Type
Advanced Tool for Real Estate Investment Evaluation
Objective
One of the top-rated real estate investment firms in France needed an integrated platform that could evaluate investments effortlessly. The objective was to design an
advanced analytical tool capable of facilitating an assessment of the property, computing the ROI, and providing a comprehensive outline of financial projections with
good decision-making while diminishing the associated risks from investments.
Technologies
.NET, C#, SQL, Azure, Kubernetes, Hadoop
Country
France
Project Attributes
Engagement Model
Dedicated hiring
Duration
Ongoing since 4 Years
App Users
Real estate analysts, investment advisors, and financial consultants.
Challenges
- Too much data in real estate: The client had multiple data about real estates,
and these were too many for them to understand and analyze them so as to
come up with actual findings. This meant that tools in existence were hard to
make use of when dealing with big numbers in analytics, which resulted in long
turn-around times as well as some terrible insights. - Uncoordinated Data Analysis: Investment projections were irregular. The
advisors could not make proper forecasting to their investors due to the
underlying manual procedures of data analysis. - Resource-intensive Calculations: The ROI, cash flow, and cap rates took a lot
of resources to calculate. This made it inconvenient for the team to discuss and
analyze many properties at any one particular time. - Limited Scalability: As the scale of data from the clients grew, so did their
infrastructure’s breakdown. Data could not be processed during their peak
analysis times.
Solutions
- Big Data Processing through Hadoop and .NET Integration: The team
integrated Hadoop with a .NET-based platform so as to efficiently manage large
datasets for proper analysis. This solution not only provided an easier way of
handling big data in real estate but also offered faster insights with a more
reliable analysis process. - Automated Financial Modeling: The team used SQL and C# to develop
automated financial modeling capabilities that could produce uniform financial
projections and cash flow analysis of numerous properties simultaneously for the
client. - Real-Time Reporting and Visualization of Data: The integration of Azure and
Kubernetes ensured real-time reporting and efficient processing of data.
Customized dashboards helped advisers monitor key performance indicators,
and the development of visual insight aided decisions based on data. - Infrastructure Scalable and Flexible: Kubernetes-based deployment enabled
the application to scale with increasing data requirements from the client. The
infrastructure would support a large volume of data without any untoward effects
on performance, thus ensuring safe investment evaluation even during maximum
usage.
Results:
- Better Processing Method for Data: The big data processing ability through Hadoop has enabled an application that could be best used in enabling better management of large amounts of data for the client to quickly evaluate his properties, hence improving accuracy on his financial projections.
- Analysis time was reduced: Automation of some critical metrics allowed the team to analyze several properties in parallel, dramatically reducing the general time of total analysis for investments.
- Optimization of Scaling Infrastructure: Scalability through Kubernetes: Scalability opens space for elastic growth of data with unbeatable performance.
Conclusion:
The Real Estate Investment Analysis Tool empowered the client to make more informed investment decisions and, hence, positioned them as a data-driven leader in the real estate industry.