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Private equity-backed real estate investments

Once you're at the top of your game, you want to stay at the top of your game.

To maintain their fast-paced growth, this start-up real estate investing company needed a lead qualification engine that would automatically aggregate and evaluate incoming data, as opposed to their existing process that slowly assessed individual leads. A lead qualification engine would determine whether a property fit within our client’s investment criteria, enabling them to make smarter and faster investment decisions and continue on their growth trajectory.



This private equity-backed real estate investing start-up was experiencing fast-paced growth. They needed to quickly evaluate potential investment properties with minimal effort.


Ollion built out a lead qualification engine, which included a centralized data hub platform and a scalable data vault solution in Snowflake.


At the end of this project, our client was able to process 900% more leads per hour and make smarter, faster real estate investment decisions.

The challenge

After being acquired by a private equity firm, this real estate investing start-up was growing rapidly and needed to be able to automatically evaluate hundreds of leads at once. They were taking a broad range of information into account when assessing potential properties, including these categories and much more:

  • School zone
  • Median income for the zip code, pulled from the census
  • Number of bedrooms/bathrooms
  • Square footage
  • Estimated insurance rates

Our client was dealing with three main challenges as they scaled up their lead qualification efforts. These obstacles slowed down their process and impacted lead qualification accuracy.

1. Manual lead evaluation in Excel

At the outset of this project, our client was storing their data in Excel sheets. As leads came in, they had to run them through their model in Excel one at a time, manually maintaining lookups and aggregating data. This significantly limited the number of leads the team could evaluate.

2. Exhaustive process to add data sources and leads

Our client lacked an effective way to sync with their new CRM of choice, Salesforce, requiring many steps to enter data sources and leads under evaluation. Data was being pulled from APIs via scripts or manually. The process was not efficient or scalable.

3. Difficulty augmenting or overriding data

Our client needed to be able to easily add new information or override existing data in Salesforce. Incoming leads often lacked details, so an initial lead evaluation would be based on inadequate information. Our client’s set-up did not allow them to easily modify records to best represent a property’s value, facilitating substandard lead qualification.

The solution

This start-up regularly received data from partner sources, including real estate valuation and property listing organizations. Some of the data was originally stored on external locations, such as Amazon S3, and some were user inputs into Google Sheets. Ollion built out a centralized data hub platform, combining all of the inputs. Our solution also cleaned the data from user inputs so when additional data was retrieved via API calls, our client received the highest quality responses possible.

Once the inputs were collected, we utilized a data vault solution in Snowflake. We chose this solution for a few reasons:

  • It enables full historical tracking. Snowflake makes additional historical storage financially feasible.
  • It is scalable for new data sources.
  • It offers high performance and minimization of dependencies as many processes had to be run in parallel.

The data was then sent to Salesforce so our client’s team could review and manually update leads, adding additional information or overriding details as necessary. Only qualified leads would go into Salesforce; leads that didn’t meet certain criteria (e.g., not a profitable zip code, lacking the appropriate bedroom to bathroom ratio, etc.) would be automatically discarded.

Once a qualified lead entered Salesforce, users could make changes as needed (adding missing school zone information, for example) and these updates would circle back through the solution and be updated in the data hub. Leads were automatically evaluated by the lead qualification engine – essentially a valuation calculator dependent on our client’s specifications – but would be reevaluated when users updated information in Salesforce, ensuring our client always had access to accurate scores based on the latest data.

The outcome

With the solution Ollion implemented, our client’s data is available in Salesforce within roughly one hour. They previously had to wait a day or longer to access new data. Now, they’re able to analyze 500 qualified leads per hour, rather than a few dozen. This amounts to a 900% increase.

Furthermore, this real estate investing start-up now has a scalable data hub, so they don’t need to build out additional pipelines or change existing structures to accommodate new data as they add partners and lead sources. With Ollion’s code accelerator, our client can easily build the required database structure to efficiently add new data sources.

As a result of this project, our client’s acquisitions team can easily evaluate deals in Salesforce rather than spending their time transferring data into Excel and adjusting lookups. This enables our client to make smarter and faster real estate investment decisions as they continue to grow.

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