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on Project, Program and Portfolio Management |
| By: | HUD - PD&R |
| Abstract: | The Department of Housing and Urban Development (HUD) Quality Control for Rental Assistance Subsidy Determinations studies provide national estimates of the extent, severity, costs, and sources of rent errors in tenant subsidies for the PHA-administered Public Housing, Section 8 Housing Choice Voucher, and Moderate Rehabilitation programs; and the owner-administered Section 8, and Section 202 and Section 811 programs with Project Rental Assistance Contracts (PRAC) or Project Assistance Contracts (PAC). These programs account for nearly all of HUD's current housing assistance outlays administered by the Offices of Housing and Public and Indian Housing, as well as the large majority of units assisted by HUD. This study was designed to measure the extent of administrative error by housing providers. The errors we evaluated in this study affect the rent contributions tenants should have been charged. The findings presented in this report are a result of data collected from February through July 2007 for actions taken by Public Housing Authority (PHA) and project staff during FY 2006 (October 2005 through September 2006). These findings show that the percent of errors, the average dollars in error and the gross dollar error rate in the Public Housing, Section 8 Housing Choice Voucher, Moderate Rehabilitation, owner-administered Section 8, and Section 202 and Section 811 programs with PRAC or PAC tenant subsidies continues to remain stable when compared with results from previous studies. |
| JEL: | D40 |
| Date: | 2007–10 |
| URL: | http://d.repec.org/n?u=RePEc:hud:wpaper:39163&r=ppm |
| By: | Eduardo Engel; Ronald Fischer |
| Abstract: | The government contracts with a foreign firm to extract a natural resource that requires an upfront investment and which faces price uncertainty. In states where profits are high, there is a likelihood of expropriation, which generates a social cost that increases with the expropriated value. In this environment, the planner’s optimal contract avoids states with high probability of expropriation. The contract can be implemented via a competitive auction with reasonable informational requirements. The bidding variable is a cap on the present value of discounted revenues, and the firm with the lowest bid wins the contract. The basic framework is extended to incorporate government subsidies, unenforceable investment effort and political moral hazard, and the general thrust of the results described above is preserved. JEL classification: Q33, Q34, Q38, H21, H25. |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:edj:ceauch:244&r=ppm |
| By: | Michael Iacono; David Levinson (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota) |
| Abstract: | Transportation analysts and the public decision-makers they support are confronted with a broad range of analytical tools for estimating the economic impacts of improvements to trans- portation networks. Many of the available models operate at different scales and have distinctly different structures, making them more or less appropriate for analyzing the impacts of differ- ent types of projects. Here, we review several of the economic methods and models that have been developed for analyzing the impact of transportation improvements, giving special atten- tion to types of projects that add highway capacity in urban areas. We review project-based methods, including beneÞt-cost analysis and several analytical software tools developed by the Federal Highway Administration (FHWA) for economic analysis of transportation investment. We then move on to aggregate and disaggregate-level econometric methods, including regional economic models, hedonic price functions, production functions and cliometric analyses. We also devote some attention to the role of induced demand in economic evaluation, since it is of- ten one of the most uncertain and confounding factors faced by those charged with conducting economic evaluation of transportation projects. |
| Keywords: | Economic Impact, Benefit-Cost Analysis |
| JEL: | R41 R48 D63 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:nex:wpaper:estimatingeconomicimpact&r=ppm |
| By: | Pavithra Parthasarathi; David Levinson (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota) |
| Abstract: | This research evaluates the accuracy of demand forecasts using a sample of recently-completed projects in Minnesota and identiÞes the factors inßuencing the inaccuracy in forecasts. The forecast traffic data for this study is drawn from Environmental Impact Statements(EIS), Transportation Analysis Reports (TAR) and other forecast reports produced by the Minnesota Department of Transportation (Mn/DOT) with a horizon forecast year of 2010 or earlier. The actual traffic data is compiled from the database of traffic counts maintained by the Office of Traffic Forecasting and Analysis section at Mn/DOT. Based on recent research on forecast accuracy, the (in)accuracy of traffic forecasts is estimated as a ratio of the forecast traffic to the actual traffic. The estimation of forecast (in)accuracy also involves a comparison of the socioeconomic and demographic assumptions, the assumed networks to the actual in-place networks and other travel behavior assumptions that went into generating the traffic forecasts against actual conditions. The analysis indicates a general trend of underestimation in roadway traffic forecasts with factors such as highway type, functional classiÞcation, direction playing an inßuencing role. Roadways with higher volumes and higher functional classiÞcations such as freeways are subject to underestimation compared to lower volume roadways/functional classiÞcations. The comparison of demographic forecasts shows a trend of overestimation while the comparison of travel behavior characteristics indicates a lack of incorporation of fundamental shifts and societal changes. |
| Keywords: | Minnesota, Minneapolis, Travel Demand Model, Transportation Planning, Forecasting |
| JEL: | R41 R48 D63 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:nex:wpaper:forecastaccuracy&r=ppm |