Alien Registration- Solak, Michael (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/32007/thumbnail.jp

Decision Models for Foreclosed Housing Acquisition and Redevelopment: A University of Massachusetts Multi-Campus Collaborative Project - Processes and Findings to Date

The recent housing foreclosure crisis has had devastating impacts on individuals, communities, organizations and government. In response, several community development corporations (CDCs) have sought new ways to assist neighborhoods suffering from the myriad effects of high foreclosures, including neighborhood instability, increased vandalism and crime, lower property values, and economic disinvestment. This research project focuses on activities of community-based organizations that acquire and redevelop foreclosed properties to support neighborhood stabilization and revitalization. However, the costs of pursuing this strategy far exceed the resources available to typical CDCs. Thus, our project seeks to solve the following decision problem: What subset of a large number of available foreclosed properties should be acquired for neighborhood stabilization and revitalization? What activities should be pursued with which properties, when should they be pursued, and to what degree? The decision models we intend to develop will yield acquisition policies that are more efficient, effective, and equitable for CDCs and their community residents. Our goal is to develop theory, models and methods that benefit from the knowledge of practitioners while providing practitioners with novel tools and perspectives that enable them to better achieve their organizations’ missions. This document lays out our knowledge to date on the scope and magnitude of the foreclosure crisis, the policy responses and actions by local CDCs to mitigate the effects of foreclosures, and the next steps in our research project, which include applying our expertise to the experiences of community partner organizations to develop models and inform theory and practice

FAIR Metadata Standards for Low Carbon Energy Research—A Review of Practices and How to Advance

The principles of Findability, Accessibility, Interoperability, and Reusability (FAIR) have been put forward to guide optimal sharing of data. The potential for industrial and social innovation is vast. Domain-specific metadata standards are crucial in this context, but are widely missing in the energy sector. This report provides a collaborative response from the low carbon energy research community for addressing the necessity of advancing FAIR metadata standards. We review and test existing metadata practices in the domain based on a series of community workshops. We reflect the perspectives of energy data stakeholders. The outcome is reported in terms of challenges and elicits recommendations for advancing FAIR metadata standards in the energy domain across a broad spectrum of stakeholders

Alien Registration- Solak, Michael (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/32007/thumbnail.jp

Alien Registration- Solak, Michael (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/32007/thumbnail.jp

Stochastic Models for Strategic Resource Allocation in Nonprofit Foreclosed Housing Acquisitions

Increased rates of mortgage foreclosures in the U.S. have had devastating social and economic impacts during and after the 2008 financial crisis. As part of the response to this problem, non-profit organizations such as community development corporations (CDCs) have been trying to mitigate the negative impacts of mortgage foreclosures by acquiring and redeveloping foreclosed properties. We consider the strategic resource allocation decisions for these organizations which involve budget allocations to different neighborhoods under cost and return uncertainty. Based on interactions with a CDC, we develop stochastic integer programming based frame- works for this decision problem, and assess the practical value of the models by using real-world data. Both policy-related and computational analyses are performed,and several insights such as the trade-offs between different objectives, and the efficiency of different solution approaches are presented

National Science Foundation funded project: Collaborative Proposal: Decision Models for Foreclosed Housing Acquisition and Redevelopment

The recent housing foreclosure crisis has had devastating impacts on individuals and their communities. To mitigate some of these impacts a number of community-based organizations are acquiring foreclosed properties in efforts to support neighborhood stabilization and revitalization. Examples of actions taken on these foreclosed properties include: land-banking, rehabilitation, demolition and re-sale or/re-rental. These actions have the potential to minimize blight, reduce unanticipated housing mobility, and provide affordable housing opportunities. However, the cost of pursuing any or all of these strategies to their fullest extent far exceeds the resources available to typical community-based organizations. In this research project, the PIs will develop decision support tools to assist community based organizations in identifying the subset of available foreclosed properties to acquired for the purposes of neighborhood stabilization and revitalization, determining which actions should be undertaken with respect to which properties, when the actions should be taken and to what degree? In terms of broader impacts, the policy/decision models developed will yield acquisition policies that are, relative to current operations and procedures: (1) more efficient, i.e. they make better use of organization resources and social subsidies, (2) more effective, i.e. they help organizations make more rapid progress towards social goals related to affordable housing and community development, and (3) more equitable, i.e. they ensure that stakeholder groups and the communities see the foreclosure acquisition process as more transparent, consistent and fair

Property Value Impacts of Foreclosed Housing Acquisitions under Uncertainty

Community development corporations seek to stabilize neighborhoods affected by the recent foreclosure crisis through acquisition and redevelopment of distressed properties. One rationale for this work is the alleviation or avoidance of negative foreclosure impacts. We estimate the lost value to proximate properties associated with a single foreclosure through a Markov chain representing probabilistic transitions between foreclosure stages. We apply our model to a case study of foreclosure properties in Chelsea, MA. A rank ordering by estimated property value impacts indicates significant potential gains in social value as compared to current community development practice. We extend our basic model to address the effects of clusters of foreclosed units upon the value of proximate properties. This study provides additional support for the use of decision modeling in foreclosed housing acquisition and redevelopment. [NOTE: The publisher, Elsevier, has generously provided *free* access to the full-text of the article through January 31, 2014

Property Value Impacts of Foreclosed Housing Acquisitions under Uncertainty

Community development corporations seek to stabilize neighborhoods affected by the recent foreclosure crisis through acquisition and redevelopment of distressed properties. One rationale for this work is the alleviation or avoidance of negative foreclosure impacts. We estimate the lost value to proximate properties associated with a single foreclosure through a Markov chain representing probabilistic transitions between foreclosure stages. We apply our model to a case study of foreclosure properties in Chelsea, MA. A rank ordering by estimated property value impacts indicates significant potential gains in social value as compared to current community development practice. We extend our basic model to address the effects of clusters of foreclosed units upon the value of proximate properties. This study provides additional support for the use of decision modeling in foreclosed housing acquisition and redevelopment. [NOTE: The publisher, Elsevier, has generously provided *free* access to the full-text of the article through January 31, 2014