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This study aims to analyze whether urban tourism affects Berlin housing rents. Urban tourism is of considerable economic importance for many urban destinations and has developed very strongly over the past few years. The prevailing view is that urban tourism triggers side-effects, which affect the urban housing markets through a lack of supply and increasing rents. Berlin represents Germany’s largest rental market and is particularly affected by growing urban tourism and increasing rents.

The paper considers whether urban tourism hotspots affect Berlin’s housing rents, using two hedonic regression approaches, namely, conventional ordinary least squares (OLS) and generalized additive models (GAM). The regression models incorporate housing characteristics as well as several distance-based measures. The research considers tourist attractions, restaurants, hotels and holiday flats as constituents of tourism hotspots and is based on a spatial analysis using geographic information systems (GIS).

The results can be regarded as a preliminary indication that rents are, indeed, affected by urban tourism. Rents seem to be positively correlated with the touristic attractiveness of a particular location, even if it is very difficult to accurately measure the real quantity of the respective effects of the urban tourism amenities, as the various models show. GAM outperforms the results of OLS and seems to be more appropriate for spatial analysis of rents across a city.

To the best of the authors’ knowledge, the paper provides the first empirical analysis of the effects of urban tourism hotspots on the Berlin housing market.

The purpose of this paper is to analyze whether urban tourism attractiveness affects young adult migration within Germany. Currently, factors like urban attractiveness, environmental qualities or vicinity to amenities play a more important role for the migration of young adults than in the past. This has highly asymmetric implications for the housing (and commercial real estate) markets in cities with an abundance of urban attractiveness, compared to cities without such attractions.

This analysis focuses on the internal migration of young adults (18-30-year-olds). First, some stylized facts regarding migration patterns are presented by means of descriptive and cluster analyses (k-means methodology) with respect to the net immigration rate for the two years, 2004 and 2014. Second, ordinary least squares-regression analyses are used to estimate the connection between urban tourism attractiveness and migration.

Young adults in Germany predominantly migrate to cities. The authors find typical migration patterns, and the regression results indicate that young adult migration is highly correlated with the indicator measuring urban tourism attractiveness. This means that urban attractions matter for young adults. Finally, the authors also find that housing rents are correlated with urban tourism attractiveness.

Good city planning must not only be concerned with new industrial sites, but also about esthetic neighborhoods and, for example, attractive squares. Moreover, because city structures and urban amenities are both path dependent and expensive to change, it is likely that the winning cities of today will remain winners in the next decade, which is good news for risk-averse investors.

To the best of the authors’ knowledge, the paper provides the first empirical analysis of the connection between urban tourism attractiveness and the migration of young adults, in the context of German cities.

Short-term rentals are mainly of small flats, which are offered to tourists. Currently, the providers of short-term rentals, in particular Airbnb (ABB), are being criticized in several German cities, on the grounds that shares of residential flats are being removed from the housing market, due to illegitimate misuse as tourist accommodation. Thus, the conventional urban housing markets are contending with a decline in housing supply and increasing rents. This paper aims to support these findings empirically.

The paper opted first for a spatial analysis with ArcGIS for ABB in Berlin. Second, different online data requests of periods of up to two months were used to analyze the extent of misuse with regard to the Zweckentfremdungsverbot (misuse prohibition law). Third, analysis of variance was used to analyze rental growth on the ABB markets. The data were collected in different approaches from the website of airbnb.com.

The paper provides evidence that 5,555 residential flats are presently being misused by ABB (0.30 per cent of the total housing stock in Berlin) and that many providers of entire flats have more than one offer simultaneously. Moreover, the paper provides the first entire-market overview of ABB in Berlin. It is evident that the ABB market is mainly located centrally and that only a few neighborhoods have large ABB markets. Rental growth is higher in the ABB markets which have a significant share of misused flats, than in the ABB markets which have insignificant shares of misused flats.

To the authors’ best knowledge, the paper provides the first empirical approach regarding misuse through short-term rentals on a housing market with an innovative design and first-hand data.

The goal is to assess performance prediction systems (PPS) that are used to assist at-risk learners.

The authors propose time-dependent metrics including earliness and stability. The authors investigate the relationships between the various temporal metrics and the precision metrics in order to identify the key earliness points in the prediction process. Authors propose an algorithm for computing earliness. Furthermore, the authors propose using an earliness-stability score (ESS) to investigate the relationship between the earliness of a classifier and its stability. The ESS is used to examine the trade-off between only time-dependent metrics. The aim is to compare its use to the earliness-accuracy score (EAS).

Stability and accuracy are proportional when the system's accuracy increases or decreases over time. However, when the accuracy stagnates or varies slightly, the system's stability is decreasing rather than stagnating. As a result, the use of ESS and EAS is complementary and allows for a better definition of the point of earliness in time by studying the relation-ship between earliness and accuracy on the one hand and earliness and stability on the other.

When evaluating the performance of PPS, the temporal dimension is an important factor that is overlooked by traditional measures current metrics are not well suited to assessing PPS’s ability to predict correctly at the earliest, as well as monitoring predictions stability and evolution over time. Thus, in this work, the authors propose time-dependent metrics, including earliness, stability and the trade-offs, with objective to assess PPS over time.

This paper contributes insights into how different firm types in the emerging market (EM) of Turkey respond to upgrading pressures in terms of internationalization and the usage of domestic political support. It seeks to highlight how the usage of and the responses to different strategies, connections and policy instruments vary with firm types.

Binary logistic regression analysis is used to differentiate and identify characteristics of firms regarding market-seeking strategies and their usage of institutional and financial support. The analysis is based on survey data from firms located in the metro-region of Istanbul: advanced market multinational enterprises (AMNEs), Turkish MNEs (TMNEs) and domestic Turkish firms (DTFs).

Different types of firms within the population of innovative firms in the EM setting of Turkey show significant variety regarding the usage of and the responses to key factors affecting internationalization. AMNEs particularly benefit from investment and export incentives as well as from establishing political connections in Turkey. DTFs significantly use tax incentives and primarily seek advanced markets. TMNEs particularly benefit from investment and export incentives and prefer to target advanced markets.

Using Turkey as a single-country setting is a limitation to the generalizability of the results. Future studies could use more cases of AMNEs to compare different countries of origin. In addition, the intended focus on R&D-related firms produces specific outcomes for such companies.

National and regional policies need to pursue different strategies for the surveyed groups of firms to attract and maintain foreign direct investments (FDIs) of AMNEs as well as to support outward FDIs of domestic firms and EM MNEs. In particular, policies for market entries and knowledge sourcing in advanced markets are becoming a crucial factor for EM firms in overcoming a shortage of resources at home.

This paper’s findings challenge existing theories such as the concept of psychic distance or liabilities of foreignness, which do not always provide an adequate explanation for internationalization activities of EM firms. In addition, it is highly relevant to apply an eclectic or multidimensional concept when conducting research in EMs in order to capture the interrelated constructs of upgrading, internationalization and political support.

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.

The purpose of this study is to investigate whether ethnic discrimination is present in the Russian labor market and whether it has a significant economic effect on the potential salaries of applicants.

Data were collected using a correspondence audit for four experimental male applicants with identical professional and personal characteristics while differing only in applicant name as a signal of applicants' ethnic background. Implied ethnicities include Russians, Armenians, Jews and North Caucasians. Résumés were sent out to 800 real unique vacancies on behalf of the experimental applicants with a geographic focus on the capital Moscow.

The results of the analysis suggest that there is a significant difference in treatment in both response rate and potential average salaries on ethnic grounds. Disadvantaged groups were found to be systematically pushed into jobs paying 15% less monthly wage.

The study investigates the existence of ethnic discrimination in the Russian labor market and furthermore economically quantifies the effects of discrimination.

Collecting customer data is increasingly becoming an automatic process at different customer touchpoints, carried out with the help of artificial intelligence. Modern telecommunication networks are necessary for collecting this data in a timely manner. This chapter describes 5G, the latest generation of mobile telecommunication networks. It outlines the current stage of development and use cases being introduced or planned by telecommunication companies worldwide. A key aspect of the chapter is to explain what 5G means for collecting customer data.

The purpose of this paper is to develop surrogate models, using deep learning (DL), that can facilitate the application of EM analysis software. In the current status quo, electrical systems can be found in an ever-increasing range of products that are part of everyone’s daily live. With the advances in technology, industries such as the automotive, communications and medical devices have been disrupted with new electrical and electronic systems. The innovation and development of such systems with increasing complexity over time has been supported by the increased use of electromagnetic (EM) analysis software. Such software enables engineers to virtually design, analyze and optimize EM systems without the need for building physical prototypes, thus helping to shorten the development cycles and consequently cut costs.

The industry standard for simulating EM problems is using either the finite difference method or the finite element method (FEM). Optimization of the design process using such methods requires significant computational resources and time. With the emergence of artificial intelligence, along with specialized tools for automatic differentiation, the use of DL has become computationally much more efficient and cheaper. These advances in machine learning have ushered in a new era in EM simulations where engineers can compute results much faster while maintaining a certain level of accuracy.

This paper proposed two different models that can compute the magnetic field distribution in EM systems. The first model is based on a recurrent neural network, which is trained through a data-driven supervised learning method. The second model is an extension to the first with the incorporation of additional physics-based information to the authors’ model. Such a DL model, which is constrained by the laws of physics, is known as a physics-informed neural network. The solutions when compared with the ground truth, computed using FEM, show promising accuracy for the authors’ DL models while reducing the computation time and resources required, as compared to previous implementations in the literature.

The paper proposes a neural network architecture and is trained with two different learning methodologies, namely, supervised and physics-based. The working of the network along with the different learning methodologies is validated over several EM problems with varying levels of complexity. Furthermore, a comparative study is performed regarding performance accuracy and computational cost to establish the efficacy of different architectures and learning methodologies.

This study attempts to segment the Swiss travel market based on holiday activities. It is based on data of the 2001 travel market in Switzerland. Cluster and discriminant analysis have been employed in order to segment the data and to explain the differences between the clusters. Hereby, five activity‐clusters could be defined, each representing a set of holiday activities most likely to be exercised. The analysis of the five clusters revealed that two demographic profile variables “occupation” and “size of household” did explain the affiliation to a certain cluster. The same could be found for the following travel profile variables: “destination and duration of the trip”, “total number of participants from a household and “type of trip”. Further research will be necessary to find out if the clusters identified really do fulfil the needed criteria for market segments in order to be used by companies in the travel industry.