Find property with comparable rental data

Understanding the rental market requires more than just a snapshot of current listings—it demands historical context, pricing trends, and insight into how properties have shifted between long-term and short-term rentals over time. By tapping into Datafiniti’s rentalStatuses and prices fields, you can build comprehensive market reports that highlight average rents, seasonal fluctuations, and the competitive landscape for any given area. Whether you’re an investor, property manager, or market analyst, this data-driven approach enables informed decisions and a clear view of where rental opportunities lie.

Sample `rentalStatuses` Structure

"rentalStatuses": [  
  {  
    "date": "2025-08-14T21:53:01.982Z",  
    "dateSeen": ["2025-08-14T21:53:00.000Z"],  
    "firstDateSeen": "2025-08-14T21:53:00.000Z",  
    "lastDateSeen": "2025-08-14T21:53:00.000Z",  
    "sourceURLs": ["https://www.airbnb.com/..."],  
    "type": "Short Term Rental"  
  },  
  {  
    "date": "2003-08-18T00:00:00.000Z",  
    "dateSeen": ["2025-06-05T23:32:16.983Z"],  
    "firstDateSeen": "2025-06-05T23:32:16.983Z",  
    "lastDateSeen": "2025-06-05T23:32:16.983Z",  
    "sourceURLs": ["https://datafiniti.co/"],  
    "type": "Rental"  
  }  
]

This field lists on-rent events—useful for tracking when a property shifted rental modes or listing activity.
docs.datafiniti.co

How to Use RentalStatuses to Compare Properties

1. Retrieve Rental Statuses

Start with a property search API query like:

{  
  "query": "country:US AND province:TX AND rentalStatuses.type:\*",  
  "num_records": 5  
}

This fetches properties with any rental history, along with location and pricing metadata.

2. Extract and Interpret Data

For each property:

Sort rentalStatuses chronologically by date. You will have to sort this JSON using another coding medium or structural database. As Datafiniti's JSON structure does not sort the API response. You can capture the following fields:

Type (e.g., For Rent, Short-Term Rental)
Start date (firstDateSeen)
Lastest date (lastDateSeen)
Source (sourceURLs)

3. Match RentalStatuses to Prices

Cross-reference with the rentalStatuses dates sub-field to get exact rental amounts based on the dateSeen within our prices field array:

"prices": [  
  {  
    "amountMin": 817,  
    "amountMax": 817,  
    "currency": "USD",  
    "dateSeen": ["2016-01-20T00:00:00Z"],  
    "period": "Per week",  
    "isSale": false,  
    "status": "For Rent"  
  }  
]

This captures the rental price values, timing, and rental period.

4. Normalize Rental Pricing

Convert all rental periods to a standard unit (e.g., per month):

If period is Per week: multiply value by ~4.345.
If Per year: divide by 12.

This lets you compare rentals on a consistent basis.

5. Analyze Brokers and Property Managers

Once you have retrieved brokers and managedBy data from your search results, you can use it to uncover the key players influencing the rental market.

Review the brokers field

Identify brokers that are most relevant in your search across your dataset.

	"brokers": [
                {
                    "agent": "Sandra Silva",
                    "company": "Charles Rutenberg Realty Ftl",
                    "dateSeen": "2025-08-06T19:07:41.307Z",
                    "emails": [
                        "[email protected]"
                    ],
                    "firstName": "Sandra",
                    "lastName": "Silva",
                    "licenseNumber": "0a03776a305607c67f6c69f0b2aa980c",
                    "phones": [
                        "954-396-3001",
                        "9546100281",
                        "9543963001",
                        "954-610-0281"
                    ],
                    "people_key": "sandra/silva/510996502"
                }
            ]

Review the managedBy field

Note the property management companies associated with each rental. Capture related metadata, such as company websites or the dates they first appear managing a property.

"managedBy": [
                {
                    "dateSeen": "2025-06-25T05:12:11.020Z",
                    "value": "EXP REALTY LLC Gisela Alpern https://www.realtor.com/rentals/details/55-SW-9th-St-Apt-1805_Miami_FL_33130_M51657-00158?f=listhub&s=MAORFL&m=A11779030&c=rent"
                },
                {
                    "dateSeen": "2020-06-06T05:27:08.536Z",
                    "value": "/ Optimar International Realty"
                },
                {
                    "dateSeen": "2022-03-11T08:22:00.000Z",
                    "value": "www.turbotenant.com"
                },
                {
                    "dateSeen": "2022-02-22T03:02:51.025Z",
                    "value": "Optimar International Realty"
                }
            ],

Aggregate the Data and Compare

Aggregating and comparing broker and property manager data reveals who controls the most listings, highlights pricing differences between market players, and uncovers whether certain brokers or managers specialize in short-term or long-term rentals, providing a clearer picture of market dynamics and competitive positioning. With this dataset you can:

Group records by broker or management company to see who holds the largest market share.
Calculate average rental prices per broker/manager.
Compare the share of short-term vs. long-term listings for each.

5. Compare Properties Across Your Results

You will also have the ability to cross reference rental property based on the following data points:

Group properties by location (e.g., city or postal code)
Average rent per month
Rent range (min/max)
Count of short-term vs. long-term listings
Trend analysis over time (e.g., rising or falling rent)

Present the Findings

Present your findings using visual aid such as tables and charts to clearly show rental averages, ranges, and trends by location or rental type.

Example Table – Monthly Rental Comparisons (Zip 75078)

Rental Type	Avg Monthly Rent	Min Rent	Max Rent	Listings Count
For Rent	$1,820	$1,550	$2,100	140
Short-Term Rental	$2,750	$2,300	$3,200	60

Example Chart – Average Monthly Rent by Rental Type

Avg Rent ($)
3000 |                  ████████
     |                  ████████
2500 |       ████████
     |       ████████
2000 | 
     |
1500 |
     |
     |________________________________
        For Rent    Short-Term Rental

(Short-Term Rentals in this sample have higher average rates than long-term rentals.)

Example Records

Here are example bulk download files of our previous query:

Conclusion

By leveraging the rentalStatuses field alongside prices, you can gain a complete picture of a property’s rental history, from listing type changes to shifts in market value. Normalizing rents to a consistent unit allows for accurate comparisons across properties and time periods, while visual summaries make trends and opportunities easy to identify. This approach transforms raw rental data into actionable market intelligence—helping you pinpoint competitive pricing, spot growth areas, and make informed investment or management decisions.