A very accessible line of thinking about applying data in the real estate space goes something like this:
Except, the “implementation details” are particularly challenging in this domain. Four specific challenges surfaced when trying to apply and monetize data in the acquisition phase of real estate development projects.
Data is typically used to uncover new potential sites for (re-) development. The time between initial identification, approaching the owner, and a transaction may be weeks (very optimistic), possibly months, and likely years. The perception of “added value” from the data therefore is quite weak, and that in turn decreases your ability to sell and commercialize.
Quality of data requires focus, while having successful acquisition channels depends on having unique access to sites, thus making use of unique data to uncover sites. There is a conflict between these approaches: developers prefer a breadth-based approach, where as data provider you are also responsible for depth (= quality).
Therefore, supplied data needs to be incredibly diverse, and more likely than not developers will ask for more esoteric and unique data than you can supply.
Ultimately the most interesting metric by which to find potential sites is the willingness-to-sell of the current owner. Most types of data in this process are considered in the degree that it correlates with that metric. We have found that overall, there are no consistent indicators of willingness-to-sell. The biggest other ingredient to turn data into deals is time.
As the transaction sums are large, to share in the transaction requires a lot of patience and trust between you and your client.
That accessible line of reasoning therefore is not as evident as it might appear. The ability to find new high-value (re-)development sites - in my experience - does not easily convert into (predictable) revenue.