Search results

By considering the rapid and continuous increase of housing prices in Turkey recently, this study aims to examine the determinants of the residential property price index (RPPI). In this context, a total of 12 explanatory (3 macroeconomic, 8 markets and 1 pandemic) variables are included in the analysis. Moreover, the residential property price index for new dwellings (NRPPI) and the residential property price index for old dwellings (ORPPI) are considered for robustness checks.

A quantile regression (QR) model is used to examine the main determinants of RPPI in Turkey. A monthly time series data set for the period between January 2010 and October 2020 is included. Moreover, NRPPI and ORPPI are examined for robustness.

Predictions for RPPI, NRPPI and ORPPI are carried out separately at the country (Turkey) level. The results show that market variables are more important than macroeconomic variables; the pandemic and rent have the highest effect on the indices; The effects of the explanatory variables on housing prices do not change much from low to high levels, the COVID-19 pandemic and weighted average cost of funding have a decreasing effect on indices while other variables have an increasing effect in low quantiles; the pandemic and monetary policy indicators have a negative and significant effect in low quantiles whereas they are not effective in high quantiles; the results for RPPI, NRPPI and ORPPI are consistent and robust.

The results of the study emphasize the importance of the pandemic, rent, monetary policy indicators and interest rates on the indices, respectively. On the other hand, focusing solely on Turkey and excluding global variables is the main limitation of this study. Therefore, the authors encourage researchers to work on other emerging countries by considering global variables. Hence, future studies may extend this study.

The COVID-19 pandemic and market variables are determined as influential variables on housing prices in Turkey whereas macroeconomic variables are not effective, which does not mean that macroeconomic variables can be fully ignored. Hence, the main priority should be on focusing on market variables by also considering the development in macroeconomic variables.

Emerging countries can make housing prices stable and affordable, which will increase homeownership. Hence, they can benefit from stability in housing markets.

The QR method is performed for the first time to examine housing prices in Turkey at the country level according to the existing literature. The results obtained from the QR analysis and policy implications can also be used by other emerging countries that would like to increase homeownership to provide better living conditions to citizens by making housing prices stable and keeping them under control. Hence, countries can control housing prices and stimulate housing affordability for citizens.

This paper aims to determine priority issues in the corporate governance (CG) principles to increase CG rating notes of publicly traded companies.

This study defines the priority issues for publicly traded companies that should be focused to increase the CG rating notes. In this context, this study considers the companies in Borsa Istanbul CG index (XKURY), use data for 2018, 2019, 2020, and applies machine learning algorithms.

Overall, importance of each CG principle changes for the CG rating notes; first five CG principles in terms of significance have a total of 43.6% importance for the CG rating notes; following a straight-line approach in completing deficiencies of the CG principles cannot help increase the CG rating notes. Hence, empirical results highlight the impact of the most significant CG principles in terms of the CG rating notes that should be focused on by publicly traded companies so that CG ratings can be increased.

This study uses Turkey data and considers publicly traded companies in the XKURY index. The main cause of this condition is that consolidated data of compliance report format for all publicly traded companies cannot be obtained.

The publicly traded companies can increase the CG rating notes by considering the results of this study while focusing on priority issues in the CG principles.

The study determines the most important CG principles that companies can focus on, highlights the importance of usage of machine learning algorithms in determining the most influential CG principles in terms of the CG rating notes and reflects on the difficulties for gathering consolidated CG principles compliance reporting data for all publicly traded companies. Hence, societies can have better companies that are ruled more efficiently and corporately by increasing their compliance with the CG principles.

To the best of the authors’ knowledge, this is the first empirical study that determines the priority issues to increase the CG rating notes of publicly traded companies based on the new CG principles compliance reporting scheme in Turkey. Following this aim, machine learning algorithms, which can present better results with regard to most of the econometric models, are used in this study.

The study is called for to eliminate the noise between the significant macro variables from the perspective of the cause-and-effect approach to indicate why and how the return of solar projects is being affected by these.

The study aims to investigate the spread between unit selling electricity prices of a monthly production of 250 KW solar project installed in Türkiye and USD/TRY.

A relational framework is designed by drawing on the variables determined as crude oil prices, United States (US) 2-year yield, Dollar Index (DXY), USD/TRY, the annual inflation rate of Türkiye, and unit selling electricity prices. Then, a multivariate approach is performed through Matlab to analyse the correlational relationships and structure the curve estimation models.

The observations show that the gradually rising spread between unit selling electricity price and USD/TRY signals the reduction in return-on-investment rate of solar energy projects because of the particular causes of the European energy crisis by the reason of Russia and Ukraine war and escalating risks in DXY and US treasury yields as a result of federal fund rate hikes against inflationary pressures. Solar energy investments are delicate instruments to global oil shocks and higher DXY in controlling Inflation and currency volatility; therefore, resilient policies should solicit the demand because of environmental and economic reasons to reduce the external dependency of Türkiye.