This post is part of our new Future of Real Estate series which interviews the leading founders and executives who are on the front lines of the industry to get a better understanding of what problems the industry is facing, what trends are taking place, and what the future looks like.
The following is an interview recently had with Joshua Fraser, CEO and founder of Data Nerds.
1. What's the history of Data Nerds?
Data Nerds began in 2014 as a digital marketing agency and in 2016 pivoted to focus on the real estate data industry. It started in Kelowna, British Columbia and while I was purchasing a home in 2015, a light-bulb went off to create a Carfax for homes. Fast forward to today and over 50,000 reports are developed a month and our latest product Estated.com is gaining a ton of traction.
2. What specific problem does Data Nerds solve?
Data Nerds currently drives solutions as a data provider, and as a property report provider. We solve the problem of inaccessible, incomplete data by offering the most comprehensive datasets around, as well as providing premium analytics and content for our consumers and clients. We solve these problems for developers, realtors, consumers, and professionals in sectors such as mortgage lending, insurance, home services, and energy/utilities, to name a few.
3. How does your solution work?
We power apps and workflows using our industry-leading residential property data and use our comprehensive nationwide coverage of US residential properties to do so.
4. What are the top 3 tech trends you're seeing in real estate?
The top three emerging tech trends in real estate currently would have to be artificial intelligence, blockchain tech, and virtual reality. We are exploring these further, and you can read about them on Medium.
5. What's the future of real estate?
The future of real estate lies in the insights we can drive from data. With predictive capabilities opening new horizons to urban development and real estate, data integrity is the nucleus of keeping it all together. By leaning cleaner, more accurate datasets and continuously training and refining algorithms, specific platforms will be able to predict prices with higher accuracy and detail over time.