CAREER: Efficient and Equitable Housing Allocation
CAREER: Efficient and Equitable Housing Allocation
批准号:
2339912
负责人:
Nick Arnosti
金额:
$53.01万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2024
资助国家:
美国
项目状态:
未结题
起止时间:
2024-07-01 至 2029-06-30
中文摘要
该学院早期职业发展奖将通过支持研究公平和有效地分配负担得起的住房的技术,为促进国家繁荣和经济福利做出贡献。它通过以下创新政策应对无家可归和住房负担能力方面的持续挑战:(1)提高住房购房券的使用率,确保更多高障碍家庭受益于住房购房券;(2)提高购房券轮候名单的流动速度,同时减少行政管理费用;(3)减少在获得快速安置支持后返回无家可归的家庭数量;以及(4)确保分配新的出租单位为不同类型的申请者提供机会。教育和推广计划包括与实践者合作,通过博客和YouTube频道传播想法,以及公开分享新课程的创新材料和活动。要实现这些研究目标,需要克服两个研究障碍。首先,住房援助计划往往有多个目标,与人们普遍认为的收入或吞吐量最大化等目标不同。第二,许多政策具有长期后果,与短期效果背道而驰。该项目将开发动态模型,以捕捉当前决策对系统未来发展的影响,制定适当的目标,并建议和分析实现这些目标的新政策。该项目将扩展库存管理、动态匹配、排队和优化的概念,考虑当前决策影响未来到达的动态匹配问题,以及来自未知分布的具有随机提前期的库存管理问题。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
This Faculty Early Career Development (CAREER) award will contribute to the advancement of national prosperity and economic welfare by supporting research to study techniques to allocate affordable housing equitably and efficiently. It addresses ongoing challenges of homelessness and housing affordability with innovative policies to (i) increase housing voucher utilization and ensure that more high-barrier households benefit from housing vouchers, (ii) increase the rate at which voucher waiting lists move while reducing administrative overhead, (iii) reduce the number of households that return to homelessness after receiving rapid rehousing support, and (iv) ensure that the allocation of new rental units offers opportunities to a diverse set of applicants. The education and outreach plan includes collaboration with practitioners, dissemination of ideas through a blog and YouTube channel, and publicly sharing innovative materials and activities from a new course.Achieving these research goals will require overcoming two research hurdles. The first is that housing assistance programs often have multiple objectives that differ from widely considered goals such as revenue or throughput maximization. The second is that many policies have long-run consequences which run counter to their short-term effects. This project will develop dynamic models to capture the effects of current decisions on the future evolution of the system, formulate suitable objectives, and recommend and analyze novel policies to achieve these objectives. The project will extend concepts from inventory management, dynamic matching, queueing, and optimization by considering dynamic matching problems in which current decisions influence future arrivals, and inventory management problems with stochastic lead time from unknown distributions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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