Latest / Real Estate Investing with RealDealCrew / Why 18 Million Americans May Never Own a Home w/ Neal Bawa

Transcript

Why 18 Million Americans May Never Own a Home w/ Neal Bawa · Real Estate Investing with RealDealCrew

• 0:00
  18 million American families are now permanently locked out of buying a home,
• 0:04
  and most of them do not even know it yet.
• 0:07
  This is the call from Neil Bawa,
• 0:09
  a multifamily investing expert who tracks this stuff obsessively.
• 0:13
  And he is back to break down why the gap between renting and buying
• 0:18
  has blown wide open. In this episode,
• 0:21
  you will learn why home prices are set to drop nationally,
• 0:25
  except in one Bay Area market.
• 0:28
  That is about to mint thousands of new millionaires.
• 0:31
  Why thirty million homeowners are frozen in place by their old mortgage rate?
• 0:37
  and one question Neil asks every morning that has completely changed how
• 0:42
  he runs his business.
• 0:43
  You can get more details and the full breakdown at RealDealCrew dot com.
• 0:49
  Let's get started.
• 0:54
  Neil Bawa joins me here today and you can learn what he can help you with
• 0:58
  by going to multifamily you dot com slash club.
• 1:02
  Take a look at the resources there.
• 1:04
  Neil is a fount of information and resources.
• 1:07
  So again it's multifamily you dot com slash club.
• 1:11
  I'm gonna have that as a clickable link in the show notes.
• 1:13
  Appreciate you coming back again,
• 1:15
  Neil. We're gonna do a round two from your previous appearance.
• 1:19
  Yeah. Yeah, yeah.
• 1:20
  I'll have a link to that as well so people
• 1:23
  can check that out.
• 1:23
  Yeah, yeah. Well thanks for having me back on,
• 1:26
  Jack. it's always good to be with the crew.
• 1:29
  Yeah, we're gonna we're gonna later talk about AI and how you're leveraging
• 1:33
  it and a few other things there.
• 1:34
  We'll kinda nerd out on some things and do some note comparing some notes.
• 1:38
  But based on what we chatted about last time,
• 1:41
  you were really kind of in that in that park where you were believing that
• 1:48
  we weren't gonna why home prices won't drop at that time,
• 1:52
  which was well, probably almost a year ago now.
• 1:55
  Are you still
• 1:56
  Thinking that that's gonna be the case?
• 1:58
  No, and I've been proven wrong.
• 1:59
  So home prices you know, in many markets dropped about one to two percent.
• 2:03
  So technically wrong. if I aggregated on a US level,
• 2:07
  technically right, I mean they've gone up about a one percent.
• 2:09
  But if you think about it, I would still say I was wrong,
• 2:12
  and I'll tell you why, Jack, because inflation was roughly three ish percent
• 2:16
  for the last year. So if home prices go up one percent when inflation
• 2:19
  is three percent, technically they're down two percent.
• 2:23
  So I'd say, you know, not sure if I was right on that one.
• 2:26
  but I I think that that's not true for the next twelve months.
• 2:29
  So for the next twelve months,
• 2:30
  I'm predicting that home prices in the US will go down single digit,
• 2:33
  probably between one and three percent.
• 2:35
  And the biggest single reason for that is that we were going to
• 2:38
  see lower interest rates, but the Iran war pushed those out by a year.
• 2:42
  You know, four or five months for the war itself,
• 2:45
  and then another eight months for the impact of that.
• 2:49
  Because a lot of the impacts of the of the oil price hikes are are delayed.
• 2:53
  the impacts on fertilizer, plastics,
• 2:55
  food are very heavily delayed.
• 2:57
  It takes ninety to one hundred and twenty days for that to even show
• 2:59
  up in the inflation data. and then it takes a good six months before you
• 3:03
  get rid of that inflation. So that basically means the Iran war is one extra year
• 3:07
  of inflation and the home prices were,
• 3:10
  you know, some of my predictions were based on interest rates going down.
• 3:14
  They haven't gone down. So I think one to three percent reduction in home prices
• 3:18
  for the country. some sent sensitive markets might be further down.
• 3:22
  the only market that I think is going to do really,
• 3:24
  really well is the San Jose the the three Metro system,
• 3:28
  which is San Jose, Oakland and San Francisco.
• 3:30
  And the biggest reason for that is both Anthropic and OpenAI will
• 3:33
  go public and create roughly, I don't know,
• 3:35
  five thousand millionaires, those people will buy in cash and that'll boost that
• 3:39
  market up tremendously.
• 3:40
  Yeah, well there's some details there that are gonna be definitely worth exploring.
• 3:44
  You know, with with the economy the way it is and the way things are
• 3:49
  are are headed or going, do you have anything that you think that real estate
• 3:55
  investors b believe today that they still completely have wrong?
• 3:59
  I think that you know there's the biggest wrong that I see in the market
• 4:05
  is not about single family. The truth is single family still continues
• 4:09
  to be overvalued. So I there are two graphs that I show in every single presentation
• 4:14
  that I do. These are the only two graphs,
• 4:15
  right? And these two graphs in two different ways show how disconnected renting
• 4:20
  has become from buying. Right?
• 4:22
  So until twenty twenty one, the
• 4:24
  Amount of money needed for a mortgage versus the amount of money needed
• 4:27
  to pay rent were similar. The amount of money for the mortgage was always
• 4:32
  a little bit higher. But if you look back going 50 years,
• 4:35
  the lines were very close to each other.
• 4:37
  In 2021, you know, I don't know what the F happened after COVID,
• 4:41
  but those lines diverged. And today the average mortgage in the United States
• 4:46
  is thirty one hundred and the average rent is eighteen hundred.
• 4:48
  You know, round rounding up numbers.
• 4:50
  So this is you a gap there of twelve hundred plus dollars.
• 4:54
  And that's crazy. We have not seen that gap in two thousand and six
• 4:57
  or two thousand five when things were bubbly.
• 4:59
  We haven't seen it in the dot com cr you know at boom time or
• 5:02
  the crisis following that. This is unique,
• 5:05
  this is new. We've never seen it.
• 5:06
  So in my mind, it is comprehensive evidence that home prices are too high.
• 5:11
  But the problem is we don't have one home price market in the US.
• 5:14
  We had one home price market until twenty twenty one.
• 5:17
  We now have two. And one market is one
• 5:20
  That you've got you know people that basically can't afford to buy a home.
• 5:24
  The median income required to buy a home in the United States today is $114,000,
• 5:28
  right? But people are earning on average $37,000 less than that.
• 5:33
  That gap between what people earn,
• 5:35
  median income, and what people need to afford the mortgage is $37,000.
• 5:39
  Previously, I've never seen it higher than five,
• 5:42
  six, seven, eight thousand dollars.
• 5:43
  So that gap is so high that on the one side we have what I called renternation,
• 5:49
  you know.
• 5:50
  18, roughly 18 million families,
• 5:52
  younger ones, that basically will never be able to afford to buy a home.
• 5:56
  They just don't know it yet. Right?
• 5:58
  They live in this hope that they will be in,
• 6:00
  you know, and these these are big numbers,
• 6:02
  eighteen million. obviously somebody could get a huge,
• 6:05
  you know, better job or you know,
• 6:07
  their income doubles and then they buy a home.
• 6:09
  But as a group, eighteen million families,
• 6:11
  these people are now forever renters.
• 6:13
  They are the first true generation and they're really composed of two generations,
• 6:18
  you know.
• 6:18
  General G Gen Z and and millennials that are never going to be homeowners.
• 6:23
  They don't know it. Nobody's telling them about it because it's you know,
• 6:26
  politicians, this is not a popular thing to say to to voters if you want their their
• 6:30
  votes. And so on the one side you have this renter nation,
• 6:33
  which is a bigger group than we've ever seen in American history.
• 6:36
  And then on the other side, you've got a ridiculous number of homeowners,
• 6:40
  somewhere between twenty and thirty million families,
• 6:43
  that are stuck in their homes.
• 6:44
  They can't leave because they have
• 6:47
  three percent to four percent mortgages.
• 6:48
  And today, you know, you know,
• 6:51
  we're we're recording this in in Q two of of twenty twenty six.
• 6:54
  Today their mortgage is going to be somewhere around six and a half percent,
• 6:58
  seven percent, at least six and a half percent,
• 7:00
  right? With some threats of the Fed that they may actually raise rents,
• 7:04
  right? So the the the Fed met last week and they didn't raise rents,
• 7:06
  but they their language clearly indicated that they're looking potentially
• 7:10
  at raising not rents, sorry, raising rates.
• 7:12
  So with the potential for rate increases,
• 7:16
  the chan what are the chances that those thirty million families that
• 7:19
  are locked into mortgages leave?
• 7:21
  You know, I live in an expensive home in the San Francisco Bay Area
• 7:24
  and my mortgage is one my my interest rate is is one point seven five percent
• 7:28
  and it's a fifteen year mortgage,
• 7:29
  right? So I'm paying it down quicker.
• 7:30
  If I were to sell my home and buy it back today,
• 7:34
  my mortgage would be six and a half percent.
• 7:36
  So I'd be paying fifty thousand dollars a year more in interest,
• 7:40
  which effect effectively means I have no mobility at all,
• 7:44
  right? I can't move.
• 7:46
  And that's bad news for America because these people they were moving generally
• 7:49
  for jobs, for progress, for making more money,
• 7:51
  right? Bettering their lives. They can't do that,
• 7:53
  they're locked in. So it's a story of two separate home price markets.
• 7:58
  Now, every year, because of you know divorces and moves and you know things like
• 8:03
  that, deaths, some of those people that are locked in,
• 8:05
  they lose their lock. And so that effect slowly wanes over time.
• 8:09
  We're beginning to see it wane.
• 8:10
  But it's still extremely, extraordinarily strong and it's preventing home prices
• 8:14
  from going down. Because they should go down,
• 8:16
  right? Multifamily, when interest rates doubled,
• 8:19
  it went down twenty five percent because multifamily is a business.
• 8:22
  You know, you're not gonna buy a business if you're paying more
• 8:23
  in mortgage and making less in cash flow,
• 8:25
  you're gonna pay more. That makes sense.
• 8:27
  But home prices are an emotional purchase.
• 8:30
  You know, people buy these for emotional reasons.
• 8:32
  I I wanna remind you, Jack, that people were buying homes when interest rates were
• 8:36
  eighteen percent in nineteen eighty three.
• 8:39
  Eighteen percent.
• 8:39
  Mm-hmm.
• 8:40
  People were buying homes. Now,
• 8:41
  granted, the average home in America was one third the cost,
• 8:44
  but they were still buying homes,
• 8:45
  right? So it's an emotional decision.
• 8:47
  Nobody should be buying a home at an eighteen percent annualized interest,
• 8:49
  but people do it all the time.
• 8:50
  It's an emotional decision. So part of it is emotion,
• 8:53
  part of it is the fact that people are locked in,
• 8:55
  and that's what's really prevented home prices from going down.
• 8:58
  The the truth is home prices should be down at least between ten
• 9:01
  and fifteen percent for them to make sense.
• 9:03
  Well,
• 9:03
  we we're you think you do you foresee that these lines are gonna diverge again where
• 9:09
  they're gonna be a little bit more in line with each other?
• 9:11
  You're talking about home prices and it's kind of frankly,
• 9:15
  I I think this would go against the the current administration when
• 9:19
  it because I think they were they've been pushing to get mortgage rates
• 9:23
  to to go down or the Fed rates to go down.
• 9:26
  So you suggesting that we might actually see an increase,
• 9:29
  that actually would probably
• 9:33
  widen that gap even more.
• 9:34
  I'm hopeful when we're recording this,
• 9:36
  you know, there's conversations about the Iran deal being signed.
• 9:39
  If it gets signed, then I just don't see any rate increases for the year,
• 9:43
  but I don't see any rate decreases.
• 9:45
  We were going to get, in my mind,
• 9:46
  two rate decreases for the year if we did not have the Iran war.
• 9:50
  Now we might get none and hopefully we don't get increases.
• 9:53
  But it all depends on how inflation comes in in Q three and Q four of this year.
• 9:56
  The Fed may be forced to raise interest rates by at least one time,
• 10:00
  maybe even two times. And I'm hopeful that the war has ended
• 10:04
  quickly enough so that we can just basically sacrifice the the rate cuts
• 10:09
  we were going to get in the second half of this year and say,
• 10:11
  okay, well we'll you know, we'll call it a day.
• 10:13
  We won't raise, we won't cut. And that's my hope.
• 10:15
  But there's still the chance that we get at least one rate increase
• 10:19
  in the second half of the year.
• 10:20
  Yeah, this this is kind of interesting how it all trickles down from from these
• 10:24
  world events, right? Y you know,
• 10:26
  we're talking about oil prices and and next thing you know,
• 10:30
  it just disrupts everything. And I don't think people even make that that level
• 10:35
  of connection.
• 10:36
  Absolutely. The the price of oil for the last four years would have been
• 10:39
  ten dollars lower, which essentially means groceries would have been about three,
• 10:44
  four, five percent cheaper if Russia hadn't attacked Ukraine.
• 10:47
  So you have this war going with two,
• 10:50
  you know, two countries trying to kill each other,
• 10:52
  burning through tens of millions of gallons of oil every week,
• 10:55
  you know, buying weapons, disrupting you know,
• 10:58
  shipping, disrupting food supply.
• 11:00
  Ukraine's the fifth largest, you know,
• 11:02
  grower of wheat in the United States or at least in the in the world,
• 11:05
  at least or at least it was before the war.
• 11:07
  so they you know, price of everything is affected by geopolitics and
• 11:10
  the two biggest ones have been the Ukraine war and and Iran.
• 11:14
  Well, with all that being said,
• 11:16
  like where would you be placing your capital now first?
• 11:21
  is are you still hot on multifamily,
• 11:23
  built to rent, student housing,
• 11:25
  or is there something else?
• 11:28
  I'm not hard on build to rent anymore and I'll explain why.
• 11:30
  I am hard on buying existing multifamily at this point of time.
• 11:34
  I like to buy things when they're inexpensive.
• 11:37
  And I also like to follow people that are much smarter than me.
• 11:39
  So when I see Warren Buffett buying builders,
• 11:43
  why is he buying builders? They're extremely cheap right now,
• 11:45
  right? They've been hammered down,
• 11:47
  their concessions are through the roof,
• 11:49
  the home prices are, you know,
• 11:50
  flat and construction costs have gone up because of ice,
• 11:54
  the
• 11:55
  the construction industry has lost at least half a million workers,
• 11:57
  good workers, trained workers that,
• 11:59
  you know, no longer show up for work.
• 12:00
  So there's a lot of bad things that have happened in the builder arena
• 12:03
  and I think that they're about to hit a bottom,
• 12:05
  which is why Warren Buffett just bought one.
• 12:06
  And I think he already owned a big one.
• 12:08
  So now, you know, he's obviously doubling down.
• 12:11
  That's a good sign in my book because,
• 12:13
  you know, Warren Buffett usually doesn't follow the the market.
• 12:16
  He basically follows analysis and buys things when they're inexpensive.
• 12:19
  Same thing with multifamily. Multifamily prices are down about twenty five percent.
• 12:22
  Rent growth is
• 12:23
  practically zero in the US for the last eighteen months.
• 12:26
  which doesn't make any sense. If you look at rent growth in the US,
• 12:29
  for the last hundred years, it's been tracking inflation and always
• 12:33
  a little higher than inflation.
• 12:34
  So if you know inflation's two percent rent growth is three,
• 12:37
  if inflation is three percent,
• 12:38
  rent growth is four. And that hasn't happened for two years.
• 12:40
  And so I I I feel like there's some pent up future rent growth that we're missing.
• 12:45
  we can't get it until this current
• 12:46
  glut of supply ends. There's this huge glut of supply that came in
• 12:50
  in twenty twenty three, twenty four,
• 12:51
  twenty-five. And in twenty six there isn't that much supply landing,
• 12:55
  but there's still these this stuff that came in in twenty twenty five that hasn't
• 12:59
  gotten filled up yet. So they're still in leasing and when they're leasing they give
• 13:02
  you huge concessions, massive,
• 13:03
  sometimes two months free, sometimes three months free on a year's lease.
• 13:06
  And that's still going on. And so that's keeping,
• 13:09
  you know, rent growth down, keeping it down at zero.
• 13:12
  But I think that that's that supply runs out.
• 13:15
  by the end of twenty twenty six.
• 13:17
  So to me, it makes sense to be a multifamily owner in twenty seven,
• 13:22
  but it definitely makes sense to be a multifamily homeowner in twenty eight
• 13:25
  and twenty nine, the two years that we don't have a lot of supply coming through
• 13:27
  the pipeline. We know that because obviously you have to pull permits.
• 13:30
  Permits cost a huge amount of money,
• 13:32
  you know, half a million dollars.
• 13:33
  So nobody pulls permits unless they're actually going to build and unless their loan
• 13:36
  is approved. Why would you do that,
• 13:37
  right? Why would you pay for permits if your loan isn't approved?
• 13:41
  so we
• 13:42
  We permits are a great indicator to what supply would be two years from now.
• 13:46
  And so multifamily supply definitely is going to crater in twenty seven
• 13:50
  and twenty-eight, definitely twenty-eight.
• 13:52
  And so it makes sense to be a buyer.
• 13:54
  Single family, not so much. I I still find that it is an extraordinarily illogical,
• 13:59
  bizarre market. And if people are like,
• 14:01
  Well, I just like single family,
• 14:03
  yes, but what's your rent growth?
• 14:04
  I mean, if you're thinking your rent growth in single family is anything less than
• 14:08
  minus three percent over the last two years.
• 14:11
  Then you haven't bothered to take 30 seconds to ask ask ChatGPT about it.
• 14:15
  Basically ask ChatGPT in the last 12 months,
• 14:18
  last 24 months, what has been single family rent growth in the United States?
• 14:23
  And I think they'll give you an answer that will be disappointing.
• 14:26
  If you so there's not much rent growth,
• 14:28
  home prices are crazy, you're basically bleeding money because your mortgage doesn't
• 14:33
  pay for you know your your the rents,
• 14:35
  the net rents that you get don't pay for your mortgage.
• 14:38
  So as a as a landlord, I I don't understand why people want to
• 14:40
  be single family landlords, but I guess I understand from an emotional perspective,
• 14:44
  they don't think that they can make the jump to a four unit or ten unit multifamily.
• 14:48
  And so people continue to buy these things.
• 14:51
  But I'm very bearish on prices for single family in any market except San Francisco,
• 14:56
  and I'm very bearish on rent growth for single family.
• 14:59
  You
• 15:00
  know, a lot of people talk about the the supposed housing crisis.
• 15:04
  you're kinda saying things that give me the impression that that might
• 15:08
  be not the case. Is w what are your thoughts on the concept of
• 15:12
  the housing crisis then and and there not being enough product for people to occupy?
• 15:18
  so there is a crisis. It's just a much smaller crisis now.
• 15:21
  So at some point the United States needed five and a half million homes
• 15:24
  for this crisis to end. Then it became four and a half million,
• 15:27
  then it became two point seven million.
• 15:28
  Now I think the real number is under two million.
• 15:31
  Is it a crisis? Sure. We're two million homes short.
• 15:34
  Is it less of a crisis than eight or nine years ago?
• 15:37
  Sure. Why did that gap, you know,
• 15:39
  reduce? Number one, US population growth slowed radically after twenty thirteen,
• 15:45
  massive slowdown.
• 15:46
  we're just not producing as many babies anymore.
• 15:48
  second, we've lost somewhere between half a million and one million people because
• 15:52
  of reverse immigration in the last eighteen months.
• 15:55
  So those people are not living in apartments anymore.
• 15:58
  They're somewhere in Mexico or in Guatemala.
• 16:00
  and then we don't have people coming in anymore.
• 16:02
  So the US had positive you know,
• 16:04
  immigration for the last thirty or forty years.
• 16:07
  As far as I know, that's almost completely stopped.
• 16:10
  Right. As a result.
• 16:13
  our need for housing, which was predicated based on continuing immigration,
• 16:16
  those numbers have not proven to be true.
• 16:19
  So our our needs are somewhere around two million.
• 16:22
  Again, it's a crisis. You can still call it a crisis until we get to,
• 16:25
  you know, much lower number than that.
• 16:27
  But is the need reducing? Yes.
• 16:29
  It's it's reduced drastically.
• 16:30
  Much slower population growth growth,
• 16:32
  negative immigration is are really the two reasons.
• 16:36
  So in the end it's it's more a l in the lines of the affordability
• 16:39
  of it than anything else.
• 16:41
  I think affordability is a key factor because if I just told you peep
• 16:45
  you know there's a thirty-seven thousand dollar gap between your median mortgage
• 16:48
  and your medi median rent, there's no way to cover that gap that I'm aware of.
• 16:53
  And so there's all there's also this bunch of people that want to buy homes,
• 16:57
  that cannot buy homes. and I suppose that that reduces the demand
• 17:01
  for homes and reduces the crisis to a certain level simply because
• 17:04
  if they're not going to be buying homes,
• 17:05
  then
• 17:07
  You know, you need less homes in the US.
• 17:09
  That's why I think that number's now below two million.
• 17:11
  and I you know, you see different numbers,
• 17:13
  you know, if Zillow's doing their calculation,
• 17:14
  it's different from Fannie Mae or Freddie Mac.
• 17:16
  Everyone does their own calculation.
• 17:18
  But there's no doubt that everyone's numbers are coming down in terms
• 17:20
  of the total crisis size.
• 17:23
  Is there any other metrics that we might be missing as investors that people seem
• 17:27
  to be ignoring in twenty twenty six?
• 17:30
  think both the positive and negative impact of AI,
• 17:33
  I I think that AI is the single greatest disruption
• 17:38
  in modern US modern world history.
• 17:40
  Right? So we've had disruptions of this size before,
• 17:44
  fire, the wheel, gunpowder, the printing press.
• 17:47
  These are all disruptions that are at the same level as AI.
• 17:51
  But in modern history, nothing that we have done,
• 17:54
  whether it's the personal computer,
• 17:56
  the internet, the smartphone
• 17:58
  Nothing that we have done comes close to the disruption that AI is going to cause.
• 18:02
  It's an astonishing level of disruption.
• 18:04
  And it's both very good for society and very bad for society.
• 18:08
  So I think that it's people haven't factored in how AI could affect demand.
• 18:13
  I I think that we are going to see waves of job growth.
• 18:16
  We've and luckily the first wave that we saw was job growth,
• 18:19
  right? Remember all these AI companies came in and you've seen what happened
• 18:22
  to the stock market. The stock market's up what,
• 18:24
  five trillion dollars? That creates wealth.
• 18:27
  When people perceive that they have more money in their stock market accounts,
• 18:30
  they spend more money and that supports the economy.
• 18:33
  So we've had an economy that's been pretty shitty for a while,
• 18:35
  but it hasn't gone into recession.
• 18:37
  We continue to move forward, people are continuing to buy because
• 18:40
  the stock market keeps going up and that creates a wealth effect for all Americans.
• 18:44
  and and so it's been very beneficial so far,
• 18:46
  but now we're beginning to see the the job losses come in.
• 18:49
  and I think that generally the US produces about two million jobs a year.
• 18:53
  And I
• 18:54
  I think roughly half a million of those jobs each year are now gone.
• 18:57
  We don't need those people anymore because we're using some form of AI to do that.
• 19:02
  the early adopters, of course,
• 19:04
  were both the banking industry,
• 19:06
  you know, laying off tens of thousands of people.
• 19:08
  And any company that had software engineers has laid off people.
• 19:11
  those are high paying jobs, so they affected the economy negatively.
• 19:14
  But at some point we're gonna start seeing,
• 19:16
  you know, sort of lower level white collar people get affected as well.
• 19:20
  It's it's inevitable. It's one hundred percent inevitable.
• 19:23
  I spend four hours a day on AI and I I see it coming.
• 19:26
  You know, th already at this point of time,
• 19:28
  if every business owner in the US fully knew how to implement AI,
• 19:32
  we'd lose five million j jobs in a year.
• 19:35
  Now, luckily they don't, which is why the job losses are distributed over time.
• 19:40
  Mm-hmm. Just to remind everybody,
• 19:42
  we're talking to Neil Bawa. You can learn more by heading over
• 19:46
  to multifamily you dot com slash club.
• 19:49
  That's going to be a clickable link in the show notes.
• 19:52
  If you found some value in what we're chatting about so far,
• 19:55
  do us a favor, share this with one of your investor friends.
• 19:57
  If you are watching us on YouTube,
• 20:00
  give us a like and subscribe. So,
• 20:02
  Neil, let's talk about that four hours you're spending in AI here today.
• 20:07
  What
• 20:07
  are some of that low hanging fruit that you implemented to really move the needle?
• 20:12
  So there we are an AI first company and we are at
• 20:17
  the fourth level of our evolution.
• 20:19
  So the fourth level started about two months ago,
• 20:22
  three months ago. when and some of it has been because technology,
• 20:26
  the the AI models themselves have improved over time.
• 20:29
  some of it simply is, you know,
• 20:31
  it takes time to go from one level to the rest.
• 20:33
  At the first level, every single employee in our company became
• 20:38
  Fluent with ChatGPT. Everyone was given ChatGPT accounts.
• 20:41
  everyone was required to use ChatGPT.
• 20:43
  We enforced it. We provided bonuses.
• 20:45
  We tied their annual salary raises to their use of AI.
• 20:48
  We counted how many times they were using AI every day.
• 20:51
  And we succeeded. A hundred percent of our employees now use AI like they use water.
• 20:56
  They're they're very good at it.
• 20:57
  But the the catch is they were still using AI to answer questions.
• 21:01
  They would ask a question, get an answer,
• 21:03
  or throw in a file and get a, you know,
• 21:05
  in an an improved file or a response.
• 21:08
  You know, maybe throw in a transcript and get an investor,
• 21:11
  an update for investors, things like that.
• 21:13
  Very manual. You throw something into chat GPT or you say something to
• 21:16
  it and it gives you an answer back.
• 21:18
  In the second round, which took us about nine months to implement,
• 21:21
  which we call you know, version two of being an AI company,
• 21:25
  we started doing what is known as a a custom GPT.
• 21:28
  A custom GPT is a trained conversation string,
• 21:32
  right? So you train on on train it on doing something.
• 21:34
  For example,
• 21:36
  You know, we buy buildings. And when we are buying buildings,
• 21:39
  you know, let's say every day we get two,
• 21:40
  you know, buildings that are for sale,
• 21:42
  we get an offering memorandum.
• 21:44
  So now what we do is we basically take the address of that company and
• 21:49
  we plug it into a trained string.
• 21:50
  The string's already been trained,
• 21:51
  so all it needs is the address.
• 21:53
  You don't s give it any instruction.
• 21:55
  That's pre-training. It's already done.
• 21:56
  Custom GPT is already created.
• 21:58
  And it knows that it has to take to the string and it has to go find rent comps
• 22:02
  or comp.
• 22:03
  pairables for that property. So if the property is,
• 22:05
  you know, in Wyoming, you know,
• 22:07
  in you know, in in you know, some city in Wyoming,
• 22:10
  it has to find comps in a two mile radius that exactly match or almost exactly match
• 22:15
  this property's age and its type.
• 22:17
  You know, obviously you don't want,
• 22:18
  you know, if you have a class C property,
• 22:20
  you don't want a class A, you know,
• 22:22
  comp because a class A has much higher prices.
• 22:24
  So that was we built four hundred custom GPTs across
• 22:31
  twenty five employees.
• 22:33
  Right. So basically roughly sixteen or seventeen custom GPTs for each employee.
• 22:37
  And that was phase two. And in phase three,
• 22:39
  we started doing more. So we went back to our custom GPTs and we said,
• 22:43
  what more? What which of the steps here can be done by AI instead of humans?
• 22:48
  For example, we would take the output of those custom GPTs and we would paste them
• 22:53
  in Slack or send an email or put them in Asana or project management system.
• 22:58
  Now we said, why can't AI do this?
• 23:01
  So we
• 23:01
  We had to switch. We had to switch from a software called ChatGPT to
• 23:05
  a new software called Claude, which was done by Anthropic,
• 23:08
  because it had the ability to go,
• 23:09
  you know, post all this stuff.
• 23:11
  So then we basically took those four hundred custom GPTs
• 23:14
  and basically started giving more steps of the process to AI with less steps
• 23:20
  to to humans. In in all inevitably humans are still needed in those processes.
• 23:23
  But now if there were seventeen steps in the process,
• 23:26
  AI instead of doing two or three was now doing ten or twelve.
• 23:30
  And humans were doing less of those processes.
• 23:32
  And that took us about six to nine months where our custom GPTs became empowered
• 23:37
  to securely post to to Slack and Gmail and Zoom and you know all these other tools
• 23:43
  that we use instead of humans doing those things.
• 23:45
  And then stage four, which we are currently at,
• 23:48
  is that we have now we now have six people within the company,
• 23:51
  not everyone's on it, that are writing custom apps.
• 23:54
  So these are applications that we design from scratch,
• 23:57
  completely from scratch.
• 24:00
  On you know, the GrowCapitus application directory now
• 24:02
  has 23 completed applications.
• 24:05
  And there's front-end interfaces,
• 24:06
  there's radio buttons, there's checkboxes,
• 24:08
  there's steps. This is us being programmers.
• 24:11
  Now, none of us knows how to program,
• 24:13
  none of us knows how to write code.
• 24:14
  But I I will say this and it'll sound really weird.
• 24:18
  I mean, all of your review your viewers are going to say this guy's full of shit.
• 24:21
  Everyone that's listening is now a programmer,
• 24:25
  right? Just because like
• 24:27
  People in the eighties and nineties when Excel came out,
• 24:30
  right? I was an early adopter of Excel.
• 24:32
  There was even a product before Excel called Lo Lotus 123.
• 24:35
  And nobody would ever thought that every single person would know how
• 24:39
  to use Excel and everybody would know how to use Word.
• 24:41
  But today everybody does. The future is about us talking to computers
• 24:46
  and computers writing code to do the things that we want them to do.
• 24:50
  Right? I do this in my personal life.
• 24:52
  You know, three months ago I wrote a piece of
• 24:54
  code which is right now on the screen,
• 24:56
  I see it on the left over there.
• 24:57
  It's called stiffness check. So I'm one of these people that won't
• 25:01
  go to a chiropractor until my neck neck is completely locked and I'm in pain.
• 25:04
  I don't basically I'm not connected with my body the way women tend to be.
• 25:08
  So I wrote this app which pops up at nine o'clock on my map every on
• 25:12
  my Mac every morning. It has neck stiff stiffness one to five,
• 25:15
  back stiffness one to five, and a chart at the bottom showing how that stiffness
• 25:19
  is going up or down over time.
• 25:20
  Right?
• 25:22
  And I click a button. Today I'm going to click,
• 25:24
  I'm gonna go do it now, three for my neck,
• 25:26
  four for my back, because I I checked it earlier on my walk.
• 25:30
  And it goes away. Right? And now what it's doing is when I consistently fall below
• 25:36
  a certain benchmark, it goes out and schedules a session with
• 25:40
  my chiropractor and puts it on my calendar and sends me a text message twenty
• 25:44
  minutes before. All of this with me talking with Claude.
• 25:49
  No code.
• 25:51
  Right, I'm not doing anything technical,
• 25:53
  but I'm confident enough to be able to do this.
• 25:55
  This is me. Hundreds of millions of us are going to do this in the coming years,
• 26:01
  and that is phase four of my company's,
• 26:03
  you know, AI first methodology.
• 26:06
  Yeah and I I actually thought
• 26:06
  That was just one example. I mean
• 26:07
  we have dozens and dozens of examples.
• 26:09
  Right. Like it this is one of those things that I've been I've been pushing really
• 26:14
  hard. You know, I I've been building a number of websites for people which
• 26:18
  are which is kind of a easier bar to of of dealing with that type of thing lately.
• 26:23
  But we're not restricted to the web apps websites anymore.
• 26:27
  They're web apps. Then we can add additional functionality,
• 26:31
  additional features that that business owner or that investor might need.
• 26:36
  down the road. It it's it becomes a a bigger thing than than
• 26:41
  an online business card, even.
• 26:42
  no, like here's here's the here's what I'll tell you,
• 26:45
  right? So like Calendly, right?
• 26:48
  Calendly is a very popular app.
• 26:49
  A lot of people use it to schedule themselves,
• 26:52
  Mm-hmm.
• 26:52
  right? You realize now you could get one serve virtual server for ten bucks
• 26:56
  a month and host hundreds of apps like Calendly on it,
• 27:00
  and you might be like, I don't know how to build the the app Calendly.
• 27:02
  You don't have to. You tell AI,
• 27:05
  go to a site called GitHub. GitHub is like magic for programming.
• 27:09
  Go to GitHub and find the best.
• 27:11
  equivalent Calendly app. Set it up on this $7 a month server and
• 27:17
  ask me any questions that are needed.
• 27:18
  I want to replace Calendly with this app.
• 27:20
  And ten minutes later it'll be done,
• 27:23
  right? It'll ask you some questions and the first month it'll be buggy
• 27:27
  and then you'll get rid of Calendly.
• 27:28
  Right? You can get rid of Calendly now.
• 27:30
  You can get rid of Zoom now. You can get rid of Loom.
• 27:32
  You can get rid of Riverside even,
• 27:34
  right? because all of these apps exist.
• 27:36
  They're they're already
• 27:37
  Mm-hmm.
• 27:37
  programmed by somebody else.
• 27:39
  Right, who who did it and put it up on GitHub.
• 27:41
  There's over five hundred thousand apps for free on GitHub.
• 27:45
  And you might say, Yeah, but you know,
• 27:47
  what if the app doesn't have a feature that I need?
• 27:49
  Well talk to Claude and say, add this feature and add it for you.
• 27:52
  It's it's not just about the feature that the app doesn't have.
• 27:55
  You can have custom features that are specific only to your company.
• 27:59
  Everyone in the world
• 28:01
  Well this is
• 28:02
  can have an app that's just yours and has more features than any app that
• 28:07
  you could ever buy.
• 28:09
  Because it's designed for you.
• 28:10
  This this is one of the things that
• 28:11
  Endlessly improve it.
• 28:13
  right, and this is what blows my mind.
• 28:14
  You know, it's it's actually when you think about it,
• 28:17
  let's I'm gonna pick on some big players,
• 28:20
  whether it's let's say Salesforce.
• 28:21
  People have implemented Salesforce,
• 28:24
  this huge app for their CRM, and they barely touch most
• 28:29
  of the function functionality in there.
• 28:31
  Imagine creating a a
• 28:33
  People use 2% of Salesforce,
• 28:34
  2%. Right? And that 2% is available on 50
• 28:36
  Just what if you just develop an app that's
• 28:38
  different GitHub applications.
• 28:40
  50. No, you don't have to develop
• 28:40
  Exac exactly.
• 28:42
  them. You just have to find them.
• 28:44
  Right? And even every month you could basically say,
• 28:47
  Did the developer improve this app?
• 28:49
  Yes, there's a new version that we see there.
• 28:50
  Okay, go go grab the new version.
• 28:52
  Now you've got
• 28:53
  Mm-hmm.
• 28:54
  more stuff. But you could be continuously,
• 28:56
  you know, writing new stuff yourself.
• 28:58
  Right? So you would have Salesforce.
• 28:59
  Yeah. And in fact, I thought this was the
• 29:02
  for the real deal crew.
• 29:04
  Right. Then th th this was something that I thought I was a had an original thought,
• 29:09
  but apparently Elon Musk had had or had had said it.
• 29:12
  We are approaching that time where we may not even have apps on our phones
• 29:17
  the way we we do today. You'll just say something to your phone and the
• 29:22
  app or the functionality just materializes.
• 29:25
  Like that's that's the route we're moving.
• 29:26
  Yes, not quite there
• 29:28
  yet because the phones are not fast enough for that in real time.
• 29:31
  But I think we we get there in phases,
• 29:33
  right? So first we get there where the apps on our phone are customizable,
• 29:37
  meaning we can actually edit them and improve them.
• 29:39
  So they become the code becomes open,
• 29:42
  right? So we can basically say something like,
• 29:44
  you know, this this U fee app that I have for my my security cameras,
• 29:49
  I don't like the way it's laid out.
• 29:51
  I want it to be laid out like this instead of like that,
• 29:54
  right? And so please go ahead and fix that.
• 29:56
  And then takes about ten or fifteen seconds and now my app's layout
• 29:59
  is fixed because it's customized to me,
• 30:02
  right? It th the app's code can be rewritten.
• 30:05
  At some point, you just talk to your phone and it basically in over the next half
• 30:10
  a second builds the app in real time for you.
• 30:13
  We're still about five five years away from that because the you know
• 30:17
  the phones have to get faster.
• 30:19
  Mm-hmm. Yeah, and if this sounds magical to you,
• 30:22
  that's where I step in. head over to realdealcrew.com and I can help
• 30:27
  you implement some of these solutions.
• 30:29
  Neil, this is it's always a such a great time to chat with you.
• 30:33
  And I know you have kind of a hard stop,
• 30:35
  so we are going to transition into the the rapid fire and start closing
• 30:40
  out this episode. Is there anything else we should have hit on though?
• 30:42
  Like you gotta come back. Maybe we just spend
• 30:46
  an hour sometime talking about AI and doing some compara comparative notes
• 30:51
  on on this. ~ been working on some projects myself that I
• 30:52
  I'll I'll let Ashley know to you know,
• 30:54
  connect. Yeah.
• 30:55
  think we could nerd out about.
• 30:57
  Okay, happy to do that. Let's
• 30:58
  Ha ha.
• 31:00
  let's go for rapid fire.
• 31:01
  What
• 31:02
  lie do real estate investors often tell themselves?
• 31:04
  that they're data driven. just having an Excel spreadsheet doesn't make
• 31:08
  you data driven. Having a set of rules that you don't break whenever you feel like,
• 31:12
  that's data driven. Going out and doing research,
• 31:15
  something that costs, you know,
• 31:17
  a hundredth of the time today at a thousandth of the cost,
• 31:20
  do research. Compare stuff. challenge all of your assumptions.
• 31:24
  Every time you think, I believe this,
• 31:26
  go out there and see if that's data driven.
• 31:29
  Very few people do that. People are not as data driven as they think they are.
• 31:33
  If you could go back in time and give your younger self a piece of advice,
• 31:35
  what would it be?
• 31:36
  that's easy. Buy as much bit Bitcoin as you can in two thousand nine.
• 31:39
  Yeah. I'd be one of the ones that would have lost my key though and then
• 31:43
  and then I'm sitting there staring at millions of dollars.
• 31:46
  And don't lose your key.
• 31:47
  Do you have a book recommendation or what are you reading right now?
• 31:51
  I like the miracle morning because I think that what what we find
• 31:54
  is that everybody gets busy during the day and having that miracle morning
• 31:58
  in the morning, having that structured one hour routine gets you just ahead
• 32:03
  of ninety nine percent of the people.
• 32:04
  I still think that less than one one thousandth of the people on the planet have
• 32:08
  a morning miracle morning time schedule.
• 32:11
  So that gets you ahead of just about everyone on the planet.
• 32:14
  And I probably know the answer to this last one,
• 32:16
  but what single tool or process have you implemented that has had
• 32:19
  the biggest time saving impact?
• 32:21
  every morning I get up and ask myself this question,
• 32:23
  who's doing this task and can AI do it better and faster and cheaper?
• 32:29
  The answer in ninety-nine percent of the cases is yes.
• 32:33
  It's just you have to have the the balls to implement it.
• 32:36
  Well, Neil, always great to have you.
• 32:38
  Hope you'll consider coming back a lot sooner.
• 32:40
  again, it is multifamily you dot com slash club.
• 32:46
  That's gonna be a clickable link in the show notes,
• 32:48
  but really appreciate your time again,
• 32:50
  Neil. It was great to catch up.
• 32:51
  Thanks for having me back, Job.
• 32:52
  Jack. Sorry.