I have software that does this as well - its not excel but there are a ton of benefits over excel, especially if you want to have multiple scenarios of the same asset, aggregate multiple assets into a fund, calculate complex equity waterfalls, integrate AI to automate data entry or write python scripts to do analysis (or have ChatGPT write it for you). 

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?

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?

Sometimes the market data is off by $50-100.

The Binder Method is a strategy that encourages tenants to request a rent increase by showing them real market data—creating a win-win situation. 

Looking forward to engaging in discussions grounded in data and operator experience.

The MLS is the best and the most accurate place to find comps, do a CMA and really know the data to be confident that you can sell it for that price you want to sell it for when you are done with the project.

Week of January 5, 2026 | Market Snapshot for Real Estate & Mortgage ProsAs we kick off the new year, fresh data confirms two important trends: the job market is quietly cooling, and new home construction is slowing more than expected.

Use airDNA to collect market data.

.• Use a full background screening, not a single data sourceAt a minimum, look for credit, nationwide criminal history, eviction records, and judgments or liens.