Why property values matter: Real Estate, Meet Data Science

At GeoPhy, we think a lot about how property values drive the global economy. Savills World Research recently conducted a study showing that by the end of 2018, the global value of real estate had reached almost $300 trillion. This makes real estate the largest asset class globally (see figure 1), with a value 3.5 times larger than the entire global GDP.

Figure 1: Global Real Estate asset value is 48.7% larger than the value of global Financial instruments and 146% larger than Global Oil Reserves

And real estate doesn’t just comprise an enormous asset class, it’s also a direct contributor to global economic output. For instance, according to Statista, construction’s value add contribution to US GDP has grown over 25% since its low of 3.4% of GDP in 2011 to 4.1% in 2018:

“The construction market in the United States is one of the largest in the world, with private spending reaching 992 billion U.S. dollars in 2018 and with about 10.69 million people employed in the industry. It is expected that new construction put in place will total 1,526 billion U.S. dollars by 2022.” (see figure 2 below)

Figure 2: Value added of the construction industry as a share of gross domestic product in the U.S. from 2007 to 2018

Neither of these figures should be surprising. Real estate has always been a big driver of economic activity. What is surprising is the extent to which the value of a property (and the aggregation of all property values) can distort business cycles and create real chaos in billions of people’s lives. We have all been witness to how inflated real estate prices predated the global financial crisis of 2009. 

With so much value tied up in global real estate, understanding property value and the underlying metrics that determine its value is paramount. Investors, financial institutions, and governments who manage their real estate portfolios wisely can generate significant returns for their shareholders and the broader community of people whose livelihoods depend on real estate such as builders, operators, and agents. Valuing a property in a way that aligns with the underlying fundamentals of the income and growth it can generate is essential to reducing risk.

Data science is changing the global economy

Since the financial crisis of 2009, institutional real estate investing and lending has changed dramatically. Regulation has become tighter and investors more disciplined—although that discipline may dissipate in the current hunger for strong yield. Over the past ten years, a number of data science trends emerged and accelerated. These include:

  1. Abundant storage/compute in the cloud
  2. Big Data architectures
  3. AI tools like machine learning
  4. Millions of newly trained data scientists

According to LinkedIn, AI and data science roles are the fastest-growing job postings in the U.S., with the number of AI Engineer job openings increasing by 74% and Data Scientist job openings by 37%. Thus, the conditions are now ripe to apply data science to a multitude of businesses, including real estate. 

These conditions are especially evident to consumers of eCommerce and social media platforms. It’s relatively easy to observe data science predictions and recommendations shaping our digital experiences on consumer platforms like Google, Amazon, and Instagram. 

Data science is also changing how we make decisions at work. There are so many examples in just about every vertical and horizontal. Whether it’s deploying ethical AI to make hiring less biased or leveraging robotic process engineering to streamline business processes, data science and AI are just beginning to transform how we make decisions at work and how we organize output. 

A recent A16Z podcast “AI in B2B” describes how businesses are seeking to deploy AI as either a co-pilot or autopilot. As a co-pilot, AI can provide data and insights to knowledge workers who can leverage those insights to make more informed decisions faster. Then as an autopilot, AI and data science can automate repetitive processes that don’t add much value (and frankly can be boring for workers). 

GeoPhy brings real estate and data science together

Real estate is no exception to being transformed by data science. However, most advances to-date have been relevant for the residential market—think about iBuyer programs launched by Zillow, Opendoor, and other platforms. Commercial real estate is now following in the footsteps of the single-family home market. 

At GeoPhy, we help clients understand property value and its underlying drivers. We source, link, and cleanse traditional and unconventional data, then apply advanced algorithms to provide a unique perspective on commercial property values. Our unique approach provides the industry’s most accurate, objective property valuations and tools that allow users to develop a deeper understanding of the factors influencing property values. 

Simply put, whether you invest or lend, your goal is to maximize returns. To do so, you must execute effectively at each key phase across the real estate lifecycle. It boils down to three simple questions:

1. Which Markets? It typically starts with a focused and data-driven strategy that answers:

  • Which markets (major or tertiary) and neighborhoods? 
  • Which property types (multifamily, industrial, office, etc.)? 
  • Which deal types (core, core plus, value add, etc.)?

GeoPhy offers a combination of neighborhood ratings and value drivers that granularly rate neighborhood attractiveness so that you can co-pilot with GeoPhy to establish a data-driven CRE strategy.

Figure 3: GeoPhy Neighborhood Rankings splits the US into 10,000+ submarkets and rates each on desirability

2. Which Properties? Once a strategy has been implemented, one is now ready to screen properties for fit and value by answering:

  • Is this property sufficiently attractive on the surface to be worth my time?
  • Is this property within the neighborhood boundary that is desirable?
  • Is this property’s current NOI and project NOI (post value-add) reasonable?

GeoPhy powers automated valuation models, visualizations, and workflow automation services so that you can co-pilot with GeoPhy to screen more properties, bid on the most attractive ones, and successfully close on a large percentage of them thanks to a faster underwriting and appraisal co-piloted processes.

Figure 4: GeoPhy Evra’s AVM provides an accurate valuation of any multifamily asset in the US and tells you which factors contribute most to value such as NOI, median area rent per SF, etc.

Taken together, we believe that better insights on markets and properties will translate to a more efficient and stable market.  

Real Estate, Meet Data Science

At GeoPhy we believe that we’ll play an important role in fully introducing the benefits of data science to the commercial real estate ecosystem. Or put alternatively: Real Estate, Meet Data Science. 

Together we will help investors, lenders, regulators, and other constituents create sustainable growth in the real estate industry and avoid costly asset bubbles that lead to financial crisis. This doesn’t just help those in the real estate industry. Since real estate is a central store of value in today’s world, the biggest beneficiaries of more stable real estate markets are citizens. Together we will uncover the underlying value of every property in the world. 

Join us—together we’ll make better investment and lending decisions

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