Baldwin, Kenneth

diBartolomeo, Dan

Gold, Richard

Baldwin, K

diBartolomeo, D

Gold, R

Nottingham Trent Institutional Repository (IRep)

From theory to practice: a new approach to real estate risk, part II

The new model allows investors to measure real estate risk similarly to other asset classes. Investors now can determine the composition of risk for individual properties and whole portfolios.Publisher statement: Copyright ©2016 by Institutional Real Estate, Inc. Material maynot be reproduced in whole or in part without the express written permission of thepublisher

Coventry University Pure Portal

  From Theory to Practice: a new approach to real estate risk, part II  Baldwin, K. , diBartolomeo, Dan and Gold, Richard  Published PDF deposited in Curve April 2016  Original citation:  Baldwin, K. , diBartolomeo, Dan and Gold, Richard (2005) From Theory to Practice: a new approach to real estate risk, part II. The Institutional Real Estate Letter, volume 17 (11): 35-37  Copyright ©2016 by Institutional Real Estate, Inc. Material may not be reproduced in whole or in part without the express written permission of the publisher.  Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.  CURVE is the Institutional Repository for Coventry University  http://curve.coventry.ac.uk/open  THE INSTITUTIONAL REAL ESTATE LETTER  November 2005  ■  THE LETTER  ■  www.irei.com 35In “Finding Equal Footing” (The Institutional Real Estate Letter, September 2005), we outlined a model that measures private equity real estate risk using a securities- market approach. It is a second-order model designed to directly forecast the uncertainty of returns at the prop-erty level. Property-level risk forecasts then can be aggregated to the portfo-lio level and further aggregated with other asset types, such as stocks and bonds. It also provides estimates of the usually unobservable correlations between the returns of different prop-erties and with other asset classes. In addition, the model can be used as a framework to estimate the incremental contribution of individual properties and groups of properties to the portfolio. By presenting the output of the model in a way that is familiar to risk managers in other asset classes, we aim to provide the missing link between private equity real estate and other more liquid asset classes in terms of risk evaluation. Such a bridge should allow real estate man-agers to operate with equal footing in the slippery worlds of risk manage-ment and asset allocation. REVIEW OF THE MODELThe model breaks the risk of each property into four pieces, which are evaluated in the Northfield Everything Everywhere (EE) model — similar to what would be done with comparable risks arising in marketable securities. First, we project the future cash-flow stream from the property, inclusive of appropriate treatments of vacancy rates, tenant rollover and tenant defaults. We then can measure directly how changes in interest rates (and, hence, congruent changes in capitaliza-tion rates) affect the valuation of the cash flows. This is akin to the way a bond portfolio manager evaluates risk.Our approach to forecasting cash flows is similar to that found in software packages such as Argus and Circle but with less detail and preci-sion. Cash flows are based on pro-jected net operating income (NOI), which is driven primarily by occu-pancy, rent levels and expenses over time. Recognizing that inflation and rental growth are not correlated per-fectly in the short run, in future ver-sions of the model we will impose a rent forecast for the first five-year period and then revert to inflation-driven rents in the out years. This should help improve the model’s robustness. NOI also depends on occupancy levels because vacant space generally is costly to the land-lord. Therefore, a building’s vacancy is assumed to move from its cur-rent level to a long-term equilib-rium structural vacancy rate, which varies by property type. The model assumes reversion to the mean with respect to market factors, such as occupancy. “Forecast Cash Flows” (page 36) shows a building whose current vacancy rate is well below the mar-ket’s. As the building trends toward market equilibrium, cash flows decline to reflect the loss of tenants to other buildings. Once it approaches market occupancy, it is assumed to trend with the market thereafter. Lease renewal rates for existing tenants also are inversely related to market vacancy levels. The higher the market’s vacancy rate, the more options a tenant has and the less likely a new lease will be signed. Downtime between leases needs to be incorporated, and it also is a func-tion of the vacancy rate at the time of renewal. Finally, rents are assumed to move with inflation in the long run, and the useful life of a building is assumed to be 50 years from the start of the analysis. Similarly, we next can analyze the outgoing cash flows associated with the provisions of the financing structure. This process is comparable to that of a bond portfolio manager with a short position in a set of fixed-interest securities. If it is cross-col-lateralized, we bundle like properties together and create a composite asset consisting of the cross-collateralized properties. The issues of fixed versus adjustable rates are modeled. Prepay-ment options also are incorporated and are similar to having embedded call options, where the exercise price is the prepayment penalty plus the outstanding loan amount at that time. A conservative approach with respect to the borrower is taken by assuming that default is not an option. The next step is to include the impact of tenant credit risk on the uncertainty of expected returns. As the effect of tenant defaults on cash flows already has been accounted for, this step concentrates on forecasting volatility in capitalization rates. One cause is changes in the credit quality and diversity level of the tenant mix. Another cause is that general inves-tor confidence varies through time, sometimes demanding more or less incremental yield from investments of perceived lower quality. Anchor credit risk is represented with the parameters estimated by the EE model based on the anchor’s credit rating. All other tenants are considered “generic” tenants, and REAL ESTATE MODELING by Dan diBartolomeo, Richard Gold and Ken BaldwinFrom Theory to PracticeA New Approach to Real Estate Risk, Part IIExecutive Summary◆ The new model allows investors to measure real estate risk similarly to other asset classes.◆ Investors now can determine the composition of  risk for individual properties and whole portfolios.  36 November 2005  ■  THE LETTER  ■  www.irei.comTHE INSTITUTIONAL REAL ESTATE LETTERtheir credit risks are determined by weighting EE credit-risk parameters by employment-sector share in the local economy. Corporate credit rat-ings provide a conservative measure of tenant risk — economic losses due to corporate bond defaults typically are higher than economic losses aris-ing from tenants defaulting on rent.Finally, we look at the impact of rent and occupancy volatility. Rather than try to assess this through Monte Carlo simulations of the cash-flow stream, we model this aspect in an option-like framework, where the prop-erty owner takes risk by betting that rent and occupancy will be constant. The rent and occupancy volatili-ties are projected from a supply-and-demand model. Supply statistics are based on the current conditions in the local market; variability of demand is based on the volatility of financial performance for the economic sectors that contribute to the local economy. Demand volatility for Palo Alto, Calif., office space, for example, would be projected based on the observed vola-tility of financial performance of the high-tech sector that dominates eco-nomic conditions in that area.In most markets, the supply of commercial space changes proportion-ally with demand over the long run, and although supply spikes are not uncommon, they largely are uncorre-lated with the EE model’s broad eco-nomic factors and are a function of local market conditions. In contrast, demand for commercial space is elastic and can be captured by relating per-centage changes in rents to the broad economic factors of the EE model.Estimating rent volatility is a two-step process. First, regressions are run in which rent volatility is a function of a local market demand-supply gap as well as an initial market conditions variable. Demand volatility is estimated as a function of the EE model factors that represent broad industrial sectors of the economy, weighted by the local market’s employment mix. Supply is the change in the metro area’s build-ing stock. The initial conditions vari-able is a moving average of vacancy rates and is important because mar-kets are rarely in equilibrium at any one moment. Therefore, we need to explicitly account for the fact changes in the demand-supply gap have a different impact when vacancies are 5 percent versus 25 percent. Each prop-erty’s or composite asset’s current exposure to rent volatility is related to current vacancy plus annual lease turnover (i.e., space subject to rent negotiation). REQUIRED INPUT DATATo make the model useful, we must define a parsimonious set of input data that can be collected for actual real estate portfolios. The inputs the model uses for each property are dominant property use (apartment, hotel, industrial, office or retail); current occupancy; anchor tenants (square footage, lease renewal date and credit rating); debt (property-only or cross-collateralized, duration, fixed or variable, coupon rate, and pre-payment options/penalties); expected capital expenditures; current effective rent; and current estimated property value. Data also is collected on local real estate market conditions for each area in which a property is located.AN EMPIRICAL EXAMPLEMuch of the motivation to create the model came from Investcorp, an insti-tutional investor based in Bahrain. Investcorp sought a methodology to measure real estate risk from various perspectives, including the individual-property and portfolio levels, as well as the amount of risk arising on its own balance sheet as a co-investor. With a statistically more accurate way to aggregate real estate risk relative to its other investments — private corporate equity and hedge fund of funds — it would be able to evaluate capital risk allocation for deals in all 30 9301,8302,7303,6304,5305,4306,3297,2308,1299,0309,93010,83011,73012,63013,53014,43015,33016,23017,13018,029$0$200$400$600$800$1,000$1,200$1,400$1,600Per unit per monthTime in daysForecast Cash Flows: Building with Below-Market VacancySources: Northfield Information Services, InvestcorpNCREIFMerrill Aggregate Bond IndexUnlevered R. E. PortfolioNAREITLevered R. E. PortfolioS&P 5000% 5% 10% 15% 20%Quarterly standard deviation annualized: 1999–2003*Relative Risk Comparison*Quarterly standard deviation times the square root of 4Source: Northfield Information ServicesTHE INSTITUTIONAL REAL ESTATE LETTER  November 2005  ■  THE LETTER  ■  www.irei.com 37stages. Additionally, the Basel II capi-tal accord permits a closer alignment between economic and regulatory risk capital for institutions able to use a model approach for risk assessment, something which Investcorp now can extend to its real estate portfolio. The model was applied to Invest-corp’s portfolio of U.S. commercial real estate. Analyses were conducted in early 2004 and in early 2005. At both points, the portfolio contained more than 40 properties, including more than 25 separate combinations of property type (office, retail, hotel and residential) and geographic loca-tion. The aggregate purchase cost of the properties was in excess of $1.5 billion, with considerably greater mar-ket value. Almost all of the proper-ties had significant financing in place, and in the case of several subgroups, the financing was cross-collateralized. “Relative Risk Comparison” (page 36) shows, for the overall portfolio, the expected return volatility was about 10.0 percent on an unlevered basis and approximately 14.0 percent given its current financing structure. These compare to about 13.0 percent for the NAREIT Equity Index, 5.0 percent for corporate bonds and 1.3 percent for the NCREIF Property Index during the same period. These volatility values were measured over the trailing five years, which is comparable to the EE model estimation sample period. At the individual-property level, the forecast return volatilities were between 8 percent and 12 percent per year on an unlevered basis, with forecasts of between 9 percent and 30 percent annual volatility for the returns on equity. To check the reasonable-ness of the results, we formed two lists of the properties, ranked from least risky to most risky. One list was created based on the unlevered risk values and the other list inclusive of financing. When the lists were pre-sented to the investment professionals who had acquired and managed the properties, the relative risk rankings were found to be highly intuitive and consistent with management beliefs. Last, we review the risk decompo-sition of a single property: a medium-size shopping center. Each row of the “Risk Decomposition” table (below left) describes the contributing influ-ence on the property return of a par-ticular factor in the EE model. The exposure column represents the sen-sitivity of the property’s return to the factor, while the factor variance col-umn describes the volatility (in vari-ance units) of the returns associated with this factor. The annual volatility of oil prices, for example, is listed at a variance of about 1,800 percent2. The product of the exposure squared and the factor volatility would sum to the value of the variance contribution col-umn if the returns to the factors over time were uncorrelated. To the extent that correlations exist among the fac-tors, the variance contribution column also includes the respective covariance terms. The factor contributions sum to a total of 89.96 percent2. We then add specific tracking variance, which accounts for risk aspects of the prop-erty that could not be included in the model. In variance units, we have a total of 90.38 percent2 or 9.51 percent per year in standard deviation terms. THE BOTTOM LINE The model provides a framework for determining how much of the risk of investing in a property arises from characteristics of the specific property and how much of the risk arises from common influences across all proper-ties. This new level of transparency will encourage investors to be con-fident in their understanding of real estate and, consequently, to allocate more resources to it. One interesting use of the model would be the development of syn-thetic “real estate” by creating securities portfolios that are constructed to some comparable exposure to the economic forces that drive returns. Such products may be an important step in overcom-ing the illiquid nature of real estate. This approach to estimating real estate risk also provides an avenue for inno-vations in real estate practice, including better benchmarks, the active hedging of interest rate risk to property values, and the more appropriate inclusion of real estate in optimal asset allocation. vDan diBartolomeo is president of Northfield Information Services, Richard Gold is director of research with Grosvenor Americas, and Ken Baldwin, Ph.D., is with Investcorp.Risk Decomposition: Medium-Size Shopping CenterFactor Exposure Factor variance Variance contributionEnglish-speaking countries 0.01 291.21%2 0.20%2Industrial sector 0.01 367.84%2 –0.19%2Consumer sector 0.15 196.03%2 2.39%2Technology and health sector 0 690.97%2 0.03%2Interest rate–sensitive sector –0.03 223.17%2 –0.30%2Non-energy materials 0 402.95%2 –0.02%2Energy mineral sector 0 285.09%2 0%2SB world government bond index 0.18 54.68%2 6.43%2Oil prices in USD 0.01 1,808.21%2 0.67%2Developing market 0.18 107.66%2 6.69%2Size 0.32 128.89%2 17.02%2Dividend yield 0.07 158.19%2 1.44%2Treasury curve factor 1 –13.4 0.31%2 56.00%2Treasury curve factor 2 –3.61 0%2 0.11%2Treasury curve factor 3 –0.79 0.02%2 –0.50%2Factor Tracking Variance 89.96%2Specific Tracking Variance 0.42%2Total Tracking Variance 90.38%2Return Standard Deviation 9.51%Sources: Northfield Information Services, InvestcorpCopyright © 2016 by Institutional Real Estate, Inc. Material may not be reproduced in whole or in part without the express written permission of the publisher.

From Theory to Practice: a new approach to real estate risk, part II

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