A renowned real-estate consulting firm wanted to foresee real-estate based investment opportunities to buy promising properties and buildings and resell or rent them in the future. The company wanted to collect property and housing data from 200 different sources to cover the entire property listings in the US. With an ever evolving market, the company wanted to have a real-time database of property listings to make sure they are covering every real-estate listing in the country.
Along with gathering the right property data, our client needed to build a decision intelligence system that provides bidding insights to identify the right price for a property. They could use this system to make reasonable bidding for the listed properties.
Challenges they faced
The data they required was shattered across disparate sources, so manual data aggregation was challenging. The process was time-consuming and the data had anomalies, which our client found difficult to detect.
They faced significant challenges in gathering the data because of the volume of the data that had to be extracted, as well as the unstructured nature of the data. Due to the lack of an automated solution, they lost both money and time in curating the right dataset to back up their decisions.
The decision-making process for selecting the finest property and the right estimate for the bid was a laborious task that required complex calculations. A higher bid price results in a loss of returns from the invested property and a lower bid price means they will lose the property.
How we solved the problem?
We designed and deployed bots in 200 different sources and aggregated the data in an automated manner. We divided the number of sources and scheduled the data extraction on an hourly basis. After aggregating the right data, we implemented a cloud database and data lake architecture to centralize all the data and to transfer the data across the organization.
For complete comprehension of the data we provide, we presented the data in dashboards using our data visualization capabilities and helped the user to make the optimal utilization of data.
We developed an ML model that works as a decision intelligence system to help them identify the right property to make investments. We analyzed parameters such as attractors, detractors, historic sales trends and government regulations in the locality to derive accurate inferences. With the help of our ML Model, we could find the right property that could provide a greater return on investment while having a fair bidding price.
Our automated data aggregation solution helped them gather the right data they needed to make better investment decisions. Using our ML based decision intelligence system, we enhanced the accuracy of bidding upto 95%. The success rate for new property acquisition increased by 20% over a span of one year. Using relevant data and accurate insights, the return on investment for the property increased by 45%.