How AI & Machine Learning Is Helping Real Estate Businesses Clean-Up & Organize Their Data
PropertyQuants was founded by a team of Ph.D. ex-quant traders with a track record of successfully deploying automated trading models in global financial markets.
They're bringing the quantitative revolution to real estate investing. Property investing today is often based on incomplete and inadequate information. Analysis, forecasts, and decisions are not rigorously produced and are sometimes driven by emotion, untested assumptions, and rules of thumb. Portfolio considerations in real estate are often ignored.
PropertyQuants works to improve real estate decision-making. Real estate decision-making should be evidence-based, data-driven, and systematic instead. PropertyQuants are leading the way forward by applying quantitative finance and data science methods to global real estate, helping investors beat the market.
---
How AI & Machine Learning Is Helping Real Estate Businesses Clean-Up & Organize Their Data
When someone thinks of AI machine learning, you think of this scientist in a coat in front of a whiteboard or chalkboard. Those are scary. I've seen you dissect a few, and that can be quite lengthy and intimidating. What I realized after running my last startup was to get the data. We realized that no matter how good your data science team is, or no matter how good those PhD modeling people are, they can't do anything unless they can access data.
The other surprising thing to me in doing your course and other courses is that I’ve realized that it takes a few lines of code and you can implement an existing algorithm. Some of these algorithms are powerful, and you don't need to reinvent the wheel. In fact, that's probably the worst thing you can do. You might want someone who can access the data, clean the data, and type a few lines of Python code, and you've performed the work that previously humans couldn't do. Even teams of humans couldn't get to the output that a machine can get to.
In that case, what we were trying to achieve with the cost seems to have been a success. We are in this era of open source technology. You're absolutely right. You probably shouldn't be writing new algorithms. You shouldn't be implementing what other people have already written, A) Because it's more time-efficient. B) Because there are teams of smart guys out there making sure it's correct, keeping it up to date, and things like that.
You're right, gather data in a format that a team of people could do the applications for. Once that's all there and it's cleaned up, running the model in any data science activity, getting the data together can be 80%, 90% of the time, and then running and optimizing your model is probably 10%, 20% of the time. That remains true.
No matter how good your data science team or those Ph.D. modeling people are, they can't do anything unless they can access data.
There's a third thing too. You’ve got to remember that there are teams of experts out there. Some scientists and PhDs and others are working at Google or Facebook, and they're producing powerful algorithms with lots of resources and they're open-sourcing that. The community is powerful. Can you think of any instance where it makes sense to go and hire a PhD-level researcher and write algorithms and models? To me, I don't see many use cases in real estate. If there are, they are very edge. What are your thoughts?
One of the reasons that we did launch the course is because we are in this open-source era and you can download all these algorithms. With the specialized domain-specific code for running some things in real estate, we don't see any libraries or packages that you can directly import. I don't know if you need a PhD-level researcher, but someone who understands what they're doing, be it Masters because they've geeked out about this for a long time and studied it, that’s fine too.
It’s somebody who can convert some of this into a library of code that you can use. That’s a part of the stuff that we do in the course as well, we do share some of the things that we've coded up. If you are trying to do something specific, in the real estate space, there are a couple of cases in which there is something new. There's a little bit of innovation.
I’ll give you another example. How do we use image recognition for real estate? We have companies that are looking into maybe grading the quality of the property based on looking at the photos from a listing. We can think about companies like Foxy.ai, Recipe.ai, and so on. There is a bunch of open-source stuff about image recognition. We can identify a cat from thousands of photos. It's a little bit different, this question is realistic. How do we determine the condition? Maybe there are specific examples like that.
Another example of image recognition is floorplan analysis. How do we automatically recognize from a JPEG or PDF, the various rooms inside the floorplan or the size? It seems to be still an unsolved problem. You're trying to solve specific cases like that for sure. For the most part, what a lot of people are trying to do doesn't necessarily require a letter. It requires somebody to take their existing algorithms and try to solve them.
That focus on data is not a glamorous role either. It can be quite a boring role sometimes for many engineers, and that's probably why it's been difficult. In my last startup, hiring good data engineers was the most important thing we could do. Companies tend to kick the can down the road of this when their architecture isn't in the right place when the data integrity isn't being respected, it's being stored in different places. It's a lot more work to unchain everything and redo the architecture, and sometimes that's what's needed. Before you can put any of that to use, garbage in, garbage out. If the data isn't clean or if the data isn't being collected and stored properly, that's where you've got to focus, and that's not an easy job.
---
Subscribe to Zain Jaffer: https://bit.ly/2SWhYW5
Follow the PropTech VC Podcast:
Listen on Apple - https://apple.co/2Izoznu
Listen on Spotify - https://spoti.fi/2STWDwq
Listen on Google Play - https://bit.ly/2H7s6c0
Follow Zain Jaffer at:
Twitter: https://twitter.com/zainjaffer
Website: https://zainjaffer.com/
Current Ventures: https://zain-ventures.com/
LinkedIn: https://www.linkedin.com/in/zainjaffer/
First Co-Living Start-Up: https://www.youtube.com/watch?v=K2MQNZnfvOA
Innovative Housing that can change the world! https://www.youtube.com/watch?v=K92fRT04Qjo
Building an Online E-Commerce Furniture Empire: https://www.youtube.com/watch?v=WhznBbYMhhg
About Zain Jaffer
Zain Jaffer is an accomplished executive, investor, and entrepreneur. He started his first company at the age of 14 and later moved to the US as an immigrant to found Vungle, after securing $25M from tech giants including Google & AOL in 2011. Vungle recently sold for $780M.
His achievements have garnered international recognition and acclaim; he is the recipient of prestigious awards such as “Forbes 30 Under 30,” “Inc. Magazine’s 35 Under 35,” and the “SF Business Times Tech & Innovation Award.” He is regularly featured in major business & tech publications such as The Wall Street Journal, VentureBeat, and TechCrunch.
Important Links
- Foxy.ai
- Recipe.ai
- https://Bit.ly/2SWhYW5
- https://Apple.co/2Izoznu
- https://Spoti.fi/2STWDwq
- https://Bit.ly/2H7s6c0
- https://Twitter.com/zainjaffer
- https://ZainJaffer.com/
- https://Zain-Ventures.com/
- https://www.LinkedIn.com/in/zainjaffer/
