Since its invention back in the 1990s, digitization has diffused its way into every conceivable sector of commerce; specially the service industry. In financial services the rise of digital technologies has dramatically changed the manner in which institutions deliver information to their clients. Zivanta Analytics has helped in data digitization of a leading real estate hedge fund company which has brought more efficiency in their work there by shortening the TAT for their bidding for each deal.
The company’s primary objective is to deploy capital for pools of real estate assets that contain a title, document or compliance issue which impacts the marketability of that asset to the current owner. It utilized an asset based due diligence review which encompassed the legal curative strategy, property value and cash flow analysis data points needed to secure the assets with an appropriate equity position to protect a potential investment risk.
The objective of the company is to acquire real estate pool which came with thousands of scanned collateral documents which told the story about the constituent properties, its lien and title status, default details, assignments and other key variables which determine the risk profile of the property. For taking a price position for bidding for a Deal, the risk profile needs to be determined.
To create the risk profile of these properties, the relevant data had to be culled out from the collateral documents for each property. Zivanta Analytics had been retained by the Hedge fund company to sift through thousands of collateral documents and the key indicators. Zivanta Analytics has also built an analytics engine which uses the key data to build the risk contours of each individual property in a deal.
The company selected a specific deal and had transferred the entire collateral set to Zivanta Analytics in pdf format. Analysts at Zivanta analytics who are trained to look into the real estate collaterals, sorted the documents and looked at the completeness for building the collateral datasets. Accordingly the digitization strategy for a given deal was decided. The collaterals which could be machine read were sent to the technical data scraping team who wrote codes to digitise. The remaining collaterals which were not machine readable were sent to the data associates who keyed in the data for each property from each collateral using either voice or key board based data entry method. As the data were entered, the data quality team concurrently checked the data for quality using proprietary checking tools and analytics.
The cleansed data was then processed by the Zivanta proprietary analytics engine to create a risk profile for each property. The data could be queried by the hedge fund company experts through a custom interface developed by Zivanta Analytics. A dashboard to help analyze the risk profile was also developed.
With the help of this newly developed process the hedge fund company got quality data on tight deadlines at very economic costs. The risk profile created using the data from the collateral documents helped make an informed decision regarding the bid price for acquiring a deal. Once a Deal is acquired, the experts at the company are now using the data set to identify and mitigate the risk associated with each individual property thereby making the property marketable at prevailing market rates.