Introduction
The Malayalam Real Estate Chat Dataset is a high-quality collection of over 12,000 text-based conversations between customers and call center agents. These conversations reflect real-world scenarios within the Real Estate sector, offering rich linguistic data for training conversational AI, chatbots, and NLP systems focused on property-related interactions in Malayalam-speaking regions.
Participant & Chat Overview
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Participants:
200+ native Malayalam speakers from the FutureBeeAI Crowd Community
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Conversation Length:
300–700 words per chat
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Turns per Chat:
50–150 dialogue turns across both speakers
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Chat Types:
Inbound and outbound
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Sentiment Coverage:
Positive, neutral, and negative interactions included
Topic Diversity
The dataset spans a broad range of Real Estate service conversations, covering various customer intents and agent support tasks:
•Property inquiries (buy/rent)
•Rental property availability
•Renovation and maintenance inquiries
•Property features and amenities
•Investment advice and ROI analysis
•Property ownership and legal history
•New property listing announcements
•Post-purchase follow-ups
•Investment opportunity alerts
•Property valuation updates
•Customer satisfaction and feedback surveys
This topic variety enables realistic model training for both lead generation and post-sale engagement scenarios.
Language Nuance & Authenticity
Conversations are reflective of natural Malayalam used in the Real Estate domain, incorporating:
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Cultural Naming Patterns:
Personal names, agency names, and developer brands
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Localized Contact Info:
Phone numbers, email addresses, and geographic locations across Malayalam-speaking regions
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Numeric and Temporal Language:
Dates, prices, unit sizes, and time references formatted in Malayalam conventions
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Informal and Domain-Specific Language:
Real estate slang, idioms, and casual tone used in property discussions
This level of linguistic realism supports model generalization across dialects and user demographics.
Conversational Structure & Flow
Conversations include a mix of short inquiries and detailed advisory sessions, capturing full customer journeys:
•Complaint handling and support
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Greetings and identity verification
•Intent identification and context gathering
•Solution explanation or recommendations
•Resolution or next steps
•Closing and optional feedback
This structure supports training of AI systems that can handle multi-turn dialogues and dynamic user needs.
Data Format & Structure
Available in JSON, CSV, and TXT formats, each record includes:
•Optional metadata such as sentiment, topic, or region tags
Format compatible with popular NLP toolkits
Applications
This dataset is ideal for a wide range of AI and NLP applications within the Real Estate domain:
•Real Estate Chatbots & Virtual Assistants
•Intent Detection and Dialogue Flow Modeling
•Lead Qualification and Sales Automation
•NER for Entity Extraction (e.g., location, price, unit type)
•Text Summarization and Generation
•Malayalam NLP Research for Real Estate Vertical
Secure & Ethical Collection
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Consent-Based Participation:
All contributors participated with informed consent
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Privacy-Preserved:
No personally identifiable information (PII) is included
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Secure Platform:
All data was handled and stored within FutureBeeAI’s secure data environment
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Ethical Compliance:
Collection and usage aligned with responsible AI and data governance standards
Dataset Expansion & Customization
This dataset is actively maintained and can be extended or customized based on your requirements:
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Custom Annotations:
Named Entity Recognition (NER), sentiment, intent labels, etc.
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Topic-Specific Collection:
e.g., mortgage advisory, vacation rentals, commercial property
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Region-Specific Language:
Country/dialect-focused data collection in Malayalam
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Multilingual Options:
Data available in other languages on request
Licensing
The dataset is developed and owned by FutureBeeAI and is available for commercial licensing. Flexible terms are available for enterprises, startups, and academic use.
