Introducing the Cadre Price Trend Indices

Cadre’s proprietary data model tracks repeat commercial real estate (CRE) sales across markets and property types. We use these correlations to construct robust price indices that provide up-to-date observations on CRE pricing trends.

Published on Apr 19, 2022
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Executive Summary:

  • Pricing trends in commercial real estate (CRE) are notoriously difficult to measure. Sparse transaction data and unique property attributes are some of the challenges specific to this illiquid asset class.
  • Typically, index providers obtain CRE price information from appraisals, which are often inaccurate proxies for market pricing.
  • Cadre’s innovative model pulls in actual transaction data on different property types across the U.S. to paint a more complete picture of the CRE pricing landscape.
  • We capture correlations from repeat sales to produce a set of robust price indices, which investors can use to make timely investment decisions.

Historically, price information has been difficult to come by for investors in commercial real estate (CRE). Transaction data is limited, so index providers typically use appraisal values instead.

Appraisals, however, do not reflect real prices. Appraisals smooth over micro-market nuances that can make an enormous difference in property values. Additionally, CRE assets have unique characteristics that complicate valuation. Appraisals can conflate properties that are not at all alike. These difficulties are compounded when investors want to track pricing trends over time.

At Cadre, we believe that indices drawn from appraisal values deprive investors of essential information about pricing trends. To help investors access this valuable trove of information, we created our own indices based on actual transaction data: The Cadre Price Trend Indices (PTI).

Introducing the Cadre Price Trend Indices

The Cadre Price Trend Indices track CRE pricing trends across property types in over 50 U.S. metropolitan areas. We’ve improved upon typical index providers by developing an innovative model that pulls in transaction data instead of appraisal values. We optimize repeat transactions to draw more accurate pricing correlations, then analyze the results to produce actionable investment insights for Cadre’s Investments team and our investors.

How Do the Price Trend Indices Work?

Cadre’s Data Science team developed a proprietary quantitative model that pulls in actual transaction data, rather than relying on appraisal values as typical index providers do.

Our model ingests a massive amount of data about CRE assets, including location, price, property type, transaction date, property size, and many other identifiers. We optimize all of this data to extract common trends across repeat transactions, charting correlations over time to show price trends across markets and property types.

Cadre’s Data Science team analyzes pricing information from the Cadre PTI on a quarterly basis to deliver actionable insights. Investors can use our price observations to gauge up-to-date price appreciation/depreciation, with a focus on long-term learning. This combination of machine-learning and human analysis helps us deliver detailed, data-driven observations that no other CRE investment manager provides to their clients.

How Can the Price Trend Indices Help Investors?

Indices are fairly straightforward measurement tools. Most investors measure the success of their investments by benchmarking their returns against the hypothetical returns of an index. This can help investors determine the relative value of their assets by comparing “apples to apples.” They are particularly useful for assets that trade frequently at publicly available prices. Stocks, for example, are easily benchmarked. Index providers such as Standard and Poor’s and Russell Investments produce well known indices for stock and bonds.

Accurately benchmarking commercial real estate is far more difficult than many other asset classes. Reliable transaction data can be hard to source for such illiquid assets. Property sales are infrequent, and prices are typically gathered from appraisals or self-reported information. As a result, many widely used CRE indices may be based on inaccurate data[1]. Many indices cannot capture nuances in price appreciation or depreciation across markets, information that can prove immensely helpful to real estate investors.

By measuring changes in actual transaction data, Cadre is arming investors with real information they can use to make decisions about specific real estate investments. This approach produces two important outcomes for investors:

Cadre’s innovative model sources actual transaction data to capture true pricing trends across a wide variety of CRE markets and property types.
Investors can use Cadre’s pricing observations and make better CRE investment decisions supported by data science.

Data Science: In-Depth

Cadre’s index construction model is based on a novel tensor decomposition routine that captures correlation among the returns of a large set of repeat-sale transactions. The approach allows us to effectively track pricing trends within different CRE property types and market groupings, even when the observed transaction data is sparse and/or heterogenous.

Cadre’s model outputs allow us to monitor price trends for multiple markets and property pairings, test relationships between demographic and fundamental variables on future price growth, and understand individual property performance in terms of systematic and idiosyncratic components. We break up this correlation along geographic and property-type dimensions, then use these correlations to construct robust indices even in low sample settings.

The ability to spot trends in low sample settings is particularly important for CRE. Property data, as we’ve mentioned, can be sparse. Our model uses machine-learning to fill in gaps to help us round out our sample set. This has proved useful to our team in Salt Lake City, for example, where fewer transactions were available. Our model was able to fill those gaps via tensor decomposition to more accurately represent the price dynamics in that market.

Index Construction

Cadre constructs its individual price indices through a combination of different components. Each component captures the effect of different factors in the CRE marketplace. We identify these factors through a low-rank tensor decomposition routine.

Our factors include:

  • Overall CRE price trends
  • Deviations in returns attributable to asset classes
  • Deviations in returns specific to markets

Cadre’s proprietary index formulation allows us to access a larger set of repeat-sales data in the construction of a single price index. The benefit is an expanded data set.

As an illustrative example: we may construct the Denver Multifamily Index by combining the overall CRE market factor, a 30% loading on the factor tracking new apartments, a 15% loading on the factor tracking urban-infill apartments, a 40% loading on the factor capturing growth trends in secondary markets, etc.

(Note: In practice the factors are statistically determined and don’t lend themselves to such simple qualitative descriptions).  

While most investors have access to minimal data relating to a specific property type in a specific market (in this example, multifamily properties in Denver), Cadre’s team can identify the main factors motivating price changes across the entire set of repeat sales transactions. We then artfully combine these factors to form our individual price indices.

This proprietary process allows us to accurately track price trends from smaller sample sizes—a huge advance in commercial real estate, where up-to-date pricing data can be extremely hard to obtain.

Become a Cadre member to get access to our investment deals and additional exclusive insights.

  1. Jim Clayton & David Geltner & Stanley W. Hamilton, 2001. "Smoothing in Commercial Property Valuations: Evidence from Individual Appraisals," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 29(3), pages 337-360, March. ↩︎


Educational Communication
Not AdviceThe 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.

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Any discussion regarding “Opportunity Zones” ⁠— including the viability of recycling proceeds from a sale or buyout ⁠— is based on advice received regarding the interpretation of provisions of the Tax Cut and Jobs Act of 2017 (the “Jobs Act”) and relevant guidances, including, among other things, two sets of proposed regulations and the final regulations issued by the IRS and Treasury Department in December of 2019. A number of unanswered questions still exist and various uncertainties remain as to the interpretation of the Jobs Act and the rules related to Opportunity Zones investments. We cannot predict what impact, if any, additional guidance, including future legislation, administrative rulings, or court decisions will have and there is risk that any investment marketed as an Opportunity Zone investment will not qualify for, and investors will not realize the benefits they expect from, an Opportunity Zone investment. We also cannot guarantee any specific benefit or outcome of any investment made in reliance upon the above.

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