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The liquidity of direct real estate has been surrounded by mystery. Research in the USA and in the UK has contributed much to clarify the liquidity issue of direct real estate. In The Netherlands, not much research exists on this issue; however, a major ALM advisory firm in The Netherlands suggests a liquidity factor of 1.5 times the standard deviation of the ROZ/IPD real estate index, leading to a 50 percent higher risk compared to the current ROZ/IPD real estate index risk. This paper aims to investigate this issue.

The paper investigates whether this is a reasonable assumption by approaching the issue from several perspectives. First, the transaction process, the effects of heterogeneity and the size of the property are reviewed. The market risk between the date of the decision to sell the property and the date on which it was actually sold is also reviewed. The last element reviewed is the reallocation risk, in other words missed opportunities that have arisen because it could take longer to sell property than to sell stocks or bonds. Extensive anonymous information from the main institutional investors in The Netherlands is used, as well as interviews with the main brokers in The Netherlands. The survey is placed in an international context by comparing the results as well as the methods to previous surveys in the UK.

As a result suggestions are presented about risk premiums as a protection against the liquidity risks which turn out to be quite low, much lower than the 50 percent increase of the risk premium on top of the ROZ/IPD real estate index's standard deviation of the total return. The results are compared to risk premiums for stocks and bonds at times of high and average returns.

So far not many surveys have been done on this subject using the bottom up approach. If there were, those have been looked at in the literature review. The unique ROZ/IPD databank allows us to come up with real quantitative results related to the different types of real estate liquidity risks. The paper has identified five of those. The survey is restricted to results in a growing market because of the time frame and it is strongly recommended to repeat it after a depressed market.

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 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 provide new insights into asset liquidity in direct commercial real estate investment in the UK. Transaction data provided by four institutional investors of commercial real estate are used to test for changes in asset liquidity as manifest in recorded times from price agreement to deal completion. Median times to completion by stage of the transaction are presented alongside industry estimates.

Stages of the transaction process are modelled and median times per stage calculated to track changes in asset liquidity over, and between, the two periods of the study (2000‐2002 and 2005‐2008). Real times to completion are considered in conjunction with estimated times compiled through interviews with senior level investment professionals. This paper applies the Wilcoxon rank‐sum test to determine the significance of variation in median times across the two study periods.

This paper provides empirical evidence that liquidity increased from 2000 to 2008. Median times from price agreement to completion decreased significantly (p=0.015) from 2000‐2002 to 2005‐2008, indicating an increase in asset liquidity in step with an overall increase in transaction volume. Furthermore, senior investment actors were found to persistently over‐estimate transaction efficiency and underestimate liquidity risk when acquiring and disposing of commercial properties.

This work offers new insights into the changing nature of asset liquidity over the last decade based on a limited number of transactions. Additional studies involving larger samples of transactions would provide still greater insight into commercial real estate liquidity dimensions.

The paper presents evidence of pro‐cyclicality; asset liquidity varies positively with overall transaction volumes, and investment actors were found to overestimate asset liquidity suggesting a persistent underestimation of liquidity risk.

This paper addresses a gap in the extant literature offering real time on market‐time to completion observations alongside investor estimates. Median times to completion have been modelled and presented, together with time estimates provided by industry experts. Also, for first time in real estate research, median times to completion are shown to shorten significantly in‐line with increasing transaction volumes.

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 minimization of financial losses and costs stemming from the credit recovery process is strictly connected with the time necessary to complete the procedure: in real estate credits, it depends on the liquidity and the efficiency of the enforcement procedures. The purpose of this paper is to test the relevance of the economic cycle in Italy on the determinants of the recovery process both at national and regional level.

The first step is to identify the determinants of the real estate loans recovery process duration by the means of the review of the existing literature. The second step develops an empirical analysis to appraise the relevance of the economic cycle on the liquidity of the real estate market and efficiency of real estate enforcement proceedings. The relevance of the economic cycle is verified through, first, a correlation analysis of the selected indexes with the national and regional gross domestic product (GDP) and, second, a regression analysis of the selected indexes on the current and lagged GDP. As it concerns the liquidity of the real estate market, a turnover index is considered stratified both at sectoral and geographical level, while for the real estate enforcement procedures the paper analyzes indexes based on both the turnover of ended and filed proceedings and the pending proceedings outstanding at the year‐end.

The empirical results demonstrated that, in some sectors and geographic areas, the market liquidity is influenced by the national and the regional economic cycles, both expressed at current values and, moreover, the sign of the relationship is frequently negative. As it concerns the enforcement procedures efficiency, empirical evidence does not support the direct influence of the current or past economic cycle on it, leaving room for the relevance of the competent court specific features.

The paper considers the Italian market, that is featured by a moderate level of the average loan to value and, above all, by lengthiness administrative procedures. The paper contributes to the existing literature through the integrated examination of the relationship between the recovery process determinants and the national and regional economic cycles over different geographic areas.

After the Global Financial Crisis in 2008, the impact of expanded liquidity in the financial market has drawn attention. The purpose of this paper is to examine the relationship between liquidity in financial markets and office markets across Asian countries. In particular, the research not only examines the effect of normal liquidity on real estate markets, but also the effects of excess liquidity are specifically highlighted.

This paper uses panel estimation utilizing quarterly data from the first quarter of 2007 to the fourth quarter of 2015. Taking both time and location dimensions into account allows for a more precise estimate of the relationship between liquidity and office market’s yields.

Per the empirical outcome, an increasing excess liquidity tends to decelerate the value of office yields in six major Asian office market centers due to the positive effect on commercial real estate value. This effect is also identified by comparing the difference between the level of fitted yields and actual yields.

The results enhance the understanding of commercial real estate yield determinants. Furthermore, the results can be used to assess the impacts of liquidity on major office markets in Asia.

This paper attempts to uncover the impact of liquidity in financial markets on the office market yields. To better understand the relationship, the concept of excess liquidity is adopted and further exploration of each office market is conducted by comparing the fitted yields, which is computed considering the effects of excess liquidity on yield levels and actual yields.

The purpose of this paper is to establish an optimum mix of liquid, publicly traded assets that may be added to a real estate portfolio, such as those held by open-ended funds, to provide the liquidity required by institutional investors, such as UK defined contribution pension funds. This is with the objective of securing liquidity while not unduly compromising the risk-return characteristics of the underlying asset class. This paper considers the best mix of liquid assets at different thresholds for a liquid asset allocation, with the performance then evaluated against that of a direct real estate benchmark index.

The authors employ a mean-tracking error optimisation approach in determining the optimal combination of liquid assets that can be added to a real estate fund portfolio. The returns of the optimised portfolios are compared to the returns for portfolios that employ the use of either cash or listed real estate alone as a liquidity buffer. Multivariate generalised autoregressive models are used along with rolling correlations and tracking errors to gauge the effectiveness of the various portfolios in tracking the performance of the benchmark index.

The results indicate that applying formal optimisation techniques leads to a considerable improvement in the ability of the returns from blended real estate portfolios to track the underlying real estate market. This is the case at a number of different thresholds for the liquid asset allocation and in cases where a minimum return requirement is imposed.

The results suggest that real estate fund managers can realise the liquidity benefits of incorporating publicly traded assets into their portfolios without sacrificing the ability to deliver real estate-like returns. However, in order to do so, a wider range of liquid assets must be considered, not just cash.

Despite their importance in the real estate investment industry, comparatively few studies have examined the structure and operation of open-ended real estate funds. To the authors’ knowledge, this is the first study to analyse the optimal composition of liquid assets within blended or hybrid real estate portfolios.

Real estate is the last major asset class without liquid derivatives markets. The reasons for that are not fully known or understood. Therefore, the purpose of this paper is to better understand the main factors that influence the propensity of commercial real estate investors in the UK to employ property derivatives.

The research methodology that was chosen for this research is grounded theory which, in its original form, goes back to Glaser and Strauss (1967). A total of 43 interviews were conducted with 46 real estate professionals in the UK from property investment management firms (investing directly or indirectly in real estate), multi-asset management firms, real estate investment trusts, banks, and brokerage and advisory firms, among others.

The research results show 29 factors that influence the propensity of direct and indirect real estate investors in the UK to employ property derivatives. Out of the 29 factors, the current research identified 12 factors with high-explanatory power, 6 with a contributing role and 11 with low explanatory power. Moreover, factors previously discussed in the literature are tested and assessed as to their explanatory power. The focus of this paper is on those factors with high-explanatory power. From the research data, three main reasons have been identified as the sources of investor reluctance to trade in property derivatives. The first and main reason is related to a mismatch between motivations of property investment managers and what can be achieved with the instruments. The second reason, which ties in with the first one, is a general misunderstanding as to the right pricing technique of property derivatives. Finally, the third reason is a general lack of hedging demand from the investor base owing to the long investment horizons through market cycles.

The research contributes to the literature on property derivatives in various ways. First, it extends the literature on market hurdles in property derivatives markets by testing and extending the hurdles that were proposed previously. Second, the research shows that the existing pricing models need to be extended in order to account for the risk perception of practitioners and their concerns with regard to liquidity levels.

For both theory and practice, the research has shown some limitations in using property derivatives for purposes such as creating index exposure or hedging. Another contribution, in this case to practice, is that this study provides a clearer picture as to the reasons that keep property investment managers away from using property derivatives.

The research results indicate that liquidity per se is not a universal remedy for the problems in the market. In addition to the need for improving the understanding of the pricing mechanism, practitioners should give more thought to the notion of real estate market risk and the commensurate returns that can reasonably be expected when they take or reduce it. This implies that property index futures currently do not price like those on any other investable asset class.

The purpose of this paper is to use a text-based sentiment indicator to explain variations in direct property market liquidity in the USA.

By means of an artificial neural network, market sentiment is extracted from 66,070 US real estate market news articles from the S&P Global Market Intelligence database. For training of the network, a distant supervision approach utilizing 17,822 labeled investment ideas from the crowd-sourced investment advisory platform Seeking Alpha is applied.

According to the results of autoregressive distributed lag models including contemporary and lagged sentiment as independent variables, the derived textual sentiment indicator is not only significantly linked to the depth and resilience dimensions of market liquidity (proxied by Amihud’s (2002) price impact measure), but also to the breadth dimension (proxied by transaction volume).

These results suggest an intertemporal effect of sentiment on liquidity for the direct property market. Market participants should account for this effect in terms of their investment decisions, and also when assessing and pricing liquidity risk.

This paper not only extends the literature on text-based sentiment indicators in real estate, but is also the first to apply artificial intelligence for sentiment extraction from news articles in a market liquidity setting.