STR vs LTR Market Lens — Airbnb, Vrbo & Apartments.com 🚀

🎉 NEW: The first cross-channel STR-vs-LTR decision tool on the Apify Store — pulls Airbnb, Vrbo, and Apartments.com in one run and normalizes every row to a single monthly_equivalent_rate_usd for side-by-side acquisition due diligence.

Built for real-estate investors, STR operators, and acquisition analysts who need to answer "Should I list this property short-term or long-term?" before they buy — not after.

📋 Overview

Should this property earn more as an Airbnb, a Vrbo, or a 12-month rental? — that's the acquisition-phase question this actor answers in a single run. Most rental scrapers serve operators inside one channel. This one serves the buyer-side decision across channels.

Out of ~25,000 actors on the Apify Store, zero ship a cross-platform STR-vs-LTR comparison. The off-Apify analogs (AirDNA, KeyData, Rabbu, Mashvisor) start at $20–$300/month subscriptions, and Mashvisor — the closest comparable — doesn't even cover Vrbo. At $0.012 per marketComparisonRow on the BRONZE tier, you undercut the cheapest of them on pay-per-use AND add the Vrbo channel they miss.

Why investors and analysts choose this actor:

• ✅ STR-vs-LTR Acquisition Lens: The only Apify actor that fuses short-term and long-term rates side-by-side — built for the moment before you buy, not for ongoing ops.
• ⚡ 3 Platforms, 1 API Call: Pull Airbnb, Vrbo, and Apartments.com in parallel from a single input. Cross-channel fan-out finishes in ~45 seconds for 50 rows/channel.
• 🎯 Normalized monthly_equivalent_rate_usd: Every row carries the cross-channel comparison primitive — nightly × 30.4 × occupancy for STR, native monthly rent for LTR. Sort, compare, decide.
• 💰 Pay-Per-Row, Not Per-Month: $0.012 per market-comparison row vs Mashvisor's $35/month floor. Casual investors stop paying when they stop researching.
• 💎 Break-Even Occupancy %: The single most useful number for an STR acquisition — "this Airbnb needs 75% occupancy to beat the LTR baseline." Computed on every STR row.
• ✨ NEW: cross_listing_candidates[] — coordinate-proximity matching (50 m + bedroom-count + property-type) surfaces "same property listed on both Airbnb and Vrbo" without an exact address join.

✨ Features

• 🏘 3-Channel Cross-Fusion: Airbnb (STR), Vrbo (STR), and Apartments.com (LTR) merged into one schema, one dataset, one report.
• 💰 Monthly-Equivalent Rate Normalization: Convert any nightly rate to a comparable monthly figure using a configurable occupancy assumption (default 0.70).
• 🎯 Break-Even Occupancy %: For every STR row, the minimum bookings needed to match the run's median LTR monthly rent — the decision-critical number.
• 🔗 Cross-Listing Candidate Detection: Identifies likely cross-listed properties using 50 m coordinate proximity + bedroom + property-type match.
• 📊 HTML Market Report: A self-contained, shareable report (report.html) with channel medians, break-even distribution, and cross-listing match counts — written to the key-value store on every run.
• 📅 Optional Live Calendar Enrichment: Toggle enrichSTRCalendar to pull forward-12-month Airbnb and Vrbo availability per listing and replace the static occupancy baseline with real bookings data.
• 🔍 3 Search Modes: Location string (Miami, FL), coordinates + radius (km), or US ZIP code — pick whichever matches your sourcing workflow.
• 🔢 Pay-Per-Event Pricing: Charged only for the rows you receive — and only for the headline event when 2+ channels return data. Single-channel runs pay commodity rates.
• 🎁 Free Tier: 25 rows per channel — enough to scope a market end-to-end before committing.
• 📈 Warehouse-Ready Output: Standard JSON-line dataset → Snowflake / BigQuery / Postgres via Apify's built-in integrations.

🎬 Quick Start

Set a market, pick your channels, run. Every row in the dataset carries a monthly_equivalent_rate_usd you can sort and compare across Airbnb, Vrbo, and Apartments.com directly.

curl -X POST "https://api.apify.com/v2/acts/sian.agency~str-investor-market-lens/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchMode": "location", "location": "Miami, FL"}'

🚀 Getting Started (3 Simple Steps)

Step 1: Pick a market

Open the actor in Apify Console and set searchMode to location with a target like Miami, FL, or coordinates for a precise neighbourhood radius, or zip for a US postal code.

Step 2: Confirm channels

Leave channels at the default ["airbnb", "vrbo", "apartments"] — the headline marketComparisonRow event only fires when 2+ channels return data, so cross-channel runs unlock the full value.

Step 3: Run and open report.html

Click Start. Dataset rows arrive in real time; the HTML market report lands in the key-value store on completion — open it in any browser to see channel medians, break-even distribution, and cross-listing hits.

That's it! In under 60 seconds for a standard market, you'll have:

• A normalized monthly_equivalent_rate_usd on every row across all three channels
• A break_even_occupancy_pct for every STR row — the minimum bookings to match LTR
• A cross_listing_candidates[] array flagging likely Airbnb↔Vrbo duplicates
• A shareable HTML market report with median rates per channel
• A warehouse-ready dataset that plumbs straight into Snowflake, BigQuery, or Google Sheets

📥 Input Configuration

Field	Type	Required	Description
searchMode	enum	Yes	location, coordinates, or zip
location	string	When searchMode=location	Free-text destination — Miami, FL, Brooklyn, NY
latitude	string	When searchMode=coordinates	Decimal latitude (-90 to 90)
longitude	string	When searchMode=coordinates	Decimal longitude (-180 to 180)
radiusKm	integer	No (default 10)	Search radius in km for coordinate mode (1-100)
zip	string	When searchMode=zip	5-digit US ZIP code
channels	array	No (default all 3)	Subset of ["airbnb", "vrbo", "apartments"]
maxResultsPerChannel	integer	No (default 50)	Per-channel row cap (FREE tier locked to 25)
occupancyBaseline	string	No (default "0.70")	STR occupancy assumption for monthly-equivalent calc
enrichSTRCalendar	boolean	No (default false)	Pull live Airbnb / Vrbo availability calendars per listing
computeCrossListingCandidates	boolean	No (default true)	Compute cross_listing_candidates[] per row (free)
checkIn	string	No	ISO YYYY-MM-DD — drives STR per-night prices for the window
checkOut	string	No	ISO YYYY-MM-DD — must be paired with checkIn

Example — single market sweep:

{
  "searchMode": "location",
  "location": "Miami, FL",
  "channels": ["airbnb", "vrbo", "apartments"],
  "maxResultsPerChannel": 50,
  "occupancyBaseline": "0.70"
}

Example — coordinate-radius scan with live calendar:

{
  "searchMode": "coordinates",
  "latitude": "25.77",
  "longitude": "-80.19",
  "radiusKm": 5,
  "channels": ["airbnb", "vrbo", "apartments"],
  "enrichSTRCalendar": true,
  "computeCrossListingCandidates": true
}

Example — US ZIP scan:

{
  "searchMode": "zip",
  "zip": "33134",
  "channels": ["airbnb", "vrbo", "apartments"]
}

📤 Output

Results are saved to the Apify dataset with 30+ fields per row, including the cross-channel comparison primitives that drive the acquisition decision:

Field	Type	Description
source_platform	string	airbnb, vrbo, or apartments
listing_id	string	Native ID (Airbnb base64, Vrbo numeric, Apartments listingKey)
url	string	Canonical listing URL
name	string	Listing title
address_line	string	Street address — Apartments.com only
city, state, postal_code	string	Location signals
latitude, longitude	number	Geographic anchor (when available per platform)
bedrooms, bedrooms_max, bathrooms, property_type	mixed	Unit profile
rental_type	string	STR for Airbnb/Vrbo, LTR for Apartments
nightly_rate_usd	number	Per night for STR; monthly_rent / 30.4 for LTR
monthly_rent_usd	number	Native for LTR; null for STR
monthly_equivalent_rate_usd	number	The cross-channel comparison primitive
occupancy_decimal_used	number	Occupancy used in the calc
occupancy_source	string	baseline, live_calendar, or ltr_default
break_even_occupancy_pct	number	STR only — minimum bookings to match LTR median
cross_listing_candidates	array	Peer-platform rows within 50 m + same bedrooms
is_market_comparison_row	boolean	True when the row was charged the headline event
channels_in_run	array	Which channels produced data in this run
review_count, rating, photos_count, primary_image_url	mixed	Quality signals
calendar_summary	object	Populated when calendar enrichment is enabled
scraped_at	string	ISO timestamp

Worked example — Miami 1BR, 2026-05-21:

[
  {
    "source_platform": "apartments",
    "listing_id": "218qyk8",
    "city": "Miami",
    "state": "FL",
    "bedrooms": 1,
    "rental_type": "LTR",
    "monthly_rent_usd": 3210,
    "monthly_equivalent_rate_usd": 3210,
    "nightly_rate_usd": 105.59,
    "occupancy_source": "ltr_default",
    "is_market_comparison_row": true
  },
  {
    "source_platform": "airbnb",
    "listing_id": "53568731",
    "city": "Miami",
    "bedrooms": 1,
    "rental_type": "STR",
    "nightly_rate_usd": 140.40,
    "occupancy_decimal_used": 0.70,
    "occupancy_source": "baseline",
    "monthly_equivalent_rate_usd": 2987.71,
    "break_even_occupancy_pct": 75.2,
    "is_market_comparison_row": true
  },
  {
    "source_platform": "vrbo",
    "listing_id": "117961754",
    "city": "Miami",
    "bedrooms": 1,
    "rental_type": "STR",
    "nightly_rate_usd": 58.00,
    "occupancy_decimal_used": 0.70,
    "occupancy_source": "baseline",
    "monthly_equivalent_rate_usd": 1235.04,
    "break_even_occupancy_pct": 182.4,
    "is_market_comparison_row": true
  }
]

Reading the row above: the Apartments.com baseline pays $3,210/month. The Airbnb 1BR at $140.40/night needs 75% occupancy to match it — achievable in South Beach. The Vrbo cabin at $58/night would need 182% occupancy (impossible) — it should stay LTR. One run, three channels, one decision.

The actor also writes a self-contained HTML market report to the key-value store at report.html — channel medians, break-even distribution, cross-listing hit count, ready to share.

💼 Use Cases & Examples

1. Acquisition Due Diligence for STR Investors

Real-estate investors evaluating a property before purchase

Input: Target zip or coordinates around a candidate property Output: Median Airbnb / Vrbo / LTR rates for the neighbourhood + the unit's break-even occupancy Use: Decide whether to underwrite the unit as a short-term rental, long-term rental, or pass on the deal entirely — before committing capital.

2. Market-Entry Feasibility for STR Operators

STR managers expanding into new cities

Input: 5 candidate markets, run sequentially or in parallel Output: Median monthly_equivalent_rate_usd per channel per market Use: Pick the city with the widest STR-over-LTR delta — that's where your operational margin is largest.

3. Channel-Mix Optimization for Mixed Portfolios

Property managers running blended STR + LTR books

Input: Coordinates around each existing unit Output: Side-by-side neighbourhood rates for both channels Use: Flag mis-channelled units — long-term leases earning what short-term could be earning, or vice versa — and re-platform them.

4. Listing-Price Benchmarking for Independent Landlords

Solo landlords with 1-5 units

Input: Single neighbourhood, default channels Output: Median market rates across STR and LTR Use: Verify your current lease rate is at market — or discover you've been under-pricing for years.

5. Investor-Report Automation for Real-Estate Funds

Acquisition analysts at REITs and PE funds

Input: Scripted batch of target markets, daily or weekly Output: Warehouse-ready dataset piped into Snowflake / BigQuery / Redshift Use: marketComparisonRow is the canonical investment-grade row — feeds straight into portfolio underwriting models.

6. AirDNA / Mashvisor Alternative for Casual Researchers

Solo investors not ready to subscribe to a $35/month tool

Input: One market, one run Output: All the rate intelligence Mashvisor charges $35/month for + Vrbo data they don't ship Use: Pay $0.50 once for a market scan vs $420/year for a subscription you'll use once.

7. Cross-Listing Discovery for Compliance Audits

City officials and HOAs investigating short-term rental compliance

Input: Specific neighbourhood coordinates Output: cross_listing_candidates[] — likely same-property cross-listings between Airbnb and Vrbo Use: Identify operators running unpermitted STR units across multiple platforms.

🔗 Integration Examples

JavaScript / Node.js

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

const run = await client.actor('sian.agency/str-investor-market-lens').call({
  searchMode: 'location',
  location: 'Miami, FL',
  channels: ['airbnb', 'vrbo', 'apartments'],
  maxResultsPerChannel: 50
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
const headline = items.filter(r => r.is_market_comparison_row);
console.log(`${headline.length} comparison rows across ${[...new Set(headline.map(r => r.source_platform))].length} channels`);

Python

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

run = client.actor('sian.agency/str-investor-market-lens').call(
    run_input={
        'searchMode': 'location',
        'location': 'Miami, FL',
        'channels': ['airbnb', 'vrbo', 'apartments'],
        'enrichSTRCalendar': False,
    }
)

for item in client.dataset(run['defaultDatasetId']).iterate_items():
    if item.get('rental_type') == 'STR' and item.get('break_even_occupancy_pct'):
        print(item['source_platform'], item['city'], item['break_even_occupancy_pct'])

cURL

curl -X POST "https://api.apify.com/v2/acts/sian.agency~str-investor-market-lens/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchMode": "location", "location": "Miami, FL", "channels": ["airbnb", "vrbo", "apartments"]}'

jq — Sort STR rows by margin over LTR baseline

curl "https://api.apify.com/v2/datasets/<DATASET_ID>/items?token=<TOKEN>" \
  | jq '[.[] | select(.rental_type=="STR" and .break_even_occupancy_pct)] | sort_by(.break_even_occupancy_pct) | .[0:10]'

The rows with the lowest break-even occupancy are the strongest STR acquisitions — they need the fewest booked nights to clear the LTR floor.

Automation Workflows (N8N / Zapier / Make)

1. Trigger: Schedule (daily / weekly) or webhook on a new sourcing record
2. HTTP Request: Call the actor with the target market
3. Process: Filter rows where is_market_comparison_row === true
4. Action: Write to Google Sheets / Snowflake / Notion / Slack

📊 Performance & Pricing

FREE Tier (Scope a market for free)

• 25 rows per channel per run — full schema, same normalization math
• Calendar enrichment disabled
• No credit card required
• Perfect for sizing up a single market before committing budget

PAID Tier (Production-ready acquisition workflow)

• Up to 500 rows per channel per run
• Live STR calendar enrichment unlocked
• Pay-per-event — you're charged only for rows you receive, and only for the headline event when 2+ channels return data
• Tiered discounts unlock automatically — BRONZE → DIAMOND, no manual upgrades

Event	What triggers it	BRONZE	GOLD	DIAMOND
apify-actor-start	One per run, post-validation	$0.005	$0.005	$0.005
marketComparisonRow	Headline — row with monthly_equivalent_rate_usd when ≥2 channels active	$0.012	$0.008	$0.005
airbnbListingExtracted	Per Airbnb row	$0.004	$0.003	$0.002
vrboListingExtracted	Per Vrbo row	$0.004	$0.003	$0.002
apartmentsListingExtracted	Per Apartments.com row	$0.004	$0.003	$0.002
strCalendarEnriched	Per STR listing with live calendar fetch	$0.015	$0.010	$0.006

💰 Best price on the market — Mashvisor starts at $35/month for a similar comparison (and skips Vrbo). At $0.012 per marketComparisonRow, $20 of credit buys ~500 cross-channel comparison rows.

Single-channel runs (only airbnb, only vrbo, or only apartments selected) do not trigger the headline event — you pay commodity rates only.

🔗 View current pricing

❓ Frequently Asked Questions

Q: Why don't Airbnb and Vrbo rows in my dataset have a street address? A: Both platforms hide street addresses by design — until the booking is confirmed. You'll get city + coordinate per row. Apartments.com publishes the full street address. This is also why FREE tier runs can produce empty cross_listing_candidates[] arrays — see the next question.

Q: My cross_listing_candidates array is empty on most rows. Is this a bug? A: No — the 50 m radius is intentionally strict. Address-key joins across STR ↔ LTR fail roughly 95% of the time because Airbnb and Vrbo never expose street addresses (they hide them until the booking is confirmed), so the actor falls back to coordinate proximity + bedroom count + property type. Real cross-listings exist but they're rare in any given run. On FREE tier with only 25 rows per channel, the probability of two near-identical coordinates lining up is genuinely low. Run a denser market (NYC, LA, Miami Beach) on PAID tier with maxResultsPerChannel: 200+ to see hit counts climb.

Q: How accurate is the monthly_equivalent_rate_usd? A: The arithmetic is exact (nightly × 30.4 × occupancy). The one assumption you control is occupancy — 0.70 is industry default. For more accuracy, set enrichSTRCalendar: true to pull live forward-12-month calendars per Airbnb and Vrbo listing — that replaces the static baseline with real bookings data.

Q: Why is the headline marketComparisonRow event only charged when 2+ channels return data? A: Because the rate normalization is only useful when there's something to compare it to. A single-channel run is just a vanilla scrape — you pay commodity rates only. The headline fires when the run produced rows on 2+ channels and the row carries a populated monthly_equivalent_rate_usd. Honest pricing.

Q: Can I run this for non-US markets? A: Airbnb and Vrbo: yes (try London, UK or Lisbon, Portugal). Apartments.com: US only — the source is a US rental aggregator. Non-US runs will return Airbnb + Vrbo rows only, and the LTR baseline / break-even occupancy fields will be null.

Q: How long does a typical run take? A: ~15 seconds for a 10-row quick scan, ~45 seconds for a 50-row standard sweep, ~3 minutes for a 50-row sweep with live calendar enrichment. Channels run in parallel — adding Vrbo to an Airbnb + Apartments run does NOT triple the wall-clock.

Q: What output formats are available? A: JSON, CSV, Excel, JSONL, XML, RSS — export directly from the Apify dataset UI or pull via the API. The HTML market report is downloadable from the key-value store as report.html.

Q: Is this legal? A: Yes — only publicly available listing data is consumed. See the legal section below.

🐞 Troubleshooting

All channels return 0 rows for my location

• Use City, ST format for US markets (Miami, FL not just Miami)
• Try the coordinates mode with a 10-20 km radius
• Confirm the location is spelled correctly (the upstream geocoder is strict)

The marketComparisonRow event never fires

• Only one channel returned data — widen the radius, pick a less-niche market, or accept commodity pricing on the per-channel events
• Check channels_in_run on the rows you got — if it's a single-element array, the headline gate intentionally blocks the charge

Calendar enrichment didn't run

• FREE tier blocks calendar enrichment regardless of the enrichSTRCalendar flag
• On PAID tier, confirm enrichSTRCalendar: true is set in your input

Airbnb rows look truncated at 25 per run

• That's the FREE-tier cap on maxResultsPerChannel. Upgrade to PAID tier for up to 500 rows per channel.

Vrbo rows arrive with null latitude / longitude

• Vrbo's search endpoint doesn't expose coordinates. Set enrichSTRCalendar: true to call the per-listing details endpoint, which populates coordinates and enables Vrbo rows in cross-listing detection.

⚖️ 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.

⚠️ Trademark Disclaimer

This is an independent tool developed by SIÁN Agency — it is not affiliated with, endorsed by, sponsored by, or otherwise officially connected to Airbnb, Inc., Expedia Group, Inc. (Vrbo), or CoStar Group, Inc. (Apartments.com). All product names, logos, and brand references appear in their nominative-fair-use form solely to describe the data the actor returns and the platforms it covers.

• Airbnb is a trademark of Airbnb, Inc.
• Vrbo is a trademark of Expedia Group, Inc.
• Apartments.com is a trademark of CoStar Group, Inc.

All trademarks are the property of their respective owners.

🤝 Support

Join our active support community

• For issues or questions, open an issue on the actor's page
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