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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome to another episode of the Odd Lots Podcast.

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Speaker 1: I'm Joe Wisenthal and I'm Tracy Alloway.

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Speaker 2: Tracy, So, you know, we had that conversation the other

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Speaker 2: day with rich Hill Cohenan Steers, and you sort of

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Speaker 2: put like a maybe not too gloomy gloss on.

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Speaker 1: The commerci not too gloomy gloss, not too gloomy gloss.

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Speaker 2: It's a good one, uh like not great, but like

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Speaker 2: maybe like not terrible. That like price discovery is happening.

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Speaker 2: It's not great, but it's salvageable. There's still room for

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Speaker 2: extending and pretending it's not the end of the world. Like,

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Speaker 2: you know, not great, but not terrible.

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Speaker 1: Yeah, And I think to some extent, we've seen that

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Speaker 1: out in markets in recent months. Right, So there were

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Speaker 1: all these stories about the looming maturity wall. Yeah, a

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Speaker 1: lot of those loans, a lot of those bonds have

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Speaker 1: been refinanced, a lot of companies are surviving, a lot

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Speaker 1: of building owners or real estate developers seem to be

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Speaker 1: surviving under the new conditions. But I guess the wild

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Speaker 1: card in a lot of this is still what happens

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Speaker 1: with interest rates.

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Speaker 2: Here's what I don't get though, Like, at least when

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Speaker 2: we're talking about office and when we're talking about office

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Speaker 2: real estate in big cities, the vacancy rates keep going up.

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Speaker 2: And I sort of thought that would stop, because you know,

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Speaker 2: especially in the middle of last year, you saw a

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Speaker 2: number of pushes where companies like, no, we're serious, it's

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Speaker 2: return to the office time, guys, And so I was like, oh, okay,

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Speaker 2: like that was it, this was the turning point. And

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Speaker 2: yet I think by like at least top line metrics,

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Speaker 2: if you just sort of look at offices and the

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Speaker 2: empty ones, things continue to deteriorate.

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Speaker 1: Office buildings are still empty in places like New York

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Speaker 1: and San Francisco. This is very true, but I think

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Speaker 1: really ate, like people still want to live here residential

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Speaker 1: real estate. I mean, I think those vacancies are pretty low.

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Speaker 1: So there is this like there's this tension where people

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Speaker 1: still want to come to the city, it feels like,

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Speaker 1: or at least move here, but they don't want to

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Speaker 1: go into the office, which fair enough I sympathize.

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Speaker 2: So just a full disclosure or you know, my own

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Speaker 2: stake in this story, which is that last year I

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Speaker 2: bought an apartment in Manhattan. So I am really invested

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Speaker 2: in the idea that Manhattan continues to be a thriving,

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Speaker 2: going concern with people on the streets, looking around, moving about.

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Speaker 2: I feel like that's you know, it's good. It feels

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Speaker 2: good for safety, things like that. I really need this

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Speaker 2: whole thing to hang together.

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Speaker 1: And I'm on the opposite side of this trade, I

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Speaker 1: think where I have no Manhattan ownership. I rent, I

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Speaker 1: would like the rent to go down actually, and I

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Speaker 1: have a house out in the country. So yeah, we'll.

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Speaker 2: Debate between the two of us. Tracy, we're perfectly.

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Speaker 1: Well well hedged.

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Speaker 2: Yeah, that's right, we're perfect He well. Anyway, so we

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Speaker 2: had that conversation with rich Hill last week and he

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Speaker 2: mentioned he said, oh, there's this NYU professor who was

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Speaker 2: one super gloomy about the urban doom loop real estate apocalypse,

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Speaker 2: and he even he's changed his tone. And then when

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Speaker 2: he said that, I thought two things. It's like, hey,

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Speaker 2: I know that guy. Yeah, he's even been to some

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Speaker 2: odd logeed trivia nights. And b I didn't realize he

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Speaker 2: had changed his tone. I didn't even know if that's true.

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Speaker 2: Got stateated on air. I hope it's true, because I

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Speaker 2: want things to be factual on the podcast. So it's

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Speaker 2: like if we should actually we should talk to we

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Speaker 2: should talk to that NYU professor.

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Speaker 1: I like that our fact checks have become sources of content.

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Speaker 1: Now it's just another episode to see if what someone

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Speaker 1: already said is actually correct.

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Speaker 2: We could have just done a fact check, or we

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Speaker 2: just put it out there and then if it's wrong

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Speaker 2: then we get it, we get it debunked on a

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Speaker 2: future episode.

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Speaker 1: All right, let's find it out if it's wrong or not.

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Speaker 2: Well, I'm very excited. We're going to be speaking with

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Speaker 2: Arbid Gupta. He's an associate professor of Finance at NYU

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Speaker 2: Stern and back in twenty twenty two, he was the

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Speaker 2: co author of a paper work from Home and the Office,

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Speaker 2: Real Estate Apocalypse. Of course, there's all this fear of

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Speaker 2: doom loops. People don't go into the office. Then the

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Speaker 2: local businesses that depended on that foot traffic, like Delhi's

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Speaker 2: and salad bars and restaurants go out of business, and

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Speaker 2: then people don't like being in the city even more,

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Speaker 2: and then they move out to the middle of nowhere

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Speaker 2: like you. And then suddenly the whole thing falls apart, Arbet,

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Speaker 2: thank you so much for coming in, Thanks so much

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Speaker 2: for having me. Let's just start with like the basic

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Speaker 2: fact check question, are you as gloomy or do you

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Speaker 2: have the same fears of an office real estate apocalypse

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Speaker 2: as you did in late twenty twenty two.

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Speaker 3: Great, So, taking a step back, my co author Teams

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Speaker 3: and I, so that includes stan One Neiberg who was

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Speaker 3: at Columbia, as well as RNDA Methol who was at

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Speaker 3: unc first began with a research paper that was looking

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Speaker 3: at the urban flight and residential real estate. So we

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Speaker 3: were comparing the price of real estate in the center

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Speaker 3: of cities against the price of residential real estate in

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Speaker 3: the suburbs. And we found is that over the course

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Speaker 3: of the pandemic, the price of suburban real estate, so

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Speaker 3: places like where Tracy lives have gone up from.

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Speaker 2: Maga's really like ex surban but yes, keep going.

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Speaker 3: No, it's really those far far exurban places, places that

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Speaker 3: weren't even really considered commutable, that actually went up in

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Speaker 3: value the most relative to real estate in the center

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Speaker 3: of the city, places like where Joe lives, right, And

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Speaker 3: so we find that that spread is corresponding to a

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Speaker 3: rent gradient or a price gradient really narrowed, and in fact,

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Speaker 3: that narrowing has continued even through twenty twenty three. So

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Speaker 3: urban real estate continues to lose value relative to suburban

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Speaker 3: and far exurban residential real estate. We find that that

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Speaker 3: pattern is really driven by remote work. So it's people

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Speaker 3: that are willing to move further and further away from

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Speaker 3: the city and are less willing to pay for urban

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Speaker 3: amenities and proximity to urban work locations. That's kind of

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Speaker 3: driving this spatial arbitrage across markets.

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Speaker 1: How do you square that spatial arbitrage idea or that

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Speaker 1: price discrepancy urban versus non urban prices with the sense

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Speaker 1: that I mean, it certainly feels reading the headlines, and again,

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Speaker 1: Joe and I might be slightly biased here because we

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Speaker 1: work in financial journalism adjacent to the financial industry, but

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Speaker 1: it feels like there is a drive to get people

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Speaker 1: back into the office.

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Speaker 3: Absolutely, and I think this really varies across cities and

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Speaker 3: across industries. Right, so when you look at the entire country,

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Speaker 3: there's really no good indication that people are more likely

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Speaker 3: to come into the office. Whether you're looking at surveys

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Speaker 3: that kind of ask people, how often are you coming

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Speaker 3: to the office or not, or surveys that ask firms

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Speaker 3: what are your policies or not. But one trend that

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Speaker 3: is pretty noticeable is it's a lot of the largest

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Speaker 3: companies that kind of make up the headlines, firms like

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Speaker 3: Goldman Sacks, they're very vocal about their back to office plans.

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Speaker 3: And you see across some industries, particularly industries like finance,

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Speaker 3: they are a little bit more inclined to get workers

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Speaker 3: into the office. So it's really kind of hard to

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Speaker 3: see a nationwide trend, but it is there in certain

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Speaker 3: industries or firms.

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Speaker 2: So is the doom looping, like, is it spiraling or

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Speaker 2: has anything arrested it?

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Speaker 3: Right? So we looked at this aspect of urban flight,

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Speaker 3: and so across many cities like New York or San Francisco,

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Speaker 3: you see a population loss of about six to eight

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Speaker 3: percent that happened over the course of the pandemic, and

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Speaker 3: this population loss has sort of stabilized. So San Francisco

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Speaker 3: gained a small amount of population last year. New York

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Speaker 3: continued to lose population, but at a smaller rate than

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Speaker 3: previous years, and that kind of motivated us to look

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Speaker 3: into what are the implications for commercial office buildings and

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Speaker 3: this concept of the of the doom loop. So in

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Speaker 3: our paper that came out a couple of years ago,

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Speaker 3: we estimated that New York City office real estate would

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Speaker 3: be down in value about forty to fifty percent. And

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Speaker 3: this can lead to two possible spillovers, right. So one

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Speaker 3: is the impact on financial institutions and banks in particular

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Speaker 3: that are holding a lot of the debt associated with

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Speaker 3: office buildings. So that's a financial doom loop risk.

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Speaker 1: Right.

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Speaker 3: So as the value of these properties declines, loans attached

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Speaker 3: these properties might enter into full and we're seeing default

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Speaker 3: rates now about six to seven percent. That might trigger

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Speaker 3: foreclosures to stress sales that then amplify back and feed

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Speaker 3: into further real estate price to clients. We're seeing the

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Speaker 3: beginnings of this play out, but modification efforts have sort

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Speaker 3: of stemmed this tide for now. The other externality or

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Speaker 3: spillover effect comes onto cities, right, And so that's this

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Speaker 3: urban doom blup idea that you're going to see a

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Speaker 3: cycle driven by remote work leading to office vacancy losses,

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Speaker 3: loss and property value that then hits city budgets. City

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Speaker 3: governments therefore lose money and are forced because of balanced

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Speaker 3: budget requirements to either raised taxes or cut services, and

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Speaker 3: people don't like either of those, and so they might

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Speaker 3: respond by leaving the city in greater numbers, which might

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Speaker 3: then further amplify those losses. And so we're seeing, i think,

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Speaker 3: is sort of the first wave of those events happen

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Speaker 3: with the population loss, the loss and value for commercial

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Speaker 3: real estate. And it's really now kind of in the

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Speaker 3: hands of policymakers to kind of decide how they choose

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Speaker 3: to respond to those treads, right, whether they're going to

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Speaker 3: respond to these losses by trying to kind of raise taxes,

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Speaker 3: cut spending, or trying to improve the quality of life

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Speaker 3: and amenities in urbine areas.

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Speaker 1: So you're the forty percent estimate. So the idea that

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Speaker 1: long run office valuations are forty percent below their pre

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Speaker 1: pandemic levels per your paper, that was kind of assuming

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Speaker 1: that everything pretty much stays the same. Is that right?

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Speaker 3: So we are trying to figure out how persistent remote

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Speaker 3: work is going to be, Right, So we're acknowledging the

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Speaker 3: possibility that these firms might be successful in bringing people

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Speaker 3: back into the office, and we're trying to estimate how

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Speaker 3: likely is that going to happen. We ultimately back that

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Speaker 3: number out by looking at publicly traded stock prices. So

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Speaker 3: the idea is we sort of solve acid pricing model

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Speaker 3: that has a key parameter being how likely are firms

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Speaker 3: to be successful in bringing people back to the office.

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Speaker 3: And basically the only way we can kind of match

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Speaker 3: publicly traded read prices so stocks like Brenado, essel Green

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Speaker 3: is by getting a parameter an estimate for how persistent

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Speaker 3: remote work is likely to be. It's going to be

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Speaker 3: pretty high. So we're basically estimating how likely is it

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Speaker 3: that we're going to stay in this remote work equilibrium,

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Speaker 3: and we sort of estimate that it's a pretty high likelihood.

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Speaker 3: So this world sort of seems to be persistent for now.

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Speaker 2: Yeah, it is striking. So prior to the pandemic, shares

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Speaker 2: of Vernado were about seventy dollars a year. In the

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Speaker 2: middle of twenty twenty three, they hit as low as

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Speaker 2: twelve seventy nine and they've rebounded, but they're only back

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Speaker 2: up to just under thirty dollars, so still about basically

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Speaker 2: cut in half versus pre pandemic levels, and I think

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Speaker 2: sl Green looks pretty similar.

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Speaker 3: Yeah, and the other major pricing indicator to look at

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Speaker 3: is the CMBX series. Right. So, back in the financial crisis,

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Speaker 3: a lot of short traders were looking at the ABX series,

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Speaker 3: which was tracking credit to fault swaps on the price

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Speaker 3: of non agency mortgage backed securities. So the CMBX index

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Speaker 3: similarly tracks the value of CMBs deals, which are backed

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Speaker 3: by bundles of large numbers of commercial real estate mortgages,

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Speaker 3: and these bundles vary in how much office they have.

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Speaker 3: But what we're seeing is a value loss in the

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Speaker 3: CMBX index, which might vary from between fifteen and twenty

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Speaker 3: five percent for a triple b CNBX references in DEX.

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Speaker 3: So that basically means that market participants expect there to

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Speaker 3: be some value loss in these depths accuritizations backed by

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Speaker 3: commercial real estate.

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Speaker 1: Since you mentioned the ABX, and now I have all

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Speaker 1: these subprime memories flooding back. But part of the problem there,

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Speaker 1: or part of the reason the losses were a lot

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Speaker 1: higher than a lot of people expected, was because we

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Speaker 1: estimated the correlation wrong. Right, mortgages when they started going bad,

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Speaker 1: it wasn't just isolated incidents. It ended up being across

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Speaker 1: the country. What do you see in terms of correlation

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Speaker 1: or relationship when it comes to vacant office buildings. Do

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Speaker 1: you see like if one office building doesn't have a

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Speaker 1: lot of business inside it or a lot of renters,

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Speaker 1: does that impact the ones around it?

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Speaker 3: So there probably is this correlation risk within office and

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Speaker 3: maybe other adjacent asset classes like urban retail. And the

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Speaker 3: reason for that is if an office building is vacant,

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Speaker 3: if people aren't going there, well, that just makes it

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Speaker 3: a less attractive destination, even for other people within the

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Speaker 3: same office district. So a good example of this might

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Speaker 3: be DC. So DC actually has a really high remote

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Speaker 3: working rate, in part because a lot of federal government

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Speaker 3: employees have fairly generous remote working policies, and so that

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Speaker 3: means that a lot of different office buildings in the

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Speaker 3: DC market are all experiencing high vacancy at the same time.

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Speaker 3: That does have some spill or effects to other corners

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Speaker 3: of commercial real estate. So if I'm looking at retail

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Speaker 3: in DC or in some markets like San Francisco, even

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Speaker 3: hospitality hotels or impacted as well, but it is confined

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Speaker 3: more to those specific asset classes and doesn't necessarily spill

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Speaker 3: over to all of commercial real estate, which, as we

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Speaker 3: both know, is a very large, heterogenious universe, a lot

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Speaker 3: of different asset tymes.

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Speaker 2: I remember we used to talk sometimes like on TV

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Speaker 2: to Emerging markets manager and it's like, oh, it's very

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Speaker 2: important to remember not all em is a monolith. And

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Speaker 2: now I feel like all CIRE discussions similar thing. You

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Speaker 2: must get in there that CIRI is not a monolith.

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Speaker 2: It includes many different categories. The key maybe dichotomy or

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Speaker 2: question as you sort of set it out, is do

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Speaker 2: cities respond to population flight by raising taxes which might

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Speaker 2: accelerate population flight, or cutting spending which might do the

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Speaker 2: same thing, or do they find some way of actually

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Speaker 2: reversing that trend and making people not want to move out?

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Speaker 2: Like what are they doing so far?

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Speaker 3: So I think the important thing to keep in mind

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Speaker 3: is the really long lags built into all of this, Right,

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Speaker 3: So the office leases are really long term in nature,

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Speaker 3: and so even to this point, many firms haven't had

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Speaker 3: to make an active space decision yet because they're still

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Speaker 3: inherited at least from before the pandemic. Then you have

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Speaker 3: the mortgages on these assets that are also really long

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Speaker 3: term in nature, and so you haven't had to hit

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Speaker 3: a refinancing point yet. For these owners. And then finally

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Speaker 3: you have the property tax assessment cycle, which is also

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Speaker 3: really long, so it takes a really long time for

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Speaker 3: cities to necessarily recognize a loss in property value in

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Speaker 3: their tax re seats. Right, So a lot of cities

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Speaker 3: haven't yet really faced this fiscal reckoning yet. It's really

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Speaker 3: more of the long term that they're going to be

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Speaker 3: potentially facing lower property tax re seats coming from lower

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Speaker 3: property tax revenue from these commercial office buildings, coming from

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Speaker 3: lower income taxes from people that have left, as well

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Speaker 3: as lower sales taxes from less spending that's happening in

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Speaker 3: urban districts. So most cities haven't really had to face

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Speaker 3: the impacts of this yet. They've also benefited from one

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Speaker 3: time federal relief funds. But in the future, a lot

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Speaker 3: of cities are projecting future deficits, and part of that

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Speaker 3: just reflects conservative accounting on the part of city governments,

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Speaker 3: so they're sort of accounting for the possible ability that

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Speaker 3: revenues in the future may just kind of come in slower.

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Speaker 3: So it's not yet clear whether these deficits will materialize

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Speaker 3: in the future. But what we can say is when

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Speaker 3: we look back in the past, the conservative forecasts on

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Speaker 3: revenues wound up being exceeded because cities actually wound up

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Speaker 3: getting a lot more money than they expected in property taxes.

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Speaker 3: So a lot of these core assets, like office buildings,

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Speaker 3: we're really providing cities with a lot of additional revenue

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Speaker 3: in the past ten to fifteen years, and so they

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Speaker 3: may not be in a position to get the same

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Speaker 3: amount of revenue from those office buildings, which then force

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Speaker 3: the cities to do a variety of different things to

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Speaker 3: try to make sure that they remain attractive, vibrant places

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Speaker 3: to be. So one big response cities are trying to

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Speaker 3: do is conversions. Right, so if you can take this

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Speaker 3: structurally obsolescent, you know, stranded asset office real estate class

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Speaker 3: and turn it into housing or some other use case. Housing,

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Speaker 3: of course is great because everyone wants more housing in

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Speaker 3: dense urban areas. That is a great way to kind

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Speaker 3: of fill up the city, fill up the subway systems.

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Speaker 3: Those subway systems are also kind of experiencing declines and

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Speaker 3: ridership as well, and so that's something a lot of

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Speaker 3: cities are looking actively at. In addition to just converting

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Speaker 3: additional office space or just changing regulation around housing. More broadly,

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Speaker 3: so we can build more housing in cities.

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Speaker 1: This reminds me actually, one thing that often comes up

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Speaker 1: in these urban doom loop discussions is the example of

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Speaker 1: New York in the nineteen seventies, nineteen eighties, or even

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Speaker 1: after two thousand and one and nine to eleven, and

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Speaker 1: the idea that well, there there was a sort of

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Speaker 1: discussion about New York is in a long run decline

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Speaker 1: and it's going to be impossible for it to get out,

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Speaker 1: and yet it did in one way or another. How

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Speaker 1: did it manage to achieve that? And what does that

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Speaker 1: say about the current policy trajectory.

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Speaker 3: So when we coined that term in our paper, we

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Speaker 3: really weren't thinking about this the industrialization shock which hit

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Speaker 3: a lot of rust belt cities, right and so you

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Speaker 3: saw cities like Detroit or Saint Louis Louis about fifty

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Speaker 3: or sixty percent of their population over that time as

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Speaker 3: they kind of lost that center industry which was holding

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Speaker 3: up their entire ecosystem. For cities like New York, it

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Speaker 3: also was a pretty bad shock. So the city lost

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Speaker 3: something like a million manufacturing jobs that were previously located

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Speaker 3: right in the heart of the city. So we had

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Speaker 3: this big surplus of real estate associated with these industrial

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Speaker 3: logistics uses that had to get redeployed towards other things.

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Speaker 3: And ultimately we found here in York that you could

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Speaker 3: take these buildings and convert them to retail housing and

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Speaker 3: support a lot of office buildings. So a lot of

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Speaker 3: white collar work came to New York in the aftermath

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Speaker 3: of that deindustrialization period, so some cities were able to

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Speaker 3: ultimately prove to be more resilient. We also see across

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Speaker 3: cities that meds and eds were a really big factor.

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Speaker 3: So having hospitals and educational institutions were a great way

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Speaker 3: for economies to kind of pivot and create new job

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Speaker 3: centers of growth in order to take over the previous

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Speaker 3: blue collar work that would have been going on in cities.

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Speaker 3: And so some of those cities, again, particularly like New York,

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Speaker 3: were able to kind of redeploy and change their economic

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Speaker 3: environment in response.

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Speaker 2: We are recording this merch twenty fifth, twenty twenty four.

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Speaker 2: So on March fifth, twenty twenty four, how are you feeling.

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Speaker 2: Let's start with New York specifically, because that's all care about. No,

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Speaker 2: it's not all I care about, just ninety percent. How

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Speaker 2: are you feeling about New York right now.

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Speaker 3: So I think New York has a lot of great

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Speaker 3: advantages going for it. So it begins with the diversified

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Speaker 3: industrial base, right so, in comparison to a lot of

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Speaker 3: these West Coast cities, in comparison to city like San

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Speaker 3: Francisco right now has something like a thirty seven percent

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Speaker 3: office vacancy rate. At those rates, if you had the

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Speaker 3: fastest office absorption in history, you would still need seven

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Speaker 3: or eight years just to fill the existing vacant office space.

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Speaker 3: If you are filling San Francisco office at the average

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Speaker 3: rate of absorption over the last twenty years, you need

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Speaker 3: something like fifteen or more years to actually fill all

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Speaker 3: of that office space. So there's just a huge vacancy

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Speaker 3: problem in a lot of these West Coast tech center

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Speaker 3: centity cities. In comparison, New York just has a broad

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Speaker 3: diversity of different industrial uses, and you have a lot

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Speaker 3: of firms like big law or financial firms that do

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Speaker 3: seem to be more able to get workers into the office.

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Speaker 3: In addition, you have a lot of great consumption amenities,

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Speaker 3: so people love being in New York for all sorts

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Speaker 3: of different reasons. That's going to mean that New York

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Speaker 3: is in the position to reinvent itself as more of

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Speaker 3: a consumption city rather than a production city. And then finally,

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Speaker 3: there's another great buffer that New York City has, which is,

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Speaker 3: if you're looking at office rents that are let's say

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Speaker 3: seventy dollars or eighty dollars a square foot, you have

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Speaker 3: the space to cut that rent down and get to

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Speaker 3: a point where you're able to find some other tenant

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Speaker 3: willing to come into that space. Now, office owners, for

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Speaker 3: variety reasons we can get into, are reluctant to lower

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Speaker 3: the rent to that point. But there is space to

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Speaker 3: lower the rent and still be able to operate that

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Speaker 3: building financially. In comparison, you have a lot of other cities,

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Speaker 3: for example, some other Roust Belt cities, where there's not

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Speaker 3: necessarily that same level of space to cut the office

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Speaker 3: rent without really eating into the ability to operate that

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Speaker 3: building successfully.

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Speaker 1: This was going to be exactly my next question. So

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Speaker 1: what is the catalyst for office owners or building owners

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Speaker 1: to finally reduce rents, because presumably that's when you get

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Speaker 1: the sort of healing process, the creative destruction. I guess

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Speaker 1: that allows new types of businesses to come into the

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Speaker 1: city and maybe start redefining some of these urban areas.

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Speaker 3: I think in many cases it's really going to require

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Speaker 3: a change in ownership of the building, right because why

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Speaker 3: you're building owners reluctant to lower the rents of their

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Speaker 3: existing buildings. Well, I think there are variety of reasons,

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Speaker 3: but some of the reasons I've heard include one, there's

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Speaker 3: this real options problem, so you don't want to lock

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Speaker 3: in a tenant for the long term at a lower rent.

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Speaker 3: You kind of want to wait and find the right tenant. Second,

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Speaker 3: you hear about issues related to debt covenants on their mortgages,

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Speaker 3: so the owners of debt may have restrictions in place

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Speaker 3: that prevent that owner from reducing the rent. And then

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Speaker 3: finally you hear about issues related to strategic vacancy, So

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Speaker 3: owners of buildings may be reluctant to lower rents because

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Speaker 3: that sends a signal to other tenants or signals to appraisals.

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Speaker 3: And there are mortgage holders that may impact their future

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Speaker 3: financing prospects. So one way to kind of deal with

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Speaker 3: these things is really for that building to go through

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Speaker 3: foreclosure or fire sale, change ownership, and that new owner

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Speaker 3: is going to be able to or just a building

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Speaker 3: at a much lower cost basis that cost spaces helps

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Speaker 3: them either a convert the building into an apartment or

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Speaker 3: other use find ways of investing in that building to

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Speaker 3: really try to compete for that trophy a plus real

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Speaker 3: estate space, or just take the building and lower the

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Speaker 3: rent and find a way of remaining profitable at a

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Speaker 3: lower rent point.

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Speaker 2: So you've mentioned and you've written a fair amount about

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Speaker 2: office Torezi conversions, and we've talked about it on this

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Speaker 2: show a handful of times. Definitely one of these things.

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Speaker 2: It sounds good, it seems expensive, it seems slow. I

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Speaker 2: can never really tell, even if everyone got their ducks

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Speaker 2: in an order, whether it would move the dial enough

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Speaker 2: to meaningfully a ease some of the housing strains or

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Speaker 2: be sort of fill in some of this empty space.

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Speaker 2: What is your work saying right now? Is this happening

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Speaker 2: on any scalers. This just a thing people tweet and

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Speaker 2: write papers about.

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Speaker 3: So we did write a paper on this and have

438
00:21:51,119 --> 00:21:53,880
Speaker 3: tweeted about it as well. So we've contributed to the

439
00:21:53,960 --> 00:21:57,640
Speaker 3: last few components. At least. What we find in our

440
00:21:57,680 --> 00:22:00,920
Speaker 3: work is again joint with Steinwin Norberg and another quatht

441
00:22:00,920 --> 00:22:05,040
Speaker 3: Candy Martinez is that there is a prospect for physically

442
00:22:05,040 --> 00:22:08,639
Speaker 3: converting about ten to fifteen percent of the nation's office stock,

443
00:22:08,880 --> 00:22:11,760
Speaker 3: and that would produce something like four hundred thousand apartments,

444
00:22:12,119 --> 00:22:14,960
Speaker 3: where our annual average apartment production is something like two

445
00:22:15,000 --> 00:22:17,960
Speaker 3: hundred and sixty thousand a year. So it's a reasonally

446
00:22:18,119 --> 00:22:21,639
Speaker 3: large prospect relative to the size of annual production, but

447
00:22:21,720 --> 00:22:25,280
Speaker 3: it's still a pretty small overall part of the you know,

448
00:22:25,320 --> 00:22:27,960
Speaker 3: the entire office ecosystem. Across cities. We do see some

449
00:22:27,960 --> 00:22:30,840
Speaker 3: cities that are exploring it more aggressively than others. Cleveland,

450
00:22:30,880 --> 00:22:32,679
Speaker 3: Actor is a city that seems to be engaging in

451
00:22:32,720 --> 00:22:35,240
Speaker 3: these office to raise the conversions a little bit more.

452
00:22:35,560 --> 00:22:37,680
Speaker 2: So, would you say, explore this is what I'm trying

453
00:22:37,720 --> 00:22:40,280
Speaker 2: to get. Is there like in New York or maybe

454
00:22:40,320 --> 00:22:42,480
Speaker 2: even Cleveland, like is this actually happening.

455
00:22:42,800 --> 00:22:45,199
Speaker 3: Yeah, it seems like these conversions are happening. You know,

456
00:22:45,240 --> 00:22:48,440
Speaker 3: here in the Financial District, we converted you know, somewhere

457
00:22:48,440 --> 00:22:51,480
Speaker 3: in the order of tens of thousands of apartment units

458
00:22:51,480 --> 00:22:53,840
Speaker 3: out of former office building. So that's something that happened,

459
00:22:54,240 --> 00:22:56,720
Speaker 3: particularly after nine to eleven, and it is happening now.

460
00:22:56,720 --> 00:22:58,719
Speaker 3: It seems like at an increasing rate across a lot

461
00:22:58,760 --> 00:22:59,199
Speaker 3: of cities.

462
00:22:59,320 --> 00:23:02,600
Speaker 1: So you mentioned timelines earlier and the idea that this

463
00:23:02,720 --> 00:23:05,639
Speaker 1: dynamic is really operating on a lag and so it

464
00:23:05,760 --> 00:23:08,840
Speaker 1: might not seem that the doom loop is upon us

465
00:23:08,920 --> 00:23:11,720
Speaker 1: right now, but a lot of those rents or leases

466
00:23:11,760 --> 00:23:14,679
Speaker 1: have yet to be renegotiated, and so it might just

467
00:23:14,720 --> 00:23:17,560
Speaker 1: take longer than a lot of people anticipated. Maybe we

468
00:23:17,600 --> 00:23:21,440
Speaker 1: could reframe the question as like, when would you expect

469
00:23:21,440 --> 00:23:25,240
Speaker 1: this to play out, Like what is the approximate timeline

470
00:23:25,240 --> 00:23:27,760
Speaker 1: that you're sort of envisioning at this stage.

471
00:23:27,920 --> 00:23:30,480
Speaker 3: So I think there are two important things to keep

472
00:23:30,480 --> 00:23:32,879
Speaker 3: in mind with this urban doom loop concept. First is

473
00:23:32,920 --> 00:23:35,800
Speaker 3: that it's sort of a cycle, not necessarily a prediction

474
00:23:35,880 --> 00:23:37,800
Speaker 3: of something that will happen, right, So we kind of

475
00:23:37,800 --> 00:23:40,520
Speaker 3: talked about how aspects of these dynamics have happened in

476
00:23:40,560 --> 00:23:42,680
Speaker 3: the past, and I think it's an open question whether

477
00:23:42,720 --> 00:23:46,119
Speaker 3: we're going to see this element of urban downward spiral

478
00:23:46,119 --> 00:23:47,720
Speaker 3: happen in the future. If it were to happen, I

479
00:23:47,760 --> 00:23:51,159
Speaker 3: would sort of expect it to really materialize as you

480
00:23:51,200 --> 00:23:55,280
Speaker 3: see this federal stimulus dry up and the impact of

481
00:23:55,400 --> 00:23:58,840
Speaker 3: these property losses start to accumulate for cities so as

482
00:23:58,880 --> 00:24:02,480
Speaker 3: one benchmark, York City had a relatively severe recession in

483
00:24:02,480 --> 00:24:05,280
Speaker 3: the early nineties. We had a big problem commercial real

484
00:24:05,359 --> 00:24:06,920
Speaker 3: estate back then, so we had a lot of failures

485
00:24:06,920 --> 00:24:10,320
Speaker 3: of banks with the SNL crisis, big losses for commercial

486
00:24:10,359 --> 00:24:12,360
Speaker 3: real estate back then, and it actually took a really

487
00:24:12,359 --> 00:24:14,640
Speaker 3: long time until the late nineties for New York City

488
00:24:14,680 --> 00:24:18,399
Speaker 3: to see the losses from those commercial real estate property

489
00:24:18,440 --> 00:24:21,399
Speaker 3: tax losses materialize, at which point the city had already

490
00:24:21,440 --> 00:24:24,040
Speaker 3: experienced a growth cycle because of the dot com bubble.

491
00:24:24,359 --> 00:24:27,040
Speaker 3: So the forecast, I think is in the next few

492
00:24:27,080 --> 00:24:29,480
Speaker 3: years you're going to potentially see the impact of these

493
00:24:29,680 --> 00:24:32,680
Speaker 3: property tax losses combined with the drying up of co

494
00:24:32,840 --> 00:24:34,080
Speaker 3: ed funds, and that's going to be the moment of

495
00:24:34,119 --> 00:24:36,680
Speaker 3: greatest stress for cities. I think. The other thing to

496
00:24:36,760 --> 00:24:39,240
Speaker 3: keep in mind is that in parallel with the urban

497
00:24:39,280 --> 00:24:42,800
Speaker 3: doom loop, you have the suburban boom loop, which is

498
00:24:42,840 --> 00:24:46,040
Speaker 3: the phenomenon going on in suburban areas. As more people

499
00:24:46,040 --> 00:24:48,480
Speaker 3: move to these areas, you're seeing more amenities pop up,

500
00:24:48,760 --> 00:24:52,040
Speaker 3: and these make them more desirable locations, enabling a positive

501
00:24:52,080 --> 00:24:55,520
Speaker 3: flywheel of economic activity. So remote work in aggregate is

502
00:24:55,560 --> 00:24:57,239
Speaker 3: probably a good thing for the economy, it just has

503
00:24:57,280 --> 00:24:59,680
Speaker 3: these concentrated pattern of winners and losers.

504
00:25:00,880 --> 00:25:03,920
Speaker 2: The suburbs are getting so good, No, I really believe that.

505
00:25:04,040 --> 00:25:06,080
Speaker 2: Like I may have mentioned it recently on an episode,

506
00:25:06,080 --> 00:25:09,880
Speaker 2: but I was like halfway between Austin and San Antonio recently,

507
00:25:10,040 --> 00:25:13,479
Speaker 2: which is now basically just one giant, contiguous suburb, and

508
00:25:13,600 --> 00:25:16,119
Speaker 2: like the sheer number of like new different kinds of

509
00:25:16,160 --> 00:25:19,360
Speaker 2: restaurants that are opening up out there and different concepts

510
00:25:19,400 --> 00:25:21,639
Speaker 2: and stuff, like, it's so wild, it's so good, Like

511
00:25:21,680 --> 00:25:25,520
Speaker 2: there's so much good stuff out there. So the New

512
00:25:25,600 --> 00:25:28,240
Speaker 2: York City has already seeing budget strands.

513
00:25:27,920 --> 00:25:30,880
Speaker 3: Right, Yeah, So there was a big back and forth

514
00:25:30,920 --> 00:25:33,320
Speaker 3: about some cuts to things like libraries, and then I

515
00:25:33,400 --> 00:25:36,200
Speaker 3: think the Controller has estimated that in the coming years

516
00:25:36,240 --> 00:25:38,520
Speaker 3: were going to see out budget gaps, you know, something

517
00:25:38,640 --> 00:25:41,480
Speaker 3: like eight nine percent of the city's budget in future years.

518
00:25:41,480 --> 00:25:44,119
Speaker 3: So I think it hasn't really yet materialized for the

519
00:25:44,160 --> 00:25:47,160
Speaker 3: city because revenues I think ultimately came in higher than expected,

520
00:25:47,280 --> 00:25:49,240
Speaker 3: enabling us to avoid some of those cuts to things

521
00:25:49,240 --> 00:25:51,960
Speaker 3: like libraries. There's the prospect of future cuts on the

522
00:25:51,960 --> 00:25:56,000
Speaker 3: horizon as we're forced with the prospect of increasing expenditures

523
00:25:56,000 --> 00:25:58,119
Speaker 3: down the road. And thinking about what will be the

524
00:25:58,160 --> 00:25:59,360
Speaker 3: revenue sources to pay for.

525
00:25:59,320 --> 00:26:02,879
Speaker 1: It, how good the suburbs are getting to which all

526
00:26:02,880 --> 00:26:05,280
Speaker 1: I can say is you're dooming, Joe, and I'm booming.

527
00:26:05,560 --> 00:26:09,199
Speaker 1: But actually this brings me to another thing that I

528
00:26:09,200 --> 00:26:11,720
Speaker 1: wanted to ask you about. And this comes up again

529
00:26:11,920 --> 00:26:15,320
Speaker 1: a lot in these conversations, but the idea of perhaps

530
00:26:15,359 --> 00:26:20,080
Speaker 1: making it easier to build, deregulating or rezoning or whatever

531
00:26:20,080 --> 00:26:21,879
Speaker 1: you want to call it. And there is an argument

532
00:26:21,920 --> 00:26:24,359
Speaker 1: that one of the reasons you've seen a lot of people,

533
00:26:24,400 --> 00:26:26,680
Speaker 1: a lot of businesses move to places in the sun

534
00:26:26,720 --> 00:26:29,760
Speaker 1: Belt is because it's easier to build there. There's more room,

535
00:26:30,200 --> 00:26:32,760
Speaker 1: it's cheaper in many ways, or at least it used

536
00:26:32,760 --> 00:26:36,520
Speaker 1: to be relatively cheap versus places like New York. Could

537
00:26:36,560 --> 00:26:39,080
Speaker 1: you talk about that sort of regulatory aspect of all

538
00:26:39,119 --> 00:26:41,440
Speaker 1: of this. Are there levers that the city could pull

539
00:26:41,960 --> 00:26:45,199
Speaker 1: to make New York an easier city in which to

540
00:26:45,320 --> 00:26:47,800
Speaker 1: construct new buildings or repurpose old buildings.

541
00:26:48,320 --> 00:26:53,000
Speaker 3: Absolutely so. The lowest hanging fruit, I think is the

542
00:26:53,080 --> 00:26:55,159
Speaker 3: far cap here in the city. So we have a

543
00:26:55,240 --> 00:26:59,000
Speaker 3: limit for multi family buildings, they can only have an

544
00:26:59,080 --> 00:27:02,520
Speaker 3: far of twelve, so that basically means for every So

545
00:27:02,560 --> 00:27:05,199
Speaker 3: it's the ratio of all the usable space in a building,

546
00:27:05,280 --> 00:27:07,600
Speaker 3: divided by the plot size. So if I have a

547
00:27:07,800 --> 00:27:10,880
Speaker 3: one square foot I can create twelve more square feet

548
00:27:10,920 --> 00:27:13,120
Speaker 3: of livable space on top of that, but no more

549
00:27:13,520 --> 00:27:15,880
Speaker 3: whereas in fact, that ratio is higher for office buildings.

550
00:27:15,920 --> 00:27:18,119
Speaker 3: And this is actually a big barrier for conversions, because

551
00:27:18,160 --> 00:27:21,080
Speaker 3: if I take a large office building and convert that

552
00:27:21,160 --> 00:27:23,359
Speaker 3: into apartments, I may only be able to use a

553
00:27:23,480 --> 00:27:25,320
Speaker 3: portion of that building for residential use, and I have

554
00:27:25,400 --> 00:27:27,480
Speaker 3: to leave the other floor is empty. This is also

555
00:27:27,520 --> 00:27:31,040
Speaker 3: why those giants super tall buildings kind of south of

556
00:27:31,160 --> 00:27:33,159
Speaker 3: Central Park are so skinny. They're sort of trying to

557
00:27:33,200 --> 00:27:36,320
Speaker 3: meet these regulatory requirements on density. So that thing is

558
00:27:36,320 --> 00:27:39,119
Speaker 3: the easiest fix, and policymakers are looking at that one

559
00:27:39,200 --> 00:27:42,320
Speaker 3: right now. More broadly, we have a lot of restrictions

560
00:27:42,720 --> 00:27:45,760
Speaker 3: on the ability to build in the city, and even

561
00:27:45,800 --> 00:27:47,639
Speaker 3: here in New York, we have some neighborhoods that are

562
00:27:47,640 --> 00:27:51,040
Speaker 3: still zoned for essentially single family homes. You're not allowed

563
00:27:51,040 --> 00:27:53,119
Speaker 3: to build a multi family building, or if you are,

564
00:27:53,200 --> 00:27:55,960
Speaker 3: they're really strong density requirements. So I think there are

565
00:27:55,960 --> 00:27:58,600
Speaker 3: a host of regulatory shifts that cities can do to

566
00:27:58,680 --> 00:28:01,320
Speaker 3: make it easier to build, which will then narrow that

567
00:28:01,359 --> 00:28:04,800
Speaker 3: cost of living advantage between urban cities and the Sun Belt.

568
00:28:04,880 --> 00:28:08,560
Speaker 2: I hadn't realized that about the sort of regulatory rationale

569
00:28:08,560 --> 00:28:09,840
Speaker 2: for the skinny buildings.

570
00:28:09,920 --> 00:28:10,159
Speaker 1: So the.

571
00:28:11,640 --> 00:28:14,760
Speaker 2: Smaller the plot on the ground, the more you're allowed

572
00:28:14,760 --> 00:28:15,119
Speaker 2: to go up.

573
00:28:15,320 --> 00:28:17,960
Speaker 3: It's more that you're taking a certain plot of land,

574
00:28:18,000 --> 00:28:20,960
Speaker 3: and if you want to sort of have a better view,

575
00:28:21,000 --> 00:28:22,520
Speaker 3: you kind of have to make the building a little

576
00:28:22,520 --> 00:28:24,720
Speaker 3: bit skinnier, and sometimes you have to leave some of

577
00:28:24,760 --> 00:28:28,000
Speaker 3: those floors in the middle unoccupied in order to really

578
00:28:28,040 --> 00:28:30,600
Speaker 3: take advantage of the high benefits.

579
00:28:30,240 --> 00:28:34,120
Speaker 2: On San Francisco, as you mentioned, like under a reasonable

580
00:28:34,359 --> 00:28:38,360
Speaker 2: normal assumption about new office leasing, I think you said

581
00:28:38,400 --> 00:28:41,000
Speaker 2: thirty seven years would take to fill that hole or

582
00:28:41,000 --> 00:28:41,600
Speaker 2: something like that.

583
00:28:41,640 --> 00:28:43,239
Speaker 3: I think at an average rate, I think it's more

584
00:28:43,280 --> 00:28:43,760
Speaker 3: than fifteen.

585
00:28:44,320 --> 00:28:47,520
Speaker 2: What happens to a city with that big of a deficit? Like,

586
00:28:48,480 --> 00:28:50,440
Speaker 2: is this like a series? Like you know, there's a

587
00:28:50,480 --> 00:28:53,400
Speaker 2: lot of excitement because basically every smart AI person in

588
00:28:53,440 --> 00:28:56,840
Speaker 2: the world pretty much lives in San Francisco, is my understanding?

589
00:28:57,040 --> 00:28:59,440
Speaker 2: Like in your view, is this a sort of deep

590
00:28:59,480 --> 00:29:02,640
Speaker 2: structure impediment to the viability of the city and the

591
00:29:02,680 --> 00:29:03,840
Speaker 2: city's budget going forward.

592
00:29:04,240 --> 00:29:06,400
Speaker 3: I think it's certainly a big problem. So the cities

593
00:29:06,440 --> 00:29:09,520
Speaker 3: own estimates I think suggest that they've lost something like

594
00:29:09,680 --> 00:29:12,760
Speaker 3: half a billion dollars due to remote work, and in

595
00:29:12,800 --> 00:29:15,720
Speaker 3: the future they're looking at a lot of property tax reassessments.

596
00:29:15,760 --> 00:29:18,280
Speaker 3: So basically a lot of people that own office buildings

597
00:29:18,320 --> 00:29:21,000
Speaker 3: are appealing their property taxes, are arguing that their building

598
00:29:21,040 --> 00:29:22,920
Speaker 3: is worth a lot less now that you have all

599
00:29:22,920 --> 00:29:25,840
Speaker 3: these vacancies, and so it's definitely a source of stress

600
00:29:25,880 --> 00:29:27,760
Speaker 3: I think for the city's budget. I think this is

601
00:29:28,040 --> 00:29:32,480
Speaker 3: really amplified by some San Francisco, California specific issues. Right,

602
00:29:32,480 --> 00:29:35,240
Speaker 3: so you have Proposition thirteen, which means that you're not

603
00:29:35,320 --> 00:29:37,960
Speaker 3: able to raise taxes as much on residential real estate,

604
00:29:38,320 --> 00:29:40,560
Speaker 3: so that puts more of the burden on other sources

605
00:29:40,600 --> 00:29:43,280
Speaker 3: of taxation. They also have these things like business gross

606
00:29:43,280 --> 00:29:46,280
Speaker 3: receips and things like that, which are additional taxes levied

607
00:29:46,320 --> 00:29:48,760
Speaker 3: on businesses in the area. So firms ate that and

608
00:29:49,200 --> 00:29:51,840
Speaker 3: don't like having to pay these additional costs. And then

609
00:29:51,880 --> 00:29:53,800
Speaker 3: in addition, I think you have the kind of spatial

610
00:29:53,840 --> 00:29:56,440
Speaker 3: development of the city. So going back to the seventies

611
00:29:56,440 --> 00:29:59,200
Speaker 3: and eighties, there was basically this deal whereby the city

612
00:29:59,240 --> 00:30:02,760
Speaker 3: agreed to adap so of most of the residential neighborhoods

613
00:30:02,760 --> 00:30:06,440
Speaker 3: in exchange for a very concentrated building of commercial riding

614
00:30:06,440 --> 00:30:09,960
Speaker 3: in the downtown area. And this allowed the residents of

615
00:30:09,960 --> 00:30:12,160
Speaker 3: San Francisco to kind of live in these small, nice,

616
00:30:12,200 --> 00:30:14,280
Speaker 3: quaint little homes, preserving their neighborhood character.

617
00:30:14,320 --> 00:30:14,760
Speaker 2: They're nice.

618
00:30:14,840 --> 00:30:17,000
Speaker 1: Well, so these are the ones with the like Victorian

619
00:30:17,040 --> 00:30:17,840
Speaker 1: woodwork and.

620
00:30:17,720 --> 00:30:19,680
Speaker 3: Stuff right right exactly, which people hate it at the

621
00:30:19,680 --> 00:30:22,479
Speaker 3: time but grew to sort of love and see as

622
00:30:22,560 --> 00:30:24,160
Speaker 3: kind of historic parts of their character.

623
00:30:24,320 --> 00:30:24,560
Speaker 1: I was.

624
00:30:24,760 --> 00:30:27,120
Speaker 2: I visited a friend a few years ago who lives

625
00:30:27,120 --> 00:30:29,920
Speaker 2: in like not one of the nice like Victorian homes,

626
00:30:29,960 --> 00:30:31,760
Speaker 2: but just like a sort of like little nice neighborhood

627
00:30:31,800 --> 00:30:33,640
Speaker 2: with a wedget single family housing. And he's like a

628
00:30:33,640 --> 00:30:35,480
Speaker 2: big MBI guy, and it's like, Oh, I love your neighborhood.

629
00:30:35,480 --> 00:30:37,280
Speaker 2: It's so nice. And he's like, Joe, you can't you

630
00:30:37,320 --> 00:30:39,680
Speaker 2: can't say that I hate it. You said the wrong thing,

631
00:30:39,720 --> 00:30:40,680
Speaker 2: but anyway, keep going.

632
00:30:40,760 --> 00:30:43,040
Speaker 3: Absolutely, so, the trade off was you're going to have

633
00:30:43,120 --> 00:30:45,160
Speaker 3: this down zoning of the rest of the city in

634
00:30:45,160 --> 00:30:48,960
Speaker 3: exchange with this very concentrated development happening in downtown, and

635
00:30:49,000 --> 00:30:51,760
Speaker 3: the problem is that that left the city really unprepared

636
00:30:51,880 --> 00:30:53,760
Speaker 3: when the pandemic hit because you had all these people

637
00:30:53,760 --> 00:30:56,480
Speaker 3: commuting from really long distances to try to get to

638
00:30:56,520 --> 00:30:59,440
Speaker 3: that downtown core because they really couldn't find or afford

639
00:30:59,480 --> 00:31:02,040
Speaker 3: any housing nearby. And that's one of the reasons why

640
00:31:02,040 --> 00:31:04,840
Speaker 3: people were so excited by remote work, because it allowed

641
00:31:04,880 --> 00:31:07,360
Speaker 3: them to take advantage of the ability to move to

642
00:31:07,400 --> 00:31:10,040
Speaker 3: Austin or wherever else and get a lower cost of

643
00:31:10,080 --> 00:31:12,760
Speaker 3: living in order to continue working in the same place

644
00:31:12,760 --> 00:31:15,840
Speaker 3: they were before. So you have just massive mismatch between

645
00:31:15,880 --> 00:31:17,440
Speaker 3: the jobs and the people, and so that has to

646
00:31:17,480 --> 00:31:19,760
Speaker 3: be corrected one way or the other. Either you move

647
00:31:19,800 --> 00:31:21,480
Speaker 3: the jobs now to where the people are, to move

648
00:31:21,520 --> 00:31:23,520
Speaker 3: the jobs over to Austin or wherever they are now,

649
00:31:23,880 --> 00:31:26,040
Speaker 3: or you find ways of bringing more people back into

650
00:31:26,040 --> 00:31:28,600
Speaker 3: San Francisco and that'll actually fill back up the transit

651
00:31:28,600 --> 00:31:31,040
Speaker 3: system to fill back up those downtown office.

652
00:31:30,760 --> 00:31:49,280
Speaker 1: Course, so people don't just want to live in cities

653
00:31:49,400 --> 00:31:53,000
Speaker 1: because there's work there, although clearly that's one aspect of it.

654
00:31:53,080 --> 00:31:56,880
Speaker 1: You know, at least in recent decades, going to a

655
00:31:56,920 --> 00:31:59,360
Speaker 1: place like New York, that's where all the jobs are

656
00:31:59,440 --> 00:32:02,760
Speaker 1: and where you need to be in order to make

657
00:32:03,120 --> 00:32:05,719
Speaker 1: a certain type of living. But there are people who

658
00:32:05,800 --> 00:32:07,840
Speaker 1: will move to a city just because they want to

659
00:32:07,840 --> 00:32:10,760
Speaker 1: be part of a city. They want that vibrant experience,

660
00:32:10,840 --> 00:32:13,280
Speaker 1: they want to be close to other human beings, they

661
00:32:13,360 --> 00:32:16,320
Speaker 1: want to have options for restaurants and going out and

662
00:32:16,600 --> 00:32:20,160
Speaker 1: experience everything that a city like New York has to offer.

663
00:32:21,040 --> 00:32:25,600
Speaker 1: Is there a world where maybe cities get divorced from

664
00:32:25,720 --> 00:32:28,840
Speaker 1: or slightly more separated from the economic opportunities.

665
00:32:29,640 --> 00:32:31,560
Speaker 3: I think so that will be essentially a world in

666
00:32:31,600 --> 00:32:35,200
Speaker 3: which cities are really defined by those consumption opportunities, right

667
00:32:35,560 --> 00:32:39,080
Speaker 3: by their dating markets rather than their labor markets. Right,

668
00:32:39,240 --> 00:32:40,680
Speaker 3: and people, we're doomed.

669
00:32:40,880 --> 00:32:43,720
Speaker 2: Sorry, I haven't heard anything.

670
00:32:43,440 --> 00:32:46,479
Speaker 3: Good about them. And people, you know, you know, what

671
00:32:46,520 --> 00:32:48,280
Speaker 3: we see in the data is a lot of people

672
00:32:48,280 --> 00:32:51,360
Speaker 3: are working remotely, even in Manhattan. Right. So clearly a

673
00:32:51,360 --> 00:32:54,160
Speaker 3: lot of people, when given the choice, will still choose

674
00:32:54,200 --> 00:32:56,760
Speaker 3: to live in these large cities. I think the challenge

675
00:32:56,800 --> 00:32:59,800
Speaker 3: is establishing the quality of life and cost of housing

676
00:32:59,840 --> 00:33:02,280
Speaker 3: to enable people to do that. So, for example, the

677
00:33:02,280 --> 00:33:05,800
Speaker 3: Citizens Budget Commission has been serving people over time, and

678
00:33:05,920 --> 00:33:08,880
Speaker 3: they find that in New York far few people rate

679
00:33:08,920 --> 00:33:11,440
Speaker 3: their quality of life as being high compared to what

680
00:33:11,440 --> 00:33:14,440
Speaker 3: they're rating it before the pandemic, So they're important concerns

681
00:33:14,480 --> 00:33:17,360
Speaker 3: about the quality of life people are experiencing in urban

682
00:33:17,400 --> 00:33:19,480
Speaker 3: areas now. They also found that people actually liked their

683
00:33:19,480 --> 00:33:22,040
Speaker 3: neighborhood more than the city, kind of the same way

684
00:33:22,040 --> 00:33:24,800
Speaker 3: that people rate their personal financial situation a lot better

685
00:33:24,840 --> 00:33:27,200
Speaker 3: than the national one. So there's maybe some issue of

686
00:33:27,240 --> 00:33:30,320
Speaker 3: negative urban vibes that are kind of impacting people's decision

687
00:33:30,320 --> 00:33:33,640
Speaker 3: making here. But I think definitely something around trying to

688
00:33:33,640 --> 00:33:36,560
Speaker 3: make sure that cities can remain these vibrant, exciting places

689
00:33:36,640 --> 00:33:38,320
Speaker 3: is really a great way to make sure that they

690
00:33:38,360 --> 00:33:40,200
Speaker 3: continue to live there even when they have options and

691
00:33:40,240 --> 00:33:41,400
Speaker 3: can choose to live elsewhere.

692
00:33:41,520 --> 00:33:44,840
Speaker 2: It's such an interesting phenomenon about like everyone's own situation

693
00:33:45,000 --> 00:33:47,440
Speaker 2: is not as bad as they perceive everyone else's. Like

694
00:33:47,600 --> 00:33:49,160
Speaker 2: I think you see the same thing, and like people

695
00:33:49,160 --> 00:33:51,720
Speaker 2: talk about their representatives too, like it's like, oh, my center,

696
00:33:51,800 --> 00:33:53,800
Speaker 2: my rep is all right, but it's not like, but

697
00:33:53,920 --> 00:33:55,040
Speaker 2: Congress is terrible.

698
00:33:55,320 --> 00:33:58,560
Speaker 1: It's like the Dunning Krueger effects, right, Like everyone just

699
00:33:58,640 --> 00:34:00,760
Speaker 1: assumes they're doing better than everyone else.

700
00:34:01,240 --> 00:34:05,920
Speaker 2: I like my neighborhood, but yeah, I guess maybe everyone

701
00:34:06,160 --> 00:34:09,080
Speaker 2: likes their neighborhood too, but you know, on this point,

702
00:34:09,160 --> 00:34:11,480
Speaker 2: so it's like there is this dimension that is not

703
00:34:11,920 --> 00:34:15,120
Speaker 2: just about numbers, and it sort of gets to the

704
00:34:15,200 --> 00:34:18,440
Speaker 2: quality of life effect. And you know, recently, for example,

705
00:34:18,880 --> 00:34:22,720
Speaker 2: there is the headline about Governor hokel deploying the National

706
00:34:22,719 --> 00:34:25,880
Speaker 2: Guard to the subway and obviously there's a lot of

707
00:34:25,920 --> 00:34:28,840
Speaker 2: anxiety about safety on the subways these days. And like,

708
00:34:29,120 --> 00:34:32,040
Speaker 2: you know, in my neighborhood, which I like, there are

709
00:34:32,080 --> 00:34:36,239
Speaker 2: certainly many public vices that one sees as I walk here,

710
00:34:36,360 --> 00:34:38,320
Speaker 2: take my kids to the park, or take them to school,

711
00:34:38,360 --> 00:34:40,560
Speaker 2: et cetera. And some of these things aren't really just

712
00:34:40,800 --> 00:34:43,120
Speaker 2: money things that can be solved with spending or taxes.

713
00:34:43,200 --> 00:34:46,200
Speaker 2: There's a certain amount of political will or political consensus

714
00:34:46,239 --> 00:34:48,440
Speaker 2: about the degree to which, you know, we crack down

715
00:34:48,520 --> 00:34:51,120
Speaker 2: on you know, illegal weed shops and stuff like that.

716
00:34:51,360 --> 00:34:54,680
Speaker 2: How much is the future of some of these cities

717
00:34:54,760 --> 00:34:58,320
Speaker 2: going to be determined by the ability to get political

718
00:34:58,360 --> 00:35:01,200
Speaker 2: consensus for some things that aren't just sort of dollars

719
00:35:01,200 --> 00:35:02,120
Speaker 2: and cents questions.

720
00:35:02,520 --> 00:35:05,040
Speaker 3: I think that's really important because I think the pandemic

721
00:35:05,120 --> 00:35:08,239
Speaker 3: was this whole desocialization period in which we were just

722
00:35:08,640 --> 00:35:11,000
Speaker 3: not near each other as much, and that sort of

723
00:35:11,040 --> 00:35:13,440
Speaker 3: broke down a lot of social norms, right, and you

724
00:35:13,480 --> 00:35:16,160
Speaker 3: think about how new people are entering the city, they're

725
00:35:16,200 --> 00:35:18,560
Speaker 3: sort of seeing people behave as they currently are and

726
00:35:18,600 --> 00:35:20,719
Speaker 3: just assuming Okay, well, these are the social norms in

727
00:35:20,760 --> 00:35:23,440
Speaker 3: the city. So it's really important, I think, to maintain

728
00:35:24,000 --> 00:35:26,040
Speaker 3: sort of pro social behavior. So just to take one

729
00:35:26,040 --> 00:35:29,160
Speaker 3: example that you raised, the subways, So I think overall

730
00:35:29,239 --> 00:35:32,080
Speaker 3: crime in the subway is down slightly compared to before

731
00:35:32,080 --> 00:35:34,359
Speaker 3: the pandemic, But if you look at felony assaults, so

732
00:35:34,480 --> 00:35:38,160
Speaker 3: people actually experiencing and being victims of crime in subways,

733
00:35:38,200 --> 00:35:40,640
Speaker 3: that's actually up pretty substantially from what it was before

734
00:35:40,640 --> 00:35:43,520
Speaker 3: the pandemic. And again back in that CBC survey, they

735
00:35:43,560 --> 00:35:46,600
Speaker 3: found that people basically rate the safety of the subway

736
00:35:46,719 --> 00:35:49,520
Speaker 3: during the daytime the same as they rated the safety

737
00:35:49,520 --> 00:35:52,120
Speaker 3: of the subway at nighttime before the pandemic. So there's

738
00:35:52,160 --> 00:35:55,160
Speaker 3: been a huge shift in the perception of crime and

739
00:35:55,200 --> 00:35:57,279
Speaker 3: safety on subways, and I think part of that is

740
00:35:57,360 --> 00:36:00,560
Speaker 3: driven by remote work, because you have fewer people taking

741
00:36:00,560 --> 00:36:04,160
Speaker 3: the subway, so that's fewer eyes on the street, less sociable,

742
00:36:04,400 --> 00:36:06,520
Speaker 3: you know, kind of enforcement coming from other people, and

743
00:36:06,520 --> 00:36:09,120
Speaker 3: that sort of drives more anti social behavior in ways

744
00:36:09,160 --> 00:36:12,520
Speaker 3: that can compound on themselves unless it's addressed by you know,

745
00:36:12,600 --> 00:36:15,360
Speaker 3: for example, putting more police officers in the subway or

746
00:36:15,400 --> 00:36:17,640
Speaker 3: sort of changing the social norms in other ways.

747
00:36:17,920 --> 00:36:21,440
Speaker 1: So you're obviously at NYU. I'm curious if you're able

748
00:36:21,520 --> 00:36:24,520
Speaker 1: to talk about it. Do city officials ever ask you

749
00:36:24,600 --> 00:36:28,560
Speaker 1: for advice or ask for additional information on your research.

750
00:36:28,960 --> 00:36:31,120
Speaker 3: We've i think talked to a number of city officials

751
00:36:31,160 --> 00:36:34,840
Speaker 3: from across the country, particularly on the office problem, you know,

752
00:36:34,880 --> 00:36:37,640
Speaker 3: thinking about conversions and things like that. I would say

753
00:36:37,640 --> 00:36:40,600
Speaker 3: that this whole research has definitely resonated far more than

754
00:36:40,680 --> 00:36:42,920
Speaker 3: any research I've done previously or probably will do in

755
00:36:42,920 --> 00:36:45,080
Speaker 3: the future. But I'm hopeful that it's a sort of

756
00:36:45,080 --> 00:36:47,359
Speaker 3: wake up call for city and local governments to think

757
00:36:47,400 --> 00:36:49,800
Speaker 3: about what do they need to do after the pandemic

758
00:36:50,040 --> 00:36:51,920
Speaker 3: in order to really continue to make sure that their

759
00:36:51,960 --> 00:36:54,640
Speaker 3: cities are really exciting envirobrant places to be Out.

760
00:36:54,440 --> 00:36:57,160
Speaker 2: Of curiosity, just because you mentioned more what has been

761
00:36:57,200 --> 00:36:59,600
Speaker 2: the general gist of your work prior.

762
00:36:59,280 --> 00:37:01,120
Speaker 3: To this, So I do a lot of work related

763
00:37:01,120 --> 00:37:04,799
Speaker 3: to financial crisis, So thinking about all those subprime mortgages

764
00:37:04,840 --> 00:37:07,600
Speaker 3: and defaults and things like that. I also do research

765
00:37:07,640 --> 00:37:09,920
Speaker 3: now thinking about housing regulations and costs right, and how

766
00:37:09,920 --> 00:37:12,719
Speaker 3: we can use AI to better extract information from these

767
00:37:12,800 --> 00:37:16,120
Speaker 3: zoning codes and better figure out ways of making housing

768
00:37:16,160 --> 00:37:17,320
Speaker 3: cheaper real quickly.

769
00:37:17,360 --> 00:37:18,719
Speaker 2: On that A, I think, I think you did a

770
00:37:18,760 --> 00:37:23,160
Speaker 2: recent research paper using AI to do something about regulations

771
00:37:23,200 --> 00:37:25,920
Speaker 2: and codes. What did you what's your experience we got

772
00:37:25,960 --> 00:37:27,319
Speaker 2: to get in an AI question on that.

773
00:37:27,640 --> 00:37:29,480
Speaker 3: So I think it's a great use case actually because

774
00:37:29,480 --> 00:37:32,600
Speaker 3: we have, you know, across the country, every municipality has

775
00:37:32,719 --> 00:37:36,120
Speaker 3: a very long, complicated document hundreds of pages long that

776
00:37:36,200 --> 00:37:39,440
Speaker 3: outlines all of their regulations, you know, these density requirements,

777
00:37:39,480 --> 00:37:41,120
Speaker 3: a lot of size restrictions, so on and so forth,

778
00:37:41,480 --> 00:37:44,200
Speaker 3: and people really haven't dug through and read all of

779
00:37:44,200 --> 00:37:46,520
Speaker 3: these documents to really categorize and figure out what is

780
00:37:46,520 --> 00:37:48,080
Speaker 3: inside them. And so that turns out to be a

781
00:37:48,120 --> 00:37:50,759
Speaker 3: great application where we can basically get chat GPT to

782
00:37:50,800 --> 00:37:53,200
Speaker 3: do it and thereby create a data set of housing

783
00:37:53,239 --> 00:37:55,000
Speaker 3: regulations across the country. So I think that's something we

784
00:37:55,000 --> 00:37:58,960
Speaker 3: can really ramp up kind of across municipal government regulations

785
00:37:58,960 --> 00:38:00,799
Speaker 3: to better understand the rules out there.

786
00:38:00,920 --> 00:38:05,360
Speaker 1: We should charge servers rent. That's the solution, like charge

787
00:38:05,400 --> 00:38:08,399
Speaker 1: the bots rent and property taxes and this will solve

788
00:38:08,400 --> 00:38:08,840
Speaker 1: the issue.

789
00:38:08,920 --> 00:38:11,719
Speaker 2: I mean, data center cre is like the hottest area

790
00:38:11,719 --> 00:38:12,120
Speaker 2: in the world.

791
00:38:12,160 --> 00:38:15,160
Speaker 1: Yeah, I know, so higher taxes there we go.

792
00:38:15,640 --> 00:38:18,800
Speaker 2: That what office to data center conversions I think is

793
00:38:18,840 --> 00:38:21,719
Speaker 2: the move rather than office to REZI, office office to

794
00:38:21,840 --> 00:38:26,200
Speaker 2: AI conversions. I like that idea. Arpi Gupta, Associate Professor

795
00:38:26,239 --> 00:38:29,080
Speaker 2: of Finance at NYU, thank you so much for coming on.

796
00:38:29,160 --> 00:38:30,000
Speaker 2: Great to find the chat.

797
00:38:30,320 --> 00:38:31,399
Speaker 3: Thanks so much for having me.

798
00:38:41,880 --> 00:38:45,720
Speaker 2: Tracy. So, I guess my takeaway is maybe it's still

799
00:38:45,719 --> 00:38:47,759
Speaker 2: coming like I don't know, maybe like the coast is

800
00:38:47,800 --> 00:38:48,799
Speaker 2: not totally clear yet.

801
00:38:48,960 --> 00:38:51,960
Speaker 1: Well, I take the point that in all of commercial

802
00:38:51,960 --> 00:38:54,560
Speaker 1: real estate, it feels like it is operating on this

803
00:38:54,719 --> 00:38:57,479
Speaker 1: incredibly lengthy lag time and it takes a long time

804
00:38:57,640 --> 00:39:01,240
Speaker 1: for Lisa's to get renegotiated. A lot of property owners

805
00:39:01,440 --> 00:39:04,359
Speaker 1: don't want to have lower rents for the reason that

806
00:39:04,719 --> 00:39:07,960
Speaker 1: are Pit discussed. But the other thing I would say is, like,

807
00:39:08,680 --> 00:39:11,120
Speaker 1: coming away from that conversation, I kind of feel like

808
00:39:11,400 --> 00:39:16,120
Speaker 1: there's two possible pathways, like two extreme pathways. One is

809
00:39:16,160 --> 00:39:19,360
Speaker 1: the doom loop scenario that ar Pit described and the

810
00:39:19,440 --> 00:39:23,120
Speaker 1: other one is the sort of like redevelopment of the

811
00:39:23,239 --> 00:39:28,040
Speaker 1: urban environment where offices do get turned into new residential units,

812
00:39:28,080 --> 00:39:30,680
Speaker 1: and maybe as rents are lower, there are new types

813
00:39:30,719 --> 00:39:34,479
Speaker 1: of businesses that come in. It's that consumption economy rather

814
00:39:34,520 --> 00:39:37,680
Speaker 1: than this is the place that you go to work idea.

815
00:39:38,040 --> 00:39:42,640
Speaker 1: If everything went perfectly, if policymakers like pursued that path,

816
00:39:42,800 --> 00:39:46,160
Speaker 1: that could be a relatively amazing outcome. I do not

817
00:39:46,480 --> 00:39:50,640
Speaker 1: always have one hundred percent confidence that policymakers are going

818
00:39:50,719 --> 00:39:54,320
Speaker 1: to be sort of visionaries when it comes to redefining

819
00:39:54,920 --> 00:39:59,280
Speaker 1: huge urban cities, but it's kind of a nice lot totally.

820
00:39:59,320 --> 00:40:01,480
Speaker 2: There are three things there that struck me.

821
00:40:01,640 --> 00:40:01,759
Speaker 3: So.

822
00:40:01,920 --> 00:40:05,320
Speaker 2: First of all, disinclination to lower rents. It is like

823
00:40:05,360 --> 00:40:08,839
Speaker 2: one of these great examples of accounting affecting the real world. Right,

824
00:40:09,160 --> 00:40:12,239
Speaker 2: So it's like something that exists on a spreadsheet somewhere

825
00:40:12,600 --> 00:40:15,680
Speaker 2: creates a reason to not find that market clearing price

826
00:40:15,719 --> 00:40:18,120
Speaker 2: for rent. And maybe the moment will come when the

827
00:40:18,120 --> 00:40:21,080
Speaker 2: new businesses that like the cheaper rent can come in,

828
00:40:21,120 --> 00:40:23,560
Speaker 2: And I think that's something interesting to watch. Two. I

829
00:40:23,760 --> 00:40:26,480
Speaker 2: liked the way Arpet put it and respond to your

830
00:40:26,560 --> 00:40:28,880
Speaker 2: question about you know people maybe people come to the

831
00:40:28,880 --> 00:40:31,400
Speaker 2: cities for the dating markets instead of the labor markets,

832
00:40:31,440 --> 00:40:33,000
Speaker 2: and that's sort of grim. But I think, like it's

833
00:40:33,080 --> 00:40:37,000
Speaker 2: interesting if you think, like about things like Tinder or

834
00:40:37,200 --> 00:40:39,000
Speaker 2: door dash or some of these apps, and like how

835
00:40:39,080 --> 00:40:41,640
Speaker 2: much better they probably are in the cities rather than

836
00:40:42,000 --> 00:40:43,600
Speaker 2: out on the sticks you to wipe and see the

837
00:40:43,640 --> 00:40:45,840
Speaker 2: same three restaurants are the same, you know, six people,

838
00:40:45,840 --> 00:40:48,480
Speaker 2: same three people, and that on that these technologies that

839
00:40:48,520 --> 00:40:52,160
Speaker 2: we think of as potentially diffusing could actually encourage physical concentration.

840
00:40:52,760 --> 00:40:55,760
Speaker 2: And then third, you know, one thing I think about,

841
00:40:55,840 --> 00:40:59,640
Speaker 2: and we talked recently about the megacity that the UAE

842
00:40:59,760 --> 00:41:02,600
Speaker 2: might build in northern Egypt, is like this idea of

843
00:41:02,600 --> 00:41:06,520
Speaker 2: like cities divorced from production generally, and I sort of

844
00:41:06,560 --> 00:41:09,440
Speaker 2: think that's like the thesis of some of these megacities

845
00:41:09,480 --> 00:41:11,839
Speaker 2: in the Middle East is like the main thing they're

846
00:41:11,920 --> 00:41:15,320
Speaker 2: selling is not some industry that exists in the city.

847
00:41:15,400 --> 00:41:18,240
Speaker 2: It's the main thing is they're selling a nice quality

848
00:41:18,280 --> 00:41:21,080
Speaker 2: of life, a quality of life and lower taxes and

849
00:41:21,120 --> 00:41:24,279
Speaker 2: low taxes and low crime, et cetera. And then like

850
00:41:24,680 --> 00:41:26,960
Speaker 2: the idea that the production has to happen there is

851
00:41:27,000 --> 00:41:29,600
Speaker 2: like sort of not particularly necessary.

852
00:41:29,320 --> 00:41:30,919
Speaker 1: That's a really good way of putting it. The one

853
00:41:30,920 --> 00:41:34,960
Speaker 1: other thing I'd say is so obviously the big cities

854
00:41:35,040 --> 00:41:40,319
Speaker 1: could develop themselves into like consumerist or human connection capitals

855
00:41:40,360 --> 00:41:43,239
Speaker 1: of the world, I guess, but you could also see

856
00:41:43,239 --> 00:41:47,359
Speaker 1: smaller cities, smaller towns, suburban areas start to build up

857
00:41:47,440 --> 00:41:51,239
Speaker 1: their own like nightlife and restaurant options, sort of what

858
00:41:51,280 --> 00:41:54,480
Speaker 1: you were talking about earlier, Like, and we have seen

859
00:41:54,600 --> 00:41:57,160
Speaker 1: some places outside of New York. I think there was

860
00:41:57,200 --> 00:42:01,000
Speaker 1: a really good Bloomberg article on a small town in Connecticut.

861
00:42:01,200 --> 00:42:04,560
Speaker 1: Am I I'm talking my own book now, but there

862
00:42:04,680 --> 00:42:07,080
Speaker 1: was a nice story about, you know, relatively small town

863
00:42:07,080 --> 00:42:09,879
Speaker 1: in Connecticut that had seen a lot of people move

864
00:42:10,000 --> 00:42:11,640
Speaker 1: away from New York. They don't want to do the

865
00:42:11,640 --> 00:42:15,040
Speaker 1: commute into the city anymore, and so their downtown area

866
00:42:15,440 --> 00:42:20,040
Speaker 1: is booming. Now that's relative for a smaller town, but

867
00:42:20,200 --> 00:42:22,719
Speaker 1: they have new restaurants opening up, they have new shops,

868
00:42:22,880 --> 00:42:26,000
Speaker 1: they have offices being built there. So you could see

869
00:42:26,080 --> 00:42:29,680
Speaker 1: these sort of like smaller versions I guess of cities

870
00:42:30,120 --> 00:42:32,839
Speaker 1: kind of develop across America, where it is to your

871
00:42:32,840 --> 00:42:36,319
Speaker 1: point about the megacities in the UAE and the Middle East,

872
00:42:36,360 --> 00:42:38,720
Speaker 1: where it is much more about that quality of life.

873
00:42:38,760 --> 00:42:41,359
Speaker 2: Tracy, I have a favor to ask. Can you go

874
00:42:41,440 --> 00:42:44,239
Speaker 2: to town planning meetings and be ultra nimby so that

875
00:42:44,280 --> 00:42:46,960
Speaker 2: people can't move out of the city? And you know, like,

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00:42:47,120 --> 00:42:49,800
Speaker 2: this is how we could exploit our natural hedge together,

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00:42:49,840 --> 00:42:53,000
Speaker 2: which is you fight to preserve your quality of life

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00:42:53,040 --> 00:42:54,880
Speaker 2: out in the middle of nowhere so that people have

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00:42:54,920 --> 00:42:57,080
Speaker 2: to stay in New York and keep rents and foot

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00:42:57,120 --> 00:42:57,879
Speaker 2: traffic up here.

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00:42:58,080 --> 00:43:00,400
Speaker 1: I think that would be a very dangerous thing for

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00:43:00,480 --> 00:43:02,840
Speaker 1: me to do personally, all right, I respect, but we

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00:43:02,840 --> 00:43:03,480
Speaker 1: can talk about it.

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00:43:03,560 --> 00:43:04,719
Speaker 2: Jall right, it sounds good.

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00:43:04,719 --> 00:43:05,480
Speaker 1: Shall we leave it there?

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00:43:05,520 --> 00:43:06,560
Speaker 2: Let's leave it there, all right.

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00:43:06,600 --> 00:43:09,440
Speaker 1: This has been another episode of the Oudlots podcast. I'm

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00:43:09,440 --> 00:43:12,480
Speaker 1: Tracy Alloway. You can follow me at Tracy Alloway.

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00:43:12,160 --> 00:43:14,840
Speaker 2: And I'm Joe Wisenthal. You can follow me at the Stalwart.

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00:43:15,080 --> 00:43:19,200
Speaker 2: Follow our guest Arpadgupta. He's at Arpitrage. Follow our producers

891
00:43:19,239 --> 00:43:22,800
Speaker 2: Carmen Rodriguez at Carmen armand dash El Bennett at Dashbot,

892
00:43:22,880 --> 00:43:25,960
Speaker 2: Kelbrooks at Kelbrooks. Thank you to our producer Moses On.

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00:43:26,640 --> 00:43:29,360
Speaker 2: From our Oddlots content, go to bloomberg dot com slash

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00:43:29,440 --> 00:43:32,000
Speaker 2: odd Lots, where we have transcripts, a blog, and a newsletter,

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Speaker 2: and you can chat about all of these things twenty

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Speaker 2: four to seven in the discord, Discord dot gg, slash

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Speaker 2: od lots.

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Speaker 1: And if you enjoy adlots, if you like it when

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