Real Estate Investment Trust Returns Research Articles

The Effects of Conventional and Unconventional Monetary Policy Shocks on US REITs Moments: Evidence from VARs with Functional Shocks

AbstractWe use a vector autoregressive model with functional shocks, capturing the shift of the entire term structure of interest rates on monetary policy announcement dates, to empirically evaluate the effects of conventional and unconventional monetary policy decisions on the Real Estate Investment Trusts (REITs) markets of the United States (US). Using 5-min interval intraday data, we analyze not only the impact on REITs returns, but also its realized variance (RV), realized jumps (RJ), realized skewness (RSK), and realized kurtosis (RKU) over the daily period of September 2008 to June 2021. While the effects of conventional monetary policy shocks on the moments of REITs returns tend to conform with economic theories, the same is not necessarily the case with unconventional monetary policy shocks. In addition, though monetary policy shocks have the most persistent and strongest effects on RJ, the extreme behaviour of the REITs market is also observed through RSK and RKU. Moreover, when we look into 10 REITs sectors, there is indeed heterogeneity in terms of the strength of the effect, but not so much in terms of the sign of responses of the various moments compared to the overall market. Our results have important implications for REITs market participants, given its exponential growth as an asset class.

Quantile dependence and asymmetric connectedness between global financial market stress and REIT returns: Evidence from the COVID-19 pandemic

Using daily data for the financial stress index of the US and real estate investment trusts (REITs) returns from February 2, 2020, to January 20, 2022, we investigate the frequency-dependent and asymmetric connectedness between global financial market stress and REIT returns for the top 12 REIT regimes in America, Europe, and Asia. We use a novel asymmetric, noise-reducing-domain EEMD-based quantile connectedness and quantile-on-quantile regression technique and the quantile vector autoregression (QVAR) connectedness approach. The findings divulge that at the upper quantile financial market stress is a major risk transmitter, transmitting risk towards Germany, France, Netherlands, New Zealand, the UK, and Canada. The findings of the study explicate the pivotal role of the financial soundness on the housing market, which is one of the main drivers of the economy. Investors and market participants should observe the conditional state of market dynamics and its associated policies for risk management and diversification strategies in real estate investment.

The Effects of Uncertainty Disclosures on REIT Returns

We examine the relationship between uncertainty-related disclosures in annual filings and market returns of REITs. We search annual filings (10-K) of Real Estate Investment Trusts (REITs) with the Securities and Exchange Commission (SEC) for uncertainty words. We then form rolling portfolios of REITs based on occurrences of uncertainty words in annual filing and track the returns. Our main finding is that there are no statically significant abnormal returns to these REIT portfolios. This supports the notion that the stock market is efficient in quickly incorporating any new information revealed in annual reports when the information becomes available.

Revisiting the nexus of REITs returns and macroeconomic variables

This paper employs the Bootstrap Fourier Granger Causality in Quantiles test to reevaluate the relationship between Real Estate Investment Trusts(REITs) returns and macroeconomic variables in the United States. Our empirical findings demonstrate a significant positive relationship between GDP and REITs returns across all quantiles, except for the lowest 0.1 quantile. Additionally, we observe a significantly negative relationship between the stock price index and REITs returns across all quantiles. Moreover, the study reveals that the unemployment rate and effective interest rate exhibit an effective increase in REITs returns.

Moments and momentum in the returns of securitized real estate: A cross-country study of risk factors driving real estate investment trusts before and during COVID-19

A real estate investment trust (REIT) is a company running a funding pool that allows people to invest in real estate without physical purchase. Since REITs are stock market-traded real estate assets, there is debate as to whether their returns are driven by stock market risk factors. In this regard, this paper examines the impact of the well-established equity market risk factors of momentum, skewness, and kurtosis on the returns of different types of REITs, including mortgage REITs (MREIT), equity REITs (EREIT), and hybrid REITs (HREIT), across five countries—Australia, the UK, the US, Japan, and Canada—during the period 2000–2022, controlling for other well-established factors in the asset pricing literature. The study first adds the skewness and kurtosis to analyze cross-national REIT returns via the Fama–French five-factor model. Next, the cross-national REIT dataset is built for the different periods and then tested for the robustness of the effect of the factors during the COVID-19 period. Findings indicate that the influence of momentum on the return of the REITs is consistently positive across countries and different types of REITs. However, the significance of momentum for different REITs in different countries varies. These results were robust during the COVID-19 period, providing further confirmation that REITs behave less like stocks rather than real estate investments, with significant implications for investors.

Predicting the returns of the US real estate investment trust market: evidence from the group method of data handling neural network

PurposeThe Group Method of Data Handling (GMDH) neural network has demonstrated good performance in data mining, prediction, and optimization. Scholars have used it to forecast stock and real estate investment trust (REIT) returns in some countries and region, but not in the United States (US) REIT market. The primary goal of this study is to predict the US REIT market using GMDH and then compare its accuracy with that derived from the traditional prediction method.Design/methodology/approachTo forecast the return on the US REIT index, this study used the GMDH neural network and the generalized autoregressive conditional heteroscedasticity (GARCH) model. In this test, the training samples, testing samples, and kernel functions of the GMDH model are controlled to investigate their impact on the accuracy of the machine learning approach. Corresponding experiments were performed using the GARCH model, and the accuracies of these two approaches were compared.FindingsCompared with GARCH, GMDH’s accuracy is much higher, indicating that the machine learning approach can provide a highly accurate prediction of REIT prices. The size of the training samples and the kernel functions in the GMDH model affect the accuracy of the prediction results. In particular, the kernel function has a significant impact on prediction accuracy. The linear and linear covariance kernel functions are simple to train and yield accurate predictions, whereas the quadratic function is difficult to train. Even with small training samples, GMDH can outperform GARCH in prediction accuracy.Research limitations/implicationsAlthough GMDH shows good performance in predicting the US REIT return, it is still a black-box model, and the algorithm is difficult for financial analysts to develop and customize. The data used in this study come from the US REIT market, which is the world’s largest and most liquid market.Social implicationsThis research shows that the GMDH model outperforms the GARCH model in forecasting REIT returns. Hence, investors can use the machine learning approach to make more accurate predictions of the target REITs’ returns and thus better investment decisions. Future investors and researchers may use GMDH to forecast the performance of REITs in other markets.Originality/valueThis is the first study to apply the GMDH neural network to the US REIT market and determine the impact of the two factors on its performance. For example, this research first discusses the impact of kernel functions on the US REIT market using the GMDH neural network. It also includes short-term daily prediction returns that were not previously considered, making it a valuable reference for financial industry analysts.

Connectedness structure of Pan-European Equity REITs

ABSTRACT This paper examines the interconnectedness among European equity REITs (Real Estate Investment Trusts) due to increased financialization of the real estate sector and European economic integration. It uses two models, Diebold and Yilmaz's generalised variance decomposition and Barunik and Krehlik's time-frequency dynamics framework. The study covers a sample of 42 Pan-European REITs over a 15-year period from 2004 to 2018 and finds that the connectedness among REITs is influenced by the geographical location and performance of commercial real estate sub-sectors. The dynamic analysis shows that the source of shocks does not determine the persistence, but rather how the markets interpret the shocks during high-frequency intervals. The paper concludes that the connectedness structure among REITs reflects uncertainty interaction, with some effects observed mainly at high frequency levels, and few unexpected shocks found in the long-term stability of REIT returns.

Effects of COVID-19 on the relationship between inflation and REITs returns in South Africa

PurposeThis research aims to ascertain the extent to which the coronavirus disease 2019 (COVID-19) epidemic affected the relationship between inflation and real estate investment trusts (REITs) returns in South Africa.Design/methodology/approachThis research used the Johansen cointegration test and effective test in establishing if there is a long-run cointegrating equation between the variables. To ascertain if COVID-19 resulted in a different relationship regime between inflation and REITs returns, the sequential Bai–Perron method was used.FindingsBetween December 2013 and July 2022, there was no evidence of a long-run relationship between inflation and REITs returns, and a restricted vector autoregressive (VAR) model with a period lag for each variable best describing the relationship. Using the sequential Bai–Perron method, for one break, the results show February 2020 as a structural break in the relationship. A cointegrating equation is also found for the period before the structural break and another after the break. Interestingly, the relationship is negative before the break and a new positive relationship (regime) is confirmed after the noted break.Practical implicationsThis research helps REITs stakeholders to position themselves in light of any changes to macroeconomic activity within South Africa.Originality/valueThis is one of the first studies to test inflation relationship with REITs returns in South Africa and the effects of COVID-19 thereof. This research helps REITs stakeholders to position themselves in light of any changes to macroeconomic activity within South Africa.

Relationship of public farmland and timberland REITs with their private equity counterparts and selected asset classes

PurposeThe purpose of this study is to examine the return sensitivity of public farmland and timberland real estate investment trusts (REITs) to private-equity farmland, timberland and real estate, long-term corporate bonds and large- and small-cap stocks. The study also examines time-dependent contributions of selected asset classes to farmland and timberland REIT volatility.Design/methodology/approachThe authors use a multi-factor asset pricing model under a seemingly unrelated regression framework to evaluate farmland and timberland REIT returns, and a state-space model with the Kalman filter to evaluate the time-dependent contributors of farmland and timberland REIT volatility. The authors first perform orthogonalized regressions to obtain pure independent factors, and then decompose volatility into individual asset components.FindingsSignificant loadings on financial assets are found for both farmland and timberland REITs, suggesting that they are generally driven by some common state variables. Large-cap stocks are found to be the major contributor of farmland and timberland REIT volatility, despite some differing patterns over time.Originality/valueEmpirical analysis of farmland REIT is very scarce. The authors compare the risk-return characteristics of farmland and timberland REITs under a state-space framework with the Kalman filter. This study can improve the understanding of the roles of farmland and timberland REITs in a multi-asset portfolio.

REITs impact on typical investment portfolio – further evidence of the sector split importance

The REIT (Real Estate Investment Trust) returns demonstrate a time-varying linear correlation with various equity indexes, therefore they are fit for multi-asset portfolio enhancement. On the one hand, each REIT sector is characterised by a unique set of return properties, and on the other, companies within those sectors remain homogenous. The aim of this research is twofold: firstly, to verify the earlier studies on how adding REITs to mixed equities/bonds portfolios affects their risk and return characteristics, and secondly, to contribute to these studies by examining the impact of adding different REIT sectors to such portfolios over a relatively long and more up-to-date sample, i.e. the period of 1990–2019. The results indicate that, in contrast to what some previous studies suggested, adding the REIT index exposure leads to a limited portfolio enhancement only. More significant and consistent effects can be achieved by the inclusion of individual REIT sectors in an investment portfolio. Apartment REITs offered diversification benefits across the entire spectrum in all the periods, while Industrials were useful across the curve in 1990s and 2010s. Self-storage exposure, on the other hand, improved the investment portfolio performance in each of the studied decades. In general, it was enough for investors who strived for portfolio improvement over the three decades between 1990 and 2019 to have a small portion of their Value holdings replaced with the REIT sector exposure to obtain a positive impact on both the returns and the risk.

Modelling the asymmetric effect of COVID-19 on REIT returns: A quantile-on-quantile regression analysis

The COVID-19 pandemic has affected all sectors of the economy resulting in unprecedented challenges for market participants, policymakers, and practitioners. This study envisages this issue from the perspective of real estate investment trusts (REITs), which is a relatively less analysed segment. We examine the impact of the COVID-19 pandemic on REIT returns for 12 top REIT regimes spread across America, Asia, and Europe under the bullish, bearish, and normal market conditions over the COVID-19 period (specifically from February 02, 2020, to January 24, 2022). We employ the quantile-on-quantile regression and causality-in-quantiles approach. We document a strong (weak) predictive power of COVID-19 cases on REIT returns within the lower (upper) conditioned quantiles. Our findings are of importance to market participants, practitioners, and regulators across REIT regimes.

Pandemic Proof Property Companies

Using a sample of 163 U.S. based equity real estate investment trusts (REITs), this paper explores the consequences of COVID-19 on securitized commercial property markets. More specifically, we first map the geographic location of each firm’s investment property holdings to gauge the degree of exposure of each REIT’s asset base to the pandemic. We next demonstrate these firm level exposure metrics are directly related to the negative returns encountered by REITs in the early months of the pandemic and explore what firm specific characteristics and attributes (notably financial flexibility and financing constraints) may moderate this relation and enhance the resiliency of their equity returns. Finally, we examine the impact of the Federal Reserve’s late-March intervention designed to address and soften the economic fallout of the pandemic and ensure the liquidity and stability of capital markets. After this intervention, previously observed relations and patterns between firm specific COVID-exposure levels and operating characteristics fail to retain their prior signs and significance. In sum, the magnitude of the government’s response to the economic challenges brought about by the coronavirus pandemic is shown to outweigh the importance of firm specific factors in predicting the resiliency of REIT returns during this crisis period.

Informational efficiency and GICS classification: Evidence from REITs

The historical promotion of the real estate industry in the Global Industry Classification Standard (GICS) system to a new headline sector should raise the informational efficiency of pricing for the asset class. We test this hypothesis for the first time using a large sample of publicly traded real estate investment trusts (REITs). Using two alternative statistical tests, we find that REIT returns become more random and less predictable following the event, suggesting better informational efficiency of REIT pricing. Furthermore, we find that the higher pricing efficiency is related to higher institutional ownership in the asset class in the same period. These findings should be of interest to academics and practitioners.

Economic Policy Uncertainty and Real Estate Market: Evidence from U.S. REITs

We examine how economic policy uncertainty (EPU) affects the monthly performance of U.S. real estate investment trusts (REITs) from 1985 to 2018. First, a positive shock in EPU impairs contemporaneous REIT returns but predicts higher positive future returns. Such a return reversion has not been previously documented, and we provide some explanations. Second, although EPU can explain for the current returns of both equity and mortgage REITs, EPU is unable to predict the future returns of mortgage REITs. Third, breaking down the main EPU index into its four components, we find that the impact of EPU is primarily from the broad newspaper coverage of policy-related economic uncertainties while the other components are less influential. Our results indicate that policy uncertainty can affect the value of real estate assets, and EPU is an economically important factor for understanding and pricing REITs.

Spillovers and Dynamic Correlations between REITs, Exchange Rates, and Equities in Japan

This paper investigates return transmission, volatility spillovers, and dynamic correlations between the Tokyo Stock Exchange (TSE) Real Estate Investment Trust (REIT) index, the Nikkei 225 index, and the yen/dollar exchange rate. As a result, we find many new findings and these all show our significant contributions as follows. First, there is return transmission from the Nikkei 225 to the TSE REIT index. Second, there is bidirectional return transmission between the Nikkei 225 and the yen/dollar exchange rate. Third, there are bidirectional volatility spillovers between the Nikkei 225 and the TSE REIT index. Fourth, there are volatility spillovers from the Nikkei 225 to the yen/dollar exchange rate. Fifth, dynamic conditional correlations (DCCs) between TSE REIT returns and Nikkei 225 returns are not low. Moreover, DCCs between Nikkei 225 returns and yen/dollar exchange rate changes are not high. Furthermore, DCCs between TSE REIT returns and yen/dollar exchange rate changes are quite low. These our new findings shall be useful for not only deepening our understanding of financial markets but also our related future research.

Overreaction‐based momentum in the real estate investment trust market

AbstractThis paper examines cross‐sectional return predictability in a real estate investment trust (REIT) market by using a continuing overreaction measure constructed based on the weighted average of signed volumes. We show that (a) trading strategies that involve buying REITs with an upward continuing overreaction and selling REITs with a downward continuing overreaction generate intermediate‐term momentum and long‐term return reversals; (b) the continuing overreaction measure provides superior predictive power in future REIT returns compared with the traditional predictor measured by past returns; and (c) overreaction‐based momentum is more pronounced when the market state continues in the same direction. Evidence from the REIT market provides direct support for the return prediction of the model based on investor overconfidence and biased self‐attribution rather than the model based on dividend growth theory.

Asset productivity, local information diffusion, and commercial real estate returns

AbstractAn extensive literature finds that indices of returns on equity real estate investment trusts (REITs) predict return indices in the private commercial real estate (CRE) market. Using a novel geographically weighted proxy for the quarterly performance of the property types within the local markets in which an REIT is invested, or property portfolio return (PPR), we find a “private predicts public” result in a cross‐sectional, firm‐level context. This finding suggests that geographically dispersed information and investors’ limited attention can delay timely stock price adjustments. Our findings also suggest that it is the diffusion of information about “local” price changes, rather than local supply elasticities, regulatory constraints, the degree of local information risk, current rental income, or local liquidity that predicts REIT returns. The PPRs associated with REIT allocations to major “gateway” markets are more predictive of REIT returns than the PPRs produced by allocations to secondary and tertiary markets. This study improves our understanding of the speed at which “local” information about the perceived productivity of a firm's assets is capitalized into stock prices.

Does listed real estate behave like direct real estate? Updated and broader evidence

ABSTRACT This study investigates a question relevant to many investors: do the broad Real Estate Investment Trust (REIT) return characteristics reflect those of the broad direct real estate markets. The paper makes several contributions to the literature in addition to using more recent data: (1) we use data for six countries (Australia, France, Germany, Netherlands, the U.K., and the U.S.); (2) we estimate both country-specific and panel models to increase the reliability and generalizability of the analysis; (3) we estimate a structural vector autoregressive model to be able to better and more reliably interpret the various shocks in the system; and (4) we investigate the effects of global liquidity shocks, among other shocks. Our results indicate that over the mid to long horizon, broad REIT and direct returns have similar characteristics and are highly correlated at the panel level. Also, the two types of exposure to real estate exhibit similar reactions to economic shocks. Thus, the paper makes a case that investors do not necessarily need to worry much about compositional effects when aiming to track broad international direct market performance by investing in listed real estate.

The Influencing Factors of Real Estate Investment Trust Yield under the Background of Internet Finance

Internet finance is an innovative financial service model formed under the background of Internet technology popularization. Based on Internet finance, the financial service mode can be rebuilt and the investment mode of many industries, including real estate, has been changed. More and more people are investing in real estate through trust funds. Real estate investment trusts (REITs) are becoming popular with investors. However, with the support of Internet finance, the factors affecting the return of real estate investment trust fund also become more and more. This paper mainly studies the influencing factors of REIT returns under the background of Internet finance, providing reference for improving the returns of trust funds and promoting the sustainable development of real estate investment. Considering the particularity of real estate investment trust on asset securitization and its as the universality of securities, real estate investment trust fund under the background of the Internet financial yield influence factors are divided into macroeconomic fundamentals, medium industry status and micro stock price fluctuations three levels, the analysis of the affecting factors are respectively.

Overreaction in the REITs Market: New Evidence from Quantile Autoregression Approach

Real estate investment trusts (REITs) provide portfolio diversification and tax benefits, a stable stream of income, and inflation hedging to investors. This study employs a quantile autoregression model to investigate the dependence structures of REITs’ returns across quantiles and return frequencies. This approach permits investigation of the marginal and aggregate effects of the sign and size of returns, business cycles, volatility, and REIT eras on the dependence structure of daily, weekly, and monthly REIT returns. The study documents asymmetric and misaligned dependence patterns. A bad market state is characterized by either positive or weakly negative dependence, while a good market state is generally marked by negative dependence on past returns. The results are consistent with under-reaction to good news in a bad state and overreaction to bad news in a good state. Past negative returns increase and decrease the predictability of REIT returns at lower and upper quantiles, respectively. Extreme positive returns in the lower (upper) quantiles dampen (amplify) autocorrelation of daily, weekly, and monthly REIT returns. The previous day’s REIT returns dampen autoregression more during recession periods than during non-recession periods. The marginal impact of the high volatility of daily returns supports a positive feedback trading strategy. The marginal impact of the Vintage REIT era on monthly return autocorrelation is higher than the New REIT era, suggesting that increased participation of retail and institutional investors improves market efficiency by reducing REITs’ returns predictability. Overall, the evidence supports the time-varying efficiency of the REITs markets and adaptive market hypothesis. The predictability of REIT returns is driven by the state of the market, sign, size, volatility, and frequency of returns. The results have implications for trading strategies, policies for the real estate securitization process, and investment decisions.