I'd add one more thing for first-time OOS buyers specifically: before you even think about unit count or financing structure, make sure the listing data actually matches public records.I've seen a duplex where the listing showed 3 bed / 1 bath per unit but county records said something different — with an entire bathroom that doesn't exist in any public record.

With markets feeling more segmented lately, I’ve been rethinking how I anchor ARV during early deal analysis.Instead of relying on a single comp or peak-sale comparison, I’ve been leaning toward:• ARV ranges (low / mid / high) rather than one number• Heavier weighting on the most recent 60–90 day sales• Noting spread between list vs. sold prices in the same pocket• Treating appreciation as a bonus, not a givenI’m finding that even within the same zip code, buyer demand and pricing tolerance can shift block by block depending on condition, financing availability, and buyer profile.Curious how others are handling market data right now:Are you tightening ARV assumptions, using wider ranges, or changing how you comp altogether?

For contact management like that its awesome to search for specific types of people and locations or just data like "how long does it take to foreclose in texas".We have some other use cases as well that we use to track not only ours but competitors results online and learning what does and does not work for different aspects of the business.

For our kids, it was awful because even data was down as well (although driving to a cafe or library for internet was still possible).

Where it really helps is organizing data — pulling census population trends, job growth, school ratings, and crime stats into one clear snapshot — but you still need to call 2–3 local property managers to confirm real rents and tenant quality.

If not, adjust strategically.Vacancy is data.

I started pulling rent comps from 3 sources instead of one - sometimes there's a decent gap that makes deals work when others walk away with bad data.

Once a system controls the payment flow, it doesn’t have to rethink its data model, workflows, or assumptions.At roughly $40,000 per year for every 1,000 properties, payment handling isn’t nickel-and-diming — it’s extraction.

The numbers tell the story every year when I prepare the data for my CPA.It works.

Rental data is thinner and inventory is smaller, but purchase prices allow for better cash-flow potential if rents are verified locally.Best suited for:Budget-conscious investorsCash-flow-oriented strategiesInvestors comfortable doing deeper rent-comp research📊 Quick ComparisonCountyAvg Purchase PriceAvg RentGross YieldEst.