Real Estate CRM

Part of the worlds-biggest-software-project initiative.

An AI-native, open-source CRM purpose-built for real estate — unifying lead management, listing pipeline, and transaction coordination in a single platform.

Real Estate CRM is a candidate open-source project to give agents, teams, and brokerages a domain-specific alternative to fragmented and expensive incumbents. It targets the gap between cheap, dated single-agent tools and complex, opaque all-in-one platforms by combining a real estate data model, speed-to-lead automation, and modern AI capabilities in one transparent system.

Why Real Estate CRM?

No purpose-built open-source real estate CRM exists today; general-purpose open-source CRMs (Twenty, SuiteCRM, Vtiger) lack a real estate data model, IDX/MLS integration, and speed-to-lead automation.
Incumbent pricing is bifurcated and often opaque: entry-level tools like Wise Agent and LionDesk run $39–$57/month with dated UX, while all-in-one platforms like kvCORE and Lofty run $499–$1,800+/month for teams under custom contracts with no published rate cards.
Most CRMs require separate tools (Dotloop, SkySlope) for transaction management; the line between CRM and TC workflows is blurring and integrating them natively is an underserved opportunity.
Seller pipeline management, investor workflows, and multi-MLS coverage are weak across nearly every reviewed product.
AI features in incumbents are either rule-based drip schedules or locked behind premium tiers; an AI-native baseline (lead scoring, adaptive follow-up, natural language queries) is overdue at every price point.

Key Features

Lead Management & Speed-to-Lead

Centralised inbox aggregating leads from major portals (Zillow, Realtor.com, Facebook, Google Ads) via Zapier and direct API
Configurable auto-response (SMS/email) within 60 seconds of lead arrival
Smart lead routing: round-robin, ZIP-code rules, and custom assignment logic
Lead source tracking and attribution with ROI reporting
Cross-source deduplication with intelligent merging

Pipeline & Transaction Coordination

Customisable buyer and seller pipelines with Kanban and list views
Transaction checklist module with configurable milestones for buyer, seller, and rental flows
Client-facing transaction portal for real-time progress sharing
Commission tracking and brokerage back-office reporting
AI contract review extracting key dates, contingencies, and flagging non-standard clauses

Communication & Nurture

Email and SMS drip campaigns with a visual sequence builder
Two-way texting and batch email with open/click tracking
Video email and text messaging
Automated market snapshot reports for past-client database nurture
Action plans guiding agents step-by-step through follow-up sequences

AI & Intelligence

AI lead scoring using engagement signals (email opens, site visits, response rate)
Adaptive follow-up sequences that adjust cadence and channel based on lead behaviour
AI-generated follow-up message drafts personalised to lead profile and stage
Natural language pipeline queries ("show me all buyers who haven't been contacted in 14 days")
Geographic farming with predictive seller scoring

MLS, Listings & Integrations

IDX/RESO Web API integration for live MLS listing data tied to lead profiles
Listing alerts and property match notifications
Native integrations with major lead portals
REST API, webhooks, and Zapier for extensibility
Mobile apps for iOS and Android with core CRM access

AI-Native Advantage

Unlike incumbents whose AI is bolted on or paywalled, this project treats AI as a baseline. Predictive lead scoring synthesises MLS search behaviour, response patterns, and market timing to rank leads by readiness to transact. Follow-up sequencing adapts cadence and channel per lead instead of running fixed drips. AI contract review extracts key dates and flags non-standard clauses before a transaction coordinator sees the document, and natural language queries replace filter builders. Market-aware nurture content personalises listing alerts and market updates to each lead's specific search history and price point.

Tech Stack & Deployment

The project is intended to be self-hostable and cloud-deployable, with an API-first architecture (REST and, where useful, GraphQL) so that teams can extend the data model and integrations. It will align with open real estate standards including the RESO Web API for MLS data exchange, IDX for syndicating listing data to agent websites, and MISMO where loan-origination data flows into transaction management. SDK and Zapier-style integrations will cover the major lead portals and document/e-sign tools (Dotloop, SkySlope, Docusign). Compliance-relevant flows (CAN-SPAM, TCPA, CCPA) will be first-class concerns in the messaging and data-storage layers.

Market Context

The global real estate CRM software market was valued at approximately $3.8B in 2025 and is projected to reach $9.6B by 2034 at a CAGR of 10.8% (Dataintelo, 2025). Pricing is bifurcated: entry-level tools start at $39–$57/month per agent, while all-in-one platforms run $500–$1,800+/month for teams, with brokerage deals negotiated under custom contracts. Primary buyers are individual agents managing their own pipeline, small teams (2–10 agents) needing shared routing and accountability, brokerages managing hundreds of agents on a single platform, and real estate investors tracking acquisition deals separately from agent workflows.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. Source materials note that an Apache 2.0 or MIT basis would be more suitable than AGPLv3 for a commercial open-source product, since AGPLv3 copyleft would require any SaaS derivative to release its source code. See discussion for context.