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Considering the importance of human capital in innovation, entrepreneurship and thus economic development, this study attempts to examine formal learning as a mechanism of human capital development in institutions of higher learning. Ironically, students in such institutions are automatically assumed to learn and accumulate pertinent capacity, which would then enable them to compete in the business world or pursue further studies in future. Consequently, lack of this cognizance culminates in little being known about how students learn to accumulate knowledge, skills and requisite competencies. Notwithstanding this, the challenges posed in the twenty-first century require well-rounded students those especially who can address the global transformations witnessed in the business arena. The purpose of this paper is an attempt to fill this gap using data from the University of Dubai (UD) to examine how formal learning takes place in an institution and what determines it.

Learning is conceptualized in terms of knowledge, skills and competencies accumulated as proxied by cumulative general point aggregate. All the data used came from the UD. In addition to in-depth descriptive analysis, the study uses limited dependent techniques to identify the most significant determinants of institutional learning.

The empirical results generated indicate that demographic characteristics such as age, nationality and gender had a positive effect on learning. Moreover, a student’s initial condition influenced his/her learning positively. Whereas the mode of study under personal preferences did not seem to affect learning, the number of course sections taken had a positive influence on learning. As anticipated, student transfer had a negative influence on learning. The number of credit hours accumulated affected learning positively.

The main limitation of this study is that results are only applicable within a limited geographical scope, and thus they cannot be generalized for global consumption. Nonetheless, the discussion and results obtained make insights to any future-related studies.

As pointed out in the previous sections, learning will be conceptualized in the form of knowledge, skills and competency acquisition. In a school setting, knowledge, skills and competencies are better captured by the grade attained in each subject. The general student learning can, therefore, be equally captured by the cumulative grade point aggregate. The authors purport that learning can be visualized, or in other words conceptualized, as a complex process that is determined by five main factors that include demographic characteristics; student initial condition; personal preferences and choices; and time factor curriculum and anticipated future career.

This paper aims to examine house price drivers in Dubai, addressing nonlinearity and heterogeneity.

The study applies a combination of linear and nonlinear, as well as quantile regression, specifications to address these concerns and better explain the real-world phenomenon.

The study shows the double-log quantile regression approach is an overarching description of house price drivers, confirming that not only the price of housing and its determinants are non-linearly related but also that their relationship is heterogeneous across house price quantiles. The findings reveal the prevalence of sub-market differentials in house price sensitivity to house attributes such as size (in square meters), location and type of house, as well as government laws. The study also identifies the peaks and deflation, as well as the rebounding nature of the house price bubble in Dubai.

The data used are limited, in that information on only a few house attributes was available. Future research should include data on other house attributes such as house quality, zip codes and composition.

The findings of this study are expected to suggest results with significant ramifications for researchers, practitioners and policy makers. From a policy perspective, there is an obvious interest in understanding whether the price of housing is affected by different attributes differently along its distribution.

This study allows policy makers, developers and buyers of higher-priced houses to behave differently from buyers of lower-priced or medium-priced houses.

Methodologically, it demonstrates alternative linear and nonlinear, as well as quantile regression, specifications to address two increasing concerns in the house price literature: nonlinearity and heterogeneity. Unlike most other studies, this study used a rich data (140,039 day-to-day transactions of 10 years’ pooled data). The Dubai housing market presents an interesting case. UAE (Dubai, in particular) is named as the second-hottest marketplace for global residential property investors, ahead of Singapore, the UK and Hong Kong (Savills plc, 2015).