Airbnb Listings Scraper & Competitor Analysis

Scrape Airbnb listings and turn them into practical competitor insights. Get normalized nightly and total pricing, comparable-market statistics, price positioning, confidence metrics, and optional target-listing benchmarking in one run.

This Actor combines Airbnb listings extraction with competitor and comparable analysis. It keeps observed source data separate from Actor-derived market intelligence, so you receive usable benchmarks without opaque claims about bookings, occupancy, or revenue.

What this Actor does

Airbnb search → comparable listings → normalized prices → market benchmark → optional target comparison

Provide a location or Airbnb search-results URL, exact stay dates, guest details, and optional comparable filters. The Actor collects up to 240 unique listings, preserves nightly-versus-total price semantics, scores eligible listings relative to relevant comparables, and writes a market summary after the listing records. Add a public Airbnb room URL to receive a target benchmark too.

Why use it?

Many Airbnb scrapers stop at rows of listing data. This Actor also answers practical questions:

• What is the median nightly price for this exact stay?
• Is a listing priced below, near, or above its comparable sample?
• How large and specific was that sample?
• Does the available data support low, medium, or high confidence?
• How does one target listing compare with the market median and quartiles?

The methodology is deterministic and documented. Missing evidence stays null and includes reason codes instead of being guessed.

Target listing benchmarking

Add targetListingUrl to compare one listing with its best available collected segment. For example, a result might show: “My property is $114/night versus a $152 comparable median, approximately 25% below the market sample.” The output also reports quartiles, percentile, price position, review strength, value score when confidence permits, and sample quality.

If the target already appears in search results, the Actor reuses it. Otherwise it requests the public listing page once and reports whether reuse occurred.

Key features

• Dated searches by location or Airbnb search-results URL
• Normalized listing, property-type, rating, review, and pricing fields
• Separate nightlyPrice and totalPrice with source provenance
• Market median, P25, P75, distributions, and coverage
• Tie-aware price percentile and price delta from the comparable median
• Plain-language price position: budget, below market, market, above market, or premium
• Transparent review strength and value score
• Comparable tiers based on property category and bedrooms
• Per-listing and market confidence with reason codes
• Optional benchmark for one public target listing
• Explicit limitations and transparent null handling
• Apify datasets ready for JSON, CSV, Excel, APIs, and automation workflows

Who is it for?

Airbnb hosts, property managers, short-term rental analysts, investors doing comparable research, hospitality consultants, researchers, and agencies building market or client reports.

Common uses include dated rate benchmarking, competitor-set research, target-listing reviews, market snapshots, portfolio pricing checks, research datasets, and automated reporting pipelines.

Input

Market and stay

Field	Required	Description
location	One market input	City, neighborhood, or region. Use this or searchUrl, not both.
searchUrl	One market input	HTTPS Airbnb search-results URL used instead of location.
checkIn	Yes	Future arrival date (YYYY-MM-DD) or relative date such as 30 days.
checkOut	Yes	Departure date after check-in, absolute or relative. Dates are required because Airbnb prices are stay-specific.

Guests and comparable filters

Field	Required	Description
adults	No	Adult guests; default 2.
children	No	Child guests; default 0.
infants	No	Infant guests; default 0.
pets	No	Pets; default 0.
bedrooms	No	Exact bedroom filter. Missing or different bedroom counts are excluded when set.
roomTypes	No	Any of entire_place, private_room, shared_room, or hotel_room.
priceMin	No	Minimum normalized nightly price in the selected currency.
priceMax	No	Maximum normalized nightly price in the selected currency.
currency	No	Requested ISO currency; default USD.
maxResults	No	Unique listing cap from 1–240; default 100.

Benchmark and proxy

Field	Required	Description
targetListingUrl	No	Public Airbnb room URL to benchmark against the collected market.
proxyConfiguration	No	Apify Proxy or custom proxy fallback. Direct access is attempted first.

Example inputs

Basic Miami market analysis

{
  "location": "Miami Beach, United States",
  "checkIn": "2027-03-10",
  "checkOut": "2027-03-14",
  "adults": 2,
  "currency": "USD",
  "maxResults": 100
}

Dubai one-bedroom market

{
  "location": "Dubai, United Arab Emirates",
  "checkIn": "2027-02-08",
  "checkOut": "2027-02-12",
  "adults": 2,
  "bedrooms": 1,
  "roomTypes": ["entire_place"],
  "currency": "AED",
  "maxResults": 100
}

Target listing benchmark

{
  "location": "Miami Beach, United States",
  "checkIn": "2027-03-10",
  "checkOut": "2027-03-14",
  "adults": 2,
  "bedrooms": 1,
  "currency": "USD",
  "maxResults": 100,
  "targetListingUrl": "https://www.airbnb.com/rooms/12345678"
}

Output

The default dataset contains three self-identifying record types:

1. listing — one normalized listing plus pricing provenance and relative intelligence.
2. target_benchmark — written only when a target URL is supplied and acquired successfully.
3. market_summary — written last, with pricing, reviews, distributions, coverage, confidence, exclusions, and limitations.

Abbreviated, sanitized examples are included with the repository in examples/outputs.

{
  "recordType": "listing",
  "listingId": "12345678",
  "title": "Condo in South Beach",
  "normalizedPropertyType": "condo",
  "bedrooms": 1,
  "rating": 4.89,
  "reviewCount": 227,
  "pricing": {
    "nightlyPrice": 113.75,
    "totalPrice": 455.0,
    "currency": "USD",
    "nightlyPriceSource": "derived_from_total"
  },
  "intelligence": {
    "pricePercentile": 0.0,
    "priceDeltaFromMedianPercent": -32.89,
    "pricePosition": "budget",
    "reviewStrength": 86.55,
    "valueScore": 91.93,
    "comparableSampleSize": 19,
    "comparableTier": "category_and_bedrooms",
    "confidence": "medium"
  }
}

Understanding the intelligence

• Price percentile shows the listing's midpoint rank from 0 to 100 within its selected comparable sample. Lower means less expensive.
• Price delta from median shows the money and percentage difference from that sample's median.
• Price position maps percentile bands to a readable label from budget to premium.
• Review strength combines observed rating quality with review volume relative to the market.
• Value score combines review strength and relative affordability. It is a transparent ranking signal—not a revenue, occupancy, or return forecast.
• Comparable tier says exactly how the sample was selected.
• Confidence summarizes sample size, segmentation, and coverage. Reason codes explain every label and unavailable metric.

Methodology in brief

Price percentile uses a tie-aware midpoint rank: 100 × (prices below + 0.5 × equal prices) / sample size. Price delta is the subject nightly price minus the comparable median; the percent form divides that delta by a positive median. Price-position bands are 0–20 budget, above 20–40 below market, above 40–60 market, above 60–80 above market, and above 80 premium.

Review strength is 70% rating quality above 4.0 and 30% log-scaled review volume relative to the market review-count P95, clamped to 0–100. Value score is 60% review strength plus 40% relative affordability (100 - pricePercentile), also clamped to 0–100. It is suppressed at low or insufficient confidence. The complete production specification—including eligibility, coverage, rounding, and all reason codes—is maintained in docs/methodology.md in the repository.

How comparable listings are selected

The Actor tries the most specific viable segment first:

category + exact bedrooms → category → exact bedrooms → broader dated market

Each tier needs at least eight eligible records. The target is excluded from its own sample. Price-relative metrics are suppressed below eight comparables. Value score is more conservative: it is suppressed at low or insufficient confidence.

Market summary

The final market_summary includes minimum, maximum, average, median, P25, and P75 prices; rating and review summaries; price, bedroom, and property-type distributions; coverage rates; duplicates removed; truncation; comparison quality; exclusion reasons; and limitations.

Median and quartiles describe the collected eligible sample—not the entire Airbnb market.

Data quality and limitations

• Results are a collected Airbnb search sample, not complete market coverage.
• Search-card fields such as bedrooms, bathrooms, ratings, or reviews can be absent.
• Prices are specific to the requested dates, guests, filters, and currency.
• nightlyPrice and totalPrice are distinct; provenance fields explain observed or derived use.
• Currency is never inferred from a symbol such as $ alone.
• Confidence and comparable sample size should accompany every interpretation.
• Null metrics mean evidence was missing or quality rules suppressed the calculation; inspect unavailableReasons and limitations.
• The Actor does not claim known bookings, occupancy, revenue, returns, or official Airbnb metrics.
• Airbnb can change public response structures; resilient parsing, retries, and quality reporting reduce but cannot eliminate that dependency.

Integrations

Use Apify dataset exports and APIs with Make, n8n, Zapier, Google Sheets, Airtable, webhooks, or custom Python and JavaScript workflows. These integrations consume normal Apify datasets; no separate proprietary dashboard is required.

Performance

During production testing, a typical 100-listing benchmark completed in approximately 16–20 seconds, used roughly 60–65 MB peak memory, and required no residential proxy traffic. Actual duration and network usage vary by market, response size, retries, target acquisition, and Airbnb availability. Direct HTTP is attempted first; a configured proxy is a retry fallback.

Pricing

Pricing: $2 per 1,000 successfully returned Airbnb listings, plus a minimal Actor-start charge. Market summaries and target benchmarks are included at no additional event charge.

• 100 listings: approximately $0.20 total ($0.00005 Actor start + 100 × $0.002)
• 240 listings: approximately $0.48 total ($0.00005 Actor start + 240 × $0.002)

FAQ

Are check-in and check-out dates required?

Yes. Airbnb pricing changes by stay, and one exact date range is necessary for a meaningful nightly and total-price comparison.

Is nightly price the same as total price?

No. The Actor preserves the displayed meaning. It derives a nightly amount from an explicit total only when the night count is known, and records that provenance.

Does a percentile of 20 mean 20% cheaper than the median?

No. Percentile is a rank. Use priceDeltaFromMedianPercent for the percentage price difference.

Why is value score null?

It requires both price percentile and review strength, and it is deliberately suppressed when comparison confidence is low or insufficient.

Can I benchmark a listing outside the search results?

Yes. Supply its public Airbnb room URL. The Actor will attempt one additional target-page request when the listing was not collected in search.

Does this estimate occupancy or revenue?

No. It reports observed public listing data and deterministic comparative metrics only.

Can I search without dates?

No. Required dates are a V1 quality rule that prevents mixing ambiguous price contexts.

How many listings should I request?

Eight is the minimum for price-relative metrics. Larger samples generally improve coverage and confidence; 100 is the default and 240 is the V1 cap.