What does Two Sigma do overall and in a real estate capacity?
Was researching different companies in New York and came across Two Sigma. Honestly a little confused what they focus on overall and then in a real estate capacity. What is a data science driven real estate platform, what proprietary technology are they using?
“Our mission is to be the leading data science-driven real estate platform. We believe that the combination of deep real estate industry expertise and Two Sigma’s technology will be a powerful driver of better investment outcomes.”
Only way to know what exactly the technology they’re using is to work there. But overall, they do the same thing we all do (acquire/manage real estate) but use different data sets to lead them. I have a colleague who works there and what they do is no different than what a PE firm or LifeCo would do
It’s a very prestigious quant hedge fund. It seems like they’re trying to diversify and thus are entering the real estate space. Pay should be very good and their technology is likely very up-to-date, but the job should be the same as any other real estate investment role.
That’s my question though, what are they using to analyze deals that’s different than any other Acqusitions team? They’re very data driven? I’m confused because to me when Acqusitions a property risk profile/returns are important, how much liquidity you have, what asset class are you targeting and does the area/city make sense for your plan.
So what outside of that are they analyzing and makes them better? Do they just think
they're making smarter decisions because of the caliber of people there?
They probably use data scientist to track certain metrics, like cellphone usage, metrocard swipes to see where the next burgeoning areas to be developed and to purchase will be.
Assuming they are buying physical assets and not REIT stocks, as a quant fund they are coding all their own proprietary software.
When it comes to real estate this probably means tracking certain metrics (as Shervin said, cellphone usage/foot traffic/transit usage and the like) to identify changes in patterns in given sub-markets to try and pick up on trends early, and more importantly I can almost guarantee you they're using some type of machine learning/AI to recognize these patterns and spit out recommendations or at least narrow the field. This can also be applied to pricing models and being able to identify when an asset is underpriced very quickly rather than relying on market knowledge and experience.
Now that all being said, this is obviously a new field and real estate is an asset class with very limited public information and great variances in the information each party in a deal has, so this is a hugely untested platform/investment method and remains to be proven whether it actually works (see the recent Zillow disaster).
If they’re a hf, does that mean they’re focuSed on l/s real estate stocks and reits or are they acquiring assets in a fund structure similar to repe?
Can be both. Several hedge funds have RE desks which do both public and private situations, examples include King Street, Davidson Kempner, DE Shaw, Baupost. Some of these have raised drawdown funds as liquidity requirements limits how much can be allocated to private investments in traditional HF structures.
They do nothing different from a REPE fund at any other company. It’s just a REPE job, don’t get your panties in a twist
I know everyone keeps saying that it’s just a normal REPE shop but I do wonder what kinds of data they’re processing. I would have to assume it’s more than a gimmick having a dedicated data scientist on the RE team. Maybe macro trends in different cities or submarkets? Coming up with different risk profiles for tenant mixes based on their view of the tenant strength (maybe based on public company analyses in their other investment arms)? I dunno there’s a lot you could do with data and real estate, and I think it’s cool.
Dude every huge real estate asset manger has a research team to show off to their LPs. They provide nothing of value because broad based research in most instances doesn’t influence a deal to any significant extent, at least in value add/opportunistic real estate.
Maybe it works for deciding on broad investment themes to follow, but end of the day real estate is micro locational and data science isn’t going to help too much with that other than a few pretty slides in the IC memo.
In my experience the research teams at large investment managers are more typically used in support of asset management and leasing rather than investments. Identifying market trends, looking at what is going on with competitors, macro themes, etc. to help better manage your portfolio and improve pitches to tenants.
At the investment level, as you said their only purpose is to support the macro thesis and maybe throw in a few data points for pitch books and IC memos.\
I'd imagine these teams are VERY different from what Two Sigma is doing given their entire investment thesis in other asset classes is driven by quantitative methods.
Yeah, you’re probably right. It might just be more gimmick than real value add. You can definitely build some cool models for macro research in an area, but there are probably very limited uses for using big data to analyze a single property for potential acquisition.
I heard they use Black Scholes to calculate renewal probabilities
Two Sigma and a handful of other traditional quant HF types have PE and REPE arms where they claim to port over their ability to work with data at scale. As far as I am aware, none have managed to deliver amazing returns.
I wonder if the have SA for RE
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