RentCast connector

Set up the RentCast connector in Kaivo: authentication, configuration, the 6 BigQuery tables it syncs, and answers to common questions.

Written By Lauri Raivio

Last updated 16 days ago

Kaivo is a fully managed data platform that syncs your RentCast data into a Google BigQuery warehouse and keeps it up to date automatically. There is no pipeline to build and no infrastructure to run, so you can spend your time analysing your payment and finance data instead of moving it.

What is the RentCast connector

Sync your RentCast property records, listings, and estimates into BigQuery with Kaivo to analyse real estate prices and rental markets.

Category	Finance & Payments
Authentication	API key
Setup	Self-service

Getting started with the RentCast connector

Sign up for Kaivo and create a workspace.
Connect your RentCast account.
Choose which tables to sync.
Wait for the initial sync to finish.
Query your data in BigQuery or your favourite AI or BI tool.

Authenticating RentCast

Authenticate with your API Key.

Field	Description
API Key

Configuring the RentCast connector

When you set up the connector, you provide:

Field	Description
Address	The full address of the property in the format Street, City, State, Zip. Used to retrieve data for a specific property, or together with the radius parameter to search for listings in a specific area.
City	The name of the city to search for listings in. This parameter is case-sensitive.
State	The 2-character state abbreviation to search for listings in. This parameter is case-sensitive.
Zip Code	The 5-digit zip code to search for listings in.
Latitude	The latitude of the search area. Use together with longitude and radius to search for listings in a specific area.
Longitude	The longitude of the search area. Use together with latitude and radius to search for listings in a specific area.
Radius	The radius of the search area in miles (maximum 100). Use in combination with latitude/longitude or address to search for listings in a specific area.
Property Type	The type of property to filter by. Accepted values: Single Family, Condo, Townhouse, Manufactured, Multi-Family, Apartment, Land.
Bedrooms	The number of bedrooms to filter by. Use 0 to indicate a studio layout.
Bathrooms	The number of bathrooms to filter by.
Status	The current listing status to filter by. Accepted values: Active, Inactive.
Days Old	The maximum number of days since a property was listed or last sold (minimum 1). Used to filter properties within a specific date range.
Data Type	The type of aggregate market data to return. Defaults to All if not provided. Accepted values: All, Sale, Rental.
History Range	The time range for historical record entries in months. Defaults to 12 if not provided.

Tables and columns synced from RentCast

Kaivo syncs 6 tables from RentCast into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.

Property Records (100 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`address_line1`	`STRING`
`address_line2`	`STRING`
`assessor_id`	`STRING`
`bathrooms`	`FLOAT64`
`bedrooms`	`FLOAT64`
`city`	`STRING`
`county`	`STRING`
`features__architecture_type`	`STRING`
`features__cooling`	`BOOL`
`features__cooling_type`	`STRING`
`features__exterior_type`	`STRING`
`features__fireplace`	`BOOL`
`features__fireplace_type`	`STRING`
`features__floor_count`	`FLOAT64`
`features__foundation_type`	`STRING`
`features__garage`	`BOOL`
`features__garage_spaces`	`FLOAT64`
`features__garage_type`	`STRING`
`features__heating`	`BOOL`
`features__heating_type`	`STRING`
`features__pool`	`BOOL`
`features__pool_type`	`STRING`
`features__roof_type`	`STRING`
`features__room_count`	`FLOAT64`
`features__unit_count`	`FLOAT64`
`features__view_type`	`STRING`
`formatted_address`	`STRING`
`id`	`STRING`
`latitude`	`FLOAT64`
`legal_description`	`STRING`
`longitude`	`FLOAT64`
`lot_size`	`FLOAT64`
`owner__type`	`STRING`
`owner__mailing_address__address_line1`	`STRING`
`owner__mailing_address__address_line2`	`STRING`
`owner__mailing_address__city`	`STRING`
`owner__mailing_address__formatted_address`	`STRING`
`owner__mailing_address__id`	`STRING`
`owner__mailing_address__state`	`STRING`
`owner__mailing_address__zip_code`	`STRING`
`owner_occupied`	`BOOL`
`property_taxes___2016__total`	`FLOAT64`
`property_taxes___2016__year`	`FLOAT64`
`property_taxes___2017__total`	`FLOAT64`
`property_taxes___2017__year`	`FLOAT64`
`property_taxes___2018__total`	`FLOAT64`
`property_taxes___2018__year`	`FLOAT64`
`property_taxes___2019__total`	`FLOAT64`
`property_taxes___2019__year`	`FLOAT64`
`property_taxes___2020__total`	`FLOAT64`
`property_taxes___2020__year`	`FLOAT64`
`property_taxes___2021__total`	`FLOAT64`
`property_taxes___2021__year`	`FLOAT64`
`property_taxes___2022__total`	`FLOAT64`
`property_taxes___2022__year`	`FLOAT64`
`property_taxes___2023__total`	`FLOAT64`
`property_taxes___2023__year`	`FLOAT64`
`property_type`	`STRING`
`square_footage`	`FLOAT64`
... and 40 more columns

Nested subtables

Repeating fields are split into their own tables, listed here with their column counts.

Property Records__owner__names (4 columns)

Rent Estimate (7 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`latitude`	`FLOAT64`
`longitude`	`FLOAT64`
`rent`	`FLOAT64`
`rent_range_high`	`FLOAT64`
`rent_range_low`	`FLOAT64`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Nested subtables

Repeating fields are split into their own tables, listed here with their column counts.

Rent Estimate__comparables (28 columns)

Rental Listings (162 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`address_line1`	`STRING`
`address_line2`	`STRING`
`bathrooms`	`FLOAT64`
`bedrooms`	`FLOAT64`
`city`	`STRING`
`county`	`STRING`
`created_date`	`STRING`
`days_on_market`	`FLOAT64`
`formatted_address`	`STRING`
`history___2022_05_23__days_on_market`	`FLOAT64`
`history___2022_05_23__event`	`STRING`
`history___2022_05_23__listed_date`	`STRING`
`history___2022_05_23__listing_type`	`STRING`
`history___2022_05_23__price`	`FLOAT64`
`history___2023_04_12__days_on_market`	`FLOAT64`
`history___2023_04_12__event`	`STRING`
`history___2023_04_12__listed_date`	`STRING`
`history___2023_04_12__listing_type`	`STRING`
`history___2023_04_12__price`	`FLOAT64`
`history___2023_09_13__days_on_market`	`FLOAT64`
`history___2023_09_13__event`	`STRING`
`history___2023_09_13__listed_date`	`STRING`
`history___2023_09_13__listing_type`	`STRING`
`history___2023_09_13__price`	`FLOAT64`
`history___2023_12_16__days_on_market`	`FLOAT64`
`history___2023_12_16__event`	`STRING`
`history___2023_12_16__listed_date`	`STRING`
`history___2023_12_16__listing_type`	`STRING`
`history___2023_12_16__price`	`FLOAT64`
`history___2023_12_16__removed_date`	`STRING`
`history___2024_05_02__days_on_market`	`FLOAT64`
`history___2024_05_02__event`	`STRING`
`history___2024_05_02__listed_date`	`STRING`
`history___2024_05_02__listing_type`	`STRING`
`history___2024_05_02__price`	`FLOAT64`
`history___2024_06_13__days_on_market`	`FLOAT64`
`history___2024_06_13__event`	`STRING`
`history___2024_06_13__listed_date`	`STRING`
`history___2024_06_13__listing_type`	`STRING`
`history___2024_06_13__price`	`FLOAT64`
`history___2024_06_13__removed_date`	`STRING`
`history___2024_07_21__days_on_market`	`FLOAT64`
`history___2024_07_21__event`	`STRING`
`history___2024_07_21__listed_date`	`STRING`
`history___2024_07_21__listing_type`	`STRING`
`history___2024_07_21__price`	`FLOAT64`
`history___2024_07_31__days_on_market`	`FLOAT64`
`history___2024_07_31__event`	`STRING`
`history___2024_07_31__listed_date`	`STRING`
`history___2024_07_31__listing_type`	`STRING`
`history___2024_07_31__price`	`FLOAT64`
`history___2024_08_12__days_on_market`	`FLOAT64`
`history___2024_08_12__event`	`STRING`
`history___2024_08_12__listed_date`	`STRING`
`history___2024_08_12__listing_type`	`STRING`
`history___2024_08_12__price`	`FLOAT64`
`history___2024_08_12__removed_date`	`STRING`
`history___2024_08_14__days_on_market`	`FLOAT64`
`history___2024_08_14__event`	`STRING`
... and 102 more columns

Sale Listings (570 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`address_line1`	`STRING`
`address_line2`	`STRING`
`bathrooms`	`FLOAT64`
`bedrooms`	`FLOAT64`
`builder__development`	`STRING`
`builder__name`	`STRING`
`builder__phone`	`STRING`
`city`	`STRING`
`county`	`STRING`
`created_date`	`STRING`
`days_on_market`	`FLOAT64`
`formatted_address`	`STRING`
`history___2021_06_21__days_on_market`	`FLOAT64`
`history___2021_06_21__event`	`STRING`
`history___2021_06_21__listed_date`	`STRING`
`history___2021_06_21__listing_type`	`STRING`
`history___2021_06_21__price`	`FLOAT64`
`history___2023_10_09__days_on_market`	`FLOAT64`
`history___2023_10_09__event`	`STRING`
`history___2023_10_09__listed_date`	`STRING`
`history___2023_10_09__listing_type`	`STRING`
`history___2023_10_09__price`	`FLOAT64`
`history___2023_11_02__days_on_market`	`FLOAT64`
`history___2023_11_02__event`	`STRING`
`history___2023_11_02__listed_date`	`STRING`
`history___2023_11_02__listing_type`	`STRING`
`history___2023_11_02__price`	`FLOAT64`
`history___2023_11_29__days_on_market`	`FLOAT64`
`history___2023_11_29__event`	`STRING`
`history___2023_11_29__listed_date`	`STRING`
`history___2023_11_29__listing_type`	`STRING`
`history___2023_11_29__price`	`FLOAT64`
`history___2023_12_05__days_on_market`	`FLOAT64`
`history___2023_12_05__event`	`STRING`
`history___2023_12_05__listed_date`	`STRING`
`history___2023_12_05__listing_type`	`STRING`
`history___2023_12_05__price`	`FLOAT64`
`history___2023_12_07__days_on_market`	`FLOAT64`
`history___2023_12_07__event`	`STRING`
`history___2023_12_07__listed_date`	`STRING`
`history___2023_12_07__listing_type`	`STRING`
`history___2023_12_07__price`	`FLOAT64`
`history___2024_01_09__days_on_market`	`FLOAT64`
`history___2024_01_09__event`	`STRING`
`history___2024_01_09__listed_date`	`STRING`
`history___2024_01_09__listing_type`	`STRING`
`history___2024_01_09__price`	`FLOAT64`
`history___2024_01_23__days_on_market`	`FLOAT64`
`history___2024_01_23__event`	`STRING`
`history___2024_01_23__listed_date`	`STRING`
`history___2024_01_23__listing_type`	`STRING`
`history___2024_01_23__price`	`FLOAT64`
`history___2024_02_07__days_on_market`	`FLOAT64`
`history___2024_02_07__event`	`STRING`
`history___2024_02_07__listed_date`	`STRING`
`history___2024_02_07__listing_type`	`STRING`
`history___2024_02_07__price`	`FLOAT64`
`history___2024_02_25__days_on_market`	`FLOAT64`
`history___2024_02_25__event`	`STRING`
... and 510 more columns

Statistics (236 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`id`	`STRING`
`rental_data__average_days_on_market`	`FLOAT64`
`rental_data__average_rent`	`FLOAT64`
`rental_data__average_rent_per_square_foot`	`FLOAT64`
`rental_data__average_square_footage`	`FLOAT64`
`rental_data__history___2023_11__average_rent`	`FLOAT64`
`rental_data__history___2023_11__date`	`STRING`
`rental_data__history___2023_11__max_rent`	`FLOAT64`
`rental_data__history___2023_11__min_rent`	`FLOAT64`
`rental_data__history___2023_11__total_listings`	`FLOAT64`
`rental_data__history___2023_12__average_rent`	`FLOAT64`
`rental_data__history___2023_12__date`	`STRING`
`rental_data__history___2023_12__max_rent`	`FLOAT64`
`rental_data__history___2023_12__min_rent`	`FLOAT64`
`rental_data__history___2023_12__total_listings`	`FLOAT64`
`rental_data__history___2024_01__average_rent`	`FLOAT64`
`rental_data__history___2024_01__date`	`STRING`
`rental_data__history___2024_01__max_rent`	`FLOAT64`
`rental_data__history___2024_01__min_rent`	`FLOAT64`
`rental_data__history___2024_01__total_listings`	`FLOAT64`
`rental_data__history___2024_02__average_rent`	`FLOAT64`
`rental_data__history___2024_02__date`	`STRING`
`rental_data__history___2024_02__max_rent`	`FLOAT64`
`rental_data__history___2024_02__min_rent`	`FLOAT64`
`rental_data__history___2024_02__total_listings`	`FLOAT64`
`rental_data__history___2024_03__average_rent`	`FLOAT64`
`rental_data__history___2024_03__date`	`STRING`
`rental_data__history___2024_03__max_rent`	`FLOAT64`
`rental_data__history___2024_03__min_rent`	`FLOAT64`
`rental_data__history___2024_03__total_listings`	`FLOAT64`
`rental_data__history___2024_04__average_rent`	`FLOAT64`
`rental_data__history___2024_04__date`	`STRING`
`rental_data__history___2024_04__max_rent`	`FLOAT64`
`rental_data__history___2024_04__min_rent`	`FLOAT64`
`rental_data__history___2024_04__total_listings`	`FLOAT64`
`rental_data__history___2024_05__average_rent`	`FLOAT64`
`rental_data__history___2024_05__date`	`STRING`
`rental_data__history___2024_05__max_rent`	`FLOAT64`
`rental_data__history___2024_05__min_rent`	`FLOAT64`
`rental_data__history___2024_05__total_listings`	`FLOAT64`
`rental_data__history___2024_06__average_rent`	`FLOAT64`
`rental_data__history___2024_06__date`	`STRING`
`rental_data__history___2024_06__max_rent`	`FLOAT64`
`rental_data__history___2024_06__min_rent`	`FLOAT64`
`rental_data__history___2024_06__total_listings`	`FLOAT64`
`rental_data__history___2024_07__average_rent`	`FLOAT64`
`rental_data__history___2024_07__date`	`STRING`
`rental_data__history___2024_07__max_rent`	`FLOAT64`
`rental_data__history___2024_07__min_rent`	`FLOAT64`
`rental_data__history___2024_07__total_listings`	`FLOAT64`
`rental_data__history___2024_08__average_days_on_market`	`FLOAT64`
`rental_data__history___2024_08__average_rent`	`FLOAT64`
`rental_data__history___2024_08__average_rent_per_square_foot`	`FLOAT64`
`rental_data__history___2024_08__average_square_footage`	`FLOAT64`
`rental_data__history___2024_08__date`	`STRING`
`rental_data__history___2024_08__max_days_on_market`	`FLOAT64`
`rental_data__history___2024_08__max_rent`	`FLOAT64`
`rental_data__history___2024_08__max_rent_per_square_foot`	`FLOAT64`
`rental_data__history___2024_08__max_square_footage`	`FLOAT64`
... and 176 more columns

Nested subtables

Repeating fields are split into their own tables, listed here with their column counts.

Statistics__rental_data__data_by_bedrooms (22 columns)
Statistics__rental_data__data_by_property_type (22 columns)
Statistics__rental_data__history___2023_11__data_by_bedrooms (8 columns)
Statistics__rental_data__history___2023_12__data_by_bedrooms (8 columns)
Statistics__rental_data__history___2024_01__data_by_bedrooms (8 columns)
Statistics__rental_data__history___2024_02__data_by_bedrooms (8 columns)
Statistics__rental_data__history___2024_03__data_by_bedrooms (8 columns)
Statistics__rental_data__history___2024_04__data_by_bedrooms (8 columns)
Statistics__rental_data__history___2024_05__data_by_bedrooms (8 columns)
Statistics__rental_data__history___2024_06__data_by_bedrooms (8 columns)
Statistics__rental_data__history___2024_07__data_by_bedrooms (8 columns)
Statistics__rental_data__history___2024_08__data_by_bedrooms (22 columns)
Statistics__rental_data__history___2024_08__data_by_property_type (22 columns)
Statistics__rental_data__history___2024_09__data_by_bedrooms (22 columns)
Statistics__rental_data__history___2024_09__data_by_property_type (22 columns)
Statistics__rental_data__history___2024_10__data_by_bedrooms (22 columns)
Statistics__rental_data__history___2024_10__data_by_property_type (22 columns)
Statistics__sale_data__data_by_bedrooms (22 columns)
Statistics__sale_data__data_by_property_type (22 columns)
Statistics__sale_data__history___2024_01__data_by_bedrooms (8 columns)
Statistics__sale_data__history___2024_02__data_by_bedrooms (8 columns)
Statistics__sale_data__history___2024_03__data_by_bedrooms (8 columns)
Statistics__sale_data__history___2024_04__data_by_bedrooms (8 columns)
Statistics__sale_data__history___2024_05__data_by_bedrooms (8 columns)
Statistics__sale_data__history___2024_06__data_by_bedrooms (8 columns)
Statistics__sale_data__history___2024_07__data_by_bedrooms (8 columns)
Statistics__sale_data__history___2024_08__data_by_bedrooms (22 columns)
Statistics__sale_data__history___2024_08__data_by_property_type (22 columns)
Statistics__sale_data__history___2024_09__data_by_bedrooms (22 columns)
Statistics__sale_data__history___2024_09__data_by_property_type (22 columns)
Statistics__sale_data__history___2024_10__data_by_bedrooms (22 columns)
Statistics__sale_data__history___2024_10__data_by_property_type (22 columns)

Value Estimate (7 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`latitude`	`FLOAT64`
`longitude`	`FLOAT64`
`price`	`FLOAT64`
`price_range_high`	`FLOAT64`
`price_range_low`	`FLOAT64`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Nested subtables

Repeating fields are split into their own tables, listed here with their column counts.

Value Estimate__comparables (27 columns)

How the RentCast sync works

After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where RentCast supports it, each sync pulls only new and changed records so it stays fast; otherwise it refreshes the whole table. Every record keeps its original ID, so you won't get duplicate rows.

Frequently asked questions

How long does the initial sync take for RentCast?

It depends on how much history is in your RentCast account. Most initial syncs finish within minutes, while large accounts can take a few hours. After that, syncs only fetch new and changed records, so they're much faster.

Can I sync only some tables or columns?

Yes. You pick which tables to sync when you set up the connection and can change the selection later. Tables you don't select are never copied to your warehouse.

What happens when RentCast's schema changes?

New fields are never added automatically. You choose which fields to sync, so data you haven't selected (sensitive personal data, for example) never lands in your warehouse. When a new field appears, it becomes available for you to add. What happens to removed or renamed fields depends on a table's sync mode: full-refresh tables always match what's currently in RentCast, so dropped fields disappear, while incremental tables keep their existing columns and history, so an old field stays and newly added fields fill in over time.

Your data lives in your own Kaivo-managed BigQuery warehouse, so the most direct option is to delete or anonymise specific records right in BigQuery. If you delete data in RentCast instead, full-refresh tables drop it on the next sync, while incremental tables keep it, so you would remove the row in BigQuery or ask us to run a full refresh. To remove everything, delete the RentCast connector in Kaivo and all of its synced data is deleted with it.

Common use cases for RentCast data

Market analysis

Use Sale Listings and Rental Listings by city and county to track prices and days on market.

Valuation tracking

Use Value Estimate and Rent Estimate to follow property and rent values over time.

Property records

Report on Property Records by features and location to profile your portfolio.

Area statistics

Use Statistics by zipCode to compare rental and sale trends across areas.

Use RentCast data in your AI and BI tools

Once RentCast data lands in your Kaivo-managed BigQuery warehouse, you can explore it with AI tools or any BI tool that connects to BigQuery. Here's how the most common destinations work with RentCast data.

Claude

Use Kaivo's MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →

Power BI

Microsoft's BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →

Data Studio

Free Google BI tool with native BigQuery support. One-click connection to your Kaivo warehouse; great for SMB teams on Google Workspace. Setup guide →

Tableau

The premium analytics standard, with native BigQuery integration. Setup guide →

Google Sheets

Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →

Excel

Connect via Power Query's BigQuery connector. Setup guide →

Metabase

Open-source BI tool with strong BigQuery support. Setup guide →

See our pricing page for RentCast connector pricing and plan details.

Chargebee: Sync Chargebee to BigQuery.
ChartMogul: Sync ChartMogul to BigQuery.
Fennoa: Sync Fennoa to BigQuery.
Fortnox: Sync Fortnox to BigQuery.
FreshBooks: Sync FreshBooks to BigQuery.
PayPal Enterprise Payments: Sync PayPal Enterprise Payments to BigQuery.

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What is the RentCast connector

Getting started with the RentCast connector

Authenticating RentCast

Configuring the RentCast connector

Tables and columns synced from RentCast

Nested subtables

Nested subtables

Nested subtables

Nested subtables

How the RentCast sync works

Frequently asked questions

How long does the initial sync take for RentCast?

Can I sync only some tables or columns?

What happens when RentCast's schema changes?

How do I handle GDPR or data deletion requests?

Common use cases for RentCast data

Market analysis

Valuation tracking

Property records

Area statistics

Use RentCast data in your AI and BI tools

Claude

Power BI

Data Studio

Tableau

Google Sheets

Excel

Metabase

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