Do you have a beautiful city area- a place to rent to some decent folks? And also for not much money, but you are not sure about the tenants, Naborly has a solution for you.
Naborly is a startup by Dylan Lenz, Zeke Kan, and Anastasia Fox. It is a group of students and friends from the University of British Columbia. The application allows landlords to create custom tenant applications to collect pertinent information from residents. This information is then processed to develop a dossier for the landlords. It works helping them to decide whether it is safe to keep the tenant or not.
The company have so far raised $500,000 and is looking forward to seeing a rise of $2 million. Naborly is receiving a revenue of $2000 per day hitting almost 20-50 new landlords every day. The company is also offering this service via the API. Naborly is also working with credit companies progressively to add more data to their system. The Naborly team is also working always to find better ways to manage tenant ratings.
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The team is constantly looking for variables that matter and can make the product more efficient and effective. Improvising Machine Learning – the primary core of the product, the team is continuously putting efforts to improve the performance.
The team says that the application instantly analyses about 500 data points on each tenant including information from social media, rental histories, Google, credit, etc. The application can now accurately predict things like- late rent payments based on interpersonal conflicts between roommates, macroeconomic events, job profiles, or other financial factors.
The Bottom Line
In this way, the application saved many landlords time to research and find out the credibility of the tenants before renting them their place. Another idea or can be said- the inspiration behind naborly was when Lenz had to deal with a professional tenant that cost him $22, 000 in property damage and unpaid rent.
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He says that the residents even threatened him at his work and home.It was the time he realized the need of such a tool that could help the landlords to identify tenants that could be risky to keep. This tool intelligently worked for them and contributed to identifying vulnerable residents saving money, efforts and time.