Real estate private equity (REPE) funds are often differentiated by risk class: core, value-added, or opportunistic. Fund class is used by investors and managers to allocate funds and to describe investment policies. In this paper, we use REPE fund cash flow data from Burgiss that allow us to calculate a variety of performance metrics. For a subset of the data, we also observe characteristics of underlying fund holdings. Despite evidence that Value-Added and Opportunistic funds differ in investment composition, we show that class does not do a good job of predicting differences in performance. Unsurprisingly, greater investment in development (as assessed ex post), predicts poor performance for funds raised just before the Great Recession.
Commercial real estate is a major asset class, with an estimated value of more than $12 trillion in the U.S. alone. But the stay-at-home orders and business closures precipitated by the COVID-19 pandemic have the potential to negatively – and disastrously – affect commercial properties. What will the short- and long-term impacts be, which types of properties will be hardest hit and what policies can be put in place to help stem the tide of losses? UNC Kenan-Flagler Business School Professor and Leonard W. Wood Center for Real Estate Studies Faculty Advisor Andra Ghent and her colleagues examine these issues in this week’s Kenan Insight.
The data-generating process of productivity growth includes both trend and business-cycle shocks, generating many counterfactuals for prices under full-information. In practice, agents cannot immediately distinguish between the two shocks, leading to "rational confusion": each shock inherits properties of its counterpart.
We introduce easy to implement regression-based methods for predicting quarterly real economic activity that use daily financial data. Our analysis is designed to elucidate the value of daily information and provide real-time forecast updates of the current (nowcasting) and future quarters.
We examine the relation between plant-level predictive analytics use and centralization of authority for more than 25,000 manufacturing plants using proprietary US Census data. We focus on headquarters authority over plants through delegation of decision-making and design of performance-based incentives.
Join part one of our two-part discussion on data privacy as we examine the integration and privacy concerns presented by contact tracing. The conversation will explore how current data protections laws address the issue, and will cover potential regulatory changes we might see in response to the current crisis.
Reliably detecting insider trading is a major impediment to both research and regulatory practice. Using account-level transaction data, we propose a novel approach. Specifically, after extracting several key empirical features of typical insider trading cases from existing regulatory actions, we then employ a machine learning methodology to identify suspicious insiders across our full sample.
Three-tiered private-label (PL) portfolio strategies (low-quality tier: economy PLs, mid-quality tier: standard PLs, and top-quality tier: premium PLs) are gaining interest around the world. Drawing on the context-effects literature, the authors postulate how the introduction of economy and premium PLs may affect the choice of mainstream-quality and premium-quality national brands (NBs) and the choice of the retailer's existing PL offering.
AI has become close to bewildering in its promises, met and unmet, its terms and tools, acronyms, “use” case examples of wild successes countered by duds and disappointments. There’s an overall lack of clear pointers for business leaders to shape the direction, priorities and pace of their organization’s AI activities. Over the past two years, we have explored the widening AI space; what stood out in our reviews is that there is today a lack of management perspective on AI.
Despite having the deepest and most diverse capital markets in the world, the United States still struggles to provide sufficient capital to many small businesses outside of major commercial centers as well as to women-owned and minority-owned businesses regardless of size or location. This paper reviews the academic literature and provides an analysis of some recent data to gain understanding of the causes of these gaps as well as the solutions for filling the gaps. Results indicate that the Small Business Administration’s SBIC program is an effective mechanism for providing capital to underserved geographies as well as to businesses owned by women and underrepresented minorities.
Prior to the COVID-19 outbreak, institutions of higher education were under immense pressure to live up to their value propositions, with underlying tensions that have been developing for years posing an existential threat to their financial viability. As colleges and universities move classes and operations online in response to the pandemic, questions arise as to what such changes hold not just for now, but for the long-term success of higher education. Can ed tech provide a way forward? Find out in this week’s Kenan Insight.
The extent to which federal investment in research crowds out or decreases incentives for investment from other funding sources remains an open question. Scholarship on research funding has focused on the relationship between federal and industry or, more comprehensively, non-federal funding without disentangling the other sources of research support that include nonprofit organizations and state and local governments. This paper extends our understanding of academic research support by considering the relationships between federal and non-federal funding sources provided by the National Science Foundation Higher Education Research and Development Survey.
Reviewing 25 years of research, we observed that the science of feedback at work is not yet a story of coherent and cumulative progress. Feedback is often generically defined, and assumptions substantially diverge. Consequently, insights often appear disconnected from the way feedback is practiced and experienced in organizations. We organize the literature by making three core assumptions explicit and identifying six distinct substreams of feedback research.
The Frank Hawkins Kenan Institute of Private Enterprise at the University of North Carolina at Chapel Hill’s Kenan-Flagler Business School, in partnership with Infinia ML, an advanced machine learning company that delivers transformative automation solutions and data science to enterprise businesses, will host a cross-sector symposium on Friday, Nov. 30 to advance the field of machine learning. Academic and business leaders will come together with students to connect the possibilities of cutting-edge ML research with the realities of practical implementation.
A large body of social science evidence indicates that objective, reliable and valid risk assessment instruments are more accurate in evaluating risk than professional human judgements alone. In the world of pretrial detention, where more than 10 million people are jailed each year in the United States after arrest, pretrial risk assessment tools may provide a more efficient, transparent and fairer basis for making assessments than having a judge quickly scan documents detailing the defendant’s prior record and current charges and make a decision in mere minutes. However, these assessments will retain any bias present in the data used by criminal justice agencies.
Caller abandonment could depend on their past waiting experiences. Using Cox regressions we show that callers who abandoned or waited for a shorter time in the past abandon more in the future. However, Cox regression approach does not shed light on callers’ prior belief about the duration of their delays.
In 2022, in-migration slowed, and out-migration accelerated, reducing the role of net migration in North Carolina statewide population growth, according to recently released Census data. For the Tarheel state, we document changes in gross and net migration flows between 2021 and 2022, highlight possible drivers, and offer anecdotal evidence as to why the revealed changes may foreshadow a longer- term shift in migration’s role in statewide population change.
Using data from two experience-sampling studies, this paper investigates the dynamic relationships between discretionary behaviors at work—voluntary tasks that employees perform—and internal somatic complaints, focusing specifically on a person’s pain fluctuations.
We disentangle and study the relative importance of different risk preferences in explaining extended warranty purchases and the high premia paid for them. Empirical and behavioral research on insurance is at odds with whether diminishing returns (curvature of the utility function), or loss aversion and nonlinear probability weighting lead to observed consumer behavior. This lack of consensus is primarily due to the inability of standard choice data to separate different risk preferences, and the consequent need to rely on strong parametric assumptions.