Search results
1 – 10 of 34Johannes Braun, Jochen Hausler and Wolfgang Schäfers
The purpose of this paper is to use a text-based sentiment indicator to explain variations in direct property market liquidity in the USA.
Abstract
Purpose
The purpose of this paper is to use a text-based sentiment indicator to explain variations in direct property market liquidity in the USA.
Design/methodology/approach
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.
Findings
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).
Practical implications
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.
Originality/value
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.
Details
Keywords
Jessica Roxanne Ruscheinsky, Marcel Lang and Wolfgang Schäfers
The purpose of this paper is to determine systematically the broader relationship between news media sentiment, extracted through textual analysis of articles published by leading…
Abstract
Purpose
The purpose of this paper is to determine systematically the broader relationship between news media sentiment, extracted through textual analysis of articles published by leading US newspapers, and the securitized real estate market.
Design/methodology/approach
The methodology is divided into two stages. First, roughly 125,000 US newspaper article headlines from Bloomberg, The Financial Times, Forbes and The Wall Street Journal are investigated with a dictionary-based approach, and different measures of sentiment are created. Second, a vector autoregressive framework is used to analyse the relationship between media-expressed sentiment and REIT market movements over the period 2005–2015.
Findings
The empirical results provide significant evidence for a leading relationship between media sentiment and future REIT market movements. Furthermore, applying the dictionary-based approach for textual analysis, the results exhibit that a domain-specific dictionary is superior to a general dictionary. In addition, better results are achieved by a sentiment measure incorporating both positive and negative sentiment, rather than just one polarity.
Practical implications
In connection with fundamentals of the REIT market, these findings can be utilised to further improve the understanding of securitized real estate market movements and investment decisions. Furthermore, this paper highlights the importance of paying attention to new media and digitalization. The results are robust for different REIT sectors and when conventional control variables are considered.
Originality/value
This paper demonstrates for the first time, that textual analysis is able to capture media sentiment from news relevant to the US securitized real estate market. Furthermore, the broad collection of newspaper articles from four different sources is unique.
Details
Keywords
Benedict von Ahlefeldt-Dehn, Marcelo Cajias and Wolfgang Schäfers
Commercial real estate and office rental values, in particular, have long been the focus of research. Several forecasting frameworks for office rental values in multivariate and…
Abstract
Purpose
Commercial real estate and office rental values, in particular, have long been the focus of research. Several forecasting frameworks for office rental values in multivariate and univariate fashions have been proposed. Recent developments in time series forecasting using machine learning and deep learning methods offer an opportunity to update traditional univariate forecasting frameworks.
Design/methodology/approach
With the aim to extend research on univariate rent forecasting a hybrid methodology combining both ARIMA and a neural network model is proposed to exploit the unique strengths of both methods in linear and nonlinear modelling. N-BEATS, a deep learning algorithm that has demonstrated state-of-the-art forecasting performance in major forecasting competitions, are explained. With the ARIMA model, it is jointly applied to the office rental dataset to produce forecasts for four-quarters ahead.
Findings
When the approach is applied to a dataset of 21 major European office cities, the results show that the ensemble model can be an effective approach to improve the prediction accuracy achieved by each of the models used separately.
Practical implications
Real estate forecasting is essential for assessing the value of managing portfolios and for evaluating investment strategies. The approach applied in this paper confirms the heterogeneity of real estate markets. The application of mixed modelling via linear and nonlinear methods decreases the uncertainty of abrupt changes in rents.
Originality/value
To the best of the authors' knowledge, no such application of a hybrid model updating classical statistical forecasting with a deep learning neural network approach in the field of commercial real estate rent forecasting has been undertaken.
Details
Keywords
In England and Wales, legislation pertaining to hate crime recognizes hostility based on racial identity, religious affiliation, sexual orientation, disability or transgender…
Abstract
In England and Wales, legislation pertaining to hate crime recognizes hostility based on racial identity, religious affiliation, sexual orientation, disability or transgender identity. Discussions abound as to whether this legislation should also recognize hostility based on gender or misogyny. Taking a socio-legal analysis, the chapter examines hate crime, gender-based victimization and misogyny alongside the impact of victim identity construction, access to justice and the international nature of gendered harm. The chapter provides a comprehensive investigation of gender-based victimization in relation to targeted hostility to assess the potential for its inclusion in hate crime legislation in England and Wales.
Details
Keywords
Daniel Wurstbauer and Wolfgang Schäfers
Similar to real estate, infrastructure investments are regarded as providing a good inflation hedge and inflation protection. However, the empirical literature on infrastructure…
Abstract
Purpose
Similar to real estate, infrastructure investments are regarded as providing a good inflation hedge and inflation protection. However, the empirical literature on infrastructure and inflation is scarce. Therefore, the purpose of this paper is to investigate the short- and long-term inflation-hedging characteristics, as well as the inflation protection associated with infrastructure and real estate assets.
Design/methodology/approach
Based on a unique data set for direct infrastructure performance, a listed infrastructure index, common direct and listed real estate indices, the authors test for short- and long-term inflation-hedging characteristics of these assets in the USA from 1991-2013. The authors employ the traditional Fama and Schwert (1977) framework, as well as Engle and Granger (1987) co-integration tests. Granger causality tests are further conducted, so as to gain insight into the short-run dynamics. Finally, shortfall risk measures are applied to investigate the inflation protection characteristics of the different assets over increasingly long investment horizons.
Findings
The empirical results indicate that in the short run, only direct infrastructure provides a partial hedge against inflation. However, co-integration tests suggest that all series have a long-run co-movement with inflation, implying a long-term hedge. The causality tests reveal reverse unidirectional causality – while real estate asset returns are Granger-caused by inflation, infrastructure asset returns seem to cause inflation. These findings further confirm that both assets represent a distinct asset class. Ultimately, direct infrastructure investments exhibit the most desirable inflation protection characteristics among the set of assets.
Research limitations/implications
This study only presents results based on a composite direct infrastructure index, as no sub-indices for sub-sectors are available yet.
Practical implications
Investors seeking assets that are sensitive to inflation and mitigate inflation risk should consider direct infrastructure investments in their asset allocation strategy.
Originality/value
This is the first study to examine the ability of direct infrastructure to assess inflation risk.
Details
Keywords
Konrad Finkenzeller, Tobias Dechant and Wolfgang Schäfers
The purpose of this paper is to provide conclusive evidence that infrastructure constitutes a separate asset class and cannot be classified as real estate from an investment…
Abstract
Purpose
The purpose of this paper is to provide conclusive evidence that infrastructure constitutes a separate asset class and cannot be classified as real estate from an investment point‐of‐view. Furthermore, optimal allocations are determined for direct and indirect infrastructure within a multi‐asset portfolio.
Design/methodology/approach
Portfolio allocations are optimized by using an algorithm, which accounts for downside risk, rather than variance. This approach is more in accordance with the actual investor behaviour and might meet their investment objectives more effectively. An Australian dataset comprising stocks, bonds, direct real estate, direct infrastructure and indirect infrastructure is applied for portfolio construction.
Findings
Although infrastructure and real estate have common characteristics, the conclusion is that that they constitute two different asset classes. Furthermore, the diversification benefits of direct and indirect infrastructure within multi‐asset portfolios are highlighted and determine efficient allocations up to 78 percent for target rates of 0.0 percent, 1.5 percent and 3.0 percent quarterly.
Practical implications
The results will help investors and portfolio managers to efficiently allocate funds to various asset classes. Most institutional investors are not familiar with investments in infrastructure. The study facilitates a better understanding of the asset class infrastructure and yields some important implications for the optimal allocation of infrastructure within institutional investment portfolios.
Originality/value
This is the first study to examine the role of direct and indirect infrastructure within a multi‐asset portfolio by applying a downside‐risk approach.
Details
Keywords
Marian Alexander Dietzel, Nicole Braun and Wolfgang Schäfers
The purpose of this paper is to examine internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve…
Abstract
Purpose
The purpose of this paper is to examine internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices.
Design/methodology/approach
This paper examines internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices.
Findings
The empirical results show that all models augmented with Google data, combining both macro and search data, significantly outperform baseline models which abandon internet search data. Models based on Google data alone, outperform the baseline models in all cases. The models achieve a reduction over the baseline models of the mean squared forecasting error for transactions and prices of up to 35 and 54 per cent, respectively.
Practical implications
The results suggest that Google data can serve as an early market indicator. The findings of this study suggest that the inclusion of Google search data in forecasting models can improve forecast accuracy significantly. This implies that commercial real estate forecasters should consider incorporating this free and timely data set into their market forecasts or when performing plausibility checks for future investment decisions.
Originality/value
This is the first paper applying Google search query data to the commercial real estate sector.
Details
Keywords
Abstract
Details
Keywords
Wolfgang Messner and Norbert Schäfer
The cultural dimensions of the Hofstede and Global Leadership and Organizational Behavior Effectiveness (GLOBE) studies are often used to capture cultural differences and…
Abstract
Purpose
The cultural dimensions of the Hofstede and Global Leadership and Organizational Behavior Effectiveness (GLOBE) studies are often used to capture cultural differences and operationalize them in academic research, corporate business, and teaching. The purpose of this paper is to investigate if this context is appropriate for the Indian information technology (IT) offshore services industry; that is, if Indian culture can be measured with group-referenced items, averaged, and explained by discrete dimensions.
Design/methodology/approach
The authors devised items based on the GLOBE study, and conducted empirical research with 291 employees of two services sourcing providers in Pune and Bangalore, India. The authors then scrutinized the data set on item and dimension level using statistical methods, such as interrater agreement, t-test, arithmetic mean, and standard deviation.
Findings
An interpretation of the analysis posits that cultural assumptions based on dimensions and means are problematic in the context of the Indian IT offshore services industry. The two digit exact values of the GLOBE study (and similarly the ordinal scale by Hofstede) suggest a level of accuracy and absoluteness which could not be replicated in the empirical research. Therefore, one authors should be very careful referring to Indian national culture when conducting intercultural awareness programs and coaching international teams who are engaging with India.
Originality/value
The GLOBE study omits to report basic statistics of questionnaire development. Through this replication study in India, the authors provide empirical evidence that the construct validity of cultural dimensions and the concept of national/group averages may be flawed.
Details