AI and the 21st Century Housing Act: What Cities Can Do

A bipartisan housing bill doesn’t usually make municipal leaders lean in. This one should.

In mid-December 2025, the U.S. House introduced the Housing for the 21st Century Act, positioning it as a practical attempt to increase housing supply by lowering the cost and complexity of development. It borrows momentum from a recent Senate push and comes with unusually broad industry backing—because it targets the friction points that slow housing down: inspections, permitting, financing limits, and outdated program rules.

Here’s the part that matters for this topic series—“Mākslīgais intelekts publiskajā sektorā un viedajās pilsētās.” Even if the bill is federal, the hardest work happens locally: zoning interpretation, plan review queues, inspection scheduling, voucher placement, and public reporting. AI in the public sector is one of the few tools that can reduce time-to-permit and time-to-occupancy without lowering standards.

This post breaks down what the legislation is aiming for, where cities typically get stuck, and exactly how AI and data-driven governance can help public agencies deliver faster, fairer housing outcomes.

What the Housing for the 21st Century Act is really trying to fix

The core idea is simple: housing production hasn’t kept pace with demand, and the development process has become expensive and slow due to regulatory delays, inflation pressures, and zoning constraints. The bill packages 27 provisions aimed at modernizing programs, streamlining federal requirements, and increasing flexibility for state and local implementation.

For smart cities and public-sector teams, the headline isn’t “more federal policy.” It’s this:

The bill’s success depends on whether local workflows can actually move faster—with transparency.

Several provisions point directly at the bottlenecks where city operations and digital government can make or break results:

• HUD zoning guidelines and best practices (Housing Supply Frameworks Act)
• HOME program updates (HOME Reform Act of 2025)
• Public reporting on land-use barriers tied to federal funding (Identifying Barriers to Housing Supply Act)
• Faster voucher placement via streamlined inspections (Choice in Affordable Housing Act of 2025)

These are operational problems. That’s good news—because operational problems are solvable.

Where housing delivery slows down (and why AI helps)

Housing is delayed in predictable places. Most cities can point to them immediately:

1. Pre-application uncertainty (confusing rules, inconsistent interpretations)
2. Permitting and plan review backlogs (limited staff, manual checks)
3. Inspections and re-inspections (scheduling chaos, uneven documentation)
4. Interagency coordination (housing, planning, utilities, transportation)
5. Public reporting requirements (painful data collection, delayed dashboards)

AI helps when the work is repetitive, document-heavy, and governed by rules. Housing delivery is all of that.

Just as importantly, AI helps public agencies stay honest. When you automate parts of the process, you also create audit trails, consistent decision logs, and measurable service levels.

The myth: “Housing delays are only a policy problem”

Policy matters, but I’ve found the bigger limiter is often execution capacity.

You can loosen zoning and still fail to deliver if:

• reviews take 10–16 weeks because the queue is unmanaged,
• staff spend hours searching old PDFs for precedent,
• inspections are booked manually by phone,
• applicants get different answers depending on who’s on duty.

AI doesn’t replace planners or inspectors. It gives them time back and makes decisions more consistent.

AI opportunities hidden inside the bill’s provisions

If you’re working in a municipality, housing authority, or regional planning body, here are the most practical “bridge points” between the bill and AI for smart cities.

1) Zoning and land-use clarity: AI copilots for code interpretation

When HUD publishes zoning guidelines and best practices, local governments still need to translate them into day-to-day decisions.

A realistic application: a zoning copilot trained on your municipal code, adopted plans, and approved variances—able to:

• answer staff and applicant questions consistently,
• cite the relevant code sections,
• generate applicant checklists based on parcel + project type,
• flag likely variance needs early.

Answer first: This reduces rework. Rework is the quiet killer of housing timelines.

If applicants submit better first drafts, plan reviewers spend less time on corrections, and projects move forward faster without lowering standards.

2) Permitting throughput: AI-assisted plan review and intake triage

Permitting teams don’t need “fancier portals.” They need fewer incomplete applications and fewer manual checks.

AI can support:

• document classification (are all required forms present?),
• rules-based validation (setbacks, unit counts, parking minimums/maximums),
• intake triage (route complex cases to senior reviewers, simple cases to fast lanes),
• automated applicant feedback (clear, structured correction requests).

This is where e-government services become tangible. Residents and developers don’t care that your city is “digital.” They care that they get an answer quickly and can track it.

3) Inspections and vouchers: scheduling optimization + risk-based targeting

The Choice in Affordable Housing Act emphasizes streamlining inspections to get voucher holders housed faster.

That problem is tailor-made for operations research plus AI:

• route optimization for inspectors,
• dynamic scheduling based on cancellations and geographic clustering,
• risk-based inspection targeting (focus deeper checks where history shows issues),
• standardized photo and note capture with structured templates.

Answer first: Faster inspections shorten vacancy time. Shorter vacancy time increases effective supply without building a single new unit.

That’s the kind of “invisible capacity” public agencies can create in months—not years.

4) Reporting barriers to housing: automated compliance dashboards

The Identifying Barriers to Housing Supply Act pushes CDBG recipients to publicly report progress in removing onerous land-use policies.

Cities should treat this as an opportunity, not a burden.

A strong approach is to build a public-facing housing delivery dashboard that updates monthly and includes:

• median time to first plan review,
• median time to permit issuance by project type,
• inspection wait times,
• appeals/variance rates,
• geographic equity indicators (where permits are and aren’t happening).

AI helps by extracting structured data from permits, staff notes, and legacy documents—so reporting isn’t a quarterly scramble.

A practical AI playbook for cities preparing for 2026

Federal bills move at federal speed. City operations can improve faster—especially if you focus on workflows instead of big-bang platforms.

Here’s a step-by-step approach I’d recommend to public-sector teams planning for 2026 housing pressure.

Step 1: Map the housing “clock,” not the org chart

Start with one metric: days from first contact to occupancy.

Break it into segments:

• pre-application
• application completeness
• first review
• resubmittals
• permit issuance
• inspections
• certificate of occupancy
• voucher placement (if relevant)

This forces cross-department clarity. AI projects fail when they’re built for departments instead of outcomes.

Step 2: Fix data basics before you “do AI”

You don’t need perfect data, but you do need:

• consistent permit IDs across systems,
• timestamps at each workflow stage,
• a shared address/parcel reference,
• clear definitions (what counts as “complete” or “in review”).

Good data-driven decision-making starts with agreeing on what the numbers mean.

Step 3: Start with two high-ROI automations

If you do only two things, make them these:

1. Application completeness checker (reduce back-and-forth)
2. Inspection scheduling optimization (reduce idle days)

These two changes typically improve timelines without provoking political fights.

Step 4: Build transparency in from day one

Housing is political. Automation makes it more political if people think decisions are hidden.

So:

• publish service-level targets (e.g., “first review in 15 business days”),
• publish actual performance monthly,
• keep AI as “recommendation,” with human sign-off where required,
• store decision logs for audits and appeals.

Transparent AI governance is not optional in the public sector. It’s how you keep legitimacy.

Common questions public-sector teams ask (and straight answers)

“Will AI speed things up without weakening safety?”

Yes—if you use AI to reduce administrative waste, not to ignore standards. The right goal is fewer incomplete applications, faster scheduling, and more consistent checks.

“What if our zoning and permitting data is messy?”

It probably is. Start with a narrow use case (like intake triage) that tolerates imperfections. Then improve data quality as a byproduct of the workflow.

“How do we avoid bias in AI-driven housing decisions?”

Don’t use AI to decide who gets approved. Use it to:

• standardize information,
• reduce arbitrary differences between reviewers,
• surface patterns (like neighborhoods where approvals stall),
• support oversight with dashboards.

Bias loves opacity. Transparency is the antidote.

Why this matters for smart cities—not just housing agencies

Housing isn’t a silo. When supply is constrained, cities pay for it everywhere else:

• longer commutes and transport load,
• higher homelessness services demand,
• workforce shortages in essential roles,
• displacement pressures that destabilize neighborhoods.

Smart city development isn’t only sensors and mobility apps. It’s the ability to run core civic workflows efficiently, fairly, and visibly. Housing delivery is one of the clearest tests.

The Housing for the 21st Century Act sets direction. The day-to-day wins will come from cities that treat permitting, inspections, and reporting as systems to be engineered, not just forms to be processed.

If you’re building out your roadmap for AI in the public sector in 2026, here’s the question worth carrying into your next meeting: what would happen if your city cut housing cycle time by 20% without changing a single zoning line—just by fixing workflow and data?