CrawlBee | Mortgage Data | Homeownership Data | Financial Services | Lender Data
| # | address |
street |
city |
county |
state |
zip |
year_built |
home_value |
home_value_updated |
home_value_min |
home_value_max |
home_value_change_yoy |
home_value_history |
tax_value |
tax_rate |
tax_change_yoy |
property_tax |
land_value |
improvement_value |
tax_history |
num_bedrooms |
num_bathrooms |
num_units |
num_rooms |
num_stories |
sqft |
sqft_first_floor |
sqft_second_floor |
sqft_basement |
sqft_finished |
sqft_unfinished_basement |
sqft_garage |
sqft_carport |
sqft_shed |
sqft_driveway |
sqft_utility_building |
sqft_canopy |
sqft_grainery |
sqft_loading_platform |
sqft_milk_house |
sqft_porch |
sqft_patio |
sqft_deck |
sqft_balcony |
sqft_pool |
sqft_storage_building |
sqft_pole_structure |
sqft_stable |
sqft_poultry_house |
sqft_attic |
sqft_gazebo |
lot_numbers |
lot_size |
lot_depth |
lot_width |
zoning |
water |
sewage |
parking_spaces |
parking_type |
architecture_type |
roof_material_type |
exterior_wall_type |
interior_wall_type |
foundation_type |
flooring_types |
plumbing_fixtures_count |
construction_type |
has_family_room |
has_game_room |
has_golf_course |
has_tennis_court |
has_mobilehome_hookup |
has_accessibility_elevator |
has_ac |
has_central_ac |
has_window_ac |
has_fireplace |
has_fire_sprinklers |
heating_type |
heating_fuel_type |
land_use_code |
county_use_code |
county_subdivision |
county_subdivision_description |
ownership |
deeds |
permits |
foreclosures |
businesses |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| 5 | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxx | xxxxx | xxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxx | xxxxxxxxxx | xxxxxxx |
| 6 | xxxxxxxx | Xxxxx | xxxxx | xxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxx | Xxxxxx | xxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxx | xxxxxx | xxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxx | xxxxxx | Xxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxx | xxxxxx | Xxxxxxxx | Xxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | Xxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxx | xxxxxxx | xxxxxxxx |
| 7 | xxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxx | xxxxxxxx | xxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxx | Xxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxxxx | Xxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx |
| 8 | Xxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | xxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxx | xxxxxxx | Xxxxxx | Xxxxxx | xxxxxxx | Xxxxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | Xxxxxx | xxxxxxxx | xxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxx |
| 9 | xxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | xxxxx | xxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxx | xxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxx | Xxxxxxx | xxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxx | xxxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxx | xxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxxxx | xxxxxx | xxxxxxxxxx | xxxxx | xxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxx | xxxxxx | Xxxxx | xxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxx | xxxxxx | xxxxxx | xxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | xxxxx | Xxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxxxxxx |
| 10 | Xxxxxxxxx | xxxxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxx | Xxxxxx | xxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | xxxxxx | xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxx | xxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | Xxxxx | xxxxxxxxx | Xxxxx | Xxxxx | xxxxx | xxxxxx | xxxxxx | xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxx | xxxxx | xxxxxxxx | xxxxx | Xxxxx | xxxxxx | xxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxx | xxxxxxxx |
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Data Dictionary
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
address
|
String | 101 WATER DR WARNER ROBINS GA 31088 | |
|
street
|
String | 101 WATER DR | |
|
city
|
String | WARNER ROBINS | |
|
county
|
String | HOUSTON | |
|
state
|
String | GA | |
|
zip
|
Integer | 31088 | |
|
year_built
|
Integer | 1987 | |
|
home_value
|
Integer | 228774 | |
|
home_value_updated
|
DateTime | 2023-06-27T00:00:00+00:00 | |
|
home_value_min
|
Integer | 224198 | |
|
home_value_max
|
Integer | 233349 | |
|
home_value_change_yoy
|
Float | -4.1 | |
|
home_value_history
|
String | [{"date":"2023-06","value":228774,"change":-0.9},{"date":... | |
|
tax_value
|
Integer | 51320 | |
|
tax_rate
|
Float | 2.51 | |
|
tax_change_yoy
|
Float | 52.5 | |
|
property_tax
|
Integer | 1289 | |
|
land_value
|
Integer | 6600 | |
|
improvement_value
|
Integer | 44720 | |
|
tax_history
|
String | [{"year":2020,"land_value":6600,"improvement_value":44680... | |
|
num_bedrooms
|
Integer | 3 | |
|
num_bathrooms
|
Integer | 2 | |
|
num_units
|
Integer | 1 | |
|
num_rooms
|
Integer | 3 | |
|
num_stories
|
Integer | 1 | |
|
sqft
|
Integer | 1441 | |
|
sqft_first_floor
|
Integer | 1441 | |
|
sqft_second_floor
|
|||
|
sqft_basement
|
|||
|
sqft_finished
|
Integer | 1441 | |
|
sqft_unfinished_basement
|
|||
|
sqft_garage
|
Integer | 460 | |
|
sqft_carport
|
|||
|
sqft_shed
|
|||
|
sqft_driveway
|
|||
|
sqft_utility_building
|
|||
|
sqft_canopy
|
|||
|
sqft_grainery
|
|||
|
sqft_loading_platform
|
|||
|
sqft_milk_house
|
|||
|
sqft_porch
|
Integer | 432 | |
|
sqft_patio
|
Integer | 168 | |
|
sqft_deck
|
|||
|
sqft_balcony
|
|||
|
sqft_pool
|
|||
|
sqft_storage_building
|
|||
|
sqft_pole_structure
|
|||
|
sqft_stable
|
|||
|
sqft_poultry_house
|
|||
|
sqft_attic
|
|||
|
sqft_gazebo
|
|||
|
lot_numbers
|
String | 2, 482 | |
|
lot_size
|
Integer | 20909 | |
|
lot_depth
|
Integer | 16000 | |
|
lot_width
|
Integer | 13100 | |
|
zoning
|
String | R1 | |
|
water
|
|||
|
sewage
|
|||
|
parking_spaces
|
Integer | 2 | |
|
parking_type
|
String | GARAGE, ATTACHED | |
|
architecture_type
|
String | OTHER | |
|
roof_material_type
|
String | ASPHALT | |
|
exterior_wall_type
|
String | ASBESTOS | |
|
interior_wall_type
|
String | WALLBOARD/DRYWALL | |
|
foundation_type
|
String | MAT/RAFT FOUNDATION (SLAB) | |
|
flooring_types
|
String | LAMINATE PLANK | |
|
plumbing_fixtures_count
|
|||
|
construction_type
|
String | GABLE/HIP | |
|
has_family_room
|
|||
|
has_game_room
|
|||
|
has_golf_course
|
|||
|
has_tennis_court
|
|||
|
has_mobilehome_hookup
|
|||
|
has_accessibility_elevator
|
|||
|
has_ac
|
Integer | 1 | |
|
has_central_ac
|
Integer | 1 | |
|
has_window_ac
|
|||
|
has_fireplace
|
Integer | 1 | |
|
has_fire_sprinklers
|
Integer | 0 | |
|
heating_type
|
String | CENTRAL | |
|
heating_fuel_type
|
|||
|
land_use_code
|
Integer | 0 | |
|
county_use_code
|
Integer | 13153 | |
|
county_subdivision
|
String | QUAIL MEA-BEAVER RUN | |
|
county_subdivision_description
|
String | LOT 2 BEAVER RUN ESTATES EXTN | |
|
ownership
|
String | [{"month_purchased":"SEPTEMBER","year_purchased":2019,"ye... | |
|
deeds
|
String | [{"date":"2019-09-06","type":"WARRANTY","is_residential":... | |
|
permits
|
String | [] | |
|
foreclosures
|
String | [{"date":"2018-04-03","type":"NOTICE OF TRUSTEE'S SALE","... | |
|
businesses
|
String |
Description
Country Coverage
History
Volume
| 150 million | records |
Pricing
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Quality
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Frequently asked questions
What is CrawlBee Mortgage Data Homeownership Data Financial Services Lender Data?
Drive informed decisions and stay ahead in the market with accurate, up-to-date mortgage and property data tailored to meet your unique business needs.
What is CrawlBee Mortgage Data Homeownership Data Financial Services Lender Data used for?
This product has 5 key use cases. CrawlBee recommends using the data for Consumer Trend Analysis, Financial Services, Property Investment, Analytics, and Property Market Analytics. Global businesses and organizations buy Mortgage Data from CrawlBee to fuel their analytics and enrichment.
Who can use CrawlBee Mortgage Data Homeownership Data Financial Services Lender Data?
This product is best suited if you’re a Medium-sized Business or Enterprise looking for Mortgage Data. Get in touch with CrawlBee to see what their data can do for your business and find out which integrations they provide.
How far back does the data in CrawlBee Mortgage Data Homeownership Data Financial Services Lender Data go?
This product has 1 days of historical coverage. It can be delivered on a secondly, minutely, hourly, daily, weekly, monthly, quarterly, yearly, real-time, and on-demand basis.
Which countries does CrawlBee Mortgage Data Homeownership Data Financial Services Lender Data cover?
This product includes data covering 1 country like USA. CrawlBee is headquartered in United States of America.
How much does CrawlBee Mortgage Data Homeownership Data Financial Services Lender Data cost?
Pricing information for CrawlBee Mortgage Data Homeownership Data Financial Services Lender Data is available by getting in contact with CrawlBee. Connect with CrawlBee to get a quote and arrange custom pricing models based on your data requirements.
How can I get CrawlBee Mortgage Data Homeownership Data Financial Services Lender Data?
Businesses can buy Mortgage Data from CrawlBee and get the data via S3 Bucket, SFTP, REST API, Streaming API, and Feed API. Depending on your data requirements and subscription budget, CrawlBee can deliver this product in .json, .csv, and .txt format.
What is the data quality of CrawlBee Mortgage Data Homeownership Data Financial Services Lender Data?
CrawlBee has reported that this product has the following quality and accuracy assurances: 98% Accuracy. You can compare and assess the data quality of CrawlBee using Datarade’s data marketplace. CrawlBee has received 4 reviews from clients.
What are similar products to CrawlBee Mortgage Data Homeownership Data Financial Services Lender Data?
This product has 3 related products. These alternatives include Alesco Home Ownership Mortgage Data - 50+ Million US Homeowners - Available for Licensing!, Factori US Home Ownership Mortgage Data Property Data Real-Estate Data - 340+ Million US Homeowners, and Mortgage Data, Property Data, Title Data, Ownership Data Over 150 MM Records and 200 Attributes. You can compare the best Mortgage Data providers and products via Datarade’s data marketplace and get the right data for your use case.
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