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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.

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?

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, with a goal of outperforming competitors that have lagged in adopting such methods.

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.

Commercial real estate timeline

While the 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 will be positioned 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:

Traditional CRE FundsCadre
Often limited technical foundation and thus the inability to structure and analyze data.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 than our competitors, while having more robust data at our fingertips, which we believe helps us identify better transactions on a risk-adjusted basis.
The propensity for narrow perspectives on asset evaluations that may not account for all potential risks and opportunities.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.
Traditional and sometimes ingrained biases that focus on old-school investing strategies.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 capitalize on the industry’s flaws by blending our deep technical expertise with our extensive investing experience in order to bring a level of rigor and sophistication that does not often 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.

commercial real estate data

Our approach to data permeates three key components of the acquisition process: Market and Asset Class Selection, Deal Sourcing, and Underwriting.

 CadreTraditionalCrowdfunding
Market and Asset Class SelectionLeveraging 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.
Traditional CRE funds often have limited ability to analyze the vast amount of data required to deeply understand the dynamics of a market, micro-location, or asset class.

The approach to market and asset class selection often produces a myopic view of investing that may introduce unnecessary risk into the investment decision, and an inability to adapt quickly to changing conditions.
Many crowdfunding sites tend to function as a marketplace rather than a fiduciary.

This can incentivize the platform to list inventory across a broad range of markets and asset classes with limited diligence, rather than focusing on attractive long-term fundamentals of markets and asset classes.
SourcingOur 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.
The traditional approach to sourcing is a very manual process, where analysis is bespoke and learnings are isolated between individuals.

The inability to aggregate real time market insights limits a manager’s ability to understand the intrinsic value of assets, often leading to inefficiencies in price discovery.
As a marketplace, the focus is connecting sponsors with capital. The concept of sourcing doesn’t exist as inventory is driven by operators that require capital to close on a transaction.

This model can lead to adverse selection where the sponsor is unable to raise capital from a top tier capital provider, potentially leading to lower quality, non-institutional transactions.
UnderwritingTaking 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.
Inability, or lack of capital, to invest in and leverage data effectively can lead to biases when aligning on assumptions, resulting in potentially unrealistic return profiles.

Human resource constraints also limit the ability to analyze more transactions, thereby limiting the investable universe.
Investment opportunities tend to be lightly diligenced, relying on the sponsor’s underwriting as the source of truth.

This can introduce a misalignment in the incentive structure where the operator is focused on selling their investment rather than providing the investor with a more balanced, risk-adjusted view on a transaction.

Our investment acumen 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.

About the Author
Cadre is a technology-empowered real estate manager built on institutional diligence enhanced by data.
Disclaimer
The views expressed above are presented only for informational and educational purposes and are subject to change in the future. 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.

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