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A unique element of EA’s value stream is the management, improvement, and development of rent “price determination models” and the identification and evaluation of those rent inputs and resulting outputs on an ongoing basis. The debate on how to best present this rent research and how to fairly measure aggregate changes in rents is ongoing.

The primary challenges in developing a reliable rent index is that suite level rent observations are missing observation attributes, tend to be volatile quarter to quarter and carry an inherent risk of not comparing buildings of similar status and space. Establishing a true measure of rent movement requires many pieces of information, meticulous processing, and creativity in the absence of transparency.

Rents are observed in two main categories: asking rents (gross or net) which represent the asking rent figure or “headline rent” at the time the space is being marketed, and net effective rents which represent the agreed upon base rent per month, free-rent periods and/or scheduled rent escalations, length of lease, tenant cash allowances and other fees incurred  during the term of a lease. Since both asking rents and net effective rents contain detailed attributes, rent observations tend to further fragment the already low sample into non-comparable subsets.

In partnership with EA’s founder, Professor Bill Wheaton from the MIT Center of Real Estate, EA has developed a new rent series that leverages a repeat rent methodology – the gold standard in economics – and at the same time achieves granularity through rent interpolation at the building level.

The repeat rent approach uses rent observations within buildings that have multiple asking rent values captured in its history. We construct this index by comparing rents of the same buildings, so we can avoid comparing rents between different properties, which tend to be heterogeneous. This is similar to the methodology used for constructing the well-known S&P CoreLogic Case-Schiller Home Price Indices.

This eliminates potential bias from a change in the sample (for example, from the development of new higher-value buildings or major renovations that increase the floor space).

The EA Asking Rent series is reported in USD per square foot per year. It will be revised each year when the benchmark rent level moves from Q4 of one year to Q4 of the next.  Rental growth estimates are not revised (unless there is a change in the underlying data).

In addition, EA expends great effort on data sourcing, collection, outlier detection and integrity enhancements to improve the quality of our series input data and solve for challenges. We are convinced of the statistical superiority of the new method and of its greater ability to accurately track market movements. At the same time, we are very aware of the important role of incentives to the market and we have launched a new project to create a granular mix-adjusted incentives series that uses a similar methodology to EA Asking Rents. 

With the new methodology for capturing rent movements and a new bottom-up forecasting approach, we have become more flexible in data rendering and more reliable in capturing changes. We believe the combination of these two innovations can provide clients with more accurate insight on market movement and better information on rent in both history and forecast.

To learn more about the methodology and new EA Asking Rent series, contact any of the authors listed below.

To become a member, click here.

THIS DOCUMENT WAS PREPARED BY:

Nathan Adkins
Sr Economist, CBRE Econometric Advisors
Nathan.Adkins@cbre.com

Ibrahiim Bayaan
Economist, CBRE Econometric Advisors
Ibrahiim Bayaan@cbre.com

Jing Ping
Sr Data Scientist, CBRE Econometric Advisors
Jing.Ping@cbre.com

Christina Tong
Economist, CBRE Econometric Advisors
Christina.Tong@cbre.com

Matt Vance
Sr Economist, CBRE Econometric Advisors
Matthew.Vance@cbre.com