Cadre’s Edge: How Data Drives Our Investment Strategy
Our real estate investment strategy is predicated upon pairing an experienced investments team with a forward-thinking, data-driven approach that we believe will drive outperformance over traditional real estate investment managers. (Private real estate investment involves significant risk, including risk of loss; see Disclaimer below.)
Like other asset classes, today’s real estate investing landscape is a challenging one. Capital markets flows have led to asset pricing at or near all-time highs in many markets, even as fundamentals are expected to continue to moderate.
As fiduciaries, we think about these challenges daily: How can we find attractive opportunities, move quickly in a competitive market, and generate outsized risk-adjusted returns?
As an investment manager, we started with a time-tested real estate investing strategy: We have cultivated an institutional investments team with over $45 billion of collective real estate investing experience to source, diligence, structure, and asset manage the best possible real estate transactions. We also invest alongside well-aligned local operating partners with substantial experience owning and operating assets in our target markets, which we believe drives alpha.
But to outperform in today’s environment, we go even further than this. We have carefully curated a group of data scientists and software engineers to take a highly analytical approach to real estate investing. By leveraging traditional, alternative, and proprietary data sets, we can supplement the traditional real estate investing strategy with the advanced quantitative methods and machine learning techniques that have transformed other areas of finance.
This approach allows us to proactively identify markets and microlocations in which we want to invest, screen more opportunities, programmatically surface both risks and opportunities, and move more quickly with greater conviction.
Public equities markets provide the roadmap
Traders on the floor of the stock exchange in the 1980s and 1990s handwrote the buy and sell orders for the day, with most information hoarded by the big brokerage firms. Over time, the industry evolved to include further standardization of reporting, a robust data and research infrastructure, and more sophisticated investing methodologies. The vast amount of structured data and ease of its access allowed for the rise of highly analytical hedge funds that can make investment decisions quickly. Today, algorithmic trading — or trading that utilizes highly advanced mathematical models to make investment decisions in a matter of seconds — represents the majority of trading volume in the stock market.
While the private commercial real estate industry has a ways to go before being compared to the public equities market, we can draw two parallels. First, technology will reduce the barrier to entry and allow access to an asset class that was traditionally reserved for the wealthy. Second, institutions that have the ability to analyze and harness the ever-growing amount of data should be positioned to use such data to outperform the broader market.
How is our approach better?
From our vantage point, the real estate industry continues to operate much like it did two decades ago — diligence is usually characterized by spreadsheets, phone calls, and disparate, non-standardized and often imprecise data that limits the ability to optimally underwrite transactions. Largely, this has been done on purpose or because there was no impetus for change.
This lack of innovation leads to a number of opportunities:
- Our data science team has built its data infrastructure from the ground up. For each transaction we source, we aggregate, scrub, and surface thousands of data points from a variety of traditional, alternative, and proprietary data sets into one centralized database for analysis. This allows us to analyze more transactions more quickly, while having more robust data at our fingertips, which we believe helps us accurately identify transactions on a risk-adjusted basis.
- We source hundreds of opportunities annually and select a small subset for investing based on an analysis of tens of thousands of data points per investment. Alongside traditional data gathering and robust diligence, automated analysis of market and micro-location fundamentals, asset and business plan profiles, and sponsor quality allow us to form more complete views on more transactions, identifying both sources of upside and risk mitigation.
- Our investment strategy supplements the “tried and true” human approach by blending our seasoned Investments Team with cutting-edge data science methodologies. We are constantly testing new theses to understand what strategies may be most attractive going forward.
We seek to blend our deep technical expertise with our extensive investing experience in order to bring a level of rigor and sophistication that may not exist at traditional commercial real estate managers. We’ve spent three years building systems that allow us to harmonize and ingest data from various sources, enabling us to develop differentiated insights to inform our acquisitions process.
Our approach to data permeates three key components of the acquisition process: Market and Asset Class Selection, Deal Sourcing, and Underwriting.
- Market and Asset Class Selection: Leveraging machine learning, we holistically analyze millions of features to identify markets, micro-locations, and asset classes that are correlated to long-term outperformance. Our ability to programmatically analyze markets and asset classes allows us to adjust our acquisitions strategy as market conditions change
- Deal Sourcing: Our senior team has hundreds of sponsor relationships developed across multiple real estate cycles that allows us to source hundreds of institutional-quality opportunities annually. Paired with our data platform, we can analyze multiple facets of a transaction immediately, which allows us to fully review more opportunities, thereby making more informed investment decisions based on both relative and absolute value.
- Underwriting: Taking a rigorous, data-driven approach, we can model assumptions to a higher degree of conviction, leading to what we believe is a more accurate underwriting. In addition to increased conviction, we are hyper-focused on risk management, have an institutional investment committee process, and our investment team co-invests in every transaction to ensure we have a vested interest and alignment of interests in the long term performance of our assets for our investors, our employees, and ourselves.
Our investment experience paired with our data and technology focus enables us to bring investors direct access to thoroughly underwritten and diligenced institutional-grade investments in a way that we believe has never been done before. To get started, please request access.
Educational Communication: The views expressed above are presented only for educational and informational purposes and are subject to change in the future. No specific securities or services are being promoted or offered herein.
Performance Not Guaranteed: Past performance is no guarantee of future results. Any historical returns, expected returns, or probability projections are not guaranteed and may not reflect actual future performance.
Risk of Loss: All securities involve a high degree of risk and may result in partial or total loss of your investment.
Cadre makes no representations, express or implied, regarding the accuracy or completeness of this information, and the reader accepts all risks in relying on the above information for any purpose whatsoever. These materials are not intended to provide, and should not be relied upon for investment, accounting, legal or tax advice. Additionally, these materials are not an offer to sell or the solicitation of an offer to buy any securities or other instruments. Actual transactions described herein are for illustrative purposes only, are presented as of underwriting and are not indicative of actual performance, and were selected based on objective, non-performance factors such as asset-type, geography or transaction date, among others. Certain information presented or relied upon in this presentation has been obtained from third party sources believed to be reliable, however, we do not guarantee the accuracy, completeness or fairness of the information presented.