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

2. Property & Market Data Scrape

Use web_extract to pull key market indicators:

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:

# 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:

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:

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:

5. Crime Statistics

Research crime data for the area:

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:

# BLS API for local area unemployment statistics (LAUS)
# Series ID format: LAUCN<area_code>0000000003 (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:

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:

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:

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