Search results

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This study only presents results based on a composite direct infrastructure index, as no sub-indices for sub-sectors are available yet.

Investors seeking assets that are sensitive to inflation and mitigate inflation risk should consider direct infrastructure investments in their asset allocation strategy.

This is the first study to examine the ability of direct infrastructure to assess inflation risk.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This is the first paper applying Google search query data to the commercial real estate sector.

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.

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.

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.

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.

Most intercultural frameworks assess intercultural competencies, but global businesses lack instruments to support the feedback loop, that is help project managers answer the question if an effective global team has been formed. The purpose of this paper is to develop and assess a new indicator for measuring the actual effectiveness of intercultural communication and collaboration at the individual and team level, the Mysore InterCultural Effectiveness (MICE) indicator.

Based on a needs analysis in global businesses, international projects, and review of existing literature, a low-touch self-report indicator was developed. A test run in several international companies with live data obtained from 154 employees helped to validate the indicator using exploratory and confirmatory factor analysis.

The MICE indicator is based on two scales: first, the effectiveness in interacting and collaborating with foreign counterparts by providing an answer to the question “how I think I am with them;” and second, the satisfaction with appropriateness of communication received from foreign interlocutors and the outcome of the collaboration by answering the question “how I think they are with me.”

Empirical results indicate that the two scale/six factor model provides a good fit to the data. Using the MICE Indicator, it is now possible for project managers to effectively address shortcomings of intercultural communication skills in their international teams with the right type of intercultural training.

As clients of India's IT services providers continue to complain about knowledge loss caused by high attrition rates in their offshore delivery factories, the linkages between organizational culture and commitment of the Indian employee base are of interest to researchers as well as practitioners. This paper seeks to address these issues.

Data was collected in the first half of 2012 through the ICCA™ appraisal framework from 291 Indian IT executives and managers working for two IT services sourcing provider organizations in Pune and Bangalore, India. To analyse the data, descriptive and inferential statistics were used together with multiple regression and confirmatory factor analysis.

Taken together, this research makes several contributions. First, the results of data analysis exhibit that, among the organizational culture dimensions, in‐group collectivism and performance orientation are the antecedents with the biggest effect on employee commitment. Other culture dimensions show varying degree of positive and negative influence on employee commitment. Second, this paper contributes to the cross‐cultural generalizability discussion of employee commitment. The data analysis unveils a stronger correlation between affective and normative commitment in the Indian context as compared to other North American studies. Third, it supports suggestions put forward in other research that continuance commitment should be split into the two subfactors c/alternative and c/sacrifice.

It is proposed that the Indian IT services sourcing industry should be adept at thinking about employee commitment from an organizational culture point of view.

The proposed model of this research posits and proves that employee commitment in an Indian IT services offshoring context is influenced by organizational culture.