Lead-to-Lease Funnel Review

Lead-to-Lease Funnel Review

Workflow purpose

Diagnose each stage of the property's demand-conversion funnel. Compare actuals against overlay-driven target bands. Isolate the binding constraint (lead volume, response, tour conversion, approval friction, or move-in conversion), surface fair-housing and screening-policy flags, and propose concrete actions scoped to the role that invoked the workflow.

Trigger conditions

• Explicit: "run funnel review", "weekly leasing funnel", "tour conversion drop", "pipeline audit", "why aren't we leasing".
• Implicit: leased_occupancy below band for two consecutive weeks; notice_exposure above band; lead_response_time median breaches SLA; step-to-step fall-off in CRM changes materially week over week.
• Recurring: weekly for every property at lifecycle in [lease_up, stabilized, renovation].

Inputs (required / optional)

Outputs

Required context

Router must resolve: asset_class, segment, form_factor, lifecycle_stage, management_mode, role, and at minimum a market. If market is missing, workflow asks before running any market-relative metric.

Process

1. Snapshot the funnel (T7 and T30). Compute, in order, lead_response_time (median and p95), tour_conversion, application_conversion, approval_rate, move_in_conversion. Add the resulting state metrics: leased_occupancy, preleased_occupancy, notice_exposure.
2. Compare to target bands. Pull reference/derived/role_kpi_targets.csv; apply any org overlay override. Color-code each stage within/below/above band. Note the reference as_of_date.
3. Identify the binding constraint. Walk stages in order; the first metric materially outside band (per overlay's materiality threshold) is the binding constraint. If multiple stages are outside band, rank by leverage on leased_occupancy.
4. Fair-housing guardrail scan.
   • Scan CRM notes, Lead.preferences, marketing copy for the protected-class term list. Any hit opens an advisory line; pattern hits open approval_request row 3.
   • Compute approval_rate trend vs. the trailing 90-day baseline. Statistically meaningful disparity flags for human review (never an autonomous adverse-action decision).
   • Confirm every ApprovalOutcome in the sample cites a policy_ref. Missing policy_ref is a screening-drift finding.
5. Concession scan. Compute concession_rate on new leases and compare to the concession policy overlay ceiling. Any lease at or above the ceiling is listed; any lease above policy opens an approval_request row 13 or verifies an existing one.
6. Source-mix attribution (if cost data present). Join leads and tours to marketing source; compute lead-volume share and tour-conversion by source. Route high-cost / low-conversion sources to the leasing manager for reallocation.
7. Diagnose with branches.
   • If lead_response_time median above SLA -> recommend staffing / coverage review, evaluate autoresponder posture, and flag the top response-time outliers.
   • If tour_conversion below band with response-time green -> recommend tour-quality review, shop-call / mystery-shop pull, amenity walk, comp audit.
   • If application_conversion below band -> recommend pricing review against market_to_lease_gap, concession-posture recheck, friction audit on the application step itself.
   • If approval_rate swing > overlay materiality vs. baseline -> route to fair-housing review and screening-policy confirmation; do not propose an ad-hoc change.
   • If move_in_conversion below band -> recommend post-approval friction review (unit readiness, keys, welcome package), review make_ready_days in coordination with workflows/unit_turn_make_ready.
8. Propose action list. Each action names an owner, a due date, and the approval gate it must pass (if any). Mark draft_for_review for any resident-facing or vendor-facing communication.
9. Surface confidence banner. List every reference cited with as_of_date and status (sample / starter / live). Outputs never cite sample data as operating fact without the tag.

Metrics used

lead_response_time, tour_conversion, application_conversion, approval_rate, move_in_conversion, leased_occupancy, preleased_occupancy, notice_exposure, concession_rate. market_to_lease_gap as a supporting metric when market rent reference present.

Reference files used

• reference/normalized/market_rents__{market}_mf.csv
• reference/normalized/concession_benchmarks__{market}_mf.csv
• reference/normalized/occupancy_benchmarks__{market}_mf.csv
• reference/derived/role_kpi_targets.csv
• reference/derived/funnel_conversion_benchmarks__middle_market.csv
• overlays/segments/middle_market/service_standards.md (concession policy pointer)

Escalation points

• Fair-housing hit (term-list match, disparity signal, missing policy_ref) routes to regional_manager and opens an approval_request row 3 before any resident-facing response is drafted.
• Proposed concession above policy opens approval_request row 13.
• If the binding constraint is a screening-policy gap, the workflow hands off to the regional_manager and the workflows/renewal_retention / screening-policy review path rather than proposing changes.

Required approvals

• Any concession action beyond policy (row 13).
• Fair-housing-flagged dispute (row 3) before public-facing response.

Failure modes

1. Treating "occupancy" as a single number. Fix: always surface physical_occupancy, leased_occupancy, economic_occupancy together; this workflow uses leased as the state reference.
2. Diagnosing from last week only. Fix: T7 and T30 side by side; weight T30 for structural signal.
3. Recommending concession as a first-line remedy. Fix: concessions are a last resort, always route above policy.
4. Missing the fair-housing scan because the funnel "looks normal". Fix: the scan is mandatory every run, not conditional on funnel state.
5. Recommending a screening policy change to close the funnel. Fix: screening criteria live in the screening_policy overlay; workflow never authors ad-hoc screening criteria.
6. Source-mix narrative without cost data. Fix: if marketing spend absent, note the gap and propose to capture cost data rather than inferring CPL.

Edge cases

• Lease-up stage: band targets pulled from lease_up overlay, not stabilized. stabilization_pace_vs_plan is the primary state metric; funnel is the mechanism.
• Renovation stage, unit mix shifting: exclude classic-to-renovated unit transitions from the market_to_lease_gap calc; note the exclusion in the output.
• Very small property: if weekly sample is below minimum_lead_volume (from overlay), widen to T30 and annotate low-sample confidence.
• TPM-managed property: the workflow produces owner-oversight view and TPM-facing data request if data is missing; never bypasses the TPM.
• New property (no 90-day history): disparity scan uses portfolio baseline; confidence: low banner applied.

Example invocations

1. "Run the weekly funnel review for Ashford Park; flag anything below band."
2. "Tours are down at Willow Creek. Diagnose which funnel stage is breaking and propose actions."
3. "Build a May-1-2026 funnel scorecard for the South End portfolio (three assets) with source mix attribution."

Example outputs

Output — Funnel review (abridged, week ending 2026-04-12, Ashford Park)

Funnel KPI table.

Binding constraint. tour_conversion on the T30 view, dragging leased_occupancy below band.

Actions.

• Tour-quality audit this week (owner: leasing_manager; due: Friday; no approval gate).
• Mystery-shop pull (owner: regional_manager; due: Friday; no approval gate).
• Comp audit invoked via workflows/market_rent_refresh (owner: property_manager; due: 10 days; no approval gate).
• No concession action proposed.

Fair-housing scan. No term-list hits. approval_rate within baseline tolerance. All ApprovalOutcome rows in sample cite policy_ref.

Approval requests opened. None.

Confidence banner. funnel_conversion_benchmarks__middle_market.csv@as_of=2026-03-31, status=sample. market_rents__charlotte_mf.csv@as_of=2026-03-31, status=sample. Funnel data live CRM through 2026-04-12 08:00 local.