Sustainable Development of Real Estate

Research, theoretical and practical tasks of sustainable real estate development process are revised in detail in this monograph; particular examples are presented as well. The concept of modern real estate development model and a developer is discussed, peculiarities of the development of built environment and real estate objects are analyzed, as well as assessment methods, models and management of real estate and investments in order to increase the object value. Theoretical and practical analyses, presented in the monograph, prove that intelligent and augmented reality technologies allow business managers to reach higher results in work quality, organize a creative team of developers, which shall present more qualitative products for the society. The edition presents knowledge on economic, legal, technological, technical, organizational, social, cultural, ethical, psychological and environmental, as well as its management aspects, which are important for the development of real estate: publicly admitted sustainable development principles, urban development and aesthetic values, territory planning, participation of society and heritage protection. It is admitted that economical crises are inevitable, and the provided methods shall help to decrease possible loss. References to the most modern world scientific literature sources are presented in the monograph. The monograph is prepared for the researchers, MSc and PhD students of construction economics and real estate development. The book may be useful for other researchers, MSc and PhD students of economics, management and other specialities, as well as business specialist of real estate business. The publication of monograph was funded by European Social Fund according to project No. VP1-2.2-ŠMM-07-K-02-060 Development and Implementation of Joint Master’s Study Programme “Sustainable Development of the Built Environment”

An Open Access Distribution Tariff: Removing Barriers to Innovation on the Smart Grid

This Article proposes that the Federal Energy Regulatory Commission (FERC) consider promulgating an Open Access Distribution Tariff (OADT) to open the nation\u27s electric grid to new products and services at the consumer (distribution) level. Design of the OADT would be comparable to the Open Access Transmission Tariff that the FERC has used previously to open the nation\u27s transmission wires. This Article argues that an OADT is necessary to create a smart electricity network that would be national, multimodal, and interactive. There is no smart electricity network at present, and there are numerous barriers to the development of open networking, such as obstacles to open access, lack of consumer demand for such a network and its products, resistance of incumbent utilities, and a variety of other factors. An open access principle will likely be necessary, but the timing of such regulation is of critical importance. This Article argues that regulatory establishment of an open access principle will eventually be necessary-and to assess when open access might be desirable, this Article examines the revolutionary transformations that took place in three regulated industries: telecommunications, electricity restructuring, and finance. This Article examines key near-term and long-term questions involving an OADT\u27s timing and development by analyzing these regulated industries using three criteria: (1) signifiers of when a transition would be necessary and conditions that might make open access more desirable to industry actors, (2) regulatory prerequisites necessary in the near term, and (3) risks involved in drawing lessons from the specific regulatory transformation (including federalism concerns). This Article concludes that rather than waiting for an organic transformation of the electric grid and evolution of open networking, a deliberate path of preparatory work will best set the foundation for open access

Sustainable Real Estate: Management, Assessment and Innovations

Production and consumption activities have determined a weakness of the sustainable real estate economy. The main problems are the subordination of public decision making, which is subjected to pressure from big companies; inefficient appraisal procedures; excessive use of financial leverage in investment projects; the atypical nature of markets; income positions in urban transformations; and the financialization of real estate markets, with widespread negative effects. A delicate role in these complex problems is assigned to real estate appraisal activities, called to make value judgments on real estate goods and investment projects, the prices of which are often formed in atypical real estate markets, giving ever greater importance to sustainable development and transformation issues. This Special Issue is dedicated to developing and disseminating knowledge and innovations related to most recent real estate evaluation methodologies applied in the fields of architecture and civil, building, environmental, and territorial engineering. Suitable works include studies on econometric models, sustainable building management, building costs, risk management and real estate appraisal, mass appraisal methods applied to real estate properties, urban and land economics, transport economics, the application of economics and financial techniques to real estate markets, the economic valuation of real estate investment projects, the economic effects of building transformations or projects on the environment, and sustainable real estate

DATA MINING IN CUSTOMER RELATIONSHIP MANAGEMENT

In the context of millennium development business, speed and adaptability to change are the key words. So to meet increasingly strong competition, information, transmitted in real time, enable companies to anticipate their customers needs and developments in the global market. Organizations now find that Peter Drucker was a visionary in the center saying that any company is customer strategy. The new economy requires strong and lasting relationships with customers, how to manage them with new information technologies is a major element of increased competitiveness of firms.Data mining, customer relationship management, machine learning

Artificial Intelligence & Machine Learning in Finance: A literature review

In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc

Opportunities and Challenges of Applying Artificial Intelligence in the Financial Sectors and Startups during the Coronavirus Outbreak

Purpose: The main goal of this article is the comprehensive study of the applications of artificial intelligence in financial sectors in addition to startups and its impacts on such cases along with Covid19. Methodology: we have tried to study the applications of artificial intelligence in different areas especially financial fields such as accounting, auditing, management, capital market, banking etc. On the other hand, we have studied the impacts of artificial intelligence on startups during Covid-19 too. Findings: The results showed that AI can be a powerful tool in financial fields such as investment advice, asset allocation, fraud detection, portfolio management and etc. and startups such as increasing production and productivity, time management, data management and analysis and etc. during the Covid-19 outbreaks and it can decrease the harmful effects of Coronavirus. Thus, timely actions can be taken. Originality/Value: The main contribution of this paper is a comprehensive and specialized look at the discussion of the applications of artificial intelligence in the field of finance as well as startups during Covid19. We have tried to consider subjects and contents which cover most of the paper