WEBVTT - The Mortgage Wall 0:00:01.400 --> 0:00:11.680 Fulturo investigates Investia. 0:00:13.440 --> 0:00:17.880 Housing as a standalone issue is now one of the 0:00:18.079 --> 0:00:22.480 top five concerns for Latino voters, and that's the first 0:00:22.480 --> 0:00:25.880 time this has happened in an election cycle. But as 0:00:25.920 --> 0:00:29.400 housing costs and mortgage rates in the country continue to increase, 0:00:29.880 --> 0:00:32.559 the dream of owning and affording a home is becoming 0:00:32.640 --> 0:00:34.080 more and more elusive. 0:00:34.720 --> 0:00:37.120 I already been preparing for this moment. I brought all 0:00:37.159 --> 0:00:41.600 the paperwork, my credit score, my list of liabilities, my 0:00:41.920 --> 0:00:46.239 employment history, the information regarding my business. 0:00:46.600 --> 0:00:52.400 That's Juscelyn Danielle. She's Argentinian and Haitian, and like many immigrants, 0:00:52.479 --> 0:00:56.440 she's many things. She's a licensed clinical social worker, she 0:00:56.560 --> 0:01:00.280 founded her own psychotherapy practice, and she's also an artist, 0:01:00.440 --> 0:01:05.360 which influences everything she does. Juselene, who's based in New Jersey, 0:01:05.400 --> 0:01:09.319 said she spent years saving money and building her credit 0:01:09.760 --> 0:01:12.160 and making sure all of her paperwork was in order 0:01:12.240 --> 0:01:16.800 before even applying for a mortgage. But for Juicelyn and 0:01:16.840 --> 0:01:19.880 her family, the dream of buying a home almost slipped 0:01:19.920 --> 0:01:21.200 by them entirely. 0:01:21.880 --> 0:01:24.560 When we went to the mortgage officer, this was the 0:01:24.600 --> 0:01:27.679 first question that he asked, was your zip code? 0:01:27.880 --> 0:01:29.319 I knew that's something I was wrong. 0:01:34.800 --> 0:01:38.080 This question about her zip code set off alarm bells 0:01:38.120 --> 0:01:42.560 for Jusselyn, who knew that historically, people living in predominantly 0:01:42.640 --> 0:01:47.680 black and Latino Latina LATINX communities had a harder time 0:01:47.840 --> 0:01:52.200 getting bank loans. It's a practice known as redlining, and 0:01:52.280 --> 0:01:54.840 even though it was outlawed more than fifty years ago, 0:01:55.240 --> 0:01:59.200 it continues to drive inequities in mortgage lending today. 0:02:00.440 --> 0:02:04.600 A black woman with an accent and a Latino guy 0:02:04.840 --> 0:02:09.320 with an accent. This is the textbook discrimination. 0:02:13.400 --> 0:02:17.640 From Futro Media and PRX. It's Latino USA. I'm Maria Rosa. 0:02:18.080 --> 0:02:21.480 Today is part of our elections series The Latino Factor, 0:02:21.639 --> 0:02:25.040 How We Vote. We dive into the disparities that make 0:02:25.120 --> 0:02:30.400 it harder for Latinos and Latinas to overcome the mortgage wall. 0:02:35.600 --> 0:02:39.000 Juslen's experience, which we heard at the top of the show, 0:02:39.080 --> 0:02:43.320 is far from unique, and there's vast data to prove that, 0:02:43.840 --> 0:02:47.520 data that we dug into and analyzed ourselves. My co 0:02:47.560 --> 0:02:50.120 executive producer, Penni letter Amidis is with me now in 0:02:50.160 --> 0:02:53.200 the studio and she led the investigation and it's with 0:02:53.280 --> 0:02:57.560 me today to share our main findings. Hey, Penni let Hey, Marianne, 0:02:58.120 --> 0:03:00.920 all right, so tell us how how we went about 0:03:00.960 --> 0:03:01.920 this investigation. 0:03:02.600 --> 0:03:06.040 Well, we first started looking into this mortgage data after 0:03:06.080 --> 0:03:09.359 we learned about what happened to Justlyan, but also happened 0:03:09.400 --> 0:03:12.600 to you know, her daughter, Milena, and to Milena's father, 0:03:12.880 --> 0:03:17.520 Martin Calvino. And Martin is from Why. He's a multimedia 0:03:17.639 --> 0:03:21.560 artist and he's also a data scientist, and after they 0:03:21.560 --> 0:03:25.160 were rejected in twenty twenty one from even applying for 0:03:25.200 --> 0:03:28.720 a loan, Martin was convinced that certain lenders in New 0:03:28.760 --> 0:03:32.840 Jersey were more likely to reject Latino borrowers more than 0:03:32.880 --> 0:03:33.960 white applicants. 0:03:34.120 --> 0:03:37.680 Okay, so how did you go about determining if Martin's 0:03:37.680 --> 0:03:38.840 suspicions were true? 0:03:40.040 --> 0:03:42.400 Well, Maria, we had a data journalist and we did 0:03:42.480 --> 0:03:47.640 our own extensive analysis using this public information, and it 0:03:47.720 --> 0:03:52.320 took us several months. We analyzed hundreds of thousands of 0:03:52.400 --> 0:03:56.640 mortgage applications filed in New Jersey from twenty eighteen to 0:03:56.760 --> 0:04:01.320 twenty twenty two, and we focused on conventional mortgages because 0:04:01.360 --> 0:04:04.800 these are the luans that are offered by private companies. 0:04:05.560 --> 0:04:09.560 So the data was already public, but someone actually has 0:04:09.640 --> 0:04:12.120 to pull it together and make sense of it all. 0:04:12.600 --> 0:04:13.880 So what did you end up finding? 0:04:14.160 --> 0:04:17.000 So we found that Latinos were more likely to be 0:04:17.120 --> 0:04:21.520 rejected than white applicants. And this was not just in 0:04:21.600 --> 0:04:24.839 one bank or in one city. This was the case 0:04:24.880 --> 0:04:29.280 across financial institutions in New Jersey and Maria. To make 0:04:29.320 --> 0:04:33.120 things even worse, those Latinos who get a mortgage loan 0:04:33.600 --> 0:04:37.880 paid higher interest rates than their fellow white borrowers, even 0:04:38.080 --> 0:04:39.640 on similar mortgages. 0:04:40.120 --> 0:04:43.560 And dear listener, what this disparity shows us is that 0:04:43.600 --> 0:04:46.920 there are deep systemic barriers to home ownership for Latinos 0:04:46.920 --> 0:04:49.800 and latinos in this country, whether it's access to credit, 0:04:50.080 --> 0:04:52.000 not being able to make enough of a down payment, 0:04:52.440 --> 0:04:54.520 or not even being in the right zip code, as 0:04:54.560 --> 0:04:56.800 we heard from Juicelyn at the top of our show. 0:04:57.520 --> 0:05:01.640 Yes, and that's interesting because the An Institute, which is 0:05:01.839 --> 0:05:05.880 a think tank in Washington that does economic and social 0:05:05.960 --> 0:05:10.560 policy research, is projecting the Latinos hear this will make 0:05:10.800 --> 0:05:14.440 up seventy percent of first time home buyers in the 0:05:14.480 --> 0:05:15.920 next twenty years. 0:05:16.360 --> 0:05:18.880 Wow, that is an extraordinary number. 0:05:19.120 --> 0:05:23.040 Latinos making up seventy percent of first time home buyers 0:05:23.040 --> 0:05:24.280 in the next twenty years. 0:05:24.640 --> 0:05:25.760 I mean that's. 0:05:25.839 --> 0:05:36.320 Huge, So, dear listener, let's really attempt to understand what 0:05:36.400 --> 0:05:39.880 these lending disparities in New Jersey show us about the 0:05:39.920 --> 0:05:44.120 future of home ownership for Latinos in this country, especially 0:05:44.279 --> 0:05:49.039 as housing access continues to be a growing concern nationwide 0:05:49.360 --> 0:05:52.279 in this election year. And we're going to start in 0:05:52.400 --> 0:05:55.440 Highland Park, which is a small town in New Jersey 0:05:55.480 --> 0:05:56.600 where Juslyn lives. 0:05:56.640 --> 0:05:56.919 Now. 0:05:57.320 --> 0:06:00.719 Benny Lay and a team of our producers for visited 0:06:00.760 --> 0:06:04.719 her in her home in November of twenty twenty three. Okay, 0:06:04.760 --> 0:06:06.560 Penny Ley, with all of that background, I think we're 0:06:06.600 --> 0:06:09.000 ready now, So why don't you take us to your 0:06:09.040 --> 0:06:13.040 reporting in Highland Park, New Jersey. 0:06:14.600 --> 0:06:18.240 We're visiting just Lean on a cool fall morning, and 0:06:18.279 --> 0:06:22.360 the wide tree lined street she lives on is quiet. 0:06:23.920 --> 0:06:27.120 Her house is two stories. It sits on the corner 0:06:27.279 --> 0:06:30.400 with a big lown in front and steps leading up 0:06:30.440 --> 0:06:34.240 to a Porsche. It's painting in a muted blue collar 0:06:34.480 --> 0:06:37.560 and it has white trim around the windows and edges. 0:06:41.520 --> 0:06:42.680 Are you I mean. 0:06:45.880 --> 0:06:46.240 Space? 0:06:46.720 --> 0:06:50.840 Fine? Just Lean takes us throughout the side door, which 0:06:50.880 --> 0:06:54.160 is where her office is located, and it's separated from 0:06:54.200 --> 0:06:57.240 the rest of the home that's where she plans out 0:06:57.360 --> 0:07:00.320 her art projects. As soon as you walk in, you 0:07:00.360 --> 0:07:03.880 can tell a visual artist lives here. The room is 0:07:03.960 --> 0:07:09.000 full of color. One corner is filled with green plants 0:07:09.440 --> 0:07:14.680 and framed artwork lines the soft blue walls. JUSTLYNI is 0:07:14.720 --> 0:07:18.880 fifty years old, and she has this bibrant voice but 0:07:19.240 --> 0:07:22.720 a quiet presence with a bright and big smile. 0:07:23.320 --> 0:07:26.160 So sometimes we get very attached to things, but then 0:07:26.200 --> 0:07:28.480 eventually you will have to release them and let them 0:07:28.520 --> 0:07:30.040 go as it becomes something else. 0:07:30.680 --> 0:07:34.000 Today she's wearing a light pink sweater with a rhyin 0:07:34.120 --> 0:07:37.320 stone ball on the front, and her dark hair is 0:07:37.360 --> 0:07:41.480 a style in a long braid, and she twisted red 0:07:41.560 --> 0:07:45.760 and pink flowers in it. We're visiting just Lean two 0:07:45.800 --> 0:07:50.040 years after she and Martin bought this house, but getting 0:07:50.080 --> 0:07:52.720 here though to way longer than that. 0:07:54.560 --> 0:07:58.880 I've been moving from place to place from Queens, Flashing 0:07:59.560 --> 0:08:03.320 Lumber and Spring Lake. I think I have multi in 0:08:03.400 --> 0:08:05.800 my whole life at least fifty times. 0:08:06.280 --> 0:08:10.160 Juselyn remembers that in one occasion, she was removed overnight 0:08:10.280 --> 0:08:13.320 from an apartment that she was ranting in Queens, New York, 0:08:14.080 --> 0:08:17.480 and it was because the landlord decided that she needed 0:08:17.480 --> 0:08:19.440 the space for her relatives. 0:08:19.160 --> 0:08:21.200 And I was like, but I already pay the rent 0:08:21.520 --> 0:08:23.120 and I have all the things, and I had to 0:08:23.160 --> 0:08:24.640 go to work and I had to go to school. 0:08:24.680 --> 0:08:26.800 And she was like, I'm sorry, but you know, like 0:08:27.160 --> 0:08:29.200 my family coming in that you cannot come back. 0:08:31.200 --> 0:08:35.439 Her experience highlights a common issue for many renters. Things 0:08:35.559 --> 0:08:39.079 can just get too unpredictable. And there is of course 0:08:39.120 --> 0:08:41.839 the rising cause of rent, which is a problem nationwide. 0:08:42.520 --> 0:08:46.040 More than half Latino renters in the US say they 0:08:46.080 --> 0:08:48.680 spend more than a third of their income on rent. 0:08:49.240 --> 0:08:53.040 And this election year, Paul's show is the economy that 0:08:53.160 --> 0:08:59.520 is concerning Latino voters the most, and of course housing 0:08:59.600 --> 0:09:05.400 is a big part of that. California is probably the 0:09:05.400 --> 0:09:07.520 first place that comes to mind when we talk about 0:09:07.520 --> 0:09:11.880 the housing crisis. Dim Hernandez is a California state director 0:09:11.920 --> 0:09:15.120 for Me Familia Vota and this is a national organization 0:09:15.240 --> 0:09:19.199 that works on building Latino political power through civic engagement. 0:09:19.720 --> 0:09:23.480 He says, the economy goes beyond numbers and percentages. It's 0:09:23.520 --> 0:09:26.840 about how it becomes real in people's daily lives. 0:09:27.120 --> 0:09:29.160 They wake up, they go off and they get a coffee, 0:09:29.200 --> 0:09:31.040 and that coffee used to be probably four or five 0:09:31.080 --> 0:09:33.520 dollars and now it's seven. They work at their job 0:09:33.679 --> 0:09:36.320 which has not increased their wages, and then when they 0:09:36.320 --> 0:09:38.040 go to the grocery store that night to get food 0:09:38.040 --> 0:09:40.040 for their family, they see the price of milk has 0:09:40.040 --> 0:09:42.640 increased and they go home and they worry about rent. 0:09:43.000 --> 0:09:45.880 And so when we talk about the economy, we use 0:09:45.920 --> 0:09:49.440 indicators like the market, but we don't use indicators like 0:09:49.640 --> 0:09:51.800 individual sentiment of the consumer. 0:09:52.360 --> 0:09:56.120 What team told us echoes what Unidos US, which is 0:09:56.160 --> 0:10:00.960 another advocacy organization in DC, founding a study that is 0:10:01.040 --> 0:10:05.480 that in this election year, Latinos want their policymakers to 0:10:05.559 --> 0:10:09.680 prioritize addressing the cause of living and the housing prices. 0:10:09.960 --> 0:10:12.400 And so it makes us election even more critical this 0:10:12.520 --> 0:10:15.199 year of ensuring that the folks that were electing and 0:10:15.240 --> 0:10:17.760 the folks that were putting in the office care about 0:10:17.760 --> 0:10:18.640 these things. 0:10:18.800 --> 0:10:23.000 And as Latinos faced multiple economic burdens, many of them 0:10:23.080 --> 0:10:27.440 still dream of owning their home. Onido's US found that 0:10:27.520 --> 0:10:30.960 sixty five percent of Latino renters want to buy a 0:10:31.000 --> 0:10:37.160 house but have not been able to After renting in 0:10:37.240 --> 0:10:40.320 Queens for a while, Justlyne eventually made them move to 0:10:40.360 --> 0:10:44.800 New Jersey. Her many experiences as a renter instill in 0:10:44.840 --> 0:10:47.960 her the goal of owning a home, and she said 0:10:48.000 --> 0:10:51.160 she wanted to achieve that stability and that will become 0:10:51.240 --> 0:10:55.000 even more pressing when her daughter, Milena was born twelve 0:10:55.080 --> 0:10:55.640 years ago. 0:10:56.240 --> 0:10:58.440 So for me, when I have my daughter, there was 0:10:58.480 --> 0:11:00.520 no question about it, like I was going to give 0:11:00.559 --> 0:11:01.600 that stability to her. 0:11:02.080 --> 0:11:05.079 Just Lyn remembers the moment when she decided that it 0:11:05.160 --> 0:11:08.080 was about time to take the big step. It was 0:11:08.080 --> 0:11:12.320 December twenty nineteen. She was renting a place in Highland Park, 0:11:12.800 --> 0:11:14.480 not too far from where she lives. 0:11:14.520 --> 0:11:17.920 Down the moment that I decided that I was going 0:11:18.000 --> 0:11:21.200 to get the home is like my previous landlord, I 0:11:21.280 --> 0:11:24.880 planted a tulip in my backyard and she went outside 0:11:25.000 --> 0:11:26.800 and she took it out and she threw her in 0:11:26.840 --> 0:11:27.800 the garage. 0:11:28.240 --> 0:11:32.520 What so No, by the moment that I decided that. 0:11:32.480 --> 0:11:38.400 I ron my home, Wow, yeah, a. 0:11:38.520 --> 0:11:42.240 Tu live in the garbage just because the landlord didn't 0:11:42.280 --> 0:11:46.760 like it. This singular moment was pivotal in just Lyn's life. 0:11:47.320 --> 0:11:51.440 Flowers helped me to think about the nature of existence. 0:11:51.920 --> 0:11:56.720 Gulyn just loved flowers. She incorporates flowers in almost all 0:11:56.840 --> 0:12:01.120 her projects. Seeing the landlord throw away the tulip she 0:12:01.200 --> 0:12:04.880 planted was the final extra for her and for Martin, 0:12:05.960 --> 0:12:12.839 So they started getting ready. Justlyne prepared all her paperwork 0:12:13.000 --> 0:12:17.200 and her savings. She learned all about the buying process, 0:12:17.320 --> 0:12:21.280 and less than two years later, just Lene, Martin and 0:12:21.440 --> 0:12:25.479 their daughter Milena made the trip to that bank investor's 0:12:25.600 --> 0:12:29.080 bank in Somerset, New Jersey, and they were ready to 0:12:29.120 --> 0:12:32.160 apply for a loan. She was already a client of 0:12:32.240 --> 0:12:35.440 the bank and said she had all her money saved there. 0:12:35.760 --> 0:12:38.840 So we get into the branch and I had to 0:12:38.880 --> 0:12:42.960 describe it like he was a guy in his late sixties, 0:12:43.440 --> 0:12:48.400 blue eyes, white American, and he goes like, hey, hi, guys, 0:12:48.600 --> 0:12:50.400 how are you And he did the fist pump to 0:12:50.480 --> 0:12:51.200 me at the fish. 0:12:51.480 --> 0:12:54.439 But it was the mortgage officer's second question that to 0:12:54.760 --> 0:12:58.120 just Lyn and her family back. It was when he 0:12:58.240 --> 0:13:01.839 asked about their zip code. What did you think at 0:13:01.880 --> 0:13:02.960 that moment that that was. 0:13:02.960 --> 0:13:06.520 A red black Because I did the real stare course, 0:13:06.840 --> 0:13:09.840 so I really knew about red lining. Why is it 0:13:09.920 --> 0:13:13.080 like a certain segment of the population can access on 0:13:13.200 --> 0:13:15.960 in the home. How is it that we got to 0:13:16.040 --> 0:13:19.440 be where we are so part of the course that 0:13:19.600 --> 0:13:22.640 I have been taken involved all of these. 0:13:22.840 --> 0:13:26.640 Again, when just lindsays redlining, she's referring to this old 0:13:26.679 --> 0:13:29.840 practice of banks deny it loans to black and Latino 0:13:29.920 --> 0:13:37.920 families and effectively preventing them from buying into certain neighborhoods. 0:13:37.960 --> 0:13:42.280 In nineteen sixty eight, the Third Housing Act banned redlining 0:13:42.400 --> 0:13:44.240 to end this racial discrimination. 0:13:45.360 --> 0:13:49.600 Now with this bill, the voice of Justice speaks again. 0:13:51.240 --> 0:13:56.960 It proclaims that fair housing for all all human beings 0:13:57.360 --> 0:14:02.080 who live in this country is now a part of 0:14:02.160 --> 0:14:04.280 the American way of life. 0:14:04.200 --> 0:14:08.760 But several investigations, including ours for this story, show how 0:14:08.840 --> 0:14:13.079 the practice is still very much alivee After the mortgage 0:14:13.120 --> 0:14:16.959 officer asked Justlyn about her zip code, he started questioning 0:14:17.000 --> 0:14:21.880 Martin's employment. Martin was said to begin a job just 0:14:21.920 --> 0:14:25.560 a couple of months later, and it was at Rodgers University, 0:14:25.840 --> 0:14:28.440 which is one of the top public research schools in 0:14:28.480 --> 0:14:33.480 the entire country. But according to Justlyn, the mortgage officer 0:14:33.680 --> 0:14:34.640 went after that. 0:14:35.000 --> 0:14:38.520 The fact that he had started the job and the 0:14:38.560 --> 0:14:43.800 fact that he didn't have employment history. Even though we 0:14:43.800 --> 0:14:47.960 were telling him that we were business owners and we 0:14:47.960 --> 0:14:49.040 were working together. 0:14:49.600 --> 0:14:53.760 In two thousand and seven, Justlyn founded her psychotherapy practice 0:14:54.080 --> 0:14:57.280 and her art collective, which is called Nice to Meet You, 0:14:57.760 --> 0:15:00.920 is also an LLC, so they have plenty of ways 0:15:00.960 --> 0:15:04.840 to demonstrate their family income. But despite this, just Lin 0:15:04.960 --> 0:15:08.600 says that things with the law an officer just continued 0:15:08.680 --> 0:15:10.280 to spiral downwards. 0:15:10.520 --> 0:15:13.280 He did at Runner Credit Escort, and he didn't verify 0:15:13.320 --> 0:15:15.040 any of the paperwork that we had. 0:15:15.200 --> 0:15:17.680 So you were rejected even before applying. 0:15:18.080 --> 0:15:21.280 Absolutely, he never put any information in a system, in 0:15:21.360 --> 0:15:24.200 a computer or anything like just to get us on 0:15:24.400 --> 0:15:29.000 sort of data to compare our qualifications to anything, to 0:15:29.080 --> 0:15:31.440 any standard that they might have at the bank. 0:15:32.280 --> 0:15:36.280 Since then, the bank was acquired by Citizens Financial Group, 0:15:36.800 --> 0:15:39.840 and we tried to contact this new bank, but the 0:15:39.880 --> 0:15:43.520 spokesperson said the company could not comment on just Lin's 0:15:43.520 --> 0:15:48.320 case because it predated the acquisition of Investors Bank. They 0:15:48.400 --> 0:15:51.840 told us that they were committed to quote creating an 0:15:51.880 --> 0:15:55.840 inclusive culture for their customers. You should know that just 0:15:55.920 --> 0:15:59.960 Lyn identifies herself as an Afro Latina. She was born 0:16:00.080 --> 0:16:02.920 in Los Angeles, but for the first half of her 0:16:02.960 --> 0:16:07.240 life she was living in Argentina, which is a predominantly 0:16:07.400 --> 0:16:12.160 white country. She said that in New Jersey she recognized 0:16:12.200 --> 0:16:16.400 that feeling from her childhood memories. I go pull you 0:16:16.440 --> 0:16:17.320 all my life. 0:16:18.040 --> 0:16:22.000 My mommy white, she's blonde, she has green eyes. So 0:16:22.240 --> 0:16:25.480 I clearly remember like one time we went to get 0:16:25.520 --> 0:16:29.400 ice cream and a lady that was sitting next to 0:16:29.440 --> 0:16:35.080 her said like, Linda Nete, oh you have two beautiful 0:16:35.320 --> 0:16:36.240 little black kids. 0:16:36.280 --> 0:16:37.480 Where did you get them from? 0:16:38.080 --> 0:16:40.360 So you know, like, I'm not putting up with any 0:16:40.400 --> 0:16:43.600 of these anymore, not at this age when I already 0:16:43.640 --> 0:16:46.640 know the laws. I'm not going to tolerate it, and 0:16:46.680 --> 0:16:48.120 I'm going to do something about it. 0:16:48.920 --> 0:16:51.960 And she did. After leaving the bank where she had 0:16:52.040 --> 0:16:54.840 hoped to start the process of buying a home, just 0:16:54.920 --> 0:16:58.040 Lean filed a complaint. She did it with the New 0:16:58.120 --> 0:17:02.160 Jersey Attorney General's Office with a division on civil rights. 0:17:02.480 --> 0:17:05.840 She said. She followed up with a claim for two years, 0:17:05.920 --> 0:17:09.600 and then she became accested with the process. There were 0:17:09.720 --> 0:17:13.159 just too many documents to submit and she ended up 0:17:13.280 --> 0:17:18.320 missing a deadline and the case was eventually dismissed. During 0:17:18.400 --> 0:17:22.240 this time, Martin started looking at mortgage application data in 0:17:22.280 --> 0:17:22.880 New Jersey. 0:17:23.320 --> 0:17:27.280 He spent hours like on the computers, you know, like Jesse, 0:17:27.520 --> 0:17:31.040 going through it, like creating different things to be able 0:17:31.080 --> 0:17:31.879 to tell the data. 0:17:32.560 --> 0:17:36.560 Martin's research led him to believe that certain lenders in 0:17:36.600 --> 0:17:39.800 the state of New Jersey were more likely to reject 0:17:39.880 --> 0:17:45.040 Latino burrowers than white applicants. Martin first reached out to 0:17:45.200 --> 0:17:48.600 us Abuduo Investigates in twenty twenty two to share what 0:17:48.720 --> 0:17:53.119 he found. We were deeply interested in his research, but 0:17:53.200 --> 0:17:56.119 we also needed to analyze the mortgage data in New 0:17:56.200 --> 0:17:59.800 Jersey ourselves, so we went to the same public database 0:17:59.840 --> 0:18:03.400 that our team us and we spent months doing our 0:18:03.520 --> 0:18:11.880 own extensive data analysis. So, using this public data, we 0:18:11.920 --> 0:18:16.199 went through hundreds of thousands of mortgage application outcomes and 0:18:16.400 --> 0:18:20.800 in the process we refined our methodology several times. We 0:18:20.840 --> 0:18:24.960 shared it with other investigators and other journalists, and we 0:18:25.000 --> 0:18:28.439 asked them to critique our methods. We wanted to be 0:18:28.600 --> 0:18:33.320 really sure that our conclusions were right, and finally we 0:18:33.400 --> 0:18:37.720 found a clear pattern. Between twenty eighteen and twenty twenty two, 0:18:38.280 --> 0:18:42.600 more than eleven percent of the conventional mortgage applications filed 0:18:42.600 --> 0:18:46.359 by latinos were rejected by lenders in New Jersey, and 0:18:46.560 --> 0:18:50.560 in the case of white residents, only around six percent 0:18:50.600 --> 0:18:53.960 were denied. So the data was there and it was 0:18:54.119 --> 0:18:59.520 pretty clear just lean story was not just an unfortunate experience. 0:19:00.119 --> 0:19:07.200 It indicated a persistent problem, one that has a long 0:19:07.359 --> 0:19:10.280 history in the state of New Jersey. 0:19:13.680 --> 0:19:17.840 Coming up on Latino USA, we dive into our data analysis, 0:19:17.960 --> 0:19:22.200 which proves that New Jersey's disparities in home ownership make 0:19:22.240 --> 0:19:25.280 it one of the most inequitable states in the country. 0:19:25.920 --> 0:19:43.399 Stay with us, hey, we're back, and before the break, 0:19:43.440 --> 0:19:46.600 we shared Juicelyne's story and we heard from Benny Lay 0:19:46.880 --> 0:19:51.960 about the findings of our investigation. Our data analysis confirmed 0:19:52.040 --> 0:19:56.320 juicelyn and Martine's suspicions that Latinos like them, like me, 0:19:56.400 --> 0:19:59.600 and like many of you listening today, were denied home 0:19:59.640 --> 0:20:04.520 loans at roughly double the rate of white borrowers. Benny 0:20:04.560 --> 0:20:06.639 Lay is back in the studio with me now to 0:20:06.880 --> 0:20:11.280 talk more about our investigation. So, Benny Lay, what else 0:20:11.359 --> 0:20:14.159 did the data end up showing you? 0:20:14.359 --> 0:20:17.320 Well, Maria, the reason why it took us several months 0:20:17.359 --> 0:20:20.600 to analyze this mortgage data is because we were looking 0:20:20.640 --> 0:20:24.440 at five years worth of raw information. So as our 0:20:24.480 --> 0:20:28.679 first step, we filtered the decisions and we focused only 0:20:28.720 --> 0:20:29.439 on New Jersey. 0:20:29.880 --> 0:20:32.320 That would still leave a lot of data to pass 0:20:32.400 --> 0:20:34.840 through though. I mean, even at that point, right. 0:20:35.080 --> 0:20:38.440 For sure, Maria, it was still in the millions. So 0:20:38.480 --> 0:20:41.359 what we did is that we narrow it down even 0:20:41.440 --> 0:20:46.439 more and we concentrated only on conventional mortgages. And we 0:20:46.520 --> 0:20:50.520 also used other criteria like, for example, making sure that 0:20:50.600 --> 0:20:53.840 the loan was to buy a house, not as an investment, 0:20:54.520 --> 0:20:57.240 and we were only looking at loans that were either 0:20:57.320 --> 0:21:01.600 approved or rejected. But even with all of this, Maria, 0:21:01.680 --> 0:21:07.320 this left us still with four hundred thousand applications to analyze. 0:21:06.720 --> 0:21:09.400 All right, and so in your analysis, how did these 0:21:09.480 --> 0:21:11.400 lenders make their decisions? 0:21:11.400 --> 0:21:12.080 What did you find? 0:21:12.600 --> 0:21:15.240 Well, there is a few things they looked at. The 0:21:15.320 --> 0:21:17.679 first thing is something that is called the debt to 0:21:17.800 --> 0:21:21.040 income ratio, and this is basically how much money you 0:21:21.119 --> 0:21:24.240 make every month pursus how much money you need to 0:21:24.240 --> 0:21:26.879 pay for your debts. Because you know, you have credit 0:21:26.880 --> 0:21:29.880 card payments, you have rent, you have car insurance, all 0:21:29.880 --> 0:21:32.240 the things that you pay every month, and when you 0:21:32.320 --> 0:21:35.720 request a loan, your lender will check how much is 0:21:35.760 --> 0:21:38.679 your income every month and how much are these expenses. 0:21:39.200 --> 0:21:42.200 So when there is a big gap between the debt 0:21:42.320 --> 0:21:45.520 and the income. That's when loans were denied the most. 0:21:46.119 --> 0:21:49.120 But lenders are also checking other things like, for example, 0:21:49.200 --> 0:21:52.760 credit history, and also they are seeing if the applicant 0:21:52.840 --> 0:21:56.160 has co applicants, so if they're buying the house by 0:21:56.200 --> 0:22:00.639 themselves or with all the people supporting them. 0:22:00.760 --> 0:22:03.720 All right, already, ding ding ding. A flag is going 0:22:03.800 --> 0:22:07.960 up for me. Because you know, Latinos, we often do 0:22:08.080 --> 0:22:10.560 things as a family. A lot of us have non 0:22:10.600 --> 0:22:13.600 traditional forms of income. It makes it hard to build 0:22:13.680 --> 0:22:15.720 up credit. So I can see that this would raise 0:22:15.760 --> 0:22:16.360 some questions. 0:22:16.640 --> 0:22:19.879 Yes, exactly, Maria, I also have those questions. So we 0:22:19.960 --> 0:22:23.199 spoke with Amii Sin and she's a researcher with the 0:22:23.240 --> 0:22:26.800 House in Finance Policy Center at the Urban Institute, and 0:22:26.840 --> 0:22:30.480 she specializes in mortgage lending and she has a focus 0:22:30.520 --> 0:22:33.000 on racial equity. And this is what she told us 0:22:33.000 --> 0:22:33.480 about this. 0:22:33.880 --> 0:22:36.240 Our current underwriting system does a really poor job of 0:22:36.240 --> 0:22:40.439 accommodating for multiple incomes, particularly more than two incomes, and 0:22:40.480 --> 0:22:42.720 so that's a real issue as Latinos are more likely 0:22:42.760 --> 0:22:45.760 to live in multi generational households as well. Kind of 0:22:45.800 --> 0:22:48.320 on a similar vein Latino people are more likely than 0:22:48.400 --> 0:22:50.800 other racial and ethnic groups of people to have non 0:22:50.840 --> 0:22:54.280 traditional forms of income. They make up a disportionately high 0:22:54.280 --> 0:22:57.399 share of self employed individuals. They're more likely than others 0:22:57.400 --> 0:23:00.160 to engage in enterprising or informal work activity. 0:23:00.560 --> 0:23:03.879 So if you, for example, sell tamlis on the street 0:23:03.920 --> 0:23:06.760 for a living, or if you turn your art collective 0:23:06.760 --> 0:23:09.960 into a business like Juice Lean, or you make a 0:23:09.960 --> 0:23:12.199 lot of your money in cash, for example, you're going 0:23:12.280 --> 0:23:16.880 to have a harder time proving that you should qualify 0:23:16.960 --> 0:23:18.080 for a mortgage loan. 0:23:18.240 --> 0:23:21.240 Or even if you live with your parents and your siblings, 0:23:21.440 --> 0:23:24.800 but also with your Auela, with your grandma. And yet 0:23:24.840 --> 0:23:29.280 Maria Latina's really go after homeownership here is amlie Again. 0:23:29.720 --> 0:23:32.160 Latino households are more likely than others to be first 0:23:32.200 --> 0:23:34.280 time home buyers, and more likely to be the first 0:23:34.280 --> 0:23:36.040 in their family to buy a home. And so I 0:23:36.080 --> 0:23:38.720 think it's like, sometimes who do you turn to? I 0:23:38.720 --> 0:23:40.840 would think for myself, I'm not a homeowner, but if 0:23:40.840 --> 0:23:42.440 I were going to buy a home, I'd probably ask 0:23:42.480 --> 0:23:45.080 my parents, who are homeowners, a lot of questions about 0:23:45.080 --> 0:23:47.240 their process. What kind of bank to go to, what 0:23:47.320 --> 0:23:49.760 kind of lender to go to, what neighborhood should I 0:23:49.760 --> 0:23:52.119 look in that are in my price range. If you 0:23:52.240 --> 0:23:54.600 don't have somebody in your family that's a homeowner, or 0:23:54.600 --> 0:23:57.119 you don't know someone that's a homeowner, that can just 0:23:57.119 --> 0:23:58.200 be a barrier right there. 0:23:58.760 --> 0:24:00.840 All right, Penny, I know this is little nerdy, but 0:24:00.920 --> 0:24:04.600 how did the team actually do this data analysis? 0:24:05.080 --> 0:24:08.320