Abstract
This paper presents an empirical analysis of the relation between energy factor markets, leasing structures, and the transaction prices of office buildings in the U.S. We employ a large sample of 15,133 office building transactions between 2001 and 2010. In addition to building characteristics, we also include information on the operating expenses, net operating income, and market capitalization rates at sale to estimate an asset-pricing model for commercial office real estate assets. A further set of important controls in our analysis is the forward/futures contract prices for electricity and natural gas. We also include weather metrics for each building’s location and sale date. Our final set of controls includes information on the dominant contractual leasing structure of the buildings. Our empirical results suggest that Energy Star labels do not explain additional variance in property prices once the key asset-pricing factors of expenses, income and market capitalization rates are included. By contrast, energy-factor market prices, the shape of the energy forward price curves, and weather metrics are consistently significant determinants of office building transaction prices, suggesting that commercial office building prices are exposed to shocks in these markets.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Two reports by the International Energy Agency (IEA 2009, 2008) provide comparisons of residential energy use in the U.S. and Europe, corrected for climate and measured per unit of GDP or per capita. McKinsey (2007) also shows substantially higher energy consumption in the U.S. than in Europe, after controlling for GDP and population. Ries et al. (2009) compare energy use in the U.S., Australia and the European Union.
Bureau of Labor Statistics, Employment Hours and Earnings, State and Metro Area, http://www.bls.gov/sae/data.htm.
These are sales between unrelated parties.
The semi-log specification is used to correct for skewness in the distribution of office building prices.
We eliminate all transactions for which there was a “non-arms-length” condition of sale due to such factors as a 1031 Exchange, a foreclosure, a sale between related entities, or a title transfer, among other conditions. All of these sale conditions would affect prices due to the trading of tax basis in the case of 1031 exchanges or the auction structure in the case of foreclosure. Instead, we focus only on market transactions between unrelated parties.
Many buildings in this sample were Energy Star rated multiple times and these ratings are often non-monotonic in time (sometimes lower ratings are obtained at later dates). This non-monotonicity may arise because the Energy Star rating is relative to the population mean performance of office buildings. Thus, if an office building simply maintained its energy consumption profile, its ranking might fall if the overall population of U.S. buildings increases its energy efficiency.
These rating vary between 75 and 100.
CoStar does not account for the date the Energy Star rating was received.
Bureau of Labor Statistics, Employment Hours and Earnings, State and Metro Area, http://www.bls.gov/sae/data.htm.
The capitalization rate is the discount rate that translates the observed net operating income into the observed transaction price at the time of sale, assuming an infinite investment horizon,
$$\sum\limits_{t = 1}^{\infty} \frac{\text{NOI}}{\text{capitalization rate}}=\text{Sales Price}. $$Of course, real options considerations might also enter this calculus, leading to consideration of the second moments of fundamental factors (Grenadier 2005).
Computed by the authors using various BOMA publications.
Power market holidays are defined by the North American Electric Reliability Corp. (NERC).
Bureau of Labor Statistics, Employment Hours and Earnings, State and Metro Area, http://www.bls.gov/sae/data.htm
References
Benth, F., Cartea, A., Kiesel, R. (2008). Pricing forward contracts in power markets by the certainty equivalence principle: explaining the sign of the market risk price premium. Journal of Banking and Finance, 32, 2006–2021.
Benth, F.E., Koekebakker, S., Ollmar, F. (2007). Extracting and applying smooth forward curves from average-based commodity contracts with seasonal variation. Journal of Derivatives, 15, 52–66.
Eichholtz, P., Kok, N., Quigley, J. (2010). Doing well by doing good? Green office buildings. American Economic Review, 100, 2492–2509.
Fuerst, F., & McAllister, P. (2011). Green noise or green value? Measuring the effects of environmental certification on office values. Real Estate Economics, 39(1), 45–69.
Geman, H., & Roncoroni, A. (2006). Understanding the fine structure of electricity prices. Journal of Business, 79, 1225–1261.
Grenadier, S.R. (2005). An equilibrium analysis of real estate leases. Journal of Business, 78(4), 1173–1214.
Heckman, J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–162.
IEA. (2008). Energy efficiency requirements in building codes, energy efficiency policies for new buildings. International Energy Agency.
IEA. (2009). Energy prices and taxes. Working paper, International Energy Agency.
Kotchen, M.J. (2006). Green markets and private provision of public goods. Journal of Political Economy, 114, 816–845.
McKinsey. (2007). Curbing global energy demand growth: The energy productivity opportunity. Technical report, McKinsey Global Institute.
Plazzi, A., Torous, W., Valkanov, R. (2010). Expected returns and the expected growth in rents of commercial real estate. Review of Financial Studies, 23, 3469–3519.
Riedhauser, C. (2000). A no-arbitrage forward commodity curve. Working paper, Pacific Gas and Electric Company.
Ries, C.P., Jenkins, J., Wise, O. (2009). Improving the energy performance of buildings: Learning from the European Union and Australia. Technical report, RAND Corporation.
Rosen, S. (1974). Hedonic prices and implicit markets: production differentiation in pure competition. Journal of Political Economy, 82, 34–55.
U.S. Department of Energy. (2012). 2011 Buildings Energy Data Book.
Vandell, K., & Lane, J. (1989). The economics of architecture and urban design: Some preliminary findings. AREUEA Journal, 17, 121–136.
Wheaton, W.C., & Torto, R.G. (1994). Office rent indices and their behavior over time. Journal of Urban Economics, 35, 121–139.
Wiley, J., Benefield, J., Johnson, K. (2010). Green design and the market for commercial office space. Journal of Real Estate Finance and Economics, 41, 228–243.
Acknowledgements
We thank Paulo Issler for excellent research assistance, and seminar participants at the Lawrence Berkeley National Laboratory, the National University of Singapore, and the 2012 Winter meetings of the American Real Estate and Urban Economics Association. We are grateful to Piet Eichholtz and Nils Kok for helpful comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Program, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
Appendices
Appendix: Geographic Structure of the Transactions Data
EPA Energy Star rated buildings in the Los Angeles, Riverside, and San Diego areas
EPA Energy Star rated buildings in the San Francisco, East Bay, San Jose, and Sacramento areas
Lease contract types for buildings in the San Francisco, East Bay, San Jose, and Sacramento areas
Lease contract types for buildings in the Los Angeles, Riverside, and San Diego areas
Appendix: Weather Data Construction
The weather data were obtained from Wolfram Schlenker at Columbia University. These data are based on the same rectangular grid system underlying PRISM that covers the contiguous United States.Footnote 15 It consists of 1405 grids in the longitude direction and 621 grids in the latitude dimension, space equidistant \(1/24\) degree steps (about 2.5 miles). The data are matched to the centroid of each grid point to the fips codes of all counties in the United States. There are 471,159 grid points with non-missing data in the PRISM data where the centroid is matched to lie within a county.
The data include the minimum and maximum temperature (Fahrenheit), and total precipitation (cm) for each day of the year for all of the 471,159 grids in the United States from 1950–2010. These data are interpolated from PRISM’s monthly weather station averages to daily data, and we aggregate them back into monthly data for our analysis. We associate the past twelve months of weather data for each building in the CoStar data with the weather data associated with the nearest grip point in the Schlenker data.
Appendix: Energy Data Construction
We extract the energy forward curve pricing from the forward contract auctions for electricity and from the futures contracts auctions for natural gas. We follow Benth et al. (2007), Benth et al. (2008), Geman and Roncoroni (2006), and Riedhauser (2000), in the construction of these curves.
C.1 Forward Market for Power (Electricity)
The forward market for power is organized around the trading of standard packages covering on-peak and off-peak consumption periods. Trading occurs for delivery hubs located at the Eastern-Central regions and delivery hubs located in the Western region of the continental United States. The Easter-Central standard forward package covers the following markets: New England, New York (several hubs), Ontario, PJM, MISO, ERCOT South, Into Entergy, Into Southern and Into TVA. The Western packages cover NP15 and SP15 among others. Packages for the Eastern-Central hubs differ from those traded for the Western hub on two dimensions: the way on-peak and off-peak are defined and the delivery months of the forward packages.
We compute the standard on-peak forward packages in Eastern and Central markets are 5x16 packages (5 days per week and 16 hours per weekday from 7:00 Am to 22:59 PM), which include power delivered during on-peak hours on weekdays and exclude weekends and holidays .Footnote 16 Similarly, on-peak forward packages in Western markets are 6x16 packages, which include power delivered during the 16 on-peak hours each day Monday through Saturday and exclude Sundays and holidays. The off-peak standard packages, the forward market trade 5x8 (5 days per week and 8 hours per day) plus a \(2\times 24\) package, this includes power for delivery during the eight off-peak hours each weekday, plus all 24 hours (around the clock) on weekends. The standard off-peak forward package for the Western markets is a \(6\times 8\) delivery block plus a 1x24 delivery block, this includes power for delivery during the eight off-peak hours Monday through Saturday plus all 24 hours (around the clock) on Sunday.
For the Eastern-Central markets, on-peak and off-peak contracts are formulated for the prompt month (nearest contract), second month, third month, and balance-of-the-year in seasonal or single month packages, two full years in seasonal or single-month packages and two subsequent calendar year packages. Separate seasonal and single-month packages include the January-February winter package, the March-April spring package, May, June, the July-August summer package, September and the fourth quarter (from October to December).
C.2 Platts-Ice Forward Curve
Using forward contract data from Platts, we construct a daily forward curve for power for on-peak and off-peak consumption. Platts gathers information on the power forward market from active brokers and traders and through the non-commercial departments of companies. Since October 2007 this information is complemented with the Intercontinental Exchange (ICE) quotes to form the Platts forward market power daily assessment. Because more liquid locations and shorter term packages trade more on ICE, while less liquid locations and longer term packages trade more over-the-counter (OTC), Platts is able to combine these sources to build a comprehensive picture of the forward market. Details of the methodology are described in the Platts Methodology and Specification Guide - Platts-ICE electricity Forward Curve (North America).
We select a sub-set of electricity hubs based on data availability for options and forward contracts from Platts and by our requirement to account for the power forward prices for all metropolitan areas with 150,000 employees in Finance, and Professional and Business Services (the major office categories).Footnote 17
C.3 Futures Market for Natural Gas
There is a very active market for natural gas in the U.S. Following the deregulation of the wholesale market for natural gas in the mid 1990s, the New York Mercantile Exchange (NYMEX) launched the trading of monthly futures contracts with similar characteristics to those of crude oil. The standard NYMEX natural gas futures contracts specify physical delivery of 10,000 MMBtu (millions of British thermal unit) ratably delivered into Henry Hub - Louisiana. Until early 2000 NYMEX provided monthly contracts covering maturities about 36 months out. More recently, the range of maturities has been extended and it now covers more then six years (72 months) on a monthly basis. The NYMEX website provides more details on how the contracts are traded and the rules for settlement.
There is an extensive network of natural gas pipelines connecting the production basins to large consumption areas (mainly large populated urban centers). Wholesale physical natural-gas trading occurs in different hubs distributed in the continental United States. These hubs are key points in the pipeline grid characterized by either being interconnections between major pipelines and/or access points to public utility gas companies. Of all those hubs, Henry Hub is the benchmark for price quotation. Henry Hub’s importance stems from both as being an interconnecting point for multiple pipelines and as being the most liquid point for trading spot and futures contracts. Prices for other hubs (spot and OTC forwards) are typically quoted as a basis to Henry Hub. These basis quotes are a very small fraction of the full benchmark quote.
Rights and permissions
About this article
Cite this article
Jaffee, D., Stanton, R. & Wallace, N. Energy Factors, Leasing Structure and the Market Price of Office Buildings in the U.S.. J Real Estate Finan Econ 59, 329–371 (2019). https://doi.org/10.1007/s11146-018-9676-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11146-018-9676-x