Spatial Concentration of Institutional Property Ownership: New Wave Atomistic or Traditional Urban Clustering

NCREIF investors acquire property in counties that meet socioeconomic filtering criteria. In contrast to atomistic predictions, these investors acquire their apartment buildings, offices, retail facilities, and warehouses in density clusters. These clusters follow a model of a negative exponential demand curve, a model that previously explained the technologically caused density gradient of urban areas. Institutional investors signal their belief that clustering of properties is a value dimension.

Commercial Loan Underwriting and Option Valuation

This article seeks to answer why over a nineteen-year period the debt-coverage ratio for commercial noninsured properties averages 1.29. The article applies the corporate liabilities extension of the Black-Scholes option pricing model to the equity valuation of a real estate project. The regression results of the modified model robustly sustain its usefulness in explaining the derivation of the debt-coverage ratio. The results confirm that commercial mortgage loan underwriters operate with a five-year horizon in creating the equity cushion needed to protect themselves against interest-rate risk.

Option-Based Prediction of Commercial Mortgage Defaults

Underwriters set loan-to-value ratios and loan contract interest rates of uninsured commercial mortgages to anticipate the likelihood of subsequent default. The results of the use of a modified Black-Scholes option model suggest that loan-to-value ratios are bound from below by borrowers' desires to maximize project leverage in a limited liability setting and constrained from above by lenders' requirement to originate loans with institutional-grade (Baa) contract interest rates. Given the prevailing risk-free rate and the investment-grade rate, this model at the time of mortgage origination predicts the possibility of default for a new commercial mortgage. The model is empirically verified with ACLI data for 1968-89.

Office Employment Growth and the Changing Function of Cities

The proportion of a city's local and regionally/nationally supported office employment changes as the city assumes more central place functions. Certain mixes of office employment should reflect the central place function of the city and promote office growth. Forty-five cities are studied using data from 1997, 1982, and 1985. The results indicate that the variance of office employment does not help predict a city's growth, but that certain cluster categories of office employment are associated with office employment growth. Finally, the results indicate that office employment profiles of cities have become more homogenized over time.

The Information Precision of CBD Office Vacancy Rates

Consistent with the rational expectations information model (REIM), the difference among reported area CBD office vacancy rates is proportional to the level of vacancy, inversely proportional to the size of the inventory, and significantly higher when both a national vendor and a local agent make simultaneous reports. Underlying volatility in the market and the cost and difficulty of acquiring information are the principal causes of the information variance. The difficulties and ambiguities in obtaining information are reviewed and the Diamond REIM model is tested using encountered published vacancy rates for central business districts for the period 1985-1989.

Results from Australia's 2014 Report Card on Physical Activity for Children and Youth

BACKGROUND: Like many other countries, Australia is facing an inactivity epidemic. The purpose of the Australian 2014 Physical Activity Report Card initiative was to assess the behaviors, settings, and sources of influences and strategies and investments associated with the physical activity levels of Australian children and youth. METHODS: A Research Working Group (RWG) drawn from experts around Australia collaborated to determine key indicators, assess available datasets, and the metrics which should be used to inform grades for each indicator and factors to consider when weighting the data. The RWG then met to evaluate the synthesized data to assign a grade to each indicator. RESULTS: Overall Physical Activity Levels were assigned a grade of D-. Other physical activity behaviors were also graded as less than average (D to D-), while Organized Sport and Physical Activity Participation was assigned a grade of B-. The nation performed better for settings and sources of influence and Government Strategies and Investments (A- to a C). Four incompletes were assigned due to a lack of representative quality data. CONCLUSIONS: Evidence suggests that physical activity levels of Australian children remain very low, despite moderately supportive social, environmental and regulatory environments. There are clear gaps in the research which need to be filled and consistent data collection methods need to be put into place