Do Political Institutions Affect the Choice of the U.S. Cross-Listing Venue?

We study the impact of political institutions on foreign firms’ choice of their U.S. cross-listing venue. Using two measures of political institutions (an index of political rights and a political constraint index) and controlling for various firm-level and country-level characteristics, we show that foreign firms from countries with weak political institutions are more likely to cross-list in the U.S. via the over-the-counter market and less likely to opt for an exchange-listed program (i.e., New York, Nasdaq, and AMEX).Cross-listing, Political institutions, Legal institutions

Two Essays in Real Estate Dynamics

Real estate dynamics encompass a multifaceted interplay of various factors that shape the market. This dissertation presents two distinct essays that delve into critical aspects of real estate dynamics. In the first essay, we investigate the influence of short-term rentals, specifically Airbnb activity, on neighboring house prices in Hampton Roads, Virginia. By employing robust measures such as active listings, reservations, and their cumulative impact over different periods, we uncover a positive association between prior Airbnb rental activity and housing sales prices. Moreover, we observe a spatial decay effect, where the localized impact diminishes with increasing geographic distance, particularly beyond 500 meters. Further analysis employing quantile regression reveals that the effect of Airbnb rentals is more pronounced for higher-priced homes, while middle-range house prices demonstrate a relatively lower sensitivity to Airbnb activity. These findings contribute to the existing literature by shedding light on the nuanced relationship between Airbnb and housing prices. The second essay delves into the relationship between media content sentiments and returns of Real Estate Investment Trusts (REITs). Leveraging proprietary investor sentiment measures from Thomson Reuters, including dimensions such as stress, emotion vs. fact, dividends, and price direction, we employ a multi-step approach to examine their impact on REIT returns. Through time series regression and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, we establish the statistical significance of media content sentiments in explaining REIT returns and market volatility. Employing Lasso analysis, we identify the sentiment related to price direction as the most influential factor impacting excess REIT returns consistently across various REIT types and weighting schemes. Our analysis enhances traditional asset pricing models, improving the adjusted R-squared, and provides insights into the role of media sentiment in shaping REIT returns. By integrating these two essays, this dissertation contributes to a comprehensive understanding of real estate dynamics. The first essay illuminates the impact of Airbnb activity on house prices, emphasizing the spatial decay effect and differential sensitivity across price distributions. The second essay highlights the significance of media content sentiments in explaining REIT returns and the findings are validated through Covariance-based Structural Equation Modeling (SEM) and path analysis. Collectively, these essays broaden our knowledge of the complex dynamics within the real estate market and provide valuable insights for researchers, policymakers, and market participants alike

Determinants of Industrial Property Rents in the Chicago Metropolitan Area

Urban economists have long understood the theoretical importance of transportation infrastructure and accessibility on the location choice of households and firms. We utilize a readily available data set of transaction rents in the Chicago metropolitan area to investigate the determinants of industrial property rents. Among the factors considered are proximity to transportation infrastructure, characteristics of the property, the term structure of lease agreements, and local attributes of the neighborhood. Empirical results suggest property, lease, and local demographics play important roles in determining rents. Despite the fact that industrial property tends to locate very close to rail lines and interstate highways, transportation infrastructure has much less influence. There is evidence that there is an upward sloping lease term structure premium and that the premium varies over time. The model is also used to develop a constant quality rent index for the Chicago commercial property market. Compared to average rents and asking rents, the estimated constant quality index shows a smaller run up in rents from 2003 through 2008 and a larger drop off in rents through the end of 2011

EPC Labels and Building Features: Spatial Implications over Housing Prices

The influence of building or dwelling energy performance on the real estate market dynamics and pricing processes is deeply explored, due to the fact that energy efficiency improvement is one of the fundamental reasons for retrofitting the existing housing stock. Nevertheless, the joint effect produced by the building energy performance and the architectural, typological, and physical-technical attributes seems poorly studied. Thus, the aim of this work is to investigate the influence of both energy performance and diverse features on property prices, by performing spatial analyses on a sample of housing properties listed on Turin’s real estate market and on different sub-samples. In particular, Exploratory Spatial Data Analyses (ESDA) statistics, standard hedonic price models (Ordinary Least Squares—OLS) and Spatial Error Models (SEM) are firstly applied on the whole data sample, and then on three different sub-samples: two territorial clusters and a sub-sample representative of the most energy inefficient buildings constructed between 1946 and 1990. Results demonstrate that Energy Performance Certificate (EPC) labels are gaining power in influencing price variations, contrary to the empirical evidence that emerged in some previous studies. Furthermore, the presence of the spatial effects reveals that the impact of energy attributes changes in different sub-markets and thus has to be spatially analysed

INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems

Is the Real Estate Market of New Housing Stock Influenced by Urban Vibrancy?

3noThe attractiveness and vibrancy of an urban area are very complex aspects that both Public Administrations and real estate developers and construction companies have to carefully consider in order to correctly address their investments and sustainable urban development projects. 'e aim of this paper is to study urban vibrancy and its relationship with the neighbourhood services and the real estate market of new housing stock. Spatial analyses are performed to study the influence of the Neighbourhood Services Index (NeSI) and its Principal Components (PCs) on listing prices and the construction activity. Spatial autoregressive (SAR) models are applied both with lattice data and data points, in order to manage spatial dependence and to identify the variables that significantly influence housing prices and construction site density. Findings highlight that the NeSI significantly influences the real estate market of new housing stock and that above the analyzed neighborhood services and the retail activities have a great, significant, and positive influence on the density of housing construction sites. 'e results of this study represent a real support for both public and private bodies to identify the most and least attractive and vibrant urban areas and to deal with important aspects of urban complexity.openopenBarreca, Alice; Curto, Rocco; Rolando, DianaBarreca, Alice; Curto, Rocco; Rolando, Dian

Modelling Non-residential Real Estate Prices and Land Use Development in Windsor with Potential Impacts from the Windsor-Essex Parkway

A study of non-residential land use in the Windsor, Ontario CMA was undertaken to examine possible local implications from construction of the Windsor-Essex Parkway. Two distinct model types were employed. The first consisted of price regressions for industrial, vacant, commercial, office, retail, restaurant, and plaza properties. The second set studied the discrete choice of land use types within commercial and industrial zoning. The commercial logit model had four alternatives: office, retail, restaurant, and other. The industrial logit model had three alternatives: warehouse, factory, and other. The results obtained from these models provide a useful account of interacting land use processes that can inform future transportation and land use policies. Moreover, the empirical analysis is particularly valuable given the larger amount of research into residential land use compared to non-residential. Finally, the models may be useful in the future as part of a more complex integrated urban model

Harnessing big data to inform tourism destination management organizations

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn the last few years, Portugal has been witnessing a rapid growth of tourism, which reflects positively in many aspects, especially in what regards economic factors. Although, it also leads to a number of challenges, all of them difficult to quantify: tourist congestions, loss of city identity, degradation of patrimony, etc. It is important to ensure that the required foundations and tools to understand and efficiently manage tourism flows exist, both in the city-level and country-level. This thesis studies the potential of Big data to inform destination management organizations. To do so, three sources of Big data are discussed: Telecom, Social media and Airbnb data. This is done through the demonstration and analysis of a set of visualizations and tools, as well as a discussion of applications and recommendations for challenges that have been identified in the market. The study begins with a background information section, where both global and local trends in tourism will be analyzed, as well as the factors that affect tourism and consequences of the latter. As a way to analyze the growth of tourism in Portugal and provide prototypes of important tools for the development of data driven tourism policy making, Airbnb and telecom data are analyzed using a network science approach to visualize country-wide tourist circulation and presents a model to retrieve and analyze social media. In order to compare the results from the Airbnb analysis, data regarding the Portuguese hotel industry is used as control data