Senior Living PPC Strategy & Policy Guide

Section 01

Legal Foundation — The Fair Housing Act

All three major ad platforms — Google, Microsoft/Bing, and Meta — built their senior living advertising restrictions in direct response to the same piece of federal law: the Fair Housing Act of 1968. Understanding the law's exact language, its implementing regulations, and how courts have interpreted it is essential context for every decision you make in a senior living ad account.

The Statute: 42 U.S.C. § 3604

The Fair Housing Act (Pub. L. 90–284, April 11, 1968, as amended by the Fair Housing Amendments Act of 1988) is codified at 42 U.S.C. §§ 3601–3619. The advertising prohibition is in subsection (c):

42 U.S.C. § 3604(c) — The Advertising Prohibition

"To make, print, or publish, or cause to be made, printed, or published any notice, statement, or advertisement, with respect to the sale or rental of a dwelling that indicates any preference, limitation, or discrimination based on race, color, religion, sex, handicap, familial status, or national origin, or an intention to make any such preference, limitation, or discrimination."

Source: U.S. House Office of the Law Revision Counsel, uscode.house.gov (current through Feb. 8, 2026)

The phrase "cause to be made, printed, or published" is critical: it means an advertiser who uses a platform's targeting tools to deliver discriminatory advertising is liable — not just the publisher. A housing advertiser who sets up a Facebook campaign excluding 65+ users has "caused" a discriminatory advertisement to be published.

HUD's Implementing Regulations: 24 C.F.R. § 100.75

HUD implemented § 3604(c) through regulations at 24 C.F.R. § 100.75. Subsection (a) tracks the statute directly and adds that "the use of words, phrases, photographs, illustrations, symbols or forms which convey that dwellings are available or not available to a particular group of persons" constitutes a violation.

24 C.F.R. § 100.75(a) — HUD Implementing Regulation

"It is unlawful to make, print or publish, or cause to be made, printed or published, any notice, statement or advertisement with respect to the sale or rental of a dwelling which indicates any preference, limitation or discrimination because of race, color, religion, sex, handicap, familial status, or national origin."

Source: Code of Federal Regulations, Title 24, Part 100.75(a)

The "Ordinary Reader / Listener" Standard

Courts interpret § 3604(c) using the "ordinary reader or listener" standard: an advertisement violates the FHA if it would indicate discrimination to an ordinary person who sees or hears it — regardless of whether the advertiser intended to discriminate. Intent is not a defense.

Rodriguez v. Village Green Realty, Inc.

788 F.3d 31, 41, 52–54 (2d Cir. 2015)

Applied the ordinary reader standard to real estate advertising. The defendant's subjective intent was irrelevant; the court examined what an ordinary member of the public would understand the advertising to communicate.

Corey v. Secretary, Dep't of Housing & Urban Dev.

719 F.3d 322, 326 (4th Cir. 2013)

The Fourth Circuit confirmed that § 3604(c) violations do not require proof of discriminatory intent. The statute is violated when the advertisement indicates a preference to an ordinary listener, full stop.

White v. Dep't of Housing & Urban Dev.

475 F.3d 898, 905–06 (7th Cir. 2007)

The Seventh Circuit extended the ordinary reader standard to visual advertising, holding that photographs and imagery can convey discriminatory preferences independent of the text of an advertisement.

Fair Hous. Council v. Roommates.com, LLC

521 F.3d 1157, 1166–75 (9th Cir. 2008) (en banc)

The Ninth Circuit held that the FHA applies to online housing search websites that structure their interfaces to require discriminatory preferences. This is the key precedent for platform liability in digital advertising.

Why this matters for digital platforms

The Roommates.com decision established that online platforms which structure their tools to facilitate discriminatory housing advertising bear liability under § 3604(c). This is the direct legal pressure that caused Google, Meta, and Microsoft to restrict demographic targeting for housing advertisers — the platforms changed their products to avoid liability for discriminatory delivery that their own algorithms would otherwise have facilitated.

Disparate Impact Liability

The Supreme Court confirmed in Texas Dep't of Housing & Community Affairs v. Inclusive Communities Project, Inc., 576 U.S. 519 (2015) that the FHA prohibits practices with a discriminatory effect, not just discriminatory intent. This means that an ad targeting system which produces a disparate demographic outcome — even if the advertiser had no discriminatory intent — can still violate the Act.

HUD's 2023 Discriminatory Effects Rule (24 C.F.R. § 100.500) formalizes this framework. It is what makes algorithmic ad delivery a housing compliance issue: if a platform's delivery algorithm systematically excludes Black residents, Latino families, or disabled adults from seeing senior living ads, the advertiser may be liable for the algorithm's outcomes.

Penalties for Violations

$21,663

Max fine per violation

First-time civil penalty

$54,157

Max fine, 2nd violation

Within 5-year window

$108,315

Max fine, 3+ violations

Pattern of violations

+$$$

Private damages

Compensatory + punitive

Beyond civil penalties, HUD can seek injunctive relief and refer matters to the Department of Justice for pattern-or-practice prosecutions. Private plaintiffs can also sue directly under 42 U.S.C. § 3613 and recover compensatory damages, punitive damages, and attorney's fees.

Senior Living & the 55+ Exemption — What It Covers

The Housing for Older Persons Act of 1995 (HOPA) created a limited exemption from the FHA's familial status (children) protections for communities where at least 80% of occupied units have one resident 55 or older. This allows 55+ communities to lawfully exclude families with children from residency. It does not create any exemption from the advertising provisions of § 3604(c). Senior living communities remain fully subject to housing advertising policy regardless of whether they qualify for the HOPA exemption.

Section 02

Google Ads — Housing & Employment Categories (HEC)

Google introduced its Housing, Employment, and Credit (HEC) policy in October 2020 following a 2019 settlement between HUD and Facebook that established platform liability for discriminatory ad delivery. Senior living is automatically classified under the Housing category. The policy restricts both targeting capabilities and certain ad formats.

What Google Restricts

Restriction	What It Means	Impact on Senior Living
No demographic audience targeting	Cannot target or exclude audiences by age, gender, parental status, or household income	Cannot target adult children 45–65 or seniors 70+; cannot exclude under-35
No ZIP code location targeting	Cannot add ZIP code location targets or audience segments	Must use city, region, or radius targeting only
No income-based audiences	Google's household income segments (top 10%, 11–20%, etc.) are unavailable	Cannot skew delivery toward high-income households
No demographic bid adjustments	Cannot set bid modifiers by age group, gender, or parental status	Algorithm optimizes equally across all demographics
No Google-hosted lead form assets	Lead form extensions/assets trigger additional HEC review and restrict delivery	Confirmed: Lone Tree search campaigns blocked for 33 days by a lead form asset
No similar audiences from housing lists	Cannot create lookalike audiences seeded from a housing advertiser's customer list	Remarketing audience expansion is severely limited
No Customer Match exclusions	Cannot upload a CRM list to exclude current residents from ads	Existing residents will continue to see your ads

What Google Does NOT Restrict

Keyword targeting — you can bid on any search term
Geographic targeting at city, region, or radius level
Device targeting and bid adjustments by device
Ad scheduling (dayparting)
Sitelinks, callouts, structured snippets, and image extensions
Smart Bidding strategies (Target CPA, Maximize Conversions)
Remarketing audiences for bid adjustment (not exclusion or targeting)

Real Incidents from the ESL Portfolio

ZIP Code Incident — November 2025

254 Gallery ZIP code targets and 63 Reserve ZIP code targets were added on November 21, 2025. The Gallery ZIPs were removed on November 26 (5 days). Simultaneously, 71 negative housing site exclusions were added — the combination is consistent with a Google HEC policy flag delivered through the Ads UI. The Reserve ZIPs were not removed until January 13, 2026 — 53 days of potential policy exposure.

Rule: Never use ZIP code targeting in any senior living campaign. Set geo targets to city or radius from day one.

Lead Form Asset Blocked Delivery — December 2025 – January 2026

A Google-hosted lead form asset was added to all three Reserve Lone Tree search campaigns at launch on December 17, 2025. Google's system classified the lead form as a housing-adjacent lead capture mechanism subject to HEC restrictions, triggering restricted delivery. The asset was not removed until January 20, 2026 — 33 days of blocked or restricted ad delivery at launch of a new community.

Rule: Never use Google-hosted lead form assets on senior living campaigns. Use website-based GA4 form submission conversions instead.

Demographic Bid Adjustments — July 2025

+900% bid adjustments were added to the 65+ age segment and Top 10% household income segment across all four Display Lone Tree ad groups, combined with -90% adjustments on under-35 and lower income brackets. The adjustments were removed the following morning across 4 sessions in 17 minutes — consistent with an automated HEC policy flag. Under Google HEC policy, age and income bid adjustments on housing-classified campaigns are explicitly prohibited. Even a one-day exposure can trigger a policy strike on the account.

Rule: Do not use age or income bid adjustments on any senior living campaign.

Section 03

Microsoft / Bing Ads — Housing Policy

Microsoft Advertising mirrors Google's HEC policy, having adopted comparable restrictions in 2022 following HUD guidance and legal pressure. Bing's policy implementation is less automated than Google's — enforcement is slower and more manual — but the substantive restrictions are identical in scope.

What Bing Restricts

Restriction	Notes
No demographic targeting by age, gender, or income	Same as Google — applies to both targeting and exclusions
No ZIP code audience targeting	City and radius targeting are allowed
No income-based LinkedIn audience segments	Bing's unique LinkedIn Profile Targeting cannot be used for income, job seniority, or company size in housing contexts
No custom audience exclusions based on demographics	Cannot exclude segments built on age, income, or other demographic data
No remarketing exclusions	Cannot exclude existing residents using a CRM upload

Bing-Specific Issues We Saw in Practice

No Tracking Templates Until January 2026

The Gallery Bing account had no tracking templates configured for months — all Bing traffic had zero UTM parameters and was invisible in analytics. This was discovered during a January 14 account rebuild. Unlike Google, Bing does not enforce or validate tracking templates automatically.

Rule: Manually verify UTM tracking templates are correct at launch. Add this to every launch checklist.

Wrong Campaign Names in Tracking Templates

North Port Bing tracking templates were configured with campaign names referencing Port Orange and Cape Coral — the source campaigns from which North Port was cloned. This mislabeled all North Port Bing traffic in the CRM and analytics, making it impossible to accurately attribute North Port leads.

Display Ad Disapprovals — January 2026

Port Orange CAT and Fort Collins CAT responsive display ads were disapproved on January 14, 2026, the same day as the Bing account rebuild. These were not resolved within the review window. Bing's editorial review is typically 1–3 business days for standard ads and longer for display creatives with policy flags.

Why Bing is worth the effort

Despite these operational challenges, Bing delivered the highest residency rate of any paid channel in our CRM data: 25% of Bing leads became residents, versus 19% for Google and 3.8% for Meta. The Bing audience skews older — it is the default browser on Windows devices and overrepresents users 55–75, which aligns well with senior living adult-child searchers. Allocate 15–20% of total search budget to Bing and set it up correctly from day one.

Section 04

Meta — Special Ad Category: Housing

Meta has the most complex and consequential set of restrictions for senior living advertisers. The backstory: in 2019, HUD filed a formal fair housing complaint against Facebook, alleging that its Custom Audiences and Lookalike Audiences tools, combined with its demographic targeting system, enabled advertisers to exclude protected classes from housing advertising. Facebook settled with the National Fair Housing Alliance and a coalition of civil rights organizations. The settlement required Facebook to create the Special Ad Category system and the Special Ad Audience tool.

The 2019 HUD Charge Against Facebook

"Facebook mines extensive data about its users and then charges advertisers to use that data to target their ads. Facebook has the ability to discriminate based on a click — and to do so instantly and almost invisibly. When a housing provider uses Facebook's advertising platform, it is Facebook that ultimately decides who sees the ad. Facebook is the last line of defense when it comes to preventing illegal advertising discrimination."

Source: HUD Secretary Ben Carson, March 28, 2019 (HUD Charge, FHEO Case No. 01-18-0323-8)

How Special Ad Category: Housing Changes Targeting

Feature	Standard Targeting	Special Ad Category: Housing
Age targeting	13–65+ with any range	18–65+ only — cannot restrict to 65+ or exclude under-35
Gender targeting	Male, Female, or All	All genders only — no gender targeting or exclusion
ZIP code targeting	Allowed	Not allowed — 15-mile minimum radius for all placements
Detailed interest targeting	Full Facebook interest graph (~2,000+ interests)	Severely restricted — AARP, retirement, caregiver, Medicare interests unavailable
Behavioral targeting	Full behavioral targeting available	Most behavioral categories removed
Lookalike audiences	Standard 1–10% similarity	Special Ad Audience: geography + behavior only, no demographic similarity
Customer exclusions	Upload CRM list, exclude existing contacts	Cannot exclude existing residents from housing ad delivery
Income targeting	Household income tiers available	Not available

Understanding the Two Meta Error Types

Error Type 1: Classification Notice (Expected — Not a Violation)

"It looks like your ad promotes housing. This falls under our Discriminatory Practices Policy and some additional rules apply."

This is automatic Special Ad Category classification. It is not a disapproval — it activates the restricted targeting ruleset. Action: confirm targeting is compliant and proceed. This will appear on every senior living campaign.

Error Type 2: Discriminatory Content Disapproval (Requires Action)

"It looks like your ad might include discriminatory content. This goes against our Advertising Standards on discriminatory practices."

This is an actual disapproval. The ad stops serving immediately and does not recover automatically. Triggered by: age-specific copy ("for seniors 65+"), ZIP code geo targets, restricted interest targeting, or ad imagery depicting exclusively elderly subjects.

Many are over-flags that can be disputed and reversed within 24–48 hours — but disapprovals arrive disproportionately on weekends when no one monitors the account. Set up Meta email alerts for ad disapprovals.

Meta Targeting Triggers to Avoid

ZIP code targeting in any ad set geo targeting field
Age range restricted to 65+ or 70+
Interest targeting that implies demographic screening: "AARP," "retirement planning," "Medicare," "senior care"
Ad copy using age-specific language: "for seniors 65 and older," "55+ community"
Images depicting exclusively elderly subjects (sometimes flagged as discriminatory visual targeting)
Boosted posts mentioning housing or senior care without checking Special Ad Category classification

Meta Campaign Structure That Works

What ESL data shows about Meta performance

The Gallery Fort Collins prospect data (240 records) shows Social Media has a 3.4% deposit rate — the lowest of any digital channel. 69% of social media leads end up in Denial stage. Despite this, Meta generated 58 leads out of 240 total (24%) and contributed to overall pipeline volume.

The right way to measure Meta: measure on pipeline contribution and cost per lead, not on the same short-term conversion benchmarks as search. Meta leads require longer nurturing cycles and more CRM touchpoints before they reach Planning or Action stage.

Use Consolidated campaigns (lifestyle + services creative combined) — outperforms separate campaign types in our portfolio
Objective: Lead Generation with instant forms — not Traffic, not Awareness, not Boosted Posts
Minimum radius: 20–25 miles for suburban communities, 15 miles (policy minimum) for urban markets
Grand Opening and Event awareness campaigns produce zero leads — confirmed $2,499 spend, zero leads in Reserve Lone Tree
Remarketing CPL is typically 3–4x higher than prospecting in smaller markets

Section 05

HUD 2024 AI & Algorithmic Advertising Guidance

On May 2, 2024, HUD released guidance explaining how the Fair Housing Act applies to algorithmic advertising delivery on digital platforms. This is the most significant development in fair housing advertising law since the 2019 Facebook settlement and directly addresses the AI-driven ad delivery systems used by Google, Meta, and Microsoft.

HUD Press Release, May 2, 2024 — Acting Secretary Adrianne Todman

"Housing providers, tenant screening companies, advertisers, and online platforms should be aware that the Fair Housing Act applies to tenant screening and the advertising of housing, including when artificial intelligence and algorithms are used to perform these functions."

Source: HUD Press Release No. 24-098, archives.hud.gov (issued pursuant to President Biden's Executive Order on AI, Oct. 30, 2023)

Key Legal Positions in the HUD Guidance

1. Algorithmic delivery that excludes protected classes violates the FHA — even without intent. HUD stated that "algorithmic delivery functions may operate to exclude protected groups from an ad's audience" without the advertiser's direction or knowledge. Because § 3604(c) applies to discriminatory advertising outcomes regardless of intent (the "ordinary reader" standard), an algorithm that systematically underdelivers housing ads to Black neighborhoods or to users with disabilities can generate FHA liability.

2. The housing provider is liable for the platform's algorithm. HUD applied the "cause to be made, printed, or published" language of § 3604(c) to algorithmic delivery: the housing advertiser who chooses to advertise on a platform bears responsibility for ensuring the platform's delivery does not produce discriminatory outcomes, even when the advertiser had no specific control over the algorithm's behavior.

3. "Mirror" lookalike audiences built from housing data are high-risk. HUD specifically called out that lookalike audiences — audiences created to match the characteristics of existing customers — may violate the FHA when the source customer data reflects historical discriminatory occupancy patterns. If a senior living community's current residents are demographically homogeneous (as many are), building a lookalike audience from that data could perpetuate the existing demographic exclusion.

4. Price discrimination through algorithmic ad delivery is a violation. HUD warned that algorithmic targeting can produce different pricing information for different demographic groups — for example, if a senior living community's ads reach high-income areas first and show introductory pricing that is not shown to lower-income areas. This constitutes a discriminatory term or condition under 42 U.S.C. § 3604(b).

HUD's Recommended Practices for Advertisers

Obtain disclosures from the advertising platform about how it mitigates discriminatory ad delivery risk
Follow platform instructions to correctly identify housing ads (enabling housing-specific policy treatment)
Carefully analyze the composition of audience datasets used in targeting to ensure they don't create discriminatory audiences
Monitor campaign outcomes for evidence of discriminatory delivery patterns

What this means in practice for senior living PPC

The 2024 HUD guidance confirms that senior living advertisers cannot treat platform restrictions as purely bureaucratic inconveniences. They are legally mandated compliance requirements with real enforcement consequences. An advertiser who circumvents Google's HEC restrictions (e.g., by using ZIP code audiences or age-based bid adjustments) is not just risking a platform policy flag — they are potentially violating federal civil rights law and exposing the senior living operator to FHA liability.

The guidance also confirms that demographic keyword targeting — which the platforms do not restrict — is the legally compliant substitute for demographic audience targeting. A searcher who types "[city] assisted living" has self-selected their intent and geography without requiring the advertiser to profile them by age, income, or race.

Section 06

Conversion Tracking & CRM Integration Failures

Platform policy compliance gets the most attention, but in practice, conversion tracking failures have caused more measurable damage to ESL's PPC performance than any policy issue. When Smart Bidding runs without conversion signal, the algorithm optimizes toward clicks — not leads — and CPAs balloon.

4+

Months blind

Google tracking inactive Sep 2025–Jan 2026

97

Missing leads

North Port Zapier never created at launch

25

Lost (expired auth)

Cape Coral 11, Falls Church 8, others

4×

Duplicate counting

Up to 4 conv. actions per form fill

Best Practice: Three-Layer Conversion Architecture

Layer 1 — GA4 form submission event: The primary conversion action imported into Google Ads. Fires at browser level before any downstream integration. Most reliable first-party signal. Never use Google-hosted lead forms (policy risk) as a substitute.
Layer 2 — Meta Pixel + Conversions API (CAPI): Implement server-side CAPI alongside the standard Pixel. Maintains measurement accuracy as Safari and Firefox block third-party cookies. CAPI sends conversion data from the server, independent of browser tracking.
Layer 3 — Zapier / CRM flow monitoring: The Zapier integration is for CRM, not for ad platform optimization. Check Zapier authorization token status monthly. Set up Zapier email alerts for failed zaps so broken flows are caught within minutes.

Duplicate Conversion Actions

Both Gallery and Reserve had multiple conversion actions enabled simultaneously, recording the same form submission up to 4 times. This inflates reported conversions, causes Smart Bidding to believe it is performing better than reality, and leads to systematic underbidding as the algorithm targets a CPA that is artificially low. Audit all active conversion actions quarterly and remove duplicates before they accumulate months of misleading data.

Section 07

Other Operational Limitations

Performance Max & HEC Policy

Google Performance Max campaigns optimize across all inventory (Search, Display, YouTube, Discover, Maps, Gmail) using AI. For senior living advertisers under HEC policy, PMax presents specific structural risks: the audience signals most useful for senior living cannot be used, keyword exclusion control is limited compared to standard Search campaigns, and PMax has been observed serving ads on YouTube and Gmail where conversion rates are very low but spend attribution is opaque. Do not use PMax for senior living until HEC policy guidance for PMax audience signals is clarified.

Google Recommendations — Do Not Bulk Apply

In December 2025, bulk-applying Google Recommendations reversed six weeks of deliberate keyword pauses and placed keywords in wrong ad groups, requiring a second full remediation sweep in February 2026. Google Recommendations are optimized for Google's revenue, not the advertiser's CPA. Disable auto-apply for all Recommendation types on senior living accounts and review each Recommendation manually before applying.

Automation Account Risk

The Gallery Port Orange "senior living" keyword was paused by an automation account on October 13, 2025 and not re-enabled for 113 days. The Reserve Lone Tree NB and Branded campaigns were paused 4 times in a single day by automation. Any script or automation account that can pause keywords must have a protected keyword override: a list of 3–5 core terms per community (branded exact, [city] assisted living, senior living phrase) that cannot be paused without explicit human approval and immediate email notification.

Third-Party Cookie Deprecation

Safari and Firefox have blocked third-party cookies since 2020. Adult children 50–65 — the primary senior living conversion audience — over-index on Apple devices and Safari. This means remarketing audiences built on third-party cookies are already significantly smaller for senior living than for most industries. Implement Google Enhanced Conversions, server-side GA4 tagging, and Meta CAPI to maintain measurement accuracy as browser tracking continues to erode.

Section 08

Keyword Strategy

Platform policies remove demographic targeting — but they do not restrict keyword targeting. This creates an important asymmetry: the targeting work that demographics would normally do must be done through keywords instead. A searcher who types "[city] assisted living" has self-identified their intent, geography, and care interest without requiring any restricted audience signal.

The Three-Tier Stack

Tier 1

Branded

$22–$43

CPA range

Community name exact and phrase. Never pause, never budget-cap. Consistently 3–8× more efficient than any other keyword category.

Exact + Phrase

Tier 2

Location-Specific LOC

$16–$100

CPA range

[City] + care type. QS 7–10 across all markets. The highest-ROI non-branded category. Fund fully before Tier 3.

Exact primary · Phrase secondary

Tier 3

Generic NB

$70–$200

CPA range

Generic NB and competitor names. Phrase match only — never broad. Fund after Tiers 1 and 2 are fully covered.

Phrase only — no broad

Keywords to Bid On

Tier 1 — Branded (exact + phrase for each)

[community name] "community name senior living" "community name assisted living" [community name city] "community name independent living"

Tier 2 — Location-Specific LOC (exact primary)

[city assisted living] [city senior living] [city independent living] [city memory care] [city retirement communities] [retirement homes city] [city assisted living]

Tier 3 — Generic NB (phrase match only)

"senior assisted living" "assisted living" "senior living" "assisted living near me" [exact] "senior care home" "competitor community name" "senior living near me"

Never bid on these

senior living [broad] [state] assisted living "senior housing" "luxury senior" [any match] nursing home [primary NB]

Match Type Rules

Match Type	Use Case	Rule	Why
Exact	Branded, city LOC terms	Always include alongside phrase	Tightest control, lowest CPC, highest QS
Phrase	LOC and NB terms	Default for all non-branded	Captures intent variants without broad waste
Broad	Brand name only (sparingly)	Avoid on NB terms	Gallery data: broad match NB = $2,248 CPA possible
Broad Match Modified	N/A	Do not use	Deprecated by Google; use phrase instead

Section 09

Channel Recommendations

These residency rates are derived from 269 Gallery CRM leads with Resident, Depositor, Pending Move-In, and Active statuses. A "residency rate" is the percentage of leads from that channel that became Residents, Depositors, or Pending Move-In.

Google Search

19%

Residency rate

Primary lead gen — highest intent
Fund branded first, then LOC, then NB
Target CPA bidding with GA4 conversion import
No lead form assets, no ZIP code targeting

Bing Search

25%

Residency rate

Highest residency rate of any paid channel
Older Windows-default demographic
Mirror Google keyword structure exactly
Set tracking templates before launch

Prospect Data — The Gallery at Fort Collins

240 prospect records exported from the Gallery Fort Collins CRM (Active + Depositor statuses). This is the most complete view of who moves from first inquiry to financial commitment at a single Gallery community.

Who the Prospects Are

81.8

Avg prospect age

Range 55–96

79.0

Avg depositor age

Range 65–91

58%

Assisted Living inquiries

Dominant care type

42.5%

IL depositors

Care type among depositors

Source Deposit Rates

Source	Leads	Depositors	Deposit Rate	Assessment
Resident Referral	2	2	100%	Best source — maximize referral program
Drive By / Signage	4	3	75%	High intent — already physically nearby
Print Advertising	14	9	64%	Local print significantly outperforms digital
Company Website	59	11	18.6%	Best digital channel — search-driven
Internet (Roobrik/Catalyst)	22	4	18.2%	Pre-qualification tools perform like search
Professional Referral	67	4	6.0%	Highest volume, lower conversion
Social Media (Meta)	58	2	3.4%	High volume, 69% Denial rate

The Care Type Paradox

Assisted Living represents 58% of inquiries but only 22.5% of depositors. Independent Living is 13% of inquiries but 42.5% of depositors. Assisted Living leads are typically more urgent (family-driven) and experience faster disqualification — health not matching, price objection, or placement elsewhere. Independent Living prospects self-select more deliberately and convert at higher rates once engaged.

PPC implication: Do not optimize purely for Assisted Living lead volume. Independent Living terms may produce fewer leads but better-quality deposits. Run both, but measure each against deposit rate, not raw lead count.

Prospect Stage Conversion

Stage	Count	Depositors	Deposit Rate	CRM Priority
Thinking	114	25	21.9%	Primary nurture target — largest stage
Denial	95	1	1.1%	Re-engagement only
Planning	16	9	56.3%	Accelerate tour scheduling immediately
Assess	8	0	0%	Care qualification stage
Action	7	5	71.4%	Closing stage — highest urgency

Section 11

Launch & Maintenance Checklist

Before Launch — Every Campaign

No Google-hosted lead form assets — use GA4 website form conversions only

Geo targeting set to city or radius (15–25 miles) — zero ZIP codes on any platform

No demographic bid adjustments — no age, gender, or income modifiers

UTM tracking templates set on all campaigns (Google + Bing) before first spend

Zapier / CRM flow created, tested end-to-end before launch day

Meta email alerts enabled for ad disapprovals

Google Recommendations auto-apply disabled

No duplicate conversion actions enabled — audit before launch

Branded sitelinks active: Assisted Living, Independent Living, Memory Care, Schedule a Tour

Protected keyword list created: branded exact, [city] assisted living, senior living phrase

Monthly

QS audit — flag all keywords below QS 4 with spend >$100 in prior month

Verify protected keywords are active on all platforms

Check Zapier auth tokens have not expired

Review change history for any automation account activity touching core keywords

Pull search terms report on all phrase/broad campaigns — add negatives for irrelevant queries

Verify Bing tracking templates return correct UTMs in analytics

Quarterly

Audit all active conversion actions — remove duplicates and inactive property actions

Review Meta campaigns for housing policy compliance — confirm no age or ZIP targeting

Check for Google Recommendations that may have been applied without review

Cross-reference CRM deposit / resident data against paid sources — rebalance budgets

Review care type split in leads vs depositors — adjust keyword budget allocation

Prepared by Newfangled · March 2026 · Based on ESL portfolio data: Gallery, Reserve, Sancerre, Crossing — keyword performance Sep 2025–Mar 2026 · 269 Gallery CRM leads · 240 Fort Collins prospect records · HUD guidance and federal case law through Feb 2026.