# Real Estate Market Intel > Use when researching markets. Demographics, comps, schools. - Skill: `loopyluci/real-estate-market-intel` (Agent Skill) - Install (CLI): `npx skillmds@latest add loopyluci/real-estate-market-intel` - Raw SKILL.md: https://api.skillmd.com/api/skills/loopyluci/real-estate-market-intel/raw - Safety review: pending (external: skill-scanner WARNING, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: LoopyLuci (https://skillmd.com/u/loopyluci) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/loopyluci/real-estate-market-intel --- # Real Estate Market Intelligence Multi-source market research for real estate agents, investors, and analysts. Gather property data, Census demographics, school ratings, crime statistics, employment trends, and compile everything into professional neighborhood profile sheets. ## When to Use - Advising a client on which neighborhood to buy in - Pricing a listing and needing market context - Writing a market report or blog post on local conditions - Evaluating an out-of-state or unfamiliar market - Preparing for a listing appointment with hyperlocal data - Deciding between two comparable neighborhoods ## Workflow ### 1. Data Sources & Query Strategy | Data Domain | Best Sources | How to Access | |---|---|---| | Property Values & Comps | Zillow Research, Redfin Data Center, local MLS | `web_extract` on public data pages | | Demographics | US Census Bureau (ACS), Census Reporter | API + `web_extract` | | Schools | GreatSchools.org, Niche, Schooldigger | `web_extract` | | Crime | AreaVibes, NeighborhoodScout, local PD reports | `web_search` + `web_extract` | | Employment / Economy | BLS, FRED, local EDD | API or `web_extract` | | Walkability / Transit | Walk Score, Redfin Walk Score API | `web_extract` | | Zillow Home Value Index | fred.stlouisfed.org (ZHVI series) | `web_extract` CSV data | | Neighborhood Reviews | Niche, Nextdoor, City-Data, Reddit | `web_search` + `web_extract` | ### 2. Property & Market Data Scrape Use `web_extract` to pull key market indicators: ```python def fetch_market_data(zip_code, city, state): """Gather market-level indicators for an area.""" data = {} # Zillow home value data via web extraction zillow_url = f"https://www.zillow.com/home-values/{zip_code}/" # Redfin data center redfin_url = f"https://www.redfin.com/zip/{zip_code}" return data # Example usage market_data = fetch_market_data("90210", "Beverly Hills", "CA") ``` ### 3. Demographic Profile Use the Census Bureau's American Community Survey (ACS) via API: ```bash # Get median household income, population, age distribution, housing data # Census API endpoints: # ACS 5-year estimates: https://api.census.gov/data/2023/acs/acs5 # Key variable groups: # B19013_001E - Median household income # B01003_001E - Total population # B25077_001E - Median home value # B25064_001E - Median gross rent # B25002_001E - Occupied housing units # B23025_005E - Unemployment count curl "https://api.census.gov/data/2023/acs/acs5?get=NAME,B19013_001E,B01003_001E,B25077_001E,B25064_001E&for=zip%20code%20tabulation%20area:90210" | python -m json.tool ``` Build a demographic profile function: ```python import urllib.request import json def census_demographics(zip_code): """Fetch key demographics for a ZIP code from Census ACS.""" vars_list = [ "NAME", "B19013_001E", # Median household income "B01003_001E", # Total population "B25077_001E", # Median home value "B25064_001E", # Median gross rent "B25002_001E", # Occupied housing units "B23025_005E", # Unemployed "B23025_002E", # In labor force ] vars_str = ",".join(vars_list) url = f"https://api.census.gov/data/2023/acs/acs5?get={vars_str}&for=zip%20code%20tabulation%20area:{zip_code}" try: req = urllib.request.urlopen(url) rows = json.loads(req.read()) if len(rows) > 1: col_names = rows[0] values = rows[1] demo = dict(zip(col_names, values)) # Compute unemployment rate labor_force = int(demo.get('B23025_002E', 0)) unemployed = int(demo.get('B23025_005E', 0)) demo['unemployment_rate'] = round(unemployed / labor_force * 100, 1) if labor_force else 0 return demo except Exception as e: return {"error": str(e)} return None # Usage demo = census_demographics("90210") print(json.dumps(demo, indent=2)) ``` ### 4. School Ratings Pull school data from GreatSchools.org: ```python def fetch_school_ratings(zip_code): """Search for school ratings in a ZIP code area.""" # Example using web extraction # In practice, GreatSchools requires scraping their public pages search_results = web_search(f"greatschools.org {zip_code} elementary school ratings") # Then extract ratings from the linked pages print(f"School data for {zip_code}:") for result in search_results: print(f" - {result.title}") return search_results ``` Build an organized school summary: | School Name | Type | Rating (1-10) | Enrollment | Student/Teacher Ratio | |---|---|---|---|---| | Lincoln Elementary | Public | 8 | 520 | 18:1 | | Washington Middle | Public | 7 | 680 | 20:1 | | Jefferson High | Public | 9 | 1200 | 22:1 | ### 5. Crime Statistics Research crime data for the area: ```python def crime_profile(city, state, zip_code): """Assemble crime statistics from multiple public sources.""" print(f"\n=== CRIME PROFILE: {zip_code} ===") # AreaVibes crime data url = f"https://www.areavibes.com/{city.lower()}-{state.lower()}/crime/" print(f"1. AreaVibes: {url}") print(" - Overall crime grade (A-F)") print(" - Violent crime rate per 100k") print(" - Property crime rate per 100k") print(" - Compare to national average") # NeighborhoodScout ns_url = f"https://www.neighborhoodscout.com/{state.lower()}/{city.lower()}/crime" print(f"2. NeighborhoodScout: {ns_url}") print(" - Crime per 100k residents") print(" - Annual crime count by type") print(" - Safety percentile rank") # FBI UCR data print("3. FBI Uniform Crime Reporting (national level)") print(" - https://ucr.fbi.gov/crime-in-the-u.s") return {"city": city, "state": state, "zip": zip_code} ``` ### 6. Employment & Economic Trends Pull employment data from the Bureau of Labor Statistics: ```bash # BLS API for local area unemployment statistics (LAUS) # Series ID format: LAUCN0000000003 (unemployment rate) # Area codes available at: https://www.bls.gov/cew/classifications/areas/area-titles.csv # FRED API - Federal Reserve Economic Data # ZHVI (Zillow Home Value Index) series # Payroll employment by MSA curl "https://api.stlouisfed.org/fred/series/observations?series_id=MSANB906URN&api_key=YOUR_API_KEY&file_type=json" ``` ### 7. Build Neighborhood Profile Sheet Compile all data into a readable sheet: ```python def neighborhood_profile(zip_code, city, state): """Generate a comprehensive neighborhood profile.""" print("=" * 70) print(f"NEIGHBORHOOD PROFILE: {city.upper()}, {state} {zip_code}") print("=" * 70) # Demographics demo = census_demographics(zip_code) if demo and 'error' not in demo: print(f"\nDEMOGRAPHICS") print(f" Population: {demo.get('B01003_001E', 'N/A')}") print(f" Median Household Income: ${int(demo.get('B19013_001E', 0)):,}") print(f" Median Home Value: ${int(demo.get('B25077_001E', 0)):,}") print(f" Median Gross Rent: ${int(demo.get('B25064_001E', 0)):,}") print(f" Unemployment Rate: {demo.get('unemployment_rate', 'N/A')}%") # Market conditions print(f"\nMARKET CONDITIONS") print(f" Walk Score: TBD (web search)") print(f" Transit Score: TBD (web search)") print(f" Bike Score: TBD (web search)") print(f" Avg Days on Market: TBD (local MLS)") print(f" Price per Sq Ft: TBD (local MLS)") # Run web searches for walkability search_walk = web_search(f"walk score maps {zip_code} walkability") if search_walk: print(f" Walk Score Ref: {search_walk[0].url}") print(f"\nSCHOOLS") print(f" TBD — run fetch_school_ratings({zip_code})") print(f"\nCRIME") print(f" TBD — run crime_profile('{city}', '{state}', '{zip_code}')") print("\n" + "=" * 70) return {"zip": zip_code, "city": city, "state": state} ``` ### 8. Market Trend Analysis Track year-over-year trends: ```python def market_trends(zip_code, months_back=12): """Analyze price trends, inventory, and days on market.""" print(f"\n=== MARKET TRENDS: {zip_code} ===") print(f"Period: Past {months_back} months") # Key data points to gather: # 1. Median sale price (current vs 12 months ago) # 2. Number of sales (current vs 12 months ago) # 3. Average days on market # 4. Sale-to-list price ratio # 5. Inventory levels (active listings) # 6. Months of supply # 7. Price reductions (% of listings) # Web search for recent market reports results = web_search(f"{zip_code} real estate market report 2025 2026") print("Recent market reports:") for r in results: print(f" - {r.title}") print(f" {r.url}") return {"zip": zip_code, "search_results": results} ``` ### 9. Investment Suitability Score Score a neighborhood across key investment dimensions: ```python def investment_score(demographics, school_ratings, crime_data, market_trends): """ Score a neighborhood 1-10 on each dimension and return composite. 10 = best for investment, 1 = worst. """ scores = {} # Income score: higher median income = stronger rental pool med_income = int(demographics.get('B19013_001E', 0)) scores['income'] = min(10, max(1, med_income / 15000)) # Home value appreciation # Placeholder — needs year-over-year comparison scores['appreciation'] = 5 # School score # Placeholder — needs GreatSchools data scores['schools'] = 5 # Crime score (inverted: lower crime = higher score) scores['safety'] = 7 # Employment score unemp = demographics.get('unemployment_rate', 5) scores['employment'] = max(1, 10 - unemp * 2) # Composite weights = {'income': 0.20, 'appreciation': 0.25, 'schools': 0.20, 'safety': 0.20, 'employment': 0.15} composite = sum(scores[k] * weights[k] for k in weights) scores['composite'] = round(composite, 1) print("\nINVESTMENT SUITABILITY SCORE") for dim in ['income', 'appreciation', 'schools', 'safety', 'employment']: print(f" {dim.capitalize():15s}: {scores[dim]}/10") print(f" {'Composite':15s}: {scores['composite']}/10") return scores ``` ## Common Pitfalls - **Relying on a single data source**: Cross-validate property values, crime, and schools from at least 2 sources. Zillow Zestimates can be off by 5-15%. - **Using outdated Census data**: ACS 5-year estimates lag by 2-3 years. Always check the survey year. Use ACS 1-year estimates for large metro areas (more current). - **Confusing ZIP code with neighborhood**: ZIP code boundaries don't align with neighborhood boundaries. Pull data at the tract or block group level for precision. - **Ignoring school boundaries**: GreatSchools ratings may not match actual attendance zones. Verify with the school district's boundary map. - **Overweighting crime stats**: Crime data has reporting bias. Compare per-capita rates, not raw counts. Check trend data (2-3 years), not just a single year. - **Forgetting seasonality**: Housing market data is seasonal. Compare same-month year-over-year, not month-over-month. - **Neglecting supply-side data**: New construction permits, zoning changes, and development pipelines affect future values. Check city planning department websites. - **Not verifying walk scores**: Walk Score can be inaccurate in suburban areas. Verify by looking at actual amenities within 1 mile. - **Blending MSAs incorrectly**: Large metros have vastly different submarkets. A downtown zip and exurb zip in the same MSA are not comparable. ## Verification Checklist - [ ] Median household income, population, and median home value pulled from Census ACS - [ ] School ratings gathered from GreatSchools or Niche for at least top-3 schools - [ ] Crime data collected (violent + property crime rates, vs. national average) - [ ] Walk/transit/bike scores checked and noted - [ ] Median sale price and price/sq ft from MLS or Redfin Data Center - [ ] Year-over-year appreciation trend confirmed - [ ] Average days on market and sale-to-list ratio checked - [ ] Unemployment rate and major employers identified - [ ] New construction / development pipeline researched - [ ] Neighborhood profile sheet compiled and formatted - [ ] Data sources documented with timestamps - [ ] At least 2 independent sources cross-referenced per data point