I'm trying to understand how they collect data so as to develop the best comping practices. It seems to me that they all whether paid of free, will all have the same exact amount of houses because all the transactions are all public and so they seem to compete with each other based on the quality of the data of the houses, how up to date it is, and the user experience in configuring the how the layout is. Is that correct or am I way off?
Real Estate Agent · Tooele, Salt Lake City UT · Member since 2020 · 136 posts · 54 votes
1y
You are correct that comping software like Privy, Propwire, and Propstream primarily rely on public records for their data, such as county assessor records, MLS feeds, and sometimes 3rd party aggregators. The differences lie in how they aggregate, update, and present the data. They focus on timeliness, accuracy, and user experience. Paid platforms often offer additional features like market analytics, off-market property leads, and filters for investor-specific criteria, giving them a leg up over free options.
You are correct that comping software like Privy, Propwire, and Propstream primarily rely on public records for their data, such as county assessor records, MLS feeds, and sometimes 3rd party aggregators. The differences lie in how they aggregate, update, and present the data. They focus on timeliness, accuracy, and user experience. Paid platforms often offer additional features like market analytics, off-market property leads, and filters for investor-specific criteria, giving them a leg up over free options.
Exactly, most platforms have to offset cost somewhere because of how expensive it is to keep the data up to date. A platform that updates their data daily or weekly is covering a staggering cost in order to provide that to the end user. Most platforms will update their data quarterly some of these other ones may take longer which is where the discrepancies come from.
Another common practice for nationally sparse datasets such as tax data is aggregation. For example, to get up-to-date property tax data you would need to scrape every county's tax data website (thousands of different websites, data formats, issues, etc...) then aggregate and clean the data. For most, this isn't a realistic option so they update some of the most important regions themselves and copy the data from other providers where they don't have coverage.
This is a big problem with these big consumer-grade real estate analytics platforms—you don't really know the source of that data, its frequency, etc. Very few providers are actually internally sourcing all of their national real estate data—there is lots of copying and stealing.