Don’t look now, but the federal government seems to have developed a website that actually works! The Consumer Financial Protection Bureau – the federal watchdog agency created under the Dodd-Frank financial reforms of 2010, has created a site that aggregates mortgage data nationwide and collects it in a series of well-executed infographics.
Here’s a compelling contrast that allows you to see the broad recovery in the home mortgage industry that occurred between 2010 and 2012.
Orange indicates a decline in year-to-year origination volume; blue indicates an increase.
2010-2011 Change in Mortgage Origination Volume
2011-2012 Change in Mortgage Origination Volume
The difference is dramatic. Look at the vast swathes of land in the Southwest, in the upper Midwest, and in Florida that experienced dramatic surges in mortgage origination of +30 percent or more between 2011 and 2012.
Perhaps the most exciting thing about the CFPB’s website is its transparency. Not only does the site provide users with access to a substantial amount of raw data, but it also provides a good deal of documentation. This means developers can fashion their own tools using the CFPB’s data, which is collected from hundreds of lenders nationwide acting in accordance with the Home Mortgage Disclosure Act.
The CFPB arranged the data so users can design their own custom data sets. You can break down the data according to these variables:
- Action taken.
- Applicant ethnicity.
- Applicant race.
- Applicant sex.
- Census tract number (see below).
- Co-applicant ethnicity, race or sex.
- Reason for denial.
- HOEPA (Home Ownership and Equity Protection Act) status.
- Lien status.
- Loan purpose.
- Loan type.
- MSM/MD (a census designation indicating location).
- Owner occupancy.
- Purchaser type.
- Responder ID.
You can filter up to three separate variables, and download them in one of several formats.
Applications for Real Estate Professionals
The data available via the CFPB’s website potentially makes it much easier for lenders, mortgage and real estate salespeople, and investors to gain market intelligence and identify trends and “sweet spots” within their own markets.
For example, a real estate agent can boil down the reasons people in his or her county have been getting turned down for loans – and narrow it down to census tracts, or ethnic or racial communities. Taken together with other resources, such as the Ellie Mae survey, this could help the agent anticipate problems with a mortgage application before a rejection becomes a problem.
Investors and marketers, on the other hand, can quickly grasp changes in the flow of mortgage dollars into a given market – and position themselves accordingly.
Enterprising real estate and mortgage professionals with some Web-savviness can create reports and custom information displays on their own websites, and position themselves as industry experts. Their websites can become regular sources of information for people looking for real estate information specific to their area. The data sets are compatible with Qu, an open-source programming language, and there is an extensive section dedicated to assisting developers.
Most comparable tools I’ve seen only have granularity down to the county level, and at best to the zip code level. The inclusion of census tracts as a sortable variable allows a finer resolution to search results. Each tract, on average, boils down to about 4,000 people, according to the U.S. Bureau.
Want to know what tract is most relevant for your needs? You can look up the desired census tract by address.
For example, keying in the street I largely grew up in, in the windsurfing town of Kailua, Hawaii (don’t hate!) I was able to find out that my little corner of the town, County Code 3, tract 0111.05, is considered middle income, with an estimated median family income of $103,405 for 2013 (based on a median income of $97,500 in the 2010 census.) Forty-four percent of the 3,205 people counted in the tract were minorities, which is low for Hawaii.
The applications are obvious. For example, an investor can quickly gauge the affordability of a given price-point compared to the rest of the tract area, with a little input from recent comp data. A real estate agent or investor can also do a bit of quick number crunching to compare the projected mortgage payment on a house with, say, 36 percent of the median income for the area, to take a reasonable debt-to-income ratio. If a house can be bought below that number, it may have some upside potential compared to a house already priced at maximum for the median income of the area, from the perspective of qualifying for an FHA or Fannie/Freddie compliant mortgage.
The data aggregation site is a significant improvement in the transparency and usefulness of the data available via the Home Mortgage Disclosure Act. Yes, the information isn’t particularly new. Lenders have been disclosing this material since the law first passed in 1975. But the data disclosed has historically been difficult to process into useable information without the benefit of dedicated research staff on the payroll.
Now, with the addition of a few programming skills and familiarity with database principle, nearly anyone can easily manipulate and customize the available data. This goes a long way toward leveling the playing filed for smaller lenders and other entrepreneurs, who can now do it themselves without having to hire dedicated number-crunchers to pour over the data.
The automation also makes it easier for policymakers and community advocates to identify potential areas of improper or unlawful discrimination in access to credit.
Read More: realestate.com