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 in client accounts: search campaigns blocked for 33 days by a lead form asset at launch
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 — Documented in Client Accounts

ZIP Code Incident

Over 300 ZIP code targets were added across two client accounts simultaneously. One account's ZIPs were removed within 5 days alongside 70+ negative housing site exclusions — the combination is consistent with a Google HEC policy flag delivered through the Ads UI. The second account's ZIPs remained active for over 50 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

A Google-hosted lead form asset was added to search campaigns at a new community's launch. Google's system classified the lead form as a housing-adjacent lead capture mechanism subject to HEC restrictions, triggering restricted delivery for 33 days before the asset was identified and removed.

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

Demographic Bid Adjustments

+900% bid adjustments were added to the 65+ age segment and Top 10% household income segment across Display ad groups, combined with -90% adjustments on under-35 and lower income brackets. The adjustments were removed the following morning within 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

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 client CRM data shows about Meta performance

Senior living CRM data consistently shows Social Media has a lower deposit rate than search channels — typically 3–4% versus 18–19% for website-driven leads. The majority of social media leads also end up in an early denial stage, reflecting longer nurturing cycles rather than immediate conversion readiness.

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 — awareness objectives have no conversion mechanism under housing policy restrictions
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 cause significant measurable damage to PPC performance. When Smart Bidding runs without conversion signal, the algorithm optimizes toward clicks — not leads — and CPAs balloon.

4+

Months blind

Google tracking inactive in client accounts

97

Missing leads

Zapier flow never created at community launch

25+

Lost (expired auth)

Across multiple markets, expired Zapier tokens

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.

Section 07

Other Operational Limitations

Google Recommendations — Do Not Bulk Apply

Bulk-applying Google Recommendations has caused documented harm in senior living accounts — reversing weeks of deliberate keyword pauses and placing keywords in wrong ad groups, requiring full remediation sweeps. 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.

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

Channel Recommendations

These residency rates are derived from senior living CRM lead data covering 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

Keyword Strategy — Google vs Bing

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 analysis below draws on actual keyword performance data from both Google Ads and Bing Ads across multiple senior living accounts, covering Gallery and Reserve communities. The two platforms behave differently enough to warrant separate keyword strategies.

Platform Efficiency Summary

$31

Google branded CPA

Lowest CPA in any account

$45

Bing avg CPA

Reserve — all converting keywords

$3

Bing "near me" CPA

Best-performing Bing NB cluster

100%

Bing match type

Phrase match drives all conversions

The Three-Tier Stack

Tier 1

Branded

$31–$62

CPA range (Google + Bing)

Community name exact and phrase. Never pause, never budget-cap. Google delivers $31–43 CPA; Bing branded runs $43–94 CPA but still efficient.

Exact + Phrase

Tier 2

Location-Specific LOC

$3–$100

CPA range (Google + Bing)

[City] + care type on Google. On Bing, "near me" + care type variants achieve $3–$15 CPA — frequently outperforming Google LOC terms.

Exact primary · Phrase secondary

Tier 3

Generic NB

$19–$200

CPA range (Google + Bing)

Generic NB phrase match only — never broad. Bing NB averages $19–$50 CPA; Google NB averages $70–$200. Bing NB is significantly more efficient.

Phrase only — no broad

Google vs Bing — Side-by-Side Keyword Performance

The table below compares directly observed performance for equivalent keyword categories across both platforms. Bing CPAs are based on actual converting keywords from the account data.

Keyword Category	Google CPA	Bing CPA	Winner	Notes
Branded exact ("[community name]")	$31–$43	$43–$94	Google	Google QS 10 vs Bing lower scores; both are worth funding
[City] assisted living exact	$16–$97	$9–$50	Bing	Bing "senior care in [city]" variants hit $9 CPA
[City] senior living phrase	$37–$100	$19–$62	Bing	Bing "senior living" phrase = $19 CPA (8 conversions)
"near me" variants	$50–$150	$3–$26	Bing	Bing's strongest category — "near me" searchers are older Windows users with high intent
"senior assisted living" phrase	$70–$170	$6–$15	Bing	"assisted living near me seniors" = $6 CPA on Bing
Independent living phrases	$87–$167	$3–$42	Bing	"independent living facilities near me" = $10 CPA on Bing
"senior citizen" variants	Rarely converts	$17–$19	Bing only	Bing demographic skews older — "senior citizen" language resonates; avoid on Google
"luxury senior living [city]"	$475 CPA	$3 CPA	Bing only	"luxury senior living denver" = 7 conv, $3 CPA on Bing. Polar opposite of Google result.
Broad match NB	$700–$2,248	Avoid	Neither	Documented waste on both platforms

The Bing "Near Me" Opportunity

The single most important Bing-specific insight from the data: "near me" keyword variants on Bing convert at dramatically lower CPAs than on Google — typically $3–$26 vs $50–$150 on Google for the same or equivalent terms. Bing's audience over-indexes on Windows desktop users 55–75 who use Bing as their default browser. This demographic searches conversationally ("homes senior citizens near me," "independent living communities near me," "senior housing no waitlist nearby") and those queries convert at elite efficiency on Bing while getting lost in broad match noise on Google. Run a dedicated "near me" ad group on Bing for every community.

Google — Recommended Keywords by Tier

Tier 1 — Branded (exact + phrase)

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

Tier 2 — Location LOC (exact primary)

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

Tier 3 — Generic NB (phrase only)

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

Bing — Recommended Keywords by Tier

Mirror Google Tier 1 and Tier 2 exactly. Where Bing diverges is in Tier 3 — the "near me" and conversational variants that underperform on Google are Bing's strongest category.

Tier 1 — Branded (phrase match — Bing-specific)

"community name" phrase "community name senior living" phrase "community name assisted living" phrase

Tier 2 — LOC + "near me" (Bing's best category)

"assisted living near me" phrase "senior living near me" phrase "independent living near me" phrase "senior care in [city]" phrase "assisted living [city]" phrase "memory care near me" phrase

Tier 3 — Bing-specific NB (phrase only — do NOT run on Google)

"homes senior citizens near me" "senior living communities retirement near me" "communities senior living near me" "independent living facilities near me" "senior citizen living options" "senior housing no waitlist nearby" "care facilities for elderly" "independent apartments living seniors" "senior living communities near me" "home for elderly" "luxury senior living [city]" "best senior living near me" "retirement apts near me" "senior living"

Never bid on — either platform

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

Match Type Rules

Match Type	Google	Bing	Rule
Exact	Branded + city LOC	Less critical — phrase dominates	Use exact on Google for all Tier 1 + Tier 2; Bing phrase match covers the same intent
Phrase	All NB + supplemental LOC	Primary match type for all tiers	Default for all Bing keywords — 100% of Bing conversions in data came from phrase match
Broad	Never on NB terms	Never	Documented: broad match NB = $2,248 CPA on Google. Avoid on both platforms.

Section 10

Sitelink Performance

Sitelinks are the single most underutilized asset in senior living search campaigns. When a searcher clicks a sitelink rather than the main ad headline, they have self-selected their specific care interest before even landing on the site — making sitelink clicks a leading indicator of intent quality. The data below is drawn from CRM lead records where the UTM term captured the sitelink clicked.

Sitelink Conversion to Residency — CRM Data

Sitelink	Total leads	Residents / Depositors	High-value rate	Lead stages	Verdict
sitelink_memory_care	1	1 Resident	100%	Resident	Highest intent sitelink — small sample, very strong signal
sitelink_independent_living	7	1 Resident, 0 Depositors	14%	6 Active, 1 Resident	Best volume — appears on NB and branded; produces residents
sitelink_pricing_floor_plans	1	0	0%	1 Active	Researching intent — still in pipeline, too early to judge
sitelink_assisted_living	2	0	0%	1 Lost Lead, 1 Active	Lower conversion — but 1 Lost Lead reflects Assisted Living's higher disqualification rate
sitelink_contact_us	2	0	0%	2 Active	Generic intent — avoid using as primary sitelink

Key insight: care-type sitelinks attract the highest-intent visitors

Sitelinks that name a specific care type — Memory Care, Independent Living, Assisted Living — outperform generic action sitelinks like "Contact Us." A searcher who clicks "Memory Care" on a branded ad has self-identified their care need before filling out a form. The Memory Care sitelink produced a direct Resident (100% conversion) despite only 1 click in the dataset. The "Contact Us" sitelink produced 0 high-value leads from 2 clicks. The data consistently points in the same direction: specificity converts; generality does not.

Sitelink Platform Distribution

Of the 13 sitelink-triggered leads in the CRM, 12 came from Google and 1 from Bing. The Bing sitelink lead clicked "sitelink_independent_living" on a branded campaign and is currently Active. Bing sitelinks are tracking correctly in the data but represent a smaller sample given lower overall Bing volume.

Which Campaigns Sitelinks Appear On

Campaign type	Sitelinks that generated leads	Recommendation
Branded search	Independent Living, Memory Care, Assisted Living	All care-type sitelinks — keep all three active
NB search (generic)	Independent Living, Contact Us	Replace "Contact Us" with "Schedule a Tour" or "Pricing & Floor Plans"
LOC search (city-specific)	Assisted Living	Add Memory Care and Independent Living to all LOC campaigns
Bing branded	Independent Living	Mirror Google sitelinks — add Memory Care and Assisted Living

Recommended Sitelink Set — Every Branded Campaign

Must-have sitelinks (branded + LOC campaigns)

Assisted Living Independent Living Memory Care Schedule a Tour Pricing & Floor Plans

Avoid as primary sitelinks

Contact Us About Us Home

Why "Contact Us" underperforms

A searcher who clicks "Contact Us" has not made a care decision — they are gathering contact information, a low-intent action. A searcher who clicks "Memory Care" or "Assisted Living" has told you exactly what they need before your landing page loads. Care-type sitelinks function as pre-qualification: they filter the traffic before the conversion, which is why their lead quality is measurably higher. Every primary sitelink slot should communicate a care type, a specific action (tour scheduling), or a decision-stage resource (pricing). "Contact Us" should be a backup sitelink at most.

Section 11

Prospect Data Analysis — Senior Living Community

240 prospect records from a single senior living community CRM (Active + Depositor statuses). This dataset provides a detailed view of who moves from first inquiry to financial commitment, and which sources produce the best-quality leads.

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 12

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

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 senior living portfolio keyword performance data, CRM lead analysis, prospect CRM records, HUD guidance, and federal case law through February 2026.