Market Efficiency in the Emerging Securitized Real Estate Markets

Felix Schindler

Centre for European Economic Research (ZEW)

Milan, 26th of June 2010

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Motivation

• Does the hypothesis of market efficiency hold for emerging securitized real estate markets?

• While focus has largely been placed on analyzing market efficiency with respect to international stock markets and other asset markets, there is comparably little research conducted on securitized real estate markets.

• It is well documented in financial literature that emerging markets are less efficient than more developed / matured markets.

• Existing research on this topic in real estate mainly focuses on the U.S. real estate stock market and is often based on the analysis of individual stocks.

• To our knowledge, no study explicitly analyzes market efficiency in emerging securitized real estate markets.

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Outline

• Motivation• Relevant Literature• Theoretical Background and Methodology• Data Description• Empirical Results• Trading Strategy• Conclusion

Outline Literature Methodology Data Description Empirical Results Trading Strategy Conclusion Appendix

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Relevant Literature

• Research is predominantly conducted on the U.S. market and for individual REITs or direct real estate; see e.g. Graff and Young (1997), Jirasakuldech and Knight (2005), Kuhle and Alvayay (2000), Mei and Gao (1995), Nelling and Gyourko (1998), Seck (1996).

• Research on international real estate markets: Kleiman et al. (2002), Schindler et al. (2009), Serrano and Hoesli (2009), Stevenson (2002).

• Conclusion of previous research results: mixed results, which might depend on the analyzed market, time period, and applied methodology (autocorrelation, variance ratios, runs, momentum, ARMA-GARCH-models, etc.).

Outline Literature Methodology Data Description Empirical Results Trading Strategy Conclusion Appendix

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Weak-Form Market Efficiency

Semistrong-Form Market Efficiency

Strong-Form Market Efficiency

Theoretical Background

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• Market efficiency is analyzed by focusing on the information contained in historical time series and the random walk model (joint hypothesis test).

• The following statistical tests are applied:– Autocorrelation test– Variance ratio test (heteroscedasticity-robust

estimators)– Multiple variance ratio test (heteroscedasticity-robust

estimators)– Runs test (direct test and less restrictive in its

assumptions than tests above)• Lead and lag structures of other time series and

determinants are not considered.

Methodology

Outline Literature Methodology Data Description Empirical Results Trading Strategy Conclusion Appendix

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Null and Alternative Hypotheses of Weak-Form Market Efficiency Tests

Significance Test Autocorrelation

Coefficient Variance Ratio Runs

Random Walk ( ) 0 0h for h ( ) 1 0VR h for h 0Z

Mean Aversion ( ) 0 0h for h ( ) 1 0VR h for h 0Z

Mean Reversion ( ) 0 0h for h ( ) 1 0VR h for h 0Z

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• 12 national emerging securitized real estate markets and 4 national matured securitized real estate markets for comparison only.

• Data on Global Property Research (GPR) indices.• Period: January 1992 – December 2009.• Frequency: monthly data.• The presented statistical results are based on

monthly data in local currency.

Data Description I

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Country Indices & Data Availability

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Data Description

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Summary of Statistical Tests

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• Clear ex-ante declaration of the position in the market and the trading guideline.

• Applying the same information set as for the statistical tests.

• Objective and restrictive decision criteria.• Keep it simple!

Trading Strategy Requirements

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• Feasible investment opportunity into the national securitized real estate indices.

• No market impact by individual investors.• Sufficient liquidity in the markets.• Tax effects, bid-ask spreads, and transaction costs

are not considered.• Two different trading strategies are applied:

• Without short selling• With short selling

Further Requirements Related to the Market Structure

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• The imposed restrictions are mainly carried out by a technical analysis based on moving averages (criticism: choice of moving average).

• Announced trading strategy:

Invest into the national securitized real estate market (long position), if index level is above the moving average, otherwise do not invest or short the index.

Moving Averages as a Suitable Instrument

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Results from the Trading Strategy I

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Results from the Trading Strategy II

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• The hypothesis of weak-form market efficiency cannot be rejected for most emerging securitized real estate markets.

• The statistical test results are supported from technical analysis. Excess returns compared to a buy-and-hold strategy seem to be limited compared to matured markets and are weakly significant only.

• The results are in contrast to the findings by Schindler et al. (2009) and Serrano and Hoesli (2009) for mainly matured markets.

• Thus, the often stated hypothesis in financial literature that emerging markets are less efficient than matured markets is of limited validity for securitized real estate markets!

Conclusion

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Thank you for your attention!

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• Campbell, J.Y.; Lo, A.W.; MacKinlay, A.C. (1997): The Econometrics of Financial Markets, Princeton/New Jersey.

• Fama, E.F. (1965): Random Walks in Stock Market Prices, Financial Analysts Journal 21(5), 55-59.

• Fama, E.F. (1970): Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance 25(2), 383-417.

• Fama, E.F. (1991): Efficient Capital Markets: II, The Journal of Finance 46(5), 1575-1617.

• Fama, E.F. (1998): Market Efficiency, Long-Term Returns, and Behavioral Finance, Journal of Financial Economics 49(3),283-306.

• Graff, R.A.; Young, M.S. (1997): Serial Persistence in Equity REIT Returns, Journal of Real Estate Research 14(3), 183-214.

• Jirasakuldech, B.; Knight, J.R. (2005): Efficiency in the Market for REITs: Further Evidence, Journal of Real Estate Portfolio Management 11(2), 123-132.

• Kleiman, R.T.; Payne, J.E.; Sahu, A.P. (2002): Random Walk and Market Efficiency: Evidence from International Real Estate Markets, The Journal of Real Estate Research 24(3), 279-297.

• Kuhle, J.L.; Alvayay, J.R. (2000): The Efficiency of Equity REIT Prices, Journal of Real Estate Portfolio Management 6(4), 349-354.

Literature (I)

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• Lo, A.W.; MacKinlay, A.C. (1988): Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test, The Review of Financial Studies 1(1), 41-66.

• Lo, A.W.; MacKinlay, A.C. (1989): The Size and Power of the Variance Ratio Test in Finite Samples, Journal of Econometrics 40(2), 203-238.

• Mei, J.; Gao, B. (1995): Price Reversal, Transaction Costs, and Arbitrage Profits in the Real Estate Securities Market, Journal of Real Estate Finance and Economics 11(2), 153-165.

• Nelling, E.; Gyourko, J. (1998): The Predictability of Equity REIT Returns, Journal of Real Estate Research 16(3) 251-268.

• Schindler, F.; Rottke, N.; Fuess, R. (2009): Testing the Predictability and Efficiency of Securitized Real Estate Markets, ZEW Discussion Paper 09-054.

• Seck, D. (1996): The Substitutability of Real Estate Assets, Real Estate Economics 24(1), 75-95.

• Serrano, C.; Hoesli, M. (2009): Are Securitized Real Estate Returns More Predictable than Stock Returns?, Journal of Real Estate Finance and Economics (forthcoming).

• Stevenson, S. (2002): Momentum Effects and Mean Reversion in Real Estate Securities, Journal of Real Estate Research, 23(1/2), 47-64.

Literature (II)

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Appendix

• Statistical test description• Statistical tests on the results from the technical

trading rule• Results from autocorrelation tests• Results from variance ratio tests• Results from runs tests

Outline Literature Methodology Data Description Empirical Results Trading Strategy Conclusion Appendix

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The examination of the random walk hypothesis in its weakest form is often conducted by analyzing autocorrelation, whereas the assumption of independence between lagged returns is reduced to uncorrelated lagged returns:

Test statistic:

Autocorrelation Test

.0

kkρ

2ktr

2tr

ktr;tr

2aK

1k

2

K χ~kT

kρ2TTQ

with: k = lag / degree of freedom, (k) = autocorrelation coefficient of order

k.

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Variance Ratio Test

)q(rPlnPlnqPlnPln t2

1tt2

qtt2

The approach suggested by Lo and MacKinlay (1988) tests the linearity of the variance of the return series related to the lag structure:

with: q = lag.

Given the validity of the random walk hypothesis, the variance ratio of the unbiased estimator of the variance is one:

.1rq

)q(r)q(VR

t2t

2

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In the Bernoulli-case, the expected value of the runs is defined by:

,)1()1(n2NE 22runs

with: n = number of observation, Nruns = number of runs,

= probability.

1).(0,N~π)]-(13ππ)[1(1nπ2

π)-(12nπ2

1N

Zaruns

Considering the adjustment suggested by Wallis and Roberts (1957), the Z-statistic is calculated as follows:

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Runs Test

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Statistical Tests on the Results I

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Statistical Tests on the Results II

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Statistical Tests on the Results III

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Statistical Tests on the Results IV

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Results from Autocorrelation Tests

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Results from Variance Ratio Tests I

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Results from Variance Ratio Tests II

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Results from Variance Ratio Tests III

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Results from Variance Ratio Tests IV

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Results from Runs Tests