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The Journal of Real Estate Finance and Economics

Erschienen in:

01.02.2013

Commercial Property Rent Dynamics in U.S. Metropolitan Areas: An Examination of Office, Industrial, Flex and Retail Space

verfasst von: Maria R. Ibanez, Anthony Pennington-Cross

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 2/2013

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Abstract

This paper is concerned with the market rental rate for space offered by commercial property and how that rental rate evolves over time. Rental rates reflect the value of the services provided by the property and can have a significant impact on the ability of its owners to make monthly debt obligations. We investigate commercial property rent dynamics for 34 large metropolitan areas in the U.S. The dynamics are studied from the second quarter of 1990 through the second quarter of 2009 and the results are compared across four property types or uses (office, industrial, flex, and retail). There is substantial heterogeneity in both the long and short run responses to changing demand and supply conditions. In general, the office market is the slowest to adjust back towards equilibrium while industrial and flex markets adjust back to the long run equilibrium very quickly. For industrial and office types, the speed of adjustment is substantially faster within quality segments and is strongest for grade A properties.

Vorheriger Artikel What Determined the Great Cap Rate Compression of 2000–2007, and the Dramatic Reversal During the 2008–2009 Financial Crisis?

Nächster Artikel Estimating Transaction-Based Price Indices of Local Commercial Real Estate Markets Using Public Assessment Data

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For many of the metropolitan areas the data does not start until later. On average the first year with data is 1999 for office, industrial and flex property and 2001 for retail property. The last quarter with data is always the second quarter of 2009.

Commercial property rents have also been studied at a micro or property level using either a hedonic or repeat sales framework (Wheaton et al. 2009; Jennen and Brounen 2009; Slade 2000; Munneke and Slade 2000, 2001) and in a structural and stock adjustment framework (Benjamin et al. 1998; Wheaton et al. 1997).

The TWR rent index is estimated from a hedonic model using transactions from CB Commercial. The hedonic model controls for size of the building, length of the lease, height of the building, if it is a new building, and if the lease is a gross lease. These rents could reasonably be described as more constant quality than a simple average, which is what the Costar data provides. Conventions in space and across property type lead to fairly homogeneous lease types, at least on average. However, the Costar rents may be biased upward if the quality of the property is increasing over time or biased downward if the property quality is decreasing over time. These biases should lead to a more noisy measure of rental rate growth patterns.

Over our sample period and metropolitan areas, the percentage of space available for rent that is being marketed as sublet space is on average 8.4% for office property, 3.4% for retail property, 5.2% for industrial property, and 6.6% for flex property.

This Hendershott and coauthors error-correction formulation is somewhat different from the classic ECM but it is typical of the empirical literature on commercial property rent dynamics and asymmetric price adjustment. Error correction has been applied in many studies to real estate (Miller et al. 2011) consumption (Lewis 2011; Borenstein et al. 1997; Davidson et al. 1978), macroeconomics and financial markets (Garratt et al. 2003; Lastrapes and McMillin 2004) and strategic interactions among firms (Gilligan and Sarkar 1998).

Office employment is calculated from the following 2002 North American Industry Classification System (NAICS) and downloaded from the Bureau of Labor and Statistics web site: 50510000 Information, 55520000 Finance and Insurance, 55530000 Real Estate and Rental and Leasing, 60540000 Professional, Scientific, and Technical Services, 60550000 Management of Companies and Enterprises, 60560000 Administrative and Support and Waste Management and Remediation Services, 65610000 Educational Services, 65620000 Health Care and Social Assistance, 90000000 Government. Retail employment corresponds to 42000000 Retail Trade. Industrial employment is the addition of 30000000 Manufacturing and 43493000 Warehousing and Storage. Finally, flexible-space employment is estimated as office plus industrial employment.

Specification tests were also conducted using the Monthly Wholesale Trade Survey, and the Manufacturers' Shipments, Inventories, and Orders Survey to proxy for output in office, industrial, and flex property using businesses. However, the results were often counter intuitive and very sensitive to the specification. Therefore, they have not been reported in the paper.

NNN indicates that the tenant pays the operating expenses. Full service or gross rents indicate that the landlord pays the operating expenses. In practice, many gross leases are modified so the tenant and landlord share the expenses. Some net lease rates, especially in retail, increase as the tenant sells more (percentage leases).

Changes in the rental rate may or may not be explicitly defined in the lease. For example, a rent schedule can indicate step ups in rents for the full term of the lease on specific dates (usually every year). Alternatively, rents could be indexed to a cost of living index such as the Consumer Price Index. Rents could even depend on how much the tenant sells, with the rent defined in part as a percentage of the sales (percentage rents).

Concessions can be used to ease moving costs, strategically time cash flows, or enhance the value of private information in a thin market. Typically the asking rent is publicly known while concessions are not, perhaps to improve the landlord’s bargaining positions.

We would like to thank William Wheaton for providing us with the national TWR rent series.

This may also due to other factors such as market coverage in space and the quality of the space.

The excluded metropolitan area is Atlanta-Sandy Springs-Marriett, GA.

There is a possibility that the cointegration is conditional on unobserved non-stationary factors such as increased use of on-line shopping, increased offshoring of manufacturing, and increased outsourcing of service jobs that would occupy office space. These factors could lead to dynamic changes to the underlying equilibrium. Unfortunately, the relatively short time series does not allow us to test this potential issue. However, we note that Fig. 1 does not show any downward trend in real rents.

On average, the coefficient for economic activity and the standard error are 0.486 and 0.351 for office, 0.406 and 0.288 for retail, 0.824 and 0.238 for industrial, and 0.807 and 0.933 for flex. On average, the coefficient for supply and the standard error are −1.613 and 0.427 for office, 1.416 and 0.334 for retail, 1.769 and 0.733 for industrial, and −0.025 and 0.726 for flex.

While regulations in housing markets is not the same as regulations in commercial property markets it may still function as a reasonable proxy for locations that tend to regulate more or less property markets.

Retail property does not report grades.

The long run results are not reported to save some space, but are available from the authors upon request.

The availability of credit could also play a role in determining the long run rental rate. The long run model was rerun using the quarterly Federal Reserve Board’s Senior Loan Officer Opinion Survey on Bank Lending Practices. The variable included was the net percentage of banks tightening standards for commercial and industrial loans to large and middle-market firms. With this variable included the point estimates and precision of the results were similar. The impact of more banks tightening lending standards was associated with higher long run rents on the vast majority of metropolitan areas. This likely reflects the need of landlords to have better cash flows when trying to refinance or rollover mortgage debt due to higher debt coverage ratio requirements (cash flow/debt payment). The detailed results are available from the authors.

It may be difficult to identify the equilibrium with few observations; therefore, the short run model was rerun using only areas with more than 40 observations. The results were qualitatively very similar to those reported in Table 9. The effect of EA was slightly higher for all property types. The short run model was also rerun including a measure of the tightness of lending standards. The results are again very similar, but the speed of adjustment is a little faster, there is a little more persistence, and Δln(EA) is a little larger. In addition, for 3 of the 4 property types, the impact of more tight lending standards increased rents in the short run.

Two additional specification tests were conducted. The first test lags EA and s and the second test weights each observation by the amount of space it represents. Both set of results are very similar to those presented in Tables 5 and 6.

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Titel: Commercial Property Rent Dynamics in U.S. Metropolitan Areas: An Examination of Office, Industrial, Flex and Retail Space
verfasst von: Maria R. Ibanez
Anthony Pennington-Cross
Publikationsdatum: 01.02.2013
Verlag: Springer US
Erschienen in: The Journal of Real Estate Finance and Economics / Ausgabe 2/2013
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-011-9347-7

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