Institutional Real Estate, Inc.

NAREIM Dialogues Fall 2017

The Institutional Real Estate Inc Sponsorship brochure, Connected-Investor Focused, We connect people, data and insights, sponsorship, events, IREI Products

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NAREIM DIALOGUES FALL 2017 23 For investors in buildings that stay around for decades, sometimes even centuries, it is tempting to dismiss technological innovations as irrelevant. But as the real estate sector is poised to become more data driven, this will 1) lead to (partial) automation of now- manual tasks, such as site selection and due diligence for acquisitions, valuations, and mortgage origination, 2) enhance efficiency and liquidity in the commercial real estate market, leading to an increased capital flow, and 3) affect traditional drivers of real estate demand, with significant consequences for the existing capital stock, including its pricing. "Big data" has quickly emerged from obscurity into a buzzword, rising from non-existent in 2011 Google Trends numbers, to peak popularity in 2016. Indeed, peak popularity of "big data" was last year. In 2017, the world no longer cares so much about how big data is, but rather, what you can do with it. Because without application and analysis, data is like raw oil without the refinery. For the commercial real estate sector, the emergence of "big data" is part of the broader rise of "proptech," another catch- all term that includes the many sides of the real estate market. As a short digression, it is worth looking at "proptech" in a bit more detail. It is easiest to decompose the hundreds, or perhaps thousands of tech firms in the real estate space by using a well-known framework for real estate market analysis: the four-quadrant model. As academic as that framework is, the workings of the real estate market can be boiled down to a combination of the construction market, the market for space (where landlords and tenants interact), and the market for assets (the capital market, where sellers, buyers, and providers of capital interact). As illustrated by the graph above, each part of the market has its own set of disruptive "tech" firms – I listed just a few names, but the list is virtually endless, and growing every day. In the construction market, some big trends include BIM modeling (increasingly commonplace), 3D printing (the first commercial building using 3D printing has been constructed, in China), and the Internet of Things (IoT) creating "smart" buildings (OVG's "Edge" building in Amsterdam and Oxford Properties' "RBC Waterpark Place" in Toronto are early adopters of IoT technology). In the space market, there are many example of disruptive technologies. From the obvious, such as the offline-to-online retail shift, more advanced brokerage platforms aimed to obviate expensive and unneeded brokers, to rideshare platforms changing the value of location. And don't underestimate the impact that platforms such as WeWork and AirBnB may have – with sticky supply (it's hard to move buildings) small changes in demand create large fluctuations in value. In the real estate capital market, there are multiple disruptive technologies that have direct relevance for fund managers, REITs, developers, lenders, and other players in the capital market. As a source of capital, crowdfunding and arbitrage platforms are emerging, albeit mostly focused on the single-family residential sector, where liquidity is typically higher and ticket size smaller (see, for example, Opendoor.com). Two other fields in the real estate capital market that are ripe for disruption include the role of traditional research and analytics, primarily focused on site and asset selection, and the archaic role of appraisers. Big data for site selection and due diligence purposes For real estate developers and investors, finding "the next Williamsburg" or "the next Soho" is often considered the holy grail. But, how to identify what areas (may) gentrify? At the macro level, site selection is often done using data on demographic trends, including population, employment, and income growth. But once a city has been selected for capital allocation, Census data is neither granular, nor frequent enough to be useful. Enter big data. Because at the level of the city, there are a multitude of data layers that can be used for further differentiation, as detailed as an inch, as frequent as every second. Think about data on bars, restaurants, coffee shops, art galleries, (music) events, crime, the location and frequency of pictures uploaded onto social media, homesharing (e.g. listings on AirBnB), WiFi hotspots, public transport stops, rideshare pick-ups and drop-offs. And all other data layers that Richard Florida wrote about in his "Rise of the Creative Class." These "modern" data layers, which can often be accessed through APIs, lead to unparalleled market intelligence that enables real estate investors, developers and lenders to make more informed, data-driven decision. As opposed to, or perhaps in addition to, "heard on the street" or gut feeling. But the trick is often to access and make sense of all these different layers of information, as most real estate firms are not set up to ingest many different sources of primary data, but are rather used to consuming data in aggregated format (think: data on rent levels, price developments, etc.). To illustrate how new, granular sources of data can be used by the real estate sector, my firm, GeoPhy, developed a simple site selection tool, the "Grapevine Dashboard" (after all, we often hear about new, upcoming, cool areas through the proverbial grapevine). The Grapevine Dashboard pulls a vast real-time dataset Construction Market OnTarget Honest Buildings Ravti BIM 3D printing Other Ridesharing (Uber/Lyft) Self-driving cars (Tesla) Telehealth (Teledoctor) Etc. Space/Tenant Market VTS CompStak Reonomy AirBnB WeWork AppearHere Asset/Capital Market GeoPhy Opendoor.com RealtyShares Blockchain ©iStock.com/RamCreativ

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