Evaluating House Price Forecasts

House prices, unlike stock prices, appear to be predictable with some degree of accuracy. We use an autoregressive process to model the time series behavior of a city-wide house price index, and then produce one-quarter ahead forecasts for individual properties. Better real estate decisions require forecasting models with desirable properties for prediction errors (PEs). We propose that managers use a battery of tests to compare PEs; in particular, non-parametric smoothing of the empirical distribution of PEs can add important information to statistics that focus on first and second moments. The decision-making framework is fitted with housing transactions from Dade County, Florida, from 1976 through the second quarter of 1997. PEs from two forecasting models, hedonic and repeat sales, show some departure from the desirable properties of any one-step-ahead forecast. Also, both show some informational inefficiency, but the hedonic is more efficient than the repeat. Nonparametric smoothing shows that the hedonic method dominates the repeat over an important range of PEs; thus, a case can be made that many risk-averse managers would prefer a forecast based on the hedonic method.

Explaining Pharmaceutical R&D Growth Rates at the Industry Level: New Perspectives and Insights

This paper uses aggregate data for the major pharmaceutical companies in the U.S. to study the rate of growth in pharmaceutical R&D intensity over the period from 1952 to 2001. The theoretical model argues and the empirical findings suggest that pharmaceutical R&D spending increases with real drug prices, after holding constant other determinants of R&D. Simulations based on our multiple regression model indicate that the capitalized value of pharmaceutical R&D spending would have been about 30 percent lower if the federal government had limited the rate of growth in drug price increases to the rate of growth in the general consumer price index during the period 1980 to 2001. Moreover, a drug price control regime would have resulted in 330 to 365 fewer new drugs brought to the global market during that same time period.Health and Safety

The Aggregate Demand for Private Health Insurance Coverage in the U.S.

This paper estimates the aggregate demand for private health insurance coverage in the U.S. using an error-correction model and by recognizing that people are without private health insurance for voluntary, structural, frictional, and cyclical reasons and because of public alternatives. Insurance coverage is measured both by the percentage of the population enrolled in private health insurance plans and the completeness of the insurance coverage. Annual data for the period 1966-1999 are used and both short and long run price and income elasticities of demand are estimated. The empirical findings indicate that both private insurance enrollment and completeness are relatively inelastic with respect to changes in price and income in the short and long run. Moreover, private health insurance enrollment is found to be inversely related to the poverty rate, particularly in the short-run. Finally, our results suggest that an increase in the number cyclically uninsured generates less of a welfare loss than an increase in the structurally uninsured