This document provides an analysis of the demand for residential real estate in Istanbul, Turkey. It begins with information about the data source, which is a monthly housing market survey conducted by Nielsen for ING Bank Turkey between December 2009 and October 2010. It then reviews the social and economic conditions in Istanbul compared to other cities in the survey. Istanbul has a large population of over 9 million, with higher levels of education and income compared to national averages. However, it also has higher unemployment. The document goes on to analyze demographic characteristics of the survey sample and present results of the housing survey regarding economic assessments, buying intentions, and financing preferences.
American Research Journal of Humanities & Social Science (ARJHSS) is a double blind peer reviewed, open access journal published by (ARJHSS).
The main objective of ARJHSS is to provide an intellectual platform for the international scholars. ARJHSS aims to promote interdisciplinary studies in Humanities & Social Science and become the leading journal in Humanities & Social Science in the world.
INVESTIGATION OF EFFECT OF SALES OF ARAB REAL ESTATE COMPANIES ON INCOME OF T...AkashSharma618775
This paper investigated the impact of sales of Arab real estate companies on Turkey's income from 2015
to 2020, the change in inflation-adjusted housing prices, residential real estate prices - index, nominal residential
real estate prices. This thesis sheds light on the conditions of Turkey's real estate investment environment during
the past six years. We used the study methodology that includes analyzing Turkey's investment environment and
studying Turkey's central property investment policies. We also used a time series model for the analyzes. The
results showed a relationship between the change in inflation-adjusted housing prices on the GDP of Turkey. They
showed an association between nominal residential real estate prices on the GDP in Turkey. However, the result
also showed no relationship between residential real estate prices - an indicator of GDP in Turkey.
This study provides an analysis of the costs and benefits of emigration for Georgia, with an emphasis on emigration to the EU. In the concluding section we dwell on the consequences of a possible liberalization of EU migration policies with regard to Eastern Partnership (EaP) countries, and how such a policy change would affect the flow and composition of migration from Georgia to the EU. The study estimates the costs and benefits of migration through the prism of recent economics developments in Georgia and in particular the sweeping liberalization reforms of recent years. While Georgia remains a poor country, its geopolitical position as a Western outpost in the Caucasus and Central Asian region, its role as a key trade and transportation hub, the superior quality of its bureaucracy, lack of corruption, etc., provide a very different context for migration processes, turning migration into a circular phenomenon, a major factor in modernizing the Georgian economy, society, and politics. The EU should give due consideration to this phenomenon as it (re)considers its policy on migration with regard to Georgia and, potentially, other EaP countries.
Authored by: Lasha Labadze and Mirian Tukhashvili
The projection examines impact of demographic changes and changes in health status on future (up to 2050) health expenditures. Next to it, future changes in the labour market participation and their imact on the health care system revenues are examined. Results indicate that due to demographic pressures health expenditures will increase in the next 40 years and health care systems in the NMS will face deficit. Moreover, health revenues, expenditures and deficit/surplus are slightly sensitive to possible labour market changes. Health care system reforms are required in order to balance the disequilibrium of revenues and expenditures caused by external factors (demographic and economic), and decrease the premium needed to cover expenditures. Such reforms should lead, on the one hand, to the rationing of medical services covered by public resources, and on the other, to more effective governance and management of the sector and within the sector.
Authored by: Stanislawa Golinowska, Ewa Kocot, Agnieszka Sowa
Published in 2008
Открытый урок в 7а классе по теме
«Проблемы окружающей среды»
Задачи урока:
1. Учебный аспект-систематизация знаний учащихся по теме, развитие навыков устной речи, чтения и аудирования.
2. Социокультурный аспект-привлечение интереса к проблемам своего города Ставрополя
3. Развивающий аспект-развитие способности к обобщению, анализу; развитие воображения, способности к распределению внимания.
4. Воспитательный аспект-формирование ответственного отношения к природе, к проблемам окружающей среды, формирование желания помочь природе.
American Research Journal of Humanities & Social Science (ARJHSS) is a double blind peer reviewed, open access journal published by (ARJHSS).
The main objective of ARJHSS is to provide an intellectual platform for the international scholars. ARJHSS aims to promote interdisciplinary studies in Humanities & Social Science and become the leading journal in Humanities & Social Science in the world.
INVESTIGATION OF EFFECT OF SALES OF ARAB REAL ESTATE COMPANIES ON INCOME OF T...AkashSharma618775
This paper investigated the impact of sales of Arab real estate companies on Turkey's income from 2015
to 2020, the change in inflation-adjusted housing prices, residential real estate prices - index, nominal residential
real estate prices. This thesis sheds light on the conditions of Turkey's real estate investment environment during
the past six years. We used the study methodology that includes analyzing Turkey's investment environment and
studying Turkey's central property investment policies. We also used a time series model for the analyzes. The
results showed a relationship between the change in inflation-adjusted housing prices on the GDP of Turkey. They
showed an association between nominal residential real estate prices on the GDP in Turkey. However, the result
also showed no relationship between residential real estate prices - an indicator of GDP in Turkey.
This study provides an analysis of the costs and benefits of emigration for Georgia, with an emphasis on emigration to the EU. In the concluding section we dwell on the consequences of a possible liberalization of EU migration policies with regard to Eastern Partnership (EaP) countries, and how such a policy change would affect the flow and composition of migration from Georgia to the EU. The study estimates the costs and benefits of migration through the prism of recent economics developments in Georgia and in particular the sweeping liberalization reforms of recent years. While Georgia remains a poor country, its geopolitical position as a Western outpost in the Caucasus and Central Asian region, its role as a key trade and transportation hub, the superior quality of its bureaucracy, lack of corruption, etc., provide a very different context for migration processes, turning migration into a circular phenomenon, a major factor in modernizing the Georgian economy, society, and politics. The EU should give due consideration to this phenomenon as it (re)considers its policy on migration with regard to Georgia and, potentially, other EaP countries.
Authored by: Lasha Labadze and Mirian Tukhashvili
The projection examines impact of demographic changes and changes in health status on future (up to 2050) health expenditures. Next to it, future changes in the labour market participation and their imact on the health care system revenues are examined. Results indicate that due to demographic pressures health expenditures will increase in the next 40 years and health care systems in the NMS will face deficit. Moreover, health revenues, expenditures and deficit/surplus are slightly sensitive to possible labour market changes. Health care system reforms are required in order to balance the disequilibrium of revenues and expenditures caused by external factors (demographic and economic), and decrease the premium needed to cover expenditures. Such reforms should lead, on the one hand, to the rationing of medical services covered by public resources, and on the other, to more effective governance and management of the sector and within the sector.
Authored by: Stanislawa Golinowska, Ewa Kocot, Agnieszka Sowa
Published in 2008
Открытый урок в 7а классе по теме
«Проблемы окружающей среды»
Задачи урока:
1. Учебный аспект-систематизация знаний учащихся по теме, развитие навыков устной речи, чтения и аудирования.
2. Социокультурный аспект-привлечение интереса к проблемам своего города Ставрополя
3. Развивающий аспект-развитие способности к обобщению, анализу; развитие воображения, способности к распределению внимания.
4. Воспитательный аспект-формирование ответственного отношения к природе, к проблемам окружающей среды, формирование желания помочь природе.
Discover malaysia with kualawww. Tripmart.comtripmart
Malaysia Holidays - Book Malaysia Tours & travel packages at Tripmart. Largest number of Malaysia Tour & holiday Packages available. Go for a Hoilday, travel to Malaysia and its various tourist attractions with Malaysia holiday packages. Explore Malaysia Tourism with cheap vacation packages.
EI3 - Intelligent Infrastructures in the Internet of the FutureMario Vega Barbas
The Internet of Things technology vision represents a future in which Internet extends into the real world including the everyday objects as new players in the ecosystem. This paper aims to show the lines of research of the authors within a future where physical objects can be remotely monitored and controlled through the virtual world, where intelligent control systems enable monitoring of human activity from a non-intrusive role.
Information extraction from sensor networks using the Watershed transform alg...M H
Wireless sensor networks are an effective tool to provide fine resolution monitoring of the physical environment. Sensors generate continuous streams of data, which leads to several computational challenges. As sensor nodes become increasingly active devices, with more processing and communication resources, various methods of distributed data processing and sharing become feasible. The challenge is to extract information from the gathered sensory data with a specified level of accuracy in a timely and power-efficient approach. This paper presents a new solution to distributed information extraction that makes use of the morphological Watershed algorithm. The Watershed algorithm dynamically groups sensor nodes into homogeneous network segments with respect to their topological relationships and their sensing-states. This setting allows network programmers to manipulate groups of spatially distributed data streams instead of individual nodes. This is achieved by using network segments as programming abstractions on which various query processes can be executed. Aiming at this purpose, we present a reformulation of the global Watershed algorithm. The modified Watershed algorithm is fully asynchronous, where sensor nodes can autonomously process their local data in parallel and in collaboration with neighbouring nodes. Experimental evaluation shows that the presented solution is able to considerably reduce query resolution cost without scarifying the quality of the returned results. When compared to similar purpose schemes, such as “Logical Neighborhood”, the proposed approach reduces the total query resolution overhead by up to 57.5%, reduces the number of nodes involved in query resolution by up to 59%, and reduces the setup convergence time by up to 65.1%.
Europe Holidays - Book Europe Tours & travel packages at Tripmart. Largest number of Europe Tour & holiday Packages available. Go for a Hoilday, travel to Europe and its various tourist attractions with Europe holiday packages. Explore Europe Tourism with cheap vacation packages.
Analysis of factors affecting urban per capita housing area in ChinaIJAEMSJORNAL
Housing problems have become one of the hottest topics, influencing people's livelihood and national economy. This paper intends to re-analyze the per capita housing area, which characterizes the residents' happiness index, in order to measure the basic living condition. Taking into account of the large expansion of the floating population in the process of urbanization, we choose “urban resident population” to amend the “registration population”, which is the denominator of the index. We selected the data of residential investment, urban residents' consumption level and residential completion area from 1978 to 2015 to analyze the influence of independent variables on the per capita housing area, we found the volatility of housing price, which reduces the average level of urban per capita housing empirically.
A sneak peek into Residential Report H1 2016
The real estate residential market in Bengaluru was reluctant to show any progress in the times preceding 2016. It hasn’t revived either in the first half of 2016, it has witnessed a dip in its absorption trend and reached an all-time low Sales Velocity of 1.02%, i.e. a 7.7% decline in the first half of 2016 in comparison to 2015’s second half.
As a negative result of the declining Sales Velocity, Months Inventory is at its highest in the first half of 2016 when compared to the periods following December 2012. An increase of 6.3% is noticed in the Months Inventory of the first half of 2016 when compared to the second half of 2015. The average time required to sell the existing primary residential stock has increased from 31 months in H2 2015 to 33.07 months in H1 2016.
As an outcome of declining Sales Velocity and ever increasing Months Inventory levels, the Average Capital Value of residential development across Bengaluru would have decreased, as a layman would analyze. The truth remains that the average Capital Value remains unchanged in H1 2016 as it remains the same in H2 2015, i.e. INR 5334 per sq. ft.
Bengaluru’s residential market had a net addition of 18,129 units in H1 2016 across developments. This net addition is 40% lower when compared to the net addition of H2 2015, which stood at 29,920 units. Considering the net addition, the total inventory in H1 2016 stood at 125,738 units across developments out of which the incremental sale of 20,169 units (16% of inventory) took place in H1 2016. Incremental Sales in H1 2016 has declined by 17% in comparison to H2 2015.
Apartment development accounts to 81% of incremental sales, whereas Plot, Villa and Row House development sums up to 15.1%, 3.5% and 0.1% respectively of incremental sales in H1 2016.
Apartment development in Bengaluru has a total residential development spread of 399.1 Mn sq. ft. of which unsold inventory stood at 133.1 Mn sq. ft. valuing INR 72,250 crores. The size of unsold inventory of Plot, Villa and Row House stood at 21.8, 17 and 1.5 Mn sq. ft. respectively in H1 2016.
Residential development in its various development forms is spread across Bengaluru- North to South and East to West. The quadrant which witnessed the largest addition to its already existing inventory in H1 2016 is the North-East quadrant. 8,127 units were added to its already existing stock. Also the North-East quadrant performed better than its counterparts in H1 2016 at an incremental sales of 7,569 units i.e. 16.5% sales from the total inventory available. The South-East quadrant follows the North-East quadrant both in terms of large net addition of 7,631 units in H1 2016 and a total incremental sale of 14.7%.
Based on the data of Japanese Prime metropolitan area from 1955 to 2013, this paper studies the effect of industry agglomeration and population aggregation on economic growth in Tokyo metropolitan area. Through the processing of the panel data, we find that the industry agglomeration in Japanese Prime metropolitan region has apparently positive impacts on its economic growth, and also, population aggregation can positively effects its economic growth. Following this paper is try to carry out research on Tokyo —the core city of Japanese Prime metropolitan area, to study the influence of industry agglomeration on its economic growth. This paper thinks that in the process of the development of the city group, due to resource constraints, the manufacturing output unit of land demand big industry will gradually from the degree of economic development is relatively high in the whole city and the city to evacuate, inside, labor resources are gradually to the regional flow of high GDP per capita output.
Discover malaysia with kualawww. Tripmart.comtripmart
Malaysia Holidays - Book Malaysia Tours & travel packages at Tripmart. Largest number of Malaysia Tour & holiday Packages available. Go for a Hoilday, travel to Malaysia and its various tourist attractions with Malaysia holiday packages. Explore Malaysia Tourism with cheap vacation packages.
EI3 - Intelligent Infrastructures in the Internet of the FutureMario Vega Barbas
The Internet of Things technology vision represents a future in which Internet extends into the real world including the everyday objects as new players in the ecosystem. This paper aims to show the lines of research of the authors within a future where physical objects can be remotely monitored and controlled through the virtual world, where intelligent control systems enable monitoring of human activity from a non-intrusive role.
Information extraction from sensor networks using the Watershed transform alg...M H
Wireless sensor networks are an effective tool to provide fine resolution monitoring of the physical environment. Sensors generate continuous streams of data, which leads to several computational challenges. As sensor nodes become increasingly active devices, with more processing and communication resources, various methods of distributed data processing and sharing become feasible. The challenge is to extract information from the gathered sensory data with a specified level of accuracy in a timely and power-efficient approach. This paper presents a new solution to distributed information extraction that makes use of the morphological Watershed algorithm. The Watershed algorithm dynamically groups sensor nodes into homogeneous network segments with respect to their topological relationships and their sensing-states. This setting allows network programmers to manipulate groups of spatially distributed data streams instead of individual nodes. This is achieved by using network segments as programming abstractions on which various query processes can be executed. Aiming at this purpose, we present a reformulation of the global Watershed algorithm. The modified Watershed algorithm is fully asynchronous, where sensor nodes can autonomously process their local data in parallel and in collaboration with neighbouring nodes. Experimental evaluation shows that the presented solution is able to considerably reduce query resolution cost without scarifying the quality of the returned results. When compared to similar purpose schemes, such as “Logical Neighborhood”, the proposed approach reduces the total query resolution overhead by up to 57.5%, reduces the number of nodes involved in query resolution by up to 59%, and reduces the setup convergence time by up to 65.1%.
Europe Holidays - Book Europe Tours & travel packages at Tripmart. Largest number of Europe Tour & holiday Packages available. Go for a Hoilday, travel to Europe and its various tourist attractions with Europe holiday packages. Explore Europe Tourism with cheap vacation packages.
Analysis of factors affecting urban per capita housing area in ChinaIJAEMSJORNAL
Housing problems have become one of the hottest topics, influencing people's livelihood and national economy. This paper intends to re-analyze the per capita housing area, which characterizes the residents' happiness index, in order to measure the basic living condition. Taking into account of the large expansion of the floating population in the process of urbanization, we choose “urban resident population” to amend the “registration population”, which is the denominator of the index. We selected the data of residential investment, urban residents' consumption level and residential completion area from 1978 to 2015 to analyze the influence of independent variables on the per capita housing area, we found the volatility of housing price, which reduces the average level of urban per capita housing empirically.
A sneak peek into Residential Report H1 2016
The real estate residential market in Bengaluru was reluctant to show any progress in the times preceding 2016. It hasn’t revived either in the first half of 2016, it has witnessed a dip in its absorption trend and reached an all-time low Sales Velocity of 1.02%, i.e. a 7.7% decline in the first half of 2016 in comparison to 2015’s second half.
As a negative result of the declining Sales Velocity, Months Inventory is at its highest in the first half of 2016 when compared to the periods following December 2012. An increase of 6.3% is noticed in the Months Inventory of the first half of 2016 when compared to the second half of 2015. The average time required to sell the existing primary residential stock has increased from 31 months in H2 2015 to 33.07 months in H1 2016.
As an outcome of declining Sales Velocity and ever increasing Months Inventory levels, the Average Capital Value of residential development across Bengaluru would have decreased, as a layman would analyze. The truth remains that the average Capital Value remains unchanged in H1 2016 as it remains the same in H2 2015, i.e. INR 5334 per sq. ft.
Bengaluru’s residential market had a net addition of 18,129 units in H1 2016 across developments. This net addition is 40% lower when compared to the net addition of H2 2015, which stood at 29,920 units. Considering the net addition, the total inventory in H1 2016 stood at 125,738 units across developments out of which the incremental sale of 20,169 units (16% of inventory) took place in H1 2016. Incremental Sales in H1 2016 has declined by 17% in comparison to H2 2015.
Apartment development accounts to 81% of incremental sales, whereas Plot, Villa and Row House development sums up to 15.1%, 3.5% and 0.1% respectively of incremental sales in H1 2016.
Apartment development in Bengaluru has a total residential development spread of 399.1 Mn sq. ft. of which unsold inventory stood at 133.1 Mn sq. ft. valuing INR 72,250 crores. The size of unsold inventory of Plot, Villa and Row House stood at 21.8, 17 and 1.5 Mn sq. ft. respectively in H1 2016.
Residential development in its various development forms is spread across Bengaluru- North to South and East to West. The quadrant which witnessed the largest addition to its already existing inventory in H1 2016 is the North-East quadrant. 8,127 units were added to its already existing stock. Also the North-East quadrant performed better than its counterparts in H1 2016 at an incremental sales of 7,569 units i.e. 16.5% sales from the total inventory available. The South-East quadrant follows the North-East quadrant both in terms of large net addition of 7,631 units in H1 2016 and a total incremental sale of 14.7%.
Based on the data of Japanese Prime metropolitan area from 1955 to 2013, this paper studies the effect of industry agglomeration and population aggregation on economic growth in Tokyo metropolitan area. Through the processing of the panel data, we find that the industry agglomeration in Japanese Prime metropolitan region has apparently positive impacts on its economic growth, and also, population aggregation can positively effects its economic growth. Following this paper is try to carry out research on Tokyo —the core city of Japanese Prime metropolitan area, to study the influence of industry agglomeration on its economic growth. This paper thinks that in the process of the development of the city group, due to resource constraints, the manufacturing output unit of land demand big industry will gradually from the degree of economic development is relatively high in the whole city and the city to evacuate, inside, labor resources are gradually to the regional flow of high GDP per capita output.
The Effects of European Regional Policy - An Empirical Evaluation of Objectiv...Christoph Schulze
The European Union provides funds to disadvantaged regions to promote economic growth and convergence (in terms of per capita income) among regions within Europe.
In this study, I apply Propensity Score Matching on NUTS 3 data for the operational period of 2007 – 2013 to evaluate European structural policy. I find that results for Objective 1 policy are not robust to changes within the control group,
leading to both, positive and negative results of structural policy. Findings from the evaluation of Objective 2 policy suggest success in terms of fighting unemployment
and long term unemployment. Programs aiming at reducing youth unemployment in turn did not succeed. In fact, treated regions showed significant higher rates in youth unemployment.
The purpose of this study is to explore and assess the costs and benefits of labour migration in Armenia and the potential of migration for contributing to the country’s development. We also examine how policy can be effectively formulated and implemented so that Armenia can get the most out of its migration experience. Lastly, we analyse how a phenomenon that emerged because of limited opportunities for employment – migration – evolved into a strategy towards development and prosperity.
Based on this analysis, this paper makes a strong argument in favour of implementing programs in Armenia that involve the active collaboration of government institutions and the Armenian Diaspora, duly considering the unusual influence the latter has on Armenia’s economic and human development.
Authored by: Gagik Makaryan and Mihran Galstyan
Published in 2013
Review of The Effectiveness Transfer Land and Building Tax (PBB-P2) as A Regi...Trisnadi Wijaya
Review of The Effectiveness Transfer Land and Building Tax (PBB-P2) as A Regional Tax
Authors:
Siti Khairani, SE.Ak., M.Si
Trisnadi Wijaya, SE., S.Kom
Published at:
2013 International Forum on Contemporary Management Issues Proceedings
ISBN : 978-986-6600-56-2
This paper investigates an impact of the government policies aimed at the enterprise sector on competitiveness of this sector. The analysis was based on an example of the Polish manufacturing sector and the eight-year period from 1996 to 2003.
The general recommendation is that the competitiveness of the Polish manufacturing sector could be increased by relaxing fiscal burden, further privatization and restructuring of state owned companies. The state aid in a form of subsidies seems to harm both internal and external competitiveness rather than to support them.
Authored by: Ewa Balcerowicz, Maciej Sobolewski
Published in 2005
This paper provides an overview of public expenditures on education and healthcare in Belarus, Georgia, Kyrgyzstan, Moldova, Russia, Ukraine and some other countries of the former Soviet Union before and during the global financial crisis. Before the crisis, the governments of these countries were substantially increasing spending on education and health. The crisis adversely affected the FSU countries and worsened their fiscal situation. The analysis indicates that during the crisis, despite the fiscal constraints, public education and health expenditures have mostly been maintained or increased in almost all of these countries. However, the crisis situation was not taken as an opportunity to address these countries' key education and healthcare problems related to demographic changes, insufficient per capita expenditure levels, the low efficiency of public spending and the insufficient quality of services. These issues form an ambitious reform agenda for these countries in the medium- and long-term.
Authored by: Alexander Chubrik, Marek Dabrowski, Roman Mogilevsky, Irina Sinitsina
Published in 2011
A decennium report of about 300 pages (in Khmer, English, Chinese versions), Phnom Penh 2030s will present insightful data and exhaustive information on Phnom Penh’s real estate sector from the last ten years and trend predictions into the next ten years. In this decennium report, ten key aspects related to real estate sector will be on the spotlight.
Michele Argiolas, Karol Coppola and Alberto Cruccas on "GIS-WEB approach to support spatial monitoring of housing market acquisition risk and urban property market dynamics definition"
This paper presents a quantitative assessment of the impact of two gas pipeline projects, Nabucco and South Stream, on Turkey's energy security. The incidence of the impact is based on three dimensions of energy security: supply-demand balance, production source diversity,and transit route diversity. This paper relies on the Herfindahl Hirschman Index (HHI) and the adjusted Shannon Weiner Index (SWI) to evaluate and compare the impact of the various project
implementation scenarios. The main findings are that both projects enhance Turkey's energy security and provide valuable and timely energy supply in the medium-term but their contribution is inadequate and marginal in the long-run. More specifically, the implementation of Nabucco significantly reduces the market concentration of producers whereas the South Stream project improves transit diversity by including Bulgaria as a major transit player. Surprisingly, implementation of Nabucco reduces transit diversity security because it includes politically volatile regions like Iraq and Georgia
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
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1. A STATISTICAL ANALYSIS OF THE DEMAND FOR RESIDENTIAL
REAL ESTATE IN ISTANBUL
VOLKAN EMRE
106621009
ISTANBUL BILGI UNIVERSITY
INSTITUTE OF SOCIAL SCIENCES
MSc. in FINANCIAL ECONOMICS
Asst.Prof.Dr. Orhan Erdem
SANTRALISTANBUL, 2011
i
2. İSTANBUL KONUT PİYASASI TALEBİ ÜZERİNE İSTATİSTİKSEL
BİR ANALİZ
VOLKAN EMRE
106621009
İSTANBUL BİLGİ ÜNİVERSİTESİ
SOSYAL BİLİMLER ENSTİTÜSÜ
FİNANSAL EKONOMİ YÜKSEK LİSANS PROGRAMI
Yrd. Doç. Dr. Orhan Erdem
SANTRALISTANBUL, 2011
ii
3. A STATISTICAL ANALYSIS OF THE DEMAND FOR RESIDENTIAL
REAL ESTATE IN ISTANBUL
VOLKAN EMRE
106621009
Proje Danışmanı :
Komisyon Üyesi:
Projenin Onaylandığı Tarih:
iii
4. ÖZET
Bu çalışma İstanbul ili konut piyasası ve konut talebinin kısa dönem istatistiksel
analizini amaçlamaktadır. Bu bağlamda ING Bank Türkiye için Nielsen Pazar
Araştırma Şirketi ve İstanbul Bilgi Üniversitesi tarafından aylık olarak
hazırlanan ‘ING Mortgage Barometre’ isimli konut piyasası araştırmasının
telefon anketi ile elde edilmiş 2009 Aralık – 2010 Ekim Dönemi verileri
kullanılmıştır. Yapılan analizlerde tüketicilerin güncel ekonomik koşullar ve
konut piyasası hakkındaki düşünceleri, beklentileri, ev alım tercihleri ve
finansman seçimleri incelenmiş ve yorumlanmıştır. Çalışmada ayrıca analiz ve
yorumlara ışık tutması amacıyla başta İstanbul olmak üzere kapsamlı ING Pazar
Araştırması’nda anket yapılan 14 il özelinde temin edilmiş nüfus, işsizlik, gelir,
harcama, eğitim ve konut istatistiklerine de dikkat çekilmiştir.
ABSTRACT
The Purpose of this study is to make a short term statistical analysis of the
demand for residential real estates in Istanbul. For this aim, data obtained by
Nielsen Market Research Company for Ing Bank Turkey’s monthly Mortgage
Report are used in the investigation. Data set used to make analysis, obtained
through telephone questionnaires in fourteen different provinces in Turkey.
Within the scope of the study, initially social and economic conditions of the
city of Istanbul (i.e. population, income, employment, expenditure, and housing
statistics) are summarized. Later on, demographic characteristics of sample
population and its components are described. In the following, results of the
ING Housing Market Survey, prepared in collaboration with Nielsen and
Istanbul Bilgi University, are dealt with. Finally evaluations are made
considering the field planning within this framework.
iv
5. TABLE OF CONTENTS
1. Information about Data Obtained by Nielsen .............................................................................................. 2
2. Information about ING Mortgage Barometer .............................................................................................. 2
3. Literature Review ........................................................................................................................................ 3
4. Social and Economic Conditions of the city of Istanbul in comparison to the cities and regions in the
ING Market Survey ......................................................................................................................................... 4
4.1 Population .................................................................................................................................5
4.1.1 Resident Population................................................................................................................5
4.1.2 Age ........................................................................................................................................5
4.1.3 Educational Level ...................................................................................................................6
4.2 Income ......................................................................................................................................7
4.3 Employment ..............................................................................................................................9
4.4 Expenditure ...............................................................................................................................9
4.5 Housing Statistics.................................................................................................................... 10
4.5.1 Number of Households ......................................................................................................... 10
4.5.2 Home Ownership ................................................................................................................. 11
5. Demographic Characteristics of the Sample Population ........................................................................... 12
5.1. Characteristics of the Sample Population ................................................................................ 12
5.1.1 Gender and Age.................................................................................................................... 13
5.1.2 Social Groups and Household Income............................................................................... 14
5.1.3 Education and Profession......................................................................................................15
5.1.4 People and Children in the Household .................................................................................. 16
5.1.5 Information about Housing ...................................................................................................17
5.2 Characteristics of the Households with Income ....................................................................19
5.2.1 Education and Profession..................................................................................................19
5.2.2 Social Groups and Household Income............................................................................... 20
6. Results of ING Housing Market Survey ................................................................................................... 21
6.1 Assessment of Current Year’s Economic Conditions ............................................................... 21
v
6. 6.2 Economic Expectations for Next Year ..................................................................................... 22
6.3 Current Housing Market Assessments ..................................................................................... 23
6.4 Investment Preferences of the Householders ............................................................................ 24
6.5 Tendency to Buy a House ........................................................................................................ 25
6.6 Planned Time Period to Buy a House....................................................................................... 26
6.7 Aim to Buy a House - Apartment ............................................................................................ 28
6.8 Value of the House –Apartment ............................................................................................... 29
6.9 Preferred Type of House-Apartment to Buy............................................................................. 32
6.10 Details of Financing .............................................................................................................. 32
6.11 Mortgage Barometer.............................................................................................................. 36
7. Conclusion ................................................................................................................................................. 39
8. Appendix ................................................................................................................................................... 42
vi
7. Introduction
Although it is widely argued housing demand is one of the important indicators that affect
Turkish Economy, this has not yet begun permeate all applications of housing market
analysis. This study tries to make a contribution to house demand studies for residential real
estates in Istanbul with several statistical analyses.
Framework of this work is composed of the Housing Market Survey conducted by ING Bank
with assistance of Istanbul Bilgi University and The Nielsen Company. The study analyzes
data for the city of Istanbul and contains results of several statistical investigations. The
choice of using this city is mainly because it shares a number of characteristics such as it is
economic center of Turkey. It is also a attractive place to live, due to its historical and cultural
heritage and because of being the most developed city of the country.
Analysis as in ‘Housing Market Analysis’, means probing into the parts comprising the entire
housing market and also the relations among those making up the whole. The Housing Market
Analysis is usually estimated by agents having through knowledge of housing market
behavior of a particular area. Nielsen Market Survey Company is taking that role in this study.
The Housing Market Analysis takes into account alterations in the following features of a
specific Housing Market Area: Economic, Demographic and Housing Stock. And reports of
the analysis deliver calculations and estimates of: Employment, Population, Households,
Expenditure, and Housing Statistics. This study follows a similar pattern.
The remainder of the paper is organized as follows. The following section is including
information about data obtained by Nielsen and about ING Mortgage Barometer. Section 3
presents Literature Review. Social and economic conditions of the city of Istanbul are
introduced in Section 4. Section 5 reports demographic characteristics of the sample
population. Section 6 contains the results of ING Housing Market Survey. Finally some
concluding remarks are presented in Section 7.
1
8. 1. Information about Data Obtained by Nielsen
Data is collected with using CATI (Computer Assisted Telephone Interview) Technique. In
this context 37 questions are asked to participants who are older than 30 years old and from
A, B, C1 and C2 Social Groups. Questions, asked by The Nielsen Company for the ING Bank
Mortgage Barometer Survey are listed in the appendix of this work. Interviews made for ING
Mortgage Barometer Survey are made in 14 provinces in Turkey. Those provinces are
respectively: Istanbul, Tekirdag, Bursa, Kocaeli, Izmir, Aydın, Ankara, Kayseri, Antalya,
Adana, Samsun, Trabzon, Erzurum, and Gaziantep. Data is collected on a monthly basis
between the period of December 2009 and October 2010.
In this study, Istanbul is on the main focus of the analysis between the time period of
December 2009 and October 2010. In the analyzed period of time, cumulative numbers of
interviewed participants are 11.266 for all provinces and 2.171 for Istanbul. Those numbers
are respectively 1042 and 198 for the time period of October 2010.
2. Information about ING Mortgage Barometer
ING Mortgage Barometer Survey is sponsored by ING Bank in collaboration with Istanbul
Bilgi University and the Nielsen Company. ING Bank’s Comprehensive Mortgage Report has
been published since January 2010 up to date of this study. This housing market analysis aims
to draw a general picture of the housing market in Turkey. For these purpose main goals of
the Survey can be summarized as following
-
Measure the tendency of householders to buy a house and determine their preferences
-
Investigate the dynamics that effect housing demand
-
Form the Mortgage Barometer Index to follow the expectations of households from
economy and their tendencies to buy a house
Mortgage Barometer is an Index these values vary from 0 to 200. Greater index values
indicate higher demand in the housing market on the other hand smaller index values show
the opposite.
2
9. 3. Literature Review
Dokmeci, Berkoz , Levent , Yurekli and Cagdas (1999) investigated the residential
preferences of individuals with respect to their socio-economic characteristics and the general
characteristics of the districts in Istanbul. The result of the survey showed that a clean and
quiet neighborhood and a stable social environment are common factors for all income
groups.
Yazgı and Dökmeci (2007) tried to explore the spatial distribution of housing prices in the
Metropolitan Area of Istanbul. The results of their regression analysis indicated that the most
important factor affecting the housing prices is the size of the floor area. The second and the
third most effective factors are the road surface ratio and the floor are consecutively.
Sari, Ewing and Aydin (2007) investigate the relation between housing starts and
macroeconomic variables in Turkey from 1961 to 2000. They use generalized variance
decomposition approach for examining the relations between housing market activity and
prices, interest rates, output, money stock and employment. Their results indicate that the
effect of the housing market on output is not necessarily reflected in labor market. Moreover,
the shocks to interest rates, output and prices have notable effects on housing activity in
Turkey.
Selim (2008) analyzed factors that determine the house prices in Turkey using 2004
Household Budget Survey Data. Results showed that the most important variables that affect
house rents are type of house , type of building, number of rooms, size and other structural
characteristics such as water system, pool, natural gas.
Badurlar (2008) analyzed the dynamic effects of macroeconomic variables on the house
prices in Turkey for the period 2000 – 2006. The results of cointegration analysis suggested
that there exists a long run relationship between house prices and macroeconomic variables. It
is observed that one-directional causality exists from gross domestic product and money
supply to house prices.
Alkay (2008) tests the hypothesis that in a segmented housing market, housing price structure
is different in each segment and whole market area price structure does not reflect a realistic
housing price structure effectively. The empirical results show that as a stratifier, average
household income in neighborhoods’ affects housing prices in each segment and, considering
the submarkets based on average household income in neighborhoods, is an effective for the
3
10. Istanbul housing market. Implicit attribute prices vary and there is a statistically significant
difference in the prices of each segment. These differences have a large effect on the overall
price of housing.
Özsoy and Sahin (2009) analyze empirically major factors that affect housing prices in
Istanbul using the classification and regression tree (CART) approach. The CART results
indicate that sizes, elevators, existence of security, existence of central heating units and
existence of view are the most important variables crucially affecting housing prices in
Istanbul.
Similarly Ebru and Eban (2009) investigate the relationship between house prices and housing
characteristics in Istanbul. Their data set includes some housing characteristics of dwellings
like numbers of room, bathroom, heating system, location of the house etc. The results show
some similarities and differences from earlier studies on housing prices. They find that age,
cable tv, security, heating system, garage, kitchen area, increasing numbers of room and
bathroom increase the house prices. Additionally, findings of the study also show that side
variable which is special factor for Istanbul real estate market has negative effect on the
prices.
4. Social and Economic Conditions of the city of Istanbul in comparison to
the cities and regions in the ING Market Survey
Istanbul is the largest city of Turkey and 5th largest city of the world with a population of
nearly 13 million that also makes it the second largest metropolitan area in Europe. Apart
from being the largest city of the country Istanbul is the center of Turkey’s economic life. The
city employs approximately %20 of Turkey’s industrial labor and contributes %38 of the
country’s industrial workplace. Istanbul generates %55 of Turkey’s trade and %45 of the
country’s wholesale trade and generates %21 of GNP. Istanbul contributes %40 of all taxes
collected in Turkey. The city has the highest rate of educational levels almost in all criterion
compared to the other regions in Turkey.
Details of population, income, employment, expenditures and housing statistics are
respectively analyzed in the following parts of this section.
4
11. 4.1 Population
4.1.1 Resident Population
Istanbul has a population of 9.822.210 residents according to the count made by Turkish
Statistical Institute (TUIK) in year 2000. 2.550.607 residents from the total population of the
city of Istanbul are classified as households. Household to Population ratio is approximately
25% which is almost the same of the average of the all provinces in the ING Housing Market
Survey. Details are on the chart below.
Chart 4. 1
4.1.2 Age
Chart.4.2 shows details of Median Age for 2000. Depending on the TUIK’s statistics, median
age of the whole population of the city of Istanbul is 26.3. Additionally median ages of the
male and female residents from the sample are respectively 25.9 and 26.6. As seen below the
median age in Istanbul
5
12. Chart 4. 2
4.1.3 Educational Level
Educational level has a great importance in the demographical analysis. Level of education of
the city of Istanbul is stated with the chart below.
Depending on the data obtained in year 2009 percentage of the illiterates is 3.84. This rate is
less than the average ratio of Turkey which is approximately 9%. Rate of literate people
without any professional education is 18.54%. Furthermore the rate of people who completed
primary school education is 28.15%. Secondary school graduates have the rate of 16.03%.
Following this high school graduates have the rate of 18.2%. The analysis of the university
education is separated in two parts. The rates of undergraduate and graduate educational
levels are respectively 8.27% and 0.85%. Unfortunately no information could be obtained
about the 5.67% of the people from the sample with regards to their level of education.
Results can be seen on the chart below.
6
13. Chart 4. 3
4.2 Income
As an important indicator of income, shares in Gross Domestic Product figures by regional
basis are shown comparatively in the chart below. While 2001 income per capita of Turkey is
accepted as 100 index when compared to other provinces, Istanbul is on the first place in
terms of income. Population living in this area can obtain more income of Turkey’s average.
Depending on the Chart 4.4, Istanbul takes 21.3% percent of Turkey’s GDP. In 2005 the city
of Istanbul had a GDP of $133 billion.
Income distribution is not fairly balanced in Istanbul like in Turkey. Based on 1994 statistics,
20% of the highest income group uses 64% of the economic resources and on the other hand
20% of the lowest income group uses only 4% of them.
7
14. Chart 4. 4
As the second very important indicator of income, Gross Domestic Product per capita figures
by regional basis are shown comparatively in the chart below. While 2001 income per capita
of Turkey is accepted as 100 index when compared to other regions, Istanbul is on the first
place in terms of Income.
Chart 4. 5
8
15. 4.3 Employment
Unemployment figures are also above the Turkey average as seen in income and education
figures above. According to 2009 year-end data, unemployment rate in Turkey was realized as
14% while this rate reached up to 16.8% in Istanbul which brings the city to the second place
in the ranking after Adana.
Chart 4. 6
4.4 Expenditure
Analyzing expenditures of the government and household sectors is important to have a better
Idea about the housing demand.
Table 4.1 shows ‘Cumulative Household Consumption Expenditures by Items’ in Turkey in
the year 2009. Housing and rent expenditures in Istanbul made up the largest share of total
consumption expenditure in 2009 with a rate of 32 percent. This result means that households
spend approximately one-third of their income for residential purposes. The difference
between housing and rent expenditures and expenditures for food and non alcoholic beverages
is remarkable in the city of Istanbul when compared with the other provinces.
As seen on the table below, expenditures for food and non alcoholic beverages are on the
second place in the ranking with a rate of 19 percent. Transportation expenditures are in the
9
16. following with a rate 13 percent. Education, entertainment and alcoholic beverages are the
lowest items on the household consumption expenditure rankings.
Table 4. 1
4.5 Housing Statistics
According to the statistics of 2001, householders live in 10.3 million houses of total 15.0
million households in Turkey. While average homeownership rate is 68%, this rate is above
the average in the city of Istanbul having the least homeownership rate with 57, 8%.
4.5.1 Number of Households
The Chart below, prepared with the data obtained by Turkish Statistical Institute, shows
number of households by ownership status on housing unit. Following results are defined
depending on the statistics on Chart 4.7. Number of householders who own their current
residences are 1.476.687.
Number of householders who are in the status of tenant are
893.427. Number of householders who live in the lodgment apartments is 28.100. On the
other hand number of people who own a house but still paying rent is 131.662. Furthermore
20.731 people are out of the classification.
10
17. Chart 4. 7
4.5.2 Home Ownership
Homeownership rate is founded by a simple calculation using number of households who own
a house and the number of household population. To calculate The Homeownership Rate,
number of householders is divided by the number of householders who own their apartmentshouses.
Following chart demonstrates home ownership rates for the 14 provinces that are examined in
the Nielsen’s housing market survey. Results show that the ownership rate of Istanbul is 58%.
This rate gives the last place to the biggest city of Turkey in the rankings.
This result can be more remarkable if analyzed with regards to the share of housing and rent
in the household expenditures in 2009. Although having the biggest share in the household
consumption expenditures, Istanbul is in the last place in terms of home ownership rates. This
result gives an Idea about the high rent prices in the Housing Market of Istanbul.
11
18. Chart 4. 8
5. Demographic Characteristics of the Sample Population
5.1. Characteristics of the Sample Population
This section analyzes demographic characteristics of sample population Results for the time
period between December 2009 and October 2010 are shown in the following parts in the
following order:
Gender and Age
Social Groups and Household Income
Education and Profession
People and Children in the Household
Information About Housing
12
19. 5.1.1 Gender and Age
The sample population consists of 2171
observations between the time period
December 2009 and January 2010
Statistics of the sample population with
regards to Gender are shown with the Chart
on the left side with the number of 5.1.
It is observed that 53% of the participants in
ING Mortgage Barometer Survey are male.
On the other hand the rest 47% of them are
female.
Chart 5. 1
Diversity in age is shown on Chart 5.2.
Again the sample population consists of
2171 observations between the time period
December 2009 and January 2010
According to the data people whose ages
are between 30 and 34 consist 27% of the
sample population. People who are older
than 34 and younger than 45 years old are
holding the majority with 34% of sample
population. People aged between 45 and 54
have the least percentage with 19. And
finally participants who are older than 54
years old consist 20% of the sample
population.
Chart 5. 2
Diversity in social groups is shown on Chart
5.3. In this investigation the sample
population consists of 2171 observations
between the time period December 2009
and January 2010
13
20. 5.1.2 Social Groups and Household Income
It is observed that 28.5% of the sample
population are from A and B social groups.
In addition to that the majority of the whole
sample population belongs to the C1 social
group with the percentage of 43.5. Share of
the participants from C2 Social group is
29%.
Level of Household Income is shown on
Chart 5.4. In this investigation the sample
population consists of 2171 observations
again between the time period December
2009 and January 2010.
Chart 5. 3
As seen on the Chart 5.4 in the left side,
percentage of people who earn less than
1000 TL per month consist 18% of the
sample population. People who get more
than 1000 TL but less than 1500 TL have
the rate of 19 percent. Share of the group
with income between 1501 TL and 2000 TL
İS 15% of the sample population. The
Income group between 2500 TL and 3000
TL is 7%. Participants who earn more than
2999 TL per month consist of 14 percent of
the sample population. Additionally, 16% of
participants refused to give information
about their monthly income.
Chart 5. 4
14
21. 5.1.3 Education and Profession
Statistics of the sample population with
regards to educational level of the sample
population are shown with the Chart on the
left side with the number of 5.5.
As seen on the chart, participants who only
have a primary school or secondary school
degrees consist respectively 15 and 17
percent of the sample population. The rate
of 43 percent of the sample population says
that the majority of the participants have a
degree from a high school. People with a
Chart 5. 5
university degree consist of 25% of the
whole population which is a very significant
rate in comparison to the rate of university
degree holders in Turkey.
ING Housing Market Survey classifies
Professions as stated on the Chart 5.6 that is
in the left side. At that point it must be
added that the group of unemployed
participants with a percentage of 44 on the
chart also includes housewives and retired
people. People with a regular salary consist
36% of sample population that makes them
the second on the ranking. Self Employed
participants consist 19% of the sample
population. Finally students have 1 % share
Chart 5. 6
from the sample population.
15
22. 5.1.4 People and Children in the Household
Details of number of people in the
household are shown on Chart 5.7. The
sample
population
consists
of
7826
observations between the time period
December 2009 and January 2010.
Majority of the people share live as four
people in the household. Percentage of 33
percent in the whole sample population is
the proof of this. 28% of the sample
population lives as 3 people in the
household. Participants who share their
house with one another person consist 15
Chart 5. 7
percent of the population. On the other hand
people who live with their own are just 4
percent.
Children are the major groups of people
who raise the population in households.
Chart 5.8 shows that the total number of
children is 2929. 37 percent of the sample
population has one child in the household.
Majority of participants (participants with
children)
have
two
children
in
the
household. Their rate is 43 percent of the
sample population. Having 3 and 4 children
in the household consist respectively 12 and
Chart 5. 8
4 percent of the sample population while
having 5 and above consists only 4 percent
of the sample population.
16
23. 5.1.5 Information about Housing
Details of ownership status of the current
residence are shown on Chart 5.9. The
sample
population
consists
of
198
observations in the time period of October
2010.
Examining the ownership status of the
current residence of the householders,
participants who own their apartments /
houses are on the first place with 67%.
People who live in rented residences are on
the second place with a percentage of 32%.
Other types of ownership status are 1% of
Chart 5. 9
the sample population.
After defining the ownership status, the type
of the current resident can be analyzed.
Majority of the participants do live in
Apartments on a street / boulevard / avenue.
As seen on Chart with a number of 5.10, the
rate is 75 percent of the whole sample
population. Additionally, 21 percent of the
sample populations do live in Apartments in
a housing complex or in a housing
development. Minority of the participants
with the rate of 3 percent do live either in
private houses or houses with their own
garden or in a villa. Just one percent of the
sample population live in lodgments. There
is no information about the details of the
houses that are in the lodgment
classification.
Chart 5. 10
17
24. Details of purchasing time of the current
resident are shown on Chart 5.11. The
sample population consists of 133 people
Chart 5.11 shows that 37% of the sample
population purchased their residences in the
last decade. In the following 23 % of the
people who are the owner of their current
residences bought them between 10 and 19
years ago. Thirteen percent of the residents
Chart 5. 11
are bought between 20 and 29 years ago.
Apartments that purchased either 30-39
years or 40-50 years ago have the same the
same percentages with 2% from the sample
population. Apartments that purchased more
than 50 years ago consists 18% of the
sample population.
Considering participant’s opinions about
their desired type of residents, type of
apartments in a housing complex or in a
housing development leads the choices with
its 43% rate. Apartments on a street
/boulevard /avenue consist of 42% of the
sample population. 9% of the participants
Chart 5. 12
desire to live either in a private house or in a
villa. One percent of the sample population
desire to live in lodgments. If we consider
the rate of people who currently in
lodgments, we can claim that people who
live in lodgments do want to extend their
stay.
18
25. 5.2 Characteristics of the Households
with Income
Statistics of the households with income
regarding to educational level are shown
5.2.1 Education and Profession
with the Chart on the left side with the
number of 5.13. In this analysis the people
with income consist of 1377 observations
between the time period December 2009
and January 2010
As seen on the chart, participants who only
have a primary school or secondary school
degrees consist respectively 4 and 19
percent of the people with income. The rate
of 48 percent of the observations says that
the majority of the participants have a
Chart 5. 13
degree from a high school. People with a
university degree consist of 29% of the
whole
population.
Depending
on
the
educational statistics, it can be said that
education level of Individuals with Income
is higher than the sample population.
Chart 5.14 demonstrates that almost half of
the participants are salaried. Rate of salaried
participants consist 49% of the people with
income. Rate of self employed people is
28%. Both of the rates of salaried and selfemployed participants are considerably
higher the ones of the sample population.
Rate of the unemployed people with income
consist 23 percent of the total observations.
Chart 5. 14
This rate also gives shows the number of
salaried housewives and retired people.
19
26. 5.2.2 Social Groups and Household Income
It is observed that 28% of the people with
income are from A and B social groups. In
addition to that the majority of the whole
observations belong to the C1 social group
with the percentage of 47. Share of the
participants from C2 Social group is 28%.
Level of Household Income is shown on
Chart 5.16. In this investigation the sample
population consists of 1377 observations
again between the time period December
2009 and January 2010.
Chart 5. 15
As seen on the Chart 5.16 on the left side,
percentage of people who earn less than
1000 TL per month consist 16% of the
people with income. Participants who get
more than 1000 TL but less than 1500 TL
have the rate of 18 percent. Share of the
group with income between 1501 TL and
2000 TL is 14% of the observations. The
Income group between 2500 TL and 3000
TL is 8%. Participants who earn more than
2999 TL per month consist of 17 percent of
the sample population. Additionally, 16% of
participants refused to give information
about their monthly income.
Chart 5. 16
20
27. 6. Results of ING Housing Market Survey
In this section, results obtained by ING Housing Market Survey are discussed for the city of
Istanbul. In the following set of analysis, investigations are made for the time period between
December 2009 and October 2010 on a monthly basis. Results are shown in the following
parts in the order below:
Assessment of Current Year’s Economic Conditions
Economic Expectations for the Next Year
Current Housing Market Assessments
Investment Preferences
Tendency to Buy a House / Apartment
Planned Time Period to Make an Investment in Residential Real Estate
Value of the House / Apartment
Preferred Type of House / Apartment to Buy
Details of Financing
Mortgage Barometer
6.1 Assessment of Current Year’s Economic Conditions
Monthly assessments of householders with regards to the current year’s economic conditions
between the time period December 2009 and January 2010 are shown on the Chart below. It
can be observed that most of the participants think that current economic conditions are worse
than the previous year’s economic conditions. People who don’t think that there is a
significant difference between the current and recent year’s economic conditions are on the
second place. Finally only the minority of participants think that there is an improvement in
the current year in terms of economic conditions
21
28. Chart 6. 1
Negative assessments have the highest rates especially in the first periods of the survey
starting from the rate of 76.3 percent of the sample population. Although, rate of this opinion
declines over the following months, after making its peak in April. This rate remains above
50% in every single month, except October 2011. The downward trend in the negative
opinions with regards to the current year’s economic conditions defines the rise of the positive
opinions by the time. As a result, the general view to current economic conditions is
pessimistic, but there is a significant decrease in those pessimistic householders’ assessments.
On the other hand there is a significant rise in the rate of the neutral opinions.
6.2 Economic Expectations for Next Year
Monthly assessments of householders with regards to the next year’s economic conditions
between the time period December 2009 and January 2010 are shown on the Chart below.
This evaluation has very similar results to the previous one that explained in the section 6.2.
Considering people’s opinion about next year's economic conditions, in general people with
pessimistic opinion have the majority of total. The rate of people with pessimistic opinion
about next year’s economic conditions decreases continuously from its peak in December to
July. After a two month increase the downward trend ends with its bottom in October 2010.
22
29. Chart 6. 2
Positive assessments play a more dominant role in the case of economic expectations for the
next year. The rate of people with optimistic opinion about next year’s economic conditions
increases continuously from December to April and reaches to its highest level in June with
32.2% just after a small decrease in May.
It is hard to say that there is any other trend in one of the other opinions except the
pessimistic and optimistic ones. Rates of the People who don’t have an opinion and
participants who expect to face similar economic conditions in the future are following quite
the same average in each month.
All in all, depending on the statistical results respectively on the charts above there is a strong
tendency to the optimism for the evaluation of current and future economic conditions.
Therefore it can be expected to have higher Mortgage Barometer values in the upcoming
period of time.
6.3 Current Housing Market Assessments
Monthly assessments of householders with regards to the current housing market conditions
between the time period December 2009 and January 2010 are shown on the Chart below. As
stated in the previous section; the general opinion of the householders about the current
23
30. economic conditions is negative with a decreasing trend. On the other hand the majority of the
householders think that the economic conditions are suitable to buy residential properties.
Chart 6. 3
Depending on the rates on the chart above, it is hard to claim that any of the opinions
regarding to invest money in the housing market has a significant trend between the time
period December 2009 and October 2010.
6.4 Investment Preferences of the Householders
Investment preferences of the households between the time period December 2009 and May
2010 are shown on the Charts below. Ranking of the investment preferences of the
households is in the following order:
I.
II.
No Investment
Real Estate
III.
Interest
IV.
Foreign Currency
V.
Gold
24
31. VI.
VII.
VIII.
IX.
Financial Institutions
Bank ( Participation Banks)
Car
Others
Chart 6. 4
For the purpose of the study, the priority in analyzing the investment preferences of the
households must be given to the real estate investments .Chart 6.5 shows that general
tendency to make investments in real estate has seriously declined in the time period between
December 2009 and May 2010. It can be also observed that real estate investments saved their
position in the rankings despite its significant decrease.
6.5 Tendency to Buy a House
Following Chart with the number of 29 shows the tendency of the households to buy a house
in the upcoming year for the time period between December 2009 and October 2010. As seen
below, there is an inversely proportional relationship between positive and negative opinions
25
32. in buying a new house in the following year. Turning points for both opinion on January and
April are especially remarkable.
Chart 6. 5
Both of the positive and negative opinions changed their directions twice in the 11 month long
observation period. There is a significant similarity between the intersection points of the
negative and positive trends. Depending on the historical data and the most recent
observation, obtained for October 2010, it can be claimed that there might be another turning
point in the first months of 2011.At that point it is important to say that such a possibility will
have a direct effect on the Mortgage Barometer.
6.6 Planned Time Period to Buy a House
Following Charts with the numbers of 30 and 31, respectively show planned time period to
buy a house and average planned time period to buy a house, between the time period
December 2009 and October 2010. Ranking of the investment preferences of the households
is in the following order
I.
II.
III.
One Year and More
Six to Twelve Months
Three to Six Months
26
33. IV.
Zero to Three Months
Chart 6. 6
As seen on the chart above majority of the participants with willingness to buy a house, plans
to their make their purchasing in one year or more. But the long term approach has been
significantly losing its strength on people’s minds. This decrease stimulates the short term
demand in the housing market in terms of planned time period to buy a house.
Chart 6. 7
27
34. Reflection of the decrease in the long term housing demand can be observed on the Chart
above.
6.7 Aim to Buy a House - Apartment
Monthly assessments of householders with regards to the reasons of buying a new apartment
between the time period December 2009 and January 2010 are shown on the Chart below.
Chart 6. 8
Main aim to buy a new house is to reside in it. Beside the residential reasons, majority of the
rest of the households consider to buy a house for investment purposes. The relationship of
the trends of both of the aims to buy a house is remarkable considering their graphs on Chart
6.8. It can be claimed that they have an inversely proportional relationship. In addition to
those findings, only a few amount of participants explained their interest in buying a new
house
In order to make a complete analysis about housing demand, it is also essential to understand
the reasons behind not having an intention to buy a house. Following Chart serves for this
purpose.
Chart 6.9 shows the reasons behind not buying a house for the time period December 2009
and October 2010. Reasons of not buying a house are respectively listed above:
28
35. I.
Lack of Funds
II.
Already Owning a House / Apartment
III.
Preference to Other Investment Option
Chart 6. 9
6.8 Value of the House –Apartment
Housing prices play important roles in housing market analysis with being one of the main
indicators of the housing demand. Data, collected by Nielsen Research Company for ING
Bank’s Mortgage Barometer Report contains valuable information to analyze the housing
demand in Istanbul with regards to the value of the house or apartment.
Chart 36 and 38 are formed for the purpose of analyzing the housing demand of the city of
Istanbul. Therefore, participants who have a demand in buying a new house-apartment are
first investigated and then classified in terms of the value of the place that they consider to
getting.
Ranking of the value of the houses depending on the Chart 6.10 are in the following order:
I.
50.000 TL – 100.000TL
II.
100.000 TL – 150.000 TL
III.
150.000 TL – 200.000 TL
29
36. IV.
V.
200.000 TL and Above
Less than 50.000 TL
Chart 6. 10
It is an undeniable fact that there is a direct relationship between income level and value of
the house-apartment that is considered to buy by households. As seen above, the majority of
the people with a demand in buying a new house are able to make a purchase from the second
group of houses that cost between 50.000 and 100.000 TL. This result exactly matches with
the major social group of the sample population.
Furthermore rate of inflation might have an important effect on people’s decision criteria.
Consumer Price Index (CPI) is one of the most beneficial tools to analyze inflation.
Examining the relationship between Istanbul home prices and inflation means looking at the
difference between growth rates for home prices and consumer price index .When adjusted by
inflation rate according to the consumer price index rise, it will create a domino effect across
economy causing the housing prices to rise or fall.
Because home prices are included in the construction of consumer price indexes, it can be
argued that higher income prices lead to higher consumer price indexes and vice versa. The
relationship between the two indexes contaminated with the impact of house prices on the
consumer price indexes.
30
37. The change in the Consumer Price Index in the time period between December 2009 and
October 2010 can be seen on the Chart below.
Chart 6. 11
Chart 6.12 shows monthly average values of the house – apartment that households consider
to buy. Calculated weighted average house value is approximately 120.000 TL in the time
period between December 2009 and October 2010. It can be claimed that that there is a nonperfect inverse proportion between the CPI and average value of the house-apartment that is
considered to buy by households.
Chart 6. 12
31
38. 6.9 Preferred Type of House-Apartment to Buy
A statistical analysis about preferred type of housing depending on the data obtained by
Nielsen between the time period December 2009 and October 2010 are shown on the Chart
below
Chart 6. 13
Results of the survey show that majority of people who wants to buy a new house desire to
make their investments in new residential real estate instead of second hand ones. Rates of the
undecided people that are obtained by subtracting the demand in the second hand housing
market from the new housing market demand are also remarkable. As seen above the
significant decrease in the demand in second hand housing, are directly raised rates of the
undecided participants and at the same time demand in new housing.
6.10 Details of Financing
In the last Chapter of this section, details of financing in the housing demand will be
discussed. It is beneficial to remember that the analyses of the study with regards to the
residential real estates of Istanbul are based on the answers of the households at the ING
Housing Market Survey. Therefore it is avoided to give detailed information about ways of
financing in order to have a better focus for the preferences of individuals who are
interviewed by Nielsen. Following charts are formed with that aim.
32
39. Chart 6. 14
In the Chart above with a number of 6.14, preferred types of financing are measured for the
time period between December 2009 and October 2010. Results are showing that majority of
the households prefer to buy a house or an apartment either with their own savings or with
previous investments that they made in the recent time. This situation ends in the last
observation period. In October 2010, main preference is given to housing loans from financial
institutions. There is a significant similarity between the rise and fall respectively in the
preferences of housing loans from financial institutions and own savings. Changes in the
mentioned investment styles are started in the periods of June and July 2010.
It can be claimed that increase in housing loans starting from June 2010 is parallel to the
changes in the interest rates. In the time period between June and October 2010 the Central
Bank of Turkey continued to cut its key rate with regards to the financial stability policy.
Decisions of the Central Bank of Turkey has been effected the interest rates of the financial
institutions. Those effects can be reasons behind the rise in housing loans from financial
institutions.
Following Chart is combined with the analysis above with regards to the preferred type of
financing and shows details of housing loans in the time period between December 2009 and
October 2010. Results show that majority of the households who prefer to use housing loans,
chose to use bank loans. Especially in June all of the participants in that classification
answered that they are going to fully finance their purchasing by housing loans. Participation
33
40. bank loans follow the bank loans in the financing preferences of the households. Minority of
the participants consider using loans from the project firms.
Chart 6. 15
Chart 6.16 shows the demand rates of housing loans in the time period between December
2009 and October 2010. Depending on the results it can be said that majority of the
participants tend to finance some part of their purchasing instead of a full financing.
Chart 6. 16
34
41. Following Chart aims to make a further analysis of the rate of Demand in Housing Loans by
using the same data and time period of the Chart6.16. Results show that the average rate of
demand has been always between 58% and 61% in the 11 month long time period.
Chart 6. 17
Chart 6.18 aims to analyze the maximum loan payment per month that can be paid by the
householders who have willingness to finance their purchasing with housing loans.
Chart 6. 18
Depending on the results, those who consider using bank loans with a monthly budget limit of
1000 TL are the majority for the whole time period. But the rate of that group significantly
35
42. decreases in time. Especially after June the ascending trend has turned to decrease. With that
effect people start to consider lending more money from the financial institutions. Rise in the
second group, namely 1000-1500 TL, is especially remarkable.
Results of Chart 6.19 are beneficial to sum up the analysis regarding the budget limits of the
households who consider using housing loans of the financial institutions and project firms.
Following chart measures the average amount of money per month for housing loans.
Chart 6. 19
Results show that the average amount of money that is considered to pay by the householders
for housing loans is between 964 and 1206 TL in the time period between December 2009
and October 2010. The weighted average of the whole period is 1050 TL.
6.11 Mortgage Barometer
The Mortgage Barometer is an index those values varies between 0 and 200. Higher index
values indicate householder’s intention to invest money in housing on the other hand lower
index values shows the opposite.
36
43. Details of Mortgage Barometers prepared for Turkey and Istanbul can be seen on the Chart
below.
Chart 6. 20
Results show that both of the indexes have similar trends till April. Starting from the fifth
period, the mortgage barometer index of Istanbul exceeds the index of Turkey. The gap
between the two indexes increases in the last 6 months period of time.
Mortgage Barometer of Turkey increased for four months starting from December 2009.
Tendency of buying a house started to decrease after making its peak in March. The trend in
decreasing has continued till September 2010. There is a significant increase in the Mortgage
Barometer of Turkey between September and October. It has jumped from 75% to 80%
approaching to its peak on March 2010 with a rate of 84.2%.
Mortgage Barometer Index of Istanbul has followed a similar pattern like Index of Turkey till
March and made its first peak with the rate of 84.2%. The upwards trend had a break for one
month after a sharp decrease to 74.6% in April. Between the time period April 2010 and
August 2010 tendency of buying a house was in the ascendant till the peak of the index in
August with a rate of 91.5%.
It can be beneficial to include the results of the Consumer Trust Index to the analysis to make
a better comparison. Chart 6.21 shows details of the Consumer Trust Index in the time period
between December 2009 and October 2010. Results show that consumer trust has
consecutively increased for 7 periods till its first peak in June with the rate of 88.04%.
37
44. Decreases in the consumer trust index is observed in July and August which are less than 1%.
There is a significant increase in September that also brings the index to its highest level in
the 11 month period of time.
Chart 6. 21
Depending on the results it can be claimed that there is a similarity between the trends of
Consumer Trust Index and mortgage barometer index of Istanbul. The total changes in both of
the indexes are approximately 12%. Changes in the Mortgage Barometer Index are sharper
than the changes in Consumer Trust Index.
It is helpful to talk about the results of the Istanbul’s Mortgage Barometer with regards to
Socio Economic Classification in order to deepen in the analysis. For this purpose the
following chart shows details of the mentioned index.
Chart 6. 22
38
45. Results vary among the four main social groups. As seen above, A and B Social groups have
the strongest tendencies to buy a house in each month except July and October. C1 social
group is in the second place in the ranking. The difference between the starting and ending
periods of C1 social group is approximately 12%, which is a remarkable percentage. That
difference is even bigger in C2 Socio Economic Classification with an approximate rate of
15%.
7. Conclusion
Housing markets are important indicators of how economies of countries are performing.
Therefore housing demand and housing prices are of great interest to real estate developers,
banks, policy makers, householders, actual and potential house owners. Although it is widely
argued housing demand is one of the important indicators that affect Turkish Economy, this
has not yet begun go through all applications of housing market analysis. This study tries to
make a contribution to house demand studies for residential real estates in Istanbul with
several statistical analyses.
The conclusions and suggestions that may contribute to the housing market following the
study of the statistical analysis of the demand for residential real estate in Istanbul between
the time period December 2009 and October 2010 are presented below.
Monthly Mortgage Barometer values of Istanbul are higher than average values of
Turkey in every observation period except in March and April. This result indicates
that householders in Istanbul have a greater tendency than the majority of the
householders in Turkey to invest money in housing.
There is an important similarity between the ascending trends of Istanbul Mortgage
Barometer’s Index Values and Consumer Trust Index. On the other hand the
relationship between Mortgage Barometer of Turkey and Consumer Trust Index
values in the last 7 months are inversely proportional.
Although majority of the participants evaluate the current year’s economic conditions
worse than the previous year’s, these negative assessments decreases from the start
and till the end of the observation period. The downward trend in the negative
opinions with regards to the current year’s economic conditions defines the rise of the
39
46. positive opinions. This result has a direct effect on the rise of the Mortgage Barometer
Index values.
Householder’s monthly assessments with regards to next year’s economic conditions
are generally pessimistic. The rate of people with pessimistic opinion about next
year’s economic conditions decreases continuously from its peak in December.
Depending on the statistical results there is a strong tendency to the optimism for the
evaluation of future economic conditions. Therefore it can be expected to have higher
Mortgage Barometer values in the upcoming period of time.
Majority of the householders think that the economic conditions are suitable to buy
residential properties. This fact has also a direct effect on the rise of the Mortgage
Barometer Index Values. Participants who find current housing market suitable for
selling the residential real estate’s are always less than %10 during the whole period of
time which has an unimportant role in the results of the analysis.
Results of the Nielsen’s Market Survey indicate that real estate investments are on the
first place in the investment preferences of the householders.
Majority of the participants demand housing for the residential purposes.
Main reason behind lack of motivation of the majority of the householders to buy a
new house or a new apartment is not having enough funds.
Calculated weighted average house value is approximately 120.000 TL in the time
period between December 2009 and October 2010.
Majority of people who wants to buy a new house desire to make their investments in
new residential real estate.
Majority of the households prefer to buy a house or an apartment either with their own
savings or with previous investments that they made in the recent time. This situation
ends in the last observation period. In October 2010, main preference is given to
housing loans from financial institutions. There is a significant similarity between the
rise and fall respectively in the preferences of housing loans from financial institutions
and own savings
40
47. Majority of the households prefer to buy a house or an apartment either with their own
savings or with previous investments that they made in the recent time. This situation
ends in the last observation period. In October 2010, main preference is given to
housing loans
Housing loans from banks are the main preferences of the participants for the whole
observation period.
Loans from participation banks and loans from project firms are
following bank loans. People generally tend to finance some part of their housing
investments by using housing loans of the banks.
Average amount of money that can be considered to pay per month for housing loans
is in between 964 TL and 1206 TL. The weighted average for the whole period is 1050
TL per month.
41
48. 8. Appendix
Questions, asked by The Nielsen Company for the ING Bank Mortgage Barometer Survey are
listed below. Data is collected with using CATI (Computer Assisted Telephone Interview)
Technique. In this context 37 questions are asked to participants from A, B, C1 and C2 Social
Groups.
Question 1:
Do you or one of the people with whom you are in a regular contact with work at one of these
listed areas: below?
-
Real Estate
-
Research,
-
Advertising
-
Marketing,
-
Public Relations
-
None of Them
Question 2:
In which one of these provinces do you live?
-
Adana
-
Ankara
-
Antalya
-
Aydın
-
Bursa
-
Erzurum
-
Gaziantep
-
Istanbul
42
49. -
Izmir
-
Kayseri
-
Kocaeli
-
Samsun
-
Tekirdağ
-
Trabzon.
Question 3:
How old are you? On which of the listed age group is yours?
-
Younger than 30,
-
Between 30-34 years old
-
Between 35-44 years old
-
Between 45-54 years old
-
Between 55 years old and older
Question 4:
Which of the following options describes your effect in your family at the decision making
process in case buying a new house-apartment?
-
I am the decision maker
-
Family members and I come to a mutual decision
-
Family members decide
Question 5:
What is the educational level of the person who has the main income in the household?
-
Primary School
-
Secondary School
43
50. -
High School
-
Undergraduate
-
Graduate
Question 6:
What is the profession of the person who has the main income in your family?
-
Unemployed (Retired, Housewife)
-
Self Employed
-
Salaried
-
Other
Question 7:
In which of the listed social groups is the person who has the main income in your family can
be classified?
-
A
-
B
-
C1
-
C2
-
D
-
E
Question 8:
How do you interpret current economic conditions compared with the previous year’s
economic conditions? Please select one of the answers below.
-
Much Better
-
Better
-
Same
44
51. -
Worse
-
Much Worse
-
No Opinion / No Idea
Question 9:
What do you think about the next year’s economic conditions if you take this year’s economic
conditions as a criterion? Please select one of the answers below.
-
Much Better
-
Better
-
Same
-
Worse
-
Much Worse
-
No Opinion / No Idea
Question 10:
Which of the listed investment tools do you prefer?
-
Real Estate
-
Interest
-
Foreign Currency
-
Car
-
Gold
-
Financial Instruments
-
Bank
-
Others
-
No Investment
45
52. Question 11:
How do you interpret the current housing market? Please select one of the answers listed
below.
-
Time to Buy
-
Time to Sell
-
Time to Wait
-
None of Them
Question 12:
What about to buy a house in the future? Please select one of the answers listed below.
-
Definitely Planning to Buy a House
-
Planning to Buy a House
-
Not Decided Yet
-
Not Planning to Buy a House
-
Definitely not Planning to Buy a House
-
No Opinion / No Idea
Question 13:
Which of the sentences listed below describes your opinion about not buying a house? Please
select one of the answers listed below.
-
Lack of Financial Support
-
Different Investment Preferences
-
Others
Question 14:
When are you planning to buy a house? Please select one of the answers listed below.
46
53. -
In 3 months
-
Between 3 and 6 months
-
Between 6 and 12 months
-
After 12 months
Question 15:
Which of the listed reasons describes your aim to buy a house best? Please select one of them.
-
To Live in It
-
To Make An Investment
-
Others
Question 16:
What is your budget to buy a House – an Apartment? Please select one of the answers below.
-
Less than 50.000 TL
-
Between 50.000 TL – 100.000 TL
-
Between 100.001 TL – 150.000 TL
-
Between 150.001 TL – 200.000 TL
-
Between 250.001 TL – 300.000 TL
-
Between 300.001 TL – 400.000 TL
-
Between 400.001 TL – 500.000 TL
-
More than 500.000 TL
Question 17:
How are you going to finance your purchase? Please select one of the answers below.
-
Borrowing from friends / family /relatives.
47
54. -
Own Savings / Previous Investments
-
Loans From Participation Banks
-
Loans From Project Firm
-
Others
Question 18:
How many percentage of the cost of the new housing purchase will be financed with banking
loans? Please select one of the answers below.
-
Less than 50 %
-
50% - 70%
-
71% - 80%
-
81% - 90%
-
91% - 100%
Question 19:
What is the maximum amount of money that you can pay for the banking loans per month?
Please select one of the answers below.
-
Less than 1000 TL
-
1000 TL – 1500 TL
-
1501 TL – 2000 TL
-
2001 TL – 3000 TL
-
3001 TL – 4000 TL
-
4000 TL and Above
Question 20:
Do you prefer to buy a new built or second hand house – apartment? Please select one the
answers below.
48
55. -
I prefer a new built house – apartment.
-
I prefer a second hand house – apartment.
Question 21:
What about the ownership status of your current residence? Please select one of the answers
below.
-
Rent
-
Own House/ Apartment
-
Lodgment
Question 22:
When you /your family did bought the current resident?
Question 23:
What is the type of residence that you are currently living in?
Question 24:
What is the type of desired residence in the future? Please select one of the answers below.
-
Apartment on a street / boulevard /
-
Apartment in a complex / housing estate / housing development
-
Villa / Farm House / Own House
-
Others
Question 25:
How did you financed you’re your current house’s purchase? Please select one the answers
below.
-
Borrowing from friends / family /relatives.
-
Own Savings / Previous Investments
-
Loans From Participation Banks
49
56. -
Loans From Project Firm
-
Others
Question 26:
What is your gender?
Question 27:
What is your marital status?
Question 28:
How many people do live in your residence?
Question 29:
Dou you have children?
Question 30:
How many children do live in the house with you?
Question 31:
How old are your children? Please select one of the answers below.
-
0 – 3 years old
-
4 – 7 years old
-
8 – 11 years old
-
12 – 17 years old
-
18 and older
Question 32:
Are you the person who has main income of the house? Please answer as yes or no.
Question 33:
What is your educational level? Please select one of the answers below.
50
57. -
Primary School
-
Secondary School
-
High School
-
Undergraduate
-
Graduate
Question 34:
What is your profession? Please select one the answers below.
-
Unemployed (Retired, Housewife)
-
Self Employed
-
Salaried
-
Other
Question 35:
Is there anyone who owns a car in your house?
Question 36:
In which of the listed income level is the total amount of money that enters to your apartment
per month? Please select one of the answers below.
-
Less than 500 TL
-
500 TL – 1000 TL
-
1001 TL – 1500 TL
-
2001 TL – 2500 TL
-
2501 TL – 3000 TL
-
3001 TL – 3500 TL
-
3501 TL – 4000 TL
51
58. -
4001 TL – 4500 TL
-
4501 TL – 5000 TL
-
5000 TL and Above
-
I Refuse to Answer
Question 37:
This interview is made for ING Bank. Do you give permission to share the content of this
interview with ING Bank?
-
52