Redfin Housing Market Data Scraper — Trends, Comps & Hotness 🚀

🎉 60 months of housing market history per ZIP code — price trends, Compete Score hotness, sold comps, schools & climate risk in ONE run

Built for real-estate investors, analysts, and data teams who rank markets before they buy

---

📋 Overview

Stop copy-pasting market stats from housing pages — this actor pulls complete US housing market intelligence for any ZIP code, city, county, or neighborhood and delivers it as clean, structured dataset rows ready for BigQuery, Snowflake, pandas, or Excel.

Why data teams choose us:

• ✅ 60-month trend series: median sale price, homes sold, days on market, sale-to-list ratio, % sold above list, % price drops — every monthly point with value AND year-over-year change
• 🔥 Compete Score market hotness: 0–100 competitiveness score with typical offer counts, waived contingencies, and nearby-ZIP comparison — data no other scraper exposes
• 🏠 Freshest sold comps per location: the latest sales with sold price, $/sqft, beds/baths, sold date, MLS number, coordinates, listing remarks — in one charged row
• 🎓 Schools + climate risk in the same run: school ratings with parent reviews counts, plus flood/fire/heat/wind risk scores — replaces two extra tools
• 💰 One row = one full dataset: a whole 60-month series (360+ datapoints) costs a single per-row fee — not hundreds of per-item charges
• ✨ NEW — Listing demand signals: tour counts, view popularity vs the top-10% threshold, and price-drop eligibility per active listing

---

✨ Features

• 📈 Market Trends: 6 metrics × 60 monthly datapoints per location, each with YoY change
• 🔥 Market Hotness: Compete Score aggregates + the same scores for nearby ZIP codes
• 🏠 Recently Sold Comps: the latest sold homes per location with full pricing detail
• 🎓 School Intelligence: elementary/middle/high schools with 1–10 ratings, parent ratings, student-teacher ratios
• 🌊 Climate Risk: flood, fire, heat, and wind scores with properties-at-risk counts
• 🇺🇸 National Baseline: US-wide supply/demand series incl. the average 30-year mortgage rate
• 🎯 Listing Demand Signals: per-listing tour counts, view counts, price-drop eligibility
• 📍 Free-form locations: ZIP, "City, ST", county, or neighborhood — resolved automatically
• 🔗 Join-ready output: every row carries a resolved region ID and region name

---

🎬 Quick Start

Enter one or more locations, tick the datasets you want, and press Start. Each location × dataset produces one structured row. Export as JSON, CSV, or Excel.

curl -X POST "https://api.apify.com/v2/acts/sian.agency~redfin-market-analytics-scraper/runs?token=YOUR_TOKEN" \
-H 'Content-Type: application/json' \
-d '{"locations": ["Austin, TX", "78759"]}'

---

🚀 Getting Started (3 Simple Steps)

Step 1: Add locations

ZIP codes, cities, counties, or neighborhoods — mix them freely.

Step 2: Pick your datasets

Market trends, hotness, and sold comps are on by default; add schools, climate risk, or the national baseline with one click.

Step 3: Run and export

In under a minute you'll have:

• One row per location per dataset with the resolved region attached
• Full 60-month time series ready for charting
• Sold comps ready for underwriting models

---

📥 Input Configuration

Field	Type	Required	Description
locations	array	No*	US locations — ZIP, "City, ST", county, or neighborhood
includeMarketTrends	boolean	No	60-month price & demand series (default: true)
includeMarketHotness	boolean	No	Compete Score + nearby comparison (default: true)
includeRecentlySold	boolean	No	Latest sold comps (default: true)
includeSchools	boolean	No	Schools & ratings (default: false)
includeClimateRisk	boolean	No	Flood/fire/heat/wind risk (default: false)
includeNationalTrends	boolean	No	US baseline series, once per run (default: false)
listingSignals	array	No*	propertyId:listingId pairs for demand signals

* Provide at least one location, one listing-signal pair, or enable the national baseline.

Example:

{
  "locations": ["Austin, TX", "78759", "Travis County, TX"],
  "includeMarketTrends": true,
  "includeMarketHotness": true,
  "includeRecentlySold": true,
  "includeSchools": true,
  "includeClimateRisk": true,
  "includeNationalTrends": true
}

Listing demand signals (get both IDs from any row of our Redfin Property Scraper):

{
  "listingSignals": ["31327561:217326962"]
}

---

📤 Output

One row per location per dataset. Key fields:

Field	Type	Description
dataset	string	marketTrends, marketHotness, recentlySold, schools, climateRisk, nationalTrends, listingSignals
queryLocation	string	Your input location
regionId / regionName	string	Resolved region — stable join key
trendMetrics	array	Per metric: label, current value, 60 monthly {date, value, yoy} points
competeScore	object	Hotness score 0–100, sale-to-list, days on market, typical offers
nearbyCompeteScores	array	The same scores for neighboring ZIP codes
soldHomes	array	Sold comps: price, $/sqft, beds/baths, sold date, MLS#, coordinates
schools	array	Level, rating, parent rating, students, ratios, links
climateRisk	object	Flood/fire/heat/wind blocks with properties-at-risk counts
tourInsights / popularityInfo / priceDropInfo	object	Listing demand signals
fetchedAt	string	Extraction timestamp

Example (marketTrends row, abbreviated):

{
  "dataset": "marketTrends",
  "queryLocation": "78759",
  "regionId": "34149",
  "regionName": "78759, TX, USA",
  "trendMetrics": [
    {
      "section": "overview",
      "label": "Median Sale Price",
      "currentValue": "$614,817",
      "series": [
        { "date": "2026-05-31", "value": 614817, "yoy": "-10.2%" }
      ]
    }
  ],
  "fetchedAt": "2026-07-02T14:00:00.000Z"
}

---

💼 Use Cases & Examples

1. Market ranking for real-estate investors

Investors rank 50 target ZIP codes by price momentum and competitiveness before allocating capital.

Input: 50 ZIP codes, trends + hotness enabled Output: 100 rows — 60-month series and Compete Scores per ZIP Use: Sort by YoY price change and hotness score to shortlist buy markets.

2. Sold-comp feeds for underwriting

Acquisition analysts refresh sold comps weekly for valuation models.

Input: target neighborhoods with includeRecentlySold enabled Output: the freshest sales per area with $/sqft and sold dates Use: Feed automated ARV and cap-rate models with fresh comparable sales.

3. Neighborhood quality scoring for relocation platforms

PropTech products score neighborhoods on schools and climate safety.

Input: city list, schools + climate risk enabled Output: rated schools and flood/fire/heat/wind blocks per city Use: Build a location-quality index behind your search UI.

4. Market-vs-nation dashboards

Analysts index every local metric against the US baseline.

Input: metro list + includeNationalTrends: true Output: local series + one national row incl. 30-year mortgage rates Use: Show whether a market outperforms the national trend line.

5. Demand tracking for listing agents

Agents monitor buyer interest on their active listings.

Input: propertyId:listingId pairs from the Redfin Property Scraper Output: tour counts, view popularity, price-drop eligibility per listing Use: Time price adjustments with hard demand data.

6. Housing research & journalism

Researchers pull 5-year price histories across dozens of counties in minutes.

Input: county names Output: consistent monthly series with YoY deltas Use: Publish data-backed housing stories without manual collection.

---

🔗 Integration Examples

JavaScript/Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });

const run = await client.actor('sian.agency/redfin-market-analytics-scraper').call({
  locations: ['Austin, TX', '78759']
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0]);

Python

from apify_client import ApifyClient
client = ApifyClient('YOUR_TOKEN')

run = client.actor('sian.agency/redfin-market-analytics-scraper').call(
    run_input={'locations': ['Austin, TX', '78759']}
)

for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item['dataset'], item['regionName'])

cURL

curl -X POST 'https://api.apify.com/v2/acts/sian.agency~redfin-market-analytics-scraper/runs?token=YOUR_TOKEN' \
-H 'Content-Type: application/json' \
-d '{"locations": ["78759"]}'

Automation Workflows (n8n / Zapier / Make)

1. Trigger: weekly schedule
2. HTTP Request: start this actor with your market watchlist
3. Process: read the dataset rows
4. Action: update dashboards, alert on hotness or price-drop shifts

---

📊 Performance & Pricing

FREE Tier (Try It Now)

• 5 locations and 5 listing-signal pairs per run — full feature access, same data quality
• No credit card required
• Perfect for evaluating a new market

PAID Tier (Production Ready)

• Unlimited locations and listing signals per run
• Pay-per-row: one flat fee per location × dataset row — a full 60-month series counts as ONE row
• Priced for scheduled, fleet-wide market monitoring

💰 Best value on the market — comparable tools charge per datapoint or per listing; here one row fee buys an entire dataset for the location.

🔗 View current pricing

---

❓ Frequently Asked Questions

Q: What location formats are supported? A: ZIP codes (78759), cities (Austin, TX), counties (Travis County, TX), and neighborhoods. Each is resolved automatically to a market region echoed back in the row.

Q: How much history do the trend series cover? A: 60 monthly datapoints (5 years) per metric, each with the value and its year-over-year change.

Q: How many sold comps do I get? A: The most recent sales per location (typically the 8 latest) — all inside one charged row. Need the full sold inventory? Use our Redfin Property Scraper with the sold filter.

Q: Where do I get the IDs for listing demand signals? A: Every row of our Redfin Property Scraper contains both propertyId and listingId.

Q: What output formats are available? A: JSON, CSV, and Excel — export directly from the Apify dataset.

Q: Can I filter trends by property type? A: Not currently — trend series cover all property types combined.

Q: Is this legal? A: We only extract publicly available market statistics. See the legal section below.

---

🐛 Troubleshooting

A location returns an error or no row

• Check the spelling and add the state, e.g. Austin, TX instead of Austin
• Very small or rural areas may not have a resolvable market region — try the county

Listing signals return empty objects

• Signals only populate for ACTIVE listings; sold or delisted homes return zeros
• Verify the pair format: propertyId:listingId (both numeric)

Fewer sold comps than expected

• The dataset returns the latest sales snapshot; quiet markets have fewer — the row's soldHomesReturned shows the real count
• For the complete sold inventory of an area, use the Redfin Property Scraper sold mode

---

⚠️ Trademark Disclaimer

Redfin is a registered trademark of Redfin Corporation. This actor is an independent tool and is not affiliated with, endorsed by, or sponsored by Redfin Corporation. All trademarks, service marks, and company names are the property of their respective owners. This actor only accesses publicly available data.

---

⚖️ Is it legal to scrape data?

Our actors are ethical and do not extract any private user data, such as email addresses, gender, or location. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe.

However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

You can also read Apify's blog post on the legality of web scraping.

---

🤝 Support

Join our active support community

• For issues or questions, open an issue in the actor's repository
• Check SIÁN Agency Store for more automation tools
• 📧 apify@sian-agency.online

---

Built by SIÁN Agency | More Tools