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The purpose of this paper is to investigate how recruiters at a college career fair perceive sustainability and the knowledge business graduates should have about it. It reports on how recruiters understand sustainability and perceive their organization’s engagement and resulting expectations for new hires. The results indicate that recruiters neither understand sustainability well, nor are suitably informed of their organizations’ needs with respect to this topic. Educators, as a consequence, face a dilemma of how to craft adequate educational experiences, as employer needs are not clearly expressed. The paper concludes with suggestions on how educational institutions can nevertheless proceed with offerings in sustainability education.

The study was performed by conducting personal, structured interviews at a college career fair.

While most respondents considered sustainability to be an important topic, there appears to be a lack of thorough understanding of sustainability. Recruiters were not overly informed about their organizations’ position and efforts toward sustainability. They considered it to be important that students learn about sustainability, but preferences for educational tools were not aligned with expected depth of knowledge. This leaves educators in search of guidance on how to align educational offerings with organizational needs.

As a pilot study, the total number of interviewed organizations was low, and therefore, the results should not be over-interpreted. The findings nevertheless point to a clear disconnect between organizations’ expressed needs for adequate trained personal and their ability to define what they are looking for. These results encourage more research to develop a better link between company strategy toward sustainability, recruiter’s know-how of it and concise expectations in new hires that could be mirrored in educational offerings.

Human resources play a critical role in providing organizations with the capabilities to become more sustainable. Organizations need to develop concise recruitment policies that better communicate what they are looking for, as well as educational programs for recruiters to ensure future hiring fulfills critical needs.

This paper closes a gap in the literature as it includes a thus-far ignored stakeholder group, namely recruiters; into the research on how to align organizational needs with the development of adequate educational offerings that generate future leaders and managers well-versed in sustainability.

The purpose of this paper is to show how an optimization model previously published by the authors is employed to study the effects of rent control on income optimization for the owner and the manager, as well as on the principal‐agent relationship. The model explains effects on management, maintenance and housing quality observed under rent control.

The optimization model is developed by applying a transaction cost framework to the context of income structures in investment properties and their management. Rent control is introduced into the model as a rent maximum.

Under rent control, the manager is forced to optimize income solely through cost control, without motivation to provide above minimum services. The owner is unable to optimize income. Income and funds available for management and maintenance are reduced by costs uncontrollable by the owner. Consequently, management and maintenance are reduced, if not eliminated, resulting in deterioration of the housing stock, notwithstanding minimum habitability standards imposed by regulators. This dilemma argues against dropping management entirely and interferes with the owner's quest to find the right incentive structure for the agent.

Rent control is modeled as a cap on rents. Many rent control policies employ a maximum in rent increase instead, with the maximum often linked to inflation. As the results of the model are in line with real‐world observations, this simplification seems to be non‐critical.

Rent control was implemented with the intent to provide affordable housing to the less fortunate in society. Economic and social effects of rent control have since been heatedly discussed, in the public arena and in academia. This paper supports the view that rent control has unintended, negative consequences not only for the property owner, but also for the property manager (who is at the brink of elimination) and for the tenant (as housing quality deteriorates). This paper therefore encourages a renewed discussion and review of rent control policies.

The paper expands existing literature by analyzing the effects of rent control on property management. By using an optimization model, theory‐based results are provided that supplements real‐word observations, which is interesting for both academics and practitioners. Furthermore, the applicability of the previously developed optimization model is strengthened.

Environmental management systems (EMS) seek to make companies simultaneously more competitive and environmentally responsible. Improved environmental performance can be sought from the adaptation of techniques that emphasize reduction of waste and process/product redesign in the quest of reducing environmental impact. However, EMS lacks a framework to quantify improvements and much of the evidence of EMS's impact on financial performance is anecdotal. This lack of theoretical development has served to diminish corporate support, thus reducing the likelihood of EMS implementation due to a perceived cost disadvantage. This paper proposes, and tests, a framework to quantify EMS improvements to determine the impact of EMS strategies on financial performance. Our findings suggest that implementation of an EMS strategy does not negatively impact a firm's financial performance.

The purpose of this paper is to present a practice briefing in the form of a user’s manual for Excel-based simulation of real estate risk. Based on a generic discounted cash flow model, the simulation incorporates the often ignored heavy tail behaviour of real estate investments, and consolidates Jensen’s inequality. The briefing attempts to explain a model that permits the user to decide whether to include extreme events in real estate risk modelling and how extreme these events may be. Practitioners can generate a variety of modelling outcomes and then choose risk comfort zones in which to contemplate a range of returns.

The paper provides an overview of the underlying mathematical concepts and challenges, as well as on the perspectives on their application from the current academic literature. It offers a step-by-step walk-through of the Excel model (the model being downloadable at: www.mathestate.com).

Existing models for real estate risk modelling fall short with respect to realistic simulation of the probability of extreme events due to challenges in the implementation of stable laws. These former barriers to the implementation of stable laws have been overcome by providing a unique combination of Excel-resident functionalities with a stable pseudo random number generator.

Investment advisers no longer need expensive add-ins to estimate risk. The presented Excel model is more robust than common approaches as it considers distribution shapes that are not otherwise easily available. The only apparent limitation is that users need to be familiar with the most basic functionality of Excel.

Practitioners are provided with an easy-to-use Excel model that does not require further software add-ins. The model simulates real estate investment returns, based on a more realistic inclusion of risk behaviour. It allows specifying how much extreme value behaviour characterizes the volatility in future projections modelled to guide investment decisions.

Risk is a cost to society. Many recent news events demonstrate the importance of including extreme values in modelling. The paper attempts to contribute to more realistic risk estimation in real estate investment.

This briefing introduces a real estate risk simulation model that includes using stable laws, using Excel, a familiar and widely used platform. Such a model has not previously been reported in the academic or practitioners’ literature.

The paper aims to provide an analysis of the principle‐agent relationship between owner (principal) and manager (agent) of investment properties by: developing an optimization model for the net profit scenario that any third party manager of properties in multiple locations faces; and describing the principal's (or owner's) problem and likewise developing an income optimization model. The model allows illustrating the misalignment of incentives and compensation arrangements common to the business of managing small investment properties.

The paper provides an in depth review of literature on the agency problem, both in general as well as in real estate research and compares the qualitative findings with analytical results provided by the model. The latter is developed by applying a transaction cost framework to the context of income structures in investment properties and their management.

The optimization model shows that profit maximization for the manger (agent) depends on an optimum number of properties to be managed. It is further shown that the compensation methods customary in small real estate management contracts are inappropriate for the manager to control and cover the transaction costs, which result from the fact that more than one location is managed. The result is a kind of impossibility theorem, stating that management of small investment properties based on customary compensation structures is unprofitable as the number of properties and their distance rises.

The analysis shows that industry practice for the compensation of management of small investment properties does not address the inherent principle‐agent problem. Consequently, additional compensation and incentive mechanisms as well as control structures need to be employed by the owner. The paper, therefore, provides a starting point to review and improve industry practice.

The paper expands the existing literature of the agency problem in real estate by providing an optimization model for management of investment properties. The model and findings are of interest to academics for its analytical treatment of agency relationships; as well as to practitioners, as the analysis reveals inefficiencies in industry practice.