Facebook Files Patent: Homeowner Data Could Reveal Socioeconomic Status

Have you scrolled by an ad on Facebook for new real estate signs—just a few hours after you searched for them on Google? Ad targeting is not a new concept, but it has transformed over the years through data analysis and the use of algorithms. Social media users are accustomed to experiencing ads that reflect their recent product searches or, more invasively, seeing ads for products mentioned in conversation that were never physically searched for. Now, the social media giant may be looking to extend its efforts on the ad targeting front. Just a week after Facebook announced its foray into local news, sources are reporting the discovery of a patent filed by Facebook on Jul. 27, 2016, that would allow the platform to inch even closer toward its consumer base through data analytics.

While the social platform has not formally announced plans for implementation, the patent shows Facebook wants to “increase awareness about products or services to online system users” by gathering data on homeownership, travel, education, internet usage, demographic and device ownership—factors that could be used to determine users’ socioeconomic status without using their income information. According to the patent application, Facebook would aggregate this data using market research questionnaires that would then build individual profiles categorized by socioeconomic group: working class, middle class and upper class.

Sources also report that Facebook commonly seeks patents for new technology, but does not always follow through with all of them. Although this plan has not been officially confirmed by Facebook, users and advertisers have questions:

How would this information be used?
If Facebook sold this data instead of keeping it for algorithm purposes, they would have to determine who gets the information and how it could be used. If available for purchase, Facebook could be opening the door to new methods of hyper-targeted marketing for the real estate industry. Agents would able to promote listings within a user’s predicted income status. For example, instead of seeing a mass-promoted, multimillion-dollar listing, a potential homebuyer categorized as middle class could begin seeing more affordable purchasing options. Likewise, that multimillion-dollar listing could be targeted toward those potential homebuyers categorized as upper class.

By finding out homeowner status, real estate professionals could vary the phrasing of their ads to attract more leads. Those that are listed as renting could receive ads on the benefits of buying over renting. Alternatively, current homeowners who are older could receive ads on downsizing, and younger homeowners could see ads that recommend upsizing.

Would users find this too invasive?
The social media platform risks further alienating its user base by gathering such personal information and choosing to sell it for ad space. Facebook users are already wary of applications such as Messenger that have access to their phone’s microphone, especially after reports of seeing ads that correspond to personal conversations. If real estate professionals did purchase this data, they would have to tread carefully to avoid offending prospects and opening themselves up to litigation based on discriminatory advertising.

Could Facebook convince users to take the survey?
Without incentives in place, it is highly unlikely that Facebook users would willingly provide this personal information through questionnaires. This could mean Facebook would look into alternative sources to obtain this information, such as contests, trivia games, etc. The social platform already has access to a variety of identifying information through users’ profiles and their posts. If the questionnaires prove futile, Facebook could simply comb through profiles to access most of the information they need.

How accurate would this information be?
Without income information, it would be difficult to determine if these categories are valid. For example, one of the factors Facebook is using to determine socioeconomic level is the number of devices a user has (assuming that just because they have five devices instead of two, the individual belongs to the upper class). It would also take education into consideration; however, a graduate degree no longer translates to upper class, especially as much of the millennial generation is encumbered with high levels of student loan debt from financing that education.

If real estate professionals did want to participate in this version of targeted ads, they would have to do so without seeing proven results. At this point, Facebook’s socioeconomic categorizing would be a coin toss—any dollars invested could see a return of value, or could convert into lost leads instead.

Dominguez_Liz_60x60_4cLiz Dominguez is RISMedia’s associate content editor. Email her your real estate news ideas at ldominguez@rismedia.com. For the latest real estate news and trends, bookmark RISMedia.com.

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