Rental data built to survive a backtest
Rent data noise becomes model error. Scraped listings and survey averages are where that noise comes from. Get unit-level, first-party data sourced directly from property management systems, backtestable against your existing feed in days, not quarters.
Never scraped • SFR asset class depth • No monthly minimum to start
Trusted by Leading Brands
You should be testing the data, not rebuilding it
Not all rent data survives a backtest
Scraped listings and survey averages carry noise that shows up directly as error in your accuracy metrics — inflated by duplication, staleness, and self-reported figures that were never verified against what a unit actually rented for.
Three weeks of cleaning before you even test it
A data science team can source a new feed and still lose weeks to it — cleaning, deduplicating, and reconciling records before a backtest can even run. Without a clean schema going in, validating a new source becomes an engineering project of its own.
Clean enough to backtest in days
Dwellsy IQ is sourced directly from property management systems through a structured quality pipeline — unit-level, deduplicated, and schema-validated — so a backtest against your existing feed is a data pull, not a cleanup project.
Built for model-driven teams
Programmatic, not manual
Data arrives through API IQ, MCP or AWS S3 as a structured feed. Your data science team can pull it directly into a feature pipeline the same way they’d pull any other model input.
Backtest in days
The fastest way to lose confidence in a new data source is to spend three weeks cleaning it before you can even test it. Dwellsy IQ data comes through a structured quality pipeline, so a backtest is a data pull, not an engineering project.
MSA-by-MSA rollout support
AVM providers rarely deploy nationally on day one — models get validated market by market, then expanded. Dwellsy IQ’s 800+ MSA footprint supports that phased rollout pattern, so scoping a pilot to a handful of markets doesn’t mean waiting on data elsewhere.
The URU for longitudinal backtesting
Every unit carries a URU — a permanent identifier that holds a rental unit fixed across time, even as it’s re-listed. That means you can backtest a model against the same unit’s actual rent history without reconciling records yourself.
No monthly minimum to start
Committing to an enterprise contract before your model has proven a measurable accuracy lift is the wrong order of operations. Dwellsy IQ supports variable, pay-as-you-go pricing billed with first use — so the financial risk sits with the data, not with you.
Legally compliant by design
Scraped data’s legal risk doesn’t show up until a model is already in production. Direct-sourced data instead: public, owner-disclosed, no PII, no scraping involved.
How AVM providers use this data
Model Validation & Backtesting
Data science teams backtest against an existing feed in days using unit-level, schema-validated data, without losing weeks to cleaning and reconciliation first.
Feature Pipeline Integration
Data engineering teams pull records programmatically via REST API or AWS S3 directly into a feature pipeline, structured the same way any other model input would be.
Phased Market Rollout
Quantitative analysts scope a pilot to a handful of MSAs and expand market by market, using the same 800+ MSA footprint the full deployment will eventually run on.
Longitudinal Accuracy Testing
Analysts backtesting long-term accuracy track a specific unit’s rent history across years using a URU that stays fixed across listings and time, no reconciling duplicate or re-listed records.
We've worked with a lot of data vendors. Dwellsy's data quality is genuinely best in class. Source-verified, unit-level, and consistent across more than 17 million listings. It shows up in our product and our users notice.
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.
Run the backtest before you commit to anything
Pull a scoped SFR dataset for the markets your model is targeting next and see what it does to your accuracy numbers. No enterprise contract required to find out.
FAQ
How is Dwellsy IQ data different from scraped listing data?
Dwellsy IQ is sourced directly from property management systems through the Dwellsy marketplace — not scraped from listing sites. Scraped data is periodic, duplicated, and carries legal exposure. Dwellsy IQ is continuous, deduplicated, and legally compliant.
Can we run a backtest before committing to a contract?
Yes. Pricing can be structured as pay-as-you-go with no monthly minimum, billed with first use, so your data science team can validate accuracy lift before any larger commitment.
What happens if our backtest doesn’t show a clear accuracy lift?
Then you’ve lost a data pull, not a contract. Pay-as-you-go pricing with no monthly minimum means there’s no enterprise commitment sitting behind the pilot — if the lift isn’t there for your model, you walk away having spent almost nothing to find out. We’d rather you know that fast than after a long evaluation cycle.
What asset classes does Dwellsy IQ cover?
SFR (single-family rental), BTR (build-to-rent), and multifamily, all in the same underlying dataset. SFR carries the deepest unit-level depth for AVM use cases.
How granular is the data — can we get unit-level records, not just ZIP averages?
Every record is unit-level, with 50+ standardized attributes and 250+ amenities including final asking rent, availability, and amenities. Aggregated views (ZIP, city, MSA) are built from that same unit-level source, not sampled independently.
How do we access the data — is there an API?
Yes. API IQ delivers the dataset programmatically via REST API, filterable by geography, property type, or attribute. Bulk delivery via AWS S3 is also available for teams that prefer to ingest full data drops.
How far back does the historical data go?
January 2020 to present, which supports backtesting model performance across the COVID-era rent cycle and the post-pandemic normalization period.
Can we scope a pilot to specific MSAs instead of buying national coverage?
Yes. Coverage can be scoped to the specific markets a model is being validated or rolled out in, which supports the market-by-market deployment pattern most AVM teams use.
What is the URU and why does it matter for model validation?
The URU (Unique Rental Unit Identifier) is a permanent, unit-level ID that stays fixed across listings and time. It lets you track a specific unit’s rent history for backtesting without reconciling duplicate or re-listed records yourself.
Is this survey data or does it reflect actual asking rents?
Neither survey nor scraped. Rents are captured directly from property management systems at the point they’re set and updated — final asking rents, not self-reported averages.
How much engineering effort does it take to integrate this data?
Data comes through a structured quality pipeline — normalized and schema-validated before delivery — so integration is a data pull and mapping exercise, not a cleanup project.
What geographic coverage does Dwellsy IQ offer?
800+ MSAs and 16,000+ ZIP codes, rolling up from unit-level data to city, MSA, state, and national views, covering roughly 75% of professionally managed U.S. rental housing.
Does Dwellsy IQ ever expose personally identifiable information?
No. All data is public, owner-disclosed listing information at the unit level. No PII, no private data, ever.
Equal Housing Opportunity