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Record W7112325275

Towards a Landscape of Rental Housing Ownership: Legal and Spatial Characteristics of Residential Landlords in the United States

2022· article· en· W7112325275 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Access to Scholarship at Harvard (DASH) (Harvard University) · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsRentingReal estateDisinvestmentMetropolitan areaRental housingLiabilityUnintended consequencesHousing tenureQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

This dissertation documents ongoing changes to the legal and spatial structures of rental housing ownership in the United States and considers potential consequences of these changes for tenants, neighborhoods, and cities. In particular, it draws attention to a frequently overlooked sector of urban rental housing markets – the small rental properties in which a majority of U.S. renters reside – and a historically understudied group of urban actors – the small and modestly-scaled landlords who own and operate a majority of these properties. Chapter One describes the legal characteristics of rental housing ownership across and within the 50 largest metropolitan areas in the United States. Findings document that the use of non-traditional ownership configurations – particularly those involving the use of limited liability companies (LLCs) – is increasingly commonplace in urban rental markets, particularly within predominantly Black neighborhoods. In real estate markets, entity-based ownership structures are typically associated with the activity of financialized, large-scale landlords. However, results from Chapter One make clear that these tools are also widely used by small and modestly-scaled landlords, a significant shift in the legal structure of housing ownership. Chapter Two investigates whether the additional protections afforded to landlords by LLC ownership might facilitate or accelerate housing disinvestment. Findings from Milwaukee, WI, indicate that signs of housing disinvestment increase when properties transition from individual to LLC ownership. This increase is not explained by selection on property characteristics or by divergent pre-transfer trends, suggesting that legal structures for ownership which circumscribe risks for real estate investors may generate unintended costs for tenants and cities. Chapter Three describes heterogeneity and change in the spatial structure of rental housing ownership and considers potential implications for housing conditions. Real estate investing has been – and remains – a highly local activity. Accordingly, the spatial relationship between landlord and property has rarely been the focus of empirical study, despite the emergence of new legal and technological tools that have made it easier to own and operate rental housing from afar. Findings from Boston, MA, indicate rising levels of absentee ownership and modest decreases in landlord proximity among absentee owned properties. Yet findings also indicate that for small property owners, even modest decreases in owner proximity may be associated with diminished property upkeep. Results suggest that sophisticated investors and landlords who possess stronger neighborhood ties may be better equipped or more motivated to overcome obstacles to property upkeep imposed by greater distance. Observed nonlinearities in the relationship between owner proximity and property upkeep raise new questions about the character of small property ownership and affirm that more granular measures of landlord characteristics may reveal potentially important aspects of the urban landscape. Together, these analyses clarify legal and spatial structures that undergird rental housing ownership. Findings from this dissertation indicate that even among small and modestly-scaled investors, the legal and spatial relationships that connect owner and property are changing. Such changes are likely to have continued consequences for landlords, tenants, and cities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.221
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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