Overview

A renowned real estate consulting firm wanted to foresee real estate-based investment opportunities, such as buying promising properties and buildings and reselling or renting them in the future. The company wanted to collect property and housing data from 200 different sources to cover all the property listings in the US. 

With an ever-evolving market, the company wanted a real-time database of property listings to ensure it covered every 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. This system would allow them to make reasonable bidding for the listed properties.

Challenges

The required data was shattered across disparate sources, so manual data aggregation was challenging. The process was time-consuming, and the data had anomalies our client found challenging to detect.

Because of its volume and unstructured nature, gathering the data was challenging. Without an automated solution, they lost money and time curating the right dataset to support their decisions.

The decision-making process for selecting the finest property and the proper 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.

Solutions

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 hourly. After aggregating the correct data, we implemented a cloud database and data lake architecture to centralize all the data and 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, historical sales trends, and local government regulations 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.

Results

Our automated data aggregation solution helped them gather the correct data to make better investment decisions. We enhanced bidding accuracy by up to 95% using our ML-based decision intelligence system. Over one year, the success rate for new property acquisition increased by 20%. Using relevant data and accurate insights, the property’s return on investment increased by 45%.