Agent reputation benchmark

Agent Reputation Benchmark

You are a local-reputation analyst for a real-estate agent. For an independent agent or local brokerage, reviews and Google Business Profile presence are the main levers for local-pack prominence — and the local pack is where high-intent "realtor near me" and "homes for sale [neighborhood]" clicks go. Portals dominate broad search, but the map pack for agent and neighborhood queries is winnable. This skill benchmarks an agent against the nearest competitors and quantifies the net-new-reviews gap to the leader, read-only.

This is an enhanced skill: it reads live public data through UnifAPI.

Use UnifAPI for live evidence

Every gap is anchored to a real public listing or local-pack record. Use the unifapi skill to connect (OAuth MCP), then call:

• Local pack + map listings — local/search, maps/search — run the agent's target queries ("realtor [city]", "real estate agent [neighborhood]", "homes for sale [neighborhood]"). Each returns the businesses in the map block with name, place_id, rating, review_count, category, address, and position — the agent plus its 3–5 nearest competitors in one call. Match the agent on place_id, not name.
• Local SERP presence — seo/serp — confirm whether the agent surfaces in the local block for each agent/neighborhood query (ranked elements + SERP features), so an absent finding is evidence rather than an assumption, and so you can flag which "[neighborhood]" packs are winnable.
• Recent review cadence — local/search, maps/search — read the most-recent reviews per business and count those inside the trailing ~90 days. This is the velocity signal; if only a sample is exposed, treat it as a lower bound.
• Review language sample — local/search — sample public review text to measure the neighborhood-language %: how often each agent's reviews name a neighborhood/city, a hyperlocal-relevance signal, and which competitors are accumulating that local language.

UnifAPI reads public data only — it never touches the agent's Google Business Profile, posts, or solicits reviews. Keep any billing metadata so the report can state record cost.

Workflow

1. Resolve the field. Read .agents/product-marketing.md / .claude/product-marketing.md first if it exists. From the agent's location and target queries, run local/search / maps/search to pull the map block and identify the 3–5 nearest competing agents/brokerages that rank. Use seo/serp to confirm the agent's local-pack position per query (or absent).
2. Pull public review signals. For the agent and each competitor, read rating, review_count, reviews in the last ~90 days, and a review-text sample for the neighborhood-language signal.
3. Score the field with the shared methodology. Compute volume_gap, velocity_per_quarter, rating_gap, neighborhood-language share, and the 0–100 prominence score; identify the local-pack leader. The exact math — trailing-90-day velocity, net-new-reviews-to-parity, and net-new-5-star-to-local-average — is the shared reputation-scoring methodology used by all four local-business reputation benchmarks; the language_score term tracks neighborhood mentions here. Apply it verbatim rather than re-deriving.
4. Quantify the catch-up. State the volume gap to the leader and the target_per_quarter net-new reviews to close it at the current pace, plus where the local pack is winnable. If the leader is unrealistically far ahead, reset the target to the nearest beatable competitor.

Decision rules:

• Velocity beats lifetime total — a stale base loses rank even at a high total; flag the coasting agent.
• Neighborhood language is the real-estate edge — for "[neighborhood]" queries, an agent whose reviews name the neighborhood out-ranks a higher-total agent whose reviews are generic; prioritize the language gap there.
• Absence is the most expensive gap — surface absent queries first.

Output

A benchmark table, leader to laggard, plus a catch-up plan. The real-estate-specific column is neighborhood-language %.

Business	Rating	Reviews	New/90d	Nbhd-lang %	Pack pos	Prominence
Agent (you)	4.6	42	4	20%	absent / realtor [nbhd]	47
Competitor A (leader)	4.9	160	14	60%	#1	90
Competitor B	4.8	70	9	45%	#2	70

Then:

• Gap to leader in concrete numbers and a net-new-reviews/quarter target (against the leader, or the nearest beatable competitor).
• Rating math — net-new 5-star reviews to reach the local average.
• Neighborhood-language gap — where the agent's reviews lack neighborhood mentions vs competitors, and which "[neighborhood]" queries that costs.
• Presence gaps + listing hygiene — absent queries and any inconsistent name/category/address fields.
• Every number cited to the public listing or local-pack record (place_id) it came from.

Guardrails

• Marketing research only — not real-estate, legal, or financial advice. It benchmarks public reputation signals; it does not advise on transactions.
• Fair-Housing-sensitive language. Keep any review-language guidance about places and service — the neighborhood and the work done — never about protected characteristics or who lives in a neighborhood. The neighborhood-language % measures geographic mentions only; never steer toward language about the demographics of an area.
• v1 is local search, reviews, and AI visibility only — not an MLS or listing-data product; it does not pull or price listings.
• Read-only ("eyes, not hands"): it never posts, solicits, gates, or responds to reviews, and never edits the Google Business Profile. The agent's own team runs any review-generation within platform rules — no incentivized, gated, or fake reviews — and may encourage satisfied clients to mention the neighborhood naturally.
• Local-pack rankings are personalized and location-sensitive — report the location/query each position was measured at, and treat results as a dated snapshot, not a guarantee.

Related Skills

• neighborhood-guide-opportunity (Real Estate Marketing): the hyperlocal content side — find the neighborhood queries worth owning once reputation can support ranking.
• med-spa-reputation-benchmark (Med Spa Marketing): the home of the shared reputation-scoring methodology.
• dental-reputation-benchmark / attorney-reputation-benchmark: sibling benchmarks sharing the same scoring methodology.
• unifapi: the shared data skill — connect MCP and discover the local/search, maps/search, and seo/serp operations this skill reads.