1 00:00:02,640 --> 00:00:16,640 Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. 2 00:00:18,680 --> 00:00:21,919 Speaker 2: Hello and welcome to another episode of the All Thoughts podcast. 3 00:00:22,040 --> 00:00:23,480 Speaker 2: I'm Tracy Alloway. 4 00:00:23,200 --> 00:00:24,360 Speaker 3: And I'm Joe Wisenthal. 5 00:00:24,720 --> 00:00:28,280 Speaker 2: Joe, do you remember a month or so ago we 6 00:00:28,320 --> 00:00:32,040 Speaker 2: recorded an episode all about why mortgage rates were going 7 00:00:32,120 --> 00:00:35,240 Speaker 2: up even though the FED has cut Yes. 8 00:00:35,200 --> 00:00:37,360 Speaker 4: This was a big one at the time, and I 9 00:00:37,360 --> 00:00:42,080 Speaker 4: think it still is really important. Basically, there's this intuition 10 00:00:42,320 --> 00:00:46,560 Speaker 4: that people have that the FED effects policy by cutting rates, 11 00:00:46,600 --> 00:00:48,800 Speaker 4: and one thing that happens when rate cuts is that 12 00:00:49,080 --> 00:00:52,120 Speaker 4: borrowing costs go down. In one form of borrowing that's 13 00:00:52,240 --> 00:00:55,600 Speaker 4: very popular is mortgage rates. But we're in the middle 14 00:00:55,640 --> 00:00:58,320 Speaker 4: of a rate cut cycle. The FED cut fifty in September, 15 00:00:58,440 --> 00:01:01,200 Speaker 4: then kind of another twenty five minutes sobsequent meeting, But 16 00:01:01,400 --> 00:01:04,319 Speaker 4: mortgage rates have generally not moved down at all, and 17 00:01:04,319 --> 00:01:07,520 Speaker 4: in fact moved up after that fifty basis point cut. 18 00:01:07,959 --> 00:01:10,920 Speaker 4: By the way we're recording this November twenty fifth, we 19 00:01:10,959 --> 00:01:13,959 Speaker 4: haven't seen much improvement at all, and so there is 20 00:01:14,000 --> 00:01:16,480 Speaker 4: this I don't know if it's really a mystery, but 21 00:01:16,520 --> 00:01:19,280 Speaker 4: there is certainly a story about the FED in the 22 00:01:19,319 --> 00:01:21,560 Speaker 4: middle of a rate cut cycle, and yet it not 23 00:01:21,600 --> 00:01:23,160 Speaker 4: really feeding through to a lot of. 24 00:01:23,200 --> 00:01:23,880 Speaker 3: Kinds of borrowing. 25 00:01:24,000 --> 00:01:25,759 Speaker 2: I don't think it's a mystery. We did a whole 26 00:01:25,760 --> 00:01:27,720 Speaker 2: episode on it. You're explaining what's right. 27 00:01:27,720 --> 00:01:29,160 Speaker 3: You're right, We explained it all. You're right. 28 00:01:29,200 --> 00:01:31,720 Speaker 2: But what I was gonna say is, as part of 29 00:01:31,720 --> 00:01:37,360 Speaker 2: that conversation, you asked a really interesting question, which for once, 30 00:01:37,480 --> 00:01:40,760 Speaker 2: for once, which is why can't we have a one 31 00:01:40,880 --> 00:01:43,280 Speaker 2: click mortgage REFI do you remember that? 32 00:01:43,440 --> 00:01:43,679 Speaker 5: Yes? 33 00:01:44,000 --> 00:01:48,000 Speaker 4: So you know, like I've refiled a mortgage in my life. 34 00:01:48,400 --> 00:01:50,760 Speaker 4: It's kind of annoying. You know, if you're in the 35 00:01:50,840 --> 00:01:54,040 Speaker 4: right situation, you can save money and it's probably worth it, 36 00:01:54,280 --> 00:01:56,720 Speaker 4: but it involves a lot of paperwork, et cetera. And 37 00:01:56,760 --> 00:01:58,800 Speaker 4: you know, I think we're so used to one click 38 00:01:58,880 --> 00:02:01,920 Speaker 4: financial transaction, maybe two clicks or whatever. 39 00:02:02,560 --> 00:02:02,720 Speaker 6: You know. 40 00:02:02,800 --> 00:02:05,360 Speaker 4: One of the things that came up is that there 41 00:02:05,360 --> 00:02:08,720 Speaker 4: are often a lot of mortgages out there that theoretically 42 00:02:08,800 --> 00:02:11,280 Speaker 4: are sort of in the money where the borrower the 43 00:02:11,280 --> 00:02:15,040 Speaker 4: homeowner could take advantage of lower rates, but they don't 44 00:02:15,080 --> 00:02:17,920 Speaker 4: for whatever reason. Perhaps one reason is they don't know 45 00:02:17,960 --> 00:02:20,360 Speaker 4: that rates have getting down. Perhaps another reason is they 46 00:02:20,440 --> 00:02:22,800 Speaker 4: can't be bothered to do all the paperwork and stuff 47 00:02:23,120 --> 00:02:25,480 Speaker 4: and so there are these lag effects, and so I've 48 00:02:25,480 --> 00:02:27,080 Speaker 4: always sort of wondered, why can't you just have a 49 00:02:27,240 --> 00:02:29,639 Speaker 4: one click, one click refive well. 50 00:02:29,520 --> 00:02:30,600 Speaker 3: As a new lower rate. 51 00:02:30,760 --> 00:02:34,200 Speaker 2: As someone who lives in fear of paperwork, I think 52 00:02:34,200 --> 00:02:37,480 Speaker 2: this is an interesting question and we should talk about it. 53 00:02:37,560 --> 00:02:40,240 Speaker 2: And it turns out we actually have the perfect guest. 54 00:02:40,560 --> 00:02:43,880 Speaker 2: We're going to be speaking with someone who was involved 55 00:02:43,960 --> 00:02:47,639 Speaker 2: in a one click mortgage lender. Mike You, co founder 56 00:02:47,680 --> 00:02:50,480 Speaker 2: and CEO of Vesta. Mike, thank you so much for 57 00:02:50,520 --> 00:02:51,679 Speaker 2: coming on all thoughts. 58 00:02:52,160 --> 00:02:53,240 Speaker 6: Yeah, thanks for having me. 59 00:02:53,760 --> 00:02:57,880 Speaker 2: So why don't you give us a very quick career summary? 60 00:02:57,960 --> 00:03:00,000 Speaker 2: Why are we talking to you? 61 00:03:00,120 --> 00:03:02,960 Speaker 6: Yeah, so I've spent my entire career in the mortgage 62 00:03:02,960 --> 00:03:05,359 Speaker 6: industry on the tech side purely. I like to joke 63 00:03:05,400 --> 00:03:08,600 Speaker 6: that everyone who ends up in mortgage origination stumbles into 64 00:03:08,639 --> 00:03:10,600 Speaker 6: it by accident. So I worked at Blend. 65 00:03:11,000 --> 00:03:13,120 Speaker 4: Kids don't dream of like one day I'm going to 66 00:03:13,120 --> 00:03:15,560 Speaker 4: be a mortgage originator. You didn't dream of that when 67 00:03:15,560 --> 00:03:17,880 Speaker 4: you were in a elementary school. Anyway, keep going. 68 00:03:17,919 --> 00:03:18,200 Speaker 3: Sorry. 69 00:03:18,840 --> 00:03:22,080 Speaker 6: I actually when I'm recruiting engineers here at Vesta, I 70 00:03:22,160 --> 00:03:23,960 Speaker 6: tell them I'm like, you know, lots of founders will 71 00:03:24,000 --> 00:03:27,040 Speaker 6: give you some weird story about how they've dreamed about 72 00:03:27,120 --> 00:03:29,120 Speaker 6: doing this thing since they were twelve. I can tell 73 00:03:29,120 --> 00:03:31,040 Speaker 6: you the story like that, and you wouldn't believe me anyways, 74 00:03:31,240 --> 00:03:34,840 Speaker 6: So let's be honestly stumbled into it. I started at 75 00:03:35,040 --> 00:03:37,920 Speaker 6: a mortgage tech startup, Blend in twenty sixteen, so the 76 00:03:37,920 --> 00:03:40,400 Speaker 6: company was about fifty people back then, and we built 77 00:03:40,400 --> 00:03:43,400 Speaker 6: a ton of the bar we're facing experience for big banks, 78 00:03:43,400 --> 00:03:45,840 Speaker 6: big mortgage lenders, et cetera. But the goal really being 79 00:03:45,840 --> 00:03:47,880 Speaker 6: how you make the process more digital, like it was 80 00:03:47,920 --> 00:03:49,920 Speaker 6: all pay performs back then, even for the barer to 81 00:03:49,920 --> 00:03:52,920 Speaker 6: fill out, and then how do you know, eventually consolidate 82 00:03:52,960 --> 00:03:54,920 Speaker 6: that down into one click or one tap if you're 83 00:03:54,920 --> 00:03:58,040 Speaker 6: on mobile. That company went public in twenty one, but 84 00:03:58,120 --> 00:04:00,120 Speaker 6: I left in twenty twenty to go and build a 85 00:04:00,200 --> 00:04:02,680 Speaker 6: different startup, Vesta, where I think a lot of what 86 00:04:02,680 --> 00:04:05,080 Speaker 6: we struggled with that Blend was the core infrastructure in 87 00:04:05,120 --> 00:04:07,440 Speaker 6: the back end of the system made it really hard 88 00:04:07,520 --> 00:04:10,520 Speaker 6: to consolidate the mortgage process, accelerated, cut costs for lenders, 89 00:04:10,800 --> 00:04:13,480 Speaker 6: save time, and make it easier for borrowers, and so 90 00:04:13,760 --> 00:04:15,600 Speaker 6: kind of working on the back end system of record 91 00:04:15,600 --> 00:04:17,200 Speaker 6: now where I think a lot of the other technology 92 00:04:17,200 --> 00:04:19,560 Speaker 6: problems are and I think, as this is about one 93 00:04:19,600 --> 00:04:23,040 Speaker 6: click mortgages, I think there are of course some technology limitations, 94 00:04:23,040 --> 00:04:24,880 Speaker 6: which is why we started the whole company, But there's 95 00:04:24,920 --> 00:04:27,520 Speaker 6: also a fun variety of regulatory implications that I'm sure 96 00:04:27,520 --> 00:04:29,039 Speaker 6: we'll dive into today too. Great. 97 00:04:29,120 --> 00:04:31,599 Speaker 4: You know what's funny is I think not only on 98 00:04:31,640 --> 00:04:34,560 Speaker 4: the podcast did we talk about why were there no 99 00:04:34,680 --> 00:04:38,360 Speaker 4: one click refise available? I think we specifically put out 100 00:04:38,400 --> 00:04:40,320 Speaker 4: a call. We were like, have you've ever been in 101 00:04:40,360 --> 00:04:44,080 Speaker 4: a startup, some y combinator thing which is attempted to 102 00:04:44,080 --> 00:04:46,600 Speaker 4: do one click? Reach out to us? And you were 103 00:04:46,640 --> 00:04:48,320 Speaker 4: one of the I don't know if Blend was ever 104 00:04:48,360 --> 00:04:50,919 Speaker 4: a y combinator thing, but that's not really that important. 105 00:04:51,000 --> 00:04:53,920 Speaker 4: But you answered the call literally and you heard it and. 106 00:04:53,880 --> 00:04:55,760 Speaker 2: Sore the odd lots call. 107 00:04:55,760 --> 00:04:58,760 Speaker 4: Answered the odd lots call to You're the perfect guest. Obviously, 108 00:04:58,839 --> 00:05:00,839 Speaker 4: we want to get into what you're doing it investa 109 00:05:00,920 --> 00:05:02,600 Speaker 4: and just how it all works. Talk to us a 110 00:05:02,600 --> 00:05:06,360 Speaker 4: little bit more. What did Blend do to attempt to 111 00:05:06,480 --> 00:05:09,360 Speaker 4: solve the problem of I don't know if it really 112 00:05:09,360 --> 00:05:13,080 Speaker 4: gets to one click, but simplifying or streamlining the mortgage 113 00:05:13,120 --> 00:05:14,280 Speaker 4: application process. 114 00:05:14,640 --> 00:05:17,280 Speaker 6: Yeah, it's really funny because it's only been ten years, 115 00:05:17,440 --> 00:05:19,320 Speaker 6: but ten years ago, actually, if you wanted to get 116 00:05:19,320 --> 00:05:22,080 Speaker 6: a mortgage, you couldn't even go and apply on the internet. 117 00:05:22,080 --> 00:05:23,799 Speaker 6: I would actually say in the early days of Blend, 118 00:05:24,040 --> 00:05:26,520 Speaker 6: you know, twenty sixteen, seventeen, we'd talked to lenders and 119 00:05:26,520 --> 00:05:29,039 Speaker 6: they would be like, you don't understand people applying for 120 00:05:29,040 --> 00:05:31,680 Speaker 6: a mortgage. They don't want to do it online, which 121 00:05:31,720 --> 00:05:33,640 Speaker 6: just blew our mind. And there are a lot of 122 00:05:33,640 --> 00:05:35,440 Speaker 6: you know, old school loan officers who are like, I 123 00:05:35,520 --> 00:05:38,359 Speaker 6: call my borrower and I interview them, and I literally 124 00:05:38,400 --> 00:05:41,200 Speaker 6: take the paper it's called the ten oh three or 125 00:05:41,240 --> 00:05:44,440 Speaker 6: the uniform Residential Loan Application. I take this paper form 126 00:05:44,480 --> 00:05:46,120 Speaker 6: and I fill it out with a pencil. I pull 127 00:05:46,160 --> 00:05:47,840 Speaker 6: over to the side of the road. My bar was 128 00:05:47,839 --> 00:05:49,440 Speaker 6: talking to me on the phone. I take them through 129 00:05:49,760 --> 00:05:52,040 Speaker 6: my process and I fill it out with pen and pencil. 130 00:05:52,240 --> 00:05:53,680 Speaker 6: Then I give it to my assistant and they go 131 00:05:53,720 --> 00:05:55,840 Speaker 6: like type it into the back end system. And we 132 00:05:55,839 --> 00:05:57,839 Speaker 6: were getting this kind of pushback left, right and center 133 00:05:58,160 --> 00:06:00,839 Speaker 6: in the early days, really just around the idea that 134 00:06:00,839 --> 00:06:03,360 Speaker 6: people wouldn't want to apply online. And then rockets big 135 00:06:03,400 --> 00:06:05,800 Speaker 6: Super Bowl, the ad came out push button Get Mortgage, 136 00:06:06,160 --> 00:06:07,880 Speaker 6: and then all the banks are like, oh, well, if 137 00:06:07,960 --> 00:06:10,240 Speaker 6: Rocket's going to do it, we'd probably need something competitive 138 00:06:10,240 --> 00:06:11,479 Speaker 6: with this, And I would say that was really an 139 00:06:11,520 --> 00:06:14,320 Speaker 6: inflection point for that company. But so much of it 140 00:06:14,360 --> 00:06:15,800 Speaker 6: was like, if you want to get to a one 141 00:06:15,880 --> 00:06:17,920 Speaker 6: click mortgage, well, first of all, you need people applying 142 00:06:17,920 --> 00:06:20,960 Speaker 6: for the mortgage on the Internet, not via physical paper. 143 00:06:21,400 --> 00:06:23,000 Speaker 6: And then it really becomes about how do you start 144 00:06:23,000 --> 00:06:24,800 Speaker 6: to pull in the data from all these various sources. 145 00:06:24,839 --> 00:06:26,440 Speaker 6: So Blend was one of the first to work with 146 00:06:27,000 --> 00:06:29,480 Speaker 6: some of the GSS on getting asset data direct from 147 00:06:29,520 --> 00:06:32,000 Speaker 6: the banks and pulling that into the mortgage application so 148 00:06:32,040 --> 00:06:34,120 Speaker 6: you can get faster underwriting and not needing to upload 149 00:06:34,120 --> 00:06:36,839 Speaker 6: a whole bunch of paperwork and bank statements. Similar things 150 00:06:36,839 --> 00:06:38,920 Speaker 6: for like income data, and then people are making big 151 00:06:38,960 --> 00:06:41,679 Speaker 6: pushes around property data and AVMs. It's a whole variety 152 00:06:41,680 --> 00:06:43,520 Speaker 6: of data sources you've kind of got to stitch together 153 00:06:43,880 --> 00:06:46,240 Speaker 6: in order to really save the barrow from sending in 154 00:06:46,279 --> 00:06:47,760 Speaker 6: a whole bunch of paperwork. Because one thing that I 155 00:06:47,800 --> 00:06:49,880 Speaker 6: think is a little less obvious when you send the 156 00:06:49,920 --> 00:06:52,200 Speaker 6: lender your pay stub, you're not just proving that you 157 00:06:52,240 --> 00:06:54,160 Speaker 6: have the income. They actually take a bunch of the 158 00:06:54,240 --> 00:06:56,640 Speaker 6: numbers on that pay stub that they didn't ask you 159 00:06:56,680 --> 00:06:58,640 Speaker 6: to fill out anywhere, and fill that out in a 160 00:06:58,640 --> 00:07:01,880 Speaker 6: spreadsheet or something to calculate your income based on their 161 00:07:01,920 --> 00:07:03,719 Speaker 6: models and what the GSS tell them to do and 162 00:07:03,760 --> 00:07:06,200 Speaker 6: things like that, and so so much of it was 163 00:07:06,240 --> 00:07:08,000 Speaker 6: just if you can digitize the process and get that 164 00:07:08,080 --> 00:07:10,400 Speaker 6: data in a structured format from a whole bunch of sources. 165 00:07:10,400 --> 00:07:12,480 Speaker 6: At the beginning, the belief was that's really going to 166 00:07:12,520 --> 00:07:14,760 Speaker 6: drive you towards a faster and more efficient process and 167 00:07:14,840 --> 00:07:16,520 Speaker 6: eventually one quick So you. 168 00:07:16,440 --> 00:07:19,680 Speaker 2: Mentioned the GSS a couple times there. I imagine if 169 00:07:19,840 --> 00:07:22,760 Speaker 2: you're doing a mortgage, at some point you're going to 170 00:07:22,800 --> 00:07:25,400 Speaker 2: have to get the guarantee from Fanny and Freddy, and 171 00:07:25,440 --> 00:07:28,360 Speaker 2: so you're going to have to go through them. What 172 00:07:28,400 --> 00:07:31,480 Speaker 2: are their systems, Like you mentioned you worked with them, Like, 173 00:07:31,680 --> 00:07:34,960 Speaker 2: how did you plug in to the GSE systems? 174 00:07:36,280 --> 00:07:39,440 Speaker 6: Yeah, So, as you might expect from a couple of 175 00:07:39,560 --> 00:07:44,000 Speaker 6: large financial institutions that are also now under conservatorship, their 176 00:07:44,040 --> 00:07:47,040 Speaker 6: systems are definitely of varying degrees of maturity. One thing 177 00:07:47,120 --> 00:07:49,440 Speaker 6: that I will say that I actually really appreciate about 178 00:07:49,480 --> 00:07:51,880 Speaker 6: both Fanny and Freddy is they have invested a lot 179 00:07:51,920 --> 00:07:54,960 Speaker 6: in technology over the last decade. I think when you 180 00:07:55,000 --> 00:07:57,640 Speaker 6: get down to it, financial products are all The nice 181 00:07:57,640 --> 00:08:00,000 Speaker 6: thing is there's no physical commodity, right, It's all money 182 00:08:00,440 --> 00:08:02,480 Speaker 6: and numbers in a ledger, And so I think they've 183 00:08:02,480 --> 00:08:04,680 Speaker 6: definitely started to embrace their role more as needing to 184 00:08:04,680 --> 00:08:07,800 Speaker 6: provide technology to the ecosystem that is modern, that is effective, 185 00:08:08,280 --> 00:08:11,240 Speaker 6: but very honestly like the core piece of technology they 186 00:08:11,240 --> 00:08:13,840 Speaker 6: built that gives you guidance on whether your loan is 187 00:08:13,840 --> 00:08:16,360 Speaker 6: going to qualify or not. For sell to Fanil Freddy. 188 00:08:16,720 --> 00:08:19,040 Speaker 6: It's called Desktop Underwriter in the Fanny case and Loan 189 00:08:19,040 --> 00:08:21,520 Speaker 6: Product Advisor in the Freddie case. The original versions were 190 00:08:21,520 --> 00:08:25,280 Speaker 6: built in the nineties and so definitely some older systems 191 00:08:25,320 --> 00:08:28,680 Speaker 6: it's all still you know, XML system to system conversation 192 00:08:28,760 --> 00:08:30,560 Speaker 6: if you can integrate to them at all. Some of 193 00:08:30,560 --> 00:08:34,160 Speaker 6: the systems don't have any capability for system to system integration, 194 00:08:34,520 --> 00:08:35,920 Speaker 6: and so if you want to actually sell the loan 195 00:08:35,960 --> 00:08:38,080 Speaker 6: to Fanil Freddy, someone has to go to their existing 196 00:08:38,120 --> 00:08:41,560 Speaker 6: loan origination system, download an XML file, log into their 197 00:08:41,559 --> 00:08:44,280 Speaker 6: website and upload it. So there's definitely, I would say 198 00:08:44,360 --> 00:08:48,480 Speaker 6: varying degrees of modernization and capability across those technology systems. 199 00:08:48,840 --> 00:08:51,880 Speaker 6: And that is I would say, no more true at 200 00:08:51,880 --> 00:08:53,680 Speaker 6: Fany and Freddi than it is that most of the 201 00:08:53,720 --> 00:08:56,280 Speaker 6: major financial institutions you think about, even most of the 202 00:08:56,320 --> 00:08:58,679 Speaker 6: smaller lenders you think about, it's all kind of on 203 00:08:58,720 --> 00:09:02,360 Speaker 6: the spectrum of everything was built between one and thirty 204 00:09:02,440 --> 00:09:04,560 Speaker 6: years ago, and everyone's got to kind of move. Thirty 205 00:09:04,640 --> 00:09:06,840 Speaker 6: years is long left in technology. Everyone's got to kind 206 00:09:06,840 --> 00:09:08,800 Speaker 6: of move and rebuild a new version of this, new 207 00:09:08,880 --> 00:09:09,320 Speaker 6: version of that. 208 00:09:09,679 --> 00:09:12,440 Speaker 2: Oh yeah, Joe, I remember you know those charts that 209 00:09:12,559 --> 00:09:16,120 Speaker 2: show like all the acquisitions that a Jpmore oh yeah, 210 00:09:16,280 --> 00:09:18,360 Speaker 2: or a Bank of America has done. It's sort of 211 00:09:18,400 --> 00:09:21,160 Speaker 2: like a flow chart. Yeah, every single one of those 212 00:09:21,240 --> 00:09:24,400 Speaker 2: probably has a different IT system, right, So I always 213 00:09:24,440 --> 00:09:28,200 Speaker 2: hear that one of the big difficulties in building a 214 00:09:28,280 --> 00:09:31,280 Speaker 2: giant bank is basically sorting out the. 215 00:09:31,280 --> 00:09:35,680 Speaker 4: IT totally big institutions, you know, it's easy to sort 216 00:09:35,720 --> 00:09:39,720 Speaker 4: of assume that sort of CLOSEI government institutions are going 217 00:09:39,760 --> 00:09:43,120 Speaker 4: to be worse on so forth, and maybe sometimes that's 218 00:09:43,120 --> 00:09:47,880 Speaker 4: correct and sometimes that's not. But big gigantic institutions, particularly 219 00:09:47,960 --> 00:09:50,400 Speaker 4: ones that had all kinds of mergers and roll ups, 220 00:09:50,400 --> 00:09:53,040 Speaker 4: et cetera. They all have this, and this has been 221 00:09:53,280 --> 00:09:56,200 Speaker 4: something that's come up a little bit in the past. 222 00:09:56,679 --> 00:10:00,240 Speaker 4: We've done some episodes on bank software in general, and 223 00:10:00,280 --> 00:10:03,280 Speaker 4: so I'm not particularly surprised to hear that Fanny and 224 00:10:03,320 --> 00:10:05,520 Speaker 4: Freddy have a lot of still work to do, even 225 00:10:05,559 --> 00:10:06,800 Speaker 4: if they have invested. 226 00:10:07,240 --> 00:10:08,079 Speaker 3: Why is it hard? 227 00:10:08,320 --> 00:10:11,840 Speaker 4: Maybe from your perspective, from the perspective of either a 228 00:10:11,880 --> 00:10:15,880 Speaker 4: Fany or Freddy or just any other gigantic financial institution, 229 00:10:16,320 --> 00:10:21,760 Speaker 4: how would you describe why it's challenging to update these 230 00:10:21,800 --> 00:10:24,440 Speaker 4: systems so that they resemble the type of software we're 231 00:10:24,520 --> 00:10:26,000 Speaker 4: used to in twenty twenty four. 232 00:10:26,320 --> 00:10:30,720 Speaker 6: Definitely, Maybe I'll start even way back, like fifty years ago. 233 00:10:30,840 --> 00:10:32,840 Speaker 2: Are we going to talk about Kobyl? I hope, so 234 00:10:33,280 --> 00:10:33,920 Speaker 2: we can. 235 00:10:33,960 --> 00:10:36,600 Speaker 6: Talk about Cobyl if you'd like. But I think it 236 00:10:36,679 --> 00:10:38,880 Speaker 6: actually when I was at Blend, I was fortunate enough 237 00:10:38,880 --> 00:10:40,960 Speaker 6: to work with tim Myoppolis, who was before I Blend, 238 00:10:41,000 --> 00:10:43,040 Speaker 6: the CEO at Fanny May and he has this line 239 00:10:43,040 --> 00:10:45,200 Speaker 6: which really stuck with me, which is that everyone says 240 00:10:45,200 --> 00:10:47,920 Speaker 6: that banks are like slow adopters of technology, but the 241 00:10:47,960 --> 00:10:50,520 Speaker 6: problem with banks actually is that they were very early 242 00:10:50,520 --> 00:10:53,599 Speaker 6: adopters of technology, right going back to everything really is 243 00:10:53,640 --> 00:10:55,640 Speaker 6: just a number inside a spreadsheet at a bank. There's 244 00:10:55,679 --> 00:10:59,280 Speaker 6: no corn that you're shipping or gold bars or whatnot. 245 00:11:00,000 --> 00:11:03,080 Speaker 6: Anctial services industry was really an early adoperative technology. What 246 00:11:03,120 --> 00:11:06,120 Speaker 6: that means is they installed a lot of technology very 247 00:11:06,200 --> 00:11:08,920 Speaker 6: very early on that then became harder and harder to 248 00:11:08,960 --> 00:11:11,360 Speaker 6: rip out. And one thing that we find, for example 249 00:11:11,480 --> 00:11:13,640 Speaker 6: at BESTA where we're replacing one of these core systems, 250 00:11:13,960 --> 00:11:16,400 Speaker 6: when I talk to other founders in the technology space, 251 00:11:16,559 --> 00:11:19,040 Speaker 6: it's much easier to install a new system to replace 252 00:11:19,080 --> 00:11:22,000 Speaker 6: a spreadsheet that just like so obviously doesn't work. The 253 00:11:22,120 --> 00:11:24,600 Speaker 6: enterprise security is terrible, the controls are terrible, et cetera. 254 00:11:25,120 --> 00:11:27,800 Speaker 6: Then to get someone to upgrade a system that you 255 00:11:27,800 --> 00:11:30,080 Speaker 6: know kind of works for them. It's clunky, it's inefficient, 256 00:11:30,080 --> 00:11:33,400 Speaker 6: it's slow, but it isn't a burning pain where they like, oh, 257 00:11:33,400 --> 00:11:36,240 Speaker 6: if I don't modernize, I'm going to lose the business 258 00:11:36,320 --> 00:11:38,640 Speaker 6: or lose my job or something. On the other hand, 259 00:11:38,679 --> 00:11:40,559 Speaker 6: I would say there's a very strong incentive in all 260 00:11:40,559 --> 00:11:42,840 Speaker 6: these big institutions if you try and do like a 261 00:11:42,920 --> 00:11:46,320 Speaker 6: huge modernization project of a big existing system of record 262 00:11:47,120 --> 00:11:49,640 Speaker 6: and it doesn't work, you're basically putting your job on 263 00:11:49,679 --> 00:11:52,040 Speaker 6: the line. And if it does work as a CIO 264 00:11:52,160 --> 00:11:55,000 Speaker 6: or a line level CIO at a bank, you're getting 265 00:11:55,000 --> 00:11:57,040 Speaker 6: a small promotion. I was just that the trade off 266 00:11:57,040 --> 00:11:57,600 Speaker 6: is pretty bad. 267 00:11:57,760 --> 00:11:59,520 Speaker 4: I was just going to ask, like, how much is 268 00:11:59,559 --> 00:12:05,920 Speaker 4: it tech qua tech versus institutional inertia and incentives that 269 00:12:06,000 --> 00:12:09,120 Speaker 4: really create that problem of why it's harder to upgrade. 270 00:12:09,240 --> 00:12:12,840 Speaker 6: I think it's mostly institutional inertial incentives. There certainly is 271 00:12:12,960 --> 00:12:14,679 Speaker 6: a lot of work that actually goes into it, right, 272 00:12:14,679 --> 00:12:16,440 Speaker 6: and so you've got to get budget, et cetera. Yeah, 273 00:12:16,480 --> 00:12:19,520 Speaker 6: but it's all very tractable. I will say in financial 274 00:12:19,520 --> 00:12:22,040 Speaker 6: services there are relatively few technology problems that are like 275 00:12:22,160 --> 00:12:25,960 Speaker 6: fundamentally hard technology problems, like we're not launching rockets over here. 276 00:12:26,120 --> 00:12:29,040 Speaker 6: They all tend to be people problems. Organizational problems are 277 00:12:29,080 --> 00:12:44,079 Speaker 6: problems that get in the way. 278 00:12:45,720 --> 00:12:48,120 Speaker 2: Can you talk to us about the sort of life 279 00:12:48,200 --> 00:12:51,920 Speaker 2: cycle of a mortgage in terms of technology, so like 280 00:12:52,080 --> 00:12:54,920 Speaker 2: what's the first thing that happens, what system is it 281 00:12:55,000 --> 00:12:57,040 Speaker 2: put into, and then where does it go next? 282 00:12:57,480 --> 00:12:59,880 Speaker 6: Sure, so we're talking one click refive today, So we'll 283 00:12:59,880 --> 00:13:02,680 Speaker 6: start with a refive in let's call it a relatively 284 00:13:02,679 --> 00:13:05,679 Speaker 6: idealized case. Let let's stop the real ideal, which is 285 00:13:05,679 --> 00:13:06,960 Speaker 6: like the bar is going to get an email from 286 00:13:07,000 --> 00:13:10,000 Speaker 6: their servicer right which says, hey, you're in the money, Like, 287 00:13:10,080 --> 00:13:11,920 Speaker 6: we service your loan, we know what you pay, we 288 00:13:12,000 --> 00:13:14,560 Speaker 6: know what rates are, we know you know roughly our 289 00:13:14,559 --> 00:13:16,920 Speaker 6: credit profile. We can tell you that you probably want 290 00:13:16,960 --> 00:13:19,280 Speaker 6: to refinance. So the consumer is going to click on 291 00:13:19,320 --> 00:13:21,319 Speaker 6: that link and they're normally going to go and fill 292 00:13:21,360 --> 00:13:23,600 Speaker 6: out an online application. Today they're going to go type 293 00:13:23,640 --> 00:13:25,480 Speaker 6: in a whole bunch of their data. Again, I think 294 00:13:25,760 --> 00:13:27,680 Speaker 6: you probably know your servicer has a ton of data 295 00:13:27,679 --> 00:13:29,320 Speaker 6: on you. Do you really need to type it in? 296 00:13:29,360 --> 00:13:31,320 Speaker 6: And this varies kind of depending on how tech forward 297 00:13:31,360 --> 00:13:33,520 Speaker 6: your servicer is, but often people are still typing in 298 00:13:33,559 --> 00:13:36,640 Speaker 6: their whole application. Again, they're uploading a whole bunch of documents. 299 00:13:36,679 --> 00:13:39,680 Speaker 6: And this is kind of sitting in the consumer facing system, 300 00:13:39,840 --> 00:13:41,440 Speaker 6: which today is you know, they call it a point 301 00:13:41,480 --> 00:13:43,719 Speaker 6: of sale. This is the space where really Blend is 302 00:13:43,760 --> 00:13:46,440 Speaker 6: the category leader now. And so the bar is going 303 00:13:46,440 --> 00:13:48,080 Speaker 6: to kind of type all that information in, They're going 304 00:13:48,120 --> 00:13:50,240 Speaker 6: to hit submit. That's going to push it to what's 305 00:13:50,280 --> 00:13:52,800 Speaker 6: called the loan origination system on the back end, and 306 00:13:52,920 --> 00:13:54,560 Speaker 6: you can think of that as both the system of 307 00:13:54,600 --> 00:13:56,679 Speaker 6: record and it's going to do all the compliance checks. 308 00:13:56,920 --> 00:13:58,319 Speaker 6: It's where the people are going to do all the 309 00:13:58,360 --> 00:14:01,839 Speaker 6: processing and the underwriting or any automated underwriting might happen. 310 00:14:02,080 --> 00:14:03,800 Speaker 6: And it's also the system that's going to be integrated 311 00:14:03,840 --> 00:14:07,240 Speaker 6: to like fifteen other systems. So one really annoying thing 312 00:14:07,280 --> 00:14:09,920 Speaker 6: about the mortgage ecosystem is to produce your loan from 313 00:14:09,920 --> 00:14:12,680 Speaker 6: front to back, you're probably hitting at least fifteen different 314 00:14:12,720 --> 00:14:13,520 Speaker 6: technology vendors. 315 00:14:14,000 --> 00:14:16,199 Speaker 4: Can you run some what are some of these in? 316 00:14:16,240 --> 00:14:18,360 Speaker 4: What different parts of the stack are they serving? You 317 00:14:18,360 --> 00:14:20,480 Speaker 4: don't have to list all at fifteen, but give us 318 00:14:20,520 --> 00:14:23,440 Speaker 4: an example of like the various things that need to 319 00:14:23,440 --> 00:14:24,680 Speaker 4: be hit and who's doing them. 320 00:14:25,000 --> 00:14:27,280 Speaker 6: Yeah, so I think of it as there's a whole 321 00:14:27,280 --> 00:14:29,240 Speaker 6: bunch of stuff around the property, right, so you've got 322 00:14:29,240 --> 00:14:30,680 Speaker 6: to go to a title company, you've got to go 323 00:14:30,680 --> 00:14:33,240 Speaker 6: to an appraisal management company kind of get the appraisal. 324 00:14:33,280 --> 00:14:34,840 Speaker 6: So there's a whole bunch of stuff around the property. 325 00:14:35,000 --> 00:14:37,160 Speaker 6: Someone's got to check what flood zone it's in. And 326 00:14:37,200 --> 00:14:39,360 Speaker 6: we'll get into why some of the rules are really 327 00:14:39,400 --> 00:14:41,000 Speaker 6: hard to change. But if you want to know what 328 00:14:41,000 --> 00:14:43,240 Speaker 6: flood zone a property is in. I mean you can 329 00:14:43,240 --> 00:14:45,280 Speaker 6: go on house Canary or Zillow and kind of figure 330 00:14:45,280 --> 00:14:46,920 Speaker 6: that out pretty quickly. If you want to sell a 331 00:14:46,920 --> 00:14:48,920 Speaker 6: mortgage to the GSS, you've actually got to hit one 332 00:14:48,920 --> 00:14:52,240 Speaker 6: of their four or five designated flood certificate providers for 333 00:14:52,280 --> 00:14:55,360 Speaker 6: an official flood certificate quote unquote, which is really just 334 00:14:55,760 --> 00:14:58,240 Speaker 6: you know, those are the providers that signed a deal 335 00:14:58,280 --> 00:15:00,320 Speaker 6: with the gs S where they get the FEMA maps 336 00:15:00,320 --> 00:15:02,160 Speaker 6: that everyone else gets and they produce a piece of 337 00:15:02,160 --> 00:15:04,760 Speaker 6: paper that's official enough and the GSS trust them. So 338 00:15:04,800 --> 00:15:07,200 Speaker 6: that's a provider you basically have to hit. And so 339 00:15:07,240 --> 00:15:09,480 Speaker 6: there's a whole variety of property you know, vendors you've 340 00:15:09,480 --> 00:15:11,600 Speaker 6: got to hit around those categories. There's a bunch of 341 00:15:11,680 --> 00:15:13,560 Speaker 6: bar er vendors you've got to hit around. The pulling 342 00:15:13,600 --> 00:15:15,520 Speaker 6: credit is the obvious one, but you're going to want 343 00:15:15,520 --> 00:15:17,240 Speaker 6: to verify their income. You're going to want to look 344 00:15:17,280 --> 00:15:19,840 Speaker 6: them up in fraud databases. So there's a whole set 345 00:15:19,880 --> 00:15:22,240 Speaker 6: of those, and then there's a bunch of compliance stuff 346 00:15:22,280 --> 00:15:24,960 Speaker 6: to do. So generally, you know, there are entire companies 347 00:15:24,960 --> 00:15:27,000 Speaker 6: that are dedicated to I have all the data in 348 00:15:27,000 --> 00:15:29,040 Speaker 6: the mortgage, and I'm going to prepare the disclosures for you. 349 00:15:29,360 --> 00:15:32,240 Speaker 6: Like the disclosures are so complicated, that's less a technology problem. 350 00:15:32,320 --> 00:15:34,480 Speaker 6: That's like that those companies have an army of lawyers 351 00:15:34,760 --> 00:15:37,760 Speaker 6: who basically read all the regulatory updates, all the updates 352 00:15:37,800 --> 00:15:39,960 Speaker 6: in each of the thirty eight hundred counties in the US, 353 00:15:40,280 --> 00:15:43,400 Speaker 6: any state updates, any investor updates around exactly what you 354 00:15:43,440 --> 00:15:45,440 Speaker 6: have to tell the consumer before they can kind of 355 00:15:45,440 --> 00:15:47,560 Speaker 6: sign a lian on their property, which, as you know, 356 00:15:47,600 --> 00:15:50,680 Speaker 6: like some states are very onerous about that. And so 357 00:15:50,840 --> 00:15:54,120 Speaker 6: between compliance and property and kind of checking the borrower, 358 00:15:54,440 --> 00:15:56,680 Speaker 6: there's just this whole constellation of stuff that has to 359 00:15:56,680 --> 00:15:58,600 Speaker 6: be done, a lot of data sources and a lot 360 00:15:58,640 --> 00:15:59,120 Speaker 6: of rules. 361 00:16:00,120 --> 00:16:02,280 Speaker 2: Talk to us a bit more about the rules then, 362 00:16:02,440 --> 00:16:06,880 Speaker 2: Like I'm curious how these rules come into place, what 363 00:16:07,120 --> 00:16:09,600 Speaker 2: sort of factors they're being based on, and then how 364 00:16:09,640 --> 00:16:11,360 Speaker 2: often they actually change. 365 00:16:11,680 --> 00:16:13,400 Speaker 6: Yeah, I kind of think of rules in two buckets. There. 366 00:16:13,400 --> 00:16:15,640 Speaker 6: Of course, the regulatory rules, so you can imagine a 367 00:16:15,640 --> 00:16:18,800 Speaker 6: ton of those regulatory rules were driven by seven eight 368 00:16:19,240 --> 00:16:21,400 Speaker 6: and a lot of the things that we saw during 369 00:16:21,440 --> 00:16:23,120 Speaker 6: the Great Financial Crisis or that kind of led up 370 00:16:23,120 --> 00:16:25,600 Speaker 6: to the Great financial crisis, so a lot of regulatory 371 00:16:25,600 --> 00:16:28,320 Speaker 6: stuff around what you disclose the consumers around you know, 372 00:16:28,360 --> 00:16:31,120 Speaker 6: you have to qualify their ability to repay in order 373 00:16:31,200 --> 00:16:34,320 Speaker 6: to have a compliant loan. So there's lots of regulatory stuff. 374 00:16:34,320 --> 00:16:36,960 Speaker 6: And then there's investor rules, which overwhelmingly you know, come 375 00:16:36,960 --> 00:16:39,520 Speaker 6: from Fanny er Freddy. There is a small private label 376 00:16:39,520 --> 00:16:42,360 Speaker 6: market and some other stuff. One thing that is true 377 00:16:42,440 --> 00:16:44,800 Speaker 6: about all of these rules, right the investor rules and 378 00:16:44,840 --> 00:16:47,440 Speaker 6: the regulatory rules, is they're both kind of set again 379 00:16:47,800 --> 00:16:51,560 Speaker 6: talking about organizational inertia by pseudo government institutions that have 380 00:16:51,640 --> 00:16:54,120 Speaker 6: really been burned by mortgages in the last two decades, 381 00:16:54,160 --> 00:16:57,320 Speaker 6: and so they're pretty you know, nervous about that, and 382 00:16:57,760 --> 00:17:00,960 Speaker 6: there's very little incentive to simplify the process to remove rules, 383 00:17:01,280 --> 00:17:03,480 Speaker 6: and so the rules change. I would say every month 384 00:17:03,560 --> 00:17:06,119 Speaker 6: or two you get a few new rules from the investors, 385 00:17:06,400 --> 00:17:09,119 Speaker 6: but they very rarely subtract rules, which tends to be 386 00:17:09,160 --> 00:17:10,840 Speaker 6: a reason that you end up with. I want to say, 387 00:17:10,840 --> 00:17:13,640 Speaker 6: the fan selling Guide is now twelve hundred pages basically 388 00:17:13,640 --> 00:17:15,880 Speaker 6: of rules that the loan has to satisfy. And these 389 00:17:15,960 --> 00:17:18,600 Speaker 6: rules can vary from you know, relatively straightforward things like 390 00:17:18,640 --> 00:17:21,960 Speaker 6: you can't refinance an FAJ loan within a certain amount 391 00:17:22,000 --> 00:17:25,080 Speaker 6: of time after the loan was originally originated, so there's 392 00:17:25,080 --> 00:17:27,280 Speaker 6: like a seasoning requirement. You know, there's a lot of 393 00:17:27,280 --> 00:17:29,719 Speaker 6: documentation rules, like if you're going to provide an income 394 00:17:29,760 --> 00:17:31,840 Speaker 6: to Fanny May, you normally have to have a pasteb 395 00:17:31,880 --> 00:17:33,719 Speaker 6: and in W two attached to it to kind of 396 00:17:34,200 --> 00:17:36,879 Speaker 6: verify that income. And you get into like really complex 397 00:17:36,920 --> 00:17:38,760 Speaker 6: and arcane rules as well, once you get into twelve 398 00:17:38,800 --> 00:17:41,200 Speaker 6: hundred pages, like are you allowed to have a ten 399 00:17:41,240 --> 00:17:43,840 Speaker 6: percent increase in income year over year and use that 400 00:17:43,880 --> 00:17:46,479 Speaker 6: new income, Well, you have to document that a certain way. 401 00:17:46,520 --> 00:17:48,199 Speaker 6: If it's about thirty percent, you have to document it 402 00:17:48,200 --> 00:17:50,840 Speaker 6: another way. So a whole kind of slew of rules, 403 00:17:50,880 --> 00:17:53,199 Speaker 6: which is why you have this huge body of people 404 00:17:53,280 --> 00:17:55,320 Speaker 6: basically that have to work on every single loan because 405 00:17:55,320 --> 00:17:56,359 Speaker 6: they have to learn all the rules. 406 00:17:57,040 --> 00:18:00,440 Speaker 4: So one thing that would be really nice and gets 407 00:18:00,440 --> 00:18:02,800 Speaker 4: to the one clickness of what we're trying to get at, 408 00:18:03,000 --> 00:18:06,840 Speaker 4: is if it were really easy to pull in data 409 00:18:06,920 --> 00:18:09,560 Speaker 4: quickly from all these disparate providers. So I go to 410 00:18:09,840 --> 00:18:13,240 Speaker 4: your website and I want you to know my income, 411 00:18:13,680 --> 00:18:16,520 Speaker 4: and maybe I want you to know my assets that 412 00:18:16,600 --> 00:18:18,560 Speaker 4: I have, but I'm not sure if that's as important 413 00:18:18,560 --> 00:18:20,639 Speaker 4: in a mortgage. And I want you to know the 414 00:18:20,720 --> 00:18:24,400 Speaker 4: location of my property so that you can do various things, 415 00:18:24,440 --> 00:18:28,960 Speaker 4: including see what the floodplain looks like. The various providers 416 00:18:29,000 --> 00:18:31,520 Speaker 4: of this stuff, so one of the providers might be 417 00:18:31,640 --> 00:18:35,399 Speaker 4: my payroll provider. Another provider might be the bank that 418 00:18:35,480 --> 00:18:39,880 Speaker 4: I use, the bank online, et cetera. How forthcoming are 419 00:18:39,920 --> 00:18:44,600 Speaker 4: they in making these systems easy for a third party, 420 00:18:44,720 --> 00:18:48,399 Speaker 4: say yours or a Blend or some other fintech or 421 00:18:48,440 --> 00:18:52,119 Speaker 4: a rocket, et cetera, to just go access them such 422 00:18:52,200 --> 00:18:55,800 Speaker 4: that I don't have to download PDFs and then re 423 00:18:55,880 --> 00:18:56,960 Speaker 4: upload them somewhere else. 424 00:18:57,640 --> 00:18:59,800 Speaker 6: Yeah. So I imagine you're asking because you know the 425 00:19:00,119 --> 00:19:02,880 Speaker 6: is going to be their very mixed results. So when 426 00:19:02,880 --> 00:19:04,919 Speaker 6: it comes to banks, for example, there have been a 427 00:19:04,960 --> 00:19:07,560 Speaker 6: whole advent of new kind of players that help you 428 00:19:07,560 --> 00:19:11,520 Speaker 6: connect your banking data. Claid is the big one exactly, 429 00:19:11,760 --> 00:19:14,000 Speaker 6: and then the banks, I would say, clearly had mixed 430 00:19:14,000 --> 00:19:16,040 Speaker 6: feelings about it. People are worried about the security of 431 00:19:16,080 --> 00:19:18,240 Speaker 6: people typing their bank password on something that's not the 432 00:19:18,240 --> 00:19:21,760 Speaker 6: bank's website. They're very notably over the last ten years 433 00:19:21,840 --> 00:19:23,600 Speaker 6: or so but a number of times in Chase just 434 00:19:23,600 --> 00:19:26,960 Speaker 6: shut off PLAIDS access, and so there certainly was some 435 00:19:27,560 --> 00:19:30,800 Speaker 6: complexity in that relationship. Earlier on with you know, some 436 00:19:30,880 --> 00:19:32,720 Speaker 6: of what's going on in open banking in the US. 437 00:19:32,760 --> 00:19:34,280 Speaker 6: I think the idea that you'll have access to your 438 00:19:34,280 --> 00:19:36,679 Speaker 6: own asset information is definitely a place the regulators are 439 00:19:36,680 --> 00:19:40,200 Speaker 6: pushing as well, and that gets easier and easier every year. 440 00:19:40,760 --> 00:19:43,560 Speaker 6: Payroll is a particularly interesting one. It's a very hot 441 00:19:43,600 --> 00:19:47,000 Speaker 6: topic in the mortgage world now because everyone basically uses 442 00:19:47,000 --> 00:19:50,080 Speaker 6: a product offered by appuifacts called the work number, which 443 00:19:50,080 --> 00:19:52,320 Speaker 6: you may have heard of. So the work number it 444 00:19:52,400 --> 00:19:55,080 Speaker 6: basically they have a partnership with ADP where ADP charges 445 00:19:55,119 --> 00:19:57,840 Speaker 6: them a very large amount of money actually to use 446 00:19:57,880 --> 00:20:00,480 Speaker 6: the borrow's social to look up the data. Work number 447 00:20:00,480 --> 00:20:02,359 Speaker 6: turns around and marks that up a whole bunch. So 448 00:20:02,400 --> 00:20:04,520 Speaker 6: they also charge a ton of money to the lender. 449 00:20:04,720 --> 00:20:07,280 Speaker 6: So I've heard of lender spending you know, like four 450 00:20:07,359 --> 00:20:09,920 Speaker 6: or five six hundred dollars a loon like to close 451 00:20:09,960 --> 00:20:13,119 Speaker 6: one loan to get that income and employment data in 452 00:20:13,160 --> 00:20:15,200 Speaker 6: this digitized way via the work number, which is I 453 00:20:15,240 --> 00:20:19,600 Speaker 6: think obviously ridiculous, And so there certainly is some struggle 454 00:20:19,640 --> 00:20:21,480 Speaker 6: going on with the data ecosystem. There are a bunch 455 00:20:21,520 --> 00:20:23,560 Speaker 6: of startups now trying to basically do a plaid did 456 00:20:23,560 --> 00:20:26,040 Speaker 6: where the barer can log into their payroll provider. One 457 00:20:26,080 --> 00:20:27,919 Speaker 6: problem you might imagine with that is like, do you 458 00:20:27,960 --> 00:20:30,159 Speaker 6: know your payroll password? Because I don't even know you 459 00:20:30,160 --> 00:20:32,480 Speaker 6: know who my provider is, no idea, and so there 460 00:20:32,520 --> 00:20:35,040 Speaker 6: certainly is some difficulty in getting the data together. I 461 00:20:35,040 --> 00:20:37,040 Speaker 6: would say that Ecosystem's made a lot of progress on 462 00:20:37,080 --> 00:20:40,119 Speaker 6: that in the last ten years, but payroll and income 463 00:20:40,160 --> 00:20:42,160 Speaker 6: tends to be a lot harder because it's much more fragmented. 464 00:20:42,200 --> 00:20:44,359 Speaker 6: And with banking and open banking and every bank you 465 00:20:44,359 --> 00:20:47,240 Speaker 6: know has some kind of electronic system of record, that's 466 00:20:47,280 --> 00:20:49,360 Speaker 6: a problem where I'd say they've made a lot more progress. 467 00:20:50,119 --> 00:20:54,400 Speaker 2: How much does mortgage financing depend on just your sort 468 00:20:54,400 --> 00:20:57,320 Speaker 2: of basic mail. I want to be able to say 469 00:20:57,359 --> 00:21:00,000 Speaker 2: the housing market is powered by FedEx or something. 470 00:21:01,200 --> 00:21:04,200 Speaker 6: So actually every closing I wouldn't say every closing package, 471 00:21:04,240 --> 00:21:06,399 Speaker 6: but the vast mandory of closing packages in this country, 472 00:21:06,440 --> 00:21:09,119 Speaker 6: like you go to your lender or a title office 473 00:21:09,119 --> 00:21:11,000 Speaker 6: and you sign the closing doc and the notes and 474 00:21:11,040 --> 00:21:14,439 Speaker 6: whatnot to actually the legally binding piece of paper that 475 00:21:14,520 --> 00:21:16,280 Speaker 6: says there is a lean on my property. Now, like 476 00:21:16,320 --> 00:21:18,440 Speaker 6: I have to pay this loan back, and that gets 477 00:21:18,440 --> 00:21:20,919 Speaker 6: FedEx back to the lender, and the lender then you know, 478 00:21:20,960 --> 00:21:23,159 Speaker 6: scans it, they upload it to their electronic system and 479 00:21:23,160 --> 00:21:24,720 Speaker 6: they turn around and they like FedEx that to a 480 00:21:24,760 --> 00:21:27,479 Speaker 6: doc custodian. And some lenders are more efficient with their 481 00:21:27,480 --> 00:21:29,640 Speaker 6: FedEx schemes than others on like it just goes straight 482 00:21:29,680 --> 00:21:31,639 Speaker 6: to the doc custodian and the DAK custodian scans and 483 00:21:31,680 --> 00:21:33,600 Speaker 6: sends it to them. But I would say that very 484 00:21:33,680 --> 00:21:37,040 Speaker 6: much all of the actual debt and recording and like 485 00:21:37,080 --> 00:21:41,439 Speaker 6: all of the legally binding stuff, probably ninety percent plus 486 00:21:41,560 --> 00:21:44,119 Speaker 6: is still physical pieces of paper that are getting mailed around. 487 00:21:44,280 --> 00:21:46,840 Speaker 6: There was a big trend, especially in twenty twenty, around 488 00:21:46,880 --> 00:21:49,600 Speaker 6: how do you digitize those notes? Hey do you e closing? 489 00:21:49,880 --> 00:21:51,240 Speaker 6: Then it's a matter off. You've got to get thirty 490 00:21:51,280 --> 00:21:53,400 Speaker 6: eight hundred counties to accept it, all the title companies 491 00:21:53,400 --> 00:21:55,560 Speaker 6: have to accept it, et cetera. So the big network 492 00:21:55,640 --> 00:21:57,719 Speaker 6: problem again, you're probably hearing a theme of it's just 493 00:21:57,800 --> 00:22:00,800 Speaker 6: like organizational inertia. Yeah, but you've very much can say 494 00:22:00,800 --> 00:22:04,320 Speaker 6: that home financing is still powered by FedEx because pretty 495 00:22:04,359 --> 00:22:06,159 Speaker 6: much everyone, I mean, we have a field in our 496 00:22:06,160 --> 00:22:08,000 Speaker 6: system where people are like, we need a FedEx tracking 497 00:22:08,040 --> 00:22:10,560 Speaker 6: number field for the note, like not even kidding. That 498 00:22:10,600 --> 00:22:12,000 Speaker 6: could be really cool if you could integrate that to 499 00:22:12,040 --> 00:22:14,399 Speaker 6: FedEx to like automate like looking at the tracking. And 500 00:22:14,400 --> 00:22:16,240 Speaker 6: it's like, of course you can do that technology wise, 501 00:22:16,359 --> 00:22:18,320 Speaker 6: but sometimes you ask yourself, like, what problem are we 502 00:22:18,359 --> 00:22:20,840 Speaker 6: really solving here? Guys, like we should just move it 503 00:22:20,960 --> 00:22:21,440 Speaker 6: to the cloud. 504 00:22:22,320 --> 00:22:25,400 Speaker 4: So I think the last time I applied for a mortgage, 505 00:22:25,520 --> 00:22:29,440 Speaker 4: it's late twenty seventeen, and I just remember like documents 506 00:22:29,440 --> 00:22:32,320 Speaker 4: and documents and checkboxes, and I didn't read any of 507 00:22:32,359 --> 00:22:34,840 Speaker 4: those documents, had just signed the checkbox, and I assumed 508 00:22:34,840 --> 00:22:37,399 Speaker 4: it is all okay, Well were all those checkboxes I 509 00:22:37,520 --> 00:22:40,199 Speaker 4: was or signature boxes that I was putting a digital 510 00:22:40,200 --> 00:22:40,960 Speaker 4: signature into. 511 00:22:42,000 --> 00:22:45,920 Speaker 6: A lot of those signature boxes are basically people disclosing 512 00:22:45,960 --> 00:22:47,560 Speaker 6: your rights, So very similar. 513 00:22:47,200 --> 00:22:49,440 Speaker 3: To yeah, yea disclosure. 514 00:22:49,880 --> 00:22:52,240 Speaker 6: Yeah, It's like you sign something that gives them authorization 515 00:22:52,280 --> 00:22:54,159 Speaker 6: to pull your credit in many cases, and then you 516 00:22:54,240 --> 00:22:56,520 Speaker 6: sign something that you know it's like, hey, here's your 517 00:22:56,520 --> 00:22:58,320 Speaker 6: credit score and here's how it was calculated. In the 518 00:22:58,359 --> 00:23:01,320 Speaker 6: state of California, you might something which is like if 519 00:23:01,359 --> 00:23:04,000 Speaker 6: you are getting an FAHA loan with lead paint, which 520 00:23:04,000 --> 00:23:06,400 Speaker 6: I hope you didn't, there's a disclosure that says, hey, 521 00:23:06,520 --> 00:23:08,960 Speaker 6: like we determined that the house has some old lead paint, 522 00:23:09,000 --> 00:23:10,919 Speaker 6: Like sign here to acknowledge that we disclosed that to you. 523 00:23:11,480 --> 00:23:13,920 Speaker 6: And then the other thing that happens is because each 524 00:23:13,960 --> 00:23:16,600 Speaker 6: of these disclosures are legally mandated and it's really hard 525 00:23:16,640 --> 00:23:19,159 Speaker 6: to make sure that you signed all of them in 526 00:23:19,200 --> 00:23:21,240 Speaker 6: the one go, they'll just say, hey, when you first 527 00:23:21,240 --> 00:23:23,120 Speaker 6: apply for the loan and we disclose through the terms, 528 00:23:23,160 --> 00:23:25,160 Speaker 6: we're going to stick all of those disclosures in there, 529 00:23:25,440 --> 00:23:26,880 Speaker 6: and then when you get to the closing table, we're 530 00:23:26,880 --> 00:23:28,320 Speaker 6: going to put them in there again, just so we've 531 00:23:28,359 --> 00:23:30,159 Speaker 6: belt and suspender that you know you've signed it. Like 532 00:23:30,160 --> 00:23:32,640 Speaker 6: you're at the closing table, you're not going to walk 533 00:23:32,640 --> 00:23:34,200 Speaker 6: away now, Like, let's just make you sign it one 534 00:23:34,200 --> 00:23:36,359 Speaker 6: more time. So, probably if I had the guests, you 535 00:23:36,440 --> 00:23:38,840 Speaker 6: got fifty or something disclosures, depending on the state you 536 00:23:38,840 --> 00:23:40,520 Speaker 6: were in, there's a whole bunch of you know, state 537 00:23:40,800 --> 00:23:43,200 Speaker 6: some states are more owners than others. We probably also 538 00:23:43,240 --> 00:23:45,160 Speaker 6: signed each one and average like two and a half times. 539 00:23:45,200 --> 00:23:47,880 Speaker 4: I did just real quickly, how different was that experience 540 00:23:47,920 --> 00:23:50,520 Speaker 4: for me in twenty seventeen then it would have been 541 00:23:50,680 --> 00:23:53,440 Speaker 4: in two thousand and seven, before the mortgage crisis. 542 00:23:53,880 --> 00:23:55,560 Speaker 6: Well, in two thousand and seven, you can imagine there 543 00:23:55,560 --> 00:23:58,560 Speaker 6: were way fewer disclosures. Actually, in twenty fifteen they passed 544 00:23:58,600 --> 00:24:01,439 Speaker 6: what's called TRID or tie the rest but integrated disclosures, 545 00:24:01,760 --> 00:24:03,679 Speaker 6: which is actually the main reason you can't have a 546 00:24:03,720 --> 00:24:07,160 Speaker 6: one click coage today. So TRID puts a minimum timeline 547 00:24:07,160 --> 00:24:09,159 Speaker 6: as well, where you have to give people, you know, 548 00:24:09,200 --> 00:24:11,879 Speaker 6: within three days of getting what's called a full application, 549 00:24:12,200 --> 00:24:13,720 Speaker 6: you have to provide them an estimate of all the 550 00:24:13,760 --> 00:24:16,679 Speaker 6: fees that like really clearly in a very standardized format, 551 00:24:16,680 --> 00:24:19,480 Speaker 6: discloses all the fees that comes with a bunch of disclosures. 552 00:24:19,720 --> 00:24:21,600 Speaker 6: And then you have to give the bar or seven 553 00:24:21,640 --> 00:24:24,720 Speaker 6: business days from giving them that loan estimate to close 554 00:24:24,760 --> 00:24:27,199 Speaker 6: the loan. And so we actually talk in mortgage now 555 00:24:27,240 --> 00:24:29,080 Speaker 6: a lot about the ten day mortgage, because you actually 556 00:24:29,080 --> 00:24:31,800 Speaker 6: can't have a one click mortgage purely by virtue of 557 00:24:31,840 --> 00:24:34,120 Speaker 6: the fact you need that seven day waiting period. There's 558 00:24:34,160 --> 00:24:36,240 Speaker 6: some other timelines and there even if you got rid 559 00:24:36,240 --> 00:24:38,000 Speaker 6: of that seven day waiting period, you'd still have to 560 00:24:38,600 --> 00:24:40,840 Speaker 6: remove a whole bunch of other regulatory timelines to really 561 00:24:40,840 --> 00:24:42,840 Speaker 6: get it down to one day. But in twenty fifteen 562 00:24:42,840 --> 00:24:44,800 Speaker 6: they released this new regulation which I would say made 563 00:24:44,800 --> 00:24:47,320 Speaker 6: it a lot more onerous, a lot more documents to sign, 564 00:24:47,640 --> 00:24:50,119 Speaker 6: and I mean it's good, right, Like pre twenty fifteen, 565 00:24:50,160 --> 00:24:52,640 Speaker 6: people were getting loans and they were getting bait and switched, 566 00:24:52,680 --> 00:24:55,760 Speaker 6: and you know, people were having new fees pop up 567 00:24:55,760 --> 00:24:57,320 Speaker 6: that they didn't know about. And now all that stuff 568 00:24:57,359 --> 00:24:59,560 Speaker 6: is really strictly regulated, but it definitely as to the 569 00:24:59,600 --> 00:25:00,359 Speaker 6: paperwork party. 570 00:25:15,760 --> 00:25:18,920 Speaker 2: So how did Blend actually try to solve all of this? 571 00:25:19,160 --> 00:25:21,840 Speaker 2: Because when I listen to you talk about all these 572 00:25:22,000 --> 00:25:25,040 Speaker 2: sort of challenges in the mortgage market, it just sounds 573 00:25:25,080 --> 00:25:29,280 Speaker 2: like an unsolvable kind of spider web of requirements. 574 00:25:29,800 --> 00:25:32,479 Speaker 6: Yeah, it certainly is very challenging. I wouldn't call it. 575 00:25:32,560 --> 00:25:35,560 Speaker 6: You know, nothing is really unsolvable except the regulatory timeline 576 00:25:35,640 --> 00:25:37,919 Speaker 6: is going to be what it is. But for a 577 00:25:37,920 --> 00:25:39,639 Speaker 6: lot of it was, hey, can you really get to 578 00:25:39,760 --> 00:25:41,760 Speaker 6: a one click and tell the bar or that they're 579 00:25:41,800 --> 00:25:43,760 Speaker 6: clear to close? And what that means is we've fully 580 00:25:43,840 --> 00:25:46,280 Speaker 6: underwritten everything. We know that you're going to close. The 581 00:25:46,320 --> 00:25:48,359 Speaker 6: only thing we're really waiting on is the compliance clock. 582 00:25:48,960 --> 00:25:51,040 Speaker 6: And so you kind of split it up into the 583 00:25:51,119 --> 00:25:54,000 Speaker 6: various things that get underwritten in the loan. So in property, 584 00:25:54,040 --> 00:25:56,600 Speaker 6: for example, the most clear thing you have to do 585 00:25:56,640 --> 00:25:58,960 Speaker 6: in order to say I can instantly underwrite your property 586 00:25:59,160 --> 00:26:00,840 Speaker 6: is they have to be able to get no appraisal 587 00:26:01,119 --> 00:26:04,000 Speaker 6: and get instant title. And today, title insurance is this 588 00:26:04,040 --> 00:26:05,520 Speaker 6: whole other thing that I'm sure you could do ten 589 00:26:05,560 --> 00:26:09,240 Speaker 6: episodes on. Some people have asked, which everyone, yes, I 590 00:26:09,240 --> 00:26:11,960 Speaker 6: would say every two years. Some Silicon Valley person tweets 591 00:26:11,960 --> 00:26:14,160 Speaker 6: that like, title insurance is a racket and someone should 592 00:26:14,200 --> 00:26:15,920 Speaker 6: go take it out, and I always get that tweet 593 00:26:15,960 --> 00:26:19,560 Speaker 6: texted to me like ten times. But there's this complexity around. Well, 594 00:26:19,640 --> 00:26:21,520 Speaker 6: the problem with title insurance is actually someone does have 595 00:26:21,560 --> 00:26:23,360 Speaker 6: to go to the county office still in a bunch 596 00:26:23,359 --> 00:26:25,800 Speaker 6: of counties, and go downstairs into the basement of the 597 00:26:25,800 --> 00:26:27,760 Speaker 6: courthouse and get the key and unlock it and go 598 00:26:27,800 --> 00:26:29,520 Speaker 6: like look up the records for that house or something 599 00:26:29,560 --> 00:26:31,719 Speaker 6: like that. So you have to figure out how you're 600 00:26:31,760 --> 00:26:33,720 Speaker 6: going to make title insurance instant, which there are a 601 00:26:33,720 --> 00:26:36,200 Speaker 6: whole bunch of startups that have worked on are working 602 00:26:36,240 --> 00:26:39,000 Speaker 6: on have had some success in digitizing that process. In 603 00:26:39,040 --> 00:26:41,680 Speaker 6: some counties, you've got to make the appraisal instant, which 604 00:26:41,680 --> 00:26:44,560 Speaker 6: basically means you have to get a message from Daniel 605 00:26:44,640 --> 00:26:47,439 Speaker 6: Freddy that for this particular property, they've written alone on 606 00:26:47,480 --> 00:26:49,480 Speaker 6: it recently enough that you don't have to appraise it again. 607 00:26:49,960 --> 00:26:52,199 Speaker 6: And so that's the property side. Those two things you 608 00:26:52,200 --> 00:26:55,200 Speaker 6: can imagine combined already, like you're taking one hundred percent 609 00:26:55,200 --> 00:26:56,880 Speaker 6: of properties in the US, and you're shrinking your hip 610 00:26:56,920 --> 00:26:59,040 Speaker 6: box to like twenty year or twenty five percent or 611 00:26:59,040 --> 00:27:01,200 Speaker 6: something like that, and then you've got to get the borrower. 612 00:27:01,280 --> 00:27:03,040 Speaker 6: And for Blend, a lot of the approach was, well, 613 00:27:03,080 --> 00:27:04,959 Speaker 6: we partner with a lot of big banks, and so 614 00:27:05,080 --> 00:27:07,879 Speaker 6: can you get the banking data directly from those banks 615 00:27:07,960 --> 00:27:10,040 Speaker 6: and use that to either figure out the income or 616 00:27:10,119 --> 00:27:12,040 Speaker 6: use something like the work number to instantly get income. 617 00:27:12,400 --> 00:27:15,399 Speaker 6: You can verify assets and income and the property, and 618 00:27:15,440 --> 00:27:17,320 Speaker 6: then you can of course, pulling credit is the easiest one, 619 00:27:17,320 --> 00:27:19,760 Speaker 6: because we've been able to pull credit digitally for decades 620 00:27:19,760 --> 00:27:21,800 Speaker 6: and decades in this country. If you can kind of 621 00:27:21,880 --> 00:27:24,919 Speaker 6: check all four of those boxes, then you're quite a 622 00:27:24,920 --> 00:27:28,080 Speaker 6: bit further towards a instant clear to close, you're still 623 00:27:28,119 --> 00:27:29,600 Speaker 6: not fully there. There's a bunch of stuff around the 624 00:27:29,600 --> 00:27:31,480 Speaker 6: margins you've got to go and sort out. But it 625 00:27:31,520 --> 00:27:34,080 Speaker 6: really is a matter of blocking and tackling, executing detail 626 00:27:34,119 --> 00:27:36,120 Speaker 6: by detail. And it's like, there are twelve hundred pages 627 00:27:36,160 --> 00:27:38,040 Speaker 6: of rules. I've probably read those twelve hundred pages of 628 00:27:38,040 --> 00:27:40,439 Speaker 6: Fanny rules three or four times, and you've just got 629 00:27:40,480 --> 00:27:42,119 Speaker 6: to systematically tick them off one by one. 630 00:27:42,240 --> 00:27:45,639 Speaker 4: What are you doing now at VESTA that you weren't 631 00:27:45,680 --> 00:27:47,000 Speaker 4: doing and blend. 632 00:27:47,920 --> 00:27:51,040 Speaker 6: Yeah, So a lot of the struggle that we had 633 00:27:51,119 --> 00:27:52,720 Speaker 6: up on was you really, because you own the front 634 00:27:52,800 --> 00:27:55,080 Speaker 6: end of the process, is you could get to fully automated. 635 00:27:55,400 --> 00:27:57,919 Speaker 6: You could pull it all in and be done. The 636 00:27:58,000 --> 00:27:59,960 Speaker 6: problem was, you know, you heard how I talked about 637 00:28:00,000 --> 00:28:02,399 Speaker 6: appraisal title. You kind of shrink the hit box for 638 00:28:02,440 --> 00:28:05,119 Speaker 6: what you can do fully automated. And so what we 639 00:28:05,240 --> 00:28:07,560 Speaker 6: found was that if you fully automated, like let's say 640 00:28:07,560 --> 00:28:09,800 Speaker 6: you really could fully automate one percent of lenders loans, 641 00:28:10,040 --> 00:28:12,320 Speaker 6: that would be great, but they're still you know, spending 642 00:28:12,320 --> 00:28:14,520 Speaker 6: a ton of manual dollars, a ton of you know, 643 00:28:15,200 --> 00:28:18,439 Speaker 6: operational people on ninety nine percent of their loans. And 644 00:28:18,520 --> 00:28:20,760 Speaker 6: the big problem was all the data that you got 645 00:28:20,760 --> 00:28:22,879 Speaker 6: at the front it was really hard to use that 646 00:28:22,880 --> 00:28:25,040 Speaker 6: to drive efficiencies at the back of the process or 647 00:28:25,080 --> 00:28:26,640 Speaker 6: for you know, any of the loans that did have 648 00:28:26,840 --> 00:28:29,359 Speaker 6: even one manual touch, like I ticked through all these rules. 649 00:28:29,640 --> 00:28:31,399 Speaker 6: You can imagine if only ten rules had to be 650 00:28:31,400 --> 00:28:33,000 Speaker 6: done by a human. Well, now it's got to go 651 00:28:33,040 --> 00:28:35,680 Speaker 6: through this manual process. And what it does today is 652 00:28:35,680 --> 00:28:37,800 Speaker 6: it goes through this old manual process where they basically 653 00:28:37,800 --> 00:28:40,400 Speaker 6: have to underwrite the whole loan manually because the system 654 00:28:40,400 --> 00:28:42,800 Speaker 6: doesn't have an understanding of what's already been done. It's 655 00:28:42,840 --> 00:28:45,520 Speaker 6: not you know, task or workflow oriented, and people basically 656 00:28:45,560 --> 00:28:47,600 Speaker 6: have muscle memory. So the underwriter is going to look 657 00:28:47,640 --> 00:28:50,040 Speaker 6: at everything, order the appraisal, whatnot, even if they don't 658 00:28:50,080 --> 00:28:52,960 Speaker 6: have to. And so a lot of what we realized 659 00:28:53,080 --> 00:28:55,040 Speaker 6: was the back end of the process was making it 660 00:28:55,120 --> 00:28:57,680 Speaker 6: really difficult to realize any efficiency from the good work 661 00:28:57,680 --> 00:29:00,000 Speaker 6: you're doing at the front end because the change management 662 00:29:00,000 --> 00:29:02,800 Speaker 6: and organizational inertia of you know, you've got three thousand 663 00:29:02,800 --> 00:29:06,480 Speaker 6: people on your mortgage manufacturing line so to speak, doing 664 00:29:06,480 --> 00:29:09,040 Speaker 6: exactly what they've always done, and the software isn't really 665 00:29:09,120 --> 00:29:11,880 Speaker 6: guiding them to do anything different, Like it's not it's 666 00:29:11,920 --> 00:29:14,000 Speaker 6: not a piece of software like you might be used 667 00:29:14,040 --> 00:29:16,440 Speaker 6: to working in today, like Slack gives you notifications for example. 668 00:29:16,600 --> 00:29:19,800 Speaker 6: It's really almost like a spreadsheet with a different UI 669 00:29:19,840 --> 00:29:21,160 Speaker 6: layer on top of it, and you've got to figure 670 00:29:21,200 --> 00:29:22,960 Speaker 6: out exactly what you're going to do. So a lot 671 00:29:22,960 --> 00:29:24,520 Speaker 6: of it was how do you change the way the 672 00:29:24,560 --> 00:29:27,800 Speaker 6: operation works so that people are doing a lot less. 673 00:29:28,080 --> 00:29:29,760 Speaker 6: And then the other big thing was with the existing 674 00:29:29,800 --> 00:29:33,120 Speaker 6: loan origination systems being so difficult to integrate to that 675 00:29:33,160 --> 00:29:35,200 Speaker 6: was one of the biggest hindrances and actually getting all 676 00:29:35,200 --> 00:29:38,640 Speaker 6: of the data and making that process one click. Was 677 00:29:39,240 --> 00:29:41,680 Speaker 6: that you couldn't actually do all of the jobs that 678 00:29:41,720 --> 00:29:43,360 Speaker 6: needed to be done by that old system, like the 679 00:29:43,400 --> 00:29:46,120 Speaker 6: old system that has all of the integrations I mentioned, 680 00:29:46,120 --> 00:29:48,960 Speaker 6: and they have hundreds of integrations to all these data providers, 681 00:29:49,280 --> 00:29:51,280 Speaker 6: like coordinating the appraisal. So you ended up having to 682 00:29:51,280 --> 00:29:53,760 Speaker 6: build around the old system instead of through the old 683 00:29:53,800 --> 00:29:56,200 Speaker 6: system to achieve a lot of this stuff, and that 684 00:29:56,280 --> 00:29:59,120 Speaker 6: just seemed like so clearly the wrong way to do it. 685 00:29:59,160 --> 00:30:00,960 Speaker 6: Now the downside is have to go and modernize the 686 00:30:00,960 --> 00:30:04,000 Speaker 6: old system. Which is a really hard problem. But by 687 00:30:04,160 --> 00:30:06,400 Speaker 6: kind of modernizing the old system, you unlock a the 688 00:30:06,440 --> 00:30:09,040 Speaker 6: operational efficiency that you actually get from all this data 689 00:30:09,360 --> 00:30:12,320 Speaker 6: and then be a much easier platform for everyone who 690 00:30:12,320 --> 00:30:15,080 Speaker 6: wants to build a front end to get that data 691 00:30:15,200 --> 00:30:18,160 Speaker 6: through your integrations and through your processes that the lender 692 00:30:18,200 --> 00:30:21,280 Speaker 6: already has that exists manually today of having to recreate 693 00:30:21,280 --> 00:30:22,760 Speaker 6: it on the side to try and automate it, if 694 00:30:22,760 --> 00:30:23,360 Speaker 6: that makes sense. 695 00:30:24,360 --> 00:30:27,479 Speaker 2: I have a slightly random question, which is, given that 696 00:30:27,520 --> 00:30:32,280 Speaker 2: we're talking about technicalities, how easy is it to commit 697 00:30:32,360 --> 00:30:37,240 Speaker 2: some sort of mortgage fraud nowadays? Totally random, not out 698 00:30:37,280 --> 00:30:38,320 Speaker 2: of personal interest. 699 00:30:38,800 --> 00:30:41,560 Speaker 6: Yeah, I did listen to your recent episode about government 700 00:30:41,560 --> 00:30:43,560 Speaker 6: Fraudry Joe was the one I think was very interested. 701 00:30:43,680 --> 00:30:46,320 Speaker 3: Yeah, I was the one interring to start doing fraud. 702 00:30:46,680 --> 00:30:50,400 Speaker 6: Yeah. Mortgage fraud, I think is actually quite difficult these days, 703 00:30:50,520 --> 00:30:53,680 Speaker 6: mostly because there are so many human eyeballs that look 704 00:30:53,680 --> 00:30:57,360 Speaker 6: at the loan and so let's take something really simple, 705 00:30:57,400 --> 00:30:59,520 Speaker 6: like you wanted to like doctor a document, like probably 706 00:30:59,520 --> 00:31:02,880 Speaker 6: the most straightforward thing because it's not like mascal fraud. 707 00:31:02,880 --> 00:31:04,400 Speaker 6: It's like somebody is like, I want a mortgage on 708 00:31:04,400 --> 00:31:06,960 Speaker 6: my primary residence. I can't afford it, and I'm just 709 00:31:07,000 --> 00:31:09,479 Speaker 6: going to doctor the documents to make my income look bigger. Well, 710 00:31:09,480 --> 00:31:11,160 Speaker 6: first you have to hope the lender doesn't check some 711 00:31:11,280 --> 00:31:13,239 Speaker 6: third party verified data source, or they don't reach out 712 00:31:13,280 --> 00:31:15,400 Speaker 6: to the employer, which they often do. And then you 713 00:31:15,440 --> 00:31:17,120 Speaker 6: have to hope that like your document makes it through 714 00:31:17,160 --> 00:31:19,160 Speaker 6: the processor are looking at it, and the underwriter looking 715 00:31:19,160 --> 00:31:20,880 Speaker 6: at it, and the closer looking at it, and the 716 00:31:20,960 --> 00:31:23,480 Speaker 6: underwriters especially they're looking for things that don't add up. 717 00:31:23,840 --> 00:31:26,320 Speaker 6: And so I would say mortgage fraud is probably really 718 00:31:26,680 --> 00:31:29,840 Speaker 6: pretty too, very difficult to actually accomplish today. It sounds 719 00:31:29,840 --> 00:31:31,880 Speaker 6: a lot harder to achieve than, like, you know, figuring 720 00:31:31,920 --> 00:31:35,200 Speaker 6: out how to get some Medicare dollars. Yeah, so it's 721 00:31:35,200 --> 00:31:39,160 Speaker 6: probably not worth squeeze. Now, we'll like generative AI make 722 00:31:39,200 --> 00:31:41,240 Speaker 6: it way easier to make fake profiles and all that stuff. 723 00:31:41,240 --> 00:31:43,560 Speaker 6: Maybe that's something that I think lots of people worry about, 724 00:31:43,920 --> 00:31:46,280 Speaker 6: But today I would say it's definitely the mortage industry 725 00:31:46,280 --> 00:31:48,720 Speaker 6: has done a pretty good job of doing that, and 726 00:31:48,720 --> 00:31:50,360 Speaker 6: I'd say the regulators have done a good job of 727 00:31:50,400 --> 00:31:52,720 Speaker 6: making it really hard, just given everything that happened two 728 00:31:52,760 --> 00:31:53,320 Speaker 6: decades ago. 729 00:31:53,840 --> 00:31:55,480 Speaker 3: Back to the question of refise. 730 00:31:55,560 --> 00:31:59,360 Speaker 4: So you mentioned that theoretically, if you're a homeowner and 731 00:31:59,400 --> 00:32:01,840 Speaker 4: you're in the money on your mortgage, that is to say, 732 00:32:01,920 --> 00:32:04,080 Speaker 4: where it would make economic sense for you to refi. 733 00:32:04,240 --> 00:32:06,400 Speaker 4: You might get an email or something it's like, hey, 734 00:32:06,440 --> 00:32:10,160 Speaker 4: you should refi and you can save this much. But it's, 735 00:32:10,320 --> 00:32:12,280 Speaker 4: as we're talking about, it's gonna be a lot of 736 00:32:12,440 --> 00:32:16,720 Speaker 4: paperwork and all this stuff. After our episode came out 737 00:32:16,880 --> 00:32:20,480 Speaker 4: several weeks ago, someone on Twitter, they said, why can't 738 00:32:20,480 --> 00:32:24,240 Speaker 4: we have a mortgage product that you pay a higher 739 00:32:24,280 --> 00:32:28,160 Speaker 4: premium upfront, but it's a floating rate mortgage that only 740 00:32:28,200 --> 00:32:32,720 Speaker 4: resets downward. In other words, basically, if rates drop lower, 741 00:32:32,840 --> 00:32:36,280 Speaker 4: your mortgage mechanically drops with it. And again, obviously, if 742 00:32:36,280 --> 00:32:39,080 Speaker 4: you're going to have that, you theoretically that's a more 743 00:32:39,200 --> 00:32:42,640 Speaker 4: valuable option and you pay some premium upfront, but then 744 00:32:42,680 --> 00:32:45,600 Speaker 4: in theory, you save all of this effort and time 745 00:32:45,720 --> 00:32:48,440 Speaker 4: and document checking and human hours that go into this. 746 00:32:48,880 --> 00:32:51,560 Speaker 4: In your mind, does that seem like a plausible financial 747 00:32:51,600 --> 00:32:52,560 Speaker 4: product that could exist. 748 00:32:52,880 --> 00:32:55,440 Speaker 6: Seems like a totally reasonable financial product. I think that 749 00:32:56,280 --> 00:32:58,160 Speaker 6: there may even be somebody doing it. And like the 750 00:32:58,160 --> 00:33:01,280 Speaker 6: private label securities market, there is a market basically hedge 751 00:33:01,280 --> 00:33:04,400 Speaker 6: funds that will like underwrite non qms what they're called 752 00:33:04,440 --> 00:33:07,280 Speaker 6: mortgage products and offer those to lenders, and lenders can 753 00:33:07,280 --> 00:33:09,680 Speaker 6: originate them. One thing I will say is it seems 754 00:33:09,760 --> 00:33:12,560 Speaker 6: unlikely to come from the GSUS just because so much 755 00:33:12,560 --> 00:33:15,440 Speaker 6: of the gs's mission these days is affordability and democratizing 756 00:33:15,440 --> 00:33:17,960 Speaker 6: home ownership. And I can't really think of a marginal 757 00:33:17,960 --> 00:33:20,000 Speaker 6: person that that products would get into a home. 758 00:33:20,240 --> 00:33:22,960 Speaker 4: Right, So even if it makes sense, that's just now 759 00:33:22,960 --> 00:33:23,720 Speaker 4: we'll move the dial. 760 00:33:24,360 --> 00:33:26,280 Speaker 6: Yeah, I think it makes sense from a you know, 761 00:33:26,320 --> 00:33:29,720 Speaker 6: like single person financial instrument perspective, like I would love 762 00:33:29,720 --> 00:33:31,760 Speaker 6: to have one of those, for example, But I think 763 00:33:31,800 --> 00:33:34,920 Speaker 6: that from the kind of stated policy goals of the 764 00:33:34,960 --> 00:33:37,520 Speaker 6: biggest investors in the market, it's just not really something 765 00:33:37,560 --> 00:33:39,320 Speaker 6: that oligns with their policy goals, and so I can't 766 00:33:39,320 --> 00:33:41,480 Speaker 6: see that being a big area of where we're going 767 00:33:41,520 --> 00:33:43,400 Speaker 6: to see a bunch of those in a decade. 768 00:33:43,680 --> 00:33:47,520 Speaker 2: So a lot of mortgages get bundled together into mortgage 769 00:33:47,520 --> 00:33:51,840 Speaker 2: backed securities. I'm curious, like how much of that granular 770 00:33:52,000 --> 00:33:56,240 Speaker 2: detail about pay and you know, lead paint in the 771 00:33:56,320 --> 00:34:00,440 Speaker 2: house and things like that gets ported over to the 772 00:34:00,480 --> 00:34:02,440 Speaker 2: securitization aspect of it. 773 00:34:02,720 --> 00:34:04,600 Speaker 6: So it's really not a lot. I actually I have 774 00:34:04,640 --> 00:34:06,600 Speaker 6: capital markets people reaching out to me all the time, 775 00:34:06,640 --> 00:34:09,080 Speaker 6: being like, if you have a modern loan origination system, 776 00:34:09,239 --> 00:34:11,439 Speaker 6: you can solve my problem if I can't actually get 777 00:34:11,480 --> 00:34:13,719 Speaker 6: any of the data or much of the data that 778 00:34:13,880 --> 00:34:17,480 Speaker 6: you underlines these instruments when I am going and securitizing 779 00:34:17,480 --> 00:34:20,080 Speaker 6: them or trading them or whatnot. But generally comes out 780 00:34:20,200 --> 00:34:24,280 Speaker 6: as much banking technology still is today is you export 781 00:34:24,280 --> 00:34:26,840 Speaker 6: a big CSV of some of the data fields. You 782 00:34:26,880 --> 00:34:28,520 Speaker 6: take a bunch of docks and you send them off, 783 00:34:28,880 --> 00:34:31,440 Speaker 6: and then that CSV, which you know, they fancily call 784 00:34:31,480 --> 00:34:34,240 Speaker 6: it tape, but really it's a spreadsheet, just gets ingested. 785 00:34:34,239 --> 00:34:36,560 Speaker 6: And now you've taken a process that had three thousand 786 00:34:36,560 --> 00:34:39,120 Speaker 6: fields and hundreds of pages of docks, and you've boiled 787 00:34:39,120 --> 00:34:41,480 Speaker 6: that down into like fifty or one hundred fields that 788 00:34:41,520 --> 00:34:44,080 Speaker 6: describe the mortgage, which maybe is for the best, Like 789 00:34:44,120 --> 00:34:46,880 Speaker 6: I'm not really sure that people buying mbs should be 790 00:34:46,880 --> 00:34:49,719 Speaker 6: thinking about, you know, the specific credit profile of the 791 00:34:49,760 --> 00:34:52,160 Speaker 6: thousand different mortgages that are chopped in there and put 792 00:34:52,200 --> 00:34:55,000 Speaker 6: in and so it's nice that there's some standardization, but 793 00:34:55,040 --> 00:34:56,759 Speaker 6: it's definitely very lossy, and I will tell you it 794 00:34:56,840 --> 00:34:59,040 Speaker 6: is something that mortgage traders complain to me about a lot. 795 00:35:00,360 --> 00:35:04,080 Speaker 4: So what's realistic. It doesn't sound like you'd ever get 796 00:35:04,120 --> 00:35:07,760 Speaker 4: like true one click, because it's you know, at a minimum, 797 00:35:07,800 --> 00:35:10,120 Speaker 4: you're probably going to have to tell the front end 798 00:35:10,200 --> 00:35:10,720 Speaker 4: who your. 799 00:35:10,560 --> 00:35:13,120 Speaker 3: Payroll is and who your bank is, and a few 800 00:35:13,120 --> 00:35:13,680 Speaker 3: other things. 801 00:35:14,000 --> 00:35:17,719 Speaker 4: What is a plausible version of if you know there's 802 00:35:17,840 --> 00:35:22,080 Speaker 4: continual coordination among different banks, if Fanny and Freddy continue 803 00:35:22,120 --> 00:35:25,480 Speaker 4: to update their technology so that you know, it's a 804 00:35:25,480 --> 00:35:27,600 Speaker 4: little easier, what could it look like if I were, 805 00:35:27,680 --> 00:35:29,840 Speaker 4: say you're say, in ten years. 806 00:35:29,600 --> 00:35:31,200 Speaker 3: I'm applying for a mortgage again. 807 00:35:31,520 --> 00:35:34,880 Speaker 6: I think it's very reasonable to strive for a world 808 00:35:35,000 --> 00:35:37,479 Speaker 6: where it is, you know, to your point, as close 809 00:35:37,520 --> 00:35:39,759 Speaker 6: to one click as possible on the very front end. 810 00:35:40,160 --> 00:35:43,000 Speaker 6: Maybe you're talking about like a ten minute application max, 811 00:35:43,000 --> 00:35:46,560 Speaker 6: where you connect some accounts once that's done. I think 812 00:35:46,600 --> 00:35:49,680 Speaker 6: that what you should get instantly is one of basically 813 00:35:50,000 --> 00:35:53,040 Speaker 6: three decisions. Hey, you are definitely clear to close. You're 814 00:35:53,040 --> 00:35:54,920 Speaker 6: going to get your disclosures, and then we're just going 815 00:35:55,000 --> 00:35:57,520 Speaker 6: to wait ten days and we'll close you. Option two 816 00:35:57,600 --> 00:36:00,640 Speaker 6: is hey, you as a barwer are definitely clear to close, 817 00:36:00,760 --> 00:36:03,200 Speaker 6: but we need some additional information on the property. So 818 00:36:03,360 --> 00:36:05,120 Speaker 6: you're going to wait ten to fourteen days, you're going 819 00:36:05,160 --> 00:36:06,520 Speaker 6: to pay for the appraisal, and we're going to close 820 00:36:06,560 --> 00:36:10,120 Speaker 6: you or three is basically unfortunately, you know, we're not 821 00:36:10,200 --> 00:36:12,080 Speaker 6: able to close you. Here some things you can do 822 00:36:12,120 --> 00:36:14,880 Speaker 6: to improve your stance. I think that property is probably 823 00:36:14,920 --> 00:36:16,799 Speaker 6: going to be the thing that even if you ask 824 00:36:16,840 --> 00:36:19,000 Speaker 6: me ten years from now, it's like it's really a 825 00:36:19,080 --> 00:36:21,200 Speaker 6: question of there will always be those corner cases. It 826 00:36:21,200 --> 00:36:22,960 Speaker 6: feels like where the gs are going to want to 827 00:36:22,960 --> 00:36:25,879 Speaker 6: see an appraisal unless they're the big credit profile change 828 00:36:25,920 --> 00:36:27,600 Speaker 6: or if they get privatized. You know, there's all sorts 829 00:36:27,600 --> 00:36:29,880 Speaker 6: of things that can happen in ten years. But I 830 00:36:29,920 --> 00:36:33,399 Speaker 6: think those three outcomes being ten minutes away fingertips wwise 831 00:36:33,400 --> 00:36:35,520 Speaker 6: from the bar when you close in ten days is 832 00:36:35,719 --> 00:36:38,200 Speaker 6: very much attainable, and it's really what everyone in the 833 00:36:38,200 --> 00:36:40,359 Speaker 6: industry is and ought to be working towards. 834 00:36:41,040 --> 00:36:45,240 Speaker 2: Does blockchain solve this? And I mean that's somewhat serious. 835 00:36:45,400 --> 00:36:46,200 Speaker 3: It's a good question. 836 00:36:46,160 --> 00:36:48,720 Speaker 2: Because, like I've often thought like one of the few 837 00:36:49,120 --> 00:36:53,080 Speaker 2: real world applications of blockchain technology could be in the 838 00:36:53,120 --> 00:36:57,200 Speaker 2: mortgage assignation space where you have that sort of chain 839 00:36:57,239 --> 00:37:01,920 Speaker 2: of title moving around constantly. But also I'm thinking, like 840 00:37:02,400 --> 00:37:05,120 Speaker 2: from a wallet perspective, if you could have like a 841 00:37:05,160 --> 00:37:08,640 Speaker 2: personal profile that carried with it you know, your pay 842 00:37:08,800 --> 00:37:12,080 Speaker 2: and yeah, how much your worth and et cetera, you 843 00:37:12,120 --> 00:37:13,040 Speaker 2: could use that too. 844 00:37:14,080 --> 00:37:16,600 Speaker 6: Yes, So those are two very interesting use cases on 845 00:37:16,680 --> 00:37:19,000 Speaker 6: the wallet perspective. The way that I think about blockchain 846 00:37:19,000 --> 00:37:21,680 Speaker 6: helping here is almost lets you build like a more 847 00:37:21,800 --> 00:37:26,280 Speaker 6: encompassing decentralized credit bureau. And decentralized is actually really important 848 00:37:26,320 --> 00:37:28,719 Speaker 6: because I don't think the banks are super excited about 849 00:37:28,760 --> 00:37:31,160 Speaker 6: the idea of helping build like a fourth credit bureau, 850 00:37:31,360 --> 00:37:33,439 Speaker 6: like they built three and now they pay the three 851 00:37:33,480 --> 00:37:36,319 Speaker 6: for their own data, which I think is a little 852 00:37:36,320 --> 00:37:38,000 Speaker 6: bit difficult for them. And then you know, the regulators 853 00:37:38,040 --> 00:37:40,479 Speaker 6: are not super excited about the centralized credit bureaus, et cetera. 854 00:37:40,800 --> 00:37:42,799 Speaker 6: And so I think the idea that each consumer could 855 00:37:42,800 --> 00:37:45,080 Speaker 6: have their own key that unlocks, you know, access to 856 00:37:45,120 --> 00:37:47,360 Speaker 6: all of their data on this decentralized credit bureau that 857 00:37:47,400 --> 00:37:50,040 Speaker 6: all the payroll providers and financial institutions, et cetera are 858 00:37:50,040 --> 00:37:52,880 Speaker 6: writing to, is very much an idea that has legs. 859 00:37:53,239 --> 00:37:55,319 Speaker 6: It's really hard to implement for you know, a lot 860 00:37:55,360 --> 00:37:58,520 Speaker 6: of similar organizational inertial reasons. But I do think that 861 00:37:58,600 --> 00:38:00,360 Speaker 6: is a real use case for blockchain, be it solves 862 00:38:00,360 --> 00:38:02,440 Speaker 6: the incentives problem where people are basically like, I don't 863 00:38:02,480 --> 00:38:04,560 Speaker 6: want there to be one middleman with every consumer in 864 00:38:04,560 --> 00:38:07,239 Speaker 6: America's financial data, and so blockchain, let's you kind of 865 00:38:07,239 --> 00:38:09,399 Speaker 6: like decentralize and split that up. So I think there's 866 00:38:09,400 --> 00:38:12,040 Speaker 6: some really interesting avenues there that people can go down, 867 00:38:12,200 --> 00:38:14,000 Speaker 6: and there are some companies I think actually looking at 868 00:38:14,040 --> 00:38:16,680 Speaker 6: that on the title front, what I usually tell people 869 00:38:16,719 --> 00:38:19,680 Speaker 6: on could title beyond blockchain? Absolutely? Is it an interesting 870 00:38:19,760 --> 00:38:22,640 Speaker 6: use case? Absolutely? The hard part of digitizing title is 871 00:38:22,640 --> 00:38:25,000 Speaker 6: getting thirty eight hundred counties to even move to like 872 00:38:25,280 --> 00:38:28,760 Speaker 6: putting capturing the records digitally and not in the courthouse basement. 873 00:38:29,120 --> 00:38:32,240 Speaker 6: Getting thirty hundred counties to move to blockchain seems further 874 00:38:32,280 --> 00:38:33,680 Speaker 6: away than that, not closer. 875 00:38:34,200 --> 00:38:36,080 Speaker 4: I don't know if I've ever mentioned it on the 876 00:38:36,480 --> 00:38:39,040 Speaker 4: show before. I once had a gig right after college 877 00:38:39,680 --> 00:38:43,000 Speaker 4: in which there was some company out in California doing 878 00:38:43,040 --> 00:38:45,920 Speaker 4: some of his bestest lawsuits, and they needed names of 879 00:38:45,960 --> 00:38:49,120 Speaker 4: all these people who had been partied to some suit. 880 00:38:49,160 --> 00:38:51,720 Speaker 4: I don't they're maybe putting it together a database for lawyers, 881 00:38:51,960 --> 00:38:54,000 Speaker 4: and part of my job was to go to various 882 00:38:54,040 --> 00:38:58,160 Speaker 4: county courthouses all around rural Texas in central Texas and 883 00:38:58,239 --> 00:39:00,760 Speaker 4: go to the basement and just literally pull out files 884 00:39:00,800 --> 00:39:02,560 Speaker 4: and ask for names. I'm just going to ask one 885 00:39:02,600 --> 00:39:05,840 Speaker 4: last question since Tracy hit one tech buzzword, which is 886 00:39:06,160 --> 00:39:10,719 Speaker 4: blockchain generative AI. Whether it's in the field of scanning 887 00:39:10,800 --> 00:39:15,360 Speaker 4: documents or understanding documents quickly in your work right now, 888 00:39:15,840 --> 00:39:18,480 Speaker 4: is there a substantive use that you're getting out of 889 00:39:18,480 --> 00:39:19,239 Speaker 4: this technology. 890 00:39:19,880 --> 00:39:22,480 Speaker 6: Yes, it is definitely to your point, it's scanning and 891 00:39:22,520 --> 00:39:25,319 Speaker 6: understanding documents, and so you can think of mortgage the 892 00:39:25,360 --> 00:39:26,839 Speaker 6: way that I think of it high level is it's 893 00:39:26,840 --> 00:39:28,279 Speaker 6: a whole bunch of data and it's a bunch of rules, 894 00:39:28,320 --> 00:39:31,080 Speaker 6: and the rules are well defined by investors, the government, etc. 895 00:39:31,640 --> 00:39:33,560 Speaker 6: And the data is just data, and so data and 896 00:39:33,640 --> 00:39:35,920 Speaker 6: rules to you know, a lot of this conversation should 897 00:39:35,920 --> 00:39:38,480 Speaker 6: be a one click experience, like we've known for decades 898 00:39:38,520 --> 00:39:40,440 Speaker 6: how to run rules on data. And a lot of 899 00:39:40,480 --> 00:39:42,480 Speaker 6: the problems come about because the rules are written in 900 00:39:42,480 --> 00:39:44,399 Speaker 6: the twelve hundred pah PDF and there's a little gray 901 00:39:44,440 --> 00:39:46,880 Speaker 6: and someone has to learn them, and the data in 902 00:39:46,920 --> 00:39:48,520 Speaker 6: a bunch of documents and a bunch of you know, 903 00:39:48,560 --> 00:39:51,200 Speaker 6: disparate places from the borrower, and so it's not structured. 904 00:39:51,480 --> 00:39:53,160 Speaker 6: And so if you can bring structure to the data 905 00:39:53,160 --> 00:39:55,680 Speaker 6: and structure to the rules, both of which generative AI 906 00:39:55,800 --> 00:39:57,480 Speaker 6: is really good at. Right. It can read the Fannia 907 00:39:57,600 --> 00:39:59,880 Speaker 6: Selling Guide and turn that into code rules. It can 908 00:40:00,040 --> 00:40:02,440 Speaker 6: read a document and turn that into data points. If 909 00:40:02,480 --> 00:40:04,680 Speaker 6: you can use generitive AI to structure those two things, 910 00:40:05,000 --> 00:40:07,600 Speaker 6: then you still have structured data, structured rules, and that 911 00:40:07,600 --> 00:40:09,520 Speaker 6: they're you know, whatever rules the GSE said, So you 912 00:40:09,520 --> 00:40:12,000 Speaker 6: don't have these compliance things with ohs AI underwriting below. 913 00:40:12,520 --> 00:40:14,759 Speaker 6: But we're seeing a lot of really promising results taking 914 00:40:14,760 --> 00:40:17,359 Speaker 6: the most cutting edge large language models and applying them 915 00:40:17,400 --> 00:40:19,719 Speaker 6: to these documents, both to write the rules for us 916 00:40:19,760 --> 00:40:21,680 Speaker 6: and to lift the data off the documents. 917 00:40:22,080 --> 00:40:24,920 Speaker 2: All right, Mike, you thank you so much for coming 918 00:40:24,920 --> 00:40:27,960 Speaker 2: on all lots and explaining to us why we can't 919 00:40:28,000 --> 00:40:29,080 Speaker 2: have a nice thing. 920 00:40:30,160 --> 00:40:31,080 Speaker 6: Yeah, thanks for having me. 921 00:40:31,840 --> 00:40:32,480 Speaker 3: That was amazing. 922 00:40:32,520 --> 00:40:34,480 Speaker 4: Mike, you're the perfect guest. Thank you so much for 923 00:40:34,480 --> 00:40:34,840 Speaker 4: coming on. 924 00:40:35,360 --> 00:40:37,400 Speaker 6: Yeah, that was fun. Thanks for having me. 925 00:40:49,680 --> 00:40:50,799 Speaker 2: Joe, that was really fun. 926 00:40:51,400 --> 00:40:52,279 Speaker 3: That was really fun. 927 00:40:52,320 --> 00:40:55,759 Speaker 4: I thought Mike was exceptionally clear at explaining how all 928 00:40:55,880 --> 00:40:59,200 Speaker 4: this works. And although it's still annoying the process of 929 00:40:59,200 --> 00:41:01,919 Speaker 4: getting more and lots of documents that I didn't read 930 00:41:02,000 --> 00:41:04,800 Speaker 4: and attached my signature to, Like, I guess I understand 931 00:41:04,800 --> 00:41:05,359 Speaker 4: a little bit more. 932 00:41:05,360 --> 00:41:08,279 Speaker 2: Why now, do you remember after the financial crisis there 933 00:41:08,280 --> 00:41:12,440 Speaker 2: were all these problems with loan documentation and I remember, 934 00:41:12,560 --> 00:41:16,800 Speaker 2: like there's a big thing about assigning mortgages in blank 935 00:41:16,960 --> 00:41:20,160 Speaker 2: that all turned into like court cases. Yeah, I kind 936 00:41:20,160 --> 00:41:21,080 Speaker 2: of wonder, like. 937 00:41:21,000 --> 00:41:23,560 Speaker 4: We did a great episode on that with David Yeah, 938 00:41:23,719 --> 00:41:26,960 Speaker 4: David Dian of the American Prospect, Like in twenty fifteen. 939 00:41:27,000 --> 00:41:32,720 Speaker 4: What was the name of his book, David chain of Title. Oh, yeah, 940 00:41:32,760 --> 00:41:35,359 Speaker 4: we did an episode with David Dan chain of Title. 941 00:41:35,400 --> 00:41:37,839 Speaker 4: At how crazy that was. It was just the state 942 00:41:37,960 --> 00:41:41,920 Speaker 4: of disarray in documentation after the mortgage crisis. But you 943 00:41:42,040 --> 00:41:44,600 Speaker 4: understand why it is when you still have, you know, 944 00:41:44,719 --> 00:41:47,440 Speaker 4: so much at the county level and the county level, 945 00:41:47,760 --> 00:41:51,480 Speaker 4: I guess for obvious reasons, not feeling any particular pressure 946 00:41:51,600 --> 00:41:53,719 Speaker 4: to update or digitize. 947 00:41:53,200 --> 00:41:55,640 Speaker 3: Or modernize or coordinate all of their systems. 948 00:41:55,800 --> 00:41:57,920 Speaker 2: Yeah, that's really it isn't It sort of like a 949 00:41:58,000 --> 00:42:01,799 Speaker 2: Hodgepodge of State and count Law. I feel like we're 950 00:42:01,800 --> 00:42:04,400 Speaker 2: going to be waiting a while for a solution to this. 951 00:42:05,600 --> 00:42:08,759 Speaker 4: Yes, and I've sort of hinted at it before on 952 00:42:08,800 --> 00:42:12,880 Speaker 4: the podcast, but for very arcane reasons that I'm not 953 00:42:12,920 --> 00:42:14,840 Speaker 4: going to get into. I do have a loan that 954 00:42:14,880 --> 00:42:17,239 Speaker 4: will need to be refinanced at some point in the 955 00:42:17,280 --> 00:42:18,200 Speaker 4: next couple of years. 956 00:42:18,520 --> 00:42:21,040 Speaker 2: You're very optimistic, Joe about interest rates. 957 00:42:21,520 --> 00:42:24,359 Speaker 4: No, I'm very pessimistic and I'm very anxious about it. 958 00:42:24,800 --> 00:42:27,960 Speaker 4: But if I'm not optimistic about the path of interest rates, 959 00:42:28,280 --> 00:42:30,960 Speaker 4: maybe I'll be optimistic that in a couple of years 960 00:42:31,120 --> 00:42:33,279 Speaker 4: the process is at least a little bit better than 961 00:42:33,280 --> 00:42:36,080 Speaker 4: it was the last time I applied for a mortgage. 962 00:42:36,400 --> 00:42:39,480 Speaker 2: Well, see, you'll have to tell me how many documents 963 00:42:39,520 --> 00:42:42,520 Speaker 2: like get mailed out and stuff, although I guess most 964 00:42:42,560 --> 00:42:44,600 Speaker 2: of that is on the sort of like lender and 965 00:42:44,640 --> 00:42:45,439 Speaker 2: servicers side. 966 00:42:45,440 --> 00:42:46,640 Speaker 3: But I'm not looking forward to it. 967 00:42:47,000 --> 00:42:48,680 Speaker 2: Yeah, all right, shall we leave it there. 968 00:42:48,760 --> 00:42:49,439 Speaker 3: Let's leave it there. 969 00:42:49,640 --> 00:42:52,520 Speaker 2: This has been another episode of the All Thoughts podcast. 970 00:42:52,680 --> 00:42:56,200 Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway. 971 00:42:55,840 --> 00:42:58,680 Speaker 4: And I'm Joe Wisenthal. You can follow me at the Stalwart. 972 00:42:58,880 --> 00:43:02,239 Speaker 4: Follow Ugust Mike thank you, He's at Michael Underscore. You 973 00:43:02,680 --> 00:43:06,360 Speaker 4: follow our producers Kerman Rodriguez at Kerman Ermann, Dashel Bennett 974 00:43:06,360 --> 00:43:09,759 Speaker 4: at Deshbot and Kilbrooks at Kelbrooks. Thank you to our 975 00:43:09,840 --> 00:43:13,200 Speaker 4: producer Moses Ondem. For more Oddlogs content, go to Bloomberg 976 00:43:13,239 --> 00:43:15,960 Speaker 4: dot com slash odd Lots, where we have transcripts, a blog, 977 00:43:16,360 --> 00:43:18,640 Speaker 4: and a daily newsletter and you can chut them up 978 00:43:18,719 --> 00:43:20,879 Speaker 4: all of these topics twenty four to seven in our 979 00:43:21,000 --> 00:43:23,800 Speaker 4: discord discord dot gg slash odlines. 980 00:43:24,120 --> 00:43:26,799 Speaker 2: And if you enjoy Oddlots, if you like it when 981 00:43:26,840 --> 00:43:30,080 Speaker 2: we dive deep into the reasons we can't have one 982 00:43:30,160 --> 00:43:33,840 Speaker 2: click mortgage revise, then please leave us a positive review 983 00:43:33,920 --> 00:43:37,560 Speaker 2: on your favorite podcast platform. And remember, if you are 984 00:43:37,600 --> 00:43:40,440 Speaker 2: a Bloomberg subscriber, you can listen to all of our 985 00:43:40,520 --> 00:43:44,160 Speaker 2: episodes absolutely ad free. All you need to do is 986 00:43:44,200 --> 00:43:48,760 Speaker 2: find the Bloomberg channel on Apple Podcasts and follow the instructions. 987 00:43:48,920 --> 00:44:19,239 Speaker 5: Thanks for listening, bend In