Monetizing Flint's Vacant Facades: Public Art and Advertisement Auctions as Blight Remediation
Bibliographic record
Abstract
Flint, Michigan, is a city that has weathered decades of population decline, economic hardship, and disinvestment, and faces a pervasive blight challenge. Recent assessments show that about 8,500 residential properties (roughly one quarter of all homes) and more than 900 commercial parcels, nearly half of the city's commercial properties, are vacant or derelict. (Bennett and Holloway, 2022). These abandoned structures depress surrounding property values, invite vandalism and crime, and strain public resources for maintenance and safety. Amid this challenge lies an opportunity to turn liabilities into assets. This paper presents an innovative proposal to transform Flint's many derelict building facades into platforms for public art and paid advertisements, with the generated revenue directly financing further blight remediation. Property owners or the City (for publicly owned buildings) would allow the exterior walls of structurally sound, high-visibility vacant buildings to be used for large murals or billboard-style displays. The rights to each facade would be allocated through a competitive auction open to advertisers and art sponsors. Winning bidders gain the right to install and display content (artwork, commercial advertising, or a combination) on a facade for a fixed term (up to about two years), after which the facade is again up for bid. By cycling through auctions every couple of years, the visual landscape remains dynamic and periodically refreshed, preventing any single installation from becoming dilapidated or stale. At its core, this project reinvests the proceeds from these facade auctions into the very properties and neighborhoods generating them, the forward-facing parts of Flint's urban landscape. Research from multiple case studies underscores the potential of such creative placemaking initiatives to drive substantial economic and social benefits (Project for Public Spaces, 2008). Moreover, broad evidence shows the arts can yield significant economic returns. In 2022, nonprofit arts and culture organizations and their audiences generated an estimated $151.7 billion in economic activity across the United States, supporting 2.6 million jobs and producing $29.1 billion in tax revenue (Brancaccio and Schroeder, 2023). Governments have taken note: states such as Minnesota, Florida, and Illinois have implemented "percent for art" programs that allocate a portion of public infrastructure budgets to public art, demonstrating confidence in the financial viability of art-driven revitalization (NASAA). Public support for such investments is likewise strong. National surveys indicate that 55% of Americans favor increasing federal arts funding, 57% favor more state arts funding, and 58% favor more local arts funding (Delaware Arts Alliance, n.d.). Within this context, the proposed facade monetization program offers a self-sustaining blight remediation mechanism that leverages private sector marketing dollars and philanthropic art sponsorships, rather than relying solely on grants or limited city budgets. The hypothesis is that a facade auction program can simultaneously achieve urban beautification, community engagement, and economic revitalization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".