The determinants of capitalization rates: evidence from the US real estate markets
| DOI | https://doi.org/10.1108/JPIF-12-2020-0140 |
| Published date | 03 August 2021 |
| Date | 03 August 2021 |
| Pages | 87-137 |
| Subject Matter | Property management & built environment,Real estate & property,Property valuation & finance |
| Author | Matt Larriva,Peter Linneman |
The determinants of capitalization
rates: evidence from the US real
estate markets
Matt Larriva and Peter Linneman
FCP, Washington, District of Columbia, USA and
Linneman Associates, Philadelphia, Pennsylvania, USA
Abstract
Purpose –Establishing the strength of a novel variable–mortgage debt as a fraction of US gross domestic
product (GDP)–on forecasting capitalization rates in both the US office and multifamily sectors.
Design/methodology/approach –The authors specifies a vector error correction model (VECM) to the data.
VECM are used to address the nonstationarity issues of financial variables while maintaining the information
embedded in the levels of the data, as opposed to their differences. The cap rate series used are from Green
Street Advisors and represent transactioncap rates which avoids the problem of artificial smoothness found in
appraisal-based cap rates.
Findings –Using a VECM specified with the novel variable, unemployment and past cap rates contains
enough information to produce more robust forecasts than the traditional variables (return expectations and
risk premiums). The method is robust both in and out of sample.
Practical implications –This has direct implications for governmental policy, offering a path to real estate
price stability and growth through mortgage access–functions largely influenced by the Fed and the quasi-
federal agencies Fannie Mae and Freddie Mac. It also offers a timely alternative to interest rate-based
forecasting models, which are likely to be less useful as interest rates are to be held low for the foreseeable
future.
Originality/value –This study offers a new and highly explanatory variable to the literature while being
among the only to model either (1) transactional cap rates (versus appraisal) (2) out-of-sample data (versus in-
sample) (3) without the use of the traditional variables thought to be integral to cap rate modelling (return
expectations and risk premiums).
Keywords VECM, Capitalization rates, Real estate markets, Multifamily cap rate, Office cap rate,
United States
Paper type Research paper
1. Introduction
At 16 tn dollars, the value of commercial real estate in the United States (NAREIT, 2019)
represents half of a percent of the world’s total wealth (Shorrocks et al.,2018). And at 65%
home-ownership in the United States (The St. Louis Federal Reserve Bank, 2020c), real
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Matt Larriva and Peter Linneman. Published by Emerald Publishing Limited. This article is published
under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute,
translate and create derivative works of this article (for both commercial and non-commercial purposes),
subject to full attribution to the original publication and authors. The full terms of this licence may be
seen at http://creativecommons.org/licences/by/4.0/legalcode
Funding: This research is part of Matt Larriva’s role as Director of Research and Data Analytics at
the Real Estate Private Equity firm FCP.
Conflict of interest: One author works for a Real Estate Private Equity firm which has ownership
interest in many office and multifamily assets throughout the US.
Availability of data and material: Data available upon request.
Code availability: Code available upon request.
Authors’contributions: Each of the authors confirms that this manuscript has not been previously
published and is not currently under consideration by any other journal.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1463-578X.htm
Received 16 December 2020
Revised 18 March 2021
14 May 2021
Accepted 15 May 2021
Journal of Property Investment &
Finance
Vol. 40 No. 2, 2022
pp. 119-169
Emerald Publishing Limited
1463-578X
DOI 10.1108/JPIF-12-2020-0140
estate represents a more invested-in asset class than equities–only 55% of Americans
own stock (Saad, 2020). As such, the value of this asset class and the underlying
determinants are of importance not only to owners and operators but also to the economy
as a whole.
While ratios (price to earnings, debt-to-equity) are commonplaces in most value
discussions, there is perhaps no other industry that relieson such a singular metric as real
estate does on capitalization rates (cap rates).
Its definitions and derivations abound, but in its simplest form, a cap rate is the quotient of
expected annual net operating income (NOI) and the current value of a property:
NetOperatingIncomenext12−months
CurrentAssetValue
that is, its stabilized yield.
This quotient is often used to calculate the value of an asset by dividing its next 12-months
NOI by the cap rate itself. Despite its simple calculation, it contains myriad information about
present and forward economic conditions. For example, a cap rate may compress because
forward growth is expected to be high, while an expanding cap rate could indicate a more
pessimistic view of future cash flows. By any calculation or formulation, understanding the
current and future cap rates provides a great deal of insight into the nature of property
valuations–so much so that it is frequently used instead of price when discussing market
valuations.
Because of this, cap rate forecasting and modelling remain active areas of research in the
field of real estate finance Lin (2019),Henig et al. (2019) and Christopoulos et al. (2019). Taken
in context of the current cap rate environment and the economic slowdown, this question
becomes especially pointed. Since 2009, cap rates have been on a near monotonic down trend
(representing increasing real estate values). And while they have slowed their decline since
2015, the question of their direction is topical, with some investors reasoning that stymied
rent-growth figures will push up cap rates while others suggest low interest rates will force
cap rates lower still.
The research which attempts to explain cap rates can largely be divided into three camps:
those which use lagged return (or price change) as a variable, those which use ratios such as
rent-to-price or price-to-income and those which use more granular property or regional data.
Underpinning many of these studies is the Gordon (1962) specification of cap rates, which
asserts their calculation as the constant cost of equity capital minus the growth rate of the
investment. We note that most of the studies use variables that focus on the risk
(unemployment, volatility), return (price-to-income) or expectation (future premiums or rent-
growth rates) components of the valuation equation. Few focus on the demand side of the
valuation equation.
Separately, as to the studies themselves, most focus on in-sample modelling of appraisal
cap rates. In-sample modelling is very telling, but a model robust to both in-sample and out-of-
sample forecasting may be more so. As to appraisal-based cap rates, practitioners widely note
the inherent unrealistic smoothness and aptly named appraisal bias.
With this background in mind, we seek to investigate the determinants of cap rates from
the demand side of the equation, and we aim to do so using transaction-based cap rates, both
in-sample and out-of-sample. Specifically, we focus on the US Office and Multifamily markets.
While we believe the results generalize to other sectors, a full exploration is beyond the scope
of this analysis.
To address the demand side of the valuation equation, we analyse the total nominal mortgage
debt outstanding as a percent of nominal US gross domestic product (GDP) (funds flows). Our
theory is that this variable is a more direct synthesis of all other variables (risk, return and
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expectation) and is a more direct influencer of cap rate movements. A surge in demand for
mortgage debt (as a percent of GDP) should accompany a period of cap rate compression, while
conversely, a surge in GDP that dwarfs mortgage debt may represent a period of high
opportunity cost of real estate and a devaluation of the asset class.
To address the transactional cap rate series, we use Green Street data, which starts in the
1990s, providing ample testing periods. Historically, practitioners would use National Council
of Real Estate Investment Fiduciaries’s (NCREIF) appraisal-based cap rate series, as this was
the only source of long-term data. Green Street is among the most respected names in the
REIT and Real Estate research space and has collected transactional cap rates since
inception, presenting another strong source of data.
With this data, we use a vector error correction model (VECM), and we assert that this
method is a superior choice (compared to vector autoregression (VAR)) for modelling cap
rates, as there is significant information contained in the levels, which is lost in the differences
(required for VAR), of the input series. Consider the difference in implications of a change in
cap rates from 10% to 9% versus a change of the same magnitude from 4% to 3%. We
confirm the appropriateness of a VECM by testing Granger causality and direction (funds
flows Granger cause cap rates), auditing stationarity, establishing the number of
cointegrating vectors, setting lag and rank order and confirming non-autocorrelation of
residuals. Despite the numerous conditions that must be met to utilize a VECM without
generating biased modelling, our input data meets these conditions, and our results benefit
from the robustness.
We model cap rates in both the office and multifamily sectors both in-sample and out-of-
sample. To generate out-of-sample models, we use a VECM which is trained on historical
data, which attempts to explain the next 1, 2, 3 and 4 quarters.
We then compare the results of our out-of-sample modelling (which uses only historical
samples to predict unseen future cap rates) to a baseline model composed of the traditionally
used variables: risk-premium, return expectations and the cap rates themselves.
We have two main findings that offer insight to the nature of cap rate determinants. First,
we confirm the robustness of using a VECM to explain transactional versus appraisal cap
rates, both in-sample and out-of-sample, both with our new variables and with the traditional
ones. The volatility of the realistic transactional series is captured by the flexibility of the
VECM model. And our findings with both sets of variables are strong relative to those in the
literature. Second, we submit that a funds flow variable is highly explanatory, accounting for
the vast majority of in-sample and out-of-sample variance in cap rate series. The models using
the traditional variables are outperformed by the model using the funds flow variable.
Variables of this nature are mentioned rarely in the literature and perhaps provide necessary
guidance in volatile and frothy valuation environments where traditional spreads,
expectations and returns are out of historical norms.
To put our paper in context, we are the first to prove the power of the funds flow variable
(discussed by Linneman, 2015) in explaining and forecasting cap rates in a statistically robust
context. And we are the first to forecast out-of-sample transactional cap rates (versus
NCREIF’s appraisal-based cap rates). In variable selection, our research is in the vein of
Chervachidze et al. (2009) who used total net debt as a percent of GDP, the spread of Moody’s
AAA bonds to the US ten-year treasury and lagged cap rates (among other factors) to forecast
cap rates. And our work is somewhat related to Arsenault et al. (2013), which investigates a
positive feedback loop between commercial mortgages and real estate appreciation. Their
work uses quarterly outstanding commercial and multifamily mortgages reported by the Fed
and examines the difference in these values, period over period. We confirm the efficacy of the
Fed’s mortgage data, but find it is more relevant not when differenced, but when analysed at
levels, and as a share of GDP.
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