Airbnb vs Zillow Rental Listings: Comparing Short-Term and Long-Term Rental Data

“Rental data” means two different things depending on who is asking. A short-term rental (STR) investor evaluating a condo for Airbnb arbitrage needs nightly price, occupancy signals and superhost density. A property manager setting rent on a 12-month lease needs monthly rent, availability date and comparable unit specs. Both live on different platforms, in different data shapes, and answering the wrong question with the wrong dataset wastes time.

This post compares the Airbnb Scraper and the Zillow Rental Listings Scraper — what each actually returns, where the fields diverge, and which one fits your use case.

The Core Difference: Nightly vs Monthly

Airbnb listings are priced per night and turn over multiple times a month. A single unit can generate 15-25 bookings across a year, each with its own price depending on season, day of week and demand. Zillow rental listings are priced per month on a standard 12-month (or month-to-month) lease and represent a single, stable asking rent until the unit is leased or the price is adjusted.

That distinction cascades into everything else about the data: what fields exist, how you’d model revenue, and who the end user of the dataset actually is.

Field-by-Field Comparison

Field	Airbnb Scraper	Zillow Rental Listings
Price	price (nightly, numeric) + priceText	price / monthlyRent (monthly, numeric) + priceLabel
Guest capacity	personCapacity	Not applicable (unit-based, not guest-based)
Bedrooms/bathrooms	Not returned directly	beds, baths
Square footage	Not returned	sqft
Property type	propertyType (e.g. “Entire rental unit”)	propertyType (APARTMENT, HOUSE, TOWNHOUSE, CONDO)
Rating / review signal	rating, reviewsCount, isSuperhost, badges	Not applicable — Zillow rentals carry no review layer
Availability	Implied by check-in/check-out search	availableFrom — explicit move-in date
Estimates	Not returned	zestimate, rentZestimate
Location	latitude, longitude, city	latitude, longitude, full address, city, state, zipCode
Identifier	listingId	zpid
Listing URL	url	detailUrl
Photo	photoUrl	imgSrc

The most telling gap runs in both directions. Airbnb data carries trust and demand signals — rating, review count, superhost status, badges like “Guest favorite” — because a guest is choosing between dozens of similar units for a few nights and reputation drives conversion. Zillow rental data carries none of that, because a 12-month tenant is evaluating the unit itself, not a host’s track record. Instead Zillow gives you unit specifics (beds, baths, sqft) and valuation context (zestimate, rentZestimate) that a long-term renter or landlord actually needs and Airbnb has no equivalent for.

Who Should Use Which

Use the Airbnb Scraper when you’re:

• Running STR arbitrage analysis — comparing nightly rate potential against a 12-month lease cost for the same neighborhood
• Building dynamic pricing models for existing Airbnb listings, using comparable price/rating/review data
• Researching a market before buying or listing a short-term rental property
• Building a travel or stay-comparison product that needs coordinates, photos and superhost signals

Use the Zillow Rental Listings Scraper when you’re:

• Benchmarking asking rents for a property management portfolio against live comparables
• Building lead lists of fresh rental inventory for outreach to landlords or property managers
• Underwriting a buy-and-hold investment where the exit is a long-term tenant, not nightly bookings
• Feeding a dashboard or BI tool that tracks rent-per-ZIP or rent-per-bedroom trends over time

The STR-vs-LTR Arbitrage Case

The most common reason to pull both datasets side by side is arbitrage math: is a unit worth more as a short-term rental or a long-term lease? That comparison only works if you pull comparable geographic data from both sources.

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

# Short-term comparables
airbnb_run = client.actor("themineworks/airbnb-scraper").call(run_input={
    "location": "Austin, TX",
    "maxItems": 100,
})
airbnb_listings = [i for i in client.dataset(airbnb_run["defaultDatasetId"]).iterate_items()]

# Long-term comparables
zillow_run = client.actor("themineworks/zillow-rental-listings").call(run_input={
    "location": "Austin, TX",
    "maxItems": 100,
    "rentalType": "APARTMENT",
})
zillow_listings = [i for i in client.dataset(zillow_run["defaultDatasetId"]).iterate_items()]

avg_nightly = sum(l["price"] for l in airbnb_listings if l.get("price")) / len(airbnb_listings)
avg_monthly_rent = sum(l["price"] for l in zillow_listings if l.get("price")) / len(zillow_listings)

# Rough STR monthly revenue at 65% occupancy
str_monthly_estimate = avg_nightly * 30 * 0.65

print(f"Avg Airbnb nightly rate: ${avg_nightly:.0f}")
print(f"Estimated STR monthly revenue (65% occupancy): ${str_monthly_estimate:.0f}")
print(f"Avg Zillow long-term monthly rent: ${avg_monthly_rent:.0f}")

This is a rough model — actual STR economics need cleaning fees, management overhead, seasonal variance and local regulation checked before it means anything commercially. But it illustrates why you’d want both datasets: neither one alone answers the arbitrage question.

Pricing

Both actors are pay-per-result. The Airbnb Scraper charges per listing returned. The Zillow Rental Listings Scraper is $1 per 1,000 results ($0.001 each). Neither charges for blocked pages, CAPTCHAs, or failed requests — you only pay for data actually returned.

Both require no login, no API key and no account with the source platform. Airbnb has no public listings API at all; Zillow’s official APIs are deprecated and partner-gated, so neither market has a legitimate self-serve alternative for this kind of bulk structured data.

Getting Started

For a full field reference and setup walkthrough on the short-term side, see Airbnb Scraper: Python guide with no API required. For the long-term rental side, see Zillow Rental Listings API alternative in Python. Both actors are also exposed as MCP tools, so you can ask an AI agent to pull comparables directly without writing a script for a one-off lookup.