Do Public Real Estate Returns Really Lead Private Market Returns? Elias Oikarinen*, Martin Hoesli**, Camilo Serrano*** 20th European Real Estate Society Conference, Vienna July 3-6, 2013 * Turku School of Economics ** University of Geneva (HEC and SFI), University of Aberdeen (Business School) and Bordeaux Ecole de Management *** IAZI AG – CIFI SA

Background The securitized real estate market is generally assumed to be more informationally efficient than the direct real estate market Therefore, the prices of securitized real estate investments are expected to react faster to shocks in the fundamentals than direct real estate prices → Public market returns lead direct market returns (?) The existence of lead-lag relations between asset returns would have notable predictability and portfolio implications The previous empirical evidence is not conclusive –The reported lead-lag relations are generally based on data that do not Correct for the different property-type mixes of the securitized and direct real estate indices Cater for the leverage of public real estate Consider the ‘escrow lags’ in the direct real estate data 2

Previous Literature Evidence of public market returns leading (predicting) direct market returns in the short-term (e.g. Gyourko and Keim, 1992; Myer and Webb, 1993; Barkham and Geltner, 1995; Newell and Chau, 1996; Liow, 2001; Oikarinen, Hoesli and Serrano, 2011) REIT returns lead direct market returns even when catering for the property-type mix (Geltner and Kluger, 1998; Seiler and Webb, 1999; Li, Mooradian and Yang, 2009; Yavas and Yildirim, 2011) Recent evidence of long-term dynamics, in terms of cointegration, between the private and public markets (Oikarinen, Hoesli and Serrano, 2011; Yunus, Hanz and Kennedy, 2012; Boudry et al., 2012; Hoesli and Oikarinen, 2012) –Only direct market returns adjust to deviation from a cointegrating long- run relation in the U.S. (Oikarinen, Hoesli and Serrano, 2011; Hoesli and Oikarinen, 2012) –Also securitized returns react to disequilibrium in the U.K. (Hoesli and Oikarinen, 2012) Price dynamics may notably differ across real estate sectors (Wheaton, 1999; Yavas and Yildirim, 2011; Hoesli and Oikarinen, 2012) 3

Previous Literature (Cont’d) Major complications in previous empirical studies on lead-lag relations between public and private real estate: –Appraisal smoothing in direct market data –The influence of property-type mix is catered for only rarely –The potential effect of REIT leverage on the findings –The influence of ’escrow periods’ on the timing of private real estate data –Ignorance of the longer-term dynamics –Lack of impulse response analysis Some authors have addressed some of these complications but never all of them simultaneously 4

Aim and Contributions Aim of this study: to provide a clearer picture on the reaction speeds and the lead-lag relationships between the securitized and direct real estate markets –Helps to understand the behavior of securitized and direct real estate and sheds light on issues such as price discovery and inflation hedging Contributions to existing literature on the theme: –First study that compares the reaction speed of securitized and direct real estate returns to shocks in the fundamentals based on impulse responses derived from an econometric model –First study examining the lead-lag relations that caters simultaneously for a number of data issues that may affect the findings: Property-type mix Leverage in the securitized market data ‘Escrow periods’ in direct market data The impact of fundamentals Asymmetries in buyers’ and sellers’ response speeds in the direct market 5

Methodology and Data Direct (TBI) and securitized (NAREIT) real estate total returns for four sectors in the U.S. –Apartments, offices, industrial property, retail property –TBI returns are restated for the effect of leverage Quarterly data for the period Vector error-correction models –Separate model for each property sector –Cointegration tests –Granger causality tests, Generalized Impulse Response Functions (GIRFs) Fundamentals –Economic growth: GDP & economic sentiment –Inflation rate –Short-term interest rate and the term structure of interest rates –Risk premium 6

Catering for Leverage REITs are levered whereas TBI returns assume 100% equity financing Leverage affects the mean and volatility of observed returns Time-variation in the leverage may hinder the cointegration tests and distort the estimated long-run parameters To make direct market returns comparable with REIT returns, we lever direct real estate returns using Modigliani and Miller’s (1958) formula: r eit = (r uit – r dt LTV it ) / (1-LTV it ) We use the actual quarterly leverage ratios of REITs as provided by NAREIT 7

Escrow Periods The direct real estate index values are based in part on prices that have been agreed upon some time in the past (Crosby and McAllister, 2004) This is because of the due diligence process Based on expert opinions, the maximum length of the escrow period is –90 days for apartments –180 days for other sectors –The average escrow periods are likely to be shorter than these, though, and in some cases the price is renegotiated during the due diligence process We base our main analysis on a 90-day escrow lag assumption As a robustness check, we lag the direct real estate index by various increments (45, 90, 135 and 180 days) 8

Demand Indices In theory, the perceived sluggish adjustment of direct real estate prices may be, at least partially, caused by asymmetries in buyers’ and sellers’ responses to shocks in the fundamentals If buyers respond prior to sellers, demand and liquidity are expected to respond prior to prices to changes in the fundamentals (Berkovec and Goodman, 1996; Hort, 2000; Fisher et al., 2003) –Empirical evidence supports this (Berkovec and Goodman, 1996; Oikarinen, 2012) As a robustness check, we test whether part of the sluggish adjustment of real estate prices is due to the sluggish adjustment of sellers’ reservation prices We use the demand (’constant-liquidity’) TBI indices 9

Sector Level (Levered) NAREIT and TBI Total Return Indices over 1994Q1-2010Q4 10

Long-Term Relations Cointegrating long-run relation can be identified in three out of the four sectors (an exception is the office sector) The long-run relations estimated by the baseline model (with 90- day escrow consideration) are used throughout the analysis Such long-term relations should not depend on slight differences in timing of the data The relations appear to be stable throughout the sample period and generally robust to the timing of the TBI data 11

Granger Causalities & Predictability Assuming that the escrow assumption is not relevant: REIT returns clearly lead direct real estate returns even when controlling for the property type and leverage Only TBI adjusts towards the long-run relation TBI returns can be predicted by previous period REIT returns (except for the retail sector) REIT returns remain significant in the equation for TBI returns even after including fundamentals in the models → emphasizes the predictive power of REIT returns with respect to TBI index returns 12

Granger Causalities & Predictability (Cont’d) The same applies, with the exception of the industrial sector, if the escrow period is 90 days, which is our ‘best estimate’ The escrow period would have to exceed 45 days for industrial, 90 for apartments, 135 for offices, and 180 for retail so not to observe lead-lag relationship between NAREIT and TBI index returns either directly through short-term dynamics or through the long-term relation This is unlikely with the exception of the industrial sector Also REIT prices seem to contain predictable elements All the estimated coefficients are stable throughout the sample period based on the Hansen stability test 13

Granger Causalities & Predictability: Office Sector 14

Impulse Responses With 90-Day Escrow In line with the Granger causality analysis Slow response of direct returns to REIT shocks in all sectors Slow response of REIT returns to direct market shocks only in the industrial sector TBI responses to sentiment and risk shocks also shown to be slower than REIT reaction The reaction signs generally as expected 15

Reaction Patterns for Offices (90-Day Escrow) 16

Robustness Check with Demand Indices: Summary with 90-Day Escrow Demand responses generally somewhat greater than TBI responses, as expected Lead-lag implications do not change except for the industrial sector Some slight changes in the significance (e.g., retail sector) Main changes: –Apartment sector: TBI Granger caused by long-term deviation but not by REITs; the lead-lag relation remains through the deviation from the long-run relation –Industrial sector: Cointegrating relation cannot be detected; TBI leads REITs both based on Granger causality tests and GIRFs (in the baseline 90-day case no clear lead-lag relations) 17

Summary and Practical Implications I Despite the potentially important practical implications, the previous empirical evidence on the lead-lag relations between securitized and direct real estate returns is not conclusive A number of data related issues could explain the observed lead-lag relations We find that REIT returns do lead direct real estate market returns even when catering for 1) property type, 2) leverage, 3) escrow periods, 4) asymmetries between buyers’ and sellers’ adjustment speeds, 5) long- term dynamics, and 6) the impact of fundamentals on the dynamics The industrial sector appears to be an exception REIT returns can be used to predict direct real estate returns (not only observed index values) 18

Summary and Practical Implications II In addition to responding slowly to shocks in REIT prices, direct RE prices typically react more sluggishly than REIT prices especially to economic sentiment (expectations) shocks The benefits of including both securitized and direct real estate in a long-horizon multiple asset portfolio are smaller than indicated by the typically reported quarterly correlations → Implications for the optimal portfolio allocation of a long-horizon buy-and-hold investor such as a pension fund 19