Most short-term rental analytical tools show you snapshots. But serious investors, analysts, and developers need strong time-series data they can model, transform, and integrate into custom analytics pipelines. Without a reliable performance history, it’s impossible to run accurate forecasts, stress test underwriting assumptions, or automate market screening.
Mashvisor’s historical performance API solves this by giving you clean, structured monthly performance data you can use in your own code, models, and applications. It’s built for large-scale investors, analyst teams, and PropTech companies that need more than dashboards – they need real Airbnb data they can work with.
Table of Contents
- What Does the Historical Performance API Provide?
- Why Historical STR Data Matters for Professional Airbnb Rental Analysis
- Practical Use Cases for Investors and Analysts
- How the Historical Performance API Works
- How to Integrate the Historical Data into Professional Workflows
- Best Practices for Using 36-Month STR Data
- Bottom Line
- FAQs
What Does the Historical Performance API Provide?
The Airbnb API that Mashvisor provides delivers 36 months of continuous STR performance data at both neighborhood and property level. This is not aggregated or modeled data. Instead, it is a time-series dataset designed explicitly for advanced financial evaluation, market research, risk assessment, and application development.
The API returns core short-term rental performance indicators. Importantly, STR historical data is refreshed daily for strong accuracy and reliability.
Key Monthly Metrics Provided by Short-Term Rental API
The main data points and analytics that come with Mashvisor’s Airbnb API include:
- Airbnb occupancy rate: Percentage of nights booked per month.
- Average daily rate (ADR): Average booked nightly rate for the month.
- Monthly revenue: Average monthly rental income earned by Airbnb hosts, derived from occupancy times ADR or provided directly.
- Revenue per available rental (RevPAR): Integrated performance metric combining daily rate and occupancy.
- Active listing count/market supply: Number of STR units available for booking in a month.
- Booking patterns and seasonal shifts: Indications of demand variability throughout the year.
Usefulness of Each Data Type
With Mashvisor’s dataset, you gain access to the full performance information needed for professional-grade analysis. This includes detailed monthly metrics, such as occupancy trends retrieved through the Airbnb occupancy rate API, that allow you to examine both market cycles and property-level behavior with precision.
These data points enable you to perform:
- Seasonality analysis
- Forecast model calibration
- Underwriting and risk adjustment
- Pricing strategy development
- Competitive supply assessment
- Performance benchmarking across markets or property types
- Longitudinal market trend evaluation
- Development of automated dashboards and analytical tools
With these monthly indicators in hand, the next question is how they support real-world investment analysis and why they are important for short-term rental professionals.
Why Historical STR Data Matters for Professional Airbnb Rental Analysis
Beginner and small-scale investors and hosts can extract all the data and analysis they need for successful investments from ready-to-use tools, such as Mashvisor’s Airbnb calculator.
Professionals in the short-term rental industry, however, demand a different level of analysis and formatting that requires their own manipulation and interpretation of the data.
But even enterprise-level STR players find it hard to get access to accurate and comprehensive Airbnb data, especially over the span of multiple years.
With Mashvisor’s 36-month STR data delivered via an API, they can perform all of the following functions:
Seasonality Modeling
STR performance is largely seasonal, with revenue cycles often fluctuating by 20-50% between peak and off-peak periods. Understanding seasonality requires:
- Identifying recurring monthly patterns over multiple years
- Quantifying variability between strong and weak months
- Assessing sensitivity to holidays, travel cycles, and local events
- Comparing seasonal volatility across markets
Using the historical performance API, STR analysts, investors, and property managers can segment data year-over-year to determine whether fluctuations are predictable and manageable or irregular and risk-increasing.
Forecasting Future Cash Flow
Accurate forecasting requires historical data structured for modeling.
With time-series observations, professional users can apply:
- Linear or exponential trend estimation
- ARIMA or seasonal decomposition models
- Moving averages to smooth irregularities
- Elasticity assumptions based on supply and ADR shifts
- Custom revenue projection algorithms
This allows professionals in the STR business to produce probabilistic forecasts rather than simple annual averages. In this way, they can underwrite deals in a much more accurate and precise way than ever before.
Underwriting and Investment Due Diligence
Institutional-level underwriting requires detailed validation of expected returns.
Historical STR data supports:
- Conservative revenue scenarios
- Stress-testing for low-season performance
- Evaluation of occupancy decline risk
- ADR compression modeling
- Sensitivity analysis for supply increases
Underwriting teams can plug these data points into internal income models to determine whether a target property meets return thresholds. This makes Airbnb investments more predictable and potentially more profitable as they’re based on solid analysis of the performance of similar properties over the course of the last three years.
Product Development, Automation, and Data Integration
PropTech developers can integrate the API into:
- Revenue management systems
- Dynamic pricing tools
- Market intelligence dashboards
- Investor underwriting software
- Automated STR valuation systems
Programmatic access ensures scalability and consistency across applications and more reliable results.
Now that we’ve covered why past performance matters for professional STR evaluation, let’s look at how different users apply it in practice. Below are several real-world use cases that illustrate the analytical power of this dataset.
Practical Use Cases for Investors and Analysts
To help you understand the full scope of what the Mashvisor API allows you to do, let’s take a look at a few use cases of past performance data via Application Programming Software.
Use Case 1: Market Selection and Comparison
An investor comparing two markets can use the API to evaluate:
- 36-month median occupancy
- Seasonal fluctuation intensity
- ADR stability across years
- Revenue durability during off-peak periods
- Supply growth trends
Side-by-side comparison of these crucial metrics across a couple of markets helps you determine which investment location offers more stable returns. This allows you to choose the best market Airbnb investments across areas and states.
Use Case 2: Property-Level Evaluation
A property manager analyzing a specific listing retrieves 36-month performance of STR comps (similar listings located in the same area) and examines:
- Month-to-month occupancy consistency
- Impact of listing changes (renovation, new photos, pricing strategy)
- ADR evolution across market cycles
- Seasonal peaks and troughs
- Alignment with neighborhood trends
This supports operational strategy, long-term planning, and revenue management. In other words, Airbnb managers can grow their business more strategically and sustainably, without unnecessary risks.
Use Case 3: Institutional Underwriting Model Integration
An institutional investor integrates the API into an automated underwriting tool that:
- Retrieves performance data for comparable listings
- Applies seasonality coefficients to revenue projections
- Runs downside, base, and upside scenarios
- Outputs Airbnb cap rate, IRR, and cash on cash return projections
- Flags properties that fall outside acceptable risk parameters
Through this in-depth analysis, the API ensures the underwriting pipeline is fully data-driven.
Use Case 4: Financial Analyst Forecasting Models
A financial analyst applies time-series forecasting using:
- 36-month occupancy trajectories
- ADR trend lines
- Seasonality indices
- Supply elasticity assumptions
The resulting model produces refined forward-looking revenue estimates and probability distributions for expected cash flow.
Use Case 5: STR Platform or Application Development
A vacation rental software developer integrates the API to:
- Populate dashboards with monthly performance curves
- Provide investors with automated ROI insights
- Enable comparative analysis across regions
- Build proprietary ranking algorithms
- Create dynamic outputs for pricing suggestions
These capabilities depend heavily on properly structured, reliable, and complete historical data, just like the performance tracked by the Mashvisor API.
Want to try these workflows with live data? Sign up for Mashvisor’s 7-day API trial and use your 30 credits to pull actual past performance metrics, compare markets, and test your own forecasting or underwriting models.
These use cases show what’s possible when you have reliable performance history. Next, let’s break down how the API itself works and how the data is structured under the hood.
How the Historical Performance API Works
Now that you saw specific examples of some of the many ways in which you can benefit from historical Airbnb data delivered via API, it’s time to look at how exactly it works.
Here are the steps you need to go through to start using Airbnb API for your STR analytical needs:
Authentication and Access
Users authenticate with an API key using request headers. The API provides endpoints at both the listing and neighborhood levels, enabling flexibility in analysis.
Query Structure
Requests may include:
- Location identifiers, such as state, city, or neighborhood
- Listing identifiers
- Timeframe parameters
- Filters for property type, property size, or specific property attributes
- Output format configuration
Response Schema
The API returns an organized, object-based structure containing:
- Metadata (listing ID, location)
- Historical performance array
- Monthly datapoints
- Supply metrics when available
Sample Request: Fetching Historical Data in Python
To help developers get started quickly, here’s a simple example showing how to make a request and extract the monthly time-series from the response:
import requests
API_KEY = “YOUR_API_KEY”
BASE_URL = “https://api.mashvisor.com/v1.1/client/rento-calculator/historical-performance”
params = {
“state”: “FL”,
“city”: “Miami”,
“neighborhood_id”: “269093”,
“resource”: “airbnb”,
“limit_recent_months”: False # set True only if you want recent months only
}
headers = {
“x-api-key”: API_KEY
}
response = requests.get(BASE_URL, headers=headers, params=params)
data = response.json()
# Access the historical time series
historical = data[“content”][“historical_performance”]
# Example: print year, month, and occupancy for each datapoint
for entry in historical:
print(entry[“year”], entry[“month”], entry[“occupancy”])
This quick snippet demonstrates the typical workflow: Authenticate, send a request, and then iterate through the returned monthly performance metrics.
Advanced Sample JSON
Below is an advanced sample JSON illustrating the structure of the response:
{
“status”: “success”,
“content”: {
“listing_id”: 982345,
“smart_location”: “Austin, TX, US”,
“property_type”: “Single Family Residential”,
“historical_performance”: [
{
“year”: 2022,
“month”: 1,
“occupancy_rate”: 0.72,
“average_daily_rate”: 198.40,
“monthly_revenue”: 3564.80,
“revpar”: 143.00,
“active_listings”: 420
},
{
“year”: 2022,
“month”: 2,
“occupancy_rate”: 0.68,
“average_daily_rate”: 190.25,
“monthly_revenue”: 3156.20,
“revpar”: 129.37,
“active_listings”: 415
},
{
“year”: 2022,
“month”: 3,
“occupancy_rate”: 0.81,
“average_daily_rate”: 215.00,
“monthly_revenue”: 5415.00,
“revpar”: 174.15,
“active_listings”: 430
}
// 35+ additional entries…
]
}
}
Visual Example: Turning Historical Data Into a Simple Trend View
To illustrate how easily the past performance dataset converts into practical insights, check out a small six-month example based on typical seasonality patterns:
| Year | Month | Occupancy (%) | ADR ($) | Monthly Revenue ($) |
| 2024 | 1 | 42 | 185 | 2,410 |
| 2024 | 2 | 48 | 190 | 2,550 |
| 2024 | 3 | 63 | 210 | 4,100 |
| 2024 | 4 | 71 | 225 | 4,790 |
| 2024 | 5 | 68 | 220 | 4,640 |
| 2024 | 6 | 55 | 205 | 3,380 |
This simple example extracted from the Mashvisor API allows analysts to:
- Identify the ramp-up from low season into peak spring months
- See how occupancy and ADR rise together, boosting revenue
- Compare property-level performance against neighborhood trends
Why This Advanced Sample Json Helps Analysts and Developers
This structure provides the following key benefits:
- Clean time-series array is suitable for statistical modeling
- Monthly granularity supports seasonality decomposition
- Active listing count allows supply elasticity analysis
- Metadata enables integration with property objects
As a result, developers can easily transform this structure into:
- DataFrames
- SQL tables
- Visualization layers
- Machine-learning inputs
This structure ensures the information can be efficiently processed, modeled, and integrated into a wide range of analytical workflows. It can be seamlessly incorporated into underwriting processes, revenue projections, and market evaluation frameworks.
You can also check out the Mashvisor API documentation to get a better understanding of how it works and what data you can obtain.
Once you’re familiar with the API structure and how to retrieve the dataset, the next step is to integrate it into your actual workflows. Different user groups can apply the data in different, highly targeted ways.
How to Integrate the Historical Data into Professional Workflows
Now it’s time to outline how various professional users can incorporate the 36-month historical dataset into their operational, analytical, and decision-making processes.
For Investment Firms
This workflow typically involves the following applications of the historical data:
- Import into internal underwriting models
- Merge with cost assumptions for automated valuation
- Use in risk assessment frameworks
- Compare target property performance to market baselines
For Airbnb Property Management Companies
Property managers can leverage the historical dataset in the following operational and strategic applications:
- Track multi-year performance patterns
- Support pricing strategy and occupancy optimization
- Benchmark managed properties against market trends
For Financial Analysts
Analysts often incorporate this data into a range of forecasting, evaluation, and risk-assessment processes, including:
- Build time-series forecasting models
- Construct scenario analyses
- Measure volatility and seasonality risk
- Evaluate long-term revenue sustainability
For PropTech Platforms
Short-term rental technology apps can integrate the dataset across multiple product features and automation workflows, such as:
- Populate comparative market dashboards
- Drive automated investment recommendations
- Enable predictive analytics for users
- Enhance property ranking or scoring logic
Access to three years of monthly performance is powerful, but the results depend on how consistently and correctly the data is used. These best practices will help you get the most reliable and actionable outcomes.
Best Practices for Using 36-Month STR Data
Having access to a full vacation rental dataset is only the first step. The ability to apply it efficiently and consistently is what enables investors and analysts to optimize their evaluations and maximize the accuracy of their results.
Here are best practices how to optimize each aspect of the analysis you can conduct with the help of this data:
Seasonality Best Practices
- Compare each month year-over-year
- Derive a seasonality index for every market
- Avoid reliance on annual averages
Forecasting Best Practices
- Use 12-month moving averages for smoothing
- Apply statistical decomposition (trend, seasonal, residual)
- Incorporate supply changes into growth projections
- Use downside scenarios as a baseline for underwriting
Underwriting Best Practices
- Model conservative occupancy and ADR assumptions
- Stress-test for volatility
- Evaluate off-season cash flow durability
- Integrate property expenses and financing costs
- Validate forecasts against previous performance
Developer Best Practices
- Cache historical data for efficiency
- Store time-series datapoints in relational or columnar databases
- Validate fields such as occupancy and ADR for missing values
- Implement versioning and monitoring for API usage
With these simple tips for API usage, you’ll be able to get optimal results in your short-term rental analysis and make more confident, data-driven decisions.
Bottom Line
Professional STR investment decisions require more than summary metrics or high-level dashboards. They demand granular, structured, and historically consistent datasets that reveal how a property or a market performs across seasonal cycles, supply changes, and revenue fluctuations.
Mashvisor’s historical performance API provides exactly that foundation. With 36 months of monthly STR performance metrics, including occupancy, ADR, revenue, RevPAR, and supply indicators, it enables robust forecasting models, accurate underwriting, institutional-grade evaluation, and scalable analytical products.
Whether you are an investor, asset manager, financial analyst, or PropTech developer, the API delivers the depth and structure necessary to make informed, forward-looking decisions in the STR market.
If you’d like to try how the Mashvisor Airbnb API works in practice, get a 1-week free trial with 30 credits.
FAQs
What Type of Data Does the Historical Performance API Provide?
The Mashvisor Airbnb API delivers 36 months of monthly STR performance metrics, including occupancy rate, average daily rate (ADR), monthly revenue, RevPAR, and active listing supply. The dataset is structured as a time series, enabling analytical modeling and integration into forecasting or underwriting workflows.
How Far Back Do the Historical Data Points Extend?
The API provides up to three full years – or 36 months – of historical monthly data. This timeframe allows for comprehensive seasonality analysis, trend identification, and year-over-year comparisons, all of which are essential for professional evaluations.
Is the Data Aggregated or Property-Specific?
The API supports both listing-level and neighborhood-level past performance retrieval. Listing-level data is tied to a specific property, while neighborhood-level data reflects aggregated market performance for comparative and location-based analysis.
How Accurate Is the Airbnb Historical Data Provided by the Mashvisor API?
The API provides highly reliable occupancy, ADR, revenue, and supply trends that align closely with real market behavior and can be used confidently. This is because Mashvisor’s historical STR dataset is built using real Airbnb performance signals sourced directly from active listings. The platform applies its own data-cleaning, validation, and modeling layers to correct outliers, estimate missing values, and ensure consistency across markets.
How Frequently Is the Historical Dataset Updated?
The underlying data is refreshed regularly – on a daily basis – to ensure accuracy. Updates incorporate changes in occupancy, pricing, and supply trends as recorded across STR platforms. Updated values automatically propagate to API responses without additional user action.
How Can Developers Integrate the API into Internal Systems?
Developers can authenticate using an API key and make GET requests to the relevant historical performance endpoints. The returned JSON structure is optimized for ingestion into data pipelines, SQL tables, dataframes, dashboards, forecasting engines, and underwriting models.
Can the Data Be Used to Compare Multiple Markets?
Professionals can retrieve and analyze data across different neighborhoods, cities, or regions to compare seasonality, occupancy stability, ADR trends, and supply dynamics. This enables market selection, portfolio diversification, and comparative risk analysis.
Does the API Support Institutional-Scale Analysis?
The structure, depth, and consistency of the dataset make it suitable for institutional workflows, including multi-property evaluation, algorithmic underwriting, automated deal screening, and STR portfolio optimization.
How Can I Combine Historical Data With Other Mashvisor API Endpoints for Deeper Analysis?
You can pair past performance data with endpoints such as property details, rental comps, occupancy projections, and neighborhood analytics to create more complete evaluation frameworks. For example, you can merge past performance trends with a listing’s property attributes or with market-level ROI data to build underwriting models, pricing engines, portfolio screening tools, or data-enriched PropTech applications.
How Much Does Access to the API Cost?
Mashvisor offers multiple API pricing tiers depending on usage needs, starting with a free trial that includes 30 credits for testing historical data, market endpoints, and other STR metrics. Paid plans are usage-based and scale with the number of API calls your platform or workflow requires. This structure makes pricing affordable for users with various needs.
What Should I Do If a Historical Data Field Returns Null or Missing Values?
In rare cases, a historical metric such as occupancy or ADR may return a null value due to incomplete data signals for that specific month or listing. Developers can handle this by applying fallback logic, such as interpolation, moving averages, or neighborhood benchmarks, to maintain continuity in their model. Indeed, they often combine historical performance with neighborhood-level metrics or the Lookup API when a listing has limited month-by-month history. Mashvisor’s dataset is cleaned daily, so missing values typically resolve automatically in subsequent refreshes without requiring user action.