Commercial real estate (CRE) is real estate held to generate income or used as an input into production by firms. It is notably different from other asset classes of a similar magnitude in that CRE is traded in private, illiquid markets. CRE is a hugely important asset class that has received less attention from the academic literature than asset classes that rival CRE in terms of sheer value. Yet pension funds, life insurance companies, sovereign wealth funds and other institutional investors seek the diversification benefits provided by CRE’s unusually steady income flow. The paper, “Commercial Real Estate as an Asset Class,” by Andra Ghent of UNC Kenan-Flagler Business School, Walter Torous of the MIT Center for Real Estate and Rossen Valkanov of UCSD’s Rady School of Management provides a much-needed overview of the CRE literature thus far, focusing on its attributes as an asset class.
Fifth Third Bank and the Kenan Institute of Private Enterprise at UNC Kenan-Flagler Business School have launched Empowering American Cities, a program that delivers local economic information tailored for business leaders looking to grow their operations.
UNC Kenan-Flagler Business School Clinical Associate Professor of Finance Arzu Ozoguz discusses the SEC's anticipated new rules around sustainability.
While economists have long theorized that wealthier individuals may purchase less life and property insurance because they can rely on their savings if something unexpected happens, a new study of more than 63,000 people shows that, in practice, quite the opposite is true. This week’s Kenan Insight offers a chance for our experts to explore the findings of their new study, which suggest disparities in insurance coverage could help explain and exacerbate existing financial inequalities.
“Every business I enter is looking for employees” was a common refrain in our Carolina Across 100 survey, with 79% of the total survey sample selecting employment/staffing concerns among their top three negative impacts of COVID-19 on their organization. Is the staffing shortage just a function of COVID-19 that will correct itself as COVID abates or are there larger demographic and economic forces at work? The answer is a bit of both.
Since 2001, the number of financial statement line items forecasted by analysts and managers that I/B/E/S and FactSet capture in their data feeds has soared. Using this new data, we find that 13 item surprises—11 income statement and 2 cash flow statement analyst and management guidance surprises—reliably explain firms’ signed earnings announcement returns.
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 develop Granger causality tests that apply directly to data sampled at different frequencies. We show that taking advantage of mixed frequency data allows us to better recover causal relationships when compared to the conventional common low frequency approach.
To enhance our understanding of emerging markets, we study a data set from the Casablanca stock exchange containing all the transaction records over a long span. The exchange was included in 1996 in the International Finance Corporation (IFC) data base roughly 3 years after important market reforms.
As AI and related technologies – such as machine learning, deep learning, natural language processing and computer vision – rapidly evolve, it's necessary to examine their limitations and ethical complexities.
Learning from past experience is central to an organization's adaptation and survival. A key dimension of prior experience is whether an outcome was successful or unsuccessful. Although empirical studies have investigated the effects of success and failure in organizational learning, to date, the phenomenon has received little attention at the individual level.
Professor of Strategy and Entrepreneurship, Phillip Hettleman Distinguished Scholar and Area Chair of Strategy and Entrepreneurship, UNC Kenan-Flagler Business School
We present a survey design that generalizes static conjoint experiments to elicit inter-temporal adoption decisions for durable goods. We show that consumers’ utility and discount functions in a dynamic discrete choice model are jointly identified using data generated by this specific design. In contrast, based on revealed preference data, the utility and discount functions are generally not jointly identified even if consumers’ expectations are known.
We propose a general GARCH framework that allows one to predict volatility using returns sampled at a higher frequency than the prediction horizon.
Innovative data sources offer new ways of studying spatial and temporal industrial and regional development. Our approach is to study the development of an entrepreneurial regional economy through a comprehensive analysis of its constituent firms and institutions over time.
Inspired by a data set from the Chinese retailer JD.com, we study the click and purchase behavior of customers in an online retail setting by employing a structural estimation approach.
Hasbrouck (2018) takes advantage of the fact that U.S. equity market data are timestamped to nanosecond precision, and explores models of price dynamics at resolutions sufficient to capture the reactions of the fastest agents. The paper therefore addresses the econometric analysis of multivariate time series models at sub-millisecond frequencies and relies on long distributed lag models to alleviate the computational complexity while still taking advantage of the inherent sparsity of price transitions.
This study uses learning theory to show how knowledge domains affect product extension decisions and how these product decisions change as firms age. Faced with the choice of new product-markets, a firm might decide to introduce a similar product, by leveraging existing firm knowledge, or to experiment with a less familiar product, which requires new knowledge.
In this work, we model the joint distribution of the error term of the OLS model, the instrumental variables, and the error term for the reduced-form equation of the endogenous regressor by a Gaussian copula. We show that exogeneity of instrumental variables is equivalent to the exogeneity of their standard normal transformations with the same CDF value. Then, we establish a Wald test for the exogeneity of the instrumental variables. We also show that this method can be used to test the exogeneity of a regressor.