Your next landlord might comb through your social media history before handing over the keys thanks to a Canadian company injecting big data and artificial intelligence into the age-old process of renting a place to live.

The idea is to make life easier for first-time landlords and level the playing field for those at risk of discrimination.

Naborly Inc.’s digital tenant screening solution uses all the traditional hallmarks of the rental process: a credit check, proof of employment, previous addresses and names of old landlords.

What sets it apart is its ability to compile 500 data points to measure your credibility as renter -- including a social media analysis, phone record search, and a criminal record check -- into a report that assigns a score to your “chance of success” as a tenant in about 15 minutes. A basic screening costs $29.99.

But a one-click solution that scrapes the Internet for personal information could empower property owners to weed out potential tenants based on a bevy of new details they would otherwise be unlikely to find -- like the picture you tweeted four years ago of you and your buddies chugging beer through a funnel on the lawn of a frat house, for example.

“It’s not just trying to gather dirt on you or gather information,” explains Naborly Inc. CEO Dylan Lenz. “It basically explains where the risk is. What is the likelihood of a person being late on rent payment, causing property damage, being evicted, jumping out of the lease early? What does their employment and income stability look like? Are there pet liabilities that the owner should be worried about?”

Getting a good score is a lot like being matched up on an online dating site. Having a high-paying job and good credit does not necessarily put you ahead of other renters. A lot depends on how well the property is suited to your lifestyle.

“We look at the demographics of the neighbourhood, the walkability score. How far it is from transit? If you are a young family, what do the schools look like?” said Lenz.

Renting has become a high-stakes game for everyone involved. It’s often the only option for young urban dwellers. And those that own pricey properties in Canada’s largest cities are increasingly looking to rent out excess space to keep up with mortgage payments and rising property costs.

Naborly’s launch in January 2016 was perfectly timed with skyrocketing real estate prices in places like Vancouver and Toronto. But the question of how such services will shape the rental market is difficult to answer.

For new landlords facing stacks of rental applications, and in some cases overseeing bidding wars for prized units, a streamlined data-centric product like Naborly is a godsend. But renters may now find themselves under pressure to part with more personal information than they are comfortable with.

Just think of a Facebook profile search turning up intimate photos that expose someone’s sexual orientation, or a person’s Twitter timeline revealing a person’s stance on abortion or racial profiling.

“The extent of it makes me nervous,” says former Ontario information and privacy commissioner Ann Cavoukian. “I understand why you would want to assure that the person renting the property is solid and reliable, but this company is collecting a ton of very sensitive personal information.”

Lenz admits the process sounds scary, but he believes there are benefits for all parties when a tenant and landlord are well matched. He and his wife know firsthand the consequences of leaving things to chance.

The couple sunk their life savings into an investment property in Kelowna, B.C. while still in university. The students who moved into the basement were model tenants. But the people who rented the upstairs unit refused to pay rent after the first month.

“Between property damage, eviction costs, unpaid rent, and legal fees, it cost me about $22,000. We ended up selling the house because we couldn’t afford it,” says Lenz. “We ended up moving in with my grandparents for a few months.”

After losing the property, Lenz found out his upstairs tenants had pulled this routine four times on different landlords. He calls people like this “professional tenants.” Helping landlords avoid them is a key focus for Naborly.

One of the women in this case showed up to interview for Lenz’s unit wearing nurse’s scrubs, saying she just finished a shift at Kelowna General Hospital. Her reference in the hospital’s human resources department confirmed that she was gainfully employed.

Dylan Lenz and his wife were forced to sell this Kelowna, B.C. home after they were victimized by so-called "professional tenants." (Dylan Lenz)

“Everybody spoke lovely words about her. Turns out they weren’t real references. She didn’t actually work at the hospital. She got her friends to take the phone calls for her,” says Lenz.

At 22 years old, he probably looked like a perfect mark for seasoned scam artists who tend to prey on part-time landlords -- people looking to pad their income by renting a basement unit in their home or by putting an investment property on the rental market.

Lenz denies that his youth was a factor. He assumes most landlords are bad at screening and certainly no match for his company’s powerful algorithm.

After all, a good tenant could stay in the same place for years, even decades. Landlords come to depend on the extra income and often want to get a new tenant in place as soon as one leaves so they don’t miss a month’s rent.

Humans get complacent, computers don’t. So-called “professional tenants” would probably turn tail in search of an easier victim before submitting to a Naborly search.

“I don’t think anyone could do what Naborly is doing,” says Cavoukian.

“That’s why it’s such a valuable tool. It’s extremely difficult to compile all this information and make sense of it.”

Lenz concedes his company has received complaints over privacy issues -- about 200 out of over a million screenings in Canada and the U.S. -- mostly pertaining to the company’s use of social media data.

The “external media scan,” as it’s known within the company, is one of the few parts of the screening done by actual humans, after the computers find the correct accounts by matching email addresses, phone numbers, names and places.

“We are not analyzing how you speak and making assumptions about your level of income based on if your spelling is correct. All we are looking for is, have you lied on your application? Is there something that is suspicious like drug use, or illegal activity that could be considered a liability for the landlord?” says Lenz..

Naborly’s first-ever social media scan for a private landlord uncovered an Etobicoke, Ont. man running a brothel out of his home. The initial analysis suggested he had several daughters.

“They weren’t actually his daughters. He was their pimp,” says Lenz. “You can’t make that story up.”

The company says it’s committed to using its data ethically. It’s even hired a cultural anthropologist to make sure its algorithms are not biased towards any one group. In fact, Lenz believes more robust data will help mitigate the discrimination some renters face when landlords rely on standard credit checks and face-to-face meetings.

“If you think a person will be a good tenant because of the colour of their skin or because you approve of their sexual orientation, or maybe because they have a good job and a four-year degree, we are telling you that they guy with the two-year education, or the person who looks different from you, or has an atypical love life can statistically be the better tenant,” he says.

One detail tends to stand out about those who complain that his company is going too far with its analysis.

“The people that complain about giving up too much information are white males,” he says.

“Most of the time, they’ve never had to provide a credit report because they just get the benefit of the doubt. If you go to the U.S., every single African American we’ve served has never had an issue or complained about having to fill out the application.”

“That is one scenario. I won’t discount that,” says Cavoukian, who now works as the executive director of the privacy and big data institute at Ryerson University. “To me, it’s all about the unintended consequences. There are no restrictions on how the landlords can use it.

Cavoukian’s is right to be concerned. Two racist rental ads made headlines this past summer. One for a basement apartment in Mississauga, Ont., posted on Kijiji suggested “Black guys pls no call.” Another Craigslist posting for an apartment in Metro Vancouver said the landlord was seeking “Asian only” tenants.

A 2009 study by the Centre for Equality Rights in Accommodation found unfair conditions exist at varying levels within Toronto’s rental market for single parents, black and Asian individuals, people with mental health issues, and individuals receiving social assistance.

While increased reliance on data-driven risk assessments may be a promising way to mitigate racial, gender, religious, and other types of biases, whether it be for predictive policing or tracking the social media habits of potential terrorists. It will also mean the so-called “tyranny of algorithms” will become an even bigger conversation.

At the end of the day, the data is only as sound as the person who created the means through which it is collected and presented. And the decisions that get made because of that data are only as ethical as the person armed with the information.

If knowledge is indeed power, landlords just got a lot stronger thanks to Naborly.

“We think there is a huge responsibility around the fact that 12 people in downtown Toronto are deciding where hundreds of thousands of people are going to live or not live,” says Lenz. “This is the future. Everything you do is being recorded or monitored in some way. The amount of data getting recorded on you just from your cell phone usage is shocking.”