Updated about 2 months ago on . Most recent reply
I tested something and it surprised me
I took my top 10 most distressed properties in Atlanta and looked them up on Zillow to see what they actually looked like.
Every single one had visible issues. Not one of them looked clean.
That caught me off guard because the data picked them up before I ever looked at them.
What really stood out to me was where the clusters were showing up. I have lived in some of those same areas before.
West Midtown was one of the highest clusters and I used to stay right there, so I already knew what was going on in that area.
Feels good to be able to use data as confirmation.
How are you determining the condition of your properties?
Most Popular Reply
Love this experiment, and really appreciate you sharing the “behind the scenes” of how you pressure‑test your own data.
As someone on the property management / BD side here in Atlanta, I’m constantly reminding investors that the real edge is exactly what you did: use data to narrow the haystack, then validate with eyes on the actual properties and streets. Data calling out distress before you even see the pictures is a powerful signal that your methodology is on the right track, especially when it lines up with your lived experience in those same pockets.
On my end, we usually look at “condition” through three lenses:
- Portfolio‑level data: maintenance frequency and cost per unit, work order types, days‑to‑complete, and recurring issues that signal deeper structural or systems problems.
- Block‑by‑block reality: Google Street View as a first pass, then either in‑person or trusted boots‑on‑the‑ground walking the street, noting neighboring properties, pride of ownership, and signs of investor activity.
- Resident experience: renewal rates, complaint patterns, and survey/feedback—tenant behavior often exposes functional condition issues that photos gloss over.
Your post is a great reminder that the best investors (and managers) don’t choose between “data” and “feel”—they let the data surface opportunities, then let local knowledge and on‑the‑ground observation decide which ones are actually worth leaning into.