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Speaker 1: M HM. This is Mesters in Business with Very Renaults

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Speaker 1: on Bloomberg Radio. This week on the podcast, I have

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Speaker 1: an extra special guest, Jonathan Miller. I've been reading his

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Speaker 1: research and writing about real estate and appraisals and home

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Speaker 1: price trends and anything related to residential real estate for

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Speaker 1: I don't know, it's got to be close to twenty years.

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Speaker 1: He is the co founder of Miller Samuel. His data,

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Speaker 1: analytics and research powers the back end of some of

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Speaker 1: the biggest real estate agencies in the country. He always

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Speaker 1: has a tremendous amount of insight as to the current

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Speaker 1: state of the market and the best way to contextualize

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Speaker 1: what's going on in real estate. And we really talked

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Speaker 1: about everything from aspirational UH pricing to our markets at

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Speaker 1: a peak, and how when markets do peak and roll over.

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Speaker 1: In real estate, why it takes so long for prices

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Speaker 1: to adjust UH newsflash, sellers are anchored via the endowment

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Speaker 1: effect to their own value of their home prices, which

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Speaker 1: lagged the actual market UH for a long time. We

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Speaker 1: we talked about the death of cities being greatly overstated

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Speaker 1: and and why it's so challenging to convert all that

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Speaker 1: excess office space into residential properties. If you are at

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Speaker 1: all interested in real estate, buying a house, selling a house,

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Speaker 1: renting an apartment, owning a condo, or cooper just want

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Speaker 1: to know what the heck is going on with real

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Speaker 1: estate today. Uh, you will find this conversation to be

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Speaker 1: absolutely fascinating. So, with no further ado, my conversation with

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Speaker 1: Jonathan Miller. You're listening to Masters in Business with Very

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Speaker 1: rid Holts on Bloomberg Radio. I'm Barry rid Holts. You're

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Speaker 1: listening to Masters in Business on Bloomberg Radio. My extra

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Speaker 1: special guest this week is Jonathan Miller. He is the

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Speaker 1: CEO and co founder of Miller Samuel, a real estate

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Speaker 1: appraisal and consulting firm he first founded in six His

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Speaker 1: data and analytics powers many of the largest brokerage firms

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Speaker 1: um reporting and information about the state of the housing market. Uh.

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Speaker 1: He is our returning champion. I think you and Scott

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Speaker 1: Galloway are tied for the most Masters in Business appearances.

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Speaker 1: Jonathan Miller, Welcome back to Bloomberg. Great to be here, Barry. Thanks. So,

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Speaker 1: so let's just talk a little bit about your background.

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Speaker 1: You co found Miller Samuel in in six what what

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Speaker 1: led you to the appraisal, real estate and consulting business

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Speaker 1: way back then. Well, UH, initially I worked for a

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Speaker 1: management company that was bought by Marriott back in the

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Speaker 1: mid eighties, and I said, well, what am I gonna

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Speaker 1: do with myself? And I got my real estate license

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Speaker 1: and I became a real estate agent in Chicago, and

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Speaker 1: UH was more on the analytics side, you know, sort

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Speaker 1: of the bean counter type. And then I moved to

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Speaker 1: New York and UH was eventually became a sales director

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Speaker 1: on a new condominium building and saw these appraisers coming

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Speaker 1: in without really data. There was no multiple listening system

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Speaker 1: in Manhattan. And I eventually got together with my family

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Speaker 1: and we started our firm, Miller Samuel in the eighties

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Speaker 1: six and it covers the New York City metro area.

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Speaker 1: And then UH, starting in n I started offering UM

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Speaker 1: market studies UH for the real estate company UH Douglas

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Speaker 1: Solomon and basically follow their footprints. I'm covering about forty

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Speaker 1: different housing markets in the US right now, and that's

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Speaker 1: really fun. And then it sort of marked in. I

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Speaker 1: teach market analysis at Columbia for grad students UM, and

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Speaker 1: so you know, it's just been this sort of progression

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Speaker 1: of but everything related to housing in real estate trends. Well,

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Speaker 1: I love that you are are one of the number

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Speaker 1: crunchers who dive into the data and come up with

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Speaker 1: analytics to really teach us about what's going on in

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Speaker 1: the marketplace. And so I have to start with that question,

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Speaker 1: what are you seeing these days in the real estate

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Speaker 1: markets you cover? Are prices and volumes still going up?

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Speaker 1: As of this recording, mortgage rates have just about doubled

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Speaker 1: to six from you know, the low threes or even

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Speaker 1: high twos um. What's going on in real estate? Well,

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Speaker 1: I think of it as you know, you know, with

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Speaker 1: affordability dropping literally by half since the end of December,

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Speaker 1: the market is going through a pause where there is

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Speaker 1: a tremendous pullback in demand and you know, this is

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Speaker 1: a significant jump in rates. So you have this pullback,

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Speaker 1: and what that's done initially is you know, new sign

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Speaker 1: contract volume is down across all the markets we cover. Uh,

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Speaker 1: And actually that's been occurring for the last couple of months.

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Speaker 1: This is you know, the slowdown now is has accelerated,

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Speaker 1: but really we started to see contract activity slowed down

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Speaker 1: at the end of March, beginning of April. Initially it

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Speaker 1: was because inventory had collapsed, uh and was keeping sales

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Speaker 1: from occurring because there was no product. But it's really

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Speaker 1: completely morphed into an affordability issue because nationally, you know,

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Speaker 1: depending on the metrics you follow, we're looking at increase

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Speaker 1: in housing prices plus rates doubling. What else could happen

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Speaker 1: to slowdown? I think you told me this, or you

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Speaker 1: got this from the internet somewhere somewhere you said, one

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Speaker 1: of the best things for high housing prices or high

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Speaker 1: housing prices. Well, that's that's the that's the old commodity

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Speaker 1: trader's joke. The cure for high poses are high prices.

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Speaker 1: At a certain point, either the demand cools or the

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Speaker 1: supply rushes to meet it. Yeah, And I think what

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Speaker 1: people generally have wrong with with the market cool down is,

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Speaker 1: you know, initially, when you have an external event like

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Speaker 1: great Spiky, you have sales activities slow immediately, and you

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Speaker 1: have inventory rise immediately, albeit from an unusually low place.

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Speaker 1: And I'll expand on that. But but the part, the

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Speaker 1: third step that I think most people get wrong is

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Speaker 1: they expect crisis to immediately fall and there's really a

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Speaker 1: one or two year period that it takes sellers to

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Speaker 1: capitulate to market conditions, although I do think that they'll

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Speaker 1: capitulate faster in this condition, uh than in the past.

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Speaker 1: But I you know, buyers are in already, you know,

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Speaker 1: that's their hesitation. And then you throw in a war,

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Speaker 1: you know, record or you know, high inflation. Um, you

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Speaker 1: have all these uncertainties, and everybody just I mean, it's

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Speaker 1: one of the biggest enemies of the housing market is uncertainty.

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Speaker 1: And we certainly have a basketfull of that right now.

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Speaker 1: You know, I have a very vivid recollection at like

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Speaker 1: cocktail parties and barbecues in two thousand and nine, even

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Speaker 1: people saying, why can't I get X from my house?

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Speaker 1: The same house down the street sold for X and

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Speaker 1: it's not as nice as mine, and that was sold

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Speaker 1: in two thousand six. And my answer is always, well,

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Speaker 1: you could get that money first. You need a time

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Speaker 1: machine go back to two thousand six and you could

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Speaker 1: get That's what the market was. And I watched what's amazing,

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Speaker 1: especially about all the online services today. I watched these

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Speaker 1: home sellers chase the price down. They were always lagging

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Speaker 1: behind the market. Is that really a two year process

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Speaker 1: for people? Before people you know, find religion and say

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Speaker 1: here's where the market is. If I want to sell this,

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Speaker 1: I have to get that. So yeah, So in my

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Speaker 1: own you know, thirty eight years of experience, you know,

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Speaker 1: it's a sort of anecdotal experience that has telling me

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Speaker 1: that it takes an average nationally about fifteen sixteen months.

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Speaker 1: But call it you know, year, two year and a half,

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Speaker 1: two years, sort of, it's not a couple of months. Um.

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Speaker 1: And to clarify, what I'm really saying is that it

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Speaker 1: takes a long time for the seller to capitulate to

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Speaker 1: the market so that they feel like closing, like they

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Speaker 1: didn't leave money on the table, right, So so it's

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Speaker 1: where they didn't like walk away from a sure thing,

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Speaker 1: and that takes a lot of time. Um. I think

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Speaker 1: what's a little different in this cycle is there's been

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Speaker 1: so much equity build up because of the rapid run

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Speaker 1: up at prices that maybe it's less painful, uh, because

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Speaker 1: it's money on paper too many um, you know in

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Speaker 1: that in the sense of um, you know, people uh,

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Speaker 1: you know, worried about walking away from something that they

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Speaker 1: had in their in their hands. So if you're a contrarian,

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Speaker 1: you become a buyer in the summer of Yes, yeah,

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Speaker 1: I think so, and and and what what I find,

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Speaker 1: uh interesting, is that, um, you know, when people look

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Speaker 1: at pivoting conditions, pivoting markets, you append the word forever,

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Speaker 1: like prices are falling forever, prices are rising forever. It's

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Speaker 1: like this linear view. And if you look at some

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Speaker 1: serious downturn periods in the housing market, like the housing bubble, um,

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Speaker 1: you know, fifteen or so years ago, thirteen fifteen years ago. Uh, really,

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Speaker 1: prices really didn't recover, took about six years, but activity

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Speaker 1: actually returned pretty quickly by two thousand, late two thousand nine,

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Speaker 1: early two thousand and ten. So this isn't like a

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Speaker 1: decade or a you know, a generational thing. Um, it's

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Speaker 1: you know, it's a shorter window than that. And um, honestly,

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Speaker 1: I felt, you know, rap should have been raised specifically

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Speaker 1: through housing market optics about a year ago. Uh. It

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Speaker 1: feels like the fed plate to the party, because you

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Speaker 1: could just see the obliteration nationwide of inventory. Like one

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Speaker 1: of the ways I think about housing supply, I think

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Speaker 1: we have a tremendous amount of over reliance on when

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Speaker 1: you look at the state of the housing market of

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Speaker 1: looking at new construction, new development, which in most local

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Speaker 1: and national we're really talking you know, ten percent, and

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Speaker 1: you know ebbs and flows of total inventory, Well what

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Speaker 1: about the other eighty five or of supply? Um? That

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Speaker 1: is the think of that supply as sort of the

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Speaker 1: life cycle of occupants of the real estate. You know,

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Speaker 1: they're they're you know, they're they're trading up to a

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Speaker 1: larger home because they have an expanding family. They're trading down,

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Speaker 1: they're downsizing. Um. You know, there's all these sort of

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Speaker 1: life changes. And when you come at them with a

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Speaker 1: two point six you know that market with a two

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Speaker 1: point six thirty year fixed uh, you create this sensatiable

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Speaker 1: demand this and you obliterate supply. And so this became

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Speaker 1: a housing market of bidding wars. Most of the forty

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Speaker 1: plus markets I covered for Douglas Sellman are seeing bidding

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Speaker 1: like in southern California two thirds to three quarters depending

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Speaker 1: on the segment of closings. In the early part of

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Speaker 1: this year, we're above the asking price. You know, Uh,

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Speaker 1: that would be our proxy forbidding wars. In Fairfield County, Westchester,

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Speaker 1: Long Island, you know that ring New York City, we

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Speaker 1: were looking at forty five. How is that sustainable? How

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Speaker 1: is that a good thing? So I'm quite relieved at

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Speaker 1: least thinking long term that we're moving out of this

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Speaker 1: untenable frenzy. And you know, you and I have spoken

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Speaker 1: about another aspect of UM, not the demand side, but

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Speaker 1: the supply side. You know, there's normally that chain of

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Speaker 1: purchases that takes place. Someone moves into a starter home,

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Speaker 1: there's a move up, there's a move to a larger home,

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Speaker 1: to a nicer neighborhood, eventually to waterfront, and that last

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Speaker 1: person is downsizing. During the pandemic, especially when people were

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Speaker 1: locked down in apartments, there were a lot of purchases

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Speaker 1: with no correlating out. People just went out and bought

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Speaker 1: a second or third house. That had to have a

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Speaker 1: massive impact on the supply out there. Yeah, there's two

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Speaker 1: things that happened. One was, uh so specifically with a

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Speaker 1: Manhattan example, after the lockdown, the pandemic lockdown, rental prices

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Speaker 1: fell a lot. They fell, um, you know, but there

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Speaker 1: was no vaccine, there was you know that the city

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Speaker 1: was the global hotspot, and so the rental demand weakened

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Speaker 1: because would be tenants with the collapse and mortgage rates

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Speaker 1: fled to the you know that sort of in air quotes,

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Speaker 1: the narrative was fleeing the city or another air quote

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Speaker 1: sort of a headline was exodus. UM. And so it

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Speaker 1: was all outbound migration and no inbound And you had

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Speaker 1: these these renters buying, and then you had people weren't

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Speaker 1: selling during the pandemic. They in the city, they were

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Speaker 1: buying a second home, something that I dubbed co primary,

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Speaker 1: which should be you know, they're looking at a second

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Speaker 1: primary residence that they can sort of toggle back and

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Speaker 1: forth between the city and the suburbs and um. And

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Speaker 1: it there was no seasonality you had. You had maybe

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Speaker 1: five years of you know, the typical you know, young

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Speaker 1: couple moves to the suburbs to have kids. You know

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Speaker 1: that compressed into like three months, UM. And they had

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Speaker 1: this whole distortion of sort of migration patterns UM. And

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Speaker 1: then on top of it all you have remote, which

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Speaker 1: became you know, zoom became ubiquitous in twenty four hours,

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Speaker 1: and all of a sudden, you have a rethinking of

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Speaker 1: what remote meant, and it became a powerful force. What

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Speaker 1: I was saying during the lockdown was the tether between

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Speaker 1: work and home became infinitely longer because you have a

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Speaker 1: lot more flexibility. And on top of it, you have

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Speaker 1: this skewed towards higher end meeting that you know, because

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Speaker 1: of the economic damage from the lockdown was heavily weighted

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Speaker 1: towards lower wage earners. The upper half of the market

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Speaker 1: was where what woke up or saw this boom first.

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Speaker 1: And part of it is because you know, just as

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Speaker 1: sort of a generalized statement is that the higher the wage,

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Speaker 1: the higher the mobility or you know, the ability to

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Speaker 1: work remote and it just dovetailed together really well, quite

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Speaker 1: quite interesting. So a couple of we're recording this UM

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Speaker 1: in the middle of this week on the June one UM,

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Speaker 1: some really interesting data points came out this week. Existing

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Speaker 1: home sale prices hit a record of four hundred and

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Speaker 1: seven thousand, six hundred dollars in May, and at the

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Speaker 1: same time, home sales decline three point four in the

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Speaker 1: face of rising interest rates. Is it sort of contradictory

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Speaker 1: that prices are going up even as sales decline or

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Speaker 1: is that more about the mix of of hiring homes

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Speaker 1: and lauren homes. So I think it's more about the

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Speaker 1: lag in UH closing information versus actual onmographic contract activity. Uh,

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Speaker 1: it is UM. You know, if you can think of

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Speaker 1: home price trends at the caboose on the end of

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Speaker 1: the train. I always use this analogy, even though there's

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Speaker 1: no cabooses on ends of trains anymore. UM. But really

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Speaker 1: the initial impact you should really for trends, you should

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Speaker 1: be looking at sales like petting, home sales, contract activity

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Speaker 1: in general, and then new inventory entering the market that's

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Speaker 1: much more fluid and in front of the prices occur

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Speaker 1: after the dust settles. So, for example, UM we published

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Speaker 1: monthly research in four different regions of the country, and

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Speaker 1: new stign contracts were already beginning to cool back in March,

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Speaker 1: and uh, you know, eventually that leads to an uptick

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Speaker 1: in UM listing activity, which then eventually, uh you know,

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Speaker 1: you know, levels off are cool sales. So I don't

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Speaker 1: think the pricing, the prices rising is a result of

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Speaker 1: a shift in the mix towards higher property. I think

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Speaker 1: it's really just a lag in the actual data itself.

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Speaker 1: So so where are we in this housing cycle? I

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Speaker 1: noticed that more supply is coming online. There was a

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Speaker 1: great chart the other day at at Calculated Risk showing

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Speaker 1: the most single family home completion. Since I think, oh, seven,

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Speaker 1: it is like a well over dead kid. Are we

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Speaker 1: seeing that many more um new homes and existing homes

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Speaker 1: come on the market as supply or is it really

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Speaker 1: just been down so long it looks like up To me,

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Speaker 1: I think the latter. So the way to think of it. Uh.

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Speaker 1: And this is my analogy just using my my hometown

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Speaker 1: in Connecticut. Uh, my town saw two hundred listings pre

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Speaker 1: pandemic for the prior four or five years plus or

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Speaker 1: minus twenty listings, but it was, you know, straddling the

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Speaker 1: two D threshold. A year after the pandemic there were

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Speaker 1: fifty and so you look at and go, wow, that's

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Speaker 1: a real drop. Uh. And then the beginning of the

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Speaker 1: year before the rate increases, there were twelve. Wow. And

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Speaker 1: so that's where I would call it a clap. So

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Speaker 1: now inventory has quadrupled, there's fifty. It's still s below

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Speaker 1: pre pandemical bubbles. So when I look at I look

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Speaker 1: at that and say, yeah, inventories rising, and that's a

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Speaker 1: good thing. Um. But I don't know if people realize

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Speaker 1: how insanely low inventory became began. Um. And and you know,

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Speaker 1: one of the things, um, you know that I think

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Speaker 1: is going to be apparent in the coming year or

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Speaker 1: two is that we have probably built too much multi

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Speaker 1: family rental product um right now. You know that it's

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Speaker 1: all sort of responding to this surge in you know,

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Speaker 1: the in rental prices. But part of that search in

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Speaker 1: rental prices is because, uh, the surge or the rising

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Speaker 1: rates are just the fact that lenders are not fast

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Speaker 1: and loose that they are. You know, we're not going

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Speaker 1: to have a banking crisis on the other side of this,

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Speaker 1: because lenders are ti or than they were in the

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Speaker 1: decades prior to the housing bubble um, and so people

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Speaker 1: that don't qualify they tipped into the rental market, right,

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Speaker 1: I mean, they're not. Um, you know, we don't have

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Speaker 1: we don't have financial engineering like we did. You know

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Speaker 1: in the mortgage world circa two thousand and five. Six,

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Speaker 1: What a what a funny coincidence that mortgage lenders were

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Speaker 1: much looser before the housing crisis. I mean, you know,

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Speaker 1: sometimes these coincidences are just just amazing and sing And

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Speaker 1: since the financial crisis, it's become much harder to get

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Speaker 1: a mortgage. Um. And I'm not talking about you know,

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Speaker 1: all day or sub prime I mean prime borrowers really

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Speaker 1: had to jump through some hoops in the years after

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Speaker 1: o ee oh nine. Is it's still that tight or

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Speaker 1: has things you know normalized a little bit. So the

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Speaker 1: way to look at it is uh uh underwriting standards,

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Speaker 1: mortgage underwriting standards. You know, during the the housing bubble,

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Speaker 1: you just had to have a pulse or bogramere. In

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Speaker 1: the years subsequent it has the restrictions have deteriorated, but

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Speaker 1: we're still not on par with sort of pre housing

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Speaker 1: bubble era lending standards. That that lending is generally more

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Speaker 1: conservative and there's less sort of maybe on the margin,

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Speaker 1: but there's really less sort of alternative workarounds for financing.

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Speaker 1: I think it's much more sophisticated. I think some of

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Speaker 1: the restraints with Dodd Frank uh you know, have have

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Speaker 1: helped to a certain degree which makes us in a

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Speaker 1: much less vulnerable, vulnerable position. But that has helped create

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Speaker 1: much more tightness in the rental market in general. And

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Speaker 1: I think we have this, you know, law large response

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Speaker 1: in multi family rental development being created, and I suspect

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Speaker 1: in over the next year or two that that's going

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Speaker 1: to be a um where it's gonna be apparent because

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Speaker 1: people when they look at the rental development being created,

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Speaker 1: there's assumption of well more units lower rent. But just

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Speaker 1: like new home construction, new rental constructions used to the

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Speaker 1: upper half of the market. It doesn't address the entire market.

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Speaker 1: So you're creating a lot more rental units, but you're

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Speaker 1: creating not necessarily the right distribution of rental units. And

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Speaker 1: I think that's the challenge for the rental market going forward.

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Speaker 1: Just for I was just gonna say, just in New York, um,

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Speaker 1: you know, we we report every month the rental um

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Speaker 1: you know, the rental market, and in Manhattan, the median

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Speaker 1: rent was four thousand dollars, cracking the four thousand threshold

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Speaker 1: for the first time in history. Average rents are just

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Speaker 1: shy of five thousand a month. It's still an all

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Speaker 1: time record. And we're not even to peek leasing season,

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Speaker 1: which is in August. The rental market sort of peaks

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Speaker 1: at the end of the summer, and so there's still

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Speaker 1: a lot of pressure in the rental market going forward

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Speaker 1: over the you know, whether or not we um you know,

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Speaker 1: we have rising inflation. It's it's very tenuous and this

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Speaker 1: is not unique to much of the US so so

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Speaker 1: let's let's address that because during the pandemic, I read

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Speaker 1: at least in the New York Post, new York City

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Speaker 1: is dead. It's never coming back, No one's ever going

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Speaker 1: to move here. It's all over for cities, and not

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Speaker 1: just New York, but San Francisco and Chicago and Austin

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Speaker 1: and Philadelphia and Boston. And go down the list. What

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Speaker 1: is the state of real estate in in the big

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Speaker 1: cities and and where are you seeing more of the

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Speaker 1: explosive of growth. Is that more the sun belt or

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Speaker 1: is that the coast? What's the state of well well, well,

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Speaker 1: so in terms of um urban markets, just starting with

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Speaker 1: New York first, since some base there, uh here we

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Speaker 1: have and this sort of speaks for what we're seeing

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Speaker 1: across the US. You're looking at a city like New

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Speaker 1: York and the office towers are two thirds empty right now.

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Speaker 1: Yet Ino we've had some of the highest sales volumes

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Speaker 1: in history. We've had record pricing in the rental market.

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Speaker 1: We've had some of the highest new leasing activity in history,

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Speaker 1: and we're having record rents. So people being in the

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Speaker 1: city again to this sort of tether between work at home,

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Speaker 1: it's you know, you have empty office because of remote

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Speaker 1: but people are in the still in the city or

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Speaker 1: want to be in the city, So it is um.

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Speaker 1: This is uh. There is a white paper in the

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Speaker 1: source escapes me from NBR talking about price growth is

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Speaker 1: related during the pandemic era, is related to remote work

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Speaker 1: in the churn that it has created. UM. And there's

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Speaker 1: a bigness nomer. You know, there has to be another

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Speaker 1: reason why people are in these urban coming back to

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Speaker 1: these urban markets. And I think part of it is

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Speaker 1: just what the cities have to offer and what um,

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Speaker 1: you know, the cultural activity. I'm not working for the

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Speaker 1: trade that the tourism bureau, but you know, there's other

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Speaker 1: reasons people are in the city, you know, lifestyle or

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Speaker 1: whatever broker term you want to call it. UM. And

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Speaker 1: this is being borne out in the fact that office

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Speaker 1: towers and two thirds empty still That almost sounds like

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Speaker 1: a problem that solves itself. And my frame of reference

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Speaker 1: was after September eleven, two and one, many of the

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Speaker 1: office towers in Lower Manhattan either were converted to residential

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Speaker 1: or partially converted to hotels and residential. How realistic is it?

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Speaker 1: And and you know, I'm glad I wasn't an investor

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Speaker 1: in Hudson Yards that came online just as demand for

389
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Speaker 1: office space went through the floor. But how realistic is

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Speaker 1: it to convert offices to to residential real estate? And

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Speaker 1: and let me point out when I was in grad

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Speaker 1: school and for about a decade afterwards, I lived at

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Speaker 1: ninety Lexington Avenue that originally was a Blue Cross Blue

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Speaker 1: Shield building that was converted to residential like a few

395
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Speaker 1: decades before I got there, and a decade after I

396
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Speaker 1: left it it went condo. So I know it's doable.

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Speaker 1: How realistic is it? Uh, It's not very realistic at

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Speaker 1: all in terms of scale. Uh. And the reason there's

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Speaker 1: a couple of reasons for it. One the financial districts

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Speaker 1: Uh conversion frenzy that was mostly Class B office older

401
00:27:08,160 --> 00:27:12,080
Speaker 1: almost laugh like a lot of the development that I'm

402
00:27:12,119 --> 00:27:17,520
Speaker 1: familiar with Class A. Yeah. So the way I look

403
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Speaker 1: at it is um when you So there's a couple

404
00:27:22,760 --> 00:27:29,159
Speaker 1: of things. One is time, so a lot of the conversion. Listen,

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Speaker 1: there there will be some conversion. I'm saying there won't,

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Speaker 1: but I think nowhere near on the scale of what

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Speaker 1: could be. Uh, you know, the sort of the the

408
00:27:38,640 --> 00:27:41,280
Speaker 1: opening question in this discussion is what are we gonna

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Speaker 1: do with all these you know, empty offices. Well, part

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Speaker 1: of what you're gonna do is you're going to drop

411
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Speaker 1: the rents um but somewhat you know, in theory, except

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Speaker 1: for that you've got them financed and this is a

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Speaker 1: collateral right and all of a sudden, you know, the

414
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Speaker 1: cash flow as much as half or you know, a

415
00:28:00,640 --> 00:28:06,200
Speaker 1: third of what it was precandemic. UM. You also have uh,

416
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Speaker 1: you know, converting these buildings to red to have residential

417
00:28:11,240 --> 00:28:16,919
Speaker 1: certificate of occupancies is tremendously expensive. And the third is

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Speaker 1: community approvals zoning approvals UM, which is a long process.

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Speaker 1: I'm not saying it can't be done, but who knows

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Speaker 1: what the conditions are going to be like five years

421
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Speaker 1: from now. Um. One of the things we're seeing is, uh,

422
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Speaker 1: we're seeing a tremendous amount of uh, you know, well

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Speaker 1: obviously vacancy, but also concessions because landlords some aren't feeling

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Speaker 1: the pain because you know, say eight of the leases

425
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Speaker 1: in the building have you know, five or more years

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Speaker 1: remaining on them. So uh, you know, if if if

427
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Speaker 1: there's if there's still an occupancy, the business hasn't gone under.

428
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Speaker 1: They're still paying their rent and they're paying sort of

429
00:29:02,000 --> 00:29:06,600
Speaker 1: pre pandemic levels. Um. What we're seeing a tremendous amount

430
00:29:06,600 --> 00:29:09,840
Speaker 1: of in the commercial world is that leasing on a

431
00:29:09,880 --> 00:29:12,800
Speaker 1: per square foot basis. Everybody talks about sort of the

432
00:29:12,880 --> 00:29:15,760
Speaker 1: asking rent, and when you look at all the reports

433
00:29:15,800 --> 00:29:18,840
Speaker 1: on the state of the office market in cities, uh,

434
00:29:18,880 --> 00:29:21,560
Speaker 1: they never talked about like the net effective you know,

435
00:29:21,800 --> 00:29:25,880
Speaker 1: less the free rent and the build build outs and

436
00:29:26,040 --> 00:29:30,080
Speaker 1: all those expenses you know, which you know in reality

437
00:29:30,200 --> 00:29:35,160
Speaker 1: many land larger seeing thirty to fift hits at least

438
00:29:35,160 --> 00:29:39,200
Speaker 1: in my you know, in my through my optics, um,

439
00:29:39,200 --> 00:29:44,160
Speaker 1: which is not an inconsequential amount. Um. And so I

440
00:29:44,200 --> 00:29:48,040
Speaker 1: think the asset that gets holding the bag on the

441
00:29:48,080 --> 00:29:54,840
Speaker 1: post pandemic world is really, um, you know, the commercial sector.

442
00:29:55,120 --> 00:29:59,040
Speaker 1: To a lesser degree, the retail sector, uh, which is

443
00:29:59,160 --> 00:30:02,400
Speaker 1: you know, gets its oxygen, especially in central business districts

444
00:30:02,440 --> 00:30:06,600
Speaker 1: from commercial office buildings. So you know, we're in, we

445
00:30:07,000 --> 00:30:10,840
Speaker 1: still are in you know, three to five years of

446
00:30:11,000 --> 00:30:13,560
Speaker 1: sort of figuring this out. This is this is a

447
00:30:13,640 --> 00:30:17,040
Speaker 1: slow motion train wreck. Um. You know I mentioned the

448
00:30:17,120 --> 00:30:21,400
Speaker 1: sun Belt earlier, where wherever I look, um, where the

449
00:30:21,480 --> 00:30:26,080
Speaker 1: weather is warmer and the taxes are lower, and that

450
00:30:26,120 --> 00:30:30,160
Speaker 1: could be places like Austin or Nashville or southern Florida.

451
00:30:30,320 --> 00:30:34,040
Speaker 1: It seems they've been a magnet for people leaving either

452
00:30:34,400 --> 00:30:38,120
Speaker 1: cold weather or higher tax state or or just sort

453
00:30:38,160 --> 00:30:41,200
Speaker 1: of you know, retiring what's going on in the sun

454
00:30:41,240 --> 00:30:45,840
Speaker 1: Belt and and and what does that look like going forward? Well,

455
00:30:45,880 --> 00:30:51,120
Speaker 1: I cover almost two dozen housing markets in Florida, for example,

456
00:30:51,920 --> 00:30:55,880
Speaker 1: And uh, what's interesting about a lot of these markets

457
00:30:56,280 --> 00:30:59,360
Speaker 1: is just like you say, they're coming there for lower

458
00:30:59,400 --> 00:31:06,040
Speaker 1: taxics bosure, but also these areas are aggressively going after

459
00:31:06,960 --> 00:31:11,600
Speaker 1: the c suite of companies to relocate in these places

460
00:31:11,680 --> 00:31:16,680
Speaker 1: for the tax credits UM and the weather and all

461
00:31:16,720 --> 00:31:20,160
Speaker 1: that sort of thing, and have been relatively successful. I

462
00:31:20,200 --> 00:31:25,040
Speaker 1: think initially after the the lockdown, you know where when

463
00:31:25,120 --> 00:31:29,440
Speaker 1: you lines screamed in like to your earlier point about

464
00:31:29,880 --> 00:31:32,600
Speaker 1: New York City is dead forever, you felt like by

465
00:31:32,640 --> 00:31:35,960
Speaker 1: the end of that there's going to be eleven people

466
00:31:36,080 --> 00:31:40,240
Speaker 1: left in New York City. You know that everybody was leaving. Um.

467
00:31:40,280 --> 00:31:43,640
Speaker 1: But what we're finding is that the people that have

468
00:31:43,800 --> 00:31:48,360
Speaker 1: left have been fairly quickly replaced. But we're still you know,

469
00:31:48,480 --> 00:31:54,680
Speaker 1: net net losing population markets like Florida or Texas. Those

470
00:31:54,680 --> 00:32:00,080
Speaker 1: housing markets are being restructured essentially because of remote and

471
00:32:00,120 --> 00:32:03,480
Speaker 1: what we're seeing for the first time really is we're

472
00:32:03,520 --> 00:32:08,800
Speaker 1: seeing um uh you know. The sort of the story

473
00:32:09,040 --> 00:32:12,080
Speaker 1: was that the affluent you know, the wall streets that

474
00:32:12,120 --> 00:32:16,240
Speaker 1: would um, you know, buy a second home somewhere, they

475
00:32:16,240 --> 00:32:20,720
Speaker 1: would buy in Florida, they buy Palm Beach and Miami.

476
00:32:20,760 --> 00:32:25,280
Speaker 1: And now we're seeing luxury or high end real estate

477
00:32:25,800 --> 00:32:28,920
Speaker 1: statewide all over the place, like you know, and that's

478
00:32:28,960 --> 00:32:32,560
Speaker 1: a sign of sort of structural I don't know if

479
00:32:32,560 --> 00:32:36,240
Speaker 1: the word is dispersion, but it's just spreading out because

480
00:32:36,440 --> 00:32:42,200
Speaker 1: the because the regional economy you know, has quickly adapted

481
00:32:42,360 --> 00:32:47,120
Speaker 1: to um, you know, to this inbound cohort of the population.

482
00:32:47,200 --> 00:32:52,200
Speaker 1: That this isn't a boom and bus cycle for Florida. UM. Sure,

483
00:32:52,360 --> 00:32:55,520
Speaker 1: their market will slow down because of the spike and

484
00:32:55,600 --> 00:32:59,720
Speaker 1: reals in interest rates. UM, but this is I don't

485
00:32:59,720 --> 00:33:01,640
Speaker 1: think this is a flash in the pan. I think

486
00:33:02,160 --> 00:33:07,040
Speaker 1: this is real because of every seemingly every part of

487
00:33:07,080 --> 00:33:10,560
Speaker 1: Florida is expanding. So it's a secular trend, not a

488
00:33:10,680 --> 00:33:14,400
Speaker 1: temporary issue. Let me let me ask you about California.

489
00:33:14,680 --> 00:33:18,400
Speaker 1: I always thought California was a wonderland. The last few

490
00:33:18,440 --> 00:33:21,720
Speaker 1: times I've been there. I've been kind of surprised at

491
00:33:22,480 --> 00:33:25,080
Speaker 1: you know, you drive to Lahoya from San Diego. There

492
00:33:25,160 --> 00:33:27,880
Speaker 1: used to be a lot of open space between the

493
00:33:27,920 --> 00:33:31,560
Speaker 1: two and and now it seems like every ridge has

494
00:33:31,600 --> 00:33:35,080
Speaker 1: a house on it, every every hill, every mountain. You know,

495
00:33:35,520 --> 00:33:41,080
Speaker 1: it really seems like it really seems like they've wildly built,

496
00:33:41,160 --> 00:33:44,400
Speaker 1: maybe even overbuilt parts of Southern California. What are you

497
00:33:44,440 --> 00:33:49,440
Speaker 1: seeing in that that region, well, well southern California of

498
00:33:49,480 --> 00:33:52,800
Speaker 1: all the regions that I cover. Uh, you know, the

499
00:33:52,880 --> 00:33:56,400
Speaker 1: average market and this is a little dated now where

500
00:33:56,400 --> 00:33:58,160
Speaker 1: it will be coming out with a second quarter in

501
00:33:58,200 --> 00:34:01,040
Speaker 1: a couple of weeks, but the first quarter data was

502
00:34:01,080 --> 00:34:07,240
Speaker 1: showing that on average, UM, about six of the transactions

503
00:34:07,280 --> 00:34:11,600
Speaker 1: from Los Angeles down to San Diego. Uh, we're selling

504
00:34:11,640 --> 00:34:16,640
Speaker 1: above the last asking price, like a tremendous demand above

505
00:34:16,800 --> 00:34:21,920
Speaker 1: asked So our proxy forbidding wars is a purchase price

506
00:34:22,040 --> 00:34:25,799
Speaker 1: that closed higher than the last asking price. UM, it's

507
00:34:25,800 --> 00:34:28,120
Speaker 1: about two thirds of the transactions and in some sub

508
00:34:28,120 --> 00:34:35,239
Speaker 1: markets that's UM we're not seeing that intensity now, but

509
00:34:35,239 --> 00:34:38,840
Speaker 1: but still there's a significant in balance between supply and demand.

510
00:34:38,840 --> 00:34:43,680
Speaker 1: Work demand is still overpowering UM. There has been a

511
00:34:43,680 --> 00:34:49,880
Speaker 1: tremendous outbound uh migration into low tax states like Texas.

512
00:34:51,239 --> 00:34:53,560
Speaker 1: But you know, at least in the housing market there's

513
00:34:53,600 --> 00:34:58,120
Speaker 1: still it's still extremely tight. UM. The thing about California,

514
00:34:58,160 --> 00:35:02,520
Speaker 1: they're going through a lot of problems draft and wildfires,

515
00:35:02,560 --> 00:35:07,360
Speaker 1: you know, partly from overbuilding and are encroaching on woodlands,

516
00:35:07,440 --> 00:35:13,360
Speaker 1: so it seems more tenuous, but it is still highly popular.

517
00:35:13,480 --> 00:35:16,480
Speaker 1: Tremendous amount of demand at least in southern California that

518
00:35:16,520 --> 00:35:22,240
Speaker 1: we're seeing. Um. But even in southern California, just like Florida, Uh,

519
00:35:22,280 --> 00:35:24,839
Speaker 1: you know, you're seeing with the rise and raids, you're

520
00:35:24,880 --> 00:35:29,839
Speaker 1: seeing a pullback in the intensity of contract volume. Really

521
00:35:29,920 --> 00:35:33,840
Speaker 1: quite interesting. You're listening to Masters in Business on Bloomberg Radio.

522
00:35:34,120 --> 00:35:37,680
Speaker 1: My extra special guest today is Jonathan Miller. He is

523
00:35:37,760 --> 00:35:41,120
Speaker 1: the CEO and co founder of Miller Samuel, a real

524
00:35:41,239 --> 00:35:45,719
Speaker 1: estate appraisal and consulting firm. His analytics power UH some

525
00:35:45,880 --> 00:35:50,239
Speaker 1: of the most influential real estate agencies, back offices and

526
00:35:50,320 --> 00:35:54,200
Speaker 1: back ends. He is also a professor at the Graduate

527
00:35:54,239 --> 00:35:59,800
Speaker 1: School at Columbia University and additionally sits on the Mayor's

528
00:35:59,840 --> 00:36:03,240
Speaker 1: at Economic Advisory Panel and the New York State Budget

529
00:36:03,280 --> 00:36:07,040
Speaker 1: Division Economic Advisory Board. So let's talk a little bit

530
00:36:07,080 --> 00:36:13,440
Speaker 1: about the the appraisal business. It has dramatically changed since

531
00:36:13,480 --> 00:36:17,520
Speaker 1: the Great Financial Crisis and the mortgage boom and bust

532
00:36:17,560 --> 00:36:20,680
Speaker 1: of the two thousands. Tell us what's going on in

533
00:36:20,680 --> 00:36:24,839
Speaker 1: the world of real estate appraisals. Sure, so you have

534
00:36:26,080 --> 00:36:33,000
Speaker 1: this uh sort of butting heads between human beings and uh,

535
00:36:33,120 --> 00:36:37,040
Speaker 1: you know, uh we call them a v MS automatee

536
00:36:37,080 --> 00:36:40,640
Speaker 1: evaluation models or press a button and you get a

537
00:36:42,360 --> 00:36:46,319
Speaker 1: a number of value for the house. UM sort of

538
00:36:46,360 --> 00:36:48,520
Speaker 1: the poster child for this. And if you don't mind

539
00:36:48,560 --> 00:36:51,520
Speaker 1: me talking about this estamate real quick, sure, just to

540
00:36:51,640 --> 00:36:56,400
Speaker 1: understand sort of the disparity between automation automating numbers and

541
00:36:56,520 --> 00:37:01,560
Speaker 1: housing and how dirty the data is UM and human beings. So,

542
00:37:02,880 --> 00:37:08,040
Speaker 1: if you look at the zestimate nationwide, the nationwide accuracy

543
00:37:08,160 --> 00:37:12,480
Speaker 1: rate that let me clarify, the median accuracy rate of

544
00:37:12,520 --> 00:37:16,960
Speaker 1: as estimate is two. So that means that fifty of

545
00:37:17,040 --> 00:37:20,640
Speaker 1: the time the z estimate is within two percent of

546
00:37:20,719 --> 00:37:25,319
Speaker 1: the actual value, and fifty at the time it's not.

547
00:37:25,680 --> 00:37:28,319
Speaker 1: Now the consumer just sees, hey, two percent, it's within

548
00:37:28,320 --> 00:37:31,200
Speaker 1: two percent. That's pretty accurate, But it's only fifty percent

549
00:37:31,239 --> 00:37:34,960
Speaker 1: of the time it's within two UM. But it gets

550
00:37:36,160 --> 00:37:39,919
Speaker 1: if you dig a little bit deeper. UM, it's only

551
00:37:39,960 --> 00:37:43,080
Speaker 1: within two percent of the property is currently listed for sale.

552
00:37:44,000 --> 00:37:47,840
Speaker 1: If it's not listed for sale as estimate, median accuracy

553
00:37:47,920 --> 00:37:51,160
Speaker 1: rate is seven percent in the US, so that means

554
00:37:51,160 --> 00:37:54,200
Speaker 1: that's seven percent at the time it's within seven If

555
00:37:54,200 --> 00:37:56,960
Speaker 1: it's not listed, it's within what it will sell for

556
00:37:57,600 --> 00:38:00,879
Speaker 1: within seven percent and five two percent of the time

557
00:38:00,920 --> 00:38:05,720
Speaker 1: it's not. So In other words, this estimate needs human

558
00:38:05,800 --> 00:38:11,560
Speaker 1: beings to price the listing to shave off five and accuracy. Listen,

559
00:38:11,600 --> 00:38:15,680
Speaker 1: I'm not saying as human beings like appraisers or brokers

560
00:38:16,400 --> 00:38:20,280
Speaker 1: don't have our flaws. But it is kind of interesting

561
00:38:20,320 --> 00:38:25,799
Speaker 1: that it's been brilliantly marketed, yet it's wildly unreliable and

562
00:38:25,840 --> 00:38:29,880
Speaker 1: half the time it works every time, exactly, I think,

563
00:38:30,200 --> 00:38:32,959
Speaker 1: well percent of the time half the time that works

564
00:38:33,000 --> 00:38:36,319
Speaker 1: every time. But but I but I look at that

565
00:38:36,640 --> 00:38:41,480
Speaker 1: and and you know, it's sort of overpromising and under delivering.

566
00:38:41,560 --> 00:38:46,279
Speaker 1: Because public record, the quality of public record across the

567
00:38:46,280 --> 00:38:53,160
Speaker 1: the US is incredibly varied by municipality, by county. You know,

568
00:38:53,600 --> 00:38:56,560
Speaker 1: however you want to break it out, UM, and they're

569
00:38:56,600 --> 00:38:59,640
Speaker 1: they're they're largely reliant on that, and just a few

570
00:38:59,719 --> 00:39:07,320
Speaker 1: years ago they started ah significantly waiting um the list price.

571
00:39:08,440 --> 00:39:11,160
Speaker 1: So if you you know, house is worth a million

572
00:39:11,200 --> 00:39:13,759
Speaker 1: five according to this estimate, you put it on for

573
00:39:13,840 --> 00:39:17,839
Speaker 1: two The next day the estimate is too and then

574
00:39:17,840 --> 00:39:20,600
Speaker 1: it doesn't sell and you take it off. This estimate

575
00:39:20,600 --> 00:39:22,600
Speaker 1: goes down to a million and a half the next day.

576
00:39:23,120 --> 00:39:26,080
Speaker 1: What is that like? That is? I don't And so

577
00:39:26,160 --> 00:39:30,239
Speaker 1: any time I see a news story that relies on

578
00:39:30,640 --> 00:39:37,120
Speaker 1: zestimate related information, to me, it's problematic because it's not

579
00:39:37,239 --> 00:39:40,319
Speaker 1: what it is. It's certainly fun to poke around and

580
00:39:40,360 --> 00:39:44,319
Speaker 1: look at stuff, but it's not necessarily it doesn't have

581
00:39:44,360 --> 00:39:49,520
Speaker 1: the precision that has been brilliantly conveyed in their marketing. Well,

582
00:39:49,920 --> 00:39:54,359
Speaker 1: obviously Zillo didn't do well trying to build a home

583
00:39:54,440 --> 00:39:59,040
Speaker 1: flipping business relying on their own home. Yeah, they took

584
00:39:59,040 --> 00:40:04,160
Speaker 1: a giant right on that. Yeah, the Zelo offers uh uh,

585
00:40:04,200 --> 00:40:07,280
Speaker 1: you know that broke And I think the same goes

586
00:40:07,400 --> 00:40:11,560
Speaker 1: for um, you know, the ie buyer, the Internet buyer

587
00:40:12,280 --> 00:40:19,680
Speaker 1: subset um, where you know, there's this automation promise UM,

588
00:40:19,719 --> 00:40:22,120
Speaker 1: and there's some validity to it I'm not saying that,

589
00:40:22,200 --> 00:40:26,640
Speaker 1: but you've got the impression initially that that was going

590
00:40:26,719 --> 00:40:29,560
Speaker 1: to just take over the entire market, and now it's

591
00:40:29,600 --> 00:40:34,280
Speaker 1: something like one of transactions. It's a very small number.

592
00:40:35,160 --> 00:40:40,080
Speaker 1: Uh and in a declining market. They haven't really been tested. Um,

593
00:40:40,239 --> 00:40:44,759
Speaker 1: they've only existed in rising housing markets, flats arising. So

594
00:40:44,800 --> 00:40:46,640
Speaker 1: it's going to be an interesting few years to see

595
00:40:46,680 --> 00:40:51,680
Speaker 1: how they do huh, really really intriguing. I'm kind of surprised,

596
00:40:52,360 --> 00:40:55,759
Speaker 1: um that they didn't do better, because you would have thought, hey,

597
00:40:55,920 --> 00:40:59,359
Speaker 1: forget the what we're sharing publicly, we have our own

598
00:40:59,360 --> 00:41:02,400
Speaker 1: internal net tricks. Let's let's see how we can use that.

599
00:41:02,560 --> 00:41:07,600
Speaker 1: And it turns out they were just relying on everybody else. Uh,

600
00:41:07,640 --> 00:41:09,879
Speaker 1: you know, everybody the same date to everybody else saw

601
00:41:10,280 --> 00:41:13,920
Speaker 1: and it didn't really work out. No, no, it didn't.

602
00:41:14,080 --> 00:41:17,239
Speaker 1: And I think it also says something about there's just

603
00:41:17,280 --> 00:41:21,120
Speaker 1: too many people in the space. Uh. And so I

604
00:41:21,120 --> 00:41:25,480
Speaker 1: think there's gonna be some compression in participation just because

605
00:41:25,520 --> 00:41:28,759
Speaker 1: the scale of investment is so enormous, the stakes are

606
00:41:28,800 --> 00:41:32,120
Speaker 1: so high. Um. Yeah, I wish so well. I just

607
00:41:32,239 --> 00:41:37,080
Speaker 1: I think it's it's been a little bit over over hyped. Um.

608
00:41:37,280 --> 00:41:39,880
Speaker 1: But back to your question, sort of the state of

609
00:41:39,920 --> 00:41:45,640
Speaker 1: the of the appraiser, sort of the appraisal world. Um,

610
00:41:45,760 --> 00:41:52,480
Speaker 1: the you know, we're going through a period. It's it's fascinating. Um.

611
00:41:52,520 --> 00:41:56,200
Speaker 1: You know the appraiser I was told by someone senior

612
00:41:56,239 --> 00:42:00,920
Speaker 1: to me a long time ago. You know, UM doesn't

613
00:42:01,000 --> 00:42:04,960
Speaker 1: have the answer in the transaction has the answer, the

614
00:42:04,960 --> 00:42:09,400
Speaker 1: answer being the price. The brokers have the answer, the

615
00:42:10,120 --> 00:42:12,560
Speaker 1: mortgage company has the answer. The buyer and the seller

616
00:42:12,640 --> 00:42:15,600
Speaker 1: all have the answer because they all have skin in

617
00:42:15,640 --> 00:42:19,400
Speaker 1: the game, right, they're all sort of part Right, everybody's smart.

618
00:42:19,480 --> 00:42:21,920
Speaker 1: They all know what the value is, what the number is.

619
00:42:22,480 --> 00:42:27,000
Speaker 1: And the external third party that doesn't have they get

620
00:42:27,080 --> 00:42:29,400
Speaker 1: paid whether it's high or you know, they're above or

621
00:42:29,440 --> 00:42:34,040
Speaker 1: below or at the the purchase price. UM. Uh, you

622
00:42:34,080 --> 00:42:37,320
Speaker 1: know they and they don't. We don't have as an industry,

623
00:42:37,320 --> 00:42:41,560
Speaker 1: we don't have much representation. Um. In Washington, we're just

624
00:42:41,680 --> 00:42:46,040
Speaker 1: a fraction. There's only about you know, optimistically, there's seventy

625
00:42:46,040 --> 00:42:49,040
Speaker 1: five thousand appraisers in the US versus a million and

626
00:42:49,120 --> 00:42:53,080
Speaker 1: a half real estate agents. So you know, we're vastly outnumbered.

627
00:42:53,080 --> 00:42:58,440
Speaker 1: In lobbying, you might have two lobbyists really of any um,

628
00:42:58,480 --> 00:43:03,919
Speaker 1: you know, UM gravitas in Washington compared to a much

629
00:43:03,920 --> 00:43:07,600
Speaker 1: different world, so we tend to be sort of run over. However,

630
00:43:07,640 --> 00:43:12,200
Speaker 1: our industry also has some real problems where we have

631
00:43:12,440 --> 00:43:16,560
Speaker 1: no diversity UM as an industry. UM the beer of

632
00:43:16,680 --> 00:43:24,160
Speaker 1: Labor Statistics ranks four occupations by diversity, you know, inclusion

633
00:43:24,200 --> 00:43:31,520
Speaker 1: of women and yes, who's dead last in the BLS

634
00:43:31,760 --> 00:43:38,280
Speaker 1: ratings the eppraisal industry, it's it's night white and so

635
00:43:38,400 --> 00:43:43,279
Speaker 1: I'm a middle aged white guy and I'm I'm I'm

636
00:43:43,400 --> 00:43:46,880
Speaker 1: the profile of of the industry. And yet we go

637
00:43:46,960 --> 00:43:50,480
Speaker 1: through periods like re five booms where those of chronic

638
00:43:50,600 --> 00:43:56,520
Speaker 1: shortage and a lot of the problems UH stem from

639
00:43:56,520 --> 00:44:01,400
Speaker 1: primarily UH an organization called the Appraisal Foundation, which I

640
00:44:01,440 --> 00:44:04,200
Speaker 1: have been a pain in the neck for them for

641
00:44:04,239 --> 00:44:07,920
Speaker 1: a few years. You are anticipating a question of mine

642
00:44:08,200 --> 00:44:11,680
Speaker 1: and let me frame this for listeners. So so you

643
00:44:11,719 --> 00:44:14,680
Speaker 1: put out a weekly email that I've been getting for

644
00:44:15,280 --> 00:44:18,799
Speaker 1: a hundred years. It comes out Friday afternoon, and it's

645
00:44:18,880 --> 00:44:21,240
Speaker 1: just a broad overview of the state of the market

646
00:44:21,280 --> 00:44:24,520
Speaker 1: with lots of charts and data, and at the end

647
00:44:24,560 --> 00:44:26,680
Speaker 1: there are a whole bunch of lengths including a bunch

648
00:44:26,760 --> 00:44:29,399
Speaker 1: of real estate links and your links and some more

649
00:44:29,440 --> 00:44:33,360
Speaker 1: interesting odd bowl lengths. But every now and then, it

650
00:44:33,480 --> 00:44:37,120
Speaker 1: seems that you and the UM I keep calling them

651
00:44:37,160 --> 00:44:41,359
Speaker 1: the Appraisal Institute, the Appraisal Foundation. You seem to have

652
00:44:41,520 --> 00:44:48,640
Speaker 1: a beef with the organization that supposedly represents your field.

653
00:44:49,640 --> 00:44:56,040
Speaker 1: Is this the underlying basis of your ongoing UM harassment

654
00:44:56,040 --> 00:45:00,600
Speaker 1: of them for everything from incompetency to I don't know

655
00:45:00,640 --> 00:45:03,520
Speaker 1: if I could say criminality, but you've certainly come pretty

656
00:45:03,520 --> 00:45:08,080
Speaker 1: close to accusing them of that. Yeah, I just think

657
00:45:08,120 --> 00:45:12,120
Speaker 1: it's um. So what the Appression Foundation does is they

658
00:45:12,480 --> 00:45:17,000
Speaker 1: essentially maintain, uh, for lack of about a word, sort

659
00:45:17,000 --> 00:45:22,200
Speaker 1: of the standards that I, as an appraiser to be certified,

660
00:45:22,800 --> 00:45:27,759
Speaker 1: have to adhere to, and that's embedded in states and

661
00:45:27,920 --> 00:45:34,080
Speaker 1: means un territories, fifty five states and territories. And every

662
00:45:34,080 --> 00:45:36,480
Speaker 1: time there's an update, you know that all the fifty

663
00:45:36,480 --> 00:45:40,520
Speaker 1: five states and territories have to update. And they've wildly

664
00:45:40,640 --> 00:45:45,200
Speaker 1: overstepped what I think their charter is. Like, they they

665
00:45:46,520 --> 00:45:49,080
Speaker 1: the diversity thing, I told you, uh, you know they're

666
00:45:50,320 --> 00:45:55,400
Speaker 1: so they hired the head of their new diversity effort

667
00:45:55,719 --> 00:45:58,279
Speaker 1: is a middle aged white guy seems that I have

668
00:45:58,360 --> 00:46:03,719
Speaker 1: a problem with it. And and also, um, they have

669
00:46:04,000 --> 00:46:10,080
Speaker 1: created a essentially UM it's called us PATH. That's our license.

670
00:46:10,320 --> 00:46:14,759
Speaker 1: Are the standards uniform standards of professional appraisal practice that

671
00:46:14,880 --> 00:46:18,279
Speaker 1: we have to follow and sign certifications and our reports

672
00:46:18,880 --> 00:46:21,000
Speaker 1: um to you know, it's a whole idea is to

673
00:46:21,040 --> 00:46:24,840
Speaker 1: protect the public trust. The problem is that it's really

674
00:46:24,880 --> 00:46:30,759
Speaker 1: a sort of an effort. The rules that are are

675
00:46:31,120 --> 00:46:35,839
Speaker 1: our world doesn't change very much, um, and yet this

676
00:46:35,920 --> 00:46:40,120
Speaker 1: is updated every two years and UM. And they what

677
00:46:40,160 --> 00:46:44,400
Speaker 1: they do is they make changes and then they and

678
00:46:44,400 --> 00:46:47,080
Speaker 1: then they embed that into state law and two years

679
00:46:47,880 --> 00:46:51,520
Speaker 1: later they remove those same changes or four years later,

680
00:46:52,440 --> 00:46:54,680
Speaker 1: and and it just kind of goes back and forth.

681
00:46:54,719 --> 00:46:57,080
Speaker 1: And the reason for that is that they can sell

682
00:46:58,040 --> 00:47:02,600
Speaker 1: and charge for classes to maintain your license by providing

683
00:47:02,640 --> 00:47:06,080
Speaker 1: new materials every two years that the praisers have to buy.

684
00:47:06,320 --> 00:47:09,560
Speaker 1: So you can show me that this, you know, year

685
00:47:10,239 --> 00:47:13,840
Speaker 1: two thousand, two thousand to two thousand four, two thousand six,

686
00:47:14,840 --> 00:47:17,120
Speaker 1: two thousand and oh four were the same O two

687
00:47:17,160 --> 00:47:20,160
Speaker 1: and oh six. I mean, is it that metronomic? Are

688
00:47:20,200 --> 00:47:26,120
Speaker 1: they that blatant? Uh? It's it's more through incompetence. So

689
00:47:26,280 --> 00:47:29,800
Speaker 1: I'll give you, I'll give you an idea. So uh,

690
00:47:29,960 --> 00:47:37,520
Speaker 1: they they changed a rule that said if you're intentionally

691
00:47:38,560 --> 00:47:44,879
Speaker 1: or unintentionally misleading, then you violated your license. So in

692
00:47:44,880 --> 00:47:50,520
Speaker 1: other words, if you made a typo, you've basically uh,

693
00:47:50,560 --> 00:47:55,000
Speaker 1: you know, can lose your license. And and uh, they

694
00:47:55,000 --> 00:47:58,640
Speaker 1: don't have counts up until at least you know, lately,

695
00:47:59,040 --> 00:48:01,280
Speaker 1: they don't have lawyers to review this. This is getting

696
00:48:01,320 --> 00:48:05,879
Speaker 1: embedded into fifty states and municiparia that keep saying MUNI

697
00:48:06,000 --> 00:48:11,600
Speaker 1: about territories uh, across the US. And yet uh, there's

698
00:48:11,640 --> 00:48:13,920
Speaker 1: no review of this. It's just a bunch of appraisers

699
00:48:13,920 --> 00:48:16,239
Speaker 1: on a board that sort of say, Hey, wouldn't it

700
00:48:16,280 --> 00:48:19,279
Speaker 1: be nice because I know this because I've been there, Uh,

701
00:48:19,440 --> 00:48:23,879
Speaker 1: would it be nice to just define misleading when definitions

702
00:48:23,920 --> 00:48:25,680
Speaker 1: like that should be done in a court of law,

703
00:48:25,719 --> 00:48:27,520
Speaker 1: interpreting a court a lot, and not just by a

704
00:48:27,520 --> 00:48:31,000
Speaker 1: bunch of appraisers on a board, um the other And

705
00:48:31,000 --> 00:48:35,480
Speaker 1: and so that came, that became challenge because of my writings, um.

706
00:48:35,520 --> 00:48:38,360
Speaker 1: And then there's just an endless example of this. The

707
00:48:38,719 --> 00:48:46,040
Speaker 1: bottom line is that, uh, I'm against anything that damages

708
00:48:46,480 --> 00:48:50,399
Speaker 1: the credibility of appraisers in the public eye, because if

709
00:48:50,440 --> 00:48:56,279
Speaker 1: you don't have that public trust, which is what this

710
00:48:56,440 --> 00:49:02,359
Speaker 1: organization is charter, you know, supposed to maintain UM, then

711
00:49:02,400 --> 00:49:06,600
Speaker 1: you're doing a disservice to everybody in the industry UM

712
00:49:06,640 --> 00:49:08,840
Speaker 1: you know that has a license that you know puts

713
00:49:08,880 --> 00:49:12,359
Speaker 1: their their job on the line every day when they

714
00:49:12,400 --> 00:49:15,680
Speaker 1: do sign off on an appraisal. It's just sort of

715
00:49:15,760 --> 00:49:19,800
Speaker 1: amateur hour. And I'm doing it, and I've I've myself

716
00:49:19,840 --> 00:49:22,759
Speaker 1: and sort of a band of other people I think

717
00:49:22,840 --> 00:49:28,120
Speaker 1: have really created pressure to UM to invoke change, but

718
00:49:28,200 --> 00:49:33,280
Speaker 1: it's slow going, quite quite fascinating. So so let's talk

719
00:49:33,280 --> 00:49:36,120
Speaker 1: a little bit about one of my favorite topics, which

720
00:49:36,320 --> 00:49:40,279
Speaker 1: is a phrase that you coined a couple of years

721
00:49:40,280 --> 00:49:46,400
Speaker 1: ago called aspirational pricing, to tell us about that. Sure,

722
00:49:46,719 --> 00:49:50,720
Speaker 1: it's uh, it's sort of a safety and numbers really

723
00:49:50,760 --> 00:49:58,879
Speaker 1: started seeing this in luxury vacation UH locations where I'll

724
00:49:58,920 --> 00:50:01,920
Speaker 1: just throw out random numb versay, a home luxury at

725
00:50:01,920 --> 00:50:05,960
Speaker 1: home is purchased for five million dollars and they put

726
00:50:06,000 --> 00:50:08,759
Speaker 1: a million into it or two million into it, and

727
00:50:08,800 --> 00:50:11,000
Speaker 1: then they put it on the market for thirty million,

728
00:50:12,120 --> 00:50:16,080
Speaker 1: and and and you're like, okay, well, that's silly, right,

729
00:50:16,880 --> 00:50:19,680
Speaker 1: but but you know, they become a bold faced name

730
00:50:19,800 --> 00:50:23,359
Speaker 1: in on page six on the post, you know, because hey,

731
00:50:23,400 --> 00:50:25,840
Speaker 1: they've got a home on the market for thirty million.

732
00:50:26,560 --> 00:50:31,880
Speaker 1: And then what happens The ten houses in that vicinity

733
00:50:32,200 --> 00:50:36,919
Speaker 1: put their homes on the market for million and none

734
00:50:36,920 --> 00:50:40,279
Speaker 1: of them ever sell because they're not worth that. But

735
00:50:40,360 --> 00:50:42,439
Speaker 1: it's like the safety and numbers and that was quite

736
00:50:42,480 --> 00:50:46,799
Speaker 1: a phenomenon, uh, leading up to the sort of the

737
00:50:46,800 --> 00:50:50,799
Speaker 1: pandemic era. And this is another sort of this is

738
00:50:50,840 --> 00:50:55,920
Speaker 1: a recent development, UM that is starting to starting to

739
00:50:56,080 --> 00:51:01,000
Speaker 1: see uh. And it's like the if we you know,

740
00:51:01,320 --> 00:51:06,840
Speaker 1: for your nationalists, be the Park Avenue, fifth Avenue, Manhattan

741
00:51:06,880 --> 00:51:14,640
Speaker 1: type apartment luxury sort of old world type apartments, and um,

742
00:51:15,200 --> 00:51:20,520
Speaker 1: you have they have boards that approved the buyers and

743
00:51:20,640 --> 00:51:23,640
Speaker 1: uh and and making the numbers up to say, someone

744
00:51:24,200 --> 00:51:29,560
Speaker 1: you know pays ten million dollars for an apartment and uh,

745
00:51:29,600 --> 00:51:32,720
Speaker 1: you know three years ago something in the same line

746
00:51:32,840 --> 00:51:38,840
Speaker 1: sold for fifteen or thirteen, um, and it sold for less,

747
00:51:38,840 --> 00:51:43,359
Speaker 1: and so the board kills the sale because they're not

748
00:51:43,400 --> 00:51:46,640
Speaker 1: happy about the price because it went down. And then

749
00:51:46,960 --> 00:51:49,000
Speaker 1: the seller put you know, puts it back on the

750
00:51:49,040 --> 00:51:51,720
Speaker 1: market and they get another offer of ten million. Department

751
00:51:51,760 --> 00:51:55,520
Speaker 1: has been clearly vetted by the market, and it's the

752
00:51:56,560 --> 00:52:01,080
Speaker 1: board kills that sale. And and I'm aware of a

753
00:52:01,160 --> 00:52:06,720
Speaker 1: handful of those recently. And and that's as operational pricing

754
00:52:06,760 --> 00:52:09,800
Speaker 1: in the context of the the co op board. Because

755
00:52:09,840 --> 00:52:15,799
Speaker 1: they think that they're protecting their you're performing their fiduciary

756
00:52:15,880 --> 00:52:20,480
Speaker 1: duties of protecting the value. However, they can't control the market.

757
00:52:20,520 --> 00:52:22,840
Speaker 1: The market is the market is, you know, as someone

758
00:52:22,880 --> 00:52:26,279
Speaker 1: once said, and um, and what they're actually doing is

759
00:52:26,480 --> 00:52:33,120
Speaker 1: damaging value of you know, they're violating their fiduciary responsibility.

760
00:52:33,120 --> 00:52:35,200
Speaker 1: And I think there's going to be We always see

761
00:52:35,200 --> 00:52:38,480
Speaker 1: this in a in a market that's cooling, We tend

762
00:52:38,560 --> 00:52:42,160
Speaker 1: to see this activity and then we get involved in litigation,

763
00:52:42,719 --> 00:52:47,799
Speaker 1: you know, as an expert, um to to um sort

764
00:52:47,800 --> 00:52:52,680
Speaker 1: of empirically demonstrate this. Just just a reminder that there's

765
00:52:52,680 --> 00:52:55,320
Speaker 1: a special room in hell for anyone who's ever served

766
00:52:55,320 --> 00:53:01,120
Speaker 1: on a condo board or cooper you know, it's they're endless.

767
00:53:01,160 --> 00:53:02,959
Speaker 1: If you're in New York for any length of time,

768
00:53:03,280 --> 00:53:07,640
Speaker 1: and I'm sure other cities have comparable things. There are endless, endless,

769
00:53:07,719 --> 00:53:12,680
Speaker 1: endless stories about UM. The relentless stupidity of the behavior

770
00:53:12,800 --> 00:53:14,839
Speaker 1: of boards. I don't know what it is you put

771
00:53:15,160 --> 00:53:18,879
Speaker 1: six people together in a room involving real estate and

772
00:53:19,400 --> 00:53:24,120
Speaker 1: their average I Q haves. It's it's quite amazing. Um

773
00:53:24,160 --> 00:53:28,040
Speaker 1: that that that's kind of fascinating and related to you know,

774
00:53:28,080 --> 00:53:31,640
Speaker 1: you mentioned old world New York City, Um, Fifth Avenue

775
00:53:31,680 --> 00:53:35,760
Speaker 1: and Park Avenue apartments. Um? Am I stating this wrong?

776
00:53:36,040 --> 00:53:39,160
Speaker 1: Or have you pretty much been into just about every

777
00:53:39,160 --> 00:53:44,640
Speaker 1: penthouse in Manhattan? How how accurate is that statement? I'd

778
00:53:44,719 --> 00:53:50,680
Speaker 1: say I've been in probably nine I mean, I'm I'm

779
00:53:50,760 --> 00:53:55,200
Speaker 1: wild guessing here nine plus percent. The one missing, the

780
00:53:55,239 --> 00:53:58,480
Speaker 1: big one missing from my list is the Pierre Hotel.

781
00:53:59,360 --> 00:54:01,760
Speaker 1: And how so been in the Sherry, Netherlands and about

782
00:54:02,160 --> 00:54:05,120
Speaker 1: most of the penthouses on Central Park South and West.

783
00:54:06,080 --> 00:54:08,600
Speaker 1: That's the one big one that's left over. I came close,

784
00:54:08,719 --> 00:54:12,439
Speaker 1: but it didn't quite make it. And it's it's it's

785
00:54:12,440 --> 00:54:17,680
Speaker 1: always very exciting, um, you know, to see very large space,

786
00:54:18,000 --> 00:54:22,080
Speaker 1: spectacular views. Um. And it's really a mixture of Some

787
00:54:22,160 --> 00:54:26,239
Speaker 1: apartments are in amazing condition and some haven't been renovated

788
00:54:26,280 --> 00:54:32,040
Speaker 1: and you know, multiple decades. But but just being and

789
00:54:32,239 --> 00:54:36,200
Speaker 1: seeing the entirety of it is quite never gets old,

790
00:54:37,080 --> 00:54:40,239
Speaker 1: you know. I can imagine and and do those go

791
00:54:40,520 --> 00:54:45,239
Speaker 1: for market prices or are we still seeing aspirational pricing now?

792
00:54:45,280 --> 00:54:49,359
Speaker 1: They go they go for market Like during this sort

793
00:54:49,360 --> 00:54:53,920
Speaker 1: of five or so years ago sort of peak aspirational pricing,

794
00:54:54,640 --> 00:54:57,680
Speaker 1: there was a penthouse I want to say it was

795
00:54:58,440 --> 00:55:00,640
Speaker 1: you know, I can't recall off and by where it was,

796
00:55:00,719 --> 00:55:07,000
Speaker 1: but it was asking a million, and uh, you know,

797
00:55:07,080 --> 00:55:09,719
Speaker 1: catch a following knife. The price kept dropping and I

798
00:55:09,760 --> 00:55:12,680
Speaker 1: think it's sold for in the forties, all right, but

799
00:55:12,800 --> 00:55:15,799
Speaker 1: you anchored people up at a hundreds, so maybe it

800
00:55:15,800 --> 00:55:18,319
Speaker 1: would have gone for twenty otherwise. Is is that the

801
00:55:18,360 --> 00:55:23,480
Speaker 1: thought process behind this, let's get people anchored higher. I

802
00:55:23,520 --> 00:55:27,759
Speaker 1: think that's the the the the idea. But you know,

803
00:55:27,840 --> 00:55:30,240
Speaker 1: after it's on the market for three or four years,

804
00:55:31,840 --> 00:55:36,759
Speaker 1: I can't imagine that being a uh sort of a

805
00:55:36,880 --> 00:55:43,520
Speaker 1: positive uh positive thought for the owner, um, especially if

806
00:55:43,560 --> 00:55:46,759
Speaker 1: they're barely living in it and they're paying twenty five

807
00:55:47,200 --> 00:55:51,759
Speaker 1: a month in homeowner association piece. You know. So so

808
00:55:52,320 --> 00:55:55,680
Speaker 1: maybe initially that was the strategy, but I find that

809
00:55:55,680 --> 00:56:03,040
Speaker 1: that almost never works. Uh. It just you know empirically that, um,

810
00:56:03,120 --> 00:56:07,640
Speaker 1: the market is not that dumb. Right, So let's talk.

811
00:56:08,920 --> 00:56:10,680
Speaker 1: You and I have talked about one of my favorite

812
00:56:10,760 --> 00:56:16,040
Speaker 1: Zillo tricks. Pick a town, especially higher in town. You

813
00:56:16,040 --> 00:56:20,239
Speaker 1: could do Greenwich, Connecticut, or Santa Barbara, California, or by

814
00:56:20,239 --> 00:56:25,040
Speaker 1: me Sands Point, New York, and uh, you'll get a

815
00:56:25,080 --> 00:56:29,440
Speaker 1: full listing of everything that's for sale. Um. And you

816
00:56:29,480 --> 00:56:32,279
Speaker 1: could then use the Zillo app to sort and I

817
00:56:32,280 --> 00:56:34,520
Speaker 1: always like to sort by. Now, show me the most

818
00:56:34,560 --> 00:56:38,759
Speaker 1: recent listings, and then I scroll all the way down

819
00:56:38,800 --> 00:56:41,879
Speaker 1: to the bottom. And your shocked that this has been

820
00:56:41,920 --> 00:56:44,839
Speaker 1: the hottest housing market certainly since O eight oh nine,

821
00:56:44,880 --> 00:56:49,000
Speaker 1: if not oh six oh seven, if not our lifetimes.

822
00:56:49,320 --> 00:56:51,640
Speaker 1: And it's amazing. There are houses that have been listed

823
00:56:51,640 --> 00:56:54,880
Speaker 1: for sale for three hundred days, five hundred days, fifteen

824
00:56:54,960 --> 00:56:58,960
Speaker 1: hundred days. I mean, is are those homes really for sale?

825
00:56:59,120 --> 00:57:02,680
Speaker 1: If they can't find a buyer after five years, that

826
00:57:02,800 --> 00:57:09,000
Speaker 1: just seems absurd. Yeah, yeah, I mean they are. And

827
00:57:09,120 --> 00:57:13,360
Speaker 1: it's all based on the notion that hey, you know, somebody,

828
00:57:13,640 --> 00:57:15,879
Speaker 1: you know, I get you know, it's sort of like

829
00:57:16,040 --> 00:57:20,240
Speaker 1: I'll get lucky. Uh. Didn't Google have that a search

830
00:57:20,320 --> 00:57:22,640
Speaker 1: button at one time? I don't know it still does?

831
00:57:23,200 --> 00:57:27,520
Speaker 1: You know? Uh? I feel lucky. Yeah, Yeah, it's sort

832
00:57:27,560 --> 00:57:31,720
Speaker 1: of analogous to that. Maybe I don't know, but it is.

833
00:57:31,920 --> 00:57:35,720
Speaker 1: It is amazing. I also think part of that is

834
00:57:35,800 --> 00:57:40,440
Speaker 1: just dirty Zello data, you know that it's just not removed. Um.

835
00:57:40,640 --> 00:57:42,960
Speaker 1: Oh no, a lot of this stuff is. I'm gonna

836
00:57:43,000 --> 00:57:47,080
Speaker 1: disagree with you on that because I sifted through a

837
00:57:47,120 --> 00:57:52,800
Speaker 1: lot of these um most recently. So most recently. Uh,

838
00:57:53,160 --> 00:57:58,600
Speaker 1: this kind of small waterfront um supposed to be on

839
00:57:58,640 --> 00:58:00,680
Speaker 1: an acre and you look at the map and it's like,

840
00:58:00,720 --> 00:58:04,320
Speaker 1: there's no way that's an acre. And they were asking

841
00:58:04,480 --> 00:58:09,080
Speaker 1: nine five, which I thought was insane, when right, you know,

842
00:58:09,320 --> 00:58:12,800
Speaker 1: three houses down. So this is like a three thousand

843
00:58:12,880 --> 00:58:19,520
Speaker 1: square foot UM, one car garage, no basement, waterfront, lovely

844
00:58:19,560 --> 00:58:25,200
Speaker 1: house on a built around a um sort of center courtyard,

845
00:58:25,360 --> 00:58:29,160
Speaker 1: but tiny down just like three or four houses down

846
00:58:29,320 --> 00:58:32,120
Speaker 1: is like a twenty five thousand square foot three acre

847
00:58:32,200 --> 00:58:35,160
Speaker 1: house is half the price. So I called the agent

848
00:58:35,160 --> 00:58:38,040
Speaker 1: and said, I don't understand. I'm not looking at bust

849
00:58:38,040 --> 00:58:41,120
Speaker 1: your chops, but how do you figure nine and a

850
00:58:41,120 --> 00:58:43,920
Speaker 1: half when for four and a half I get four

851
00:58:44,000 --> 00:58:47,040
Speaker 1: times out? Well, you know, the owner is a collector

852
00:58:47,120 --> 00:58:50,160
Speaker 1: of fine arts, and he thinks eventually he'll get his price.

853
00:58:50,600 --> 00:58:52,280
Speaker 1: So now I have to go back through the Zillo

854
00:58:52,440 --> 00:58:55,400
Speaker 1: history and it was originally up for sale for like

855
00:58:55,520 --> 00:58:59,480
Speaker 1: twelve or fourteen, six years ago, off back on for

856
00:58:59,560 --> 00:59:02,360
Speaker 1: eleven and off back on for ten off, And I'm like,

857
00:59:03,160 --> 00:59:05,560
Speaker 1: so this house really isn't for sale. This is just

858
00:59:05,600 --> 00:59:09,800
Speaker 1: someone goofing around. Call me when the call me when

859
00:59:09,800 --> 00:59:13,040
Speaker 1: the estate has to dispose of it, and the market

860
00:59:13,040 --> 00:59:16,440
Speaker 1: will find a real price. Yeah. I mean if if

861
00:59:16,440 --> 00:59:20,280
Speaker 1: you're looking at a listing that in this market that

862
00:59:20,920 --> 00:59:23,800
Speaker 1: you know is a hundred eighty days or older just

863
00:59:23,880 --> 00:59:27,240
Speaker 1: as us, or it's not even serious, right, I mean,

864
00:59:27,560 --> 00:59:31,760
Speaker 1: it's not even Um I had a situation. Um I

865
00:59:31,800 --> 00:59:35,120
Speaker 1: remember this is four or five years ago when Grenwage

866
00:59:35,360 --> 00:59:37,600
Speaker 1: was really a weak market. I may have told you

867
00:59:37,640 --> 00:59:41,400
Speaker 1: the story of the past. And you know, some well

868
00:59:41,440 --> 00:59:45,440
Speaker 1: known financier you know, had a house I'm guessing you

869
00:59:45,480 --> 00:59:48,840
Speaker 1: know was worth six or seven million, put it on

870
00:59:48,960 --> 00:59:52,280
Speaker 1: for fifteen million, no offers. You know, this is that

871
00:59:52,440 --> 00:59:58,960
Speaker 1: aspirational pricing era. Uh, no no activity. And then they

872
00:59:59,000 --> 01:00:01,280
Speaker 1: cut the price to a eleven you know, it's worth

873
01:00:01,320 --> 01:00:04,240
Speaker 1: six or seven probably on a good day, cut it

874
01:00:04,280 --> 01:00:09,000
Speaker 1: to eleven four million dollars and uh, sat on another

875
01:00:09,120 --> 01:00:14,160
Speaker 1: year and no offers, and then they go on stage

876
01:00:14,160 --> 01:00:18,080
Speaker 1: and say you can't give a house away in Grants, Connecticut.

877
01:00:18,960 --> 01:00:22,120
Speaker 1: And it's like, well, you know, that's really not fair

878
01:00:22,160 --> 01:00:26,760
Speaker 1: to Gramwich, Connecticut because you're, you know, your view of

879
01:00:26,840 --> 01:00:29,680
Speaker 1: the value of your property has nothing to do with

880
01:00:29,680 --> 01:00:33,960
Speaker 1: what the market will support. And I find that I

881
01:00:34,440 --> 01:00:36,560
Speaker 1: you know, we would see that all the time, you know,

882
01:00:36,600 --> 01:00:41,040
Speaker 1: where it's like they view the original price as like

883
01:00:41,280 --> 01:00:44,120
Speaker 1: their equity, and then out of the goodness of their heart,

884
01:00:44,160 --> 01:00:47,720
Speaker 1: they're giving away, um, you know, a chunk of it,

885
01:00:48,000 --> 01:00:50,680
Speaker 1: even though you know it's all based on nothing. One

886
01:00:50,680 --> 01:00:53,800
Speaker 1: of the things that I do as a hobby is

887
01:00:54,000 --> 01:00:59,800
Speaker 1: I collect listings I'm sorry, closed sales around the u

888
01:01:00,040 --> 01:01:04,880
Speaker 1: US that sold at or above million dollar. You've had

889
01:01:04,920 --> 01:01:09,480
Speaker 1: a chart of this that you update on a regular basis. Yes, yes,

890
01:01:09,560 --> 01:01:13,360
Speaker 1: And it's sort of a hobby gone wild because in

891
01:01:13,440 --> 01:01:17,320
Speaker 1: two thousand and fourteen, I remember I was approached by

892
01:01:17,360 --> 01:01:22,920
Speaker 1: a homeowner that had a house in California and I

893
01:01:23,000 --> 01:01:25,400
Speaker 1: don't even know what it was probably worth, but you know,

894
01:01:25,800 --> 01:01:28,320
Speaker 1: it was single digit, you know, but a lot of money,

895
01:01:28,920 --> 01:01:31,320
Speaker 1: and they put on the market for like thirty plus

896
01:01:31,360 --> 01:01:34,680
Speaker 1: million and that's when we started to see this sort

897
01:01:34,680 --> 01:01:40,080
Speaker 1: of aspirational prop pricing phenomenon occur. And it never sold.

898
01:01:40,520 --> 01:01:45,120
Speaker 1: And um, I just started tracking them nationwide and not

899
01:01:45,280 --> 01:01:52,000
Speaker 1: listings but just actual sales and U as in one

900
01:01:52,120 --> 01:01:56,000
Speaker 1: was the biggest year by far, and they were well

901
01:01:56,080 --> 01:02:01,760
Speaker 1: over forty transactions in the US residential that whether single

902
01:02:01,800 --> 01:02:07,160
Speaker 1: family condo or co op that sold for UM fifty

903
01:02:07,240 --> 01:02:11,840
Speaker 1: million or higher. And and this is sort of just

904
01:02:11,920 --> 01:02:17,120
Speaker 1: before uh, you know, well up through today. I think

905
01:02:17,120 --> 01:02:20,600
Speaker 1: there's been if you if you were to annualize, you know,

906
01:02:20,680 --> 01:02:24,120
Speaker 1: so optimistically, if you annualize the sales to date, it

907
01:02:24,120 --> 01:02:26,680
Speaker 1: would be the second highest in history. And there was

908
01:02:26,720 --> 01:02:30,479
Speaker 1: just a closing of a hundred seventy five million dollar

909
01:02:30,560 --> 01:02:34,520
Speaker 1: transaction in Florida just this week. Um. And so what

910
01:02:35,040 --> 01:02:37,040
Speaker 1: in two thousand and fourteen was something I would add

911
01:02:37,080 --> 01:02:39,920
Speaker 1: to the list once a month or two months, It

912
01:02:40,080 --> 01:02:44,760
Speaker 1: literally is now once a week. I remember the I

913
01:02:44,840 --> 01:02:48,880
Speaker 1: remember the developer that had created that five hundred million

914
01:02:48,880 --> 01:02:52,600
Speaker 1: dollars spec house in l A, which seemed absurd. He

915
01:02:52,640 --> 01:02:56,040
Speaker 1: filled it with you know, high end sports cars and art,

916
01:02:56,560 --> 01:03:00,400
Speaker 1: and I believe, if I remember correctly, it was ventually

917
01:03:00,440 --> 01:03:04,320
Speaker 1: auctioned off at bankruptcy for about a quarter of that price.

918
01:03:05,200 --> 01:03:07,360
Speaker 1: Yeah about him. I think it was a million five.

919
01:03:07,480 --> 01:03:11,560
Speaker 1: Yeah that was yeah, that was the one. It was

920
01:03:11,600 --> 01:03:14,200
Speaker 1: called the one, and well now you can call it

921
01:03:14,320 --> 01:03:20,160
Speaker 1: the quarter. Yeah, the corn exactly. Uh. But but you know,

922
01:03:20,160 --> 01:03:22,760
Speaker 1: it's funny because it's sort of this circus side show

923
01:03:22,840 --> 01:03:26,000
Speaker 1: for housing, because you know, just like in New York

924
01:03:26,000 --> 01:03:28,880
Speaker 1: there was a two nine million dollar condo, and there

925
01:03:28,920 --> 01:03:31,360
Speaker 1: was a hundred million dollar condo, and at least these

926
01:03:31,400 --> 01:03:36,320
Speaker 1: big numbers and so um. You know, you'd see people

927
01:03:36,360 --> 01:03:40,240
Speaker 1: with modest houses saying, hey, if someone's willing to pay,

928
01:03:40,920 --> 01:03:45,439
Speaker 1: you know, h million for a home, then my home

929
01:03:45,480 --> 01:03:48,320
Speaker 1: has to be worth ten more. And you're like, no,

930
01:03:48,720 --> 01:03:50,640
Speaker 1: it has nothing to do with you. It's a it's

931
01:03:50,680 --> 01:03:54,520
Speaker 1: a circus side show, you know. That's the aspirational pricing

932
01:03:54,840 --> 01:03:59,320
Speaker 1: is great for your realistic neighbors. So the six million

933
01:03:59,320 --> 01:04:02,360
Speaker 1: dollar house that's up for sale for thirty million, Hey,

934
01:04:02,360 --> 01:04:05,320
Speaker 1: if you're two houses down and your house is five,

935
01:04:05,440 --> 01:04:07,960
Speaker 1: you could say, hey, I really am looking for seven.

936
01:04:08,000 --> 01:04:09,960
Speaker 1: Look at that house down the street. It's thirty million.

937
01:04:10,400 --> 01:04:14,480
Speaker 1: Seven is a bargain if you're realistic. Yeah, Well, we

938
01:04:14,480 --> 01:04:19,479
Speaker 1: were having people in Manhattan in a you know, six

939
01:04:19,560 --> 01:04:24,240
Speaker 1: floor tenement walkout. Uh. You know, looking at you see

940
01:04:24,200 --> 01:04:25,840
Speaker 1: you have to walk up six flights of sarious your

941
01:04:25,880 --> 01:04:31,760
Speaker 1: your apartment, you know, much more modest pricing. Uh, making

942
01:04:31,840 --> 01:04:34,800
Speaker 1: comments that, you know, when Michael Bell bought his for

943
01:04:34,840 --> 01:04:38,280
Speaker 1: a little over a hundred million at seven that hey,

944
01:04:38,440 --> 01:04:41,280
Speaker 1: you know mine has to be worth more. And we're like, no,

945
01:04:42,240 --> 01:04:44,640
Speaker 1: it's a different market. It's a different market, has nothing

946
01:04:44,680 --> 01:04:47,840
Speaker 1: to do with you. Plus, he's worth twenty three billion

947
01:04:47,920 --> 01:04:51,720
Speaker 1: dollars and a hundred million is a rounding era to him,

948
01:04:52,040 --> 01:04:55,600
Speaker 1: right right, exactly. People, people forget that. All right, I

949
01:04:55,640 --> 01:04:58,439
Speaker 1: have one curve bawl question for you before we get

950
01:04:58,480 --> 01:05:03,240
Speaker 1: to our favorite question, and that is why does your

951
01:05:03,240 --> 01:05:09,120
Speaker 1: Twitter bio mention that you're a lobster fisherman. Well, let's

952
01:05:09,120 --> 01:05:12,520
Speaker 1: just say I'm kind of hanging onto the past. Um.

953
01:05:12,560 --> 01:05:17,760
Speaker 1: I lived close to Long Island Sound, um, big body

954
01:05:17,800 --> 01:05:21,120
Speaker 1: of water, and when I I'm an empty nest now.

955
01:05:21,200 --> 01:05:23,800
Speaker 1: But I had four sons and we used to drop

956
01:05:23,880 --> 01:05:27,800
Speaker 1: lobster pots and you know, fish for lobster. But I

957
01:05:27,840 --> 01:05:32,000
Speaker 1: only caught We only caught one legal lobster a year.

958
01:05:32,040 --> 01:05:33,560
Speaker 1: Everything else we had to throw back the end to

959
01:05:33,600 --> 01:05:36,360
Speaker 1: be a certain weight, and you know, we had a

960
01:05:36,800 --> 01:05:40,479
Speaker 1: instrument to measure them and um. And I always said

961
01:05:40,480 --> 01:05:43,400
Speaker 1: it was the most expensive lobster caught on the Eastern

962
01:05:43,440 --> 01:05:46,560
Speaker 1: Seaboard every year, because when you add up the cost

963
01:05:46,640 --> 01:05:51,000
Speaker 1: of the boat, the dock, the fuel, the winterization, um.

964
01:05:51,200 --> 01:05:54,760
Speaker 1: The time, uh, you know, and then half the time

965
01:05:54,840 --> 01:05:57,520
Speaker 1: when we caught a legal lobster, I just threw it

966
01:05:57,560 --> 01:06:02,320
Speaker 1: back in cast and release. But it was it was really,

967
01:06:02,400 --> 01:06:05,640
Speaker 1: it was really fun. Um. Uh. They I think they've

968
01:06:05,680 --> 01:06:09,120
Speaker 1: pretty I think they've banned UM. I sold my boat

969
01:06:09,160 --> 01:06:11,800
Speaker 1: two years ago, so we don't lobster fish anymore. But

970
01:06:11,960 --> 01:06:14,640
Speaker 1: I'm very proud that I was once a lobster fisherman,

971
01:06:14,800 --> 01:06:17,440
Speaker 1: even though if you delve in deeper, I wasn't that

972
01:06:17,600 --> 01:06:20,840
Speaker 1: good at it. All Right, let's jump to our favorite

973
01:06:20,920 --> 01:06:24,560
Speaker 1: questions that we ask all of our guests, starting with

974
01:06:25,360 --> 01:06:27,840
Speaker 1: tell us what you're streaming these days? What was keeping

975
01:06:27,880 --> 01:06:37,240
Speaker 1: you entertained during lockdown and beyond? So I'm going to UH.

976
01:06:37,280 --> 01:06:40,360
Speaker 1: I was anticipating this question as I listened to other

977
01:06:41,120 --> 01:06:47,160
Speaker 1: UH interviews, and I'm very sad to say that I

978
01:06:47,400 --> 01:06:51,640
Speaker 1: hardly watch any television and I don't stream anything. My

979
01:06:51,680 --> 01:06:54,640
Speaker 1: wife does. I could answer for her, but I literally

980
01:06:56,000 --> 01:06:59,320
Speaker 1: and I think, I don't know where this came from.

981
01:06:59,520 --> 01:07:04,840
Speaker 1: But my grandfather was a motion picture projections at the

982
01:07:05,480 --> 01:07:09,560
Speaker 1: Town Theater. And my my dad was a latchkey kid

983
01:07:09,800 --> 01:07:13,720
Speaker 1: and watched you know the same movie a hundred times

984
01:07:13,840 --> 01:07:16,800
Speaker 1: only in sections and did his homework with his dad

985
01:07:17,760 --> 01:07:20,400
Speaker 1: and uh, and so as a kid, I we never

986
01:07:20,400 --> 01:07:25,120
Speaker 1: went to movies. Um. And so I'm like, it's funny.

987
01:07:25,160 --> 01:07:27,400
Speaker 1: I'm just I don't know what it is. I guess

988
01:07:27,960 --> 01:07:30,880
Speaker 1: maybe that makes me old fashioned or something. I don't

989
01:07:30,880 --> 01:07:33,600
Speaker 1: know what it is. I just don't care. So I

990
01:07:33,720 --> 01:07:38,560
Speaker 1: became a full blown television idiot during the lockdown, just

991
01:07:38,800 --> 01:07:41,200
Speaker 1: we couldn't go anywhere, we couldn't do anything, and it's

992
01:07:41,280 --> 01:07:44,760
Speaker 1: just stream stream stream, And now I'm trying it's become

993
01:07:44,800 --> 01:07:49,120
Speaker 1: a habitual. And now I'm trying to break that habit

994
01:07:49,360 --> 01:07:52,120
Speaker 1: and uh get out of the house more as the

995
01:07:52,160 --> 01:07:56,320
Speaker 1: world rights up again. But it's a rabbit hole that

996
01:07:56,360 --> 01:07:58,760
Speaker 1: you can use. I just I just like screen time

997
01:07:58,880 --> 01:08:03,240
Speaker 1: for me is I just I try to fight it.

998
01:08:03,240 --> 01:08:07,200
Speaker 1: I'm on my laptop so much, you know, working at

999
01:08:07,240 --> 01:08:12,080
Speaker 1: all that that I try not to um, try not

1000
01:08:12,160 --> 01:08:14,960
Speaker 1: to do it. It probably makes me dull and boring,

1001
01:08:15,080 --> 01:08:17,240
Speaker 1: but not at all. So so I'm gonna give you

1002
01:08:17,280 --> 01:08:21,400
Speaker 1: one streaming recommendation, which is a very broad one. I

1003
01:08:21,439 --> 01:08:26,680
Speaker 1: signed up for YouTube Premium, so there's no ads, no commercials.

1004
01:08:26,800 --> 01:08:34,080
Speaker 1: YouTube is an endless world of just whatever your favorite

1005
01:08:34,120 --> 01:08:38,240
Speaker 1: topic is, There's an infinite amount of it and more

1006
01:08:38,320 --> 01:08:44,559
Speaker 1: every day. It's genuinely astonishing, right. I mean, if you

1007
01:08:44,600 --> 01:08:49,160
Speaker 1: think HBO or Netflix has a lot of stuff, YouTube

1008
01:08:49,160 --> 01:08:56,679
Speaker 1: believes them in the dust. Amazing. Yeah, I I, Um,

1009
01:08:56,720 --> 01:08:59,400
Speaker 1: I have Hulu, but I really just use that to watch,

1010
01:09:00,800 --> 01:09:05,120
Speaker 1: you know, the occasional sports or whatever. Um. We cut

1011
01:09:05,160 --> 01:09:10,280
Speaker 1: the cable connection during the pandemic. Interesting, Um, tell us

1012
01:09:10,320 --> 01:09:13,160
Speaker 1: about some of your early mentors who helped to shape

1013
01:09:13,160 --> 01:09:20,920
Speaker 1: your career. Uh So, one of them was, um, it

1014
01:09:21,080 --> 01:09:25,320
Speaker 1: was a gentleman named John Nelson, who was my first

1015
01:09:25,360 --> 01:09:30,599
Speaker 1: job out of college. I worked as a department head

1016
01:09:30,600 --> 01:09:38,200
Speaker 1: in a hospital and and he taught me. Um. First

1017
01:09:38,200 --> 01:09:41,840
Speaker 1: of all, you know, very organized guy, but he spoke

1018
01:09:41,920 --> 01:09:45,800
Speaker 1: very softly. And one of the things I learned when

1019
01:09:45,800 --> 01:09:51,439
Speaker 1: you're speaking in public, uh, is that it the less.

1020
01:09:51,640 --> 01:09:55,519
Speaker 1: I just found that the audience leans in a little

1021
01:09:55,520 --> 01:09:59,559
Speaker 1: bit more when you're not shouting, you're soft spoken. I

1022
01:09:59,640 --> 01:10:02,840
Speaker 1: just found bit very It's been very effective for me

1023
01:10:02,960 --> 01:10:05,560
Speaker 1: and sort of that's just how I present. And I

1024
01:10:05,960 --> 01:10:09,439
Speaker 1: just before that, I always, you know, thought public speaking

1025
01:10:09,479 --> 01:10:13,920
Speaker 1: was sort of shouting and you know, really being it's

1026
01:10:13,960 --> 01:10:18,320
Speaker 1: if you're slower and delivery in a little bit less um,

1027
01:10:18,479 --> 01:10:21,800
Speaker 1: I don't want to say less animated. I just find

1028
01:10:21,800 --> 01:10:24,080
Speaker 1: at least in the subject material that I speak with,

1029
01:10:24,200 --> 01:10:29,240
Speaker 1: I'm I'm I'm a better presenter than I would be otherwise.

1030
01:10:29,280 --> 01:10:32,080
Speaker 1: And so I've always appreciated it was like a subtle thing.

1031
01:10:32,120 --> 01:10:35,160
Speaker 1: And I've always appreciated that. Let's talk about books. Tell

1032
01:10:35,240 --> 01:10:38,080
Speaker 1: us some of your favorite books, and what are you

1033
01:10:38,200 --> 01:10:44,520
Speaker 1: reading right now? So I'm I'm on an auto industry

1034
01:10:45,040 --> 01:10:51,320
Speaker 1: kick and uh and so um, partly because my father

1035
01:10:51,400 --> 01:10:54,160
Speaker 1: in law, I'm in Detroit. Um, my father in law

1036
01:10:54,320 --> 01:10:58,000
Speaker 1: worked for Ford for thirty nine years and like most

1037
01:10:58,000 --> 01:11:01,200
Speaker 1: of my wife's family, had some connecttion to the auto industry.

1038
01:11:01,200 --> 01:11:03,840
Speaker 1: So I've always been intrigued by it. And I just

1039
01:11:03,960 --> 01:11:07,679
Speaker 1: read an amazing book called finns Um and it's it's

1040
01:11:07,680 --> 01:11:13,719
Speaker 1: all about Harley Earl, who was essentially the GM uh

1041
01:11:13,760 --> 01:11:18,840
Speaker 1: you know sort of yeah, just lad lead the industry

1042
01:11:19,040 --> 01:11:22,639
Speaker 1: brought Finns into the mix. And uh. And one thing

1043
01:11:22,680 --> 01:11:30,080
Speaker 1: I learned in the book was I didn't didn'tonomy that that.

1044
01:11:30,240 --> 01:11:34,240
Speaker 1: You know, the president of GM his last name was Sloan,

1045
01:11:34,560 --> 01:11:37,960
Speaker 1: and the second in command was Kettering. And my one

1046
01:11:37,960 --> 01:11:41,559
Speaker 1: of my daughter, Dodden Lawyers, works at Sloan Kettering because

1047
01:11:41,600 --> 01:11:45,639
Speaker 1: they were sort of behind the that hospital in terms

1048
01:11:45,680 --> 01:11:48,920
Speaker 1: of getting it started, is my understanding. And who would

1049
01:11:48,920 --> 01:11:51,720
Speaker 1: have thought. I'm reading a book about Detroit auto industry

1050
01:11:51,760 --> 01:11:57,479
Speaker 1: and they had this cancer hospital in Manhattan, which is

1051
01:11:57,560 --> 01:11:59,439
Speaker 1: sort of, you know, this world class saying. So I

1052
01:11:59,479 --> 01:12:01,880
Speaker 1: thought that was really interesting. And then the other thing,

1053
01:12:01,920 --> 01:12:05,240
Speaker 1: and I've read like that. I just reread The Reckoning

1054
01:12:05,360 --> 01:12:12,439
Speaker 1: by David Halberstan and and and and I just never

1055
01:12:13,120 --> 01:12:18,360
Speaker 1: realized how backwards Henry Ford was, uh, you know, beginning

1056
01:12:18,360 --> 01:12:21,080
Speaker 1: in from the Depression on, he just built one car.

1057
01:12:21,360 --> 01:12:23,960
Speaker 1: You know, the famous saying you can you can have

1058
01:12:23,960 --> 01:12:26,599
Speaker 1: any color you want as long as it's black, um.

1059
01:12:26,880 --> 01:12:30,559
Speaker 1: All that um. And how the industry really changed and

1060
01:12:30,720 --> 01:12:35,160
Speaker 1: Harley Earl really sort of brought it into accessorizing and

1061
01:12:35,360 --> 01:12:39,880
Speaker 1: making cars decorative and a sort of a statement of yourself. Um.

1062
01:12:40,000 --> 01:12:43,599
Speaker 1: And and that's been my my focus. I've read several

1063
01:12:43,880 --> 01:12:46,759
Speaker 1: auto industry books just in the last couple of months,

1064
01:12:46,800 --> 01:12:52,320
Speaker 1: just because, Um. I think I was subconsciously because I

1065
01:12:52,360 --> 01:12:54,920
Speaker 1: was preparing to go to Detroit and I wanted something

1066
01:12:54,920 --> 01:12:57,160
Speaker 1: to talk about with me and loss. You know. The

1067
01:12:57,720 --> 01:13:01,240
Speaker 1: funny thing is, I just read the book some seven

1068
01:13:02,040 --> 01:13:04,760
Speaker 1: UM trying to remember who who wrote it, but I

1069
01:13:04,800 --> 01:13:08,439
Speaker 1: love everything he writes and he talks about. That was

1070
01:13:08,600 --> 01:13:11,360
Speaker 1: kind of a myth that it was you can get

1071
01:13:11,360 --> 01:13:13,280
Speaker 1: a Model T in any color you want, as long

1072
01:13:13,320 --> 01:13:18,280
Speaker 1: as it it's black, um, Bill bright. It turns out

1073
01:13:18,320 --> 01:13:20,240
Speaker 1: of if you've got the coupe, you could get in

1074
01:13:20,240 --> 01:13:23,040
Speaker 1: this color and the convertible and that color or or

1075
01:13:23,120 --> 01:13:26,800
Speaker 1: they were like adding, Yeah, I started. It's like three

1076
01:13:26,880 --> 01:13:30,040
Speaker 1: or four colors they started adding, but only after intense

1077
01:13:30,120 --> 01:13:32,360
Speaker 1: pressure and shutting the factory down for a year to

1078
01:13:32,439 --> 01:13:39,080
Speaker 1: retool for the next Really interesting Yeah, Ford was in

1079
01:13:39,280 --> 01:13:42,240
Speaker 1: Ford was in a lot of trouble and and GM

1080
01:13:42,280 --> 01:13:44,759
Speaker 1: had really given them a run for the money in

1081
01:13:44,920 --> 01:13:51,559
Speaker 1: the twenties, UH by introducing design and new innovations right,

1082
01:13:51,600 --> 01:13:54,000
Speaker 1: and a lot of people because of the car itself,

1083
01:13:54,040 --> 01:13:56,920
Speaker 1: made fun of ed Salt like what EDEL stood for,

1084
01:13:57,720 --> 01:14:00,519
Speaker 1: but exel is really the one that kept are going

1085
01:14:01,479 --> 01:14:04,000
Speaker 1: as you know, the father sort of you know, was

1086
01:14:04,080 --> 01:14:06,000
Speaker 1: sort of anchored to the past and not such a

1087
01:14:06,080 --> 01:14:10,080
Speaker 1: nice guy, um to say the least. But yeah, yeah,

1088
01:14:10,160 --> 01:14:14,799
Speaker 1: for sure. Let's talk about advice to a recent college

1089
01:14:14,800 --> 01:14:18,000
Speaker 1: grad who was interested in a career in either real

1090
01:14:18,160 --> 01:14:21,000
Speaker 1: estate or appraisal. What what sort of advice would you

1091
01:14:21,000 --> 01:14:27,559
Speaker 1: give them? Well, uh, I think, you know, in when

1092
01:14:27,600 --> 01:14:31,920
Speaker 1: I think of real estate or valuation, I think there's

1093
01:14:31,960 --> 01:14:35,719
Speaker 1: a lot more upside in terms of future potential going

1094
01:14:35,760 --> 01:14:39,439
Speaker 1: into the commercial side rather than the residential side of

1095
01:14:39,560 --> 01:14:44,280
Speaker 1: the of the business. Um, I think there's more opportunity. Um,

1096
01:14:45,360 --> 01:14:48,040
Speaker 1: it's a on the residential side. And this is going

1097
01:14:48,120 --> 01:14:52,320
Speaker 1: back to my earlier criticism the appraisal foundation. To get

1098
01:14:52,360 --> 01:14:56,360
Speaker 1: into residential you essentially have to work for two years

1099
01:14:56,520 --> 01:15:00,840
Speaker 1: for nothing or barely anything. You have to hire somebody.

1100
01:15:01,000 --> 01:15:02,960
Speaker 1: You have to be hired by somebody is willing to

1101
01:15:03,000 --> 01:15:06,160
Speaker 1: mentor you. And the reason why they generally don't is

1102
01:15:06,200 --> 01:15:09,960
Speaker 1: because after they teach you everything you know for two years,

1103
01:15:10,920 --> 01:15:14,120
Speaker 1: many many times the new person goes out on their

1104
01:15:14,120 --> 01:15:18,360
Speaker 1: own right. So there is a UM, it's an industry

1105
01:15:18,439 --> 01:15:21,439
Speaker 1: that's aging out and That's part of the reason why

1106
01:15:21,520 --> 01:15:26,800
Speaker 1: I'm so emphatically you know, trying to um sort of

1107
01:15:27,200 --> 01:15:31,080
Speaker 1: hit a swing a baseball bat against what the Appraisal

1108
01:15:31,200 --> 01:15:34,400
Speaker 1: Foundation has done. They've essentially you know, part of the

1109
01:15:34,520 --> 01:15:39,320
Speaker 1: problem is this to your mentoring system, which accountants and

1110
01:15:39,520 --> 01:15:42,719
Speaker 1: lawyers and like, no one has this. After you take

1111
01:15:42,760 --> 01:15:46,679
Speaker 1: your license recensing test, you can function and make a living.

1112
01:15:46,720 --> 01:15:48,840
Speaker 1: You don't make as much as somebody with more experience,

1113
01:15:49,320 --> 01:15:51,920
Speaker 1: but you let the market sort it out in terms

1114
01:15:51,920 --> 01:15:56,240
Speaker 1: of you know, feed for service. In our industry, you

1115
01:15:56,320 --> 01:15:59,440
Speaker 1: can't do that. So I wonder if there's a correlation

1116
01:15:59,520 --> 01:16:04,479
Speaker 1: between mentoring process and the lack of diversity. If you're

1117
01:16:04,640 --> 01:16:07,519
Speaker 1: hiring a friend or a family member, hey, maybe that

1118
01:16:07,600 --> 01:16:13,200
Speaker 1: person is less likely to be different than you are. Oh. Absolutely,

1119
01:16:13,240 --> 01:16:14,960
Speaker 1: that's a big part of it, And that's part of

1120
01:16:14,960 --> 01:16:20,080
Speaker 1: the problem. Uh, you know, it just it just um,

1121
01:16:20,120 --> 01:16:25,440
Speaker 1: you know, sort of continues the sort of the composition

1122
01:16:25,520 --> 01:16:32,240
Speaker 1: of the industry, which industry desperately needs, you know, new voices. Um.

1123
01:16:32,280 --> 01:16:36,080
Speaker 1: So that's why I've been very outspoken about it. Um. So,

1124
01:16:37,400 --> 01:16:41,880
Speaker 1: I have four boys. I they're all adult men now,

1125
01:16:41,920 --> 01:16:44,360
Speaker 1: and they all are gainfully employed. So I feel like

1126
01:16:44,800 --> 01:16:48,080
Speaker 1: we did our job as parents. Um, they're all, you know,

1127
01:16:48,360 --> 01:16:51,400
Speaker 1: doing well and all that, But I with my wife

1128
01:16:51,400 --> 01:16:56,040
Speaker 1: and I wouldn't let them take over from us because

1129
01:16:56,080 --> 01:17:01,600
Speaker 1: I fear for the future. Um of the infloor are

1130
01:17:01,800 --> 01:17:07,479
Speaker 1: of the residential industry. Uh And and you know also too,

1131
01:17:07,680 --> 01:17:10,479
Speaker 1: I think kids taking over the family business. I feel

1132
01:17:10,479 --> 01:17:12,360
Speaker 1: like they need to be out in the world for

1133
01:17:12,400 --> 01:17:15,559
Speaker 1: five or seven years, UM, and then come back if

1134
01:17:15,600 --> 01:17:18,919
Speaker 1: they really want to come back. But I think that

1135
01:17:18,920 --> 01:17:22,759
Speaker 1: that ship has sailed really interesting. And our final question,

1136
01:17:23,479 --> 01:17:25,799
Speaker 1: what do you know about the world of real estate

1137
01:17:26,000 --> 01:17:31,080
Speaker 1: appraisals market pricing today that you wish you knew way

1138
01:17:31,120 --> 01:17:33,959
Speaker 1: back in the nine eighties when you were first launching

1139
01:17:34,000 --> 01:17:40,360
Speaker 1: Miller Samuel. Uh So, this is really a a I

1140
01:17:40,439 --> 01:17:45,840
Speaker 1: thought a lot about this. UM. People with experience in

1141
01:17:45,920 --> 01:17:50,360
Speaker 1: real estate don't know as much about market pricing as

1142
01:17:50,400 --> 01:17:54,320
Speaker 1: you think they do. Just because someone is experienced has

1143
01:17:54,360 --> 01:17:58,439
Speaker 1: been around a long time doesn't mean they're any better

1144
01:17:58,479 --> 01:18:02,080
Speaker 1: than someone that has experience that is a fraction of that.

1145
01:18:02,320 --> 01:18:05,000
Speaker 1: And the reason why I say that is there's a

1146
01:18:05,040 --> 01:18:09,920
Speaker 1: lot of lethargy where UM, individuals, Hey, I've been doing

1147
01:18:09,960 --> 01:18:14,320
Speaker 1: it this way for twenty five years, UM. And As

1148
01:18:14,360 --> 01:18:17,760
Speaker 1: it turns out, real estate in the last decade or

1149
01:18:17,880 --> 01:18:21,720
Speaker 1: maybe a little bit longer last years, has become a

1150
01:18:21,720 --> 01:18:25,640
Speaker 1: lot more volatile. So all the rules of thumb, so

1151
01:18:25,800 --> 01:18:29,920
Speaker 1: to speak, that someone you know with my my sort

1152
01:18:29,920 --> 01:18:35,040
Speaker 1: of level of experience in terms of you know, time, um,

1153
01:18:35,160 --> 01:18:39,559
Speaker 1: is it relevant and uh and so I think I

1154
01:18:39,560 --> 01:18:41,880
Speaker 1: think you don't want to have that sort of age

1155
01:18:41,960 --> 01:18:46,720
Speaker 1: bias towards you know that youth is is also a

1156
01:18:48,640 --> 01:18:53,719
Speaker 1: good source of feedback on market conditions. Huh, really really

1157
01:18:53,760 --> 01:18:58,040
Speaker 1: quite interesting. Thank you Jonathan for being so generous with

1158
01:18:58,240 --> 01:19:01,400
Speaker 1: your time. That was Jonathan Miller. He is the CEO

1159
01:19:01,479 --> 01:19:06,120
Speaker 1: and co founder of Miller Samuel. If you enjoyed this conversation,

1160
01:19:06,160 --> 01:19:09,440
Speaker 1: well check out any of the previous four hundred discussions

1161
01:19:09,479 --> 01:19:12,080
Speaker 1: we've had over the past oh, I think it's just

1162
01:19:12,200 --> 01:19:16,320
Speaker 1: about seven years. You can find those at iTunes, Spotify,

1163
01:19:16,479 --> 01:19:19,919
Speaker 1: wherever you get your favorite podcasts. We love your comments,

1164
01:19:19,920 --> 01:19:23,200
Speaker 1: feedback and suggestions right to us at m I be

1165
01:19:23,400 --> 01:19:27,280
Speaker 1: podcast at Bloomberg dot net. Sign up from my daily

1166
01:19:27,360 --> 01:19:30,360
Speaker 1: reading list at Rid Halts dot com. Follow me on

1167
01:19:30,400 --> 01:19:33,439
Speaker 1: Twitter at rid Halts. I would be remiss if I

1168
01:19:33,479 --> 01:19:36,280
Speaker 1: did not thank the team that helps put this conversation

1169
01:19:36,720 --> 01:19:42,120
Speaker 1: together each week. Sebastian Escobar is my audio engineer. Sean

1170
01:19:42,200 --> 01:19:45,920
Speaker 1: Russo is my head of research. Attica Valbron is my

1171
01:19:46,080 --> 01:19:51,080
Speaker 1: project manager. Paris Wald is my producer. I'm Barry Rihlts.

1172
01:19:51,680 --> 01:19:55,479
Speaker 1: You've been listening to Masters in Business on Bloomberg Radio.