Overview

Government housing and insurance policies often fail because they ignore systemic constraints-such as supply elasticity and risk pricing-which directly influence policy effectiveness and public trust.  Recognizing these systemic factors is essential for designing resilient policies that address root causes rather than surface symptoms.  Government employees are not concerned with root causes; instead, they focus on expending assets to ensure the next round of funding.

Below are case studies of failed or backfiring policies—not to criticize anyone, but to surface recurring systemic flaws—such as misaligned incentives and overlooked market responses—that make certain decisions predictably ill-advised and costly.

 Understanding these patterns helps policymakers avoid repeating mistakes.

I.  HOUSING: When Policy Pretends Supply Won’t React

Case Study 1 — San Francisco Rent Control Expansion (1994): “Protect Tenants” → “Shrink Supply”

What it tried to do

Rent control expansion is usually framed as an anti-displacement tool: stabilize rents for incumbent tenants and reduce forced moves. 

What happened?

A landmark study shows that rent control reduced Displacement but also reduced supply by about 15%, posing risks to long-term affordability and urging the audience to consider the broader impacts of such policies.

The paper estimates that landlords reduced the supply of affected rental housing by approximately 15% and concludes that the supply loss likely increased long-run market rents, undermining citywide affordability goals. 

Why this is ill-advised (mechanically): Rent control assumes that supply won’t reprice, reconfigure, or exit, thereby ignoring fundamental market responses that undermine its goals.

Rent control can “work” politically because benefits are concentrated and visible (protected tenants) while costs are diffuse and delayed (higher market rents, fewer units, worse entry for newcomers).

Bone-headedness isn’t the goal; it’s the tool choice that assumes supply won’t reprice, reconfigure, or exit. 

Better design principle: If you want tenant stability, adopt systemic thinking by pairing tenant protections with automatic, meaningful supply expansion, such as fast approvals, by-right infill, and reduced compliance costs, to create resilient housing policies that adapt to market responses.

Case Study 2 — Minimum Parking Requirements: “Prevent Spillover” → “Raise Housing Costs + Block Units”

What it tried to do

Many cities require developers to build a fixed number of off-street parking spaces per unit or per square foot to prevent neighborhood spillover parking and “ensure convenience.”

What the research says happens.

Donald Shoup’s classic work argues that minimum parking requirements are often set to satisfy peak demand for free parking, bundling parking costs into development costs and increasing the price of housing and local goods—even if users don’t value the parking equally.

The core problem is that these requirements reduce price signals and distort land-use and transportation decisions; Shoup recommends pricing curb parking rather than mandating off-street parking. 

Why has this become a housing failure?

The core problem is that mandated parking imposes land consumption and construction costs, which are reflected in rents and prices, thereby disproportionately harming supply-expanding projects.

Boneheaded pattern:

 A regulation intended to reduce a nuisance ends up acting like a tax on housing production. 

Better design principle:

Replace rigid minimums with:

·       Market-determined parking supply,

·       Priced curb parking,

·       Demand management—so housing isn’t forced to “buy” parking it doesn’t need. 

Case Study 3 — Public Housing Megaproject Failure:

What it tried to do

Pruitt‑Igoe was a federally funded public housing project in St. Louis (first tenants in the 1950s) intended to replace slums with modern housing. 

What happened

By the mid-1960s, it faced surging crime, rising vacancy rates, and deteriorating conditions; St. Louis began demolishing towers in 1972 and completed demolition by 1976.

HUD’s own retrospective highlights that popular “single cause” explanations (architecture alone) overlook broader drivers: economic and demographic decline, segregation dynamics, flawed planning assumptions, and a funding structure in which construction was subsidized but maintenance/operations were effectively unsupported, leaving the project financially fragile as vacancies rose.

Why this is boneheaded (structurally)

Two mistakes occurred in housing policy:

·         Overconfidence in a centralized design to solve multi-causal poverty and neighborhood decline.

·         Underfunding operations and maintenance, which guarantees physical decline and social disorder once occupancy or rent revenue falls.

Better design principle: If the government builds or subsidizes housing, the policy must treat long-run operations as part of the system, not an afterthought. 

Case Study 4 — HOPE VI: “Revitalize Distressed Housing” → Slow Delivery + Displacement Risks

What it tried to do

HOPE VI (Urban Revitalization Demonstration) funded demolition/redevelopment of severely distressed public housing into mixed-income communities and offered supportive services. 

What GAO/CRS found

GAO reported uneven progress and identified factors contributing to the slow delivery: legal issues, administrative changes, complex redevelopment plans, local opposition, coordination burdens associated with leveraged financing, and constraints on HUD oversight capacity.

CRS notes that the program was credited with replacing dangerous housing but also faced scrutiny for slow spending and for displacing low-income families, leading to political controversy and changing funding levels over time. 

Why this is boneheaded (from a systems view)

HOPE VI shows how government can “win” at physical redevelopment while “losing” on the human objective if relocation and return pathways aren’t engineered as rigorously as demolition and construction.  When program success is measured by units torn down and projects launched, displacement risk becomes a predictable blind spot. 

Better design principle: Put relocation/return outcomes on the same scoreboard as capital projects, with complex metrics and enforceable commitments. 

II.  INSURANCE: When Government Misprices Risk (or Politicizes the Price)

Case Study 5 — NFIP Flood Insurance: Subsidized Risk → Repetitive Loss + Persistent Federal Debt

What it tried to do

NFIP exists because private flood insurance markets are limited; it provides coverage and aims to reduce flood losses through mapping, pricing, and mitigation. 

What GAO found

GAO reported NFIP’s significant financial challenges, highlighted by catastrophic flood events and $20.5 billion debt to the U.S. Treasury (as of the 2020 report), with repetitive-loss properties contributing materially to fiscal exposure.

GAO also found that while FEMA-funded mitigation (including property acquisition) has addressed tens of thousands of properties over decades, the number of non-mitigated repetitive-loss properties has increased, thereby sustaining fiscal exposure.

FEMA now publishes an “NFIP Multiple Loss Properties” dataset describing structures with multiple claims, reflecting the program’s persistent concentration of repeat losses.

Why this is boneheaded (incentives)

If premiums are kept below risk (directly or indirectly), the policy can encourage continued development and rebuilding in high-risk areas while shifting costs to taxpayers and cross-subsidizing repeated losses.  That creates moral hazard and chronic pressure for insolvency. 

Better design principle: Gradual risk-based pricing paired with means-tested affordability assistance and aggressive mitigation/retreat tools—so pricing sends a signal without politically impossible “rate shock.”

Case Study 6 — California’s Homeowners Insurance Crunch: Restricted Pricing Tools → FAIR Plan Surge

(This is the same failure mode as NFIP: mispricing and delayed adjustment—except here the symptom is private-market retreat and “insurer of last resort” expansion.)

What’s happening structurally

California’s FAIR Plan (the state’s insurer of last resort) reports rapidly rising exposure and policy count.  As of September 2025, the FAIR Plan reports:

• Total exposure $696 billion (a 52% increase since Sept 2024, 317% since Sept 2021)
• Policies in force 645,987 (a 39% increase since Sept 2024, 169% since Sept 2021)
  It explicitly links this growth to fewer options in the voluntary market and “lack of adequate insurance rates,” pushing more consumers into last-resort coverage. 

Government response

In October 2025, California enacted a bipartisan package of bills to reform the FAIR Plan (financing mechanisms for faster claims payment, oversight, policyholder experience, and additional coverage categories).

Why this is boneheaded (if mispricing persists)

When risk rises and the regulatory framework resists timely repricing, insurers restrict underwriting or exit segments.  That doesn’t make risk disappear; move it to the FAIR Plan and ultimately to a broader assessment or public backstop.

The bone-headedness is treating pricing tools as “just profits” rather than the signal and capital mechanism that keeps coverage available—especially under catastrophe risk. 

Better design principle: Make risk pricing more forward-looking while using targeted subsidies and hardening incentives to avoid pricing everyone out of coverage.  (Otherwise, the “last resort” becomes the default.)

Case Study 7 (Contrast/Partial Repair) Florida Property Insurance Litigation Spiral: Legal Incentives → Market Instability

This matters because it shows how non-catastrophe policy choices (litigation rules) can overwhelm catastrophe economics.

What reforms targeted

Reporting on Florida’s market points to legislative reforms in 2022–2023 aimed at reducing litigation abuse and stabilizing the property insurance system.

Industry analysts describe reforms affecting “one-way attorney fees,” assignment of benefits, and related litigation drivers, with subsequent declines in lawsuits and improved reinsurance conditions. 

Why it belongs in a “boneheaded decisions” essay

A system that unintentionally rewards litigation volume (or claim inflation) becomes an invisible tax on every policyholder.  That’s not a hurricane problem—it’s a policy design problem. 

Takeaway for housing + insurance: the affordability crisis isn’t only “nature” or “greed.” It’s frequently a rulebook problem that either blocks supply (housing) or misprices risk and legal cost (insurance). 

III.  The Unifying Pattern: Bone-headedness = Denying Constraints

Across housing and insurance, the same denial shows up:

·         Deny supply response → rent control and parking minimums shrink/raise the cost of new supply. 

·         Deny operating economics → public housing megaprojects become fragile when maintenance funding doesn’t match reality.   

·         Deny risk pricing → NFIP debt and FAIR Plan growth are predictable when pricing and mitigation don’t match hazard exposure. 

IV.  A Practical “Less Boneheaded” Playbook (Housing + Insurance)

Housing

• Target demand-side relief without killing supply: don’t choose tools that make marginal units unprofitable to produce. 
• Remove cost-embedding mandates (e.g., parking minimums and procedural bottlenecks) and price externalities directly (e.g., curb parking and congestion). 
• Measure human outcomes in redevelopment programs (return rates, neighborhood stability), not just capital milestones. 

Insurance

• Price risk is forward-looking and transparent, while cushioning households with targeted affordability tools—not through across-the-board suppression. 
• Attack repeat-loss concentration with mitigation, buyouts, and development standards that stop building tomorrow’s repetitive-loss inventory. 
• Fix legal incentive distortions that amplify claim costs independent of hazard risk.