Regional analysis of housing price bubbles and their determinants in the Czech Republic

In this article we focus on factors affecting property prices in the Czech regions. We apply an empirical analysis to identify periods of property price overvaluation by three alternative approaches: using ratios related to house prices (price-to-income and price-to-rent), using simple analysis of time trends in regional dimension (HP filter) and using panel regression. The analysis identified overvalued property prices in 2002/2003 and partly also in 2007/2008. Compared to simple HP filters in the panel regression the size of the housing price overvaluation in 2007/2008 was relatively low, as the rise in property prices in this period was largely determined by fundamentals. Looking across individual region, there is apparent tendency for higher degree of overvaluation in regions with higher property prices. The only exception from this rule is Prague, which, as a capital city, seems to have many “specific” features.Web of Science611916

Development of Methods for Cross-Sectional HIV Incidence Estimation in a Large, Community Randomized Trial

Background Accurate methods of HIV incidence determination are critically needed to monitor the epidemic and determine the population level impact of prevention trials. One such trial, Project Accept, a Phase III, community-randomized trial, evaluated the impact of enhanced, community-based voluntary counseling and testing on population-level HIV incidence. The primary endpoint of the trial was based on a single, cross-sectional, post-intervention HIV incidence assessment. Methods and Findings Test performance of HIV incidence determination was evaluated for 403 multi-assay algorithms [MAAs] that included the BED capture immunoassay [BED-CEIA] alone, an avidity assay alone, and combinations of these assays at different cutoff values with and without CD4 and viral load testing on samples from seven African cohorts (5,325 samples from 3,436 individuals with known duration of HIV infection [1 month to >10 years]). The mean window period (average time individuals appear positive for a given algorithm) and performance in estimating an incidence estimate (in terms of bias and variance) of these MAAs were evaluated in three simulated epidemic scenarios (stable, emerging and waning). The power of different test methods to detect a 35% reduction in incidence in the matched communities of Project Accept was also assessed. A MAA was identified that included BED-CEIA, the avidity assay, CD4 cell count, and viral load that had a window period of 259 days, accurately estimated HIV incidence in all three epidemic settings and provided sufficient power to detect an intervention effect in Project Accept. Conclusions In a Southern African setting, HIV incidence estimates and intervention effects can be accurately estimated from cross-sectional surveys using a MAA. The improved accuracy in cross-sectional incidence testing that a MAA provides is a powerful tool for HIV surveillance and program evaluation