Structured rent data built for model training and retrieval
Get unit-level rental data pulled from 30+ PMS integrations and standardized across 50+ attributes. Clean to train on and current to ground retrieval-based applications.
Never scraped • Unit-level • Delivered via API or MCP
Trusted by Leading Brands
A model is only as good as the data it’s trained on
Bad data compounds inside a model
Scraped rental listings carry duplication, staleness, and legal risk that compound inside a model every time it reproduces or is traced back to its training set. Survey data is too sparse and too infrequent to train on at all.
Sourced from PMS, not scraped
Dwellsy IQ is sourced directly from property management systems through the Dwellsy marketplace, never scraped. That’s what makes 50+ attributes and 250+ amenities arrive in a consistent schema across every listing, so there’s no cleanup pass before a model can use it.
Legally compliant by design
Dwellsy IQ data is owner-disclosed, public listing data with no PII, sourced with permission at every step, removing the legal exposure that comes with scraped training data.
What makes Dwellsy IQ’s data different
Training data from the most accurate source
Dwellsy IQ is built from rental inventory flowing into our marketplace through direct integrations with 30+ property management systems.
No business incentive to preserve noisy listings
Unlike lead-driven rental platforms, Dwellsy is free for renters and property managers. Our revenue depends on the accuracy of the data, not the number of leads or listings we generate.
A consistent signal across every record
Data from different property management systems is normalized into one shared structure, with 50+ attributes and 250+ amenities across units and properties.
Historical data for training across changing market conditions
Track rent trajectories from January 2020 through the pandemic disruption, rapid rent growth, and subsequent market normalization.
Continuous, current data
Continuously updated through PMS integrations, the data allows retrieval-based applications to ground responses in current market conditions instead of last year’s snapshot.
Delivered via MCP, API or cloud
Model Context Protocol (MCP) brings Dwellsy IQ data directly into AI-native workflows and LLM applications without custom integration work.
How AI and LLM teams use Dwellsy IQ
Tech Platforms & PropTech Builders
Grounding RAG pipelines
Ground retrieval-augmented generation in current, accurate rental market data instead of static or scraped context.
Building AI-native agents and assistants
Plug rental data directly into an agent or assistant via MCP, without building a bespoke connector first.
Avoiding compounding legal risk
Train on owner-disclosed, no-PII data instead of scraped listings that carry legal exposure every time the model reproduces or is traced back to its training set.
AVM Providers
Clean training data, no cleanup pass
Feed structured, unit-level rent data into a valuation model without normalizing across sources first — every listing arrives in the same schema.
Benchmark before you commit
Pull sample data and backtest it against your current source to measure accuracy lift before any pipeline work begins.
Programmatic, filtered pulls
API IQ delivers unit-level or aggregated data filtered by geography, asset type, or attribute — built for repeatable ingestion into a model training pipeline.
Dwellsy is one of the most accurate and reliable data comps tools we've used. As an investor, having clean, factual, and real-time comps is a game-changer. It helps us more efficiently and effectively do our work, and we love the product.
See the data before you talk to anyone
Download our data dictionary and a sample dataset to see exactly what fields, coverage, and granularity you’d be working with.
Test it against your current source
Get sample data in front of your engineers and benchmark it against whatever you’re training on now, scraped listings included.
FAQ
Can this data be used to train a model?
Yes. Dwellsy IQ’s structured, unit-level data with 50+ standardized attributes and 250+ amenities is built to be training-ready, no scraped noise, no inconsistent formatting to clean up first.
Can we access this data programmatically for a training pipeline?
Yes. API IQ delivers the full dataset via a structured REST API, unit-level or aggregated, filtered by geography, property type, or attribute, built for high-volume, repeatable pulls.
How do you handle schema changes?
Our schema is versioned and standardized, so downstream applications don’t break when new attributes are introduced. New fields are additive whenever possible.
What formats do you support?
Data can be delivered through REST APIs, MCP, or flat files, depending on your workflow and infrastructure.
Can I filter data before retrieval?
Yes. Queries can be filtered by geography, property type, bedroom count, price range, amenities, and hundreds of additional attributes to minimize unnecessary data transfer.
Is the data suitable for fine-tuning, embeddings, or both?
Yes. Customers use Dwellsy IQ for supervised training, embedding pipelines, RAG systems, analytics, and traditional ML workflows.
How do you ensure data quality?
Listings come directly from integrated property management systems and pass through normalization and validation before entering the dataset, resulting in a consistent schema across 50+ attributes and 250+ amenities.
What are the API limits?
Limits depend on the licensing tier and expected usage. High-volume ingestion and recurring pipeline workloads are supported.
Can this be used in production AI applications?
Yes. The data is designed for production environments, including AI agents, search, recommendation systems, forecasting models, and analytics platforms.
Do you support retrieval-augmented generation (RAG) use cases specifically?
Yes. The combination of structured attributes, continuous updates, and MCP delivery is built for RAG pipelines that need to ground responses in current, accurate rental market data rather than static or scraped context.
Can I combine Dwellsy IQ with my own internal data?
Yes. The standardized schema makes it straightforward to enrich internal datasets or combine Dwellsy IQ with proprietary features in ML pipelines.
Do you provide metadata or provenance?
Yes. Records include metadata that helps users understand where and when the data originated, supporting traceability and governance.
What licensing rights come with the data?
Licensing depends on the agreement, but customers can use the data for internal AI training, analytics, and production applications as defined in their contract.
Can we test the data before committing to a contract?
Yes. Getting sample data in front of engineering teams to benchmark against a current source is the natural first step, reach out and we’ll get you access.
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