Refugee housing in the United States: improving the Refugee-Welcoming Rental Market
Bibliographic record
Abstract
In this thesis, I explore the vital connection between the US Reception & Placement (USRAP) refugee resettlement program and the health of the US’s refugee-welcoming rental market (RWRM). I focus on USRAP because it plays a unique and vital role in the international protection system for refugees and because the Biden Administration pledged in 2021 to scale the system’s capacity to resettle refugees back up to pre-2017 levels. The housing system within USRAP is understudied compared to those of much-smaller refugee resettlement programs in Europe, Australia and Canada. Recently, however, housing access has imposed an unignorable operational parameter on USRAP as evidenced by resettlement agency (RA) implementation offices falling more than 50% short of housing placement targets in FY 2023. In this thesis, I engage with the issue of USRAP-based refugee housing from a constructivist perspective, identifying and analyzing the systems and processes that shape a prospective RWRM match to search for practical changes that may lead to improvement. Informed by both a desk review and field research with RWRM stakeholders, I present a fictional narrative case study with two scenarios illustrating two frequent stories of RWRM matching in the housing journey of USRAP clients. The case shows that a wide variety of factors can drastically impair the rent capacity and, by extension, the open-market matching prospects for RWRM households. I then explore other key issues experienced on either side of the RWRM when matching and identify three major challenges: guaranteeing unit-tenant fit, managing risk perception amongst landlords, and streamlining the RWRM tenant placement process. To improve efficiency in tenant placement, resettlement agencies can harness better information systems and adopt clearer processes when liaising with landlords. Addressing the gaps in finances or knowledge that impede unit-tenant fit and landlords’ perceptions of renting to refugees must involve fostering partnerships with third party service providers. I identify six opportunities for stakeholders and partners to fortify the RWRM, and I consider the role of a social impact start-up called ReHome that I founded in 2023 to serve as a marketplace platform bringing new RWRM partnerships together. Finally, I consider what additional possibilities might open up within local rental markets if the government were to orient USRAP rental housing access toward non-market housing providers. USRAP has a unique ability to optimize the initial access step of the housing journey of the individuals it resettles because it is the global resettlement program that is most involved in the practicalities of rental market matches and because it is insulated geographically from refugee-producing countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".