RANK TARGET CITIES

“The bottom line is that we end up significantly setting ourselves up for better returns or results when we use data science. There's so much room for analysis, data analysis, for comparisons of various cities and how they're doing in terms of job growth, population growth, income growth, home price growth. You don't always get things right, but we end up significantly setting ourselves up for better returns beyond what typical Excel spreadsheet analysis can do.”

“Each employee in our company is required to be highly competent in the use of AI. There is a minimum of one AI training per week that 100 percent of employees must attend, and you have to provide proof that you use AI for one hour a day. We have currently over 400 documented helpers or tools that we have built for AI, and it allows us to do things that human beings simply cannot do by themselves.”

“What we found was there was strong correlation between job growth, income growth, home price growth, and crime reduction, and population growth. These five areas seem highly correlated to real estate profits. All of them make sense initially, but the devil is in the details, because the question is which one's better and how much better? How do you quantify something like this?”

“We need more than 1% annualized population growth for rents to increase aggressively. 3% job growth in a certain market can lead to very aggressive rent growths; 4% is phenomenal and 5% is rarely achieved, but it's absolutely incredible because if a city has 5% job growth, everyone at that point is employed and everyone is very aggressively looking to rent or buy.”

“I became obsessed in 2008 and 2009 with the idea of ranking cities for real estate investments. How about bringing that level of clarity, that level of objectivity to ranking cities for real estate, which cities are likely to be more profitable? You can never say it with any level of certainty, but you can certainly improve your chances pretty substantially of making money if you are data driven.”