That is pretty much the point of my post; since I only have experience with full-service PM, data from other investors’ experiences with other alternatives would be extremely helpful in doing such an analysis.  

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?

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?

Quote from @Joe Daniel: Quote from @Dave Harlan: Quote from @Joe Daniel: Hey Dave, lots of investors build their buyers list by tracking activity from local public records especially recent cash purchases, LLC transfers, and repeat buyers in the county.It helps you identify who’s actually buying right now instead of collecting random emails.If you ever want to talk through ways to source or organize that data efficiently, happy to share what I’ve seen work well for others. 

I’m also a data scientist, so the idea of a ZIP-level screener definitely resonates.A few things that could make this more useful:• clearer sourcing/definitions for metrics (e.g. whether home price reflects list prices, sale prices, or a model)• visibility into which ZIPs are included in the upgrade• coverage beyond core cities to include inner-ring suburbs• more market context (e.g. competitiveness, school ratings, renter/owner mix)• ability to segment by property type (e.g.

I've been rocking with them since 2019 and it's the best data on the market, in my opinion.

Thank you.OWNERSHIP ENTITY- Obtain copy of deed to confirm legal owner- If domestic LLC, confirm annual registration is current & who can legally sign- If foreign LLC, confirm registered in Michigan (where we & property are) & who can legally sign- If Trust, get paperwork showing who can legally sign- Confirm city assesssor & water dept have correct owner & mailing addressPROPERTY- Confirm we are added to insurance policy- Confirm property taxes are current- Lookup public records for property data- Confirm rental license active- Confirm no outstanding permits, assessments, etc.- TakeOver Evaluation Video & Property Data Sheet, share video with owner for possible repairs- Per owner selections on our PMA, have tax docs, water bills, etc. mailed to us.TENANTS- Send tenant(s) Welcome Aboard Package with TakeOver Questionnaire (confirms occupants, pets, how long there, rent rate, security deposit, who owns appliances, etc.)- Review previous rent ledger for patterns to be aware of, note their lease in PMC software- Try to convert to our Lease, to make it easier on our staff (we don't tell tenants they don't have to do this)- Opportunity to renew/extend lease with increase?

You can read all the details for yourself here:Why Viceroy Research is Short on Arbor Realty TrustBelow are some snippets that are included to link this report to individuals and entities that have promoted here or have had investors discuss the investment here so when people search for them they can find this data."

I'm calling on Title companies to get a list but learned that they charge a fee for some data

I started looking into more things a LLM (large language model) would be capable of doing and started working on a couple features to help with some of the repetitive data entry I was doing every month.I'm curious about learning how people do bookkeeping and some of the things they dread doing?