Latest / Private Markets 360° / Unpacking the State of the Market (With David Hedalen, Head of Private Markets Strategy & Research at Aviva Investors)

Transcript

Unpacking the State of the Market (With David Hedalen, Head of Private Markets Strategy & Research at Aviva Investors) · Private Markets 360°

• 0:19
  Hello, and welcome to Private Markets 360,
• 0:22
  your insiders guide to the world of private markets.
• 0:25
  I'm Jocelyn Lewis.
• 0:26
  And today, I'm joined by my co-host,
• 0:28
  Chris Sparenberg,
• 0:29
  and our guest, David Hedalen,
• 0:32
  Head of Research and Strategy
• 0:34
  at Aviva Investors.
• 0:36
  David, thank you so much for joining us.
• 0:38
  How are you today?
• 0:40
  Very well.
• 0:40
  Thanks, Jocelyn.
• 0:41
  Chris, thank you for having me.
• 0:42
  Yes.
• 0:43
  Thanks for coming in.
• 0:44
  So David, we'd like to start off with you
• 0:47
  giving a little information
• 0:47
  bit about your background,
• 0:49
  your current role, and we want to hear
• 0:52
  how your team approaches
• 0:54
  research in this vast world
• 0:56
  of private markets.
• 0:57
  Sure.
• 0:58
  Yes, happy to start at the beginning,
• 0:59
  if you want a little trip down
• 1:00
  memory lane, if you want to Jocelyn.
• 1:02
  But yes, so
• 1:02
  I grew up on the West Coast of Canada.
• 1:05
  I did my undergrad there as well.
• 1:06
  And then after a bit of traveling
• 1:08
  I moved to London,
• 1:09
  started working in financial services
• 1:10
  at that point, not in private market
• 1:12
  specifically,
• 1:13
  and then actually went back to university
• 1:14
  to do a Master's
• 1:15
  in a sort of property-related degree.
• 1:17
  And then got hired at a company
• 1:20
  called IPD (Investment
• 1:21
  Property Databank),
• 1:22
  which was laterally acquired
• 1:24
  by MSCI Real Assets.
• 1:27
  So I spent 4 years at IPD,
• 1:29
  and this was really diagnosing
• 1:33
  real estate
• 1:33
  portfolios, private
• 1:34
  real estate portfolios, aggregating data,
• 1:37
  establishing valuation approaches
• 1:40
  or examining those,
• 1:41
  I should say, as well as methodologies
• 1:44
  around performance diagnostics.
• 1:46
  So I spent 4 years
• 1:47
  there from 2006 to 2010,
• 1:49
  so right in the jaws
• 1:51
  of the financial crisis.
• 1:52
  So fair to say I learned a lot there.
• 1:54
  Maybe we could touch
• 1:54
  on some of the lessons
• 1:55
  learned a little bit later.
• 1:56
  And then laterally,
• 1:58
  I was brought internally
• 1:59
  by Standard Life Investments,
• 2:00
  so a large U.K.
• 2:01
  investment manager that was in 2010.
• 2:04
  And then about 3.5 years ago,
• 2:06
  I moved to Aviva
• 2:07
  Investors, where I am today
• 2:09
  to head up their private markets
• 2:11
  strategy and research function.
• 2:12
  And really,
• 2:13
  the goal of that team is a holistic
• 2:16
  private market research function.
• 2:18
  And the business covers
• 2:20
  a number of areas,
• 2:21
  but really the primary research functions
• 2:23
  for my team are infrastructure,
• 2:26
  real estate, both debt and equity.
• 2:28
  So we're
• 2:28
  large investors,
• 2:29
  both on the equity and debt side
• 2:31
  as well as private corporate
• 2:34
  debt, structured finance
• 2:35
  and actually venture capital
• 2:36
  and natural capital as well.
• 2:37
  So it runs the whole spectrum of
• 2:40
  private markets.
• 2:41
  And really,
• 2:41
  the way that we approach things is
• 2:43
  at least my brief from the CIO
• 2:45
  when I joined
• 2:45
  was really establish the team
• 2:47
  as sort of consistent top-down view
• 2:50
  to private markets across the franchise.
• 2:52
  So I'm not sure how familiar people are
• 2:54
  with Aviva
• 2:55
  and Aviva Investors more broadly,
• 2:57
  but Aviva as a function
• 2:59
  is one of the U.K.'s
• 3:00
  largest insurance companies.
• 3:02
  And Aviva Investors
• 3:04
  is their investment manager
• 3:06
  that they use for
• 3:08
  investing some of the --
• 3:09
  or a lot of their capital.
• 3:11
  And if you think about the numbers, Aviva
• 3:13
  Investors is $350 billion
• 3:15
  of assets under management
• 3:17
  dollars total,
• 3:18
  of which about $128 billion
• 3:20
  -- around $130 billion is fixed income
• 3:22
  and $130 is around multi-asset.
• 3:25
  There's another slice of equity
• 3:26
  and around $63
• 3:27
  billion is private markets.
• 3:29
  So it gives you a feel for
• 3:31
  how big the private market
• 3:33
  franchise is at Aviva Investors.
• 3:35
  And I should just clarify, most of
• 3:37
  that is within Europe.
• 3:39
  Is that an area of growth for you,
• 3:41
  too, private markets?
• 3:43
  Absolutely.
• 3:44
  And we've seen a lot of growth there.
• 3:46
  I think, Jocelyn, from the perspective
• 3:48
  of how private markets is sort of
• 3:50
  plugging into the wider
• 3:52
  investment ecosystem.
• 3:54
  From my point of view, and Aviva
• 3:57
  Investors is, again, a large U.K.
• 3:59
  insurer is also one of the largest
• 4:00
  DC providers in the U.K.
• 4:02
  So we're seeing material growth
• 4:05
  in the DC channels
• 4:07
  and the allocations to private markets
• 4:09
  through those DC solutions for investors.
• 4:12
  And I think this is
• 4:14
  an interesting dynamic
• 4:15
  for private markets
• 4:16
  - probably something we'll explore
• 4:17
  later - is how do we
• 4:20
  get private to talk to public
• 4:22
  so that these allocations work because
• 4:25
  despite a lot of the default funds,
• 4:27
  at least that Aviva Investors
• 4:28
  runs anywhere
• 4:29
  from 10% to 25%, in fact,
• 4:31
  some of the funds are private,
• 4:33
  it's still a minority or at least a
• 4:36
  relatively small
• 4:37
  share of that overall allocation.
• 4:39
  So it's really important that
• 4:41
  private can
• 4:42
  talk to public in that sense.
• 4:44
  So private talking to public
• 4:46
  and then also being focused
• 4:48
  very much on Europe,
• 4:49
  you're seeing multiple jurisdictions.
• 4:51
  I'm sure you're taking
• 4:52
  in a lot of information
• 4:53
  about different market dynamics.
• 4:55
  How does that inform the research
• 4:57
  that you're doing,
• 4:57
  the way you think about structuring --
• 4:59
  just what perspectives does it
• 5:00
  give you overall? Yes.
• 5:02
  That's a good question, Chris.
• 5:02
  So I think
• 5:03
  when we think about how we use research
• 5:05
  in a way
• 5:06
  or the data that comes through
• 5:08
  from the private market ecosystem,
• 5:10
  and let's call it the shop floor,
• 5:11
  if you will, in terms of our business.
• 5:13
  So as I mentioned, we
• 5:15
  run these large areas of private
• 5:17
  markets across Europe in real estate,
• 5:19
  both debt and equity, et cetera,
• 5:21
  infrastructure as well.
• 5:22
  We get a lot of sort of real-time intel
• 5:24
  coming through.
• 5:25
  And we do use that.
• 5:26
  We also are able to
• 5:27
  buy in third-party data.
• 5:29
  So we do
• 5:29
  sort of augment
• 5:30
  the third-party data provision
• 5:32
  with our own intel.
• 5:34
  And then from there,
• 5:35
  the team is tasked with sort of
• 5:37
  building up the sort of proprietary tools
• 5:39
  that we would use to help
• 5:40
  make better investment
• 5:41
  decisions and help inform.
• 5:42
  And I think I should
• 5:43
  probably clarify as well, Chris.
• 5:44
  The research, the top-down views
• 5:47
  and for those listeners
• 5:48
  who are in the weeds of private markets,
• 5:50
  they know that a top-down view
• 5:51
  is probably more applicable
• 5:53
  in the public space than it is
• 5:54
  the private space, right?
• 5:55
  So we may have a view
• 5:56
  on a certain market,
• 5:56
  but actually,
• 5:57
  the application
• 5:57
  is harder in the private space
• 5:59
  because of access routes,
• 6:03
  constraints, liquidity
• 6:05
  you might have to develop out
• 6:06
  an asset that brings in its own
• 6:07
  type of risk, et cetera.
• 6:08
  So really,
• 6:09
  whilst we offer that top-down view,
• 6:11
  there is that sort of,
• 6:11
  let's call that, the theory
• 6:13
  of where we think the markets are going.
• 6:15
  But the reality
• 6:16
  is the actual implementation
• 6:17
  of the investment,
• 6:18
  there's a journey in there,
• 6:20
  which my team supports
• 6:21
  the business on in terms
• 6:22
  of how we do that.
• 6:23
  But it's not a one-for-one.
• 6:25
  The views from my team
• 6:26
  are not necessarily dogmatic in a sense
• 6:28
  because the application
• 6:29
  will be very, very challenging to do
• 6:31
  because we may like
• 6:31
  a certain sector
• 6:32
  that there may be no product
• 6:33
  available, for example, right?
• 6:34
  But either way, having that top-down view
• 6:37
  is critical
• 6:37
  when we think about how we look
• 6:39
  at the various types of private markets.
• 6:41
  So third-party data is bought in.
• 6:44
  We use that -- we use our own
• 6:46
  sort of intel from the shop
• 6:48
  for that operational leverage
• 6:50
  to really refine and reinforce
• 6:52
  and come out
• 6:53
  with what we think is better options
• 6:57
  and for our investor base.
• 7:00
  And then
• 7:00
  thinking about those different options
• 7:03
  and solutions that you have across
• 7:05
  the investor base.
• 7:06
  So Aviva is mainly investing in Europe.
• 7:09
  But when you think about Europe
• 7:11
  and private markets,
• 7:12
  that's all different geographies
• 7:14
  really
• 7:15
  because you have all these different
• 7:17
  European countries
• 7:19
  so -- or European jurisdictions.
• 7:21
  So how do you bring that
• 7:24
  into your research?
• 7:25
  And what unique insights are you
• 7:27
  then able to get from taking
• 7:30
  in all of that data and research to
• 7:33
  then say, here's
• 7:34
  how we're going to structure
• 7:35
  our outcomes. Yes.
• 7:37
  No, again,
• 7:40
  this is, I guess, in a way,
• 7:41
  the advantage of Europe
• 7:42
  and that some of the --
• 7:43
  there's some barriers
• 7:44
  to certain countries
• 7:45
  for tax reasons, et cetera,
• 7:46
  depending on where you're
• 7:47
  domiciled, Justin.
• 7:48
  So absolutely,
• 7:48
  there are considerations there.
• 7:49
  And that probably relates
• 7:50
  back to
• 7:51
  my previous point
• 7:51
  that we may like a certain country
• 7:53
  or a certain sector,
• 7:54
  but actually --
• 7:55
  it may not be tax beneficial
• 7:56
  depending on the jurisdiction
• 7:57
  where the fund is domiciled, right?
• 7:59
  And so that's another layer,
• 8:01
  I think, that a lot of investors
• 8:04
  have to consider in Europe.
• 8:06
  So if you think about
• 8:07
  how we would break that down,
• 8:09
  we would look at the country risk.
• 8:11
  And so we're supported by a wider
• 8:13
  macro strategy
• 8:14
  team that sort of
• 8:15
  look at the various
• 8:16
  -- have views on country
• 8:18
  dynamics, cyclicality of countries,
• 8:20
  GDP growth per country.
• 8:21
  So we have a view on the sort of
• 8:22
  top down.
• 8:23
  We take that view
• 8:23
  and then think about, okay,
• 8:25
  how does that impact,
• 8:26
  let's say, the built environment
• 8:27
  in the real estate case
• 8:28
  or the demand for energy
• 8:29
  in certain countries
• 8:30
  or from a lending perspective,
• 8:32
  spreads, et cetera, in various
• 8:33
  markets, clearly,
• 8:34
  liquidity is a consideration there.
• 8:37
  And that sort of cooks
• 8:38
  down to your sort of,
• 8:39
  I guess, in a way, Jocelyn,
• 8:40
  on your risk premium
• 8:41
  per country and how we think about that.
• 8:43
  And how we do that and how we navigate
• 8:45
  that is through what we call
• 8:47
  relative value.
• 8:48
  So we look at everything through
• 8:50
  a relative value lens.
• 8:52
  In order to get there,
• 8:53
  it is not very straightforward
• 8:55
  in private markets
• 8:55
  because as you both know, sometimes
• 8:57
  the data is not available
• 8:58
  to be able to base anything off.
• 9:00
  So you almost have to manufacture that.
• 9:01
  So we will pull in this data
• 9:03
  for various markets,
• 9:04
  and we'll have visibility
• 9:05
  because we have visibility
• 9:06
  from deal flow, either maybe deals
• 9:09
  we didn't win,
• 9:10
  but we have evidence of where we outbid,
• 9:12
  potentially bids we did win,
• 9:14
  and we know
• 9:14
  potentially exact underbidder number,
• 9:17
  but we may have a feel for
• 9:18
  how many underbidders there were
• 9:19
  and is there a lot of demand
• 9:21
  for this type.
• 9:21
  And clearly, we're sellers as well.
• 9:23
  So we know what market is pricing
• 9:24
  when we sell.
• 9:26
  So we can
• 9:27
  sort of triangulate all that data,
• 9:28
  and that is proprietary, right?
• 9:30
  That is
• 9:30
  the sort of informational advantage,
• 9:32
  I would say, in this market.
• 9:34
  And what we do then is we put that into
• 9:36
  a relative value
• 9:37
  framework to be able to establish,
• 9:38
  okay, where are we seeing the best value
• 9:40
  today on a risk-adjusted basis?
• 9:43
  Now that's not for everyone
• 9:45
  because that's a coefficient,
• 9:46
  that's a ratio.
• 9:47
  And clearly,
• 9:48
  if the absolute return of that doesn't
• 9:50
  meet the hurdle,
• 9:50
  then the investor doesn't care.
• 9:53
  We may have a good risk-adjusted return,
• 9:55
  but the absolute return
• 9:56
  doesn't hit the hurdle,
• 9:57
  it's not a viable investment.
• 9:59
  But this is where I think
• 10:00
  private markets
• 10:01
  -- and back to the point
• 10:02
  that you mentioned, Chris,
• 10:03
  in terms of that public-private dynamic,
• 10:06
  this is where private markets,
• 10:08
  I think, really needs
• 10:11
  better data, and we're getting there.
• 10:13
  And I joined the industry 20 years ago,
• 10:15
  working for what was a data provider,
• 10:18
  hoovering up valuation
• 10:20
  data on private assets,
• 10:22
  putting that into a sort of confidential
• 10:24
  mixer and out came an index for
• 10:27
  New York
• 10:27
  offices or
• 10:28
  out came an index for the London City,
• 10:30
  whatever it might be.
• 10:31
  And what that gave investors
• 10:34
  was a sort of basis
• 10:35
  to be able to establish some sort
• 10:37
  of reference point, right?
• 10:40
  And that gave people
• 10:42
  that are looking to model that out,
• 10:43
  okay, here's
• 10:43
  the relationship
• 10:44
  with different drivers
• 10:45
  in the macro economy, right?
• 10:46
  Here's the relationship
• 10:48
  of this asset class versus
• 10:49
  this asset class.
• 10:51
  Now that's real estate, right?
• 10:53
  That's alongside
• 10:54
  sort of core private equity,
• 10:56
  that's probably
• 10:58
  the most invested
• 11:00
  part of the private market stack, right?
• 11:02
  And we can see that
• 11:03
  from our own survey data,
• 11:04
  and that's not a surprise to anybody.
• 11:07
  So it's quite mature
• 11:08
  in the private market sense.
• 11:09
  It's often the first investment
• 11:10
  people in private markets is real
• 11:12
  estate makes sense.
• 11:13
  But what the index gave
• 11:15
  investors was the ability to
• 11:17
  then start to model out a basis
• 11:19
  and a framework
• 11:20
  to think about how they do it.
• 11:21
  To your point, Jocelyn, on data
• 11:22
  where maybe there isn't a reference point
• 11:24
  in a European market
• 11:26
  that's maybe more developing,
• 11:28
  more nascent
• 11:28
  or the transparency is not there,
• 11:29
  it is hard.
• 11:30
  So we have to think about ways
• 11:31
  that we can do that without necessarily
• 11:35
  having some sort of reference
• 11:36
  point to base Chris, does
• 11:38
  it make sense to talk a little bit
• 11:40
  about our new data set?
• 11:41
  And since we have real assets
• 11:44
  and private credit
• 11:46
  as the first 2 segments
• 11:48
  that are available within that data
• 11:50
  set it might be helpful.
• 11:52
  I think it does, and this is a topic
• 11:54
  that's near and dear to my heart.
• 11:56
  especially, but I want to frame
• 11:58
  it almost as the complement
• 12:00
  to what you're describing
• 12:01
  is all the proprietary data
• 12:02
  that you collect.
• 12:03
  There's everything happening on the shop
• 12:05
  floor.
• 12:05
  There's the deal pipeline,
• 12:07
  you're on both sides of it,
• 12:08
  you're buying and selling.
• 12:09
  Then there's this layer of market data
• 12:11
  that you need.
• 12:12
  Some of that comes from public sources.
• 12:14
  Other sources,
• 12:14
  I think, are newly developing.
• 12:17
  And so what we've done
• 12:18
  with our partnership
• 12:19
  with Cambridge Associates
• 12:20
  and Mercer is really to take a look
• 12:22
  at the institutional
• 12:23
  reported universe almost, right?
• 12:25
  So funds
• 12:26
  reporting into LPs and what signals
• 12:29
  you can draw from a data
• 12:30
  set like that in aggregate.
• 12:32
  So we did start in March,
• 12:34
  we released our credit and real assets
• 12:37
  data product.
• 12:37
  For us, real assets
• 12:38
  is everything
• 12:39
  you described, it's
• 12:40
  real estate infrastructure, nat res.
• 12:42
  But can you talk
• 12:42
  about the importance of market data
• 12:44
  and where you see
• 12:45
  maybe some inefficiencies
• 12:46
  or opportunities
• 12:47
  and what role
• 12:48
  that would play in your process?
• 12:49
  Yes.
• 12:50
  So this is a live conversation.
• 12:52
  We're always looking for more data.
• 12:53
  So obviously,
• 12:54
  when I heard about the project, Chris,
• 12:56
  that was coming out
• 12:57
  and obviously spoken to you about it
• 12:59
  as well, really, really interested to see
• 13:01
  what data was coming out
• 13:02
  and how it could be used.
• 13:04
  And I think the use case for us,
• 13:06
  right, is,
• 13:07
  as I mentioned to Jocelyn's question,
• 13:10
  this idea that
• 13:11
  we can look at
• 13:14
  post returns for
• 13:17
  any type of private asset historically
• 13:20
  and give the journey,
• 13:20
  the volatility of the journey
• 13:22
  is really critical,
• 13:23
  particularly looking back to the point
• 13:25
  on DC
• 13:26
  and public investors,
• 13:28
  that's the language that they speak.
• 13:30
  They're used to seeing NAV volatility,
• 13:33
  valuation volatility.
• 13:34
  They're not used to seeing
• 13:36
  a 5-year IRR projection.
• 13:38
  That's one number, right?
• 13:40
  And that's a really
• 13:41
  important distinction.
• 13:42
  So I'll paint this with a live example
• 13:44
  about how we've done it,
• 13:45
  and this hopefully will
• 13:47
  get to your point, Chris.
• 13:49
  Historically,
• 13:49
  where we didn't have
• 13:50
  a reference point
• 13:51
  for a market data series,
• 13:53
  a nascent
• 13:54
  infrastructure asset class, something,
• 13:57
  let's say, EV charging, right?
• 13:59
  Now your data set may have a series
• 14:02
  for EV charging,
• 14:02
  but even if it did, it's probably not
• 14:04
  historic enough to be able to sort of
• 14:06
  hang your hat on, Sure.
• 14:10
  and this is our
• 14:11
  original relative value
• 14:12
  framework was basically say, okay,
• 14:13
  well, we're doing these deals.
• 14:14
  We've got the models,
• 14:15
  we've got the cash flows.
• 14:16
  Let's hand them over to some quants
• 14:18
  on the research team to basically stress
• 14:21
  those models and give you a sort of
• 14:23
  stochastic projection
• 14:25
  of your IRR permutation.
• 14:27
  So 5,000 IRR scenarios versus various.
• 14:29
  So you have a base case
• 14:30
  and then you have a stress
• 14:31
  and then you end up a situation, okay,
• 14:33
  what's your level of confidence
• 14:34
  in your central IRR
• 14:35
  because you now
• 14:36
  have the potential distribution, right?
• 14:38
  Now that's great.
• 14:39
  That's an IRR
• 14:40
  that gives you a risk-adjusted
• 14:41
  -- sorry, it's not risk-adjusted
• 14:44
  as we think
• 14:44
  in terms of the volatility
• 14:45
  over the return.
• 14:46
  It's actually risk-adjusted
• 14:47
  in terms of your --
• 14:48
  probably your predictability
• 14:50
  of hitting your central
• 14:51
  case. Distribution
• 14:54
  So that's fine.
• 14:55
  And that actually worked.
• 14:56
  For a long time, that worked.
• 14:58
  And it was prior to
• 15:00
  my time in the business,
• 15:01
  but it was
• 15:01
  one of the first things I was asked to do
• 15:02
  is look at this model,
• 15:03
  and it did work, and it was great.
• 15:06
  It was very clunky because you had to
• 15:07
  then take a new deal.
• 15:09
  And if you didn't have a deal,
• 15:11
  we are a big player
• 15:11
  in the European private market space.
• 15:12
  But if it was a new asset class
• 15:14
  you hadn't done an underwrite
• 15:15
  on, guess what, you have to build that.
• 15:17
  You're not going through
• 15:19
  that level of detail
• 15:19
  necessarily because nothing sharpens
• 15:22
  the mind
• 15:23
  like a live deal, right?
• 15:27
  we were using these deals
• 15:29
  where we've done a material amount of DD
• 15:31
  and be able to stress it.
• 15:32
  And this was --
• 15:32
  the problem is
• 15:32
  this was effectively a country-specific
• 15:36
  EV charging program, right?
• 15:37
  It wasn't a sort of wider context of
• 15:39
  how is this working?
• 15:40
  Whether it's a nascent
• 15:41
  sector like battery storage
• 15:43
  now, again, new, very popular
• 15:45
  and a lot of investors
• 15:46
  looking at that sector.
• 15:47
  These are things that are
• 15:48
  challenging to do.
• 15:49
  So you had this situation where we had
• 15:51
  this really interesting output,
• 15:52
  IRR-based outcomes
• 15:55
  across all these different sectors,
• 15:56
  and you could do it
• 15:56
  for real estate as well, clearly, right?
• 15:58
  We knew that.
• 16:00
  You had to make some assumptions
• 16:01
  on correlations and how,
• 16:02
  but you could start to run
• 16:04
  various outputs and sort of say,
• 16:05
  how does this work and then assess,
• 16:07
  okay, where is the best
• 16:08
  risk-adjusted return here for this
• 16:10
  ecosystem of private markets.
• 16:12
  The problem is
• 16:15
  the DC, the
• 16:17
  public side would say,
• 16:19
  how can this talk to my
• 16:21
  book, which is using
• 16:22
  some sort of post modeled out
• 16:25
  volatility of the future
• 16:27
  and the past,
• 16:28
  and we know the max drawdown
• 16:30
  and we know the potential banana skins
• 16:32
  or what will react
• 16:32
  if there's a macro event.
• 16:34
  That doesn't give you that.
• 16:35
  That's basically one number.
• 16:37
  So weaving that in was challenging.
• 16:38
  So we've started to augment that now
• 16:42
  and using
• 16:43
  where we can, to your point,
• 16:44
  Chris, this data.
• 16:44
  So where there's
• 16:45
  viable data sources that have history,
• 16:47
  you can actually do things very similar
• 16:50
  to what they do in public now,
• 16:51
  where you're actually modeling very,
• 16:53
  very similar
• 16:53
  total return forecast
• 16:55
  with similar types of volatility metrics.
• 16:57
  You can de-smooth the series
• 16:58
  and you're actually having
• 17:00
  for a public market
• 17:01
  CIO - and let's be frank, most CIOs
• 17:04
  in investment firms cut their teeth
• 17:08
  in the public sector
• 17:09
  -- public sector,
• 17:10
  the public side, excuse me, right?
• 17:11
  And so this sort of IRR
• 17:14
  conversation is often
• 17:15
  a challenge sometimes.
• 17:17
  So when you can then say, well,
• 17:18
  this is now on the same metrics
• 17:19
  on the same basis.
• 17:20
  So where you have to your data set,
• 17:24
  where you have that ability
• 17:25
  to sort of say, okay,
• 17:26
  this is a long enough series,
• 17:27
  a deep enough series,
• 17:28
  even if it's not as robust
• 17:30
  as you would like,
• 17:30
  you can still start to use it as a base,
• 17:33
  that's extremely helpful to start
• 17:35
  to think about how you potentially
• 17:36
  look at some of the factors
• 17:37
  influencing that asset
• 17:38
  class, how sensitive
• 17:40
  are the various market dynamics to rates,
• 17:43
  GDP growth, whatever energy,
• 17:45
  whatever it is you're looking at.
• 17:47
  And I think where the market is going
• 17:49
  is very much down that route,
• 17:51
  at least from my perspective.
• 17:52
  And look, I'm speaking from a major
• 17:54
  DC player, private market investor.
• 17:57
  We don't run a lot of closed-end
• 17:59
  vehicles.
• 18:00
  We don't run a lot of sort of 7-year
• 18:02
  life vehicles.
• 18:03
  We're mainly open-ended funds, LTAFs,
• 18:06
  evergreen structures for DC solutions.
• 18:09
  So this is sort of the lens
• 18:10
  that I look at it through.
• 18:11
  But I can tell you now,
• 18:12
  we've got people on my team
• 18:13
  who solely look at getting
• 18:15
  that private market
• 18:17
  output to talk to public,
• 18:19
  so it can be modeled.
• 18:20
  So strategic asset
• 18:21
  allocation makes sense.
• 18:22
  And that's, I think, to the point,
• 18:25
  Jocelyn, is what's happening
• 18:26
  there is how it's used
• 18:27
  and how the data is used in public --
• 18:31
  private is coming to public to
• 18:32
  a degree, Chris.
• 18:33
  So the more of these lines
• 18:35
  you can get, which show the sort of
• 18:39
  ex post
• 18:39
  returns and Even if it's investors
• 18:42
  questioning the depth of the data,
• 18:45
  it's still better than nothing.
• 18:47
  We're at the moment,
• 18:48
  for a nascent sector,
• 18:49
  one that we don't have data
• 18:51
  for, we're having to effectively
• 18:53
  create it for nothing.
• 18:54
  We're here,
• 18:54
  you at least have a basis
• 18:56
  to be able
• 18:56
  to sort of reference that to a degree.
• 18:59
  I think this is definitely an area
• 19:01
  where we'll see a lot more developing.
• 19:03
  For us,
• 19:04
  the goal is always to
• 19:05
  capture the total market.
• 19:06
  And that's not just from a data
• 19:09
  or performance
• 19:09
  metric standpoint,
• 19:10
  but then also how granularly
• 19:11
  we can detail the assets themselves,
• 19:13
  the sectors
• 19:14
  that we're covering, hopefully,
• 19:15
  to give you more
• 19:15
  and more of those inputs.
• 19:16
  And I think that's the next evolution,
• 19:19
  I would say, right?
• 19:20
  So this is now -- if you think about
• 19:23
  – you are flying the
• 19:23
  flag for private markets, you say, okay,
• 19:25
  so now you're starting to
• 19:25
  model out against
• 19:28
  public.
• 19:29
  It's a total return methodology.
• 19:33
  It's not a sort of specific
• 19:35
  private market IRR or any type of private
• 19:38
  market-only equation that's used.
• 19:40
  I don't think those will go away.
• 19:42
  But in order for people
• 19:44
  sort of understand
• 19:44
  and be able to do
• 19:45
  proper SAA,
• 19:46
  they need to be consistent, right?
• 19:48
  And yes, there's --
• 19:50
  you need to look at these.
• 19:51
  And as I said, this isn't dogmatic.
• 19:52
  This is not something that we say
• 19:53
  this is the outcome, this is the SAA.
• 19:55
  It's more
• 19:56
  this is your sort of a skeleton
• 19:57
  of how we would view
• 19:58
  it based on the data.
• 19:59
  And then you start to
• 20:00
  put the meat on the bones
• 20:01
  of this thing and say, okay,
• 20:03
  there's lateral movement from the asset
• 20:04
  allocator, there's
• 20:04
  lateral movement from the fund manager
• 20:06
  to do what you want.
• 20:06
  But fundamentally,
• 20:07
  this didn't exist before and now
• 20:09
  starting to come to fruition.
• 20:12
  But if that's the sort of input, right,
• 20:14
  the other side of it
• 20:15
  is, to your point,
• 20:16
  Chris, on the output
• 20:17
  and starting to assess
• 20:18
  performance, right?
• 20:19
  And this is where I really think --
• 20:21
  and when I spent
• 20:23
  4 years at IPD,
• 20:25
  I was presenting to clients.
• 20:27
  So IPD, again,
• 20:29
  this sort of index provider
• 20:30
  that was hovering up private data
• 20:32
  on behalf of private investors.
• 20:37
  It wasn't a forecasting shop.
• 20:38
  It was a consultancy service
• 20:40
  that basically diagnosed performance.
• 20:41
  So it was very much backward looking.
• 20:43
  But I probably did
• 20:44
  up to maybe 100 presentations
• 20:46
  a year on portfolios.
• 20:48
  And you're sitting down
• 20:50
  with investors
• 20:51
  through the good times in '06,
• 20:53
  through wobbly times in '07,
• 20:55
  the bad times in '08
• 20:56
  and then out the other side, right?
• 20:58
  And you see -- you learn a lot.
• 21:00
  And there's a lot of behaviors
• 21:02
  that you saw that --
• 21:03
  and lessons learned from that experience.
• 21:06
  And a big one was
• 21:08
  around performance and investors moving
• 21:12
  or having some sort of viable reference
• 21:14
  that they could show
• 21:14
  that they were good managers.
• 21:16
  So even though -- I mean,
• 21:18
  we're in London today,
• 21:19
  that market took a 40%
• 21:21
  drawdown during the financial crisis
• 21:23
  from a capital value point of view,
• 21:24
  there were some investors out there
• 21:26
  that probably lost money
• 21:28
  because of higher quality product,
• 21:31
  better underwriting on their tenants,
• 21:32
  better location,
• 21:33
  whatever it might have been, the factors
• 21:35
  that influence
• 21:35
  that haircut on their values
• 21:37
  to not be as severe.
• 21:38
  -- they want to show investors
• 21:40
  that actually they were good.
• 21:42
  So yes, it was bad,
• 21:43
  but they were much better off
• 21:45
  than the market average.
• 21:46
  Now that's 40% average for the index.
• 21:48
  There were some actually that it's around
• 21:51
  that 40%, right, which wasn't pretty.
• 21:53
  And it was difficult for investors to --
• 21:55
  and fund managers to handle and explain.
• 21:58
  But having viable data to do that
• 22:00
  and reliable data to show
• 22:01
  that is critical, right?
• 22:03
  And I think when you get into
• 22:05
  the realms of public as well,
• 22:06
  then you start to get into things
• 22:08
  around attribution.
• 22:09
  So not just
• 22:10
  absolute returns and thinking,
• 22:12
  okay, well, the market did minus 40%
• 22:14
  on the capital, but we did minus 20.
• 22:16
  It's okay, well, where and why?
• 22:19
  And how is that important, right?
• 22:20
  And as private markets becomes
• 22:22
  more sophisticated,
• 22:23
  right, as more capital flows into this,
• 22:25
  not just from the traditional
• 22:26
  institutional channels, but also from DC,
• 22:29
  which is a huge growth
• 22:30
  channel globally,
• 22:30
  but also retail channels as well, wealth
• 22:34
  advisory, massive opportunity
• 22:35
  for the private market space.
• 22:37
  they are used to seeing diagnosis
• 22:40
  of performance, not just, well,
• 22:43
  here I am, here's a number,
• 22:44
  and I beat this number.
• 22:44
  It's actually, well, why did you do that?
• 22:46
  And actually,
• 22:47
  how is your risk control working?
• 22:48
  So the better data
• 22:50
  you have,
• 22:50
  whether it's in the private credit space
• 22:52
  or in the credit space
• 22:53
  or the equity space
• 22:54
  and the ability to slice and dice
• 22:56
  that and diagnose
• 22:57
  that is absolute absolutely critical.
• 22:59
  And we see it now.
• 23:00
  We see it
• 23:01
  now where you can do that and say,
• 23:02
  okay, well, the reason we did
• 23:04
  well here is
• 23:05
  because we weren't
• 23:05
  invested in this market
• 23:07
  or the reason we did
• 23:08
  well is because we're overweight
• 23:09
  in this market.
• 23:10
  It's very, very powerful,
• 23:11
  not just for getting people up
• 23:14
  to speed with private markets
• 23:15
  and how it operates,
• 23:15
  but also from a manager
• 23:17
  fundraising point of view
• 23:18
  in terms of saying
• 23:18
  I see we're pretty good at this
• 23:20
  and we make the right call,
• 23:21
  whether that's a thematic call,
• 23:23
  whether that's a cyclical call
• 23:24
  or something...
• 23:25
  Can explain what we do
• 23:26
  and what we don't do.
• 23:28
  -- which is bread and butter for the
• 23:31
  public space, right?
• 23:32
  -- overweight
• 23:33
  tech, we're underweight tech,
• 23:34
  whatever it is,
• 23:35
  you can do that very easily
• 23:36
  and show should have been here.
• 23:38
  I was overweight here,
• 23:38
  this was a benefit.
• 23:40
  Private is getting there,
• 23:41
  and it will only benefit the sector
• 23:43
  as it gets more
• 23:44
  and more pronounced. Yes.
• 23:45
  And that track record that you have,
• 23:47
  that's another data
• 23:48
  point, too. Exactly, Jocelyn, exactly.
• 23:50
  And that's, again, to the point on DC
• 23:53
  with evergreen funds,
• 23:55
  a lot of these funds favor evergreen.
• 23:57
  That track record is important, right?
• 23:59
  Because they're
• 24:01
  constantly investing into this vehicle
• 24:03
  as opposed to sort of one set of DD
• 24:05
  and then 5, 7 years
• 24:07
  later, there's potentially
• 24:09
  another fundraising...
• 24:12
  Let's talk about investments
• 24:14
  for a minute.
• 24:15
  So in discussing Aviva,
• 24:16
  obviously, the insurance
• 24:18
  side of the business is immense,
• 24:21
  and you have a lot of
• 24:21
  insurance-based capital in your funds.
• 24:24
  You talked about a strategy of
• 24:26
  insurance-backed
• 24:27
  buy and maintain for private debt.
• 24:29
  Can you talk to us
• 24:30
  about how that expresses itself
• 24:32
  in the portfolio,
• 24:33
  how you handle portfolio construction?
• 24:35
  Just
• 24:36
  what --
• 24:36
  how does that
• 24:37
  inform your strategy overall?
• 24:39
  Yes.
• 24:40
  So it does a number of things, Chris.
• 24:42
  We talked about this
• 24:43
  sort of intel from the shop floor.
• 24:46
  It obviously
• 24:47
  means that we're very active
• 24:48
  in the market.
• 24:49
  And so
• 24:50
  Aviva being one of the largest insurers
• 24:52
  in the U.K.,
• 24:55
  absolutely, there's a lot of private debt
• 24:58
  originated on behalf of the insurance
• 25:00
  parent, absolutely.
• 25:03
  That tends to be
• 25:04
  liability driven the buy and maintain.
• 25:07
  And so it tends to be investment grade.
• 25:09
  A large portion of it
• 25:11
  is asset-backed as well,
• 25:12
  so I think
• 25:12
  infrastructure and real estate.
• 25:14
  And there's also an element of senior
• 25:16
  corporate and structured funds.
• 25:18
  So the majority of the book from
• 25:20
  that slice is investment grade.
• 25:24
  Now there is a
• 25:26
  subsector to
• 25:27
  that, which is the fund space, which is
• 25:31
  a multi-sector private debt approach.
• 25:33
  So in a way -- and I would say that
• 25:35
  how does the --
• 25:36
  so the question is how does the
• 25:39
  insurance book sort of drive
• 25:40
  the strategy?
• 25:41
  I would say
• 25:41
  it helps us get visibility
• 25:43
  on the market dynamics.
• 25:44
  So we originated
• 25:49
  almost $5 billion last year in U.K.
• 25:52
  and Europe on behalf of the
• 25:55
  insurance book,
• 25:56
  but in terms of our total private
• 25:57
  debt exposure,
• 25:58
  that's a pretty typical run
• 25:59
  rate for us anywhere
• 26:00
  between $4 to $5 billion -- so we're
• 26:02
  constantly in the market doing this.
• 26:05
  Where it really starts to sort of
• 26:08
  manifest itself is in the multi-sector
• 26:10
  private debt.
• 26:10
  So what it's done is we've done
• 26:12
  this -- we've been
• 26:13
  working on this --
• 26:14
  or we've been in this sector
• 26:14
  for over 40 years.
• 26:16
  And we've got a huge proprietary database
• 26:19
  that we use.
• 26:20
  And this is something that we do share
• 26:22
  snippets with the market,
• 26:23
  the illiquidity premium.
• 26:24
  We release it every quarter.
• 26:26
  But effectively, we've got over
• 26:28
  2,000 deals.
• 26:29
  We know the excess spread
• 26:31
  we would get over a commensurate public
• 26:33
  proxy, and we know that by
• 26:37
  investment banding,
• 26:38
  and we also know it by duration.
• 26:41
  And so we can get a feel for
• 26:44
  how these asset
• 26:45
  classes are behaving through
• 26:46
  cycles, particularly private debt.
• 26:49
  We actually talked a lot about equity,
• 26:51
  private debt.
• 26:53
  this database really helps us
• 26:56
  think about how we approach
• 26:58
  portfolio construction
• 26:59
  from a multi-sector private debt.
• 27:00
  So back to your point on the data
• 27:02
  set that you're building out
• 27:03
  with Mercer and Cambridge,
• 27:06
  this is very much proprietary data
• 27:08
  where we can see the different spreads
• 27:10
  we're achieving across the cycle.
• 27:13
  And what we can see from this
• 27:15
  is that illiquidity premium, which,
• 27:18
  I mean,
• 27:18
  goes back to our sort of university
• 27:20
  days, it's
• 27:20
  not some sort of a static variable.
• 27:22
  It's very much dynamic,
• 27:24
  and it does ebb and flow.
• 27:25
  And actually,
• 27:25
  the different characteristics
• 27:26
  of private debt
• 27:28
  have different
• 27:29
  sort of pinch points
• 27:30
  with illiquidity premium,
• 27:31
  the excess spread
• 27:32
  you receive over that public proxy.
• 27:34
  So something like real estate
• 27:36
  and infrastructure,
• 27:36
  unsurprisingly is very, very sticky.
• 27:39
  So it tends to reprice quite slowly.
• 27:41
  So what tends to happen with
• 27:43
  real estate, for example, and for a 2
• 27:45
  degree is
• 27:47
  public spreads will tighten quickly
• 27:48
  and the private space won't react.
• 27:50
  -- quick.
• 27:51
  So you end up with a sort of exaggerated
• 27:53
  or a higher illiquidity
• 27:54
  premium, you can lock that in through the
• 27:56
  structure of the credit.
• 28:00
  Private corporate debt
• 28:01
  structured finance,
• 28:02
  much more dynamic, right?
• 28:03
  -- tends to move much more -- priced
• 28:05
  much more like public.
• 28:06
  That's unsurprising.
• 28:07
  The deals can be done quicker, et cetera.
• 28:09
  So that makes sense.
• 28:10
  And so actually, what you find is
• 28:12
  whilst the spread --
• 28:13
  the average spread achievable is higher
• 28:16
  for something
• 28:17
  like a private corporate debt,
• 28:19
  the volatility of that is also elevated.
• 28:20
  So back to our --
• 28:21
  to go back to coefficient variation
• 28:23
  risk over return.
• 28:24
  But fundamentally, the ratio
• 28:27
  is predicated on a higher volatility
• 28:31
  number through that cycle
• 28:32
  or through the cycle,
• 28:33
  whereas infrastructure
• 28:34
  and real estate, whilst the long-term
• 28:37
  average is lower,
• 28:38
  it tends to not be as volatile.
• 28:40
  So it's more stable.
• 28:41
  It does move.
• 28:42
  It does have with the real estate
• 28:43
  cycle for sure,
• 28:44
  or wide infrastructure inflows, et
• 28:46
  cetera, but it's not to the same degree.
• 28:48
  So this sort of big book
• 28:51
  of origination
• 28:52
  on behalf of -- it's not just Aviva
• 28:54
  insurance, it's also other
• 28:55
  third-party managers
• 28:56
  that invest with us as well
• 28:58
  because of our breadth
• 29:00
  and depth in the market.
• 29:00
  We have other insurance
• 29:03
  partners
• 29:04
  alongside us
• 29:04
  that we originate for as well.
• 29:06
  But if you take a multi-sector approach
• 29:08
  to private debt,
• 29:09
  the data that we have
• 29:10
  can help us illustrate at
• 29:12
  what point is a good time to maybe
• 29:14
  go overweight to certain sectors.
• 29:15
  So back to the portfolio
• 29:16
  construction piece, back to the point
• 29:18
  around private debt not being a monolith.
• 29:21
  And actually,
• 29:21
  there's a lot of moving parts
• 29:22
  under the surface,
• 29:23
  something both of you
• 29:24
  will know very well.
• 29:25
  And this is really important around
• 29:27
  portfolio construction.
• 29:28
  So this is
• 29:29
  the sophistication of the private market
• 29:31
  space, zeroed in on private
• 29:33
  debt, where actually we look at, okay,
• 29:35
  we know how these spreads
• 29:36
  are going to move or expect to move,
• 29:38
  we should be
• 29:38
  focusing efforts here or there.
• 29:41
  And when you think of
• 29:42
  the private market space,
• 29:44
  because it's private,
• 29:45
  that means that it's not as transparent
• 29:48
  as the public markets, but
• 29:50
  you are bringing in all this data.
• 29:52
  You mentioned the proprietary data
• 29:54
  set that you have.
• 29:55
  What is the balance that you have
• 29:58
  of giving transparency
• 30:00
  to those who need it.
• 30:01
  So whether it's your partners
• 30:03
  that you're investing on behalf of,
• 30:04
  you mentioned
• 30:05
  external insurance companies as well.
• 30:08
  What level of transparency
• 30:10
  can you provide
• 30:11
  because that has to be
• 30:12
  somewhat of a differentiator
• 30:13
  when you're able to give comfort in
• 30:15
  the fact that you have transparency
• 30:18
  and are willing to
• 30:18
  at least provide
• 30:20
  some of it to help with explanations?
• 30:22
  Yes. Great question.
• 30:23
  And I think something
• 30:24
  -- my view on this,
• 30:25
  Jocelyn, and again, 20 years in privates,
• 30:28
  working for a company in the past,
• 30:30
  at least that was
• 30:32
  their business case was confidentiality
• 30:34
  that REITs and port
• 30:36
  cos and private vehicles
• 30:38
  submitted data into a confidential pot,
• 30:40
  and I'm sure your business is the same.
• 30:43
  And -- I feel like the private markets
• 30:46
  focus on confidentiality
• 30:47
  is maybe a little bit overcooked.
• 30:49
  And I think it varies a little too
• 30:52
  between some groups or some investors,
• 30:56
  I think,
• 30:56
  are a little more transparent
• 30:58
  than others because going back 20 years
• 31:01
  when I was in the asset management,
• 31:03
  I sat with some of the investors
• 31:05
  and gave them transparency,
• 31:06
  but it wasn't all of them.
• 31:08
  So I think that
• 31:10
  matters as well,
• 31:11
  the relationship as you have.
• 31:12
  But correct me
• 31:13
  if you see something different.
• 31:14
  I think we're in agreement there.
• 31:16
  I think where it's going
• 31:17
  is more transparency.
• 31:18
  And I think that's the way --
• 31:20
  and that's the way that Aviva Investors
• 31:21
  definitely approach it.
• 31:22
  The fact that we
• 31:23
  release our illiquidity
• 31:25
  premium,
• 31:26
  we don't give obviously
• 31:27
  the fully behind the curve of the data,
• 31:29
  but you can look at the calendar year
• 31:31
  average for 2025
• 31:32
  and see what the illiquidity
• 31:33
  premium was for infrastructure debt
• 31:36
  in Europe versus real estate
• 31:37
  debt in Europe, right?
• 31:37
  Like nobody else is doing that.
• 31:39
  And we just felt we've got this data.
• 31:42
  rising
• 31:43
  tide lifts all boats to a degree,
• 31:44
  this helps the wider market.
• 31:46
  But clearly,
• 31:47
  we then can go to investors
• 31:48
  and say, look, we're sharing this.
• 31:50
  We've got this edge here.
• 31:51
  We've got this data.
• 31:52
  We use this,
• 31:53
  and we can show them that we use it.
• 31:54
  And the level of sophistication
• 31:56
  we can do back to the modelling point,
• 31:58
  we can
• 31:58
  then run optimal portfolios, right?
• 32:00
  So we run optimal portfolios
• 32:02
  for multi-sector
• 32:03
  strategies in private debt, for example,
• 32:05
  because we've got a view of, a,
• 32:07
  where
• 32:08
  potentially spreads are trading, right?
• 32:10
  We have a view.
• 32:10
  Obviously,
• 32:11
  we know where the rate environment
• 32:12
  is going or
• 32:12
  certainly environment is going.
• 32:14
  We know where rates are today
• 32:16
  and potentially where they could go.
• 32:17
  Clearly,
• 32:17
  if it's
• 32:18
  floating, that changes
• 32:19
  some of the projections going forward.
• 32:20
  But also,
• 32:21
  we also know
• 32:22
  the potential liquidity premium.
• 32:23
  So this helps us navigate that.
• 32:25
  So Jocelyn,
• 32:26
  I think what we've seen
• 32:27
  is an evolution within the market of --
• 32:29
  there's a lot more data
• 32:30
  providers out there, for sure.
• 32:33
  There's a lot of
• 32:37
  shops that
• 32:37
  are looking to have an angle with data,
• 32:41
  right?
• 32:42
  a lot of it, I would say, is absent of,
• 32:45
  I think, what we're speaking to Chris
• 32:46
  about a minute ago
• 32:47
  around the sort of long history of deep
• 32:49
  data, right?
• 32:50
  So some of it will be specific
• 32:54
  niches in private markets
• 32:55
  or might be consultancy services,
• 32:57
  but there's not
• 32:58
  the sort of what we're after
• 32:59
  is the sort of big, deep data
• 33:00
  sets where we can really
• 33:01
  dig drill into it.
• 33:02
  Now arguably,
• 33:03
  if we were a smaller investment house
• 33:05
  or I was a team of one, I may need that.
• 33:07
  But for a team,
• 33:09
  the depth and breadth of my team, we need
• 33:12
  probably the baseline data
• 33:13
  to be able to build off
• 33:14
  and make our --
• 33:14
  but on the transparency point,
• 33:17
  we will pull in third-party data.
• 33:18
  We buy that in from various sources.
• 33:20
  We then augment that with the
• 33:23
  bottom
• 33:23
  up or the shop floor intel that we have.
• 33:25
  We then put it through the various models
• 33:27
  that we have, and we do play that back
• 33:29
  to investors.
• 33:30
  And investors,
• 33:31
  particularly on the DC side
• 33:34
  and more sophisticated plans
• 33:36
  will ask for, okay, let's
• 33:38
  see how this works.
• 33:38
  Let's see what assumptions have you made?
• 33:40
  And I think the whole thing around
• 33:42
  private markets
• 33:43
  is this education point, right?
• 33:45
  And you've got very, very clever people
• 33:47
  who are new to the sector, right?
• 33:50
  And they're asking very valid questions
• 33:52
  about how it works,
• 33:53
  and I think that's fair, right?
• 33:54
  And so I think from a private markets,
• 33:56
  we've got to help them out.
• 33:59
  We've covered a lot of ground
• 34:00
  talking about data.
• 34:01
  There is one source of data
• 34:03
  that we haven't touched on,
• 34:03
  which I'm thrilled to here,
• 34:05
  and that is survey data.
• 34:07
  So Aviva recently released
• 34:09
  a survey of institutional investors.
• 34:11
  Would love for you
• 34:12
  to take us through
• 34:12
  some of the information
• 34:13
  you got
• 34:14
  on attitudes
• 34:15
  toward the space, expectations,
• 34:16
  just what other conclusions you
• 34:18
  drew from that.
• 34:19
  Yes. No, it's
• 34:22
  a really interesting study, Chris.
• 34:23
  So our study is done
• 34:25
  every year,
• 34:28
  500 institutional investors
• 34:29
  around the world, $6.5 trillion of AUMs.
• 34:33
  And this covers
• 34:34
  insurance, sovereign wealth,
• 34:37
  DC and DB plans.
• 34:39
  and really
• 34:41
  only focused on private markets,
• 34:43
  but we control the questions I'm involved
• 34:46
  in the survey construction,
• 34:47
  and we can ask questions that,
• 34:48
  quite frankly, I'm interested in
• 34:50
  sometimes proving points or
• 34:52
  investigating things
• 34:53
  a little bit further.
• 34:54
  So main takeaway
• 34:55
  and the fact we're on this podcast
• 34:58
  today is that private markets
• 34:59
  remains in favor very much.
• 35:01
  So I think around
• 35:02
  88% of investors across that entire $6.5
• 35:05
  trillion
• 35:06
  are looking to either hold
• 35:07
  or increase allocations.
• 35:08
  Now that hold is
• 35:12
  running at about
• 35:13
  12.5% is the average allocation
• 35:15
  to privates across that data set.
• 35:19
  North America
• 35:20
  is slightly higher, around 14.5%.
• 35:22
  Asia Pac, slightly lower.
• 35:24
  Europe is around that 12% rate.
• 35:26
  So that's come from roughly
• 35:28
  10% 2, 3 years ago.
• 35:30
  So again,
• 35:31
  this growth has been material
• 35:33
  for private markets.
• 35:34
  So that's probably one takeaway
• 35:36
  that private markets
• 35:37
  very much remains
• 35:38
  in the crosshairs of investor
• 35:41
  thinking around the globe.
• 35:44
  The second one,
• 35:45
  I think, was a major
• 35:45
  takeaway from the study was around
• 35:47
  actually illiquidity premium.
• 35:48
  I know we've touched on this a lot,
• 35:50
  but it was really interesting
• 35:51
  that this was now the second most reason
• 35:54
  to allocate to private markets
• 35:55
  across the survey.
• 35:57
  Number one was diversification.
• 36:00
  So my thinking on this is
• 36:01
  if you think about how --
• 36:03
  if you were new to private markets,
• 36:05
  let's say,
• 36:06
  and you didn't have necessarily
• 36:09
  a full understanding.
• 36:10
  And as we talked about,
• 36:12
  it's still quite opaque.
• 36:13
  There's still areas
• 36:14
  where it is uncertain for sure,
• 36:15
  and you are going to get paid
• 36:16
  through the excess return
• 36:17
  you should achieve.
• 36:19
  But fundamentally,
• 36:20
  diversification is the #1
• 36:22
  reason investors look at private markets.
• 36:23
  That intuitively makes sense, right?
• 36:25
  Some of these
• 36:26
  sectors are
• 36:27
  less linked to the business cycle.
• 36:29
  Some of them have different
• 36:30
  demand drivers and supply considerations
• 36:33
  than other asset classes for sure.
• 36:35
  So that kind of intuitively makes sense,
• 36:37
  I don't know if anyone needs data
• 36:38
  to prove that necessarily.
• 36:39
  It's helpful
• 36:40
  when it's there
• 36:40
  and the data is getting better.
• 36:42
  But I think intuitive,
• 36:43
  you could probably get there.
• 36:44
  illiquidity premium, this was #10
• 36:47
  3 or 4 years ago, and now it's #2.
• 36:49
  So it's really shot up the ranking.
• 36:51
  And my view,
• 36:53
  and I was speaking to my team
• 36:53
  about this as well, the sort of consensus
• 36:55
  was that
• 36:56
  the data is just getting better, that
• 36:57
  although they're not nailing the
• 37:00
  illiquidity premium to a sort of third
• 37:02
  decimal place,
• 37:03
  they're starting to see with better data,
• 37:05
  the relationship with public,
• 37:07
  they're starting to see more ability
• 37:11
  to start to unpick that
• 37:12
  and triangulate
• 37:13
  that using different data sources.
• 37:14
  So part of this will just be
• 37:16
  there's more assets in the market.
• 37:17
  There's more people looking at this.
• 37:19
  Therefore, naturally,
• 37:20
  people start to say, okay,
• 37:21
  how can I sort of measure this, right?
• 37:22
  That makes sense.
• 37:23
  But secondly, combine
• 37:24
  that with more data.
• 37:25
  So it's more eyeballs and more data means
• 37:27
  actually people
• 37:28
  get more comfortable with this
• 37:29
  illiquidity is there.
• 37:31
  And therefore, it's
• 37:32
  become the second most reason
• 37:34
  to allocate.
• 37:35
  So that was probably the second big
• 37:37
  takeaway from the study.
• 37:41
  The third
• 37:41
  one was the rise of evergreen funds.
• 37:44
  Evergreen funds, a large majority
• 37:47
  saw the rise of Evergreen funds
• 37:48
  is here to stay.
• 37:50
  So this is quite a shift in terms
• 37:52
  of how
• 37:53
  investors have been thinking
• 37:54
  about private markets access historically
• 37:56
  and the rise of Evergreen
• 37:57
  was something that was really,
• 37:58
  really clear in the survey.
• 38:00
  And I think that makes sense, right?
• 38:02
  I think when you think
• 38:02
  about what Evergreens offer,
• 38:04
  and we talked about DC solutions,
• 38:06
  but also just wider
• 38:09
  investment committees
• 38:09
  having to do
• 38:10
  a renewed round of due diligence
• 38:12
  or bring new things to market or
• 38:14
  you've got a seed fund ready to go
• 38:17
  that you can invest in on a regular basis
• 38:20
  and potentially
• 38:21
  arguably at some point
• 38:22
  when a DC is going to wind down,
• 38:24
  redeem, right?
• 38:25
  So this is
• 38:26
  -- there's a natural sort of partnership
• 38:29
  there in our view with that DC and that
• 38:32
  evergreen structure.
• 38:33
  And that was clear in the survey
• 38:35
  that DC investors were clearly looking
• 38:36
  at that Evergreen as
• 38:39
  an access here to stay for private.
• 38:42
  I think some parts of the private markets
• 38:45
  landscape maybe don't lend themselves
• 38:46
  so well to
• 38:50
  evergreen.
• 38:51
  If you think about
• 38:52
  maybe something
• 38:53
  like real estate development,
• 38:54
  which is much more buy it, fix it,
• 38:56
  potentially sell it,
• 38:58
  back to the journey of the NAV
• 39:00
  and what investors
• 39:01
  want out of that, that lends itself
• 39:03
  more towards, in my view,
• 39:04
  an IRR type structure
• 39:05
  where you're thinking about, okay,
• 39:06
  what's my return on capital
• 39:08
  in 7 years, let's say, or 5 years,
• 39:10
  whereas a sort of
• 39:11
  more core stabilized product,
• 39:13
  you can see the rationale for Evergreen,
• 39:15
  where investors are looking at
• 39:16
  what's the NAV volatility
• 39:17
  of this vehicle on a quarterly basis,
• 39:19
  whatever it might be and how does that
• 39:22
  relationship work
• 39:22
  with the rest of my market book.
• 39:24
  So those are probably the
• 39:27
  illiquidity premium,
• 39:28
  the evergreen structure is the other one.
• 39:30
  And then actually, the rise of infra.
• 39:31
  The rise of infra equity
• 39:34
  was probably another
• 39:35
  big takeaway from the study.
• 39:37
  That was the #1 destination for capital
• 39:39
  across the board and for equity
• 39:41
  in particular.
• 39:43
  And again,
• 39:45
  this was done
• 39:46
  -- the survey was probably done
• 39:47
  in November, December, results
• 39:51
  come sort of
• 39:51
  come out in February,
• 39:52
  March time,
• 39:53
  and here we are in May speaking about it.
• 39:55
  And infrastructure
• 39:56
  is a really interesting one.
• 39:57
  This one,
• 39:57
  I think, has evolved in investor
• 40:00
  mindsets as well and what it can deliver.
• 40:02
  And I think through the hiking cycle,
• 40:05
  I think infrastructure really proved
• 40:07
  its sort of defensive
• 40:09
  characteristics, right?
• 40:10
  You think about the reasons
• 40:11
  why the hiking cycle was triggered.
• 40:14
  There was coming out of COVID,
• 40:16
  there was a demand shock,
• 40:17
  there was a surplus
• 40:18
  demand that opened up.
• 40:19
  There was a big impulse on demand,
• 40:22
  but also energy shocks
• 40:24
  from -- particularly in Europe,
• 40:25
  I'm the European lens,
• 40:26
  there was the war in Ukraine
• 40:28
  sort of putting a choke point
• 40:30
  on energy from Russia.
• 40:32
  So that caused a material energy spike.
• 40:35
  But if you think about the reasons why
• 40:38
  infra held up
• 40:38
  well was actually a lot of the cash flows
• 40:41
  either
• 40:41
  have some sort of implicit inflation
• 40:43
  linkage or explicit inflation linkage
• 40:45
  or they're linked to energy,
• 40:46
  they're energy producers, right?
• 40:47
  And so despite rising discount rates
• 40:50
  for infra,
• 40:51
  that was offset
• 40:52
  by the other side of the discount
• 40:53
  rate equation, which is cash flows.
• 40:55
  So infra,
• 40:55
  and we can see this through data
• 40:57
  back to the data point,
• 40:57
  we're starting to get better
• 40:58
  data on infrastructure equity.
• 41:00
  And we can see that
• 41:02
  relative to another equity
• 41:03
  class, a hard asset
• 41:04
  like real estate,
• 41:05
  infrastructure held up much,
• 41:06
  much better from that sort
• 41:08
  of less correlation to that sort of real
• 41:10
  re-rating that experience.
• 41:12
  So that's the other big one,
• 41:14
  I would say
• 41:14
  that infrastructure has been there.
• 41:16
  And if you look at allocations
• 41:17
  more broadly for investors,
• 41:19
  you could argue that infrastructure
• 41:21
  underweight infrastructure.
• 41:22
  And so whether this is
• 41:23
  also a part of the catch-up
• 41:25
  that real estate
• 41:26
  and PE have been
• 41:27
  sort of dominant homes
• 41:28
  for -- outside of private
• 41:29
  credit in the equity space
• 41:30
  has been mainly private equity
• 41:31
  and real estate equity, there's
• 41:34
  clearly space emerging now
• 41:37
  for infrastructure equity as well.
• 41:39
  Yes.
• 41:39
  And then it goes back to that kind
• 41:41
  of rebalancing question, too,
• 41:43
  because you think of private markets
• 41:44
  and it's like
• 41:45
  we are in the private markets,
• 41:46
  do you want to invest?
• 41:48
  And there's so many options.
• 41:50
  So how do you choose the right one
• 41:52
  based again on the strategy
• 41:55
  that you're seeking or the returns
• 41:57
  that you're looking to achieve?
• 41:59
  David, another thing
• 42:01
  I'm curious about, too, is when investors
• 42:04
  want to get into private markets
• 42:06
  or they want to expand their footprint,
• 42:09
  there's operational challenges
• 42:10
  that are associated with that.
• 42:12
  So when you're speaking with investors,
• 42:15
  what types of questions
• 42:16
  are you
• 42:17
  getting about some of the challenges
• 42:20
  that they're facing with getting into
• 42:22
  private markets and being able to support
• 42:24
  that operationally? Yes.
• 42:27
  It's a good question, Jocelyn.
• 42:29
  I think -- I would say,
• 42:31
  actually,
• 42:31
  you probably hinted at it
• 42:32
  with your question actually
• 42:35
  is to me, the big one is
• 42:38
  a lot --
• 42:39
  the private markets
• 42:40
  is not sort of a single entity.
• 42:42
  So I would say if there's one barrier,
• 42:44
  it's education and knowledge.
• 42:47
  And as I mentioned,
• 42:48
  there's some very clever people here
• 42:50
  who are looking at the sector
• 42:51
  for the first time,
• 42:52
  and they're asking
• 42:53
  very valid questions around
• 42:54
  how does this work, right?
• 42:55
  And that's totally acceptable.
• 42:57
  But I think in terms of more momentum
• 43:00
  for private asset class as an entity,
• 43:02
  whether that's any component underneath,
• 43:04
  there's an education here.
• 43:05
  So you're
• 43:06
  right, people do
• 43:07
  look at private markets
• 43:08
  as a monolith, right?
• 43:09
  They'll say,
• 43:10
  okay, this is --
• 43:10
  but it's --
• 43:11
  you can't allocate
• 43:12
  to private markets, right?
• 43:13
  And even as you peel the onion further,
• 43:15
  you can allocate to
• 43:18
  German real estate, right?
• 43:19
  And further
• 43:20
  and then you get down to a single asset
• 43:21
  in Berlin, right?
• 43:22
  So that's,
• 43:23
  I think, really,
• 43:24
  really interesting
• 43:25
  when you start to do that education.
• 43:26
  So from an operational point of view,
• 43:28
  and as we start this journey
• 43:30
  with a lot of investors that we have
• 43:31
  and a lot of investors
• 43:31
  that we're speaking to, -- it is about,
• 43:33
  okay, let's break this down.
• 43:34
  And the best way to do that
• 43:36
  and the language that they speak
• 43:38
  is data, right?
• 43:40
  It comes back to showing
• 43:42
  the classic example
• 43:43
  I often point to,
• 43:43
  Jocelyn, is let's
• 43:45
  just put up a scatter
• 43:45
  plot of risk and return
• 43:47
  on a 10-year basis,
• 43:48
  right, for the various public equity
• 43:52
  -- public
• 43:52
  equity sectors or
• 43:53
  indices around the world. And
• 43:57
  your private counterpart
• 43:58
  is --
• 43:59
  has some challenge or there's no dot
• 44:01
  because there's no data, right?
• 44:02
  And so slowly, over time,
• 44:05
  we're starting to get to ways
• 44:06
  where we can des-smooth.
• 44:07
  We go to understand
• 44:08
  the limitations
• 44:09
  of some of the valuation approaches
• 44:11
  and the smoothing nature
• 44:11
  that comes with it.
• 44:12
  And you're starting
• 44:13
  to put those dots onto that scatter plot.
• 44:15
  And that is so beneficial
• 44:17
  when you sit down with investors and
• 44:19
  say, right,
• 44:19
  this is the landscape
• 44:20
  we're talking about here.
• 44:21
  And then it comes back, okay,
• 44:22
  what do you want?
• 44:23
  And actually,
• 44:24
  I think what private markets
• 44:25
  can really offer
• 44:26
  investors is solutions, right?
• 44:28
  So we talked about the liability piece.
• 44:30
  earlier, Chris, that is effectively
• 44:33
  a solution
• 44:34
  that private markets can offer investors,
• 44:35
  whether that's floating
• 44:37
  rate income, whether that's fixed, right?
• 44:40
  Again,
• 44:41
  different investors
• 44:41
  want different things,
• 44:42
  whether that's long duration,
• 44:43
  short duration.
• 44:44
  You can bespoke this
• 44:46
  and you can originate
• 44:46
  based on that basis, right?
• 44:48
  And if you want that value add,
• 44:49
  then you go down the equity space
• 44:51
  looking for that sort of
• 44:53
  -- maybe it's a real estate development,
• 44:54
  maybe it's some buildup
• 44:55
  from an infrastructure point of view,
• 44:56
  whatever it might be.
• 44:57
  But yes, operations, I think
• 44:59
  that's probably the main bit.
• 45:01
  I think particularly for Europe,
• 45:04
  the regulator, and I don't
• 45:05
  want to get too
• 45:05
  into the weeds of sort
• 45:06
  of jurisdictional differences.
• 45:07
  I think we've been pretty good
• 45:08
  about speaking more broadly
• 45:09
  across the globe here.
• 45:10
  But there has been sort of
• 45:12
  jurisdictional implementation
• 45:14
  of private market asset
• 45:15
  classes through the LTAF, long-term asset
• 45:17
  funds and the LTIF,
• 45:18
  which is the European equivalent.
• 45:19
  These are effectively
• 45:22
  structure is designed
• 45:23
  to hold private market asset classes
• 45:26
  that can be held by various
• 45:28
  institutions slightly
• 45:29
  easier, for example.
• 45:30
  And there's very clear liquidity
• 45:32
  requirements.
• 45:32
  So they're evergreen in a sense,
• 45:34
  but there's rules around
• 45:36
  redemption notices,
• 45:37
  which are very, very clear.
• 45:38
  And that structure
• 45:39
  means that they're authorized,
• 45:40
  which means it's helpful
• 45:41
  from at least
• 45:42
  some operational point of view.
• 45:43
  But fundamentally,
• 45:45
  the valuations are different, Jocelyn.
• 45:48
  So when you think about any
• 45:49
  private market,
• 45:50
  so valuing
• 45:51
  infra deal will be different
• 45:52
  to a real estate deal,
• 45:53
  which would be different
• 45:54
  to any sort of private credit
• 45:55
  deal, right?
• 45:56
  So there's different
• 45:56
  valuation approaches.
• 45:57
  So again,
• 45:59
  private markets
• 45:59
  value the same way, right?
• 46:00
  It's not done on the same basis.
• 46:02
  So this again poses a chance.
• 46:04
  So it is an education.
• 46:06
  It's an education.
• 46:07
  But I think to
• 46:08
  me, the story will sell itself
• 46:10
  when investors can see what it can bring,
• 46:12
  the diversification
• 46:13
  that it can bring
• 46:14
  and also depending on what they want,
• 46:16
  whether that's the return spectrum
• 46:17
  or the liability spectrum
• 46:18
  or somewhere
• 46:19
  in between,
• 46:20
  private markets can deliver that.
• 46:22
  And with private
• 46:24
  markets being so vast, too,
• 46:26
  and you probably touched on some of it,
• 46:29
  but why the popularity
• 46:30
  with the evergreen funds?
• 46:32
  So I touched on it briefly, yes, with the
• 46:36
  DC channels.
• 46:37
  But I just feel that
• 46:39
  from the perspective of investors
• 46:42
  with continual flow,
• 46:43
  whether that's DC or other providers,
• 46:46
  the fact that there's continual
• 46:47
  investment opportunity
• 46:50
  rather than a big tranche upfront
• 46:52
  and then potentially not seeing
• 46:55
  that for a period of time.
• 46:55
  Now that's fine.
• 46:56
  Often the returns are
• 46:57
  what the investor wanted, but
• 46:59
  you're also getting a lot of capital back
• 47:01
  at the end.
• 47:02
  right,
• 47:03
  and then you have to find a home for
• 47:04
  that again. And that's not efficient.
• 47:06
  And I think given that the access routes
• 47:08
  are there
• 47:08
  and given the structures are in place,
• 47:10
  the evergreen funds are becoming more
• 47:13
  and more favorable
• 47:14
  because of that ability
• 47:15
  to arguably come in and come out.
• 47:17
  Now to be candid,
• 47:19
  there's challenges around that.
• 47:20
  There's got to be very, very clear
• 47:21
  guidelines on liquidity constraints
• 47:24
  and how money is --
• 47:26
  how capital is brought in
• 47:27
  and out of the funds.
• 47:28
  But selection of the right manager
• 47:30
  and the right vehicle
• 47:31
  and the right sort of
• 47:33
  requirements around those vehicles
• 47:34
  as long as investors are clear
• 47:36
  right going
• 47:36
  in, I think private markets
• 47:38
  can thread that needle.
• 47:41
  I'd like to circle
• 47:42
  back on infrastructure for a moment.
• 47:44
  I'm old enough to remember
• 47:45
  when data centers were a real estate play
• 47:48
  and infrastructure
• 47:49
  was roads and bridges...
• 47:51
  Right?
• 47:53
  The market has evolved so much
• 47:55
  since then,
• 47:55
  there are so many different flavors.
• 47:56
  Can you give us a sense
• 47:57
  of how you define infrastructure
• 47:59
  and how you have that conversation
• 48:00
  with investors
• 48:01
  and even with your own research team?
• 48:02
  Yes.
• 48:03
  So
• 48:05
  interesting, it relates
• 48:06
  to Jocelyn's question
• 48:07
  earlier around private markets.
• 48:09
  I guess we would view it as a
• 48:11
  does it --
• 48:12
  is it a solution for an investor?
• 48:13
  Is it giving the investor what they want?
• 48:15
  So there is a blurred line here for sure.
• 48:18
  And the line --
• 48:18
  the Venn diagram of real estate and infra
• 48:21
  is very much
• 48:22
  sort of increasingly overlapping.
• 48:23
  You touched on data centers there.
• 48:25
  We differentiate in the team.
• 48:27
  So I -- there is a sort of,
• 48:29
  let's call
• 48:30
  it, a primary researcher focusing
• 48:32
  on infra that sort of --
• 48:34
  and that team is doing
• 48:35
  the sort of deep dive
• 48:35
  into the infra sectors.
• 48:37
  But we also have a primary research team
• 48:38
  doing real estate, right?
• 48:40
  Now they talk to each other.
• 48:42
  They're both reporting to me,
• 48:43
  so I have visibility on what's happening.
• 48:45
  But it's probably more
• 48:46
  of a matrix structure, Chris.
• 48:47
  I think from an org chart,
• 48:48
  it kind of looks like a tree,
• 48:49
  but actually it's much more of a matrix
• 48:51
  and the teams are very much
• 48:52
  alive to this overlap
• 48:55
  and what characteristics it brings.
• 48:57
  But I think it is really interesting how
• 49:00
  Asset classes
• 49:00
  that were historically viewed
• 49:02
  as real estate
• 49:03
  are morphing into infrastructure.
• 49:05
  You touched on data centers.
• 49:07
  I worked on real estate
• 49:08
  funds 10 years ago
• 49:09
  that had data centers in there,
• 49:10
  and that was firmly considered
• 49:12
  a real estate asset.
• 49:13
  I think what's changed
• 49:15
  is the critical nature.
• 49:16
  There's a couple
• 49:17
  of things that have changed.
• 49:17
  One is the critical nature
• 49:19
  of data centers now
• 49:20
  and just how they're seen
• 49:22
  as more systemic
• 49:23
  and sort of economically material
• 49:26
  to the wider economy.
• 49:28
  I also think
• 49:29
  infrastructure, the roads, the bridges,
• 49:31
  the utilities,
• 49:33
  those were ring-fenced,
• 49:35
  high barriers to entry,
• 49:37
  inelastic demand, contractual cash
• 49:39
  flows,
• 49:39
  all the things
• 49:40
  that classic Infrastructure 1.0.
• 49:42
  But if you want to call it
• 49:43
  infrastructure 2.0 is much more around
• 49:46
  digital, around, yes,
• 49:48
  they need pricing power for sure,
• 49:50
  but highly predictable
• 49:52
  cash flows are also in the mix
• 49:54
  now, not just contractual,
• 49:55
  something that's highly predictable.
• 49:57
  And clearly,
• 49:58
  some of these asset classes that were
• 50:00
  there are moving towards
• 50:01
  that sort of infrastructure
• 50:03
  like characteristic.
• 50:03
  I think data centers is one,
• 50:05
  given how systemically important
• 50:07
  these are becoming
• 50:09
  or are in society.
• 50:11
  Another smaller example that maybe,
• 50:14
  I guess, for listeners
• 50:14
  kind of brings it home to life, maybe
• 50:17
  not such a behemoth
• 50:18
  as data centers is cold storage.
• 50:21
  right?
• 50:21
  So again, cold storage
• 50:24
  was viewed
• 50:24
  historically as a sort of real estate
• 50:27
  and arguably some sort of logistics,
• 50:29
  cold logistics, right,
• 50:30
  a cold logistics storage
• 50:31
  where a van would pick up
• 50:33
  anything required deep freezing or frozen
• 50:35
  product and move from place to place now.
• 50:37
  That has evolved very much to something
• 50:40
  and COVID
• 50:41
  kind of turbocharges
• 50:42
  because of the need
• 50:43
  for super cool vaccines.
• 50:44
  If you remember, the vaccine requirement
• 50:46
  had some of the vaccines
• 50:47
  needed to be extremely cold,
• 50:49
  and this became sort
• 50:51
  of mission-critical real estate.
• 50:52
  And as soon as that starts to happen,
• 50:54
  it then moves into infrastructure
• 50:56
  like these are supply chain critical.
• 50:58
  They're used for food
• 50:59
  and they're used for pharmaceuticals.
• 51:01
  And right away, they start to sort of
• 51:03
  come into the sort of
• 51:04
  crossover space said, okay, well,
• 51:06
  hang on, it's a building.
• 51:08
  It's got a lease,
• 51:09
  but the barriers to entry are high,
• 51:11
  the power requirements, again,
• 51:13
  this is becoming a sort of moat in itself
• 51:15
  is that if you have the power connection,
• 51:16
  whether it's a data center
• 51:18
  or something like cold storage, that's
• 51:20
  a really interesting dynamic now,
• 51:21
  which is creating its own moat
• 51:22
  that the power plug-in
• 51:23
  is critical for these things,
• 51:24
  and that creates its own
• 51:26
  defensive characteristic as well.
• 51:27
  So yes, the
• 51:30
  Venn diagram is overlapping.
• 51:31
  But I think when it comes back
• 51:32
  to the investor lens and saying,
• 51:34
  okay, well,
• 51:34
  how does this satisfy the investor
• 51:37
  requirement, right?
• 51:38
  And things
• 51:39
  like are
• 51:39
  probably where
• 51:41
  we would look at both across
• 51:42
  the business is things
• 51:43
  like potentially social housing, right?
• 51:45
  They're residential housing,
• 51:47
  potentially subsidized by a government,
• 51:49
  let's say,
• 51:50
  or have some sort of contractual
• 51:51
  cash flow in there
• 51:52
  to some local government
• 51:54
  or municipal entity,
• 51:55
  let's say, for example,
• 51:58
  their real estate,
• 51:59
  people live in them, it's
• 52:00
  a residential property,
• 52:01
  but the cash flows could be seen
• 52:03
  maybe as a little more infra-like, right?
• 52:06
  And so the crossover there,
• 52:07
  I think you could see a case
• 52:08
  where potentially both
• 52:11
  the real estate team
• 52:11
  and the infrastructure team
• 52:12
  potentially would look
• 52:13
  at something like that
• 52:13
  and would it satisfy
• 52:14
  their investor requirement.
• 52:16
  And from there,
• 52:17
  they would make a decision
• 52:18
  on their underwriting
• 52:19
  and the hold periods might be different.
• 52:21
  The long-term projections
• 52:22
  might be different.
• 52:23
  But fundamentally,
• 52:26
  I think
• 52:27
  the investor wins here because it's
• 52:28
  actually something new
• 52:29
  that the investor could help
• 52:30
  satisfy their goal
• 52:31
  of saving for their retirement,
• 52:33
  whatever it might be. I So David,
• 52:35
  could you talk also about how you're
• 52:37
  leveraging your expertise
• 52:39
  to attract external investors?
• 52:42
  through the lens of sort
• 52:44
  of third-party
• 52:44
  investment, Jocelyn,
• 52:45
  that's sort of your question.
• 52:47
  The way it's
• 52:49
  leveraged is really around
• 52:50
  the fund suite that we offer.
• 52:52
  And so we've got a number of solutions
• 52:55
  for clients,
• 52:56
  depending on
• 52:56
  their risk return
• 52:57
  profile across the capital stack,
• 53:00
  where we use the large footprint
• 53:03
  we have across the business
• 53:04
  to help triangulate, iterate,
• 53:08
  develop models
• 53:10
  to sort of
• 53:10
  make those decisions
• 53:11
  to show investors our capabilities
• 53:13
  and to really leverage that in-house
• 53:16
  data.
• 53:17
  David Hedalen,
• 53:18
  thank you so much for joining us today.
• 53:20
  And thank you to our viewers
• 53:21
  and listeners
• 53:22
  for another great episode
• 53:23
  of Private Markets 360.