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Single-Period Location Models for Subsidized Housing: Tenant-Based Subsidies

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  • Michael Johnson

Abstract

Previous research has established a need for operations research models to help urban public housing authorities (PHAs) in the U.S. better manage the transition from the traditional model of high-rise public housing developments to tenant-based housing subsidies for market-rate rental units and project-based housing subsidies for scattered-site, low-density public housing. This paper presents the tenant-based subsidized housing location model (TSHLP) that is simplified and applied to a larger and more representative data set than has been done previously. Base-case and sensitivity analyses indicate that model solutions, which are approximations to a Pareto frontier of nondominated potential family allocations, give planners considerable flexibility in choosing alternative housing configurations that can satisfy the needs of various interest groups. Copyright Kluwer Academic Publishers 2003

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  • Michael Johnson, 2003. "Single-Period Location Models for Subsidized Housing: Tenant-Based Subsidies," Annals of Operations Research, Springer, vol. 123(1), pages 105-124, October.
    • Handle: RePEc:spr:annopr:v:123:y:2003:i:1:p:105-124:10.1023/a:1026119128524
    DOI: 10.1023/A:1026119128524
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    Citations

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    Cited by:

    1. Karsu, Özlem & Morton, Alec, 2015. "Inequity averse optimization in operational research," European Journal of Operational Research, Elsevier, vol. 245(2), pages 343-359.
    2. Johnson, Michael P., 2006. "Single-period location models for subsidized housing: Project-based subsidies," Socio-Economic Planning Sciences, Elsevier, vol. 40(4), pages 249-274, December.
    3. Dieudonné Tchuente & Serge Nyawa, 2022. "Real estate price estimation in French cities using geocoding and machine learning," Annals of Operations Research, Springer, vol. 308(1), pages 571-608, January.
    4. Nicola Camatti & Giacomo Tollo & Gianni Filograsso & Sara Ghilardi, 2024. "Predicting Airbnb pricing: a comparative analysis of artificial intelligence and traditional approaches," Computational Management Science, Springer, vol. 21(1), pages 1-25, June.
    5. Farahani, Reza Zanjirani & Fallah, Samira & Ruiz, Rubén & Hosseini, Sara & Asgari, Nasrin, 2019. "OR models in urban service facility location: A critical review of applications and future developments," European Journal of Operational Research, Elsevier, vol. 276(1), pages 1-27.
    6. Rishi Talreja & Ward Whitt, 2008. "Fluid Models for Overloaded Multiclass Many-Server Queueing Systems with First-Come, First-Served Routing," Management Science, INFORMS, vol. 54(8), pages 1513-1527, August.
    7. Michael P Johnson, 2007. "Planning Models for the Provision of Affordable Housing," Environment and Planning B, , vol. 34(3), pages 501-523, June.

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