Housequot
Real estate value predictor
HouseQuot stands out by using advanced machine learning to deliver precise real estate value predictions, helping you make informed investment decisions. Trust its data-driven insights for a competitive edge in the real estate market.
Housequot
Housequot is a cloud-based platform that offers a range of services, including property management, real estate investment, and financial services. It is designed to streamline the process of buying, selling, and managing properties, as well as providing financial advice and support to its users.
Market
Digitalization of the real estate industry is accelerating, as Covid-19 forces a digital transformation upon any industry, even the more conservative sectors that have been resisting it so far
Problem
Real estates value estimate is a multi-step, long process and usually involves several people
Technology
We want to improve the user experience by minimizing the effort needed to evaluate a house. We use the power of artificial intelligence and ML to achieve this goal
HouseQout - Real Estate Value Prediction for Multiple Use Cases
HouseQout offers a versatile platform that applies machine learning algorithms to predict real estate values, tailored for a variety of use cases including investment analysis, property appraisal, and market trend forecasting.
Real Estate Agent
Speed up your workflow with precise property value predictions, empowering you to provide faster, data-backed recommendations to clients.
Owner
Get accurate home value estimations to confidently price your property for sale, maximizing your chances of a successful transaction.
Buyer
Discover detailed price predictions for homes in specific areas, ensuring you make informed decisions and find the perfect property within your budget.
Housequot - Estimate feature
Real Estate Characteristics
The user fills in the form with the real estate characteristics. He can also select the address through the Google Maps.
Estimate
The system will show the estimate. Per square min value, Per square max value, Total min value, Total max value, Most probable value.
Estimated history
The system will show the estimated history of the property. It will show the estimated value of the property.
Tech stack
Our comprehensive technology stack ensures robust and scalable solutions
Reference Architecture
Our reference architecture provides a comprehensive framework that integrates PWA apps, mobile services, and third-party integrations within a robust microservices ecosystem. Built on Google Cloud, it leverages API management, container orchestration, and observability tools to ensure scalability and reliability. The architecture emphasizes DevSecOps practices with integrated CI/CD pipelines and automated testing, while maintaining strong security measures through infrastructure as code and automated compliance checks.
Google Cloud Architecture (HLD)
Our High-Level Design on Google Cloud orchestrates a seamless integration of cloud-native services, leveraging Google Cloud's robust infrastructure for optimal performance and scalability. The architecture implements advanced load balancing, auto-scaling, and regional redundancy to ensure high availability. It incorporates sophisticated security measures with Identity and Access Management (IAM), while utilizing Cloud Storage, Cloud SQL, and various managed services to create a resilient and efficient system that meets enterprise-grade requirements.
MLOps Lifecycle
Our MLOps lifecycle implements a comprehensive approach to machine learning operations, encompassing data management, model development, training operationalization, and continuous monitoring. The workflow integrates automated ML pipelines with robust version control for both code and data, ensuring reproducibility and traceability. It features continuous training mechanisms, automated model validation, and deployment pipelines, complemented by sophisticated monitoring systems that track model performance and data drift in production environments.
ML Model on Vertex AI
Our implementation on Vertex AI leverages Google's advanced ML platform for end-to-end machine learning model development and deployment. The pipeline integrates cloud storage, BigQuery for data processing, and Vertex AI's powerful training infrastructure for model optimization. It utilizes automated ML features for model selection and hyperparameter tuning, while implementing robust monitoring and logging systems for model performance tracking. The solution ensures seamless model serving with auto-scaling capabilities and integrated monitoring tools.
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