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This paper presents a hedonic office rent model under the decentralized structure of Istanbul Office Market. The data set in the study includes 2,348 office spaces for the first quarter of 2018. This study aims to find determinants that affect the level of rent and examine whether the effects of office rent determinants are global or not.

To consider both global and local effects, the paper uses mixed geographically weighted regression approach in hedonic office rent analysis.

The empirical results indicate that office rent determinants such as physical, locational, neighborhood and market operational characteristics have significant impacts on the level of the rent. The findings also show that one of the office rent determinants has a global effect and the other determinants have local effects. According to the estimation results, local effects and statistical significances of these determinants vary from lower quartiles to upper quartiles.

To the best of the authors’ knowledge, this is the first paper to consider global and local effects of office rent determinants on the level of rent, with mixed geographically weighted regression approach. The paper provides new insights into the hedonic valuation of commercial real estates, especially for decentralized office markets.

The purpose of this paper is to combine the critical parameters used to study financial inclusion into a composite index. The idea is to rank Indian states and union territories (UTs) on the basis of this index, determine change in ranks during 2011 to 2014 and identify factors affecting high/low scores on the index.

Data for the study were collected from secondary sources published by Reserve Bank of India (RBI) and Central Statistical Organization. Applying technique of order preference by similarity to ideal solution (TOPSIS), a composite multi-dimensional index of financial inclusion (IFI) has been built by using three broad parameters of penetration, availability and usage of banking services. Factors significantly influencing scores of states/UTs on IFI were identified using multiple regression analysis.

The value of financial inclusion for India on composite IFI has increased by 0.045 points during the study period. Share of agriculture to state gross domestic product, literacy ratio, population density, infrastructure development and farmer suicides are significant factors affecting financial inclusion.

The multi-dimensional IFI is a useful tool to measure financial inclusion using several parameters for various states/regions. The index can also be used to compare the performance of states/regions over same/different periods.

This paper is unique in its attempt to construct multi-dimensional IFI for Indian states/UTs by applying TOPSIS. It will prove useful for future researchers by combining several aspects of financial inclusion into single index.

The manufacture of polymer composites with a lower environmental footprint requires incorporation of sustainably sourced components. In addition, the incorporation of novel components should not compromise the material properties. The purpose of this paper is to demonstrate the use of a synthetic amine functional toluidine acetaldehyde condensate (AFTAC) as a modifier for fiber-reinforced epoxy composites. One of the fiber components was sourced from agricultural byproducts, and glass fiber was used as the fiber component for comparison.

The AFTAC condensate was synthesized via an acid-catalyzed reaction between o-toluidine and acetaldehyde. To demonstrate its efficacy as a toughening agent for diglycidyl ether bisphenol A resin composites and for the comparison of reinforcing materials of interest, composites were fabricated using a natural fiber (mat stick) and a synthetic glass fiber as the reinforcing material. A matched metal die technique was used to fabricate the composites. Composites were prepared and their mechanical and thermal properties were evaluated.

The inclusion of AFTAC led to an improvement in the mechanical strengths of these composites without any significant deterioration of the thermal stability. It was also observed that the fracture strengths for mat stick fiber-reinforced composites were lower than that of glass fiber-reinforced composites.

To the best of the authors’ knowledge, the use of the AFTAC modifier as well as incorporation of mat stick fibers in epoxy composites has not been demonstrated previously.

The purpose of this paper is to examine the potential role of remittances on renewable energy consumption in the top recipient developing countries from 1990 to 2016.

The paper uses autoregressive distributed lag (ARDL) technique to fulfil the purpose.

The empirical findings divulge that remittances positively affect renewable energy consumption. This finding implies that remittances can potentially increase the level of renewable energy consumption by increasing affordability if proper incentives and encouragement are offered.

Given the enormous potential that renewable energy can bring to an economy, the government should offer indirect incentives to encourage recipients to allocate a portion of their remittances to renewable energy projects, either as minor investors or users.

To the best of the authors’ knowledge, this paper is novel for two reasons. First, this study adds to the existing literature by empirically examining the link between remittances and renewable energy consumption in the top five remittance recipients, which have never been studied before. Second, the findings of this study will have policy implications not only for the top remittance recipients but also for other remittance recipients, particularly for developing countries.

This study aims to analyze the volatility spillover effects of crude oil, gold price, interest rate (yield) and the exchange rate (USD (United States Dollar)/INR (Indian National Rupee)) on inflation volatility in India.

This study uses the multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models (Baba, Engle, Kraft and Kroner [BEKK]-GARCH and dynamic conditional correlation [DCC]-GARCH) to examine the volatility spillover effect of macroeconomic indicators and strategic commodities on inflation in India. The monthly data are collected from January 2000 till December 2020 for the crude oil price, gold price, interest rate (5-year Indian bond yield), exchange rate (USD/INR) and inflation (wholesale price index [WPI] and consumer price index [CPI]).

In BEKK-GARCH, the results reveal that crude oil price volatility has a long time spillover effect on inflation (WPI). Furthermore, no significant short-term volatility effect exists from crude oil market to inflation (WPI). However, the short-term volatility effect exists from crude oil to inflation while considering CPI as inflation. Gold price volatility has a bidirectional and negative spillover effect on inflation in the case of WPI. However, there is no price volatility spillover effect from gold to inflation in the case of CPI. The price volatility in the exchange rate also has a negative spillover effect on inflation (but only on CPI). Furthermore, volatility of interest rates has no spillover effect on inflation in WPI or CPI. In DCC-GARCH, a short-term volatility impact from all four macroeconomic indicators to inflation is found. Only crude oil and exchange rate have long-term volatility effect on inflation (CPI).

In an economy, inflation management is an essential task. The findings of the current study can be beneficial in this endeavor. The knowledge of the volatility spillover effect of all the four markets undertaken in the study can be significantly helpful in inflation management, especially for inflation-targeting policy.

It is observed that no other study has addressed this issue. We do not find any other research which studies the volatility spillover effect of gold, crude oil, interest rate and exchange rate on the inflation volatility. The current study is novel with a significant contribution to the vast knowledge in this context.

There are a plethora of putative influencing variables available in the literature for modelling real estate prices using AI. Their choice tends to differ from one researcher to the other, consequently leading to subjectivity in the selection process. Thus, there is a need to seek the viewpoint of practitioners on the applicability and level of significance of these academically established variables.

Using the Delphi technique, this study collated and structured the 35 underlying micro- and macroeconomic parameters derived from literature and eight variables suggested by 11 selected real estate experts. The experts ranked these variables in order of influence using a seven-point Likert scale with a reasonable consensus during the fourth round (Kendall's W = 0.7418).

The study discovered that 16 variables are very influential with seven being extremely influential. These extremely influential variables include flexibility, adaptability of design, accessibility to the building, the size of office spaces, quality of construction, state of repairs, expected capital growth and proximity to volatile areas.

The results of this study improve the quality of data available to valuers towards a fortified price prediction for investors, and thereby, restoring the valuers' credibility and integrity.

The “volatility level of an area”, which was revealed as a distinct factor in the survey is used to add to current knowledge concerning office price. Hence, this study offers real estate practitioners and researchers valuable knowledge on the critical variables that must be considered in AI-based price modelling.