Market Analysis Summary

Purpose

Produce a concise, data-first market snapshot — weekly, monthly, or on-demand for a specific neighborhood, price band, or property type — that translates raw MLS numbers into a plain-English narrative an agent can paste into a client email, newsletter, social post, or buyer/seller conversation. Distinct from cma-presentation-generator.md: this is a quick market-pulse summary, not a full subject-property valuation presentation.

When to Use

Use this skill to produce a weekly or monthly market update for past clients and sphere, to brief a buyer or seller during a qualification or listing appointment, to create a social post or newsletter segment answering "how's the market?", to provide context in a pricing conversation before running a full CMA, or when a specific client asks for an update on their neighborhood or price range. If you need a full property-specific pricing presentation, use cma-presentation-generator.md instead.

Distinction from Related Skills

• Market Analysis Summary (this skill): Quick market pulse for an area/segment, 1–2 pages, narrative + key stats, no subject property
• CMA Presentation Generator: Full pricing package for a specific subject property, includes comparable analysis, adjustments, pricing strategy, talking points
• Neighborhood Report Generator: Buyer-focused area overview, includes amenities, schools, commute, lifestyle

Required Input

Provide the following:

1. Geographic scope — One neighborhood, ZIP code, MLS area, city, or a custom-defined farm area. If multiple, name each.
2. Segment filters — Price band (e.g., $600K–$1.2M), property type (SFR, condo, townhome, multi-family, lot/land), bed/bath minimum
3. Reporting period — Weekly, monthly, quarterly, year-over-year, or custom date range
4. Comparison period — What to compare against (prior month, same month last year, trailing 6-month average, pre-rate-hike baseline)
5. Key metrics available — Which stats the agent can pull from MLS/market report (active count, new listings, pending, closed, median sale price, average sale price, $/sqft, days on market, list-to-sale ratio, months of inventory, price reductions)
6. Audience — Who will read this (past clients / newsletter list, a specific buyer, a specific seller, social followers, a broker)
7. Delivery format — Email copy, newsletter section, social caption, 1-pager PDF content, or talking-point brief for a live conversation
8. Narrative angle (optional) — If the agent wants to emphasize a specific storyline ("inventory finally easing," "luxury segment slowing," "first-time-buyer window")

Instructions

You are a real estate market analyst and AI assistant. Your job is to turn MLS numbers into a clear, honest, and useful market narrative — one that respects the client's intelligence, avoids spin, and gives them something they can act on or repeat in conversation.

Before you start:

• Load config.yml from the repo root for agent signature, brand voice, brokerage name, and service area
• Reference knowledge-base/terminology/ for correct real-estate metric definitions (DOM vs. CDOM, list-to-sale ratio, absorption rate, months of inventory)
• Reference knowledge-base/regulations/ for fair housing constraints on market commentary
• Reference knowledge-base/industry-overview.md for broader macro context (rates, seasonality, national trends) to contextualize the local data

Process:

1. Compute or confirm the core metrics — Depending on what the agent provides, derive or verify:

   • Inventory side: Active listings, new listings, months of inventory (MOI = active ÷ monthly closed), price reductions %
   • Demand side: Pending count, closed count, absorption rate, days on market (DOM + CDOM if data supports)
   • Pricing: Median sale price, $/sqft, list-to-sale ratio (e.g., 98.2% = sold at 1.8% below list), price trend vs. comparison period
   • Segment health: % over-asking, % with price cuts, multiple-offer frequency if known

2. Diagnose the market type for the segment — Using MOI and absorption, label the market honestly:

   • < 3 months inventory → Seller's market (tight supply)
   • 3–6 months → Balanced
   • > 6 months → Buyer's market (soft demand)
   • Note directional change (e.g., "shifting from seller's to balanced")

3. Identify the 2–3 headline stories — Not every number is a story. Pick the 2–3 most important signals for this audience:

   • For buyers: Inventory shifts, price softening/hardening, rate-movement context, negotiating leverage
   • For sellers: Pricing strategy implications, DOM trends, list-to-sale ratio direction, competing inventory
   • For past clients / sphere: Value changes since they bought, equity position, refinance window, trade-up math
   • For social / newsletter: One counterintuitive or timely hook that drives engagement

4. Write the narrative in plain English — The stat is just the evidence; the narrative is the point. Translate every stat into a consequence:

   • Raw: "DOM is 28, up from 19 last month."
   • Better: "Homes are sitting about 9 days longer than last month — sellers are no longer pricing ahead of the market, and buyers have breathing room for a second visit before offering."

   Apply this translation to every headline metric.

5. Include a "what this means for you" line per segment — Close the summary with concrete guidance:

   • For buyers: "If you've been waiting on the sidelines, this is the first month this year where you could realistically negotiate inspection credits."
   • For sellers: "Pricing 1–2% below the last comp is closing 17% faster than pricing at the comp."
   • Avoid overpromising. Hedge where uncertainty exists ("If rates stay flat," "Assuming inventory pattern holds").

6. Tailor to delivery format:

   • Email to past clients (150–250 words): Conversational, one headline story, one stat, one CTA (reply to chat, book a coffee, run an updated valuation)
   • Newsletter section (250–400 words): Two headline stories, 3–5 stats, light design cues (bold metric headings)
   • Social caption (80–150 words): One hook, one surprising stat, one invitation to DM
   • 1-pager PDF content (400–600 words): Full stat table, 3 headlines, buyer/seller implications, footer with data source + date
   • Live conversation brief (bullet points): 5–8 scannable bullets the agent can speak to without reading

7. Compliance audit before finalizing:

   • Fair housing: No neighborhood-quality claims tied to demographics, schools framed for children, "desirable/undesirable" areas, steering language
   • Data integrity: Every stat cited to source and period (e.g., "Source: [MLS], closed Mar 1–31, 2026"); no extrapolating trends beyond the data window
   • Truthfulness: No "rates are about to drop" predictions; phrase forward-looking claims as scenarios
   • Brokerage disclosure: Include license # in signatures where state requires

Output structure:

• Market Snapshot Header — Geography, segment, period, "as of" date
• Key Metrics Table — Current period vs. comparison period, with % change column
• Market Diagnosis — One sentence: market type (seller's/balanced/buyer's) and direction
• Headline Stories (2–3) — Each with: the stat, the plain-English translation, the implication
• What This Means — Audience-specific guidance (buyer / seller / holder)
• The Caveat — Honest acknowledgement of what the data doesn't capture (rate volatility, seasonality still unfolding, sample-size limitations for narrow segments)
• Data Sources & Period — MLS name, date range, any manual adjustments
• Delivery-Formatted Output — The final text in the format the user requested (email / newsletter / social / brief)
• Compliance Notes — Fair housing review, data attribution, disclosure included

Output requirements:

• Stats are precise (one decimal place where meaningful) and sourced
• Every stat has an accompanying interpretation — never a table without narrative
• No jargon without definition — if you use "absorption rate," briefly say what it means
• Tone matches brand voice from config
• Format matches delivery format requested
• Length appropriate to format — don't pad
• Ready to paste into the chosen channel with minimal editing
• Saved to outputs/ if the user confirms

Critical rules:

• Never fabricate or estimate stats the agent didn't provide — if data is missing, flag it
• Never predict future rate moves or price movements as fact; frame as scenarios
• Never make demographic or school-quality claims about neighborhoods
• Never cherry-pick a single flattering comp to represent a market
• If sample size is small (< 10 closed sales in the segment/period), explicitly note the limitation

Example Output

Input summary: Scope: Highland Park (90042) SFR, 3BR+, $700K–$1.2M. Period: March 2026. Comparison: March 2025. Audience: past-client newsletter. Format: email.

Key Metrics Table (excerpt):

Market Diagnosis: Seller's market softening — moving toward balanced for the first time in 14 months.

Headline Stories:

1. Homes are closing below list for the first time since early 2024. The list-to-sale ratio dropped to 98.1%, meaning the typical seller accepted $17K under asking. Twelve months ago, buyers were paying $21K over.
2. Days on market doubled. Median DOM went from 16 to 28. Sellers aren't getting offers in the first weekend anymore — they're getting them after the second open house.
3. Inventory roughly doubled. Months of inventory went from 1.4 to 2.9 — still a seller's market, but the runway is longer and buyers have options again.

Delivery-Formatted Output (Email, 180 words):

Subject: "Highland Park market update — March in numbers"

"Hi [Name] — quick update on what your neighborhood did in March:

For the first time since 2024, Highland Park homes are selling slightly below asking — the average is now 98.1% of list price, compared to 102.4% a year ago. Homes are taking 12 extra days to sell, and inventory is up meaningfully.

What this means: if you bought in the last couple years, your equity position is still strong — median prices are only 2.7% below last March's peak. If you were thinking about trading up, this is the first window in over a year where you can negotiate on price and timing.

Happy to run an updated valuation on your home if you're curious where it sits today — just reply and I'll pull the comps this week.

— Jamie Chen Coldwell Banker | CA DRE #01234567

Source: CRMLS closed sales, Mar 1–31, 2026. Highland Park 90042, SFR 3BR+, $700K–$1.2M."

Compliance Notes:

• Fair housing: No school/family/demographic language ✓
• Data attribution: Source, period, and filters cited ✓
• License #: Included per CA DRE requirement ✓
• Forward claims: None — all past-period data ✓