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

Geoffrey M Ngene,Ivan F Julio,Catherine Anitha Manohar

doi:10.3390/jrfm13110282

Abstract

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.

Highlights

Real estate investment trusts (REITs) are broadly categorized into equity REITs (EREITs) and mortgage REITs (MREITs)
The results for the AR (1) and quantile autoregression (QAR) (1) models are gathered in Table 2a,b for EREIT and MREIT, respectively
Consistent with the autocorrelation test based on the data-driven Portmanteau tests7, all the AR (1) coefficients, except for the daily returns of EREIT, are insignificant at daily, weekly, and monthly frequency, implying that the behavior of EREIT and MREIT returns at the means is characterized by a white noise process or unpredictability

Summary

Introduction

Real estate investment trusts (REITs) are broadly categorized into equity REITs (EREITs) and mortgage REITs (MREITs). The presence of significant positive or negative autocorrelation (serial dependence) of REIT returns at any quantile, either due to under-reaction or overreaction of the investors, coupled with an intermittent absence of autocorrelation, supports the time-varying (adaptive) market hypothesis (AMH) postulated by Lo (2004, 2017) and Brennan and Lo (2011). The dependence structure is a function of the state of the market, sign, size, volatility and time aggregation (frequency) of returns, and the class (EREIT and MREIT) of REITs. The episodic predictability and non-predictability of REITs returns at different quantiles, and return frequencies support the REITs’ markets time-varying efficiency and adaptive market hypothesis.

Data Sources and Data Characterization

Econometric Model

Empirical Results

Summary and Conclusions