speaker 1: the farm belt crisis or the California crisis. What are 631 00:29:51,160 --> 00:29:53,840 Speaker 1: you talking about? National home prices going down? And oh, 632 00:29:53,960 --> 00:29:56,200 Speaker 1: by the way, the defaults in those micro markets were 633 00:29:56,240 --> 00:29:58,640 Speaker 1: ten or fifteen percent and the losses were five percent. 634 00:29:59,360 --> 00:30:01,320 Speaker 1: So if you we had five percent losses on a 635 00:30:01,880 --> 00:30:04,480 Speaker 1: market and the market was only five percent of a pool, 636 00:30:05,040 --> 00:30:07,360 Speaker 1: the losses are going to be nearly zero, right, And 637 00:30:07,440 --> 00:30:09,560 Speaker 1: we're like, yeah, except for none of that's going to 638 00:30:09,600 --> 00:30:11,120 Speaker 1: happen this time. And they were like sure, Sean, pat 639 00:30:11,200 --> 00:30:12,360 Speaker 1: you on the head and send you down the road. 640 00:30:12,640 --> 00:30:14,160 Speaker 1: So so one of the problems is once you see 641 00:30:14,200 --> 00:30:16,200 Speaker 1: something like that, you kind of look for them everywhere, 642 00:30:16,600 --> 00:30:18,280 Speaker 1: So we spend our time a lot of time looking 643 00:30:18,360 --> 00:30:22,760 Speaker 1: for looking for sasquatch. And so the other thing is 644 00:30:22,960 --> 00:30:25,959 Speaker 1: is that, and I think it's our core risk management culture, 645 00:30:26,560 --> 00:30:28,360 Speaker 1: is that we think that tiller risk is way more 646 00:30:28,440 --> 00:30:31,160 Speaker 1: probable than everyone else does. So we manage the business 647 00:30:31,240 --> 00:30:34,240 Speaker 1: for extreme shocks to prices, for home prices moving twenty 648 00:30:34,280 --> 00:30:36,520 Speaker 1: five to thirty percent in a year, for interest rates 649 00:30:36,560 --> 00:30:39,560 Speaker 1: moving dramatically in a short period of time. And we 650 00:30:39,680 --> 00:30:43,640 Speaker 1: found you know that check check is all these tail risks. Well, 651 00:30:43,640 --> 00:30:45,560 Speaker 1: it's like the one hundred year floods every ten years 652 00:30:45,640 --> 00:30:47,560 Speaker 1: or so. I've been doing this for thirty years, and 653 00:30:47,600 --> 00:30:49,920 Speaker 1: I've had how many hundred your floods? More than more 654 00:30:49,960 --> 00:30:50,560 Speaker 1: than point three? 655 00:30:51,280 --> 00:30:54,000 Speaker 2: You know. The fascinating thing is I have a vivid 656 00:30:54,120 --> 00:30:57,320 Speaker 2: recollection of a paper, a white paper coming out by 657 00:30:57,400 --> 00:31:01,680 Speaker 2: professors rein Hart and Rogueoff. I remembered it was five 658 00:31:02,120 --> 00:31:08,280 Speaker 2: financial crises. So it was Helsinki, it was Sweden, it 659 00:31:08,600 --> 00:31:12,320 Speaker 2: was Japan, it was Mexico, maybe US, and the Great 660 00:31:12,360 --> 00:31:14,560 Speaker 2: Depression was the fifth one. I don't remember exactly what. 661 00:31:14,800 --> 00:31:18,000 Speaker 2: By the way that paper eventually becomes this time is different. 662 00:31:18,080 --> 00:31:21,960 Speaker 2: Eight hundred years of financial folly. But the average of 663 00:31:22,040 --> 00:31:26,040 Speaker 2: the real estate drop in any modern financial we're not 664 00:31:26,120 --> 00:31:29,720 Speaker 2: talking about tulips. Right. The last century was over thirty 665 00:31:29,760 --> 00:31:32,520 Speaker 2: percent in real estate. And once you once I saw 666 00:31:32,600 --> 00:31:35,000 Speaker 2: that paper, I remember saying, hey, this is in a 667 00:31:35,160 --> 00:31:38,760 Speaker 2: theoretical possibility. This has happened once in decades. 668 00:31:38,840 --> 00:31:41,000 Speaker 1: Right. So people think of home prices as being sort 669 00:31:41,040 --> 00:31:44,880 Speaker 1: of four to five percent price movers per annum, and 670 00:31:45,000 --> 00:31:46,840 Speaker 1: that's the case most of the time. But the problem 671 00:31:46,920 --> 00:31:48,000 Speaker 1: is we don't get to live most of the time. 672 00:31:48,040 --> 00:31:50,640 Speaker 1: We get to live all the time, and so sometimes 673 00:31:50,720 --> 00:31:52,600 Speaker 1: that five percent move can be thirty five percent and 674 00:31:52,640 --> 00:31:55,280 Speaker 1: forty percent. So think about that eighty percent l TV mortgage. 675 00:31:55,560 --> 00:31:57,280 Speaker 1: That doesn't seem like a risky loan. The bar will 676 00:31:57,320 --> 00:31:59,280 Speaker 1: put up twenty percent, the lender put up eighty percent, 677 00:32:00,120 --> 00:32:02,440 Speaker 1: but there's a one in something chance that the home 678 00:32:02,520 --> 00:32:05,800 Speaker 1: price goes goes to sixty five, And if the home 679 00:32:05,920 --> 00:32:07,920 Speaker 1: goes to sixty five, the loan is no longer going 680 00:32:07,960 --> 00:32:10,960 Speaker 1: to pay off. So that was the sort of the 681 00:32:11,080 --> 00:32:13,640 Speaker 1: thing that we built that people hadn't thought through, is 682 00:32:13,680 --> 00:32:16,560 Speaker 1: how to you stochastically forecast a range of outcomes for 683 00:32:16,600 --> 00:32:19,000 Speaker 1: the asset price? Then how does it affect the repayment 684 00:32:19,080 --> 00:32:19,360 Speaker 1: risk on. 685 00:32:19,360 --> 00:32:22,800 Speaker 2: The loan, So you have to have boots on the ground. 686 00:32:22,880 --> 00:32:26,800 Speaker 2: With fifty thousand homes, how big a staff do you have? 687 00:32:27,080 --> 00:32:30,400 Speaker 2: Is it regional? How do you manage since since you're 688 00:32:30,480 --> 00:32:33,000 Speaker 2: now the landlord for these homes, how do you manage 689 00:32:33,080 --> 00:32:36,520 Speaker 2: the regular maintenance the one off you know, things break 690 00:32:36,640 --> 00:32:39,600 Speaker 2: or refrigerator stops, the toilet kits backed up. How do 691 00:32:39,680 --> 00:32:40,200 Speaker 2: you manage that? 692 00:32:40,600 --> 00:32:43,680 Speaker 1: Yeah, it's complicated. So we have a both of an 693 00:32:43,760 --> 00:32:47,000 Speaker 1: on balance sheet group of repairmen. So we're an investment 694 00:32:47,440 --> 00:32:51,000 Speaker 1: management platform that also has trucks with plumbers crews around 695 00:32:51,040 --> 00:32:55,800 Speaker 1: the country and fixing air conditioners. We also have a 696 00:32:55,920 --> 00:32:59,040 Speaker 1: great vendor network and we have a lot of technology 697 00:32:59,240 --> 00:33:02,640 Speaker 1: that the team, as you mentioned, is about fifteen hundred 698 00:33:02,680 --> 00:33:05,120 Speaker 1: people that are just in that single venderaial platform. This's 699 00:33:05,120 --> 00:33:07,280 Speaker 1: is one of the things Amherst does. But that fifteen 700 00:33:07,360 --> 00:33:10,240 Speaker 1: hundred person team is augmented by about two thousand vendors 701 00:33:10,280 --> 00:33:15,160 Speaker 1: of companies and we're able to handle the properties because 702 00:33:15,160 --> 00:33:16,960 Speaker 1: we have a team in the field. So we literally 703 00:33:17,040 --> 00:33:20,600 Speaker 1: have a repair and maintenance team that's assigned to a 704 00:33:20,640 --> 00:33:23,320 Speaker 1: group of homes. So that person has their three hundred 705 00:33:23,320 --> 00:33:25,600 Speaker 1: homes or something, and then they're part of a local 706 00:33:25,640 --> 00:33:28,400 Speaker 1: team that's managing about fifteen hundred units. So it's not 707 00:33:28,720 --> 00:33:31,160 Speaker 1: that different from how you would manage a multi family 708 00:33:31,560 --> 00:33:34,040 Speaker 1: an apartment complex. It's just that the rooms are further apart, 709 00:33:34,120 --> 00:33:36,600 Speaker 1: the units are further apart, and it causes our drive 710 00:33:36,680 --> 00:33:38,000 Speaker 1: time to be higher. But one of the things that 711 00:33:38,080 --> 00:33:39,280 Speaker 1: we went into this that was one of the big 712 00:33:39,400 --> 00:33:41,400 Speaker 1: questions is could you provide good service and could you 713 00:33:41,480 --> 00:33:43,080 Speaker 1: manage them? And we don't get it right all the time. 714 00:33:44,000 --> 00:33:46,440 Speaker 1: But if you think about the fact that how easy 715 00:33:46,440 --> 00:33:48,600 Speaker 1: it is to get someone out to a home, and 716 00:33:48,720 --> 00:33:50,560 Speaker 1: that's part of our filtering criteria of how we buy 717 00:33:50,600 --> 00:33:53,480 Speaker 1: a home. But think about the fact that for ten bucks, 718 00:33:53,560 --> 00:33:56,320 Speaker 1: you can have Dominoes bringing a pizza and somehow of 719 00:33:56,360 --> 00:33:58,680 Speaker 1: that ten bucks they get the delivery person from their 720 00:33:58,720 --> 00:34:01,760 Speaker 1: store to your home with a hot pizza, and they 721 00:34:01,800 --> 00:34:03,400 Speaker 1: were able to pay for the Super Bowl add out 722 00:34:04,160 --> 00:34:07,040 Speaker 1: embedded in that ten dollar cost, like the transportation cost 723 00:34:07,160 --> 00:34:08,719 Speaker 1: to get people to and from these homes, it just 724 00:34:08,800 --> 00:34:12,000 Speaker 1: isn't a barrier. It's really timing and technology to really 725 00:34:12,040 --> 00:34:12,480 Speaker 1: to rout them. 726 00:34:12,800 --> 00:34:15,480 Speaker 2: So let's talk a little bit about technology over the 727 00:34:15,560 --> 00:34:21,800 Speaker 2: past two decades, real time monitoring of things like fire, flood, 728 00:34:22,560 --> 00:34:28,520 Speaker 2: carbon monoxide break ins, whatever, they've become very inexpensive, very ubiquitous. 729 00:34:28,840 --> 00:34:32,640 Speaker 2: Everybody can have it on a phone. Is that anything 730 00:34:32,680 --> 00:34:33,839 Speaker 2: that you've explored in terms? 731 00:34:33,880 --> 00:34:35,600 Speaker 1: We spent a lot of time on it. There's big 732 00:34:35,640 --> 00:34:38,600 Speaker 1: privacy concerns. So we have families, we have fifty thousand 733 00:34:38,640 --> 00:34:40,600 Speaker 1: families living in their homes and they're their homes and 734 00:34:40,920 --> 00:34:43,880 Speaker 1: we're proud to be part of that process. So, you know, 735 00:34:44,120 --> 00:34:45,880 Speaker 1: a lot of that stuff gets a little creepy to us, 736 00:34:45,920 --> 00:34:46,640 Speaker 1: and so we haven't done well. 737 00:34:46,680 --> 00:34:49,040 Speaker 2: There's a difference between a puppy cam where you're seeing 738 00:34:49,040 --> 00:34:52,000 Speaker 2: what's going on in the bedroom and I know in 739 00:34:52,080 --> 00:34:55,080 Speaker 2: my basement I have a flood. 740 00:34:54,800 --> 00:34:56,480 Speaker 1: Like a high water alarm, that sort of thing. So 741 00:34:56,600 --> 00:34:59,120 Speaker 1: that we're still on their network, we're still so that 742 00:34:59,239 --> 00:35:02,359 Speaker 1: technology for us to go at it stronger. We would 743 00:35:02,400 --> 00:35:06,600 Speaker 1: like for those devices to communicate back to us, uh directly, 744 00:35:07,120 --> 00:35:07,440 Speaker 1: not like. 745 00:35:07,520 --> 00:35:09,319 Speaker 2: A like a cell phone. 746 00:35:09,600 --> 00:35:11,400 Speaker 1: So we are looking at that. There's locks now you 747 00:35:11,440 --> 00:35:13,960 Speaker 1: can buy that have little cell phone transmitters in them, right, 748 00:35:14,080 --> 00:35:15,920 Speaker 1: So we may we may look at things like that, 749 00:35:16,040 --> 00:35:17,759 Speaker 1: but at this point we have so many people on 750 00:35:17,800 --> 00:35:19,799 Speaker 1: the field. We're touching the houses six, eight, nine times 751 00:35:19,840 --> 00:35:22,960 Speaker 1: a year. We have good relationships with our with our residents. 752 00:35:23,320 --> 00:35:24,480 Speaker 1: A lot of that stuff is a little bit of 753 00:35:24,520 --> 00:35:27,920 Speaker 1: pizaz and we see other people charging residents, you know, 754 00:35:28,000 --> 00:35:30,800 Speaker 1: fifty dollars a month for electronic door lock or something. 755 00:35:31,280 --> 00:35:32,719 Speaker 1: We don't think that that's sustainable cost. 756 00:35:32,760 --> 00:35:34,600 Speaker 2: It's a fifty dollar product. How do you chrusee fifty 757 00:35:34,600 --> 00:35:35,120 Speaker 2: dollars a month. 758 00:35:35,360 --> 00:35:37,120 Speaker 1: I no, I don't don't. I don't get it. So 759 00:35:37,640 --> 00:35:39,799 Speaker 1: we will will it's coming along. If I can get 760 00:35:39,960 --> 00:35:42,080 Speaker 1: direct cell phone connections to a high water alarm, I 761 00:35:42,120 --> 00:35:43,880 Speaker 1: would take it. But really, what we have as a 762 00:35:43,920 --> 00:35:45,800 Speaker 1: person go out there and look and touch the property 763 00:35:45,840 --> 00:35:47,800 Speaker 1: eight times a year, and that's how that's how we 764 00:35:47,840 --> 00:35:49,520 Speaker 1: do it. A lot of this is not so complicated, 765 00:35:49,560 --> 00:35:51,959 Speaker 1: but we have you know, through COVID was fascinating because 766 00:35:52,000 --> 00:35:54,439 Speaker 1: that field team and we have a big construction management team. 767 00:35:54,480 --> 00:35:56,920 Speaker 1: So these guys, those fifty thousand homes have all been renovated. 768 00:35:57,480 --> 00:36:00,440 Speaker 1: So that those teams during COVID man they up and 769 00:36:00,520 --> 00:36:02,400 Speaker 1: they went out and they made us so proud they 770 00:36:02,680 --> 00:36:05,359 Speaker 1: provide a service to the residents. They finished construction jobs. 771 00:36:05,360 --> 00:36:08,200 Speaker 1: They got homes back in service so people could move 772 00:36:08,280 --> 00:36:10,120 Speaker 1: out of wherever they were and get into a home. 773 00:36:11,000 --> 00:36:13,319 Speaker 1: So it's been fascinating to watch this business run through 774 00:36:14,080 --> 00:36:17,080 Speaker 1: a crazy COVID cycle, and then a crazy post COVID cycle, 775 00:36:17,120 --> 00:36:19,200 Speaker 1: and now an interest rate cycle. The team has had 776 00:36:19,239 --> 00:36:20,319 Speaker 1: to be pretty nimble. Huh. 777 00:36:20,840 --> 00:36:24,960 Speaker 2: Really quite intriguing. Let's talk a little bit about about 778 00:36:25,040 --> 00:36:27,840 Speaker 2: your space. What are you doing these days in mortgage 779 00:36:27,880 --> 00:36:31,440 Speaker 2: backed securities? Does that market exist remotely like it did 780 00:36:31,520 --> 00:36:32,360 Speaker 2: in the two thousands. 781 00:36:32,440 --> 00:36:34,480 Speaker 1: Well, it's great that you ask about it. So the 782 00:36:34,600 --> 00:36:36,719 Speaker 1: bulk of my career was spent in the mortgage backed 783 00:36:36,719 --> 00:36:40,800 Speaker 1: securities and structure products markets. The single family riendal of 784 00:36:40,880 --> 00:36:43,920 Speaker 1: business kept us very busy while the FED was monetizing 785 00:36:44,040 --> 00:36:46,239 Speaker 1: so many mortgages, so, as you know, they own about 786 00:36:46,239 --> 00:36:48,960 Speaker 1: a third of all mortgages that were ever issued. The 787 00:36:49,080 --> 00:36:53,200 Speaker 1: relative value for non government investors was so bad that 788 00:36:53,320 --> 00:36:56,000 Speaker 1: we wound down a lot of our capabilities in that space. 789 00:36:56,040 --> 00:36:59,600 Speaker 1: We actually sold our investment bank to Manco Santander as 790 00:36:59,640 --> 00:37:03,319 Speaker 1: part of just the frustration with how much intervention had 791 00:37:03,440 --> 00:37:06,040 Speaker 1: sort of driven down value in that space. Well, now 792 00:37:06,160 --> 00:37:09,160 Speaker 1: that's completely reversed and there's a real vacuum today, a 793 00:37:09,280 --> 00:37:12,919 Speaker 1: real vacuum. As the FED stopped buying mortgages, they bought 794 00:37:12,920 --> 00:37:15,719 Speaker 1: a third of the whole market when they stopped buying them. 795 00:37:15,760 --> 00:37:18,600 Speaker 1: I think the belief was that the market would get 796 00:37:18,680 --> 00:37:21,760 Speaker 1: back to its regulary scheduled programming and the traditional investors 797 00:37:21,800 --> 00:37:24,440 Speaker 1: would show up to buy them, and they didn't because 798 00:37:24,440 --> 00:37:26,520 Speaker 1: a lot of those traditional investors don't exist anymore. 799 00:37:26,640 --> 00:37:30,080 Speaker 2: You lose a whole generation, there's no succession beyond. 800 00:37:30,440 --> 00:37:32,279 Speaker 1: This is the largest debt cautter market in the world, 801 00:37:32,440 --> 00:37:36,279 Speaker 1: the largest, most liquid, and it's lost its sponsor. So 802 00:37:36,360 --> 00:37:39,719 Speaker 1: the sponsor went from being the big investment banks, the 803 00:37:40,120 --> 00:37:43,680 Speaker 1: government agencies, the big bank balance sheets, a lot of 804 00:37:43,680 --> 00:37:45,760 Speaker 1: the insurance can be balance sheets, and the money managers. 805 00:37:46,080 --> 00:37:49,880 Speaker 1: The FED displaced all of them. Then they changed regulations 806 00:37:49,920 --> 00:37:52,279 Speaker 1: to where the investment banks can't really step in. The 807 00:37:52,360 --> 00:37:55,840 Speaker 1: agencies are no longer allowed to run balance sheets, the 808 00:37:55,920 --> 00:37:58,520 Speaker 1: reats are not really well positioned to step up in 809 00:37:58,560 --> 00:38:00,359 Speaker 1: the size, as we just saw in the four quarter. 810 00:38:00,960 --> 00:38:02,960 Speaker 1: So there's a real lack of sponsorship with the assets, 811 00:38:02,960 --> 00:38:06,280 Speaker 1: and they've become incredibly attractively priced. So we've been ginning 812 00:38:06,320 --> 00:38:08,840 Speaker 1: back up those strategies. We still we've always run strategy 813 00:38:08,880 --> 00:38:11,320 Speaker 1: that space, but they've been very sort of boring strategies, 814 00:38:11,360 --> 00:38:14,759 Speaker 1: index tracking, index outperformance, that kind of thing. But now 815 00:38:14,880 --> 00:38:17,399 Speaker 1: there's opportunity to really go in and build proper hedge 816 00:38:17,400 --> 00:38:20,960 Speaker 1: fund strategies, proper total return strategies. The relative value is 817 00:38:21,040 --> 00:38:22,600 Speaker 1: sort of startlingly attractive now. 818 00:38:22,920 --> 00:38:26,200 Speaker 2: So I always hated the term financial repression, but what 819 00:38:26,360 --> 00:38:31,240 Speaker 2: you're describing really is the FED engaging in financial repression 820 00:38:31,400 --> 00:38:32,919 Speaker 2: on that corner of the market. 821 00:38:33,000 --> 00:38:34,560 Speaker 1: Well, what I would say is that they were investing 822 00:38:34,640 --> 00:38:38,160 Speaker 1: for a non monetary focus or motivation. Right, They didn't 823 00:38:38,160 --> 00:38:41,160 Speaker 1: care what their returnal the mortgages were they trice and sensitive, right, 824 00:38:41,239 --> 00:38:44,280 Speaker 1: they cared what the lower mortgage rate did to the economy. 825 00:38:44,800 --> 00:38:47,520 Speaker 1: So as a person that's just investing for an economic return, 826 00:38:47,560 --> 00:38:49,440 Speaker 1: you can't compete with that, right, So their motivations were 827 00:38:49,480 --> 00:38:52,480 Speaker 1: totally different, and they and they basically drove down the 828 00:38:52,560 --> 00:38:56,480 Speaker 1: relative value to where on a hedge adjusted basis, if 829 00:38:56,520 --> 00:38:58,560 Speaker 1: you looked at a mortgage and you'd sort of get 830 00:38:58,600 --> 00:39:00,600 Speaker 1: it back to where it's got the same risk a treasury, 831 00:39:01,120 --> 00:39:03,680 Speaker 1: it was yielding almost half a percent less than a treasury. 832 00:39:04,239 --> 00:39:07,719 Speaker 1: They normally yield half a percent more. Now they yield 833 00:39:08,280 --> 00:39:10,719 Speaker 1: one percent more. So in fixed income terms, that's a lot. 834 00:39:11,200 --> 00:39:13,719 Speaker 1: So so now we're really focused on mortgages that we're 835 00:39:13,719 --> 00:39:15,319 Speaker 1: way more active than we have been in the past, 836 00:39:15,360 --> 00:39:17,440 Speaker 1: and we're excited about the opportunities there, and we have 837 00:39:17,480 --> 00:39:19,120 Speaker 1: a commercial morgage lending strategy as well. 838 00:39:19,640 --> 00:39:22,399 Speaker 2: Huh, that's kind of interesting. So let's talk a little 839 00:39:22,440 --> 00:39:26,600 Speaker 2: bit about what's going on in the commercial space. We 840 00:39:26,719 --> 00:39:29,560 Speaker 2: were talking earlier about sixty minutes. Did a piece recently 841 00:39:30,200 --> 00:39:33,160 Speaker 2: on the New York real estate market is never coming 842 00:39:33,280 --> 00:39:37,000 Speaker 2: back and all these big office towers are you know, empty. 843 00:39:37,760 --> 00:39:40,959 Speaker 2: I'm old enough to remember the sea through office towers right. 844 00:39:40,960 --> 00:39:43,680 Speaker 1: Dallas back in the eighties and Dulles the whole right 845 00:39:43,800 --> 00:39:45,759 Speaker 1: the Washington Dulles Quarter was full of sea through right 846 00:39:45,960 --> 00:39:46,640 Speaker 1: see through buildings. 847 00:39:46,800 --> 00:39:50,880 Speaker 2: So we're not there, but certainly the typical high rise 848 00:39:51,040 --> 00:39:54,000 Speaker 2: has you know, a vacancy rate of ten fifteen to 849 00:39:54,040 --> 00:39:58,880 Speaker 2: twenty percent, and the occupancy rate during the day is 850 00:39:59,080 --> 00:40:02,440 Speaker 2: probably another ten to fifteen percent less than that. What's 851 00:40:02,520 --> 00:40:03,920 Speaker 2: going on in the office space. 852 00:40:04,040 --> 00:40:07,680 Speaker 1: So the castle data is pretty fascinating and you can 853 00:40:07,760 --> 00:40:09,880 Speaker 1: get it on your Bloomberg terminal the castle. The castle 854 00:40:09,920 --> 00:40:12,520 Speaker 1: ocup data as we talked about before. 855 00:40:12,719 --> 00:40:15,600 Speaker 2: But by the way, that's all swipe cards of employees 856 00:40:15,640 --> 00:40:16,279 Speaker 2: literally going down. 857 00:40:16,680 --> 00:40:19,799 Speaker 1: The real time physical occupancy data is pretty and it's 858 00:40:19,840 --> 00:40:22,160 Speaker 1: not perfect, like no data set is, but it's pretty startling. 859 00:40:22,400 --> 00:40:24,920 Speaker 1: The last time I looked at it, most markets are 860 00:40:25,000 --> 00:40:28,960 Speaker 1: peaking at fifty percent physical occupancy. Remember I said before 861 00:40:29,000 --> 00:40:31,239 Speaker 1: that in the mortgage market, in the residential mortgage market, 862 00:40:31,280 --> 00:40:33,840 Speaker 1: a borrower can stop making payments and it might be 863 00:40:33,880 --> 00:40:37,400 Speaker 1: two years before the investor actually takes a loss, sometimes 864 00:40:37,480 --> 00:40:40,120 Speaker 1: five years. Well, I think that same thing's been happening 865 00:40:40,200 --> 00:40:43,520 Speaker 1: commercial now for the last since twenty twenty one. Is 866 00:40:43,600 --> 00:40:48,080 Speaker 1: that physical occupancy is the leading indicator to economic occupancy. 867 00:40:48,480 --> 00:40:52,000 Speaker 1: Economic occupancy is who's paying the rent, and in corporate 868 00:40:52,120 --> 00:40:55,440 Speaker 1: leases are of incredibly high credit quality, incredible very few 869 00:40:55,520 --> 00:41:00,440 Speaker 1: leases ever default. Those leases, however, are going to come 870 00:41:00,560 --> 00:41:04,080 Speaker 1: do and the renewal rates are tragically tragically low. So 871 00:41:04,239 --> 00:41:06,480 Speaker 1: if you model out what's going to happen to the 872 00:41:06,520 --> 00:41:09,600 Speaker 1: commercial space from an economic perspective, you don't have to 873 00:41:09,640 --> 00:41:13,480 Speaker 1: be a wizard to figure out that monetary or physical, 874 00:41:13,760 --> 00:41:16,840 Speaker 1: fiscal or financial occupancy is going to track physical accuracy. 875 00:41:17,239 --> 00:41:19,600 Speaker 1: Companies aren't going to be able to give back one 876 00:41:19,680 --> 00:41:21,640 Speaker 1: for one as much space as they're not using because 877 00:41:21,680 --> 00:41:24,440 Speaker 1: they've got this peak and load problem where everyone likes 878 00:41:24,480 --> 00:41:26,040 Speaker 1: to come to work on Wednesdays. So you still need 879 00:41:26,120 --> 00:41:29,120 Speaker 1: the space, but that quantum of space that people need 880 00:41:29,200 --> 00:41:31,920 Speaker 1: has been reduced dramatically and we're seeing it in that 881 00:41:32,040 --> 00:41:34,560 Speaker 1: Castle data. So it's a scary thing to do. But 882 00:41:34,640 --> 00:41:38,760 Speaker 1: if you forecast that the lease payments track the physical usage, 883 00:41:39,280 --> 00:41:42,320 Speaker 1: meaning that what you're seeing today, it's fifteen percent vacancy 884 00:41:42,400 --> 00:41:44,880 Speaker 1: because some leases expired and to get renewed, well, all 885 00:41:44,960 --> 00:41:47,759 Speaker 1: those leases that are being underutilized by half, if those 886 00:41:47,800 --> 00:41:51,440 Speaker 1: don't renew or they renew it much smaller spaces, you 887 00:41:51,520 --> 00:41:55,960 Speaker 1: could create thirty forty percent physical you're actually financial vacancy 888 00:41:56,239 --> 00:41:59,640 Speaker 1: in the commercial space. Now, it's dangerous to forecast that 889 00:41:59,760 --> 00:42:03,520 Speaker 1: far in the future because behavior can change. How much 890 00:42:03,600 --> 00:42:05,840 Speaker 1: space do people need or do they do out the 891 00:42:05,880 --> 00:42:07,600 Speaker 1: fact they want their whole team to get together three 892 00:42:07,680 --> 00:42:09,560 Speaker 1: days a week, so they do they just eat the 893 00:42:09,640 --> 00:42:13,120 Speaker 1: space on the Mondays and Fridays. Some companies are never 894 00:42:13,160 --> 00:42:15,520 Speaker 1: coming back, some jobs are never coming back. So the 895 00:42:15,560 --> 00:42:17,840 Speaker 1: way we look at it, we have some loans in 896 00:42:17,920 --> 00:42:20,920 Speaker 1: the office space. We do feel like it's like bottom 897 00:42:20,960 --> 00:42:24,920 Speaker 1: fishing time and we're taking back real estate. Now that 898 00:42:25,160 --> 00:42:28,560 Speaker 1: is fifty dollars sixty dollars a square foot space for big, 899 00:42:28,600 --> 00:42:32,800 Speaker 1: beautiful buildings that need to be repopulated. But so the 900 00:42:32,840 --> 00:42:34,680 Speaker 1: way we think about is this is that occupancy is 901 00:42:34,680 --> 00:42:36,759 Speaker 1: probably going to drop by a third, but it won't 902 00:42:36,760 --> 00:42:39,160 Speaker 1: be a third for everyone. Right, some places going to 903 00:42:39,200 --> 00:42:40,960 Speaker 1: go to zero, and some guys they won't, They won't 904 00:42:40,960 --> 00:42:43,480 Speaker 1: feel it. So asset selection becomes incredibly important. 905 00:42:43,520 --> 00:42:46,320 Speaker 2: So there's a huge difference between the A class buildings 906 00:42:46,520 --> 00:42:49,200 Speaker 2: and the B and C class. And I've heard people 907 00:42:49,280 --> 00:42:51,879 Speaker 2: say even within A there's a big ring. 908 00:42:51,880 --> 00:42:53,920 Speaker 1: There's the super A stuff, you know, the one Vanderbilt 909 00:42:53,920 --> 00:42:56,359 Speaker 1: thing at two hundred bucks half foot that you can't 910 00:42:56,440 --> 00:42:59,759 Speaker 1: get enough of it. But a block away some traditional 911 00:43:00,360 --> 00:43:03,360 Speaker 1: commodity office space that's just a little draft or whatever, 912 00:43:04,120 --> 00:43:06,200 Speaker 1: you know, that people just don't want it at any price. 913 00:43:06,520 --> 00:43:09,000 Speaker 1: So now that super A space is a very very 914 00:43:09,040 --> 00:43:10,960 Speaker 1: small fracture of the market. So it's not what happens 915 00:43:11,000 --> 00:43:13,960 Speaker 1: there probably isn't going to be sort of impactful, but 916 00:43:14,080 --> 00:43:17,080 Speaker 1: we think that, you know, they're people have to adjust 917 00:43:17,200 --> 00:43:20,640 Speaker 1: to a new normal of demand, like demand function for 918 00:43:21,040 --> 00:43:23,440 Speaker 1: commercial real estate has come down. Now this is, by 919 00:43:23,480 --> 00:43:26,040 Speaker 1: the way, just another domino in a long series of 920 00:43:26,880 --> 00:43:29,959 Speaker 1: what the Inrice Norwitz guys call software eating the world. 921 00:43:30,360 --> 00:43:32,520 Speaker 1: This is technology eating real estate. And so if you 922 00:43:32,560 --> 00:43:34,000 Speaker 1: look at this over longer the time, the way we 923 00:43:34,040 --> 00:43:37,279 Speaker 1: think about it is that technology eight retail and we 924 00:43:37,360 --> 00:43:39,600 Speaker 1: all kind of saw it, right. It was Amazon killed 925 00:43:39,640 --> 00:43:43,800 Speaker 1: the shopping mall. Airbnb has eaten up a lot of 926 00:43:43,880 --> 00:43:47,160 Speaker 1: hotel demand. So technology matching a home to a to 927 00:43:47,560 --> 00:43:50,600 Speaker 1: a rent or a leaser has eaten up a bunch 928 00:43:50,640 --> 00:43:53,439 Speaker 1: of the hotel demand. Now work from home is eating 929 00:43:53,640 --> 00:43:55,800 Speaker 1: is eating office. So we can we kind of have 930 00:43:55,880 --> 00:43:57,919 Speaker 1: a playbook for how this goes. And it's not great. 931 00:43:58,719 --> 00:44:02,120 Speaker 2: And all of these are technology enabled. Without tech, you 932 00:44:02,160 --> 00:44:04,440 Speaker 2: wouldn't be able to do this. The ironic thing is 933 00:44:05,239 --> 00:44:09,080 Speaker 2: the I love people discovered like screen sharing in twenty 934 00:44:09,160 --> 00:44:11,320 Speaker 2: twenty one, right, that tech has been around for a 935 00:44:11,440 --> 00:44:13,000 Speaker 2: dozen plus fifteen years. 936 00:44:13,200 --> 00:44:15,280 Speaker 1: I know, I think about the people that created Skype. 937 00:44:15,400 --> 00:44:17,360 Speaker 1: They must be sort of jumping off a bridge somewhere 938 00:44:17,400 --> 00:44:20,160 Speaker 1: because you know, you couldn't give away Skype pre covid, 939 00:44:20,200 --> 00:44:22,600 Speaker 1: and now now I don't even have calls on my phone, 940 00:44:22,600 --> 00:44:25,359 Speaker 1: my office phone ever anymore. Everything happens over teams are 941 00:44:25,400 --> 00:44:28,279 Speaker 1: over over zoom. So the behavior has changed so quickly. 942 00:44:28,320 --> 00:44:30,560 Speaker 1: But I think that, you know, the CEO from Cisco 943 00:44:30,640 --> 00:44:32,960 Speaker 1: made a good point that the home has become the enterprise, 944 00:44:33,320 --> 00:44:35,040 Speaker 1: and what it was saying is that Cisco is seeing 945 00:44:35,080 --> 00:44:38,920 Speaker 1: people buying really sophisticated communications equipment for their homes because 946 00:44:38,960 --> 00:44:41,960 Speaker 1: now they're they're they're pushing their use case. I so 947 00:44:42,040 --> 00:44:44,520 Speaker 1: for us, it's also kind of fascinating. And this is 948 00:44:44,560 --> 00:44:47,560 Speaker 1: a little bit about how the single trintal trade has 949 00:44:47,600 --> 00:44:52,040 Speaker 1: becomes interesting. Is as people stop going out to the 950 00:44:52,120 --> 00:44:56,440 Speaker 1: mall and they shop at home, as high speed communications 951 00:44:56,520 --> 00:44:59,799 Speaker 1: allows them to stream at home, as delivery allows them 952 00:44:59,840 --> 00:45:04,560 Speaker 1: to and eat at home. Right, these real estate sectors 953 00:45:04,600 --> 00:45:07,520 Speaker 1: are all seeing their demand dry up, the demand for usage. 954 00:45:07,840 --> 00:45:10,359 Speaker 1: All that demand is showing up in the home. It's 955 00:45:10,360 --> 00:45:13,840 Speaker 1: showing up in that eighteen hundred square foot three bedroom 956 00:45:13,880 --> 00:45:17,279 Speaker 1: home because and everyone's use case and demand for real 957 00:45:17,400 --> 00:45:19,600 Speaker 1: estate's changing because they're spending so much more time there. 958 00:45:20,120 --> 00:45:22,799 Speaker 2: So I kind of feel like a lot of those 959 00:45:23,080 --> 00:45:27,560 Speaker 2: big technological shifts we'll post the peak of that like, 960 00:45:28,080 --> 00:45:31,759 Speaker 2: I'm a big online shopper and I've kind of come 961 00:45:31,840 --> 00:45:35,279 Speaker 2: to recognize there's certain things that you just can't buy them. 962 00:45:35,360 --> 00:45:37,160 Speaker 1: Yeah, I have all the time with clothes and things. 963 00:45:37,680 --> 00:45:40,600 Speaker 2: Close is a perfect example. A lot of times you 964 00:45:40,760 --> 00:45:43,760 Speaker 2: order certain things like it's hilarious. You think you're getting 965 00:45:43,760 --> 00:45:47,719 Speaker 2: a four foot tall you know, lamp and this miniature 966 00:45:47,800 --> 00:45:49,759 Speaker 2: and I guess the photo is what the photo is, 967 00:45:50,160 --> 00:45:53,640 Speaker 2: there's just no tape mail tape measure next to it. 968 00:45:53,760 --> 00:45:55,840 Speaker 1: But let me ask you about this, because pre COVID, 969 00:45:55,920 --> 00:45:58,400 Speaker 1: you couldn't have convinced me I could buy groceries on 970 00:45:58,520 --> 00:45:58,839 Speaker 1: an app. 971 00:45:58,960 --> 00:46:01,080 Speaker 2: Oh I was doing that, That's easy. Now. 972 00:46:01,080 --> 00:46:02,480 Speaker 1: I don't think I would ever go back to grocery. 973 00:46:02,560 --> 00:46:05,439 Speaker 2: In fact, Amazon began that when they bought Whole Food. 974 00:46:05,520 --> 00:46:07,719 Speaker 1: So think about what that means that grocery store, that 975 00:46:07,840 --> 00:46:11,840 Speaker 1: grocery store anchored retail. Ordinarily, the grocery store space was 976 00:46:11,880 --> 00:46:14,040 Speaker 1: undwritten at a loss by the real estate developer, right 977 00:46:14,320 --> 00:46:16,000 Speaker 1: because that was your magnet. 978 00:46:16,280 --> 00:46:18,720 Speaker 2: Now it's your distribution hub and there's no people. 979 00:46:18,880 --> 00:46:20,600 Speaker 1: So what happens to the dry clear, what happens to 980 00:46:20,640 --> 00:46:22,520 Speaker 1: the ice cream shop? What happens to the T shirt shop? 981 00:46:22,560 --> 00:46:24,040 Speaker 1: What happens to the travel agent. 982 00:46:24,719 --> 00:46:27,480 Speaker 2: They have to adapt the same technology and do pick 983 00:46:27,560 --> 00:46:28,160 Speaker 2: up and delver. 984 00:46:28,480 --> 00:46:31,440 Speaker 1: So e commerce is changing, like the footprint for a business, 985 00:46:31,560 --> 00:46:34,719 Speaker 1: it's addressable market. And so I don't think this is over. 986 00:46:34,920 --> 00:46:37,200 Speaker 1: I think that that the pricing of it kind of 987 00:46:37,239 --> 00:46:40,040 Speaker 1: like we talked about the loan starts, the loan defaults, 988 00:46:40,040 --> 00:46:42,480 Speaker 1: and then two years later someone takes a loss. Today 989 00:46:42,920 --> 00:46:46,760 Speaker 1: we're CPI prints higher than people expected because owner equivalent 990 00:46:46,800 --> 00:46:51,000 Speaker 1: rents is higher that OER number was calculable four months ago. 991 00:46:51,440 --> 00:46:54,600 Speaker 1: So the market isn't doing a good job of forecasting 992 00:46:54,640 --> 00:46:56,799 Speaker 1: what it already pricing in what it what already knows 993 00:46:56,840 --> 00:46:59,680 Speaker 1: in any cases. And I think that we're still in 994 00:46:59,800 --> 00:47:02,520 Speaker 1: the repricing phase of real estate for a new a 995 00:47:02,600 --> 00:47:04,360 Speaker 1: new type of demand. 996 00:47:04,560 --> 00:47:08,520 Speaker 2: So some of the solutions to these are wholesale changes 997 00:47:09,160 --> 00:47:11,400 Speaker 2: to the way we built out suburbia, which is so 998 00:47:11,560 --> 00:47:15,799 Speaker 2: car dependent. If we were creating these more walkable communities 999 00:47:16,440 --> 00:47:19,560 Speaker 2: like back in the Andy Griffith days, it's fascinating, suddenly 1000 00:47:20,120 --> 00:47:24,479 Speaker 2: fascinating you have retailed that's survivable because everything isn't. Getting 1001 00:47:24,520 --> 00:47:27,200 Speaker 2: your car and drive to Target that's right, or have 1002 00:47:27,320 --> 00:47:28,920 Speaker 2: Target make a delivery exactly. 1003 00:47:29,080 --> 00:47:31,120 Speaker 1: So we spend it. You think about how European cities work. 1004 00:47:32,200 --> 00:47:34,320 Speaker 1: That's that's what that's how they're that's how they're designed. 1005 00:47:34,719 --> 00:47:37,680 Speaker 2: So so the question is is that something we can 1006 00:47:37,840 --> 00:47:40,400 Speaker 2: build here? Is there an appetite for that? Is there? 1007 00:47:41,120 --> 00:47:43,480 Speaker 1: I'm spending a fair amount of time on just that is. 1008 00:47:44,040 --> 00:47:46,160 Speaker 1: Can you respond to this? Should you respond to it? 1009 00:47:46,200 --> 00:47:48,600 Speaker 1: Because as you said, like you know, maybe this is 1010 00:47:48,680 --> 00:47:50,640 Speaker 1: a flash in the pan. If all the companies decide 1011 00:47:50,680 --> 00:47:53,120 Speaker 1: that employees have to come to work every day, then 1012 00:47:53,320 --> 00:47:55,759 Speaker 1: then these trends and occupancy will change and quantum of 1013 00:47:55,800 --> 00:47:58,759 Speaker 1: demand will change. But I recently was given a book 1014 00:47:58,760 --> 00:48:01,359 Speaker 1: and I read it. It's a companion of essays called 1015 00:48:01,400 --> 00:48:03,160 Speaker 1: The City Is Not a Tree. It was written in 1016 00:48:03,239 --> 00:48:04,960 Speaker 1: nineteen sixty five, and it was about this. It was 1017 00:48:05,000 --> 00:48:09,640 Speaker 1: about how a city should work to optimize the experience 1018 00:48:09,760 --> 00:48:11,800 Speaker 1: for its residents and think of a city as a product. 1019 00:48:11,880 --> 00:48:13,560 Speaker 1: And so we give the speech to mayors when we're 1020 00:48:13,560 --> 00:48:16,200 Speaker 1: asked about sort of how we think about their city 1021 00:48:16,280 --> 00:48:19,160 Speaker 1: from a migration investment perspective, and we try to tell 1022 00:48:19,239 --> 00:48:21,239 Speaker 1: people that a city is a product. So New York 1023 00:48:21,280 --> 00:48:24,680 Speaker 1: City is a product and the customers can choose a 1024 00:48:24,680 --> 00:48:27,480 Speaker 1: different product, and it's a great product. It's one of 1025 00:48:27,480 --> 00:48:30,279 Speaker 1: the greatest products in the world. But like all customers, 1026 00:48:30,680 --> 00:48:34,200 Speaker 1: like all businesses, in all product delivery systems, you have 1027 00:48:34,360 --> 00:48:36,880 Speaker 1: to freshen your product to keep your customers happy. And 1028 00:48:36,960 --> 00:48:38,759 Speaker 1: we see some cities doing that and some cities not 1029 00:48:38,840 --> 00:48:40,719 Speaker 1: doing that. So you have to modify. You can't just 1030 00:48:41,320 --> 00:48:42,560 Speaker 1: completely tear down and change. 1031 00:48:42,760 --> 00:48:46,040 Speaker 2: So one of my favorite YouTube channels is this kind 1032 00:48:46,080 --> 00:48:50,839 Speaker 2: of wacky Canadian expat who moved to Amsterdam, and it's 1033 00:48:50,920 --> 00:48:55,400 Speaker 2: called Not Just Bikes, and he talks about walkable cities 1034 00:48:55,920 --> 00:49:00,319 Speaker 2: and how different countries in Europe do a better job 1035 00:49:00,400 --> 00:49:01,960 Speaker 2: of it, and how there are pockets of it in 1036 00:49:02,000 --> 00:49:04,800 Speaker 2: the US right and North America, but they're few and 1037 00:49:04,880 --> 00:49:05,440 Speaker 2: far between. It. 1038 00:49:05,560 --> 00:49:07,600 Speaker 1: Yeah, I think it's something we're spending time on because 1039 00:49:07,640 --> 00:49:11,920 Speaker 1: we're with our vertical integration of manufacturing homes, building homes, 1040 00:49:12,160 --> 00:49:15,160 Speaker 1: real estate development. The ability to monetize a home either 1041 00:49:15,200 --> 00:49:17,160 Speaker 1: as a cell to a consumer or a rent and 1042 00:49:17,239 --> 00:49:19,600 Speaker 1: have into an investor. It gives us the ability to 1043 00:49:19,640 --> 00:49:22,839 Speaker 1: think big about development and I haven't seen anyone pull 1044 00:49:22,880 --> 00:49:25,280 Speaker 1: off yet. So the master plan community the United States, 1045 00:49:26,000 --> 00:49:28,400 Speaker 1: other than maybe the woodlands in Houston very few of 1046 00:49:28,440 --> 00:49:31,160 Speaker 1: them are actually master planned for multiple product types where 1047 00:49:31,200 --> 00:49:37,040 Speaker 1: you have office, medical, civil, residential, entertainment, all kind of 1048 00:49:37,120 --> 00:49:39,520 Speaker 1: thought about together the way you would the way European 1049 00:49:39,600 --> 00:49:42,160 Speaker 1: cities were developed. But remember Europe, like you said, you 1050 00:49:42,200 --> 00:49:45,680 Speaker 1: said a very key thing. European cities were developed before 1051 00:49:45,760 --> 00:49:49,240 Speaker 1: the cars became years a lot of our cities stopped 1052 00:49:49,320 --> 00:49:53,000 Speaker 1: growing as core cities and started growing as these suburban 1053 00:49:53,080 --> 00:49:55,719 Speaker 1: driven cities because of the car. And so this will 1054 00:49:55,719 --> 00:49:58,120 Speaker 1: be simple, this will be think if will you reverse? 1055 00:49:58,160 --> 00:50:00,200 Speaker 1: And this is something that global real estate investor are 1056 00:50:00,239 --> 00:50:02,279 Speaker 1: thinking about a full time basis. There was a paper 1057 00:50:02,320 --> 00:50:04,000 Speaker 1: written about five years ago, I think it was put 1058 00:50:04,040 --> 00:50:06,080 Speaker 1: out by the research team Prudential, and it was all 1059 00:50:06,080 --> 00:50:09,640 Speaker 1: about urbanization and all of the investment themes across our 1060 00:50:09,719 --> 00:50:12,160 Speaker 1: investor base, the biggest invests in the world. We're very 1061 00:50:12,200 --> 00:50:14,759 Speaker 1: focused on urbanization as a global theme, and you could 1062 00:50:14,760 --> 00:50:16,520 Speaker 1: see it in Southeast Asia, you could see it all 1063 00:50:16,600 --> 00:50:19,000 Speaker 1: over China, you could see it. Of course, has happened 1064 00:50:19,000 --> 00:50:20,799 Speaker 1: in the United States where people left the small town 1065 00:50:20,880 --> 00:50:23,360 Speaker 1: to go to the big city. COVID may have reversed 1066 00:50:23,400 --> 00:50:26,520 Speaker 1: one of the largest global trends in investing in the 1067 00:50:26,640 --> 00:50:28,520 Speaker 1: last one hundred years. It may have turned. It may 1068 00:50:28,600 --> 00:50:33,320 Speaker 1: have turned us from urbanization to de urbanization and the 1069 00:50:33,360 --> 00:50:35,680 Speaker 1: impact of that. Now we're not calling that just yet, 1070 00:50:35,760 --> 00:50:37,560 Speaker 1: but it is probably one of the most important things 1071 00:50:37,600 --> 00:50:39,600 Speaker 1: that people can focus on, or are we going to 1072 00:50:39,800 --> 00:50:42,960 Speaker 1: shrink the size of these megacities that all benefited from 1073 00:50:43,600 --> 00:50:45,920 Speaker 1: urbanization for the last you know, sort of fifty years 1074 00:50:45,960 --> 00:50:48,840 Speaker 1: in the US, maybe the last fifteen years in Southeast Asia. 1075 00:50:49,600 --> 00:50:52,120 Speaker 1: So it's an interesting time where the I wish I 1076 00:50:52,120 --> 00:50:54,000 Speaker 1: could say I was going to turn out, but there 1077 00:50:54,160 --> 00:50:56,400 Speaker 1: is a the ball is bouncing around and we need 1078 00:50:56,440 --> 00:50:57,719 Speaker 1: to understand which way it's going to land. 1079 00:50:58,040 --> 00:50:59,680 Speaker 2: Tell us about Main Street Renewal. 1080 00:51:00,160 --> 00:51:02,439 Speaker 1: Is that so that's the operating platform for the single 1081 00:51:02,520 --> 00:51:06,600 Speaker 1: fundamental business. That's our construction management, our real estate brokers platform, 1082 00:51:06,760 --> 00:51:10,360 Speaker 1: our leasing platform, the customer service platform. So that's the 1083 00:51:10,480 --> 00:51:13,720 Speaker 1: brand name that the consumers see, that our their operating 1084 00:51:13,760 --> 00:51:18,040 Speaker 1: partners see for the whole vertically integrated single family rental strategy. 1085 00:51:18,680 --> 00:51:22,239 Speaker 1: That's basically analogous to the entire ecosystem of the mortgage market, 1086 00:51:22,520 --> 00:51:24,560 Speaker 1: wrapped up under one one corporate label. 1087 00:51:25,440 --> 00:51:28,040 Speaker 2: And we've been talking a lot about single family homes 1088 00:51:28,120 --> 00:51:30,759 Speaker 2: to be purchased and rented a couple of years ago 1089 00:51:30,920 --> 00:51:34,360 Speaker 2: sixty Minutes to a piece talking about, hey, is private 1090 00:51:34,400 --> 00:51:37,920 Speaker 2: equity pushing out local buyers? I know you have an 1091 00:51:37,920 --> 00:51:40,480 Speaker 2: opinion on this. Tell us a little bit about your 1092 00:51:40,520 --> 00:51:42,320 Speaker 2: experience with sixty minutes. 1093 00:51:42,560 --> 00:51:45,120 Speaker 1: Sure, sure, So, first of all, I love sixty Minutes. 1094 00:51:45,120 --> 00:51:46,640 Speaker 1: I don't know it's just because I'm finally old enough 1095 00:51:46,680 --> 00:51:48,319 Speaker 1: to age into their demographic. But I think it's one 1096 00:51:48,320 --> 00:51:50,880 Speaker 1: of the best news shows on television because in that 1097 00:51:51,000 --> 00:51:53,640 Speaker 1: twelve or fifteen minute segment, they really can simplify a 1098 00:51:53,719 --> 00:51:55,840 Speaker 1: topic and make it and make it understandable to everyone. 1099 00:51:56,760 --> 00:52:00,480 Speaker 1: The topic of where do we fit in the system 1100 00:52:00,520 --> 00:52:02,799 Speaker 1: of the single family housing market is what we're doing 1101 00:52:02,840 --> 00:52:05,279 Speaker 1: a good thing or a bad thing? Obviously, you know, 1102 00:52:05,320 --> 00:52:06,759 Speaker 1: I've got a couple of thousand people that wake up 1103 00:52:06,760 --> 00:52:08,160 Speaker 1: every day and go to work. They don't think they're 1104 00:52:08,160 --> 00:52:10,200 Speaker 1: doing a bad thing. So I can tell you our 1105 00:52:10,239 --> 00:52:11,640 Speaker 1: perspective of it. I can kind of give you both 1106 00:52:11,680 --> 00:52:13,560 Speaker 1: sides of the argument, and people can decide for themselves. 1107 00:52:13,600 --> 00:52:17,239 Speaker 1: I mean, part of the argument is that if Sean 1108 00:52:17,320 --> 00:52:18,920 Speaker 1: buys the home, or if amorist buys the home, some 1109 00:52:19,000 --> 00:52:23,200 Speaker 1: family couldn't buy the home. And it's true that if 1110 00:52:23,239 --> 00:52:24,600 Speaker 1: we buy the home, no one else could buy the home. 1111 00:52:24,760 --> 00:52:27,800 Speaker 1: I'll give you that part. Now, in the US, we 1112 00:52:27,960 --> 00:52:31,080 Speaker 1: track the home ownership rate over time. The home ownership 1113 00:52:31,120 --> 00:52:33,399 Speaker 1: rate has grown to sort of mid sixties and babble around. 1114 00:52:33,440 --> 00:52:34,960 Speaker 1: It got really really high when we were giving away 1115 00:52:35,000 --> 00:52:37,319 Speaker 1: mortgages in two thousand and seven, and then it came 1116 00:52:37,360 --> 00:52:40,760 Speaker 1: back down. But that number has been a six handle 1117 00:52:40,880 --> 00:52:43,560 Speaker 1: for the last fifty years, right, So sixty something percent 1118 00:52:43,640 --> 00:52:46,120 Speaker 1: of people own their homes. The inverse of that number 1119 00:52:46,280 --> 00:52:48,120 Speaker 1: is the people that don't own their homes. So that 1120 00:52:48,320 --> 00:52:52,040 Speaker 1: number has been between thirty and call it thirty and 1121 00:52:53,120 --> 00:52:55,520 Speaker 1: twenty five percent for a very long time. So that 1122 00:52:55,719 --> 00:52:58,879 Speaker 1: third of how of families in the US that rent 1123 00:52:58,920 --> 00:53:01,440 Speaker 1: their home rent for a mere reasons. One of the 1124 00:53:01,480 --> 00:53:03,600 Speaker 1: reasons that they rent is because they can't get amorage. 1125 00:53:04,160 --> 00:53:07,000 Speaker 1: And part of our bet in two thousand and nine 1126 00:53:07,440 --> 00:53:09,320 Speaker 1: was that the group of people who are going to 1127 00:53:09,360 --> 00:53:10,759 Speaker 1: be locked out of the mortgage market is going to 1128 00:53:10,800 --> 00:53:15,640 Speaker 1: grow substantially, partially because the standards became higher, and partially 1129 00:53:15,680 --> 00:53:19,080 Speaker 1: because student loans became kind of a predatory financial product. 1130 00:53:19,640 --> 00:53:23,160 Speaker 1: So having a student loan makes it way more difficult 1131 00:53:23,200 --> 00:53:25,279 Speaker 1: to get a mortage. So in this argument of are 1132 00:53:25,360 --> 00:53:27,080 Speaker 1: we buying a home that a family is not moving into, 1133 00:53:27,520 --> 00:53:30,040 Speaker 1: I put the paradigm in a slightly different way. When 1134 00:53:30,080 --> 00:53:33,000 Speaker 1: that home comes up for sale, a lot of families 1135 00:53:33,040 --> 00:53:34,800 Speaker 1: show up that want to live in that home. A 1136 00:53:34,880 --> 00:53:37,000 Speaker 1: group of those families show up and they can get 1137 00:53:37,000 --> 00:53:39,399 Speaker 1: a mortgage and they can buy the home. A group 1138 00:53:39,440 --> 00:53:40,719 Speaker 1: of those famili show up and they can't get a 1139 00:53:40,719 --> 00:53:42,840 Speaker 1: mortage for that second group of families to get to 1140 00:53:42,880 --> 00:53:44,960 Speaker 1: live with that home, and investors got to buy the home. 1141 00:53:45,480 --> 00:53:47,839 Speaker 1: And that investor can be and historically has been very 1142 00:53:47,880 --> 00:53:50,560 Speaker 1: small investors, people that own one or two homes. Maybe 1143 00:53:50,600 --> 00:53:53,200 Speaker 1: they owned a home, lived there, moved away, kept it, 1144 00:53:53,440 --> 00:53:57,239 Speaker 1: rented it. And now through the through technology and through 1145 00:53:57,400 --> 00:54:00,960 Speaker 1: significant investment platforms like ours, allow larger investors to go 1146 00:54:01,320 --> 00:54:04,000 Speaker 1: and invest in that home. So when I sit down 1147 00:54:04,000 --> 00:54:07,000 Speaker 1: with policy makers and they're sort of of this mindset 1148 00:54:07,120 --> 00:54:10,120 Speaker 1: that I should have stayed away and let the family 1149 00:54:10,200 --> 00:54:12,000 Speaker 1: buy the home, what I like to do is say, 1150 00:54:12,160 --> 00:54:14,400 Speaker 1: can you guys just put together the pictures of these 1151 00:54:14,440 --> 00:54:17,160 Speaker 1: two families and who's going to get to live in 1152 00:54:17,239 --> 00:54:19,480 Speaker 1: that home? If the only people who can get a 1153 00:54:19,520 --> 00:54:21,799 Speaker 1: mortgage can live there? And who can live there if 1154 00:54:21,840 --> 00:54:24,840 Speaker 1: Sean buys the home, because demographically they look more like 1155 00:54:24,960 --> 00:54:27,440 Speaker 1: the people that get served by the home when I 1156 00:54:27,560 --> 00:54:29,279 Speaker 1: buy it, look a lot more like the people the 1157 00:54:29,320 --> 00:54:32,160 Speaker 1: government should be trying to help. And that usually takes 1158 00:54:32,200 --> 00:54:33,799 Speaker 1: people and they step back and they go, wait a minute, 1159 00:54:33,800 --> 00:54:35,319 Speaker 1: what do you mean. I'm like, well, so Shawn doesn't 1160 00:54:35,320 --> 00:54:37,759 Speaker 1: live in fifty thousand homes. Someone's living in there. And 1161 00:54:37,840 --> 00:54:39,879 Speaker 1: the people that live in those homes, for the most part, 1162 00:54:40,280 --> 00:54:42,440 Speaker 1: are not candidates to get a mortgage in the twenty 1163 00:54:42,719 --> 00:54:44,560 Speaker 1: twenty four mortgage standards. 1164 00:54:45,400 --> 00:54:47,279 Speaker 2: And it's not because they don't have a jobs and 1165 00:54:47,400 --> 00:54:48,280 Speaker 2: they aren't. 1166 00:54:48,440 --> 00:54:51,560 Speaker 1: Currently they're paying two thousand dollars a month in rent. 1167 00:54:51,640 --> 00:54:54,239 Speaker 1: Our average customer only pays twenty five percent of their 1168 00:54:54,320 --> 00:54:57,920 Speaker 1: income in rent. For two thousand dollars. They cover everything, 1169 00:54:58,080 --> 00:55:00,680 Speaker 1: They cover the chance that the ac breaks. They don't 1170 00:55:00,760 --> 00:55:03,480 Speaker 1: have to pay for that, property taxes, insurance, the whole 1171 00:55:03,560 --> 00:55:05,920 Speaker 1: nine yards. So right now, the cost to rent is 1172 00:55:05,920 --> 00:55:08,200 Speaker 1: probably thirty percent cheaper than the cost to own. But 1173 00:55:08,320 --> 00:55:10,680 Speaker 1: more importantly, if you're not given a chance to get 1174 00:55:10,719 --> 00:55:13,080 Speaker 1: a mortgage, it doesn't matter what the cost to own is. 1175 00:55:13,239 --> 00:55:15,200 Speaker 1: The cost for you is infinite because you're not allowed 1176 00:55:15,200 --> 00:55:17,440 Speaker 1: to get a mortgage. So when they when Dodd Frank 1177 00:55:17,520 --> 00:55:20,719 Speaker 1: passed and the standards for mortage credit became unfairly high, 1178 00:55:21,320 --> 00:55:23,640 Speaker 1: we said, Okay, this is what's gonna this is what 1179 00:55:23,719 --> 00:55:25,520 Speaker 1: the nation to decided it wants to do now against 1180 00:55:25,560 --> 00:55:27,440 Speaker 1: my advice when I sat, when I sat at full reserve, 1181 00:55:27,480 --> 00:55:29,480 Speaker 1: I said, this doesn't have to happen. This way, we 1182 00:55:29,560 --> 00:55:31,520 Speaker 1: can sort out for you what the good subprime was 1183 00:55:31,600 --> 00:55:35,000 Speaker 1: from the bad subprime. People are like, we agree, you can, 1184 00:55:35,160 --> 00:55:38,680 Speaker 1: but that's not how policy works. That mortgage market has 1185 00:55:38,680 --> 00:55:40,239 Speaker 1: been shut down and it's going to stay shut down. 1186 00:55:40,280 --> 00:55:43,000 Speaker 2: So what should we do to reopen that mortgage market 1187 00:55:43,719 --> 00:55:47,680 Speaker 2: for people who are currently employed have a half decent credit. 1188 00:55:47,960 --> 00:55:49,359 Speaker 1: Now, you're baby, we're gonna give in the two hours 1189 00:55:49,400 --> 00:55:50,719 Speaker 1: for the podcast. I got a whole list of things 1190 00:55:50,800 --> 00:55:52,719 Speaker 1: need to do, but the give us a short PNT. 1191 00:55:53,080 --> 00:55:54,880 Speaker 1: The primary, the primary thing you have to do is 1192 00:55:54,920 --> 00:55:56,719 Speaker 1: you have to put risk. You have to make risk 1193 00:55:56,840 --> 00:56:00,920 Speaker 1: based pricing legal in the US mortgage system. Dodd Frank 1194 00:56:01,080 --> 00:56:05,000 Speaker 1: made risk based pricing illegal. So if someone comes in 1195 00:56:05,120 --> 00:56:07,920 Speaker 1: with a lower credit score, a higher likelihood of default, 1196 00:56:08,200 --> 00:56:11,120 Speaker 1: and remember the likelihood of default could mean that they 1197 00:56:11,200 --> 00:56:13,640 Speaker 1: go from being five percent likely to ten percent likely, 1198 00:56:13,800 --> 00:56:16,719 Speaker 1: not ninety percent likely. But if someone comes in that 1199 00:56:17,320 --> 00:56:19,719 Speaker 1: that has a likelihood default above a certain level, the 1200 00:56:19,760 --> 00:56:21,960 Speaker 1: answer is you can't make them the mortgage. 1201 00:56:21,560 --> 00:56:24,719 Speaker 2: At any price as opposed to where it's I'll make 1202 00:56:24,800 --> 00:56:27,200 Speaker 2: up a round number. If we're at five percent, they 1203 00:56:27,239 --> 00:56:28,840 Speaker 2: could buy get a mortgage at six and. 1204 00:56:28,880 --> 00:56:31,759 Speaker 1: Three we used the rate used to be three points 1205 00:56:31,840 --> 00:56:34,880 Speaker 1: hire two points are so Dodd Frank basically carved out 1206 00:56:34,960 --> 00:56:37,759 Speaker 1: the maximum premium you can charge to anyone, and then 1207 00:56:37,800 --> 00:56:40,920 Speaker 1: they created recourse for the borrower. So I give this 1208 00:56:41,040 --> 00:56:43,920 Speaker 1: presentation in the UK, and I gave this presentation to 1209 00:56:43,960 --> 00:56:46,400 Speaker 1: France once and I said, okay, the US passed. They 1210 00:56:46,440 --> 00:56:48,080 Speaker 1: were like, why is the demand for rental so high? 1211 00:56:48,080 --> 00:56:50,560 Speaker 1: And I said, people can't get mortgages. He said why, 1212 00:56:50,880 --> 00:56:53,080 Speaker 1: I said, well, Dodd Frank created a precedent that said 1213 00:56:53,480 --> 00:56:55,239 Speaker 1: that if I lend you money to buy your home 1214 00:56:56,360 --> 00:56:59,320 Speaker 1: and then you can't pay me back, you can sue me. 1215 00:57:00,440 --> 00:57:02,200 Speaker 1: And even in France, the guy would say no, no, no, 1216 00:57:02,440 --> 00:57:05,080 Speaker 1: you mean the other way around. I lend you the 1217 00:57:05,120 --> 00:57:07,759 Speaker 1: money you don't pay, I can sue you and I'm like, no, no. 1218 00:57:08,600 --> 00:57:11,759 Speaker 1: So there's there's this concept that that that was part 1219 00:57:11,840 --> 00:57:15,279 Speaker 1: of the the ether in the financial crisis, that the 1220 00:57:15,360 --> 00:57:18,480 Speaker 1: banks were the proximate cause for the default, and so 1221 00:57:18,600 --> 00:57:20,680 Speaker 1: the bank should not be allowed to make these loans. 1222 00:57:22,000 --> 00:57:23,360 Speaker 1: There were some bad back that's a. 1223 00:57:23,400 --> 00:57:28,160 Speaker 2: Wild statement because as someone literally wrote a book on this, 1224 00:57:28,920 --> 00:57:31,440 Speaker 2: banks did a bunch of stuff that wasn't very smart. 1225 00:57:31,920 --> 00:57:35,640 Speaker 2: But it's hard to say the banks making loans were approximate, 1226 00:57:35,720 --> 00:57:38,680 Speaker 2: cause now there's a handful of banks doing the ninja stuff, 1227 00:57:38,760 --> 00:57:39,640 Speaker 2: and but that was. 1228 00:57:39,640 --> 00:57:41,800 Speaker 1: There really enough bad acts to go around. The banks 1229 00:57:41,840 --> 00:57:47,600 Speaker 1: had culpability, the securitization industry had culpability, Serving industries culpability, 1230 00:57:47,720 --> 00:57:50,760 Speaker 1: The ratings agency agencies had culpability. And this is why 1231 00:57:50,760 --> 00:57:52,600 Speaker 1: I spend time washing trying to explain to people. But 1232 00:57:52,760 --> 00:57:54,920 Speaker 1: the consumers had culpability as well. So a lot of 1233 00:57:54,960 --> 00:57:57,320 Speaker 1: people with fraudulent loans six eight loans. So we bought 1234 00:57:57,320 --> 00:58:00,160 Speaker 1: a bunch of these loans. Something people don't know is 1235 00:58:00,160 --> 00:58:02,919 Speaker 1: that we audited eighty thousand loan contracts that we bought, 1236 00:58:03,400 --> 00:58:06,040 Speaker 1: and we there's a return to cinder clause in mortgage 1237 00:58:06,040 --> 00:58:07,480 Speaker 1: contracts that most people don't know about. 1238 00:58:07,560 --> 00:58:07,680 Speaker 2: Right. 1239 00:58:07,840 --> 00:58:10,000 Speaker 1: And if the bar were defaulted and the contracts were 1240 00:58:10,080 --> 00:58:11,800 Speaker 1: in a certain way, the person that sold your loan 1241 00:58:11,840 --> 00:58:14,200 Speaker 1: has to buy it back. So in these eighty thousand 1242 00:58:14,280 --> 00:58:17,120 Speaker 1: loans you kind of had sort of two big populations 1243 00:58:17,200 --> 00:58:21,360 Speaker 1: of predatory borrowers. One with a little mini we call 1244 00:58:21,360 --> 00:58:23,280 Speaker 1: the little Minnie Donald Trump's. They would have like twenty 1245 00:58:23,320 --> 00:58:25,880 Speaker 1: five or thirty or forty homes, no equity down. They're 1246 00:58:25,880 --> 00:58:29,320 Speaker 1: all rented, no management, kind of like yolo of like 1247 00:58:29,400 --> 00:58:30,960 Speaker 1: if they go up, we're going to refinance them. If 1248 00:58:30,960 --> 00:58:32,320 Speaker 1: they don't, we're going to send the keys back in. 1249 00:58:32,440 --> 00:58:35,760 Speaker 1: And these were loans that were made with no equity 1250 00:58:35,800 --> 00:58:39,200 Speaker 1: from the barrower eighty percent first, twenty percent second investor loans. 1251 00:58:40,160 --> 00:58:42,200 Speaker 1: And then then there were a group of people who 1252 00:58:42,320 --> 00:58:44,480 Speaker 1: really just wanted a house and they were willing to 1253 00:58:44,520 --> 00:58:47,560 Speaker 1: fib about their financial standards to get there, right, and 1254 00:58:47,680 --> 00:58:49,680 Speaker 1: so and the banks and the mortga originators. In many 1255 00:58:49,720 --> 00:58:52,640 Speaker 1: cases there's eighty thousand files. You would open up the 1256 00:58:52,720 --> 00:58:54,680 Speaker 1: file and it would say the person was a dental 1257 00:58:54,760 --> 00:58:57,760 Speaker 1: hygienist and made one hundred thousand dollars a year, no document, 1258 00:58:58,000 --> 00:59:00,200 Speaker 1: and that loom was loans approved. Now in this you 1259 00:59:00,280 --> 00:59:03,280 Speaker 1: file would be the application that got denied that said 1260 00:59:03,320 --> 00:59:05,640 Speaker 1: that they were a dental assistant and they made fifty 1261 00:59:05,680 --> 00:59:07,520 Speaker 1: thousand dollars a year, so they would give us the 1262 00:59:07,600 --> 00:59:08,920 Speaker 1: file that so they was. 1263 00:59:09,000 --> 00:59:11,240 Speaker 2: So those were the I heard stories at the time 1264 00:59:11,360 --> 00:59:15,280 Speaker 2: of the mortgage brokers who were able to guide an 1265 00:59:15,360 --> 00:59:19,040 Speaker 2: applicant through coaching. Coach, don't write this, don't write here 1266 00:59:19,240 --> 00:59:23,080 Speaker 2: what you got to say? And basically, you know, we're 1267 00:59:23,680 --> 00:59:26,320 Speaker 2: co conspirators to fraud. 1268 00:59:26,440 --> 00:59:28,600 Speaker 1: And you know the Mortriges broker was making five or 1269 00:59:28,640 --> 00:59:30,560 Speaker 1: six percent of the loan amount, right, it's a lot 1270 00:59:30,600 --> 00:59:31,120 Speaker 1: of incentives. 1271 00:59:31,120 --> 00:59:33,480 Speaker 2: So I blame them much more than the person who 1272 00:59:33,600 --> 00:59:35,800 Speaker 2: just did what they were told they were wrong. At 1273 00:59:35,840 --> 00:59:37,920 Speaker 2: this point, really the professional is the one got a 1274 00:59:37,920 --> 00:59:38,440 Speaker 2: whole scount. 1275 00:59:38,480 --> 00:59:40,200 Speaker 1: I think that we're hung up on who to blame, 1276 00:59:40,360 --> 00:59:41,840 Speaker 1: not you and me. But if the market is who 1277 00:59:41,880 --> 00:59:43,480 Speaker 1: to blame, and the market isn't paying attention of who 1278 00:59:43,480 --> 00:59:46,360 Speaker 1: got harmed, because in the first degree, the person that 1279 00:59:46,440 --> 00:59:48,720 Speaker 1: got harmed was the person who got four clothes up 1280 00:59:48,760 --> 00:59:50,440 Speaker 1: on and got addicted from their home, that's a very 1281 00:59:50,480 --> 00:59:53,560 Speaker 1: clear harm to see. The harder harm to see is 1282 00:59:53,680 --> 00:59:56,600 Speaker 1: that maybe eight million families that haven't been able to 1283 00:59:56,640 --> 00:59:59,520 Speaker 1: buy a home since this law went and it's fifteen 1284 00:59:59,600 --> 01:00:03,320 Speaker 1: years and there's no progress, So the rental market has 1285 01:00:03,360 --> 01:00:06,600 Speaker 1: to grow. Institutional capital is going to play a part 1286 01:00:06,720 --> 01:00:09,720 Speaker 1: in every home transaction. Its social capital has to be 1287 01:00:09,760 --> 01:00:12,040 Speaker 1: there to make the loan if they're not going to 1288 01:00:12,080 --> 01:00:14,880 Speaker 1: buy the home. Providing service to the third of American 1289 01:00:14,920 --> 01:00:17,880 Speaker 1: families who rent for various reasons. Now, about a third 1290 01:00:17,880 --> 01:00:19,800 Speaker 1: of our customers, or twenty percent of our customers move 1291 01:00:19,840 --> 01:00:21,560 Speaker 1: out every year, so they were never like long term 1292 01:00:21,600 --> 01:00:26,400 Speaker 1: committed to that location to begin with. The credit scores 1293 01:00:26,520 --> 01:00:30,520 Speaker 1: of our customers suggest and the financial condition of our 1294 01:00:30,560 --> 01:00:32,600 Speaker 1: customers suggests it would be very difficult. It's not it 1295 01:00:32,680 --> 01:00:35,160 Speaker 1: possible for them to get a mortgage on average. So 1296 01:00:35,840 --> 01:00:37,880 Speaker 1: this is the solution for people to move out of 1297 01:00:38,560 --> 01:00:39,880 Speaker 1: the Other thing people think about it is that it's 1298 01:00:39,880 --> 01:00:42,880 Speaker 1: okay to rent apartments. So that's socially acceptable to invest 1299 01:00:42,960 --> 01:00:45,520 Speaker 1: in apartments and rent them. But apartments are primarily one 1300 01:00:45,560 --> 01:00:48,520 Speaker 1: and two bedroom products, so we're a three bedroom product. 1301 01:00:48,760 --> 01:00:50,960 Speaker 1: So as you age out of an apartment or you 1302 01:00:51,080 --> 01:00:52,560 Speaker 1: need more space because you work from home, or you 1303 01:00:52,600 --> 01:00:54,640 Speaker 1: have a family or whatever, and you age into the 1304 01:00:54,720 --> 01:00:58,120 Speaker 1: single family product, which is location driven, local amenities driven 1305 01:00:58,160 --> 01:01:00,720 Speaker 1: blah blah blah. So you would go and get a 1306 01:01:00,760 --> 01:01:03,880 Speaker 1: mortgage and buy. But that cross section of the customer 1307 01:01:03,960 --> 01:01:06,240 Speaker 1: base that the mortgage market serves has shrunk so much 1308 01:01:07,040 --> 01:01:08,720 Speaker 1: that we set up this platform because we knew they 1309 01:01:08,720 --> 01:01:10,200 Speaker 1: were coming. We knew that they're going to want to 1310 01:01:10,240 --> 01:01:11,880 Speaker 1: live in that product and they're going to need to 1311 01:01:11,920 --> 01:01:14,200 Speaker 1: get there with a different financial solution than a mortgage. 1312 01:01:14,480 --> 01:01:17,960 Speaker 1: So we developed an institutional scale, securitized financing vehicle for 1313 01:01:18,040 --> 01:01:20,680 Speaker 1: the pool of homes. We developed the services that wrap 1314 01:01:20,720 --> 01:01:22,600 Speaker 1: around the pool of home to lower its cost to capital, 1315 01:01:23,000 --> 01:01:25,000 Speaker 1: So the cost of capital for single time really today 1316 01:01:25,080 --> 01:01:26,840 Speaker 1: is in the five to five and a half percent range. 1317 01:01:27,680 --> 01:01:29,800 Speaker 1: Prior to us getting involved, the cost of capital for 1318 01:01:29,840 --> 01:01:32,720 Speaker 1: rental was probably eight hundred over and nine hundred over 1319 01:01:32,760 --> 01:01:36,560 Speaker 1: because it was provided by small investors taking very specific 1320 01:01:36,680 --> 01:01:40,400 Speaker 1: location risk. Now we can have a thousand homes, all 1321 01:01:40,400 --> 01:01:43,439 Speaker 1: the adiosyncratic risk is pretty much gone. So we feel 1322 01:01:43,520 --> 01:01:45,040 Speaker 1: very proud of what we're doing. And I wish that 1323 01:01:45,400 --> 01:01:48,320 Speaker 1: the conversation about this crowd out would focused more on 1324 01:01:48,400 --> 01:01:51,360 Speaker 1: the specifics of who didn't get to buy, but who 1325 01:01:51,440 --> 01:01:53,600 Speaker 1: got to live there, and when people see that and 1326 01:01:53,680 --> 01:01:56,000 Speaker 1: they see that, Oh wait a minute, these are three 1327 01:01:56,040 --> 01:01:58,720 Speaker 1: hundred thousand OAR homes. These are not you know, these 1328 01:01:58,760 --> 01:02:01,360 Speaker 1: are homes that that bar, that resident would have a 1329 01:02:01,480 --> 01:02:05,200 Speaker 1: very difficult time getting into without us, and we were 1330 01:02:05,200 --> 01:02:08,520 Speaker 1: able to provide a really good service at a very 1331 01:02:08,560 --> 01:02:10,320 Speaker 1: effective price for that customer base. 1332 01:02:10,760 --> 01:02:14,520 Speaker 2: That's a really interesting answer to a complicated question, and 1333 01:02:15,200 --> 01:02:18,560 Speaker 2: it still leaves open the problem that there are eight 1334 01:02:18,640 --> 01:02:23,240 Speaker 2: million people that might otherwise be on be homeowners. But 1335 01:02:24,280 --> 01:02:25,960 Speaker 2: the rule change has been and the. 1336 01:02:25,920 --> 01:02:27,600 Speaker 1: Way I think about it, the way you get me 1337 01:02:27,640 --> 01:02:29,880 Speaker 1: a slopbox. But in the worst of the worst mortgage 1338 01:02:29,880 --> 01:02:33,520 Speaker 1: pools that we were short and the dirtiest of the pools, 1339 01:02:33,560 --> 01:02:36,560 Speaker 1: where everybody was lying the bar, where the banker, the securitizer, 1340 01:02:36,560 --> 01:02:38,800 Speaker 1: everything age, everybody was lying the worst of the worst. 1341 01:02:39,280 --> 01:02:42,200 Speaker 1: About thirty five percent of the loans defaulted, which means 1342 01:02:42,240 --> 01:02:45,000 Speaker 1: that two thirds of even those dodgy things paid. So 1343 01:02:45,120 --> 01:02:47,600 Speaker 1: those are two thirds of those families got to get 1344 01:02:47,640 --> 01:02:50,920 Speaker 1: on the economic ladder and own the piece of America. 1345 01:02:52,200 --> 01:02:55,360 Speaker 1: Because the third worked out so poorly, we shut out 1346 01:02:55,360 --> 01:02:57,600 Speaker 1: the two thirds. And that's kind of the frustration I 1347 01:02:58,000 --> 01:03:00,360 Speaker 1: had with Washington. It is like guys like I know 1348 01:03:00,440 --> 01:03:01,920 Speaker 1: there's to throw the baby out with the bath or 1349 01:03:01,920 --> 01:03:03,920 Speaker 1: what other. But you're thrown out. You're throwing out an 1350 01:03:04,000 --> 01:03:06,160 Speaker 1: opportunity for people to own a piece of the country 1351 01:03:06,960 --> 01:03:10,720 Speaker 1: and act as owners in their community because you don't 1352 01:03:10,760 --> 01:03:12,400 Speaker 1: have a good way to manage the ones that don't 1353 01:03:12,400 --> 01:03:14,280 Speaker 1: work out. So we should be focused on what to 1354 01:03:14,360 --> 01:03:17,080 Speaker 1: do when they don't work out. We shouldn't prohibit the 1355 01:03:17,120 --> 01:03:19,120 Speaker 1: activity because some of it doesn't. 1356 01:03:18,920 --> 01:03:21,240 Speaker 2: Work out well. Congress seems to have its act together. 1357 01:03:21,320 --> 01:03:24,080 Speaker 1: I'm sure, I'm sure it's next to the docket, right. 1358 01:03:24,160 --> 01:03:26,720 Speaker 2: This will be worked out, all right, So I only 1359 01:03:26,880 --> 01:03:29,560 Speaker 2: have you for a limited amount of time. Let's jump 1360 01:03:29,640 --> 01:03:32,640 Speaker 2: to our favorite questions. We ask all of our guests 1361 01:03:33,240 --> 01:03:36,480 Speaker 2: starting with what have you been entertained with these days? 1362 01:03:36,520 --> 01:03:38,360 Speaker 2: Tell us what you're either watching or listening to. 1363 01:03:38,720 --> 01:03:40,960 Speaker 1: Oh wow, So I'm a very boring person. I spent 1364 01:03:41,000 --> 01:03:43,440 Speaker 1: a lot of my time buried in data and analytics. 1365 01:03:43,960 --> 01:03:46,760 Speaker 1: I think that I really love the whole Yellowstone series. 1366 01:03:46,800 --> 01:03:49,480 Speaker 1: I'm upset that Costner backed out because I thought the 1367 01:03:49,520 --> 01:03:51,560 Speaker 1: production quality was so good. So I've seen all of 1368 01:03:51,600 --> 01:03:54,320 Speaker 1: the pre the you know, the prequels and so forth. 1369 01:03:54,360 --> 01:03:56,960 Speaker 1: So on the entertainment side, I think that streaming has 1370 01:03:57,000 --> 01:03:59,880 Speaker 1: set a whole new bar for quality of broke. 1371 01:04:00,880 --> 01:04:04,360 Speaker 2: Yeah, no, that's absolutely on my list. Tell us about 1372 01:04:04,440 --> 01:04:07,800 Speaker 2: your early mentors who might have helped shape your career. 1373 01:04:08,880 --> 01:04:10,800 Speaker 1: Wow. Well, so I've got a big family. I'm one 1374 01:04:10,880 --> 01:04:15,200 Speaker 1: of five kids. My parents were serial entrepreneurs. I've got 1375 01:04:15,240 --> 01:04:18,880 Speaker 1: four big sisters, and so they're all successful in various ways, 1376 01:04:18,880 --> 01:04:22,040 Speaker 1: and so the family has always been the primary motivator 1377 01:04:22,560 --> 01:04:26,600 Speaker 1: and leaders. You have to this in our business. You know, 1378 01:04:26,800 --> 01:04:29,400 Speaker 1: in finance, who you marry really matters. So I've been 1379 01:04:29,440 --> 01:04:31,360 Speaker 1: married for twenty eight years. My wife was in finance. 1380 01:04:31,400 --> 01:04:33,360 Speaker 1: She ran an investment management business, built it up and 1381 01:04:33,440 --> 01:04:36,720 Speaker 1: sold it. So having support at home and having a 1382 01:04:36,800 --> 01:04:39,560 Speaker 1: real partner in the business is super super important our jobs. 1383 01:04:39,800 --> 01:04:41,960 Speaker 1: When you're the founder of a business, you know, the 1384 01:04:42,040 --> 01:04:45,760 Speaker 1: hours are long and the mental exercise is significant. So 1385 01:04:46,240 --> 01:04:49,520 Speaker 1: having the right teammate at home is absolutely paramount. 1386 01:04:50,520 --> 01:04:51,120 Speaker 2: I was. 1387 01:04:51,320 --> 01:04:54,960 Speaker 1: I had a high school economics teacher who later went 1388 01:04:55,040 --> 01:04:57,040 Speaker 1: to work for the Federal Home Loan Bank of Dallas 1389 01:04:57,160 --> 01:05:00,560 Speaker 1: named Sandy Hawkins, who was just fantastic for a high 1390 01:05:00,560 --> 01:05:03,600 Speaker 1: school economics teacher. She covered everything from Milton Friedman to 1391 01:05:04,560 --> 01:05:07,200 Speaker 1: free lunches in a way that made it fun for 1392 01:05:07,280 --> 01:05:09,800 Speaker 1: high school kids, and I absorbed every second of that 1393 01:05:09,880 --> 01:05:12,520 Speaker 1: I could. And then I had this really unusual situation 1394 01:05:12,640 --> 01:05:14,280 Speaker 1: because I was at this brokerage firm when I was 1395 01:05:14,360 --> 01:05:17,800 Speaker 1: very young, and mortgages were just getting some science around them, 1396 01:05:17,840 --> 01:05:20,280 Speaker 1: and I was always good at math and I had 1397 01:05:20,320 --> 01:05:22,520 Speaker 1: been writing code since I was in the sixth grade. 1398 01:05:23,720 --> 01:05:27,680 Speaker 1: So I had real support around Wall Street because at 1399 01:05:27,720 --> 01:05:30,280 Speaker 1: the time there was a small club of firms that 1400 01:05:30,440 --> 01:05:33,320 Speaker 1: were helping solve this problem together. And so I had 1401 01:05:33,920 --> 01:05:36,240 Speaker 1: a guy named Frank Gordon who ran mortgage research at 1402 01:05:36,280 --> 01:05:39,040 Speaker 1: First Boston. That was just a great support to kind 1403 01:05:39,040 --> 01:05:40,560 Speaker 1: of bring me up up the learning curve. 1404 01:05:41,000 --> 01:05:44,640 Speaker 2: Huh interesting. Tell us about some of your favorite books 1405 01:05:44,680 --> 01:05:46,440 Speaker 2: and what have you been reading recently. 1406 01:05:46,800 --> 01:05:48,800 Speaker 1: Well, I mentioned I read A City Is Not a Tree. 1407 01:05:48,840 --> 01:05:50,880 Speaker 1: It's a little bit boring, but it's fascinating because I 1408 01:05:50,920 --> 01:05:52,960 Speaker 1: do think that there's an opportunity for us to rebuild 1409 01:05:53,720 --> 01:05:58,240 Speaker 1: microcities instead of instead of going to the exerbs and 1410 01:05:58,400 --> 01:06:00,160 Speaker 1: trying to adjoin a city. I do think that is 1411 01:06:00,320 --> 01:06:02,600 Speaker 1: something that we're working on, to just PLoP in the 1412 01:06:02,640 --> 01:06:04,800 Speaker 1: middle of nowhere and build a full stand up city, 1413 01:06:04,800 --> 01:06:08,680 Speaker 1: which would be fascinating. My my daughter and I listened 1414 01:06:08,680 --> 01:06:11,240 Speaker 1: to crime Junkies and on the entertainment side, I think 1415 01:06:11,240 --> 01:06:13,240 Speaker 1: it's one of the most popular, other than Years of course, 1416 01:06:13,280 --> 01:06:15,560 Speaker 1: one of the most popular podcasts in the country. It's fascinating. 1417 01:06:15,960 --> 01:06:18,600 Speaker 1: It's it's a couple of young women that that tell 1418 01:06:18,680 --> 01:06:22,400 Speaker 1: the story of some sort of unsolved mystery or solved 1419 01:06:22,440 --> 01:06:25,680 Speaker 1: mystery of real time what they call it there, it's 1420 01:06:25,720 --> 01:06:28,320 Speaker 1: a it's the real crime dramas. I think it's been 1421 01:06:28,320 --> 01:06:30,520 Speaker 1: pretty fascinating. And I've got we have two kids, and 1422 01:06:30,560 --> 01:06:33,560 Speaker 1: my wife and I have a freshman at Columbia and 1423 01:06:33,720 --> 01:06:36,520 Speaker 1: a sophomore at Stanford, so we're spending a lot of 1424 01:06:36,600 --> 01:06:38,400 Speaker 1: time learning about the college. 1425 01:06:38,120 --> 01:06:40,840 Speaker 2: Experience freshmen at Columbia. Oh, so you're you're back and. 1426 01:06:40,920 --> 01:06:43,400 Speaker 1: Forth, but my poor wife is on like the coast 1427 01:06:43,440 --> 01:06:44,320 Speaker 1: to coast tour. 1428 01:06:44,560 --> 01:06:47,120 Speaker 2: Are you are you guys in Austin? 1429 01:06:47,160 --> 01:06:50,920 Speaker 1: A lot home is in Austin, so you're halfway or exactly, 1430 01:06:51,000 --> 01:06:53,640 Speaker 1: we're equally it's equal travel to either place. 1431 01:06:54,320 --> 01:06:57,160 Speaker 2: And uh So, our final two questions, what sort of 1432 01:06:57,200 --> 01:07:00,680 Speaker 2: advice would you give a recent college grad interested in 1433 01:07:00,760 --> 01:07:06,280 Speaker 2: a career in mortgages real estate cra anything along those lines. 1434 01:07:06,600 --> 01:07:09,160 Speaker 1: Yeah, So whenever we have interns come in or we 1435 01:07:09,240 --> 01:07:12,400 Speaker 1: have young executives start, I buy them a couple things. 1436 01:07:12,400 --> 01:07:15,760 Speaker 1: So I buy them the Frank Fobose Handbook on mortgagsback securities, 1437 01:07:15,920 --> 01:07:19,760 Speaker 1: the Mortgage back Nerds Bible, and I buy them a 1438 01:07:19,800 --> 01:07:23,439 Speaker 1: book Bernstein's book called Against the Gods. And I really 1439 01:07:23,560 --> 01:07:26,080 Speaker 1: think that maybe it's just because I'm such a quant nerd, 1440 01:07:26,520 --> 01:07:28,400 Speaker 1: but I think that Against the Gods it's a very 1441 01:07:28,440 --> 01:07:32,040 Speaker 1: small book, a very quick read, but it does a 1442 01:07:32,120 --> 01:07:36,080 Speaker 1: really good job of teaching people that you can apply 1443 01:07:36,240 --> 01:07:39,560 Speaker 1: quantitative analytics and probably a theory to almost anything and 1444 01:07:39,680 --> 01:07:42,600 Speaker 1: to everything, to your life decisions, to everything. And I 1445 01:07:42,680 --> 01:07:44,840 Speaker 1: think it provides a nice paradigm in a world where 1446 01:07:44,920 --> 01:07:47,760 Speaker 1: today it feels like, because of the political environment, people 1447 01:07:47,760 --> 01:07:49,560 Speaker 1: are sort of it's black or it's white, it's zero 1448 01:07:49,680 --> 01:07:52,080 Speaker 1: or it's one, and it's never zero one, right, There's 1449 01:07:52,120 --> 01:07:55,440 Speaker 1: always some difference in between. So that's a book that 1450 01:07:55,480 --> 01:07:57,280 Speaker 1: I think is sort of required reading at Amherst to 1451 01:07:57,320 --> 01:07:59,680 Speaker 1: really understand the history of risk management, the history of 1452 01:08:00,040 --> 01:08:03,080 Speaker 1: ability theory, how it first turned into what are the 1453 01:08:03,120 --> 01:08:06,160 Speaker 1: big missed pricings have been? So it's not a super 1454 01:08:06,200 --> 01:08:07,800 Speaker 1: complicated read, but I think it does a really good 1455 01:08:07,880 --> 01:08:10,880 Speaker 1: job of taking people from thinking about the world as 1456 01:08:10,920 --> 01:08:13,959 Speaker 1: trying to predict a thing, instead of saying, wait a minute, 1457 01:08:13,960 --> 01:08:16,360 Speaker 1: there's a range of things. Can I be okay with 1458 01:08:16,720 --> 01:08:19,120 Speaker 1: a broadery of outcomes versus just betting on that one thing? 1459 01:08:19,320 --> 01:08:22,160 Speaker 2: And pretty much everything Peter Bernstein writes is great, awesome. 1460 01:08:22,320 --> 01:08:23,760 Speaker 1: The gold One's even good too. 1461 01:08:24,640 --> 01:08:26,640 Speaker 2: And our final question, what do you know about the 1462 01:08:26,720 --> 01:08:29,439 Speaker 2: world of real estate investing today? You wish you knew 1463 01:08:29,600 --> 01:08:32,719 Speaker 2: thirty so years ago when you were first getting started. 1464 01:08:33,080 --> 01:08:37,120 Speaker 1: Wow, that's fascinating. The ecosystem of real estate has been 1465 01:08:37,160 --> 01:08:39,280 Speaker 1: hard for me to follow, coming at it from the 1466 01:08:39,520 --> 01:08:42,240 Speaker 1: fixed income markets, so just understanding the various players of 1467 01:08:42,280 --> 01:08:44,960 Speaker 1: what they do and how they're motivated has been something 1468 01:08:45,000 --> 01:08:46,679 Speaker 1: I wish I would have just sat down and mapped 1469 01:08:46,680 --> 01:08:50,479 Speaker 1: out early on, because understanding how people are sort of 1470 01:08:50,560 --> 01:08:54,240 Speaker 1: economically rewarded really helps you predict their behavior. And I 1471 01:08:54,360 --> 01:08:56,519 Speaker 1: was kind of confused by that for a long time, 1472 01:08:56,600 --> 01:08:58,320 Speaker 1: trying to pick the thing that was the right answer 1473 01:08:58,400 --> 01:09:00,880 Speaker 1: instead of the thing that would benefited to most people. 1474 01:09:00,920 --> 01:09:03,639 Speaker 1: It's like in the financial crisis, we were we were 1475 01:09:04,800 --> 01:09:09,840 Speaker 1: short countrywide in scale hundreds of millions of dollars, and 1476 01:09:10,880 --> 01:09:11,840 Speaker 1: Bank of America. 1477 01:09:11,560 --> 01:09:15,240 Speaker 2: Bought them but for like next to nothing though right, well, 1478 01:09:15,320 --> 01:09:16,080 Speaker 2: but but. 1479 01:09:16,160 --> 01:09:19,200 Speaker 1: Yeah, but it was worth less than nothing, and so 1480 01:09:19,479 --> 01:09:21,360 Speaker 1: zero was a good was a good outcome for that thing. 1481 01:09:21,840 --> 01:09:24,280 Speaker 1: So at that point we realized that the consequence of 1482 01:09:24,320 --> 01:09:27,920 Speaker 1: countrywide failing was so great that the system was going 1483 01:09:28,040 --> 01:09:31,479 Speaker 1: to find an alternate outcome. So we switched to our 1484 01:09:31,600 --> 01:09:33,439 Speaker 1: thesis to that point to understand that the value an 1485 01:09:33,439 --> 01:09:35,720 Speaker 1: asset might have more to do with the consequences of 1486 01:09:35,760 --> 01:09:39,080 Speaker 1: that asset failing than the assets actually probably a failing. 1487 01:09:39,520 --> 01:09:41,479 Speaker 1: And that's something I wish I would have figured out before, 1488 01:09:41,600 --> 01:09:42,000 Speaker 1: because it. 1489 01:09:42,080 --> 01:09:44,080 Speaker 2: Was so you and I could go down this rabbit 1490 01:09:44,160 --> 01:09:48,679 Speaker 2: hole because we were short c T, we were short Leahman, 1491 01:09:48,680 --> 01:09:55,000 Speaker 2: and we were short AIG and AIG similarly too systemically important. 1492 01:09:55,439 --> 01:09:58,240 Speaker 2: It couldn't be allowed to crash and burn. But what 1493 01:09:58,479 --> 01:10:03,160 Speaker 2: was so fascinating was, Okay, how come Leman Brothers was 1494 01:10:03,320 --> 01:10:08,120 Speaker 2: left out to fall on its face, uniquely amongst the 1495 01:10:08,280 --> 01:10:11,760 Speaker 2: giant financial players. And I have a pet theory which 1496 01:10:11,800 --> 01:10:16,800 Speaker 2: I've never been able to validate anywhere. People forget, you know, 1497 01:10:18,400 --> 01:10:21,880 Speaker 2: Warren Buffett very famously made alone to Goldman. Sachs sure 1498 01:10:22,040 --> 01:10:25,000 Speaker 2: that at very advantageous price has got a nice piece 1499 01:10:25,000 --> 01:10:28,680 Speaker 2: of Goldman. Great bit of business for Berkshire Hathaway. What 1500 01:10:28,840 --> 01:10:32,439 Speaker 2: people forget is a few months earlier he had offered 1501 01:10:32,439 --> 01:10:35,760 Speaker 2: that deal to Dick Folds, and Dick Fold said, what 1502 01:10:35,960 --> 01:10:37,960 Speaker 2: is this so man trying to do? Steal the company? 1503 01:10:38,400 --> 01:10:41,360 Speaker 2: Tell him to go jump? And once you turned down 1504 01:10:41,400 --> 01:10:44,760 Speaker 2: Warren Buffett, how can the Treasury Department or the Fed? 1505 01:10:45,080 --> 01:10:47,400 Speaker 2: Yeah right, you know, all right, we're gonna bail you 1506 01:10:47,439 --> 01:10:50,599 Speaker 2: out of a couple hundred billion dollars. Chuse you had 1507 01:10:50,640 --> 01:10:53,400 Speaker 2: a chance to save yourself, but you waited for us. 1508 01:10:54,040 --> 01:10:55,760 Speaker 1: It's super complicated. We were a little bit on the 1509 01:10:55,800 --> 01:10:57,960 Speaker 1: outside looking in on that deal. We did price Leman, 1510 01:10:58,040 --> 01:11:01,320 Speaker 1: We priced more than Stanley for a lot of different investors. 1511 01:11:01,360 --> 01:11:05,160 Speaker 1: We priced bear Stearns. The magnitude of the losses was 1512 01:11:05,240 --> 01:11:06,920 Speaker 1: hard to get your head around, but it felt like 1513 01:11:06,960 --> 01:11:08,960 Speaker 1: the capital markets had it about right. So when bear 1514 01:11:09,000 --> 01:11:12,920 Speaker 1: Stearns was sold, their CDs was trading thirty five points 1515 01:11:13,000 --> 01:11:16,000 Speaker 1: up front for the senior unsecured piece. So it's meant 1516 01:11:16,040 --> 01:11:18,400 Speaker 1: that the bond portion of the capital structure had about 1517 01:11:18,400 --> 01:11:21,080 Speaker 1: a sixty five dollars recovery if you marked the market 1518 01:11:21,120 --> 01:11:23,840 Speaker 1: bear Stearns, that was about right. But the consequence of 1519 01:11:23,920 --> 01:11:27,799 Speaker 1: wiping out the equity, it had effects that we couldn't 1520 01:11:27,840 --> 01:11:30,439 Speaker 1: even years later I figured out what the effects were. 1521 01:11:30,960 --> 01:11:33,280 Speaker 1: But like the you know, it's kind of like the 1522 01:11:33,400 --> 01:11:35,439 Speaker 1: old anti hall, Like there's what they're saying, and then 1523 01:11:35,439 --> 01:11:38,280 Speaker 1: there's what's in the subtitles, like the macro of who 1524 01:11:38,400 --> 01:11:40,639 Speaker 1: owned the equity, who was going to get crammed down, 1525 01:11:41,040 --> 01:11:42,840 Speaker 1: who owned the fixed income? Who was going to end 1526 01:11:42,920 --> 01:11:45,639 Speaker 1: up with control? Like there was a much bigger That's 1527 01:11:45,680 --> 01:11:47,000 Speaker 1: what I'm trying to say about what to learn is 1528 01:11:47,040 --> 01:11:50,000 Speaker 1: that the first instance of what you see if something 1529 01:11:50,120 --> 01:11:52,120 Speaker 1: probably is a fraction of the story. 1530 01:11:52,520 --> 01:11:56,439 Speaker 2: Sure, and if you remember, oh, you have a weekend 1531 01:11:56,760 --> 01:11:59,840 Speaker 2: to figure this out. We expect a deal before markets open. 1532 01:12:00,240 --> 01:12:02,719 Speaker 1: These trillion dollar ballance sheet's a full of complex liquid 1533 01:12:02,720 --> 01:12:05,640 Speaker 1: assets and you have a weekend. So it was I 1534 01:12:05,680 --> 01:12:07,960 Speaker 1: think that's the thing is, it's probably never as obvious 1535 01:12:07,960 --> 01:12:11,320 Speaker 1: as it looks would be one advice, and to understand 1536 01:12:11,360 --> 01:12:14,400 Speaker 1: the whole ecosystem, not just one asset's you know sort 1537 01:12:14,439 --> 01:12:15,200 Speaker 1: of risk profile. 1538 01:12:15,600 --> 01:12:18,120 Speaker 2: Huh. Well, Sean, thank you for being so generous with 1539 01:12:18,200 --> 01:12:22,200 Speaker 2: your time. This has been absolutely fascinating. We have been 1540 01:12:22,280 --> 01:12:26,320 Speaker 2: speaking with Sean Dobson. He is the chairman, chief executive 1541 01:12:26,360 --> 01:12:31,320 Speaker 2: officer and chief investment officer at Amherst Group, managing about 1542 01:12:31,360 --> 01:12:35,679 Speaker 2: sixteen point eight billion dollars. If you enjoy this conversation, well, 1543 01:12:36,000 --> 01:12:38,840 Speaker 2: be sure and check out any of our previous five 1544 01:12:39,000 --> 01:12:43,160 Speaker 2: hundred or so. You can find those at iTunes, Spotify, YouTube, 1545 01:12:43,280 --> 01:12:47,400 Speaker 2: wherever you find your favorite podcasts. Check out my new 1546 01:12:47,479 --> 01:12:52,799 Speaker 2: podcast at the Money ten minutes of conversation about earning, spending, 1547 01:12:52,880 --> 01:12:56,400 Speaker 2: and investing your money with an expert. You can find 1548 01:12:56,479 --> 01:12:59,120 Speaker 2: that in the Master's and Business feed, or wherever you 1549 01:12:59,240 --> 01:13:03,240 Speaker 2: get your favorite Sign up for my daily reading list 1550 01:13:03,280 --> 01:13:05,960 Speaker 2: at rehelts dot com. Follow me on What's left of 1551 01:13:06,080 --> 01:13:09,040 Speaker 2: Twitter at rahelts dot com. Follow all of the Bloomberg 1552 01:13:09,120 --> 01:13:13,559 Speaker 2: Family of podcasts at Podcasts. I would be remiss if 1553 01:13:13,600 --> 01:13:15,280 Speaker 2: I did not thank the correct team that helps us 1554 01:13:15,320 --> 01:13:18,760 Speaker 2: put these conversations together each week. Kaylie Lapara is my 1555 01:13:18,880 --> 01:13:21,920 Speaker 2: audio engineer. A Tick of Albron is my project manager. 1556 01:13:22,400 --> 01:13:26,120 Speaker 2: Pariswold is my producer. Short Rousso is my head of research. 1557 01:13:26,720 --> 01:13:30,640 Speaker 2: I'm Barryhots. You've been listening to Masters of business on 1558 01:13:30,800 --> 01:13:31,719 Speaker 2: Bloomberg Radio