R
Randy I. Anderson
Researcher at City University of New York
Publications - 44
Citations - 1344
Randy I. Anderson is an academic researcher from City University of New York. The author has contributed to research in topics: Real estate investment trust & Real estate. The author has an hindex of 18, co-authored 43 publications receiving 1269 citations. Previous affiliations of Randy I. Anderson include University of Central Florida & University of Louisiana at Monroe.
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Measuring efficiency in the hotel industry: A stochastic frontier approach
TL;DR: In this article, the authors employ a stochastic frontier technique to estimate managerial efficiency levels in the hotel industry and obtain an average efficiency of 89.4%, with the most and least efficient hotels operating at a 92.1% and a 84.3% efficiency level, respectively.
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Production Efficiency in the Austrian Insurance Industry: A Bayesian Examination
TL;DR: In this paper, the authors examined the developments in the production efficiency of the Austrian insurance market for the period 1994-1999 using firm-specific data on life/health and non-life insurers obtained from the Austrian Insurance Regulatory Authority.
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Technical efficiency and economies of scale: A non-parametric analysis of REIT operating efficiency
TL;DR: Using data from the National Association of Real Estate Investment Trusts for the years 1992–1996, it is found that REITs are technically inefficient, and the inefficiencies are a result of both poor input utilization and failure to operate at constant returns to scale.
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Another Look at the Efficiency of Corporate Travel Management Departments
TL;DR: In this article, the authors examined how efficiently corporate travel management departments are operating by employing a stochastic frontier technique in addition to a linear programming procedure, and found that the results indicated that these departments are relatively efficient.
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The US Housing Market: Asset Pricing Forecasts Using Time Varying Coefficients
TL;DR: In this article, the authors employ alternative estimation methodologies where the estimated parameters are allowed to vary over time and provide strong empirical evidence in favor of utilizing the rolling Generalized Autoregressive Conditional Heteroskedastic (GARCH) Model and the Kalman Filter with an Autoregression Presentation (KAR) for the parameters' time variation.