by Changepond Posted on March 19, 2013
Banks use BIG DATA to sell real-estate faster
Information & Intelligence without Barrier
Organizations and their related associates are all generating more data than ever before, out of which some are structured, a few semi-structured and a majority is unstructured. Social media streams are perhaps some of the largest contributors of unstructured data that doesn’t fit neatly into categories, but if it can be understood, it is as valuable as structured data. The rapid expansion of big data means abundance of consumer information, but more data may not really pose a composite picture of the customer.
Leading banks are enriching their data stores by accumulating data in real time from numerous business sources. The market-centric business model propels the need for data collection and processing, which is considered useful for assimilating effective customer insights.
Our tryst with Big Data
The industry experts generally source trade information by getting privileged access to transaction data, bids information and purchase behavior of buyers. Acquiring both demands a significant expense and effort. Nowadays, experts in property data and related analytics have begun to venture away from traditional workflows, thereby permitting key stakeholders to exchange useful perspectives on the value of assets and thus creating alternate sources of data.
Banks traditionally sit on similar volumes of data on the supply and demand of the housing market, but the traditional intelligence has been too often little or too late. Now with Big Data able to harvest data from both unstructured enterprise and social sources, Banks are able to precisely map the inventory to the demand, selling homes faster and for the right price.
Changepond has helped banks to sell foreclosed houses for an optimum price. This was made a reality by effective use of relevant data, with the appropriate data architecture and analytical tools, helping banks leverage a deeper view of the housing market. Banks were able to sell by proactively contacting customers based on behavioral triggers and key life stages.
Sites such as zillow, realtytrac, airbnb and several others were scrutinized on a regular basis to understand the selling price/rent of the houses in that location. Based on the scraped data, the value of the houses was calculated. This information enabled the banks to prioritize the sale of houses. Potential buyers for a location were zeroed on from the information collected from Social media feeds (twitter). The Apache Hadoop framework was employed to calculate the average price of houses. Hbase was used to store the data scraped from the websites.