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Understanding the pricing of real estate equities is a central objective of real estate research. This paper aims to investigate the impact of liquidity on European real estate equity returns, after accounting for well-documented systematic risk factors.

Based on risk factors derived from general equity data, the authors extend the Fama-French time-series regression approach by a liquidity factor, using a pan-European sample of 272 real estate equities.

The empirical results indicate that liquidity is a significant pricing factor in real estate stock returns, even after controlling for market, size and book-to-market factors. In addition, the authors detect that real estate stock returns load predominantly positively on the liquidity risk factor, suggesting that real estate equities tend to behave like illiquid common equities. These findings are underpinned by a series of robustness checks. Running a comparative analysis with alternative factor models, the authors further demonstrate that the liquidity-augmented asset-pricing model is most appropriate for explaining European real estate stock returns.

The inclusion of sentiment and downside risk factors could provide further insights into real estate asset pricing in European capital markets.

This is the first study to examine the role of liquidity as a systematic risk factor in a pan-European setting.

The purpose of this paper is to investigate the pricing of European real estate equities. The study examines the main drivers of real estate equity returns and determines whether loadings on systematic risk factors – the excess market return, small minus big (SMB), HIGH minus low (HML) – can explain cross‐sectional return differences in unconditional as well as in conditional asset pricing tests.

The paper draws upon time‐series regressions to investigate determinants of real estate equity returns. Rolling Fama‐French regressions are applied to estimate time‐varying loadings on systematic risk factors. Unconditional as well as conditional monthly Fama‐MacBeth regressions are employed to explain cross‐sectional return variations.

Systematic risk factors are important drivers of European real estate equity returns. Returns are positively related to the excess market return and to a value factor. A size factor impacts predominantly negatively on real estate returns. The results indicate increasing market integration after the introduction of the Euro. Loadings on systematic risk factors have weak explanatory power in unconditional cross‐section regressions but can explain returns in a conditional framework. Beta – and to a lesser extent the loading on HML – is positively related to returns in up‐markets and negatively in down markets. Equities which load positively on SMB outperform in down markets.

The implementation of a liquidity or a momentum factor could provide further evidence on the pricing of European real estate equities.

The findings could help investors to manage the risk exposure more effectively. Investors should furthermore be able to estimate their cost of equity more precisely and might better be able to pick stocks for time varying investment strategies.

This is the first paper to examine the pricing of real estate equity returns in a pan‐European setting.

The risk-return relationship of real estate equities is of particular interest for investors, practitioners and researchers. The purpose of this paper is to examine, in an asset pricing framework, whether the systematic risk factors play a significantly different role in explaining the returns of listed real estate companies, compared to general equities.

Running the difference test of the Fama-French three-factor and the liquidity-augmented asset pricing model, the authors analyze the effect of the systematic risk factors related to market, size, BE/ME and liquidity in a time-series setting over the period July 1992 to June 2012. By applying the propensity score matching (PSM) algorithm, the authors bypass the “curse of dimensionality” of traditional matching techniques and identify a comparable control sample of general equities, in terms of the relevant firm characteristics of size, BE/ME and liquidity.

The empirical results indicate that European real estate equity returns load significantly differently on the size, value and liquidity factor, while the influence of the market factor seems to be equivalent. In addition, the authors find an economically and statistically significant underperformance of European real estate equities, after accounting for the diverging role of systematic risk factors. Running the conditional time-series regression, the authors further reveal that these findings are predominately caused by the divergent risk-return behavior of real estate equities in economic downturns.

Due to the diverging role of the systematic risk factors in pricing real estate equities, the authors provide evidence of potential diversification benefits for investors and portfolio managers.

This is the first real estate asset pricing study to dissect the unique risk-return relationship of real estate equities by employing propensity score matching.

The purpose of this paper in this context is to examine the impact of asset liquidity on real estate equity returns, after taking well-documented systematic risk factors into account. Due to their unique characteristics, real estate equities constitute an inherently low degree of underlying asset liquidity.

Following the Fama-French time-series regression approach, the authors extend the conventional asset pricing model by a real estate-specific asset liquidity factor (ALF), using a sample of 244 real estate equities.

The results, based on monthly data for the period 1999-2012, reveal that asset liquidity is a relevant pricing factor which contributes to explaining return variations in real estate equity markets. Accordingly, investors expect a risk premium from listed real estate companies with a low degree of asset liquidity, which is especially the case for companies facing financial constraints and during economic downturns. Furthermore, an investment strategy exploiting differences in the underlying asset liquidity yields considerable average excess returns of upto 8.04 per cent p.a.

Considering the findings presented in this paper, asset liquidity should receive special attention from investors, as well as from the management boards of listed real estate companies. While investors who ignore the magnitude of asset liquidity may systematically misprice real estate equities, management can influence the firm’s cost of capital by adjusting the underlying asset liquidity.

This is the first study to examine the role of an ALF in a real estate asset pricing framework.

The purpose of this paper is to investigate changes in the commercial real estate market dynamics as a function of and conditional to the shifts in market state-space environment that can influence agent responses.

The analytical design uses a comparative computational experiment to address the performance of property assets in the current market based on comparison with prior structural patterns. The latent variables developed across market sectors are used to test agent behavior contingent on the perspectives of capital asset pricing conditionals (CAPM) and a behavioral momentum/herd construct. The state-space momentum analysis can assist the comparative analysis of current levels and shifts in property asset performance given the issues that have arisen with the financial crisis of 2007-2009.

An analytic approach is employed framed by a situation-dependent model. This frame considers risk profiles characterizing the perspectives and preferences guiding a delineated market state. This perspective is concerned with the possibility of shifts in market momentum and representativeness conditioning investor expectations. It is observed that the current market (post-crisis) has changed significantly from the prior operations (despite the diversity observed in prior market states). The dynamics of initial findings required an additional test anchored to the performance of the general capital market and the real economy across time. This context supports the use of a modified CAPM model allowing the consideration of opportunity cost in a space-time dynamic anchored with the consideration of equity, debt, riskless asset and liquidity options as they varied for the representative agents operating per market state.

This paper integrates neoclassical and behavioral economic constructs. Combines asset pricing with prospect theory and allows the calculation of endogenous time-preferences, risk attitudes and formulation and testing of hyperbolic discounting functions.

The research shows that market structure and agent behavior since the financial crisis has changed from the investment and valuation perspectives operating as observed and measured from 1970 up to 2007. In contradiction to the long-term findings of Reinhart and Rogoff (2008), but in compliance with common perspectives and decision heuristics often employed by investors, this time things have changed! Discounting and expected rates of return are dynamic and are hyperbolic and not constant. Returns and investment for property assets are situational (market state-space specific) and offer a distinct asset class, not appropriately estimated by many of the traditional financial models.

Assist in supporting insights to measure in errors and equations that result in inefficient resource allocation and beta discounting that supports the financial crisis created by assets subject to long-term decision needs (delta function).

The paper offers a combination and comparison of neoclassic asset pricing using a modified CAPM (two-pass) approach within the structural frame of Kahneman and Tversky’s (1979) prospect theory. This technique allows the consideration of the effects of present bias, beta-delta functions and the operation of the Allais Paradox in market states that are characterized by gains and losses and thus risk aversion and risk seeking behavior. This ability for differentiation allows for the development of endogenous time-preferences and hyperbolic discounting factors characteristic of commercial property investment.

The home is a substantial investment for most individual investors but the assessment of risk and return of residential real estate has not been well explored yet. The existing real estate pricing literature using a CAPM-based model generally suggests very low risk and unexplained excess returns. However, many academics suggest the residential real estate market is unique and standard asset pricing models may not fully capture the risk associated with the housing market. The purpose of this paper is to extend the asset pricing literature on residential real estate by providing improved CAPM estimates of risk and required return.

The improvements include the use of a levered β which captures the leverage risk and Lin and Vandell (2007) Time on Market risk premium which captures the additional liquidity risk of residential real estate.

In addition to presenting palatable risk and return estimates for a national real estate index, the results of this paper suggest the risk and return characteristics of multiple cities tracked by the Case Shiller Home Price Index are distinct.

The results show higher estimates of risk and required return levels than previous research, which is more consistent with the academic expectation that housing performs between stocks and bonds. In contrast to most previous studies, the authors find residential real estate underperforms based on risk, using standard financial models.

Recent studies in the field of behavioral finance have highlighted the importance of investor sentiment in the return-generating process for general equities. By employing an asset pricing framework, this paper aims to evaluate the performance of European real estate equities, based on their degree of sentiment sensitivity.

Using a pan-European data set, we classify all real estate equities according to their sentiment sensitivity, which is measured relative to the Economic Sentiment Indicator (ESI) of the European Commission. Based on their individual sentiment responsiveness, we form both a high- and low-sensitivity portfolio, whose returns are included in the difference test of the liquidity-augmented asset pricing model. In this context, we analyze the performance of sentiment-sensitive and sentiment-insensitive real estate equities with a risk-adjusted perspective over the period July 1995 to June 2012.

While high-sensitivity real estate equities yield significantly higher raw returns than those with low-sensitivity, we find no evidence of risk-adjusted outperformance. This indicates that allegedly sentiment-driven return behavior is in fact merely compensation for taking higher fundamental risks. In this context, we find that sentiment-sensitive real estate equities are exposed to significantly higher market risks than sentiment-insensitive ones. Based on these findings, we conclude that a sentiment-based investment strategy, consisting of a long-position in the high-sensitivity portfolio and a short-position in the low-sensitivity one, does not generate a risk-adjusted profit.

Although this study sheds some light on investor sentiment in European real estate stock markets, further research could usefully concentrate on alternative sentiment proxies.

This is the first study to disentangle the relationship between investor sentiment and European real estate stock returns.

This paper aims to investigate the repercussions and impact of corporate real estate on the returns of non-real-estate equities in a time-series setting. While the ownership of real estate constitutes a considerable proportion of most listed firms’ balance sheet, in the existing literature, whether or not the benefits outweigh the risks associated with corporate real estate, is the subject of controversy.

The role of corporate real estate ownership in the pricing of returns is examined, after taking well-documented systematic risk factors into account. Employing a data sample from 1999 to 2014, the conditions and characteristics faced by firms with distinct levels of corporate real estate holdings are identified and analyzed.

The findings reveal that corporate real estate intensity indeed serves as a priced determinant in the German stock market. Among other results, the real-estate-specific risk factor shows countercyclical patterns and is particularly relevant for companies within the manufacturing sector.

The findings provide new insights into the interpretation of corporate real estate and expected general equity returns. Thus, the present analysis is of particular interest for investors, as well as the management boards of listed companies.

To the best of the author’s knowledge, this is the first paper to investigate the ownership of corporate real estate as a priced factor for German equities, after accounting for the well-documented systematic risk factors, namely, market (market risk premium), size (small minus big) and book-to-market-ratio (BE/ME) (high minus low).

The article analyzes the effects of the coronavirus disease 2019 (COVID-19) pandemic on commercial real estate prices, with a particular focus on European markets.

The authors start by highlighting caveats to bear in mind when referring to direct real estate indices. The authors then analyze the behavior of commercial real estate prices during the pandemic, emphasizing differences across property types. For that purpose, the authors use data for both direct and listed real estate and further discuss changes in the main factors affecting commercial real estate pricing. The article then turns to discussing the likely trajectory of commercial real estate prices in the future.

The authors report that retail and hospitality properties and to a lesser extent office buildings have been affected the most by COVID-19, while the residential and industrial sectors have been less affected by the crisis. The authors maintain that the future trajectory of prices will vary across sectors and that the type and location of assets will become increasingly important in their valuation.

This paper provides for a better understanding of the behavior of commercial real estate prices during the COVID-19 pandemic.

The paper aims to form system dynamics modeling in introduced in conjunction with econometric analysis and planned scenario analysis which will uniquely structure the process whereby the ex ante capital values of the prime retail real estate sector.

The integrated system dynamics model investigates the structural factors affecting a unique expectation‐centered capital value (CV) formation of the prime retail real estate sector, through system dynamics modeling, econometric analysis , and the analysis of planned scenarios. This model extends beyond the usual lags and time line aspect of the price discovery process. The retail real estate sector is investigated within the Singapore context, as this sector changes dynamically and non‐linearly in relation to rental, cost and general demand expectations and to exogenous shocks like the Severe Advanced Respiratory Syndrome (SARS) outbreak. These macroeconomic factors are introduced to investigate their impact on retail space CVs through sensitivity analysis, during the simulation period of 20 quarters from the zero reference quarter (2Q2002).

The paper finds that simulation runs of the expectations‐centered system dynamics model are based on three scenarios. Sensitivity analysis is conducted for each scenario. Optimistic scenarios' CVs are lower than those of the likely scenario, owing to developers forming excessively high expectations that cannot be met by the actual rental levels. Pessimistic scenarios' CVs are highest. Based on bounded logic and the conditions for all scenarios, there are huge differences in expectations resulting in a large disparity in the endogenous CVs. Low actual rents are primarily due to poor informational efficiency, as the prime retail real estate sector is not transparent enough, and that many transactions are privately closed. Expectations cannot be met as the market information is not disseminated extensively through the agents and players. The scenarios clearly highlight the problem of informational non‐availability in the sector. The main policy implication is a need for a more transparent system of sharing rental and pricing information for the retail real estate sector, which is meaningful for real estate developers, investors and urban planners to sustain the retail real estate sector's viability.

This paper takes system dynamics modeling to the next level of incorporating econometric analysis, to estimate the sensitivity of retail rent to cost and the change in retail rent, for effectively structuring the dynamic process whereby the ex ante CVs of the prime retail sector in Singapore are formed and assessed, through a unique and rigorous expectations‐centered system dynamics model of rents, cost, retail stock, general demand and exogenous factors.