What's unique about this API? Unlike other API that just shows raw result scraped from Airbnb, this API shows meaningful Airbnb listings' historical performance data. We are a short-term rental data company and have been specialised in dealing with Airbnb data for the past 4 years. We have developed an accurate classifer to distinguish between booked nights vs blocked nights in Airbnb calendars.
You can pass property details 1) number of bedrooms, 2) bathrooms, 3) location and 4) number of maximum people that can sleep in the property. Then, our backend will scan through our Airbnb historical performance data and aggregate the results from similar listings.
We track short-term rental data globally, from the US to the UK, to Indonesia, to Nigeria, and all.
Unlike the name, our current version doesn't provide "prediction", but only the past 12 months' average, median revenue, and other key metrics. We are training an ML algorithm.
This API answers "How much a property can earn as a short-term rental".
Airbtics tracks 7M Airbnb rentals globally and take snapshots of those listings on a daily / weekly basis. Using this information, we are able to estimate the income of individual Airbnb listings. This API simply fetches similar listings and calculates the average, median, 25, 75% revenues.
Given a property detail, the API will return the past 12 months' revenue of similar listings that are within N meters radius from the property.
Find out how much your Airbnb can earn based on short-term rental historical data points.
Our algorithm will scan through our Airbnb historical performance data and aggregate the results from similar listings. We track short-term rental data globally. Unlike the name, our current version doesn’t provide “prediction”, but only the past 12 months’ key metrics. We are training an ML algorithm.
Airbnb Income Prediction API - Endpoint Features
| Object | Description |
|---|---|
coordinate |
[Required] Latitude and longitude coordinates of the location in the format (latitude, longitude). |
bedrooms |
[Required] Number of bedrooms. |
bathrooms |
[Required] Number of bathrooms. |
{
"input": {
"coordinate": "(40.729932,-73.989256)",
"bedroom": "2",
"bathrooms": "2"
},
"message": {
"no_of_sample": 10,
"radius_used": 475,
"last_updated_date": "2023-06-30",
"last_12_months_summary": {
"average": {
"average_daily_rate": 440,
"occupancy_rate": 58,
"revenue": 92691
}
}
}
}
curl --location --request GET 'https://zylalabs.com/api/4577/airbnb+api+revenue+estimation/5642/airbnb+income+prediction+api?coordinate=(40.729932,-73.989256)&bedrooms=2&bathrooms=2' --header 'Authorization: Bearer YOUR_API_KEY'
| Header | Description |
|---|---|
Authorization
|
[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
It's in meter.
our current version doesn’t provide “prediction”, but only the past 12 months’ key metrics. We are training an ML algorithm.
we've been scraping airbnb listings since 2020.
Answer: Yes we only accept integers as bathroom parameters for now.
Returned this instead - "Not enough listings to calculate income history simliar to your listing. Self explanatory. Our algorithm didn't find matching comps within 50km radius
The API returns historical performance metrics for Airbnb listings, including average daily rate, occupancy rate, and total revenue over the past 12 months for similar properties.
Key fields include "average_daily_rate," "occupancy_rate," and "revenue" within the "last_12_months_summary" section, along with input details like "coordinate," "bedroom," and "bathrooms."
The response is structured in a JSON format, with an "input" section detailing the request parameters and a "message" section containing the summary of historical metrics and sample data.
The API provides insights into the average and median revenue, occupancy rates, and daily rates for Airbnb listings based on historical data from similar properties.
Users can customize requests by specifying parameters such as the number of bedrooms, bathrooms, location coordinates, and maximum occupancy to find relevant historical data.
The data is sourced from scraping Airbnb listings globally since 2020, with a focus on maintaining accuracy through daily and weekly snapshots of approximately 7 million rentals.
Typical use cases include evaluating potential earnings for new Airbnb listings, comparing performance with similar properties, and making informed investment decisions in short-term rentals.
Users can analyze the average daily rate and occupancy rate to estimate potential earnings, compare their property metrics against similar listings, and adjust their pricing strategies accordingly.
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