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Real Estate is Getting Left Behind in the Data Revolution

April 27, 2022
Auren Hoffman

The real estate world has gone from being not very data oriented to being highly data driven in the last decade.  Data’s been a real game-changer for commercial real estate companies, retailers, and logistics businesses alike.  

But there’s one problem: data adoption is not evenly distributed. 

As you can see in the graph above, some areas of real estate, like logistics companies and big chain retailers, are very sophisticated in their use of alternative data.  They have large data science teams building complex models that leverage raw data sources to uplevel their analysis. 

Regional brokers, investors and retailers, on the other hand, are far behind. 

On top of that, while some areas of real estate are forward thinking and advance in their data adoption, as a category, real estate still has a long way to go.  It’s almost at the very bottom of the data maturity curve. 

The good news is that the number of data scientists in the real estate industry has grown 10x in the last four years.  The bad news is that it is growing from a super small base.

The market is evolving – adapt or get left behind

The days of deal teams spending months on diligence and working with a litany of consultants are long gone.  The big retailers and large investment managers understood this 5 years ago and have adjusted accordingly.

The real estate world is changing fast and there are 3 major trends to watch:

  • Bigger data science teams: Real estate investment and brokerage firms are hiring data scientists at a faster rate than ever before – and it’s only accelerating
  • Deal cycles are shrinking: Those who move fast win deals. The best way to build conviction quickly is to have confidence in your models and data. 
  • Data options are growing: Alternative data market expected to grow at a CAGR of 39% through 2025

Same data, different use cases

The real estate “industry” is fairly broad with lots of different types of organizations optimizing for various use cases.  You have investment firms, developers, operators, and brokers, all of which can play across different asset classes like office, retail, industrial, hospitality and residential. 

There are lots of different use cases across lots of different verticals. But they can all use the same few datasets to drive meaningful insights.  

Data is your alpha. Use it or be outperformed

Underwriting is probably the most important aspect of investing.  There are two dimensions of underwriting: depth and speed.  You want to be able to go deep on an asset while moving quickly. 

Going deep means doing lots of research and truly understanding the quality of an asset.  It means going beyond just the marketing materials and the market studies broadly available on the location and the sub-market.  

It means finding sources of information that can create a sort of information asymmetry between you and your competitors.  That often comes in the form of data. 

For example, a large commercial real estate investment firm that invests in retail properties uses a variety of datasets to power their underwriting models. 

They use Point of Interest (POI) data to understand the POIs surrounding their target asset to learn the landscape.  They use lease data to stay ahead of shifts in leasing comps in the area while underwriting. 

They also leverage consumer transaction data to quickly evaluate trends at competitive and adjacent locations to estimate stabilized top-line performance.

The second component of underwriting is speed.  

When underwriting new assets, you really have three options

  1. Do manual research on the asset and source information points
  2. Outsource the research and have someone else do this work
  3. Have pre-built model that updates by pulling in raw data feeds instantaneously  

Most companies use some combination of options 1 and 2.  That’s because option 3 requires an upfront investment.  You have to have a data science team, trusted data sources and refined models that can enable you to move fast.  While these require time and effort upfront, they save you many many hours down the line.

Those who move fast win deals. 

Location, location, location

Picking the right location is one of the most, if not the most, critical factors for any decision around real estate. 

For retail businesses, it’s a matter of survival.  

Let’s use chain coffee shop operators as an example.  When entering a new market, you need to deeply understand the quality of neighborhoods, the competitive landscape within each neighborhood and the demographics.  

All competitors are looking at reports from research companies and consultants – not many are diving into the actual data.

Here’s how retail businesses like coffee shop operators can leverage data to gain a quick view into a new market:

  • Point of Interest (POI) data: Locate all other coffee shops in the market and their proximity to desired sites
  • Transaction data: Understand transaction patterns across all coffee shops in a market to influence their decision
  • Census: Learn which neighborhoods have income demographics aligned with target customer base

The bigger the investment size, the more data you need

To state the obvious – the bigger the investment size, the greater the risk.  Combining various types of data can help avoid potential investment mistakes.

Hotel operators and multi-family developers are great examples of organizations that can combine various alternative data sources when acquiring or developing an asset that requires a large investment size.

One of the most powerful analyses they do is neighborhood scoring which helps them assess the quality of a sub-market and neighborhood.  By using POI data, they can very quickly analyze an asset’s proximity to amenities such as transportation centers, bars, restaurants and event venues. 

Hotel operators need to understand the location of the nearest airport and convention centers.  Multi-family residential investors need to know the location of the closest Whole Foods, proximity to an Equinox gym and the quality of schools nearby.  POI data helps solve for all of this. 

But neighborhood scoring is not enough.  Real estate investors also look at property data to compare the asset’s history and construction details to comparable buildings to ensure deal terms are in line with asset quality. 

There are probably 5-10 other data types that can help developers, operators and investors make sure their investment decisions are optimized.  But you get the point.  

Data is powerful and it becomes more powerful as the stakes get higher.  

It’s not all about commercial assets

The power of data goes beyond commercial real estate.  There’s significant opportunity to generate alpha in residential real estate by using good data. 

We’re all aware of the IBuying phenomenon that has overtaken residential real estate.  The likes of OpenDoor and Zillow have stirred a lot of buzz in the real estate world.  IBuyers are equal parts investors and market-markers.  They deploy capital very quickly and shorten home-buying cycles.  

But diligence is often an afterthought and it really shouldn’t be. 

IBuyers can use POI data to quickly understand open / close trends for retail establishments in the area to enhance their forecasts of growth in home prices for the sub-market.  They can also analyze consumer spending patterns for nearby retailers which can serve as a strong signal for homes in the area. 

You don’t have to compromise diligence for speed.  Be the Tiger Global of real estate. 

Data is useful for more than just investment decisions

You may think that data can only uplevel your investment strategy – but it can help with much more.

Data can also help improve marketing and transform your operations. 

Developers and brokers spend a lot of effort in marketing assets coming to market.  Building properties is step 1 but leasing them can sometimes be just as hard. 

When opening a new multi-family residential building, operators and brokers can run a quick radius analysis of all the POIs in the area.  Leveraging an API to determine which bars, restaurants, grocery stores and gyms near your property will better enable you to market amenities and streamline your leasing process.

When it comes to operations, let’s take big box retailers as an example.  Large retailers can merge alternative data with their own first-party data to optimize hours of operation.  

Here’s how.  By looking at Point of Interest (POI) data, you can look at the hours of operation of all other retailers in the area and nearby complementary venues.  This can help you optimize your own hours and maintain operating discipline. 

Another dataset that can enhance operations is first-party transaction data.  By evaluating transactions happening at their stores by the hour, retailers can quickly see when customers are coming in and adjust accordingly. 

Go beyond just data about places

One of the best investment firms writ large is Blackstone.  They move fast, do deep diligence and have made tremendous returns on their investment in real estate.  Of course they are a huge firm that has lots of resources, but they also look at lots of interesting data. 

Tyler Henritze, Head of Strategic Investments at Blackstone Real Estate, explained how Blackstone uses alternative data on the World of DaaS Podcast

In the 2000s, Blackstone bought homes across the southeast USA upon realizing that more and more people were moving to states like Florida for better weather and friendlier tax environments.  How did they realize this?  Uhaul data. 

If you follow the Uhauls, you’ll find the opportunity.

Other interesting ways Blackstone uses alternative data that isn’t about places:

  • Truck traffic data on highways to assess logistics routes 🚚
  • LinkedIn job postings for certain markets to understand future demand 🏢
  • Employee count trends for tenant companies to assess tenant health 📈

All this is great… but how do you pick a good data provider?

Picking the right data provider can be hard.  Data companies are mysterious and most don’t post their schema or pricing publicly.  Majority of them don’t have a self-serve option either.

Finding the right dataset usually requires undergoing multiple evals over the course of months and even then you may not be able to discern who is the best.

Here are a few heuristics that can help when choosing a provider:

  • Accuracy and Veracity: the data needs to be highly accurate and true, otherwise it could lead you astray.  A few wrong data points could end up manifesting as hundreds of bps off your IRR.  Metadata about the data like fill rates and confidence scores can really help. 
  • Accessibility: Is the solution raw data that can be imported into a dashboard / is there an API available for automated workflows?  As you scale your operations, being able to access raw data seamlessly is critical to getting insights quickly – especially in times of macroeconomic events.
  • Cost: the solution needs to be affordable.  Customized pricing based on use case and dataset size is important.

These heuristics are simple in nature but powerful in practice.

Real Estate as an industry has made progress in leaps and bounds when it comes to data adoption, but it’s still behind most industries on the data maturity curve. 

Data can create opportunities for information asymmetry, help win better deals faster, and seriously uplevel operations.  Those who are more eager to adopt data will win.  

You can follow me on twitter @auren

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