Using data for the 1978-2008 period, this study presents evidence for cointegration between securitized (NAREIT) and direct (NCREIF) total return indices. Cointegration between the indices indicates that REITs and direct real estate are substitutable in the portfolio of a longhorizon buy-and-hold investor. Since the real estate indices are not found to be cointegrated with the stock market, REITs and direct real estate are likely to have similar long-term diversification benefits in a stock portfolio. In line with prior expectations, only direct real estate is found to currently adjust towards the cointegrating relation, with NAREIT returns leading NCREIF returns. However, giving support to the often stated argument regarding weaker informational efficiency of the REIT market prior to the "new REIT era", the results show evidence for the predictability of NAREIT returns during the 1980s. It is also found that at the beginning of the "new REIT era" a large and long-lasting deviation from the long-run relation between NAREIT and NCREIF emerged. However, there is no evidence of a permanent structural break in the long-run relation since the deviation appears to have been only temporary.

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We use sector level REIT and transaction-based direct real estate data for the period 1994-2010 to provide a clearer understanding of the dynamic relations between public and private real estate returns. We add leverage to private returns to make the private data more comparable with the REIT data. We also include economic fundamentals in the analysis to take account of the influence of fundamentals on real estate market dynamics. Moreover, we consider the influence of the 'escrow lag' in the recording of private market prices. The estimated vector error-correction and vector autoregressive models, Granger causality tests, impulse response functions and variance decompositions all provide evidence of REIT returns leading private returns in the office, retail, and apartment sectors. These lead-lag relations appear to be due to the slow reaction of private market returns to shocks in REIT returns and in some economic fundamentals. In the industrial sector, such lead-lag relation cannot be observed, however.

Predictability, Time series models, ARMA–EGARCH, REITs, Securitized real estate, C53, C22, G15,

This paper uses fractional cointegration analysis to examine whether long-run relations exist between securitized real estate returns and three sets of variables frequently used in the literature as the factors driving securitized real estate returns. That is, we examine whether such relationships are characterized by long memory (long-range dependence), short memory (short-range dependence), mean reversion (no long-run effects) or no mean reversion (no long-run equilibrium). The forecasting implications are also considered. Empirical analyses are conducted using data for the U.S., the U.K., and Australia. We find strong evidence of fractional cointegration between securitized real estate and the three sets of variables. Such relationships are mainly characterized by short memory although long memory is sometimes present. The use of fractional cointegration for forecasting purposes proves particularly useful since the start of the financial crisis.

This paper analyzes the role played by financial assets, direct real estate, and the Fama and French factors in explaining EREIT returns and examines the usefulness of these variables in forecasting returns. Four models are analyzed and their predictive potential is assessed by comparing three forecasting methods: time varying coefficient (TVC) regressions, vector autoregressive (VAR) systems, and neural networks models. Trading strategies on these forecasts are compared to a passive buy-and-hold strategy. The results show that EREIT returns are better explained by models including the Fama and French factors. The VAR forecasts are better than the TVC forecasts, but the best predictions are obtained with neural networks and especially when they are applied to the model using stock, bond, real estate, size, and book-to-market factors.

Choosing an appropriate benchmark is not unproblematic for academics or practitioners. Index construction methodologies vary from index to index as tradeoffs are made between the breadth of market coverage and the investability of the securities in the index. This paper examines the nuances between the most commonly used global securitized real estate benchmarks. A comparison of their construction methodologies, returns, and risk is performed, and the correlations between the various benchmarks are assessed. The composition of global securitized real estate markets is also analyzed. We conclude that the GPR General Property Share Index and the S&P/Citigroup World Property Index are more appropriate to examine the performance of the market as a whole, while the GPR 250 Property Share Index and the FTSE EPRA/NAREIT Global Real Estate Index are better suited to evaluate portfolio performance.

This paper contributes to the literature by identifying the response patterns of direct and indirect real estate returns to shocks in the market fundamentals. The response speeds are estimated with vector autoregressive models using TBI and NAREIT returns for the period 1994-2009 in the United States. To avoid the potential influence of different property mixes and of leverage on the dynamics, we use sector level data and deleveraged NAREIT returns. The findings indicate that REIT returns lead direct real estate returns even when catering for the property type and for leverage. Our estimations suggest that this lead-lag relationship is due to the sluggish reaction of direct real estate prices to unexpected changes both in the fundamentals and in REIT prices. The findings further suggest that the perceived lead-lag relations are not only due to the slow adjustment of sellers' reservation prices, but also due to the sluggish reaction of demand in the direct real estate market.