How to Get Zillow Property Data with Python in 5 Minutes

Need Zillow property data for a Python project? Whether you're building an investment analysis tool, tracking home prices, or feeding data into a spreadsheet, the APIllow API makes it straightforward. In this tutorial, you'll go from zero to fetching property prices, Zestimates, and 50+ data fields in under 5 minutes.

Prerequisites

Python 3.7+
The requests library (pip install requests)
A free APIllow API key (get one here)

1 Get Your API Key

Sign up at apillow.co with your email. You'll receive an API key instantly. The free tier gives you 50 requests per month — enough to follow this tutorial and prototype your project.

2 Search for Properties

The APIllow API has one primary endpoint: POST /v1/properties. You can search by city, ZIP code, street address, Zillow URL, or property ID. Here's a basic search by city:

import requests
import time

API_KEY = "your_api_key_here"
BASE_URL = "https://api.apillow.co"

# Search for homes in Austin, TX
response = requests.post(
    f"{BASE_URL}/v1/properties",
    headers={"X-API-Key": API_KEY},
    json={
        "search": "Austin TX",
        "type": "sale",
        "max_items": 5
    }
)

data = response.json()
job_id = data["job_id"]
print(f"Job submitted: {job_id}")

The API is asynchronous — it returns a job_id immediately. This means you can submit large batch requests (up to 1,000 properties) without waiting for all results before getting a response.

3 Poll for Results

Use the job_id to check when your results are ready:

# Poll until results are ready
while True:
    result = requests.get(
        f"{BASE_URL}/v1/results/{job_id}",
        headers={"X-API-Key": API_KEY}
    ).json()

    if result["status"] == "complete":
        break
    elif result["status"] == "failed":
        print("Job failed:", result["errors"])
        break

    time.sleep(2)

properties = result["results"]
print(f"Got {len(properties)} properties")

4 Extract the Data You Need

Each property in the results contains 50+ structured fields. Here's how to extract the most useful ones:

for prop in properties:
    print(f"""
    Address:    {prop['street_address']}, {prop['city']}, {prop['state']} {prop['zipcode']}
    Price:      ${prop['price']:,}
    Zestimate:  ${prop.get('zestimate', 'N/A'):,}
    Beds/Baths: {prop['bedrooms']}bd / {prop['bathrooms']}ba
    Sqft:       {prop.get('living_area', 'N/A'):,}
    Year Built: {prop.get('year_built', 'N/A')}
    Status:     {prop.get('home_status', 'N/A')}
    """)

    # Access price history
    for event in prop.get("price_history", [])[:3]:
        print(f"    {event['date']}: {event['event']} - ${event.get('price', 0):,}")

    # Access nearby schools
    for school in prop.get("nearby_schools", [])[:2]:
        print(f"    School: {school['name']} (rating: {school.get('rating', 'N/A')}/10)")

5 Search by Address

Need data for a specific property? Pass a street address:

response = requests.post(
    f"{BASE_URL}/v1/properties",
    headers={"X-API-Key": API_KEY},
    json={
        "addresses": ["742 Evergreen Terrace, Springfield, IL"]
    }
)

You can also search by ZIP code, Zillow property ID (ZPID), or paste a Zillow URL directly:

# Search by ZIP code
{"zipcodes": ["90210", "90211"], "type": "sale", "max_items": 20}

# Search by Zillow URL
{"urls": ["https://www.zillow.com/homedetails/123-Main-St/12345_zpid/"]}

# Search by ZPID
{"zpids": ["12345", "67890"]}

Full Working Example

Here's a complete script that searches for properties and saves results to a CSV file:

import requests
import time
import csv

API_KEY = "your_api_key_here"
BASE = "https://api.apillow.co"
HEADERS = {"X-API-Key": API_KEY}

# Submit search
resp = requests.post(f"{BASE}/v1/properties", headers=HEADERS, json={
    "search": "Denver CO",
    "type": "sale",
    "max_items": 25
})
job_id = resp.json()["job_id"]

# Wait for results
while True:
    result = requests.get(f"{BASE}/v1/results/{job_id}", headers=HEADERS).json()
    if result["status"] != "processing":
        break
    time.sleep(2)

# Save to CSV
fields = ["street_address", "city", "state", "zipcode", "price",
          "zestimate", "bedrooms", "bathrooms", "living_area", "year_built"]

with open("properties.csv", "w", newline="") as f:
    writer = csv.DictWriter(f, fieldnames=fields, extrasaction="ignore")
    writer.writeheader()
    writer.writerows(result["results"])

print(f"Saved {len(result['results'])} properties to properties.csv")

Available Data Fields

Every property response can include these fields (when available on the listing):

Location: street_address, city, state, zipcode, latitude, longitude
Pricing: price, zestimate, rent_zestimate, last_sold_price, tax_assessed_value, hoa_fee
Details: bedrooms, bathrooms, living_area, lot_size, year_built, property_type, home_status
History: price_history[], tax_history[]
Agent: listing_agent (name, phone, email, company)
Schools: nearby_schools[] with ratings and distance
Media: image_urls[], virtual_tour_url
Extras: description, features, comps, days_on_market, page_views, favorites_count

Ready to try it?

Get your free API key and start fetching Zillow data in Python today.

Get Your Free API Key

Next Steps

Read the full API documentation for advanced options
Check out the pricing comparison to find the right plan
Set up the MCP server to query Zillow data from Claude or Cursor