Neighborhood-by-Neighborhood: Comparing Real Estate Markets with Redfin Data

#api #webscraping #opensource #python

You're house hunting in a new city. You've narrowed it down to five neighborhoods that seem promising based on research. But now what? You could spend weeks touring each area, comparing prices, understanding market temperatures, and trying to figure out which neighborhood actually offers the best value.

Or you could extract data on hundreds of properties across all five neighborhoods in a single afternoon, then make a decision backed by analysis instead of gut feeling.

Real estate decisions often come down to data: price per square foot, how long properties sit on the market, what's actually selling versus what's listed. Redfin is the most comprehensive source of this data. But manually collecting it is time-consuming and error-prone.

What if you could scrape every property listing in five neighborhoods, compare pricing tiers, analyze market velocity, and identify hidden opportunities? That's exactly what automated Redfin scraping enables.

In this guide, I'll show you how to systematically compare neighborhoods and make data-driven real estate decisions.

Why Neighborhood Comparison Matters

Before committing $500K+ to a home purchase, you need answers that go beyond "nice area" or "good schools."

Price stratification : Is the neighborhood firmly $400-500K or does it break into affordable ($300K), mid-market ($500K), and premium ($750K+) segments? This tells you about diversity and future growth potential.

Days on market (DOM) : Properties in Neighborhood A sell in 15 days. Neighborhood B takes 45 days. That DOM difference signals buyer demand. Low DOM means competitive market, high DOM might mean overpriced or undermarketed properties.

Property age and condition : Are you buying in a vintage neighborhood (pre-1960s character, likely higher maintenance) or a newer suburban area (built 2000+, modern systems)? This impacts future costs.

Market velocity : How many homes sold last month versus last quarter? High velocity signals active buyers. Low velocity might signal a cooling market.

Price trends : Is the market appreciating, stable, or declining? A neighborhood that gained 8% last year signals growth. One that declined might signal softening demand.

Without data, you're guessing. With data, you're strategic.

Setting Up Your Neighborhood Comparison

Decide which neighborhoods you want to compare. For a typical search, I'd compare 5 neighborhoods and look for 100+ active listings in each.

Use the Apify Redfin Scraper to extract listing data. Your input configuration:


    {
      "startUrls": [
        {
          "url": "https://www.redfin.com/neighborhood/Capitol-Hill/Seattle-WA"
        },
        {
          "url": "https://www.redfin.com/neighborhood/Fremont/Seattle-WA"
        },
        {
          "url": "https://www.redfin.com/neighborhood/Ballard/Seattle-WA"
        },
        {
          "url": "https://www.redfin.com/neighborhood/Wallingford/Seattle-WA"
        },
        {
          "url": "https://www.redfin.com/neighborhood/Green-Lake/Seattle-WA"
        }
      ],
      "proxyConfiguration": {
        "useApifyProxy": true
      },
      "useChrome": true,
      "maxListings": 150
    }

Run this and you'll get structured data on every listing in each neighborhood.

Understanding Redfin Scraper Output

Here's what you'll get (abbreviated):


    {
      "neighborhoods": [
        {
          "name": "Capitol Hill",
          "city": "Seattle",
          "state": "WA",
          "listings": [
            {
              "id": "redfin_12345",
              "address": "1234 E Pike Street, Seattle, WA 98102",
              "price": 750000,
              "beds": 3,
              "baths": 2,
              "sqft": 1850,
              "yearBuilt": 1925,
              "pricePerSqft": 405,
              "listingStatus": "for_sale",
              "daysOnMarket": 14,
              "lastSoldPrice": 695000,
              "lastSoldDate": "2025-10-15",
              "propertyType": "single_family",
              "url": "https://www.redfin.com/WA/Seattle/1234-E-Pike-Street-98102"
            },
            {
              "id": "redfin_12346",
              "address": "5678 E Olive Street, Seattle, WA 98102",
              "price": 1200000,
              "beds": 4,
              "baths": 3,
              "sqft": 2650,
              "yearBuilt": 1932,
              "pricePerSqft": 453,
              "listingStatus": "for_sale",
              "daysOnMarket": 8,
              "lastSoldPrice": 1100000,
              "lastSoldDate": "2024-06-20",
              "propertyType": "single_family",
              "url": "https://www.redfin.com/WA/Seattle/5678-E-Olive-Street-98102"
            }
          ],
          "neighborhoodStats": {
            "medianListPrice": 850000,
            "medianPricePerSqft": 425,
            "medianDaysOnMarket": 16,
            "activeListings": 127,
            "soldLastMonth": 34,
            "priceChange90Days": 2.3,
            "averageYearBuilt": 1948
          }
        },
        {
          "name": "Fremont",
          "city": "Seattle",
          "state": "WA",
          "listings": [
            {
              "id": "redfin_12347",
              "address": "9012 Wallingford Avenue N, Seattle, WA 98103",
              "price": 625000,
              "beds": 3,
              "baths": 1.5,
              "sqft": 1650,
              "yearBuilt": 1950,
              "pricePerSqft": 379,
              "listingStatus": "for_sale",
              "daysOnMarket": 22,
              "lastSoldPrice": 580000,
              "lastSoldDate": "2025-02-10",
              "propertyType": "single_family",
              "url": "https://www.redfin.com/WA/Seattle/9012-Wallingford-Avenue-N-98103"
            }
          ],
          "neighborhoodStats": {
            "medianListPrice": 675000,
            "medianPricePerSqft": 389,
            "medianDaysOnMarket": 24,
            "activeListings": 98,
            "soldLastMonth": 28,
            "priceChange90Days": -0.8,
            "averageYearBuilt": 1956
          }
        }
      ],
      "scrapedAt": "2026-04-04T14:30:00Z"
    }

This raw data is the foundation. Now analyze it.

Building Your Neighborhood Comparison Matrix

Extract key metrics and compare:


    import statistics
    from collections import defaultdict

    def analyze_neighborhood(listings):
        """Extract summary metrics from listings"""
        prices = [l['price'] for l in listings]
        sqft_values = [l['sqft'] for l in listings]
        dom_values = [l['daysOnMarket'] for l in listings]
        price_per_sqft = [l['pricePerSqft'] for l in listings]

        analysis = {
            'totalListings': len(listings),
            'medianPrice': statistics.median(prices),
            'avgPrice': round(statistics.mean(prices), 0),
            'priceRange': f"${min(prices):,} - ${max(prices):,}",
            'medianPricePerSqft': statistics.median(price_per_sqft),
            'medianSqft': statistics.median(sqft_values),
            'medianDaysOnMarket': statistics.median(dom_values),
            'avgDaysOnMarket': round(statistics.mean(dom_values), 1),
            'priceStdDev': round(statistics.stdev(prices), 0),
            '3bedCount': len([l for l in listings if l['beds'] == 3]),
            'avgYearBuilt': round(statistics.mean([l['yearBuilt'] for l in listings]), 0),
            'priceAppreciation': calculate_appreciation(listings)
        }

        return analysis

    def calculate_appreciation(listings):
        """Compare last sold price to current list price"""
        appreciations = []

        for listing in listings:
            if listing['lastSoldPrice'] and listing['lastSoldDate']:
                appreciation_pct = ((listing['price'] - listing['lastSoldPrice']) / listing['lastSoldPrice']) * 100
                appreciations.append(appreciation_pct)

        if appreciations:
            return round(statistics.mean(appreciations), 1)
        return None

    # Example: Compare 5 neighborhoods
    neighborhoods = {
        'Capitol Hill': 750000,
        'Fremont': 675000,
        'Ballard': 625000,
        'Wallingford': 595000,
        'Green Lake': 680000
    }

    comparison_table = {
        name: analyze_neighborhood(data[name]['listings'])
        for name, neighborhood_data in data.items()
    }

Output table:

| Neighborhood | Median Price | $/Sqft | Median DOM | Avg Year Built | Appreciation | |---|---|---|---|---|---| | Capitol Hill | $850K | $425 | 16 | 1948 | +7.2% | | Fremont | $675K | $389 | 24 | 1956 | +5.8% | | Ballard | $625K | $379 | 19 | 1952 | +3.1% | | Wallingford | $595K | $368 | 21 | 1961 | +2.4% | | Green Lake | $680K | $405 | 18 | 1958 | +6.9% |

What this tells you:

Capitol Hill is the most expensive but also the hottest (lowest DOM). Buyers move quickly.
Fremont is cheaper but slower to sell (24 days). Could be softer demand or overpriced inventory.
Wallingford is the most affordable but also appreciated least (2.4%). Possibly stabilizing after growth.
Ballard represents middle ground: decent price, reasonable DOM, moderate appreciation.

Analyzing Price Segmentation

Don't just look at medians. Understand how prices distribute:


    def price_tier_analysis(listings):
        """Break listings into price tiers"""
        prices = sorted([l['price'] for l in listings])
        n = len(prices)

        tiers = {
            'under_500k': len([p for p in prices if p < 500000]),
            '500k_650k': len([p for p in prices if 500000 <= p < 650000]),
            '650k_850k': len([p for p in prices if 650000 <= p < 850000]),
            '850k_1.2m': len([p for p in prices if 850000 <= p < 1200000]),
            'over_1.2m': len([p for p in prices if p >= 1200000])
        }

        return tiers

    # Per neighborhood, see where inventory concentrates
    for neighborhood, listings in data.items():
        tiers = price_tier_analysis(listings)
        print(f"{neighborhood}: {tiers}")

    # Output:
    # Capitol Hill: {'under_500k': 3, '500k_650k': 12, '650k_850k': 45, '850k_1.2m': 52, 'over_1.2m': 15}
    # Fremont: {'under_500k': 8, '500k_650k': 38, '650k_850k': 42, '850k_1.2m': 8, 'over_1.2m': 2}

This reveals inventory concentration. Capitol Hill has strong inventory above $850K (premium market). Fremont concentrates in the $500-850K range (mid-market). This distribution affects your buying power and future resale potential.

Days on Market as a Market Temperature Signal

DOM is critical. It tells you if a neighborhood is hot or cooling:


    def market_temperature(listings):
        """Classify market based on DOM"""
        dom_values = [l['daysOnMarket'] for l in listings]
        median_dom = statistics.median(dom_values)

        if median_dom < 10:
            return 'HOT - Inventory moves fast, competitive offers expected'
        elif median_dom < 20:
            return 'WARM - Normal market, standard competition'
        elif median_dom < 40:
            return 'COOL - Slower market, negotiation potential'
        else:
            return 'COLD - Buyer advantage, potential price concessions'

    # Check each neighborhood
    for neighborhood, stats in comparison_table.items():
        temp = market_temperature(data[neighborhood]['listings'])
        print(f"{neighborhood}: {temp}")

    # Output:
    # Capitol Hill: HOT - Inventory moves fast, competitive offers expected
    # Fremont: COOL - Slower market, negotiation potential
    # Ballard: WARM - Normal market, standard competition

A HOT market means you need to move fast and be prepared to compete on price. A COOL market gives you negotiating leverage.

Building a Multi-Neighborhood Workflow

Here's a Python script for analyzing 5 neighborhoods end-to-end:


    import requests
    import json
    from datetime import datetime

    def run_neighborhood_analysis(neighborhood_urls):
        """Main workflow for comparing neighborhoods"""

        # Step 1: Scrape all neighborhoods
        print("Scraping Redfin data...")
        scraping_results = scrape_redfin(neighborhood_urls)

        # Step 2: Parse and analyze each neighborhood
        print("Analyzing neighborhoods...")
        comparisons = {}
        for neighborhood, listings in scraping_results.items():
            comparisons[neighborhood] = {
                'summary': analyze_neighborhood(listings),
                'priceTiers': price_tier_analysis(listings),
                'temperature': market_temperature(listings)
            }

        # Step 3: Create comparison matrix
        print("Building comparison matrix...")
        matrix = create_comparison_matrix(comparisons)

        # Step 4: Generate insights
        print("Generating insights...")
        insights = generate_insights(comparisons)

        # Step 5: Export results
        export_results(matrix, insights, comparisons)

        return comparisons, insights

    def generate_insights(comparisons):
        """Extract actionable insights"""
        insights = {
            'bestValue': min(comparisons.items(), key=lambda x: x[1]['summary']['medianPrice'])[0],
            'fastestMarket': min(comparisons.items(), key=lambda x: x[1]['summary']['medianDaysOnMarket'])[0],
            'appreciatingFastest': max(comparisons.items(), key=lambda x: x[1]['summary']['priceAppreciation'])[0],
            'largestInventory': max(comparisons.items(), key=lambda x: x[1]['summary']['totalListings'])[0],
        }

        return insights

    # Example usage
    neighborhood_urls = [
        "https://www.redfin.com/neighborhood/Capitol-Hill/Seattle-WA",
        "https://www.redfin.com/neighborhood/Fremont/Seattle-WA",
        "https://www.redfin.com/neighborhood/Ballard/Seattle-WA",
        "https://www.redfin.com/neighborhood/Wallingford/Seattle-WA",
        "https://www.redfin.com/neighborhood/Green-Lake/Seattle-WA"
    ]

    comparisons, insights = run_neighborhood_analysis(neighborhood_urls)
    print(json.dumps(insights, indent=2))

Real-World Application: Making Your Buy Decision

You're torn between three neighborhoods. The data shows:

Neighborhood A : $850K median, HOT market (15 days DOM), appreciating 7.2%
Neighborhood B : $650K median, COOL market (28 days DOM), appreciating 2.1%
Neighborhood C : $725K median, WARM market (19 days DOM), appreciating 5.9%

The choice depends on your priorities:

If you value appreciation and can handle competition: Neighborhood A
If you want negotiation leverage and affordability: Neighborhood B
If you want balance: Neighborhood C

With data, you're not guessing. You're making an informed decision.

Getting Started

Visit the Apify Redfin Scraper and scrape five neighborhoods you're considering. Run the analysis scripts above. You'll have clearer decision-making in a few hours.

Your home is likely the biggest financial decision you'll make. Make it backed by data.