1 00:00:04,200 --> 00:00:07,200 Speaker 1: From Bloomberg News and I Heart Radio. It's the Big Take. 2 00:00:09,360 --> 00:00:14,160 Speaker 1: I'm West Cassova. Today more people are working from home, 3 00:00:14,600 --> 00:00:26,639 Speaker 1: and that has some cities very nervous. We've talked a 4 00:00:26,680 --> 00:00:29,360 Speaker 1: few times on this podcast about the tug of war 5 00:00:29,480 --> 00:00:34,280 Speaker 1: between companies and employees over flexible work arrangements. People whose 6 00:00:34,360 --> 00:00:36,920 Speaker 1: jobs don't require them to be there in person want 7 00:00:36,960 --> 00:00:38,960 Speaker 1: to work from home at least part of the time, 8 00:00:39,360 --> 00:00:42,880 Speaker 1: and a lot of companies are letting them. That's left 9 00:00:42,920 --> 00:00:46,760 Speaker 1: cities with this pressing problem. Fewer people downtown means a 10 00:00:46,880 --> 00:00:49,880 Speaker 1: lot less money being spent there every day. There's less 11 00:00:49,880 --> 00:00:53,400 Speaker 1: demand for all the buildings and buses, shops, restaurants and 12 00:00:53,560 --> 00:01:00,520 Speaker 1: services that downtowns are built around and can't survive without companies. Politicians, 13 00:01:00,680 --> 00:01:03,600 Speaker 1: urban planners are now taking a hard look at how 14 00:01:03,640 --> 00:01:07,680 Speaker 1: their cities can change to adapt to a new reality. 15 00:01:07,880 --> 00:01:11,000 Speaker 1: Reporters Emma Court and Donna Borak have written a big 16 00:01:11,040 --> 00:01:13,880 Speaker 1: story about what all this looks like for New York 17 00:01:14,080 --> 00:01:16,880 Speaker 1: and other cities too, and they're here with me now 18 00:01:17,080 --> 00:01:22,000 Speaker 1: from New York. Donna, let me start with you, and 19 00:01:22,280 --> 00:01:25,360 Speaker 1: you began reporting this story. What is it that you 20 00:01:25,360 --> 00:01:29,760 Speaker 1: wanted to find out? When we began this reporting project, like, 21 00:01:29,800 --> 00:01:32,119 Speaker 1: we wanted to be able to answer the question of 22 00:01:32,480 --> 00:01:36,119 Speaker 1: what was the economic impact of remote work in Manhattan right? 23 00:01:36,480 --> 00:01:39,679 Speaker 1: And we wanted to be able to show in more tangible, 24 00:01:39,760 --> 00:01:42,760 Speaker 1: concrete ways, how the daily rhythm of life had changed 25 00:01:42,800 --> 00:01:46,520 Speaker 1: across the city. We know that wridership is down. We 26 00:01:46,560 --> 00:01:49,279 Speaker 1: know that people are not badging into the office nearly 27 00:01:49,320 --> 00:01:52,560 Speaker 1: as often as pre pandemic. You go downtown, it's not 28 00:01:53,160 --> 00:01:57,000 Speaker 1: nearly as vibrant, you know, as it once was. But 29 00:01:57,120 --> 00:01:59,240 Speaker 1: what does it really feel like on a Monday in 30 00:01:59,280 --> 00:02:02,440 Speaker 1: Manhattan versus a Thursday, And like, does it really matter? 31 00:02:02,960 --> 00:02:05,960 Speaker 1: You do see this sort of clumping around Tuesday, Wednesday, 32 00:02:05,960 --> 00:02:08,560 Speaker 1: Thursday in New York City, you know, people are in 33 00:02:08,600 --> 00:02:10,560 Speaker 1: the office, and we've seen that. You know, we use 34 00:02:10,639 --> 00:02:12,640 Speaker 1: a ton of data for the story, So we saw 35 00:02:12,680 --> 00:02:17,600 Speaker 1: this trend pretty black and white in a variety of ways. 36 00:02:17,639 --> 00:02:20,120 Speaker 1: We saw it in black card data, so for people 37 00:02:20,120 --> 00:02:23,360 Speaker 1: who are expensing business rights to work, we saw them 38 00:02:23,400 --> 00:02:26,640 Speaker 1: sort of coming in in force on Tuesday, Wednesday, Thursday. 39 00:02:26,680 --> 00:02:29,200 Speaker 1: We saw it in subway data, we saw it an 40 00:02:29,240 --> 00:02:32,200 Speaker 1: office badge data, we see it in foot traffic data. 41 00:02:32,360 --> 00:02:34,919 Speaker 1: And so I think, you know, we feel really confident 42 00:02:34,960 --> 00:02:37,280 Speaker 1: after looking at all this data and really crunching the 43 00:02:37,360 --> 00:02:40,320 Speaker 1: numbers to be able to say, this is a big 44 00:02:40,400 --> 00:02:43,000 Speaker 1: trend that's happening. You know, people are all wringing their 45 00:02:43,000 --> 00:02:45,520 Speaker 1: hands about hybrid work, but the reality of it is, 46 00:02:45,600 --> 00:02:47,640 Speaker 1: at least in New York and in many parts of 47 00:02:47,639 --> 00:02:53,239 Speaker 1: the country, people are back in offices, but it's extremely uneven, 48 00:02:53,760 --> 00:02:55,840 Speaker 1: especially at the start and the end of the week. 49 00:02:55,960 --> 00:02:58,000 Speaker 1: And you know, we have to think about what that 50 00:02:58,080 --> 00:03:02,160 Speaker 1: means for cities, for their economy, means for offices for workers. 51 00:03:02,240 --> 00:03:05,799 Speaker 1: That does have economic effects, and you know, cities are 52 00:03:05,880 --> 00:03:08,400 Speaker 1: only beginning to sort of grapple with what that means 53 00:03:08,440 --> 00:03:12,600 Speaker 1: for them. Sure. I mean, we're even seeing this at 54 00:03:12,639 --> 00:03:16,480 Speaker 1: like Sweet Green and Chopped, the popular lunch places where 55 00:03:16,520 --> 00:03:19,680 Speaker 1: you get a salad or something exactly right. Even just 56 00:03:19,720 --> 00:03:22,519 Speaker 1: standing in line, you're overhearing people talk about, well, what's 57 00:03:22,560 --> 00:03:24,440 Speaker 1: your three day, like when are you coming in? Are 58 00:03:24,480 --> 00:03:26,959 Speaker 1: you coming in on Tuesdays? Is Monday your anchor day? 59 00:03:27,040 --> 00:03:29,480 Speaker 1: And so we did a little bit of an experiment 60 00:03:29,680 --> 00:03:32,160 Speaker 1: to find out just how long it would take right 61 00:03:32,280 --> 00:03:35,600 Speaker 1: to get a Sweet Green salad on a Monday versus 62 00:03:35,640 --> 00:03:38,840 Speaker 1: a peak day of like Wednesday, or Thursday. I and 63 00:03:39,040 --> 00:03:42,160 Speaker 1: one of our interns at the time, Christian, went to 64 00:03:42,280 --> 00:03:44,480 Speaker 1: one specific Sweet Green over the course of a week 65 00:03:45,000 --> 00:03:49,400 Speaker 1: and came up with this like very scientific methodology, which 66 00:03:49,480 --> 00:03:51,520 Speaker 1: is like, you know, get online by a certain time, 67 00:03:51,600 --> 00:03:54,160 Speaker 1: like I think it was twelve ten pm, and then 68 00:03:54,160 --> 00:03:57,160 Speaker 1: see how long it takes to get from this, you know, 69 00:03:57,240 --> 00:04:00,240 Speaker 1: getting in line to picking up your salad. And so 70 00:04:00,440 --> 00:04:02,960 Speaker 1: on a Monday, it was about nine minutes and twenty seconds. 71 00:04:03,000 --> 00:04:05,720 Speaker 1: There's a little longer. On a Tuesday, nine minutes and 72 00:04:05,840 --> 00:04:12,440 Speaker 1: almost forty seconds. Wednesday, whopping, thirteen plus minutes. Thursday actually 73 00:04:12,440 --> 00:04:15,240 Speaker 1: a lot shorter, about six minutes, and on Friday it 74 00:04:15,400 --> 00:04:18,240 Speaker 1: was just four minutes. And I think, you know, this 75 00:04:18,360 --> 00:04:20,400 Speaker 1: kind of speaks to like, if you are, you know, 76 00:04:20,480 --> 00:04:22,480 Speaker 1: so bold as to go into the office on a Friday, 77 00:04:22,520 --> 00:04:24,960 Speaker 1: you will see there are no lines for lunch, your 78 00:04:25,000 --> 00:04:27,840 Speaker 1: subway car is not going to be busy, and you 79 00:04:27,839 --> 00:04:29,520 Speaker 1: you know, kind of reap the benefits of that. But 80 00:04:29,600 --> 00:04:32,520 Speaker 1: the businesses around you and the other kind of systems 81 00:04:32,560 --> 00:04:36,080 Speaker 1: reliant on commuters certainly don't. Some of this kind of 82 00:04:36,120 --> 00:04:38,119 Speaker 1: gets out. I think what the heart of this story 83 00:04:38,279 --> 00:04:40,360 Speaker 1: is really, which is, you know, there's been a lot 84 00:04:40,400 --> 00:04:43,920 Speaker 1: of handwringing about what does it all mean? Right, people 85 00:04:44,000 --> 00:04:46,320 Speaker 1: are not in offices the way they used to be. 86 00:04:46,920 --> 00:04:49,680 Speaker 1: Why does this matter? A couple of I think insights 87 00:04:49,680 --> 00:04:51,680 Speaker 1: that we bring to the table that hasn't been sort 88 00:04:51,680 --> 00:04:55,360 Speaker 1: of sharply looked at before. Is one this difference by 89 00:04:55,440 --> 00:04:57,840 Speaker 1: day of week, which I think everyone is observing if 90 00:04:57,839 --> 00:05:00,680 Speaker 1: they're in the office in a big city. And then 91 00:05:00,800 --> 00:05:02,960 Speaker 1: to sort of like what is the cost of that? 92 00:05:03,040 --> 00:05:05,280 Speaker 1: And we were able to come up with a number 93 00:05:05,640 --> 00:05:08,440 Speaker 1: to sort of figure out how much this is costing 94 00:05:08,720 --> 00:05:12,880 Speaker 1: New York City or specifically Manhattan in terms of you know, 95 00:05:13,400 --> 00:05:16,520 Speaker 1: sales to businesses like Sweet Green that sort of weekday 96 00:05:16,520 --> 00:05:20,200 Speaker 1: warriors favor, right, and uh, you know, to transit systems 97 00:05:20,240 --> 00:05:23,680 Speaker 1: because there's like misfares for instance, and all these other 98 00:05:24,120 --> 00:05:27,240 Speaker 1: you know, knock on effects of me working from home 99 00:05:27,480 --> 00:05:30,080 Speaker 1: on a Friday. It doesn't seem so bad, but in 100 00:05:30,279 --> 00:05:33,640 Speaker 1: mass an aggregate that really adds up. Our reporting and 101 00:05:33,680 --> 00:05:36,960 Speaker 1: analysis show that worker spending has reduced by at least 102 00:05:37,000 --> 00:05:40,800 Speaker 1: twelve point four billion dollars a year in Manhattan alone 103 00:05:41,160 --> 00:05:44,239 Speaker 1: due to remote work. Down in your story, you write 104 00:05:44,360 --> 00:05:47,039 Speaker 1: that to get that number, you asked for help from 105 00:05:47,080 --> 00:05:50,479 Speaker 1: a professor. His name is Jose Maria Barrero and he 106 00:05:50,560 --> 00:05:54,880 Speaker 1: is at Mexico's Instituto Technological Autonomo and he's also part 107 00:05:54,880 --> 00:05:59,280 Speaker 1: of Stanford University's Work from Home research groups. So how 108 00:05:59,320 --> 00:06:04,120 Speaker 1: does he sculate this in Barrero and his colleagues had 109 00:06:04,200 --> 00:06:06,600 Speaker 1: asked in their survey as part of their work from 110 00:06:06,680 --> 00:06:10,640 Speaker 1: home research, what workers had estimated they were spending on 111 00:06:10,680 --> 00:06:15,000 Speaker 1: a weekly basis on meals, shopping, and entertainment near the 112 00:06:15,000 --> 00:06:19,320 Speaker 1: workplace a year prior. So this research really was closest 113 00:06:19,360 --> 00:06:21,640 Speaker 1: to to get a sense of what were people spending 114 00:06:21,640 --> 00:06:24,760 Speaker 1: pre pandemic. Using that number, he was able to show 115 00:06:25,160 --> 00:06:29,039 Speaker 1: how much less workers were spending. By adjusting for a 116 00:06:29,120 --> 00:06:33,800 Speaker 1: thirty percent reduction in return to office that ends up 117 00:06:33,800 --> 00:06:37,720 Speaker 1: translating to about four thousands, six hundred and sixty one 118 00:06:37,960 --> 00:06:42,680 Speaker 1: dollars per person per year in Manhattan. They calculated similar 119 00:06:42,680 --> 00:06:46,480 Speaker 1: analysis and other major cities, including San Francisco, which was 120 00:06:46,560 --> 00:06:49,800 Speaker 1: just over three thousand dollars a year per worker that 121 00:06:49,880 --> 00:06:52,359 Speaker 1: was being lost due to remote work, and in a 122 00:06:52,400 --> 00:06:56,320 Speaker 1: city like Chicago just a little over. So we came 123 00:06:56,400 --> 00:06:59,040 Speaker 1: up with this number for New York. But it's cool 124 00:06:59,080 --> 00:07:01,760 Speaker 1: about you know what jose has done and his colleagues 125 00:07:01,800 --> 00:07:05,680 Speaker 1: at w FH Research Group is that they also came 126 00:07:05,760 --> 00:07:09,680 Speaker 1: up with all these estimates for how much in person 127 00:07:10,000 --> 00:07:12,520 Speaker 1: work has been reduced in all these other cities in 128 00:07:12,560 --> 00:07:15,600 Speaker 1: the United States, and they came up with spending calculations, 129 00:07:15,800 --> 00:07:19,040 Speaker 1: you know, similarly per person per week reductions and spending 130 00:07:19,400 --> 00:07:21,840 Speaker 1: for all these different places. So it's really interested to 131 00:07:21,840 --> 00:07:24,480 Speaker 1: see this list because at the top of it is 132 00:07:24,520 --> 00:07:28,560 Speaker 1: actually it's not New York, it's Washington, d C. Interestingly, 133 00:07:28,600 --> 00:07:33,080 Speaker 1: with a thirty seven percent reduction in person days. And 134 00:07:33,080 --> 00:07:36,240 Speaker 1: that's probably because you have all these federal workers who 135 00:07:36,280 --> 00:07:38,840 Speaker 1: have lenient work from home policies. And in fact, the 136 00:07:38,920 --> 00:07:42,520 Speaker 1: mayor of Washington, d C, Neuraler Browser, has been kind 137 00:07:42,520 --> 00:07:46,840 Speaker 1: of begging the Biden administration to require federal employees to 138 00:07:46,880 --> 00:07:49,000 Speaker 1: get back to work so that they can make up 139 00:07:49,040 --> 00:07:51,840 Speaker 1: the loss money exactly. And in fact, in New York, 140 00:07:52,360 --> 00:07:55,680 Speaker 1: Mayor Eric Adams has been a huge proponent of in 141 00:07:55,800 --> 00:07:59,880 Speaker 1: person work. He's you know, has federal employees in the office, 142 00:08:00,040 --> 00:08:02,280 Speaker 1: you know, depending on their jobs obviously like you know, 143 00:08:02,480 --> 00:08:05,040 Speaker 1: during the week, um, and it's been a big issue 144 00:08:05,120 --> 00:08:08,280 Speaker 1: because people don't really want to be in fun days 145 00:08:08,280 --> 00:08:11,040 Speaker 1: a week the way they were used to before. So, yeah, 146 00:08:11,120 --> 00:08:12,880 Speaker 1: do you see at the top of the list, followed 147 00:08:13,120 --> 00:08:16,720 Speaker 1: by Atlanta, number two, Number three is Phoenix, Number four 148 00:08:16,800 --> 00:08:19,640 Speaker 1: is l A, New York City coming in at number five, 149 00:08:19,720 --> 00:08:22,000 Speaker 1: but it's very similar to l A with a thirty 150 00:08:22,040 --> 00:08:29,840 Speaker 1: three reduction in person days. Then San Francisco, Boston, Miami, Chicago, Dallas, Philly, Houston, 151 00:08:30,440 --> 00:08:33,200 Speaker 1: And so we're seeing this happening all over the country 152 00:08:33,200 --> 00:08:36,920 Speaker 1: where cities are taken big hits because people aren't coming 153 00:08:37,000 --> 00:08:41,160 Speaker 1: downtown as much. Yeah, and we're seeing it all over 154 00:08:41,200 --> 00:08:43,440 Speaker 1: the world in fact, but you know, their data was 155 00:08:43,520 --> 00:08:47,840 Speaker 1: pretty u S specific. So we also have the spending reductions. 156 00:08:47,880 --> 00:08:49,800 Speaker 1: And even though New York is only sort of number 157 00:08:49,800 --> 00:08:54,400 Speaker 1: five in terms of reduction in person days, it's number 158 00:08:54,400 --> 00:08:58,160 Speaker 1: one in terms of spending because New York is expensive. 159 00:08:58,320 --> 00:09:00,480 Speaker 1: So New York is at the top of the list 160 00:09:00,520 --> 00:09:03,120 Speaker 1: with a reduction of four thousands, six hundred and sixty 161 00:09:03,120 --> 00:09:06,720 Speaker 1: one dollar per person per year. L A is four thousand, 162 00:09:06,800 --> 00:09:09,920 Speaker 1: two hundred d C is closer to about four thousand. 163 00:09:10,520 --> 00:09:14,240 Speaker 1: Atlanta is three thousand, nine hundred and so on. But 164 00:09:14,360 --> 00:09:16,319 Speaker 1: we thought it was a really interesting kind of window 165 00:09:16,480 --> 00:09:20,480 Speaker 1: into sort of how cities are getting impacted by this trend, 166 00:09:20,720 --> 00:09:23,000 Speaker 1: and you know how big a hit they're taking. There 167 00:09:23,080 --> 00:09:24,640 Speaker 1: is a so what to all of this, Right, they 168 00:09:24,679 --> 00:09:28,760 Speaker 1: have ripple effects of causing a decline and consumption of 169 00:09:28,800 --> 00:09:33,040 Speaker 1: food and beverages, right, transit rides impacting small business owners, 170 00:09:33,320 --> 00:09:37,120 Speaker 1: lowering sales tax revenue, transit operating revenue, which we're clearly 171 00:09:37,120 --> 00:09:39,800 Speaker 1: seeing in New York with the m T a shortfall, 172 00:09:40,240 --> 00:09:43,080 Speaker 1: and those drop offs have been especially acute as our 173 00:09:43,120 --> 00:09:46,280 Speaker 1: reporting is showed on Mondays and Fridays, right when employees 174 00:09:46,360 --> 00:09:50,280 Speaker 1: have shown the strongest preference to work from home. And 175 00:09:50,320 --> 00:09:54,240 Speaker 1: so cities really have to figure this out. The importance 176 00:09:54,280 --> 00:09:58,199 Speaker 1: of these revenue sources varies from city to city in 177 00:09:58,240 --> 00:10:00,520 Speaker 1: some cases for cities and regions, the US is there 178 00:10:00,600 --> 00:10:04,880 Speaker 1: so urgent and are just requiring immediate fiscal help in 179 00:10:04,920 --> 00:10:07,120 Speaker 1: the very near term. And even if you look at 180 00:10:07,200 --> 00:10:10,280 Speaker 1: New York City, Eric Adams ruled out his hundred and 181 00:10:10,320 --> 00:10:13,920 Speaker 1: three billion dollar budget and is already projecting tax revenue 182 00:10:13,960 --> 00:10:16,600 Speaker 1: shortfalls in the out years, like, yes, we can balance 183 00:10:16,640 --> 00:10:19,079 Speaker 1: this year, but there's going to be a lagging effect 184 00:10:19,160 --> 00:10:23,800 Speaker 1: to the impact of all of this. So all that 185 00:10:24,080 --> 00:10:27,200 Speaker 1: money has to go somewhere. It's not that people aren't 186 00:10:27,240 --> 00:10:29,880 Speaker 1: spending at all anymore, they're just spending closer to home 187 00:10:29,920 --> 00:10:32,720 Speaker 1: when they work from home. And so we are seeing, 188 00:10:33,080 --> 00:10:36,160 Speaker 1: you know, MasterCard has this really cool kind of data 189 00:10:36,800 --> 00:10:41,400 Speaker 1: and it measures in store and online retail sales across 190 00:10:41,480 --> 00:10:45,000 Speaker 1: all forms of payment. So this is literally New York 191 00:10:45,080 --> 00:10:48,400 Speaker 1: City spending data that they have, which is such a 192 00:10:48,400 --> 00:10:52,280 Speaker 1: cool data source we found. And what we saw in 193 00:10:52,320 --> 00:10:57,840 Speaker 1: their data is that you know, Manhattan growth in like 194 00:10:57,880 --> 00:11:04,520 Speaker 1: in certain months like September, versus that retail sales was growing, 195 00:11:04,840 --> 00:11:07,480 Speaker 1: you know, a little bit, but not a lot. And 196 00:11:07,480 --> 00:11:10,880 Speaker 1: then you saw in the outer boroughs higher levels of 197 00:11:10,920 --> 00:11:16,360 Speaker 1: growth relative versus in Manhattan, and I think that tells us, 198 00:11:16,640 --> 00:11:18,880 Speaker 1: you know, we don't know obviously exactly what's going on, 199 00:11:18,920 --> 00:11:20,920 Speaker 1: but we were looking at week days and so we 200 00:11:21,000 --> 00:11:24,120 Speaker 1: believe what's happening is that, you know, work from home 201 00:11:24,360 --> 00:11:29,000 Speaker 1: is providing an economic benefit in these other parts of 202 00:11:29,080 --> 00:11:32,880 Speaker 1: the city. Emma and Donna Lease stay with me, will 203 00:11:32,960 --> 00:11:44,000 Speaker 1: continue talking after the break. Donna, Emma, you've painted this 204 00:11:44,200 --> 00:11:47,520 Speaker 1: very vivid picture of how cities are being affected by 205 00:11:47,559 --> 00:11:51,120 Speaker 1: workers not coming into the office as much on certain days. 206 00:11:52,120 --> 00:11:55,520 Speaker 1: So the big question now is what are cities actually 207 00:11:55,720 --> 00:11:59,840 Speaker 1: doing about it? Businesses affected mass transit. You have lots 208 00:11:59,840 --> 00:12:03,480 Speaker 1: of office buildings that are standing empty or underused part 209 00:12:03,600 --> 00:12:06,680 Speaker 1: of the week. How is this affected cities and like 210 00:12:06,720 --> 00:12:10,240 Speaker 1: sort of why is it so important to cities? Cities 211 00:12:10,280 --> 00:12:14,240 Speaker 1: really care about this because for one thing, they use, 212 00:12:14,720 --> 00:12:19,000 Speaker 1: you know, tax revenue from consumer spending to fund the city. 213 00:12:19,120 --> 00:12:21,600 Speaker 1: They also care about it because another big form of 214 00:12:21,600 --> 00:12:25,400 Speaker 1: revenue for cities is income taxes. And if I don't 215 00:12:25,400 --> 00:12:27,360 Speaker 1: have to live in New York City because I only 216 00:12:27,360 --> 00:12:29,360 Speaker 1: have to be in the office one time a week, 217 00:12:29,400 --> 00:12:31,120 Speaker 1: and I can, you know, take the hit of a 218 00:12:31,240 --> 00:12:34,160 Speaker 1: two hour commute from some other state, maybe I'm going 219 00:12:34,200 --> 00:12:36,960 Speaker 1: to move away. Uh. And in a city that's as 220 00:12:37,040 --> 00:12:39,840 Speaker 1: expensive and difficult to live in as New York and 221 00:12:39,920 --> 00:12:43,000 Speaker 1: many cities are expensive and difficult to live in, that 222 00:12:43,200 --> 00:12:46,679 Speaker 1: is a big existential risk. What is the point of 223 00:12:46,720 --> 00:12:49,199 Speaker 1: living in New York City if you don't really have 224 00:12:49,320 --> 00:12:52,520 Speaker 1: to live there and you're paying a big price? Yeah, So, 225 00:12:52,559 --> 00:12:55,320 Speaker 1: I think for city officials, the big shift to work 226 00:12:55,360 --> 00:12:58,720 Speaker 1: from home has really increased the sensitivity of the local 227 00:12:58,760 --> 00:13:02,040 Speaker 1: tax base. So people residents in New York are going 228 00:13:02,040 --> 00:13:04,200 Speaker 1: to be a lot more attuned to the quality of 229 00:13:04,200 --> 00:13:07,079 Speaker 1: local governance, the quality of local public goods that are 230 00:13:07,120 --> 00:13:10,120 Speaker 1: being provided to the people that live and work here. 231 00:13:10,400 --> 00:13:12,640 Speaker 1: So the stakes are really high. So if you have 232 00:13:12,679 --> 00:13:15,240 Speaker 1: a city that gets it right, either by virtue of 233 00:13:15,360 --> 00:13:18,640 Speaker 1: a great functioning political system or is just endowed with 234 00:13:18,720 --> 00:13:21,800 Speaker 1: the kinds of amenities that people value, like here in Manhattan, 235 00:13:22,160 --> 00:13:25,080 Speaker 1: those cities might be like well positioned to benefit from 236 00:13:25,200 --> 00:13:27,640 Speaker 1: work from home. But for the cities that don't get 237 00:13:27,679 --> 00:13:30,959 Speaker 1: it right, like due to a number of factors, whether 238 00:13:30,960 --> 00:13:34,560 Speaker 1: they're economic or political reasons, some researchers worry that they 239 00:13:34,600 --> 00:13:37,920 Speaker 1: may face a potential doom loop right as downward spiral 240 00:13:37,960 --> 00:13:41,959 Speaker 1: where residents leave and inward commuters diminished. I think are 241 00:13:42,000 --> 00:13:45,480 Speaker 1: reporting has shown that really the economic benefit of having 242 00:13:45,520 --> 00:13:49,240 Speaker 1: these inward commuters into the city is incredibly important. And 243 00:13:49,280 --> 00:13:53,160 Speaker 1: so there's this monumental task that policymakers are now faced with, 244 00:13:53,400 --> 00:13:56,240 Speaker 1: and they're trying to work out how to best make 245 00:13:56,280 --> 00:13:59,480 Speaker 1: this transition happen, which they anticipate are going to take 246 00:13:59,640 --> 00:14:04,200 Speaker 1: years to achieve, and what does it look like? What changes? 247 00:14:04,280 --> 00:14:06,880 Speaker 1: Because there's a thing happening now in a lot of 248 00:14:06,920 --> 00:14:09,520 Speaker 1: cities where politicians answer to it is to try to 249 00:14:09,559 --> 00:14:11,640 Speaker 1: force people to get to come back to work five 250 00:14:11,720 --> 00:14:13,800 Speaker 1: days a week, because that's what the system is set 251 00:14:13,880 --> 00:14:16,080 Speaker 1: up for. But it doesn't seem realistic. A lot of 252 00:14:16,080 --> 00:14:18,360 Speaker 1: workers they're saying, well, why should I have to pay 253 00:14:18,440 --> 00:14:21,200 Speaker 1: to sustain the system that no longer works for me? 254 00:14:21,800 --> 00:14:24,560 Speaker 1: So change will have to come. What does it actually 255 00:14:24,720 --> 00:14:28,040 Speaker 1: look like? City officials right now are really trying to 256 00:14:28,080 --> 00:14:33,080 Speaker 1: wrap their arms around possible solutions. Office conversions is one 257 00:14:33,120 --> 00:14:35,640 Speaker 1: of them. You know, as Emma mentioned, this is a 258 00:14:35,760 --> 00:14:39,560 Speaker 1: very difficult undertaking. There's ten million square footage that Mayor 259 00:14:39,600 --> 00:14:42,600 Speaker 1: Adams thinks is like something that could be repurposed to 260 00:14:42,640 --> 00:14:45,080 Speaker 1: get people back into downtown so that they're not just 261 00:14:45,600 --> 00:14:48,040 Speaker 1: there to go to the office, but they're living and 262 00:14:48,080 --> 00:14:51,840 Speaker 1: going out to restaurants and helping support small businesses. How To, 263 00:14:52,080 --> 00:14:54,280 Speaker 1: it's going to be really tough, and we don't really 264 00:14:54,280 --> 00:14:57,840 Speaker 1: want to overdo the office conversions because you still need 265 00:14:58,200 --> 00:15:01,120 Speaker 1: office buildings to be there as an like an economic 266 00:15:01,160 --> 00:15:03,440 Speaker 1: engine of growth for the city. The one thing that 267 00:15:03,480 --> 00:15:05,040 Speaker 1: I also want to add on to this, like with 268 00:15:05,080 --> 00:15:08,040 Speaker 1: the rise of office vacancies. Right so, because we have 269 00:15:08,200 --> 00:15:11,120 Speaker 1: this like stark contrast from the old ebb of daytime 270 00:15:11,160 --> 00:15:13,960 Speaker 1: population surges in the middle of the day, you know, 271 00:15:14,000 --> 00:15:16,840 Speaker 1: you end up having these declining office rents that reduce 272 00:15:17,240 --> 00:15:21,320 Speaker 1: the value of commercial buildings and ultimately end up negatively 273 00:15:21,440 --> 00:15:24,840 Speaker 1: impacting property revenue, which is such an important part of 274 00:15:24,880 --> 00:15:28,880 Speaker 1: the city's livelihood. So some of the researchers at Colombia 275 00:15:29,040 --> 00:15:33,680 Speaker 1: n YU have modeled this drop in office market value, 276 00:15:33,680 --> 00:15:36,920 Speaker 1: which their estimating could cost like five billion dollars in 277 00:15:37,000 --> 00:15:40,440 Speaker 1: annual loss tax revenue, or really five percent of the 278 00:15:40,480 --> 00:15:44,960 Speaker 1: city's budget. That is a significant chunk. Having that five 279 00:15:45,000 --> 00:15:47,880 Speaker 1: percent of the city's budget be eaten a way because 280 00:15:47,920 --> 00:15:52,120 Speaker 1: of office building depreciation. Market value depreciation, I mean, really 281 00:15:52,160 --> 00:15:55,840 Speaker 1: eats into the coffers of the city. But there's a 282 00:15:55,880 --> 00:15:58,480 Speaker 1: big hole that will be left to be filled, right 283 00:15:58,480 --> 00:16:02,440 Speaker 1: that you need that additional tax revenue to fund schools, 284 00:16:02,760 --> 00:16:05,960 Speaker 1: to fund the rats are so that like the streets 285 00:16:05,960 --> 00:16:09,360 Speaker 1: are clean, you know, pick up trash, policing, all of 286 00:16:09,400 --> 00:16:12,560 Speaker 1: these things that are also at the crux of you 287 00:16:12,600 --> 00:16:14,720 Speaker 1: know what Eric Adams has to wrestle with in order 288 00:16:14,760 --> 00:16:18,600 Speaker 1: to even create a feeling of economic recovery, right that 289 00:16:18,680 --> 00:16:21,560 Speaker 1: the city is back, that they don't have these ongoing 290 00:16:21,640 --> 00:16:25,280 Speaker 1: issues for every different piece of the puzzle, there's perhaps 291 00:16:25,280 --> 00:16:27,920 Speaker 1: a different solution. One thing we've heard from Sweet Green 292 00:16:28,080 --> 00:16:30,160 Speaker 1: is in these stores that have been really hard hit 293 00:16:30,200 --> 00:16:33,800 Speaker 1: from this, they're actually trying to renegotiate their agreements with 294 00:16:33,880 --> 00:16:36,680 Speaker 1: landlords so they can instead of paying sort of fixed 295 00:16:36,720 --> 00:16:38,600 Speaker 1: rent every month, maybe they can do it as a 296 00:16:38,600 --> 00:16:41,600 Speaker 1: percentage of sales. For instance. Um, you know, with the 297 00:16:41,720 --> 00:16:45,640 Speaker 1: m t A, with these big kind of budget gaps 298 00:16:45,640 --> 00:16:48,240 Speaker 1: that they're facing down, you know, they've said, unless we 299 00:16:48,320 --> 00:16:50,880 Speaker 1: get some more money from you know, the city and state, 300 00:16:50,920 --> 00:16:53,440 Speaker 1: we're going to need to cut service. And in fact, 301 00:16:53,520 --> 00:16:56,680 Speaker 1: they are already cutting service on Mondays and Fridays. They're 302 00:16:56,720 --> 00:17:01,360 Speaker 1: planning to on several lines that you know, largely connect 303 00:17:01,680 --> 00:17:05,160 Speaker 1: Manhattan and other borrows. You know, there's a lot of 304 00:17:05,320 --> 00:17:09,760 Speaker 1: emphasis by city leaders trying to inch your way back 305 00:17:09,760 --> 00:17:13,080 Speaker 1: to what things look like in and I think something 306 00:17:13,160 --> 00:17:16,880 Speaker 1: we found really fascinating from this reporting is might there 307 00:17:16,920 --> 00:17:20,480 Speaker 1: be another path forward, for instance, by leaning into more 308 00:17:20,480 --> 00:17:23,600 Speaker 1: of that outer borrow silver lining that we're seeing. And 309 00:17:23,640 --> 00:17:25,960 Speaker 1: so I think what's really interesting about kind of putting 310 00:17:26,000 --> 00:17:29,760 Speaker 1: some numbers on these problems that hopefully helps city officials, 311 00:17:29,800 --> 00:17:33,359 Speaker 1: business leaders, everyone else find a way to take a 312 00:17:33,400 --> 00:17:36,760 Speaker 1: path forward and create a better way for the city 313 00:17:36,800 --> 00:17:40,679 Speaker 1: to recover. Emma Court, Donna Bora, thanks so much for 314 00:17:40,760 --> 00:17:42,960 Speaker 1: coming on the show today. Thank you so much, Wess, 315 00:17:43,200 --> 00:17:46,160 Speaker 1: it's been a pleasure. When we come back, we'll talk 316 00:17:46,200 --> 00:17:57,199 Speaker 1: about ways cities can reinvent themselves. So what are some 317 00:17:57,280 --> 00:17:59,520 Speaker 1: of the things cities can do to meet all these 318 00:17:59,600 --> 00:18:03,560 Speaker 1: changing needs and not go broke? Tracy Hadn't Low thinks 319 00:18:03,600 --> 00:18:06,000 Speaker 1: about these questions for a living. She's a fellow at 320 00:18:06,000 --> 00:18:10,280 Speaker 1: the Brookings Institution and her research focuses on commercial real 321 00:18:10,400 --> 00:18:13,920 Speaker 1: estate and how cities can become better places to work 322 00:18:14,040 --> 00:18:18,600 Speaker 1: and live. And she joins me from Baltimore. Tracy, if 323 00:18:18,640 --> 00:18:21,000 Speaker 1: employees won't come back five days a week, and it 324 00:18:21,040 --> 00:18:24,680 Speaker 1: seems like in a lot of businesses where physical presence 325 00:18:24,760 --> 00:18:26,919 Speaker 1: isn't required, that seems to be the new way of 326 00:18:26,960 --> 00:18:30,160 Speaker 1: doing things. How are cities going to have to adapt? 327 00:18:30,240 --> 00:18:32,159 Speaker 1: We have a lot of office buildings which will no 328 00:18:32,240 --> 00:18:34,800 Speaker 1: longer be needed. You have a lot of businesses that 329 00:18:34,920 --> 00:18:37,640 Speaker 1: can't make as much money. So I guess what we're 330 00:18:37,640 --> 00:18:40,919 Speaker 1: looking at down the road or cities changing as they 331 00:18:40,960 --> 00:18:44,600 Speaker 1: always do. What sort of changes do you see? So 332 00:18:44,880 --> 00:18:47,119 Speaker 1: a trend that was going on before the pandemic that 333 00:18:47,200 --> 00:18:50,919 Speaker 1: has certainly been accelerated is that employers are consuming fewer 334 00:18:50,960 --> 00:18:54,679 Speaker 1: square feet of office space per worker. That's been declining 335 00:18:54,960 --> 00:18:57,360 Speaker 1: in the top ten U S office markets for over 336 00:18:57,400 --> 00:19:00,159 Speaker 1: a decade, and it's going to hit new low is 337 00:19:00,240 --> 00:19:02,879 Speaker 1: now and it's going to hit more markets. This is 338 00:19:02,920 --> 00:19:04,639 Speaker 1: going to be everywhere in the US, not just in 339 00:19:04,680 --> 00:19:09,120 Speaker 1: the biggest office markets. So the question there is how 340 00:19:09,160 --> 00:19:11,920 Speaker 1: low is it going to get? Because if your employees 341 00:19:11,960 --> 00:19:13,960 Speaker 1: are in the office three days a week, you still 342 00:19:14,000 --> 00:19:17,399 Speaker 1: need an office. The question is like how much obsolete 343 00:19:17,400 --> 00:19:20,080 Speaker 1: office space is there? And that depends on how much 344 00:19:20,080 --> 00:19:24,400 Speaker 1: employers can reduce their footprints. Relatively few employers are taking 345 00:19:24,440 --> 00:19:28,320 Speaker 1: those footprints to zero and going fully remote. So the 346 00:19:28,400 --> 00:19:32,440 Speaker 1: question is what does the in person component of a 347 00:19:32,600 --> 00:19:36,680 Speaker 1: hybrid workplace look like, how much space does it take? 348 00:19:37,119 --> 00:19:39,800 Speaker 1: And then of the remaining space, how much is now 349 00:19:39,840 --> 00:19:44,840 Speaker 1: obsolete because it doesn't match these new demands for what 350 00:19:45,000 --> 00:19:48,119 Speaker 1: offices have to be like and how much of it 351 00:19:48,200 --> 00:19:54,520 Speaker 1: can be adapted. From a city planning perspective, when you 352 00:19:54,520 --> 00:19:57,680 Speaker 1: know planning commissions and mayors and city councils start looking 353 00:19:57,760 --> 00:20:00,359 Speaker 1: down the road at their cities. What sort of things 354 00:20:00,400 --> 00:20:04,600 Speaker 1: are you hearing about the way they want downtowns to change, 355 00:20:05,040 --> 00:20:09,280 Speaker 1: not just office buildings, but restaurants and shops that depended 356 00:20:09,359 --> 00:20:13,120 Speaker 1: on that nine to five revenue that is now no 357 00:20:13,160 --> 00:20:15,159 Speaker 1: longer as big as it used to be. There's a 358 00:20:15,200 --> 00:20:18,720 Speaker 1: few obvious things that cities are already doing and baking 359 00:20:18,720 --> 00:20:20,920 Speaker 1: into their strategies that makes sense. So the first is 360 00:20:20,960 --> 00:20:26,400 Speaker 1: incentivizing the conversion of office into housing. So saying like, okay, 361 00:20:26,480 --> 00:20:29,880 Speaker 1: you can strengthen demand for local retail, for example, by 362 00:20:29,920 --> 00:20:32,840 Speaker 1: making sure that downtown is a place where people are 363 00:20:32,960 --> 00:20:35,639 Speaker 1: not just between nine and five, but also five and 364 00:20:35,720 --> 00:20:40,040 Speaker 1: nine and uh. And you can also do that by 365 00:20:40,080 --> 00:20:44,280 Speaker 1: increasing the quality of the other amenities that are in 366 00:20:44,280 --> 00:20:48,520 Speaker 1: a downtown neighborhood, like waterfronts, other high quality open spaces, 367 00:20:48,800 --> 00:20:52,439 Speaker 1: places just for people to spend time, um, whether they're 368 00:20:52,480 --> 00:20:56,920 Speaker 1: working or doing something else. Right, Since uh majority of 369 00:20:56,960 --> 00:20:59,919 Speaker 1: travel is non work travel, and a majority of the 370 00:21:00,040 --> 00:21:04,280 Speaker 1: time in our lives is not spent at work, it 371 00:21:04,359 --> 00:21:08,240 Speaker 1: makes sense that if you just want more people, then 372 00:21:08,280 --> 00:21:15,760 Speaker 1: you need more nonwork amenities and activities and experiences and things. 373 00:21:15,800 --> 00:21:19,119 Speaker 1: How hard will it be for cities to begin to 374 00:21:19,240 --> 00:21:23,360 Speaker 1: adapt to this new reality. So was what is holding 375 00:21:23,440 --> 00:21:28,600 Speaker 1: up downtowns from adapting is that of the built environment 376 00:21:28,720 --> 00:21:32,880 Speaker 1: is controlled by the private sector individuals or companies who 377 00:21:32,920 --> 00:21:35,879 Speaker 1: make their own decisions about their own assets. What cities 378 00:21:35,920 --> 00:21:39,160 Speaker 1: can control is what's happening in the public right of way, 379 00:21:39,359 --> 00:21:42,919 Speaker 1: and there are huge untapped opportunities in most cities to 380 00:21:42,960 --> 00:21:45,560 Speaker 1: rethink that, not just in terms of high quality open spaces, 381 00:21:45,560 --> 00:21:49,880 Speaker 1: but traffic safety, and to think about supporting retail, for example, 382 00:21:49,920 --> 00:21:54,560 Speaker 1: by reallocating space from car storage and to retail. Like 383 00:21:54,680 --> 00:21:58,080 Speaker 1: there's there's plenty of opportunities there that cities can take. 384 00:21:58,320 --> 00:22:01,640 Speaker 1: But what about this that the built environment that's controlled 385 00:22:01,640 --> 00:22:06,880 Speaker 1: by the private sector. Figuring out how to incentivize the 386 00:22:06,960 --> 00:22:11,240 Speaker 1: owners of these assets that are located at the heart 387 00:22:11,280 --> 00:22:15,080 Speaker 1: of our cities. That is the big question right now 388 00:22:15,400 --> 00:22:20,600 Speaker 1: of how to motivate these players to think differently about 389 00:22:20,680 --> 00:22:23,399 Speaker 1: how they use their assets and put them to work. 390 00:22:24,160 --> 00:22:28,359 Speaker 1: If we make adapting buildings really hard to do, people 391 00:22:28,400 --> 00:22:31,239 Speaker 1: just won't do it and they'll just walk away, like 392 00:22:31,840 --> 00:22:35,000 Speaker 1: there's always another option. What's going to happen with some 393 00:22:35,040 --> 00:22:36,879 Speaker 1: of these office buildings is like there is going to 394 00:22:36,960 --> 00:22:39,480 Speaker 1: be a wave of foreclosures, people are just going to 395 00:22:39,600 --> 00:22:41,199 Speaker 1: give the keys back to the bank, and you know 396 00:22:41,240 --> 00:22:45,880 Speaker 1: who's really going to do nothing with it? Banks? Are 397 00:22:45,920 --> 00:22:49,640 Speaker 1: there tax incentives or there other things that cities are 398 00:22:49,680 --> 00:22:55,160 Speaker 1: already starting to offer to make those switchovers. Yeah, very 399 00:22:55,280 --> 00:22:58,959 Speaker 1: few office buildings are a hundred percent vacant. Even if 400 00:22:59,000 --> 00:23:02,640 Speaker 1: they're half empty, that means they're also have full So 401 00:23:02,840 --> 00:23:05,920 Speaker 1: when you're talking about an office building, you're talking about 402 00:23:05,960 --> 00:23:10,000 Speaker 1: an income producing asset that has current tenants in it. 403 00:23:10,680 --> 00:23:12,840 Speaker 1: First off, the motivation is low for the owner to 404 00:23:12,880 --> 00:23:15,680 Speaker 1: do anything with it because it is generating income, maybe 405 00:23:15,840 --> 00:23:18,600 Speaker 1: not as much as it used to, but if you 406 00:23:18,640 --> 00:23:21,200 Speaker 1: wanted to do anything else with it, that would come 407 00:23:21,200 --> 00:23:25,399 Speaker 1: with costs. So that's a disincentive to do anything different 408 00:23:25,440 --> 00:23:28,560 Speaker 1: with it, especially when you do have some revenue. So 409 00:23:28,720 --> 00:23:32,920 Speaker 1: in terms of incentivizing it, that means that the carrying 410 00:23:33,000 --> 00:23:36,840 Speaker 1: cost of the vacant space has to increase, and the 411 00:23:36,920 --> 00:23:41,439 Speaker 1: trick is increasing that carrying cost in a way that 412 00:23:41,560 --> 00:23:47,359 Speaker 1: doesn't further disincentivize the owner from actually doing anything with 413 00:23:47,400 --> 00:23:50,399 Speaker 1: it and investing in the property. Doing something different with 414 00:23:50,440 --> 00:23:55,760 Speaker 1: the property attractive. For example, Pittsburgh and Harrisburg in Pennsylvania, 415 00:23:56,240 --> 00:23:59,320 Speaker 1: both have a land value tax system in which they 416 00:23:59,359 --> 00:24:03,720 Speaker 1: separately acts the land under a building and the building itself. 417 00:24:04,160 --> 00:24:07,560 Speaker 1: So even if an office building is dwindling in value 418 00:24:07,800 --> 00:24:10,960 Speaker 1: as it becomes more and more vacant, the land and 419 00:24:11,000 --> 00:24:15,600 Speaker 1: the location remain valuable, and that increases the carrying cost 420 00:24:15,800 --> 00:24:19,760 Speaker 1: of the asset because you can't escape the land tax. 421 00:24:20,400 --> 00:24:23,200 Speaker 1: I was speaking to a couple of my colleagues earlier 422 00:24:23,359 --> 00:24:25,919 Speaker 1: about how all of this is playing out in New 423 00:24:26,000 --> 00:24:28,239 Speaker 1: York City, which of course is you know, sort of 424 00:24:28,280 --> 00:24:31,720 Speaker 1: the center of attention a lot of the time whenever 425 00:24:31,760 --> 00:24:34,480 Speaker 1: it comes to real estate, and in New York and 426 00:24:34,560 --> 00:24:37,240 Speaker 1: other big cities, one of the huge complaints is no 427 00:24:37,280 --> 00:24:39,600 Speaker 1: one can afford to live there, and so it's hard 428 00:24:39,680 --> 00:24:42,800 Speaker 1: to live where you work. Do you see this as 429 00:24:42,840 --> 00:24:47,280 Speaker 1: being an opportunity to change cities into places where people 430 00:24:47,280 --> 00:24:51,200 Speaker 1: can actually afford to live and work. So I don't 431 00:24:51,200 --> 00:24:53,959 Speaker 1: think that converting offices into housing is going to make 432 00:24:53,960 --> 00:24:56,720 Speaker 1: a meaningful down in the housing crisis, because we were 433 00:24:56,720 --> 00:25:00,080 Speaker 1: talking about a pretty small number of units relative of 434 00:25:00,119 --> 00:25:03,000 Speaker 1: to the need that is out there. What it can 435 00:25:03,119 --> 00:25:07,960 Speaker 1: do is revitalized downtowns as places by bringing more people 436 00:25:08,040 --> 00:25:12,280 Speaker 1: into them, and everyone should have that chance, not just 437 00:25:12,440 --> 00:25:16,000 Speaker 1: the people with the most money. So in terms of 438 00:25:16,480 --> 00:25:19,919 Speaker 1: policies and incentives that cities can offer in order to 439 00:25:20,119 --> 00:25:23,560 Speaker 1: encourage the highest and best use of these locations, I 440 00:25:23,600 --> 00:25:27,119 Speaker 1: think there's a strong argument to be made that incentivizing 441 00:25:27,119 --> 00:25:31,800 Speaker 1: the production of housing, including affordable housing, and including housing 442 00:25:31,880 --> 00:25:35,560 Speaker 1: for particular communities of concern that are concentrated downtown, like 443 00:25:35,680 --> 00:25:41,240 Speaker 1: people experiencing homelessness, there's a clear case to take these 444 00:25:41,280 --> 00:25:44,600 Speaker 1: underused real estate assets and serve a greater public good. 445 00:25:45,080 --> 00:25:49,400 Speaker 1: So I think that we will see more policies incentivizing 446 00:25:49,760 --> 00:25:53,720 Speaker 1: the production of housing and affordable housing in downtowns. And 447 00:25:53,720 --> 00:25:55,640 Speaker 1: I think that there's a role for the federal government 448 00:25:55,720 --> 00:25:59,760 Speaker 1: here too. Just like the federal government offers tax credits 449 00:25:59,800 --> 00:26:04,320 Speaker 1: for the preservation of historic buildings, why not offer a 450 00:26:04,400 --> 00:26:09,720 Speaker 1: tax credit for the recycling of buildings into affordable housing. 451 00:26:11,000 --> 00:26:14,639 Speaker 1: As someone who looks at especially commercial real estate and 452 00:26:14,720 --> 00:26:17,520 Speaker 1: how that's changing all the time, look down the road 453 00:26:17,560 --> 00:26:20,840 Speaker 1: for us in I don't know, three years, five years, 454 00:26:20,880 --> 00:26:25,280 Speaker 1: in cities like Baltimore and New York Chicago, what looks 455 00:26:25,520 --> 00:26:29,840 Speaker 1: much different than it looks right now. I Mean, my 456 00:26:30,240 --> 00:26:34,800 Speaker 1: greatest fear is that nothing looks different, and that cities 457 00:26:34,960 --> 00:26:41,400 Speaker 1: enter generation of decline because of the lack of dynamism 458 00:26:41,520 --> 00:26:45,320 Speaker 1: in the real estate sector that prevents the built environment 459 00:26:45,440 --> 00:26:49,720 Speaker 1: from adapting in a timely way. That is my greatest fear. 460 00:26:50,680 --> 00:26:55,520 Speaker 1: My greatest hope is that we see uh boom in population. 461 00:26:56,560 --> 00:27:00,439 Speaker 1: We see all kinds of new housing downtown, that we 462 00:27:00,480 --> 00:27:06,000 Speaker 1: see right of ways reimagined from being enormous channels for 463 00:27:06,080 --> 00:27:12,920 Speaker 1: commuters into um live work play spaces for people, and 464 00:27:13,200 --> 00:27:18,640 Speaker 1: that we see office buildings incorporating some of the comforts 465 00:27:18,640 --> 00:27:22,280 Speaker 1: of home with all of the important things that we 466 00:27:22,359 --> 00:27:26,640 Speaker 1: need for work, like spaces to collaborate and incredible connectivity. 467 00:27:26,880 --> 00:27:30,480 Speaker 1: Tracy had Low, Thanks so much for talking with me today. 468 00:27:30,520 --> 00:27:34,399 Speaker 1: Thanks Wes. You can read more from Emma Court and 469 00:27:34,600 --> 00:27:38,760 Speaker 1: Donna Borak at Bloomberg dot com. Thank you for listening 470 00:27:38,760 --> 00:27:40,760 Speaker 1: to us here at The Big Take. It's a daily 471 00:27:40,800 --> 00:27:44,080 Speaker 1: podcast from Bloomberg and I Heart Radio. For more shows 472 00:27:44,080 --> 00:27:46,840 Speaker 1: from my Heart Radio, visit the I Heart Radio app, 473 00:27:47,200 --> 00:27:50,800 Speaker 1: Apple Podcast, or wherever you listen. And we'd love to 474 00:27:50,840 --> 00:27:53,800 Speaker 1: hear from you. Email us questions or comments to Big 475 00:27:53,840 --> 00:27:58,159 Speaker 1: Take at Bloomberg dot net. The supervising producer of The 476 00:27:58,160 --> 00:28:02,000 Speaker 1: Big Take is Vicky Bergoline Huh. Our senior producer is 477 00:28:02,200 --> 00:28:06,639 Speaker 1: Katherine Fink, our producer is Rebecca Chasson, and our associate 478 00:28:06,680 --> 00:28:11,280 Speaker 1: producer is Sam Gebauer. Raphael I'm Seely is our engineer. 479 00:28:11,640 --> 00:28:16,280 Speaker 1: Our original music was composed by Leo Sidrin I'm West Casova. 480 00:28:16,520 --> 00:28:19,280 Speaker 1: We'll be back tomorrow with another Big Tig