Note the insurance pays all but the deductible but rebuilds take effort from the asset manager/owner.NAR just pulled down their ADU data on the premise of it being dated (it was getting old but it was the most complete source of ADU valuations).  

How are these deal websites getting this data, or did the marketing team mess up?

Without it you simply don't have enough data. 

But the ones I've been involved with use SDE (Seller Discretionary Earnings) rather than just EBITDA for cash flow portion of the sum of the parts valuation analysis.As for market comps, LoopNet is fine, but I use Crexi and others on here probably use Argus, but those 2 generally require a subscription for the better data.

It’s about moving from descriptive data (what is) to predictive trajectory (what will be).How are you weighting rent affordability against the risk of neighborhood transience?

Most agents use peak season data year-round, so I always run numbers assuming 10-15% lower occupancy.

IMHO $15 for a book is a steal and I happily pay it, but I will never sign up for their $1,000 course...Highly recommended books, in order of importance:Real Estate Investing For DummiesProperty Management For DummiesABCs of Real Estate InvestmentBuilding Wealth One House At A TimeThe Small and Mighty Real Estate InvestorAs far as real estate data, look at population trends and free real estate reports. 

Clean, localized, well-segmented data tends to outperform UI once costs tighten.From what I’ve seen working with different datasets, how those addresses are sourced makes or breaks downstream efficiency.Curious how you’re sourcing or filtering the addresses that feed into the API flow — manual criteria, stacked lists, or something else?

Sold comps are really the only data that shows what buyers are actually willing to pay, so focusing there makes sense.

100% agree — list quality beats volume.From working with different datasets and campaigns, one pattern that keeps showing up is that segmentation by actual seller pain points (not just surface filters) drastically improves connect rates and conversation quality.We’ve also seen that dialing efficiency improves when data is refreshed and cleaned more frequently, even if overall list size is smaller.Curious if you’re seeing similar lift when lists are stacked by motivation vs. single-criteria pulls?