Gayrimenkul yatırım ortakları'nın ticari taşınmaz kiralarını belirleyen faktörlerin İstanbul örneğinde coğrafi bilgi sistemleri ve bulanık mantık yöntemleri ile mekansal analizi

TÜBİTAK SOBAG Proje01.10.20101980’lerden bu yana hizmetler sektörü istihdama ve Gayri Safi Yurtiçi Hasıla’ya yaptığı katkılar nedeniyle Batı ekonomilerinin en hızlı gelişen sektörü haline gelmiştir. Hizmetler sektöründeki büyümeye bağlı olarak, metropoliten alanlardaki ofis alanına olan talep ve ofis alanı inşaatı/arzı belirgin bir şekilde artış göstermiştir. Akademik literatürde ofis piyasası dinamiklerini inceleyen çok sayıda çalışma mevcuttur. Bu çalışmaları iki ana grupta toplamak mümkündür. Đlk gruptaki çalışmalar, ofis alanına olan talebi ve ofis alanı arzını belirleyen faktörlerin incelenmesine yönelik çalışmalardır. Đkinci grup çalışmalar ise, ofis kira değerlerini belirleyen fiziksel, sosyo-ekonomik ve konumsal faktörleri analiz eden çalışmalardır. Đkinci grup çalışmalar genellikle Amerika’daki ve Đngiltere’deki ofis piyasalarını inceleyen çalışmalardır ve son 25 yıldır önem kazanarak artmaktadır. Literatürde, Türkiye’deki ofis piyasalarının analizi ve özellikle ofis alanı kiralarının belirlenmesine yönelik sadece iki çalışma mevcuttur. Bu çalışmalardan ilkinde Ustaoğlu (2003) “hedonik fiyatlama” metodunu kullanarak Ankara’daki ofis alanı kiralarını belirleyen fiziksel ve sosyo-ekonomik faktörleri incelemiştir. Đkinci çalışma ise, Öven ve Pekdemir (2006) oldukça kısıtlı bir örnekleme (17 ofis binası) için “faktör analizi” metodunu kullanarak Đstanbul’daki ofis kira değerlerini belirleyen faktörleri incelemiştir. Önerilen projenin amacı; Đstanbul Merkezi Đş Alanı (MĐA)’nda, özellikle finans sektörünün (bankacılık, emlak ve sigorta işleri) kullandığı ofis alanları kira değerlerini belirleyen fiziksel, sosyo-ekonomik ve konumsal faktörlerin Coğrafi Bilgi Sistemleri (CBS) ve Bulanık Mantık (fuzzy logic) gibi gayrimenkul literatüründe oldukça yeni sayılan metodları kullanarak mekansal analizini yapmaktır. Projenin başında, ofis alanı örneklemesi için Gayrimenkul Yatırım Ortaklıkları (GYO)’nın sahip olduğu Đstanbul’daki ofis alanları kullanılması hedeflenmiştir. Ancak, karşılaşılan veri sorunlarından dolayı Đstanbul Avrupa yakası MĐA’daki ofislerin kira değerlerini belirleyen faktörlerin belirlenmesi ve ofis kiralarının mekansal dağılımının incelenmesi amaçlanmıştır. Bu çalışma sonuçlarının ülkemiz gayrimenkul sektöründeki yerli ve yabancı kurumsal yatırımcıları oldukça yakından ilgilendiren bir konu olduğu açıktır. Bu proje kapsamındaki çalışmalarımızın uluslararası akademik literatüre, gayrimenkul sektörü yatırımcılarına ve gayrimenkul değerleme uzmanlarına önemli katkıları olacağı düşünülmektedir. Projenin temel katkıları şu şekilde özetlenebilir: Đstanbul MĐA’daki ofislerin kira değerlerinin mekansal dağılımının incelenmesi; kira değerlerini belirleyen fiziksel, sosyoekonomik ve konumsal faktörlerin ağırlıklı ortalamalarından Gayrimenkul Değerleme Endeksi (GDE) oluşturulması gayrimenkul sektörü yatırımcıları ve özellikle gayrimenkul değerlemesinden sorumlu ekpertiz şirketleri için oldukça önemlidir ve uygulamada yararlı sonuçlar verecektir. Đkinci olarak, projede kullanılacak olan metodların gayrimenkul değerleme literatürüne büyük katkısı olacağı şüphesizdir. Projenin ilk aşamasında kullanılması planlanan, CBS’ye dayalı gayrimenkul değerleme metodu Türkiye örneklerinde çok az çalışılmış bir analiz metodudur. Projenin ikinci aşamasında kullanılacak olan “bulanık mantık” metodu ise, ofis alanı kira değerlerinin belirlenmesinde Türkiye’deki ilk uygulama olacaktır. Bulanık mantık metodunun uluslararası gayrimenkul değerleme literatüründeki uygulamaları da çok sınırlı olduğundan, projeden elde edilecek sonuçların uluslararası literatüre önemli katkıları olması beklenmektedir. Son olarak, gayrimenkul sektörü gelişimi, gayrimenkul yatırım araçları (mortgage kredileri, GYO hisseleri) risk ve getiri analizi, gayrimenkul talebi ve arzı, ülkemizde özellikle akademik çerçevede, göreceli olarak oldukça az çalışılan konulardır. Gayrimenkul sektörü çalışmaları şehir planlama, ekonomi, finans, istatistik, matematik ve bilgi teknolojileri alanları arasında işbirliğini gerektiren disiplinlerarası çalışmalardır. Bu proje, ülkemiz gayrimenkul sektörü gelişiminde önemli yeri olan Đstanbul metropoliten alanındaki ofis alanı değerlerinin belirlenmesi konusunda oldukça kapsamlı ve yeni analiz tekniklerini kullanan interdisipliner nitelikte bir çalışma olacaktır.Service sector has developed as the most progressed sector of the Western economies due to its contributions to the Gross Domestic Product and employment since the 1980s. The demand for and the supply of office spaces in the metropolitan areas have increased significantly based on the service sector growth. Extant academic literature shows that there is a considerable number of studies conducted to analyze the dynamics of the office markets. These studies can be categorized under two groups. The first group of studies attempts to determine the factors that affect the demand for and the supply of office space. The second group focuses on analyzing the office space rent determinants based on physical, socio-economic and locational parameters. The second group studies have been conducted dominantly for analyzing the US and UK office markets and have gained an increasing importance during the last 25 years. In the existing literature, there are only two academic studies that analyzed the Turkish office market and office space rent determinants. In the first study, Ustaoğlu (2003) has studied the physical and socioeconomic determinants of office space rents in Ankara using “hedonic price modelling”. In a most recent study, Öven and Pekdemir (2006) analyzed the office space determinants in Istanbul by using “factor analysis” method with a very limited sample data of 17 offices. The objective of the proposed project is to make spatial analysis of the physical, socioeconomic and locational rent determinants of office properties in Istanbul Central Business District (CBD) using Geographic Information Systems (GIS) and Fuzzy Logic as new methodologies used in the real estate literature. The analyses of rent determinants of office properties and the spatial variation of rental prices across the CBD attach great importance to invest in Turkish real estate sector both for the domestic and foreign institutional investors. It is believed that, the results of this research will contribute to international academic literature, investors in real estate sector and property valuation experts. Major contributions of the current project can be summarized as follows: First, analyzing the physical, socioeconomic and locational factors that effect the rental values of office properties and constructing a Real Estate Valuation Index (REVI) based on a weighted combination of these factors are significantly beneficiary both for individual and institutional investors in real estate sector and appraisal companies that are responsible for real estate valuation. Secondly, it is certain that the methodologies proposed to be used in the project will make a substantial contribution to the real estate valuation literature. The first methodology for real estate valuation is based on Geographic Information Systems (GIS) and is not applied in Turkish commercial property market except the study by Oven and Pekdemir (2006). To the best of our knowledge, the second part of the project will be the first attempt to apply Fuzzy Logic method to analyze the commercial property rents in Turkey. The extant literature shows that, the use of Fuzzy Logic method for real estate valuation is very limited; therefore, the results of this project are expected to provide major contributions to the international literature. Finally, it is obvious that there is a limited number of works on the subjects of development of real estate sector, risk-return analysis of real estate investment tools (mortgages, REIT stocks) and demand-supply analysis for real estate space in Turkey, especially in the academic world. Research on real estate markets requires interdisciplinary work with the collaboration of urban planning, economics, finance, statistics, mathematics, and information technologies. This project will perform such a multidisciplinary work, which uses new methods and techniques in a comprehensive way, to analyze the commercial property rent determinants in Istanbul Metropolitan Area

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

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

An empirical investigation of the demographics of Top Management Team (TMT) and its influence in forecasting organizational outcome in international architecture, engineering and construction (AEC) Firms : a fuzzy set approach

Whereas Top Management Teams (TMTs) are selected to fit a firm’s strategy, prior studies have evidenced that TMTs have significant impact on firm performance. The challenge of the two-way causality has been reflected in previous findings being ambiguous, inconsistent and sometimes conflicted. Pursing the same line of research may lead to incomplete and even error-prone conclusion. In contrast, this research suggests that inconsistency of findings among TMT demographics shown in prior work may point the possibility of studying the black-box nature of such relationships, and provide a tool to future forecast the organization outcome. More specifically, a multi-input (TMT demographics) multi-output (organization outcome) structure was used in this research to explore the future predictability power of TMT demographics for international Architects, Engineers and Construction firms (AEC firms). In order to build a reliable forecasting model, those contradictions were avoided by the utilization of artificial intelligence methods by training, testing and producing results without any prior assumptions or known structures. In particular, the Adaptive Neural Fuzzy Inference System (ANFIS) have been employed as a basis for constructing a set of fuzzy “if– then” rules with pre-tested input–output pairs. Three different forecasting strategies were constructed, the findings have demonstrated the learning and potential of the ANFIS model (time series based) in forecasting organization outcome, but at the same time, suggest that distinction should be established among different constructs of TMT demographics and outcome constructs. The results demonstrated that job-related demographics (i.e., TMT Educational Diversity, TMT Functional Diversity and TMT Tenure) could provide a satisfactory forecasting accuracy for the short-span (Liquidity) and medium-span (Cash Flow Stability and Capital Structure) outcome constructs. The future predictability power of other non-job demographics could not be evidenced in this research. Additionally, outcome constructs with dynamic nature could not be forecasted. Lastly, future research opportunities have been suggested for researchers. Most importantly, it includes the need to re-define diversity in the context of TMT composition (having different meaning as in: Variety, Separation and Disparity). Other methodological future opportunities are also suggested at the end of this study

Operations Management

Global competition has caused fundamental changes in the competitive environment of the manufacturing and service industries. Firms should develop strategic objectives that, upon achievement, result in a competitive advantage in the market place. The forces of globalization on one hand and rapidly growing marketing opportunities overseas, especially in emerging economies on the other, have led to the expansion of operations on a global scale. The book aims to cover the main topics characterizing operations management including both strategic issues and practical applications. A global environmental business including both manufacturing and services is analyzed. The book contains original research and application chapters from different perspectives. It is enriched through the analyses of case studies

Data mining in computational finance

Computational finance is a relatively new discipline whose birth can be traced back to early 1950s. Its major objective is to develop and study practical models focusing on techniques that apply directly to financial analyses. The large number of decisions and computationally intensive problems involved in this discipline make data mining and machine learning models an integral part to improve, automate, and expand the current processes. One of the objectives of this research is to present a state-of-the-art of the data mining and machine learning techniques applied in the core areas of computational finance. Next, detailed analysis of public and private finance datasets is performed in an attempt to find interesting facts from data and draw conclusions regarding the usefulness of features within the datasets. Credit risk evaluation is one of the crucial modern concerns in this field. Credit scoring is essentially a classification problem where models are built using the information about past applicants to categorise new applicants as ‘creditworthy’ or ‘non-creditworthy’. We appraise the performance of a few classical machine learning algorithms for the problem of credit scoring. Typically, credit scoring databases are large and characterised by redundant and irrelevant features, making the classification task more computationally-demanding. Feature selection is the process of selecting an optimal subset of relevant features. We propose an improved information-gain directed wrapper feature selection method using genetic algorithms and successfully evaluate its effectiveness against baseline and generic wrapper methods using three benchmark datasets. One of the tasks of financial analysts is to estimate a company’s worth. In the last piece of work, this study predicts the growth rate for earnings of companies using three machine learning techniques. We employed the technique of lagged features, which allowed varying amounts of recent history to be brought into the prediction task, and transformed the time series forecasting problem into a supervised learning problem. This work was applied on a private time series dataset