Investing in rental property doesn’t have to be a research headache. The traditional approach is all about evaluating deals one-by-one. This is great for specialized properties and is the norm in the commercial world, but falls short in the high-volume single family residential market where there is a sea of opportunities. Evaluating deals and then driving the neighborhoods to get a sense of their viability is a time-consuming task. It’s a process is begging for a little automation and help from a data-driven approach. Why spend time vetting deals when 80%+ of them can be easily eliminated, allowing you to focus your time and energy on the other 20%?
Much of this legwork can be simplified. We start by building a model that estimates rents for every location in the metro area using machine learning. From there, we look at holding costs, and finally, risks.
Doing this at the city level, or even ZIP code level gives a very general sense of how locations differ, but does not provide much specificity. Within a single ZIP code, there are often very large differences between neighborhoods or even opposite sides of the street. The solution is to bring this down to the micro level using much more granular data. Conventional wisdom says that a good balance of risk and return is in “B” neighborhoods that are reasonably priced but have important stability characteristics such as:
An investor looking for high yield while still remaining in a “B” neighborhood would filter out areas that don’t meet those criteria and get this result. Right away, we now have an idea where to start looking.