Real Estate Industry

Services Involved: Strategy, Development & Deployment

Tech Stack

AWS Cloud
MongoDB
GCP Cloud
React Native
Redis
NodeJs

PROJECT BRIEF

As an ultimate AI solution for Real-Estate Investors and Brokers, this product is designed to streamline the process of identifying, valuing, and closing informed investment decisions for purchase and rental in the USA. The platform extracts, standardizes, and saves data from over 30+ multiple listing services (MLS) to deliver standardized MLS data with all data points to a single RESO format schema in real-time with standardized property information and important predictions about each property. This can also be integrated with your back office systems via API or your team can access these modules through their user interface.

BRIEF

Key Challenges

  • MLS data is an essential source of real estate opportunities. Buyers and brokers use multiple, non-standardized MLS portals with outdated user interfaces to find investment opportunities. This process is time-consuming, error-prone, and results in lost and missed opportunities.
  • The goal was to develop a platform by using which users can easily get updated details in a standardized form about the real estate properties they are looking to invest upon in the US region.
  • The biggest challenge was to extract & handle the large non-standardized database from multiple MLS portals consisting of details of a large number of properties and analyze and depict the information on the portal.
Real Estate - Key Challenges

PROJECT GOALS

To deliver standardized, unified MLS data across multiple markets in a single, unified, RESO schema, delivered near-real time while the data can be seamlessly integrated with your backoffice system.

PROCESS

Conceptualization

  • Requirements gathering
  • Market analysis
  • Project scope definition
Conceptualization

Design

  • Architecture planning & Design
  • User interface (UI) design
  • Database schema creation
  • Wireframing and prototyping
Design

Development

  • Coding and scripting
  • Version control usage
  • Feature implementation
  • Continuous integration
Development

Testing

  • Unit testing
  • Integration testing
  • User acceptance testing
  • Bug tracking and fixing
Testing

Deployment

  • Release preparation
  • Deployment automation
  • Rollout to production
  • Post-launch monitoring
Deployment

Key Features

  • Encapsulated contextual search using machine learning (ML)
  • Introduced underwriting for accurate comparables for informed decision making
  • Managing millions of property data through our robust data pipeline
  • Extract, transform, and load (ETL)
Real estate case study

Impact

  • 0M+

    Data of Property Records Updated in Real-time

  • 0+

    MLS Listings data fetched for maximum data reach

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