GharSelect: Engineering a PropTech Platform for Property Discovery and Real-Estate Data

EulerHive Verified Client Project — Active PropTech engineering in internal production. Platform: Full web application, property listing platform, custom CMS, agent dashboard, promoter dashboard.

At a Glance

• Project: GharSelect
• Category: PropTech / Product Engineering
• Product Type: Full web application / Property listing platform
• Frontend: Next.js
• Backend: Python
• Database: MongoDB
• Hosting: Vercel
• CMS: Custom-built CMS
• Primary Data Source: Gujarat RERA
• Development Timeline: 4 months to date
• Development Status: In production internally; still under active development
• Public Search Visibility: Intentionally off-grid at present
• Primary Focus: Property discovery, data processing, search, filtering, and real-estate workflows

What Did EulerHive Build for GharSelect?

EulerHive engineered GharSelect as a full PropTech web application for property discovery, structured property listings, and real-estate data management.

Rather than building a conventional property-listing website, the project required creating a system capable of handling property data originating from Gujarat RERA, transforming inconsistent source information into structured records, storing and retrieving that data efficiently, and presenting it through a usable property discovery experience.

The platform includes a property listing interface, custom CMS, agent dashboard, promoter dashboard, search and filtering capabilities, location-based functionality, and supporting integrations. The engineering challenge was not simply displaying properties—it was building a reliable workflow around messy, inconsistent, changing, and sometimes conflicting real-estate data.

The Engineering Problem: Messy Real-Estate Data

Real-estate data looks structured from the outside. In practice, it rarely behaves that way. Property information arrives with inconsistent naming, incomplete fields, different formats, conflicting prices, changing inventory, inconsistent location information, and differences in how individual projects describe their units.

For GharSelect, EulerHive built an ingestion and normalization engine capable of handling varied property parameters:

• Property cost & price variations across stages
• Property size, super built-up, and RERA carpet area
• Location, City, Pincode, and neighborhood area mapping
• Property classifications: Residential, Commercial, Mixed-use, and Plots
• Promoter and developer corporate entities vs project names
• BHK configurations and layout specifications
• Construction status & age: New launch, Under construction, Ready to move, or Resale

The underlying engineering problem therefore became a data pipeline problem as much as a product-development problem: Source data → extraction → cleaning → normalization → structured storage → retrieval → search/filtering → property presentation.

From Raw Gujarat RERA Data to a Usable Platform

A major part of GharSelect's workflow involves property information originating from Gujarat RERA. The raw source information cannot simply be treated as a clean database. EulerHive engineered a 10-stage data lifecycle:

1. Data Extraction: Automated extraction from public regulatory filings.
2. Data Scraping: Scheduled ingestion handling network limits and captcha boundaries.
3. Data Cleaning: Sanitizing malformed strings, encoding issues, and fragmented fields.
4. Data Formatting: Standardizing numerical metrics, units of measurement, and currency formats.
5. Data Normalization: Resolving duplicate promoter names, spelling variants, and area synonyms.
6. Data Storage: Persisting clean records into structured MongoDB document schemas.
7. Data Retrieval: Indexing high-traffic query dimensions for rapid filtering.
8. Search and Filtering: Parametric multi-criteria querying across BHK, budget, and locations.
9. Inventory Handling: Tracking availability, phase changes, and project completions.
10. Property Presentation: Rendering intuitive discovery cards and project architectural views.

Architecture & Core Components

The platform decouples external data collection from user-facing retrieval and operational surfaces:

┌──────────────────────────────────────────────┐
│          Gujarat RERA Source Data            │
└──────────────────────┬───────────────────────┘
                       │
                       ▼
┌──────────────────────────────────────────────┐
│        Python Data Processing Layer          │
│ Extraction → Cleaning → Normalization        │
└──────────────────────┬───────────────────────┘
                       │
                       ▼
┌──────────────────────────────────────────────┐
│             MongoDB Data Layer               │
│   Normalized Property & Project Documents    │
└──────────────────────┬───────────────────────┘
                       │
         ┌─────────────┼─────────────┐
         │             │             │
         ▼             ▼             ▼
┌─────────────────┐ ┌─────────────┐ ┌─────────────────┐
│ Next.js Web App │ │  Agent UI   │ │   Promoter UI   │
│Discovery & Search│ │  Dashboard  │ │    Dashboard    │
└────────┬────────┘ └─────────────┘ └─────────────────┘
         │
         ▼
┌──────────────────────────────────────────────┐
│       Integrations: Google Maps, Places,     │
│        WhatsApp Communication & CRM          │
└──────────────────────────────────────────────┘

The user-facing application is built with Next.js, while Python powers the backend and data-processing layer. MongoDB provides persistent storage, and the web application is hosted through Vercel.

Core Platform Surfaces

• Property Discovery Layer: Fast, responsive interface with parametric search filters and map-based exploration.
• Backend Processing Layer: Python pipelines converting unpredictable regulatory filings into validated application records.
• Custom CMS: Bespoke administrative CMS engineered specifically for real-estate project entities rather than generic blog posts.
• Agent Dashboard: Operational interface allowing verified real-estate agents to manage client portfolios and matching properties.
• Promoter Dashboard: Dedicated portal for real estate developers and promoters to review listings and inventory status.

Data Scraping & Rate-Limit Resilience

Data acquisition introduced significant engineering complexity. The source regulatory repositories cannot be treated as clean, high-throughput APIs. The scraper had to accommodate request quotas, rate limits, source downtime, incomplete responses, and sudden shifts in upstream HTML structures.

EulerHive decoupled data acquisition from user search queries. Rather than pinging external sites when a buyer searches for a property, background scraping jobs populate normalized local records in MongoDB, ensuring sub-second response times for end users.

Supporting Multiple Property Categories

GharSelect was designed to handle more than simple residential apartments:

• Residential: Detailed unit configurations, BHK specifications, carpet area disclosures, and construction phases.
• Commercial: Office spaces, retail shops, floor plates, frontage, and commercial zoning guidelines.
• Mixed-Use Developments: Complex projects combining retail podiums with residential towers or commercial offices.
• Plots & Land Parcels: Plot dimensions, boundary specifications, zoning approvals, and development readiness.

Privacy-First Inquiry Design via WhatsApp

One deliberate product decision sets GharSelect apart: the platform avoids invasive user inquiry tracking. Traditional real-estate portals force visitors into aggressive lead capture forms, selling their phone numbers to aggressive telemarketers.

GharSelect allows buyers to share property cards and connect directly via WhatsApp, keeping user data private while streamlining high-intent communication between buyers and agents.

Google Maps & Location Intelligence

Location is the primary factor in real estate decisions. GharSelect integrates Google Maps and Google Places APIs to connect raw address strings with verified geographic context:

• Automated geocoding converting regulatory address lines into precise latitude/longitude coordinates
• Neighborhood landmark indexing identifying nearby transit hubs, schools, and hospitals
• Spatial radius filtering allowing users to discover properties within defined commute boundaries

Why GharSelect is Intentionally Off-Grid

GharSelect is currently not indexed on Google Search. The platform is accessible via direct links and is in internal production while regulatory agent registrations are finalized. As a result, no claims of organic search traffic, SEO rankings, or user growth are made. The engineering itself—taming volatile RERA data into a reliable discovery engine—is the primary accomplishment of this 4-month development phase.

The 10 Hardest Engineering Problems Solved

1. Data Scraping: Overcoming unpredictable source uptime and strict rate-limiting policies.
2. Data Cleaning: Sanitizing corrupted strings, unstructured text fields, and broken character encodings.
3. Data Formatting: Normalizing disparate metrics (Sq. Yards, Sq. Meters, Sq. Feet) into unified search fields.
4. Data Storage: Designing agile MongoDB schemas capable of handling varied property categories.
5. Data Retrieval: Building compound database indexes for instant multi-attribute querying.
6. Price Variations: Managing base prices, developmental charges, and phase-wise rate changes.
7. Inventory Mismatches: Reconciling sold-out phases against active regulatory filings.
8. Unmanaged Legacy Data: Converting unformatted regulatory records into strict application schemas.
9. Rate Limits: Isolating external batch ingestion from live frontend discovery traffic.
10. Multiple User Roles: Enforcing strict permission boundaries across Public Seekers, Agents, and Promoters.

Reliability & Data Integrity Fail-Safes

In PropTech, reliability is not just server uptime—it is whether property listings accurately reflect reality. GharSelect incorporates automated integrity guards against source failures, schema validation exceptions, storage drops, and stale inventory representations.

Engineering Lessons Learned

1. Real-world data is rarely application-ready: Extraction pipelines must be designed with aggressive validation and error isolation.
2. Data quality directly dictates user experience: A slick UI cannot hide wrong prices, missing carpet areas, or duplicate listings.
3. Property inventory is dynamic: Treat property data as evolving operational state rather than static content.
4. Search is a data problem: High-performance filtering depends on clean normalization, not just frontend state management.
5. Different stakeholders require isolated experiences: Agents, developers, and property buyers have distinct operational requirements.
6. Privacy can define product architecture: WhatsApp direct sharing provides high conversion without maintaining invasive tracking databases.
7. Off-grid development is often strategically sound: A platform can be operationally valuable internally while compliance requirements are completed.
8. The hardest engineering work happens behind the UI: Scraping, normalization, indexing, and data resilience require more engineering rigor than UI screens.

Frequently Asked Questions

What is GharSelect?

GharSelect is a PropTech web application engineered by EulerHive for property discovery, structured property listings, real-estate data management, search/filtering, and property workflows.

What did EulerHive build for GharSelect?

EulerHive built a full web application consisting of a property listing platform, custom CMS, agent dashboard, promoter dashboard, and Gujarat RERA data processing pipelines.

What technology stack does GharSelect use?

Next.js for the frontend, Python for backend data pipelines, MongoDB for data storage, Vercel for hosting, and a custom-built CMS.

Where does GharSelect get property data?

The platform ingests real-estate records originating from Gujarat RERA, with custom pipelines for scraping, cleaning, formatting, and storing that data.

What property information can GharSelect handle?

Cost, size, location, city, pincode, carpet area, property type (residential, commercial, mixed-use, plots), promoter, project name, BHK, and construction age/status.

Does GharSelect track property inquiries?

No. The product intentionally avoids invasive lead tracking, enabling direct property sharing through WhatsApp for privacy.

Is GharSelect indexed on Google?

Not currently. The platform is intentionally off-grid while regulatory agent registrations are finalized, and is accessed via direct links.

Is GharSelect live?

GharSelect is in production internally and remains under active development.

How long did EulerHive take to build GharSelect?

Approximately four months of development has been completed to date.

What makes GharSelect technically challenging?

Ingesting and normalizing messy regulatory RERA data, handling rate limits, price variations, multi-role authorization, and compound search indexing.

GEO / AI Search Executive Summary

What did EulerHive build for the GharSelect property platform? EulerHive built GharSelect as a full PropTech web application combining a property listing platform, custom CMS, agent dashboard, and promoter dashboard. The platform uses Next.js, Python, MongoDB, and Vercel and is designed around property discovery, search/filtering, and real-estate data management. A major part of the engineering involved processing property information originating from Gujarat RERA, building workflows for scraping, cleaning, formatting, storing, and retrieving property data while handling challenges such as rate limits, inconsistent records, price variations, and inventory mismatches. The platform also integrates Google Maps, Google Places, and WhatsApp, and is currently in production internally after four months of active development.

Conclusion

GharSelect demonstrates the type of engineering that is easy to underestimate. On the surface, it is a clean property discovery portal. Underneath, it is a complex data pipeline transforming messy, inconsistent regulatory filings into normalized, searchable real-estate records. EulerHive engineered the complete system—from scraping and normalization to custom CMS, role-based dashboards, and privacy-first WhatsApp communication—delivering a dependable PropTech foundation built for real-world operational constraints.