python.md

Redfin Scraper — Python (apify-client) examples

Call the hosted Redfin Scraper from Python with the official apify-client.

Install

pip install apify-client

Basic run

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("logiover/redfin-scraper").call(run_input={
    "searchUrls": ["https://www.redfin.com/city/30818/TX/Austin"],
    "listingType": "forSale",
    "sortBy": "newest",
    "minPrice": 300000,
    "maxPrice": 900000,
    "minBeds": 3,
    "maxResults": 500,
})

for home in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(home["address"], home["price"], home["beds"], "bd /", home["baths"], "ba")

Sold comps for a ZIP into a pandas DataFrame

import pandas as pd
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("logiover/redfin-scraper").call(run_input={
    "searchUrls": ["90210"],
    "listingType": "sold",
    "sortBy": "recommended",
    "maxResults": 500,
})

items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
df = pd.DataFrame(items)

cols = ["address", "price", "soldDate", "beds", "baths", "sqFt", "pricePerSqFt", "daysOnMarket", "mlsId"]
print(df[cols].head(20))
df.to_csv("comps_90210.csv", index=False)

Rentals for a ZIP (yield analysis input)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("logiover/redfin-scraper").call(run_input={
    "searchUrls": ["33139"],
    "listingType": "rent",
    "minBeds": 2,
    "maxResults": 300,
})

rentals = list(client.dataset(run["defaultDatasetId"]).iterate_items())
priced = [r for r in rentals if r.get("price")]
avg_rent = sum(r["price"] for r in priced) / len(priced)
print(f"{len(rentals)} rentals, average asking rent ${avg_rent:,.0f}")

Compare price-per-sqft across several ZIPs

from collections import defaultdict
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("logiover/redfin-scraper").call(run_input={
    "searchUrls": ["78704", "78745", "78702"],
    "listingType": "forSale",
    "minBeds": 3,
    "maxResults": 1000,
})

by_zip = defaultdict(list)
for home in client.dataset(run["defaultDatasetId"]).iterate_items():
    if home.get("pricePerSqFt"):
        by_zip[home["zip"]].append(home["pricePerSqFt"])

for zip_code, ppsf in by_zip.items():
    print(f"{zip_code}: {len(ppsf)} homes, avg ${sum(ppsf) / len(ppsf):,.0f}/sqft")