If you've ever tried scraping Zillow with BeautifulSoup or Selenium, you know the pain. CAPTCHAs, IP bans, constantly changing HTML selectors, headless browser detection — it's an arms race you're not going to win.

I spent way too long fighting anti-bot systems before switching to an API-based approach. This post walks through how to pull Zillow property data, search listings, get Zestimates, and export everything to CSV/Excel — all with plain Python and zero browser automation.

What You'll Need

• Python 3.7+

• The requests library (pip install requests)

• A free API key from RealtyAPI

That's it. No Selenium. No Playwright. No proxy rotation.

Getting Started: Your First Property Lookup

Let's start simple — get full property details for a single address:

import requests

url = "https://zillow.realtyapi.io/pro/byaddress"

params = { "propertyaddress": "1875 AVONDALE Circle, Jacksonville, FL 32205" }

headers = { "x-realtyapi-key": "YOUR_API_KEY" }

response = requests.get(url, headers=headers, params=params) data = response.json()

print(f"Price: ${data['price']:,}") print(f"Bedrooms: {data['bedrooms']}") print(f"Bathrooms: {data['bathrooms']}") print(f"Sqft: {data['livingArea']:,}") print(f"Zestimate: ${data['zestimate']:,}") print(f"Year Built: {data['yearBuilt']}")`

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Sample output:

Price: $349,900 Bedrooms: 4 Bathrooms: 2 Sqft: 2,150 Zestimate: $355,200 Year Built: 1926

Price: $349,900 Bedrooms: 4 Bathrooms: 2 Sqft: 2,150 Zestimate: $355,200 Year Built: 1926

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The response includes 100+ fields — tax history, lot size, HOA, days on market, description, agent info, and a lot more. This single call replaces what would take dozens of Selenium page loads and parsing logic.

Searching for Listings (For Sale, Rent, or Sold)

This is where it gets powerful. You can search any location with filters, just like Zillow's own search page:

import requests

url = "https://zillow.realtyapi.io/search/byaddress"

params = { "location": "Austin, TX", "listing_status": "For_Sale", "sort_order": "Newest", "bed_min": "3", "bathrooms": "TwoPlus", "list_price_range": "min:300000, max:600000", "home_type": "Houses,Townhomes", "page": 1 }

headers = { "x-realtyapi-key": "YOUR_API_KEY" }

response = requests.get(url, headers=headers, params=params) results = response.json()

print(f"Total results: {results['totalResultCount']}") print(f"Returned: {len(results['searchResults'])} listings\n")

for listing in results["searchResults"][:5]: print(f"{listing['address']} — ${listing['price']:,} | {listing['bedrooms']}bd/{listing['bathrooms']}ba | {listing['livingArea']} sqft")`

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Sample output:

Total results: 847 Returned: 200 listings

Total results: 847 Returned: 200 listings

4512 Crestway Dr, Austin, TX 78731 — $579,000 | 4bd/3ba | 2,340 sqft 1208 Payton Falls Dr, Austin, TX 78748 — $425,000 | 3bd/2ba | 1,876 sqft 9305 Bradner Dr, Austin, TX 78749 — $549,900 | 3bd/2ba | 2,100 sqft 7201 Elm Creek Dr, Austin, TX 78744 — $310,000 | 3bd/2ba | 1,450 sqft 2814 S 5th St, Austin, TX 78704 — $599,000 | 3bd/3ba | 1,920 sqft`

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Each page returns up to 200 listings, and you can paginate up to 5 pages (1,000 results total per search).

Search for rentals:

params = {  "location": "Los Angeles, CA",  "listing_status": "For_Rent",  "sort_order": "Price_Low_to_High",  "bed_min": "1",  "home_type": "Apartments/Condos/Co-ops",  "page": 1 }

params = {  "location": "Los Angeles, CA",  "listing_status": "For_Rent",  "sort_order": "Price_Low_to_High",  "bed_min": "1",  "home_type": "Apartments/Condos/Co-ops",  "page": 1 }

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Search recently sold homes:

params = {  "location": "Miami, FL",  "listing_status": "Sold",  "sold_in_last": "30_days",  "sort_order": "Newest",  "page": 1 }

params = {  "location": "Miami, FL",  "listing_status": "Sold",  "sold_in_last": "30_days",  "sort_order": "Newest",  "page": 1 }

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Getting Zestimate History (10 Years of Estimates)

Track how Zillow's estimated value has changed over time:

import requests

url = "https://zillow.realtyapi.io/graph_charts"

params = { "which": "zestimate_history", "byaddress": "1875 AVONDALE Circle, Jacksonville, FL 32205", "recent_first": "True" }

headers = { "x-realtyapi-key": "YOUR_API_KEY" }

response = requests.get(url, headers=headers, params=params) history = response.json()

for point in history["data"][:6]: print(f"{point['date']}: ${point['value']:,}")`

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Sample output:

2026-04-01: $355,200 2026-03-01: $352,800 2026-02-01: $351,000 2026-01-01: $348,500 2025-12-01: $345,100 2025-11-01: $342,700

2026-04-01: $355,200 2026-03-01: $352,800 2026-02-01: $351,000 2026-01-01: $348,500 2025-12-01: $345,100 2025-11-01: $342,700

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Great for building valuation charts or tracking market trends programmatically.

Finding Comparable Homes (Comps)

If you're doing investment analysis or appraisals, comps are essential:

import requests

url = "https://zillow.realtyapi.io/comparable_homes"

params = { "byaddress": "1875 AVONDALE Circle, Jacksonville, FL 32205" }

headers = { "x-realtyapi-key": "YOUR_API_KEY" }

response = requests.get(url, headers=headers, params=params) comps = response.json()

for comp in comps["comparables"][:5]: print(f"{comp['address']} — ${comp['price']:,} | {comp['bedrooms']}bd | {comp['livingArea']} sqft")`

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You can also look up by ZPID or Zillow URL instead of address — the API accepts all three.

Walk Score, Transit Score, and Bike Score

import requests

url = "https://zillow.realtyapi.io/walk_transit_bike"

params = { "byaddress": "350 5th Ave, New York, NY 10118" }

headers = { "x-realtyapi-key": "YOUR_API_KEY" }

response = requests.get(url, headers=headers, params=params) scores = response.json()

print(f"Walk Score: {scores['walkScore']}/100") print(f"Transit Score: {scores['transitScore']}/100") print(f"Bike Score: {scores['bikeScore']}/100")`

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Sample output:

Walk Score: 98/100 Transit Score: 100/100 Bike Score: 76/100

Walk Score: 98/100 Transit Score: 100/100 Bike Score: 76/100

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Saving Results to CSV / Excel

Here's a practical pattern for bulk lookups saved to a spreadsheet:

import requests import csv

import requests import csv

API_KEY = "YOUR_API_KEY" BASE_URL = "https://zillow.realtyapi.io" headers = {"x-realtyapi-key": API_KEY}

List of addresses to look up

addresses = [ "1875 AVONDALE Circle, Jacksonville, FL 32205", "350 5th Ave, New York, NY 10118", "1600 Pennsylvania Ave, Washington, DC 20500", "3828 Double Oak Ln, Irving, TX 75061", "742 Evergreen Terrace, Springfield, IL 62704" ]

results = []

for addr in addresses: print(f"Fetching: {addr}") resp = requests.get( f"{BASE_URL}/pro/byaddress", headers=headers, params={"propertyaddress": addr} )

if resp.status_code == 200: d = resp.json() results.append({ "address": addr, "price": d.get("price", ""), "zestimate": d.get("zestimate", ""), "bedrooms": d.get("bedrooms", ""), "bathrooms": d.get("bathrooms", ""), "sqft": d.get("livingArea", ""), "year_built": d.get("yearBuilt", ""), "lot_size": d.get("lotAreaValue", ""), "home_type": d.get("homeType", ""), "days_on_zillow": d.get("daysOnZillow", ""), }) else: print(f" Error {resp.status_code} for {addr}")

Write to CSV (opens in Excel, Google Sheets, etc.)

with open("zillow_data.csv", "w", newline="") as f: writer = csv.DictWriter(f, fieldnames=results[0].keys()) writer.writeheader() writer.writerows(results)

print(f"\nDone! Saved {len(results)} properties to zillow_data.csv")`

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Output CSV looks like:

address price zestimate bedrooms bathrooms sqft year_built home_type days_on_zillow

1875 AVONDALE Circle, Jacksonville, FL 349900 355200 4 2 2150 1926 SINGLE_FAMILY 12

350 5th Ave, New York, NY 2850000 2910000 3 2 1800 1931 CONDO 45

... ... ... ... ... ... ... ... ...

You can open this directly in Excel or Google Sheets.

Want pandas instead?

import pandas as pd

df = pd.DataFrame(results) df.to_excel("zillow_data.xlsx", index=False) # requires openpyxl df.to_csv("zillow_data.csv", index=False)`

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Saving Search Results to CSV

Same idea works for search results — pull hundreds of listings and dump to a spreadsheet:

import requests import csv

import requests import csv

API_KEY = "YOUR_API_KEY" headers = {"x-realtyapi-key": API_KEY}

all_listings = []

Paginate through up to 5 pages

for page in range(1, 6): print(f"Fetching page {page}...") resp = requests.get( "https://zillow.realtyapi.io/search/byaddress", headers=headers, params={ "location": "Denver, CO", "listing_status": "For_Sale", "bed_min": "2", "list_price_range": "min:200000, max:500000", "page": page } )

data = resp.json() listings = data.get("searchResults", []) if not listings: break

for l in listings: all_listings.append({ "address": l.get("address", ""), "price": l.get("price", ""), "bedrooms": l.get("bedrooms", ""), "bathrooms": l.get("bathrooms", ""), "sqft": l.get("livingArea", ""), "zpid": l.get("zpid", ""), "url": l.get("detailUrl", ""), })

with open("denver_listings.csv", "w", newline="") as f: writer = csv.DictWriter(f, fieldnames=all_listings[0].keys()) writer.writeheader() writer.writerows(all_listings)

print(f"Saved {len(all_listings)} listings to denver_listings.csv")`

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Searching by Coordinates (Radius Search)

Useful if you're building a map-based tool or have lat/lng data:

import requests

url = "https://zillow.realtyapi.io/search/bycoordinates"

params = { "latitude": "40.599283", "longitude": "-74.129194", "radius": "0.5", "listing_status": "For_Sale" }

headers = { "x-realtyapi-key": "YOUR_API_KEY" }

response = requests.get(url, headers=headers, params=params) data = response.json()

print(f"Found {data['totalResultCount']} listings within 0.5 miles")`

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More Endpoints Worth Knowing

Beyond what's shown above, here are some other useful endpoints:

Endpoint Route What It Returns

Property Images /propimages All listing photos for a property

Price History /pricehistory Full price change + tax history

Similar Properties /similar_properties Properties Zillow considers "similar"

Nearby Properties /nearby_properties Properties in the immediate area

Climate Risk /climate Flood, fire, heat, wind risk scores

Tax History /taxinfo_history Tax assessments over time

Owner/Agent Info /owner-agent Listing agent and owner details

Housing Market Data /housing_market Zillow Home Value Index (ZHVI) for any city

Agent Search /agent/search Find agents by location, specialty, language

Agent Reviews /agent/reviews Agent ratings and review text

Skip Trace /skip/byaddress Find property owner contact info

MLS Search /search/bymls Look up a listing by MLS number

Autocomplete /autocomplete Search suggestions (like Zillow's search bar)

All endpoints use the same authentication header and base URL. Just swap the route and params.

There's Also a Ready-Made Python Scraper

If you don't want to write the API calls yourself, there's an open-source CLI tool that wraps all of this with interactive prompts, bulk processing from JSON files, progress bars, and CSV/JSON export built in:

github.com/realtyapi/Zillow-Scraper-API-Python

Clone it, add your API key, and run python scraper.py — it gives you a menu to test any endpoint or run bulk jobs from a JSON file.

Other Real Estate APIs on the Same Platform

RealtyAPI also has endpoints for other platforms if you need data beyond Zillow:

• Redfin API — Redfin listings, agent data, search by address/coordinates

• Airbnb API — Short-term rental data, pricing, availability, reviews

• StreetEasy API — NYC-specific listings and building data

• Bayut API — UAE/Dubai property listings and agent data

All use the same API key and the same x-realtyapi-key header pattern.

Quick Reference

Base URL: https://zillow.realtyapi.io Auth Header: x-realtyapi-key: YOUR_API_KEY Method: GET (all endpoints) Format: JSON responses Get API Key: https://www.realtyapi.io

Base URL: https://zillow.realtyapi.io Auth Header: x-realtyapi-key: YOUR_API_KEY Method: GET (all endpoints) Format: JSON responses Get API Key: https://www.realtyapi.io

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Hope this saves you the headaches I went through. If you have questions, drop a comment — happy to help.