Real estate investing is reaching an inflection point powered by data
The pace of technological innovation across the real estate industry is accelerating. With increased access to different types of data, faster than ever before, there’s an onus on real estate investors to evolve beyond traditional investment analysis. Real estate investors who embrace and understand the complexity and value of data and advanced analytics—and implement strategies based on both unconstrained data analysis as well as boots-on-the-ground investment experience—may be able to capture a competitive edge in forecasting, market selection, and enhance the entire investment decision-making process.
Combining traditional and nontraditional data may uncover alpha opportunities
The limitation of traditional investment analysis
Real estate investors have typically relied on traditional investment analysis, using a combination of historical analysis along with an examination of economic conditions to make investment decisions. This type of analysis, however, relies on traditional data such as operating indicators, recent sales, and developments. While this information is vital, its very strength is also its limitation. Traditional data is backward looking and limited by several factors:
- Sourcing bias—Operating indicators, such as occupancy rate, asking and taking rent, and concessions are supplied by operators, owners, and brokers and aren’t consistently processed through quality assurance or approved by a formal governance structure. The quality and delivery of this data may sometimes represent the broader interests of the organization providing the data or may be affected by relationships or partnerships with data providers.
- Selection bias—Only properties that transact can provide sales prices and cap rates. During times of severe economic dislocation transaction volumes drop significantly, leading to large bid/ask spreads and uncertainty about pricing.
- Behavioral bias—Commercial real estate markets aren’t fully transparent, and investors are often faced with an information gap. Most investors complement traditional data with intuition, experience, and broker relationships. This may lead to cognitive error and emotional biases when making investment decisions.
Nontraditional data can help create a more holistic view of markets
The standardization and increased availability of data through better warehousing and digitization, along with the rise of third-party analytics providers, have created an environment where information is more accessible. As a result, it’s now possible to develop a more holistic view of commercial real estate markets.
Nontraditional data—information that comes from unconventional sources—may reveal a more behavior-oriented, trend-based story of variables affecting real estate performance. For example, consider a study conducted by Zillow, which found a relationship between the value of homes and their proximity to certain grocery stores. Between 1997 and 2014, American homes near Trader Joe’s or Whole Foods appreciated an average of 148% (and 140%), while other U.S. homes appreciated by an average of only 71%. Diligent traditional investment analysis may capture such relationships on a case-by-case basis. Real estate investors wishing to apply this type of holistic market analysis in a more consistent, faster way, using nontraditional data, can adopt a systematic approach to data warehousing and analytics.
It’s now possible to develop a more holistic view of commercial real estate markets.
Nontraditional data can also be used to decipher what’s happening in the market in real time. Consider, for example, access to U-Haul rental data that may reveal important migration patterns that may influence demand for local real estate. On an even more localized level, Yelp restaurant reviews offer crowdsourced data points that could suggest how neighborhoods may evolve. Understanding what’s happening in the market in (relative) real time may create an edge in understanding the forces driving change and may allow real estate investors an edge over those who only confine their analysis to lagging indicators.
Examples of traditional and nontraditional data
Traditional real estate data
Nontraditional real estate data
Net absorption; occupancy/vacancy; completions; under construction; rent growth
U-Haul rentals; restaurant reviews; cell tower data on foot traffic and mobility patterns
Cap rate; price per square foot; volume
Bidder pool for live deals; fundraising; public market real estate investment trust price movements
Environmental, social, and governance
Data science may be key to uncovering alpha that are not easy to replicate or copy
The real game changer comes from combining traditional and nontraditional data to find patterns that glean fresh insight into which markets and assets have the potential for greater income and capital appreciation—the age-old search for alpha. Managing data flow and standardizing different data structures, however, remain a challenge. Fortunately, predictive analytics and machine learning algorithms have now made it possible to stitch traditional and nontraditional data together and make forecasts about the future faster. While the application for real estate investing is new, the potential is tremendous.
The real game changer comes from combining traditional and nontraditional data to find patterns.
In a study using a large database of traditional and nontraditional data, a model predicted three-year rent for multifamily buildings in Seattle with an accuracy rate of over 90%; factors related to nontraditional data alone explained 60% of the changes in rent. Deciphering such information could help investors better forecast the performance of markets and assets.
Proportion of predictive power (% share)
Source: “Getting ahead of the market: How big data is transforming real estate,” McKinsey & Company, October 18, 2018.
The key challenge to deploying advanced analytics and machine learning algorithms is in identifying what traditional and nontraditional data have predictive power. This process is complex and requires data scientists to work closely with real estate investors with actual market experience to ensure that:
- The right questions about the real estate market are asked
- Data sorting, quality checks, and flow of future information are processed correctly
- Results are interpreted properly
- Decision platforms are systematized to help make strategic decisions
We believe embedding the results into the investment process may not only help real estate investors make better, faster investment decisions but also help identify, with precision, where to develop, what to acquire, what to divest, and when to do so.
New data-driven investment decisions demand a different way of thinking
Leveraging the power of data isn’t easy. It’s not about integrating more data into one particular investment decision; rather, real estate investors will need to completely reimagine how they do things now. To make the most use of it, data needs to drive investment decisions and it’ll need to be embedded into almost every single step of the investment process.
While rethinking the entire investment model will require significant capital expenditure into technology infrastructure and data expertise, the potential that data and data analytics pose for identifying trends and patterns and helping real estate investors optimize investment returns may be too significant to ignore. In fact, some investment management firms are already taking advantage of this trend, suggesting competition in the space is heating up.
An investment approach that relies more on data and analytics isn’t meant to replace the experience and intuition of real estate investors; rather, it’s meant to enhance the investment decision-making process and has the power to be a game changer in identifying opportunities that traditional investment analysis methods only relying on retrospective data may be overlooking.
Developing a new mindset requires embracing challenges
Adopting a new approach to investment analysis and investment management comes with challenges.
Whereas public market data can easily be pulled from a Bloomberg terminal, in comparison real estate data is more limited in terms of history, access, and availability. Not all data points are consistently available across all markets and property types. Particularly for niche sectors, such as self-storage, senior housing, and single-family rentals, data tracking is in the early stages as institutional investment in these sectors has only recently become more prominent.
Governance around data collection and quality assurance is another factor to consider. As it exists today, it’s limited and at the discretion of the data provider rather than through any external governing body.
It’s also still unclear what level of time and capital investment, in terms of technology needed to support collection and analysis, is required to harness data and apply it to an investment strategy. We’re in the early innings of exploring data science, and we’ve yet to discover its full potential in driving performance and strategy.
Despite these challenges, we believe there’s a significant opportunity for the real estate industry to embrace a new data-driven mindset; however, investment management firms have a fiduciary responsibility to their investors not to underestimate these risks and others that may arise. When contemplating which real estate investment managers to hire, investors should consider partnering with those who are open to new approaches to technology, data, and how these developments can help the business make better, faster decisions.
Investors who can successfully harvest the power of data and research have the potential to create sources of alpha that are not easy to replicate.
An evolving approach to data and analytics may help real estate investors gain a competitive edge
Technological change and access to an ever-evolving menu of data and analytics sources are trends that aren’t necessarily new. However, the pace of change appears to be accelerating, and real estate investors who can successfully harvest the power of data and research have the potential to create sources of alpha that are not easy to replicate.
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