Mortgage Data: Best Mortgage Datasets & Databases
What is Mortgage Data?
Mortgage data refers to information related to loans secured by real estate property. It includes details about the borrower, such as credit score, income, and employment history, as well as the property’s value and location. Mortgage databases help assess the risk associated with granting a mortgage and to determine the terms and conditions of the loan.
Best Mortgage Datasets & APIs
BatchService - Mortgage Data + Mortgage Loan Data - Transactions, Liens, and Mortgage History
CrawlBee | Mortgage Data | Homeownership Data | Financial Services | Lender Data
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
McGRAW Mortgage Data, Property Data, Title Data, Ownership Data | Over 150 MM Records and 200 Attributes
Mortgage Advertisement data for independent mortgage lenders
The Data Group’s 128 Million Residential USA Mortgage Data Records|200+ Mortgage Attributes| Emails & Phones Available
Mortgage Rates (Front Line Prices) - Live Feed
Opah Labs | Mortgage Loans Data | USA | (Weekly Updates w/ 400M records | Consumer Data) | 4134
US National Real Estate Sale and Mortgage Data (228+Million Records)
Monetize data on Datarade Marketplace
Top Mortgage Data Providers
Datarade considers factors such as data accuracy, coverage of mortgage data, data freshness, data granularity, data source reliability, compliance with regulations, data delivery methods, and pricing models when recommending mortgage data providers.
Mortgage Data Explained
How is Mortgage Data collected?
Mortgage data is collected from various credible sources. These sources include social media platforms, online news sites, online publications, lending agencies, the government, and any related public records. In the form of a list or charts, these sources are gathered and analyzed to create mortgage datasets which undergo cleansing and filtering to eliminate duplications and incomplete data. Then, data is organized and compiled for data storage.
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What are the typical attributes of Mortgage Data?
Here are some typical attributes of mortgage data:
- Credit Score – this refers to the available data on the credit rating of an individual based on their previous loans.
- Customer information – refers to the customers’ buying patterns, purchasing power, contact data, geographical location, and employment status.
- Mortgage home – this refers to information about the homes available to purchase using a mortgage model, companies offering loans on properties, and terms and related legal information.Â
ÂWhat is Mortgage Data used for?
Mortgage data is used by lenders and banks. This data serves as an additional record in approving a loan application. Lenders benefit from this data to ensure the transparency and credibility of a borrower to pay. Mortgage data also serves as a strong decision indicator for the lending institution to avoid unreliable borrowers. Another benefit of using this data is to target a better audience based on their previous mortgage loan. Mortgage data is valuable to lending institutions for risk management, as it allows them to conduct KYC procedures so that they don’t grant loans to borrowers who won’t repay them.
How can a user assess the quality of Mortgage Data?
To assess the quality of mortgage data, users must ask providers where they got their data and what processes are involved in creating their datasets. Good quality mortgage data must also be accurate and reliable. Furthermore, sources of mortgage data must be credible and must be timely. Since some borrowers are unable to maintain a good credit score, data updating important so that the data reflects their most current credit score. Data providers must also offer excellent customer service, and provide extra mortgage information in case questions about the data are raised. Lastly, users may read online reviews to ensure credibility and ask for a mortgage data sample to counter-check if it is worthy.
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