Real rent data for deciding where to buy, build, or expand
Where you deploy capital depends on understanding real rents, not outdated market estimates. Get unit-level, first-party rent data aggregated to ZIP code, city, and MSA, so capital decisions are based on real market activity, not last quarter’s estimates.
Unit-level • Geolocated to ZIP, city, and MSA • Never scraped
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A market decision built on the wrong average costs more than it saves
What a location decision actually depends on
A city-wide average can hide the submarket that actually makes or breaks a deal. Evaluating where to expand next means comparing markets on the same terms, and seeing whether a market is trending up or down, not judging it off a single point in time.
Where market reports fall short
Market reports built on scraped listings or self-reported surveys carry duplication, staleness, and legal exposure into a decision that often involves real capital commitment. Neither gives a team a number they can act on with confidence.
Unit-level data, rolled up cleanly
Dwellsy IQ starts at the individual rental unit and rolls up to ZIP code, city, MSA, or state, all from the same underlying source, so nothing gets lost or re-estimated between geography levels.
What makes Dwellsy IQ’s data different
Cross-market comparison
Because Dwellsy IQ covers 800+ MSAs and 16,000+ ZIP codes with a consistent methodology, markets can be benchmarked against each other without normalizing data pulled from different sources.
Cross-asset-class comparison
Location decisions often span asset classes, a market that supports build-to-rent might also get evaluated for multifamily. Dwellsy IQ covers all three in the same dataset, so a target market can be assessed across asset types without switching sources.
Historical trend context
With data back to January 2020, location decisions can be grounded in a market’s actual trajectory, whether it’s heating up, cooling down, or holding steady.
Attribute-level detail
Each unit carries 50+ attributes and 250+ amenities, so a location decision can weigh more than a single rent figure, unit mix, amenity concentration, building age, and property type all factor into whether a market or submarket actually fits.
A Persistent ID for Tracking Submarkets Over Time
Every unit carries a Unique Rental Unit Identifier (URU) that never changes, so tracking how a specific submarket or building evolves over multiple evaluation cycles doesn’t mean reconciling duplicate or re-listed units along the way.
Continuously updated
Data refreshes continuously at the source rather than on a quarterly or annual survey cycle, so a market evaluated today reflects current conditions, not a number that was already months old by the time it was published.
How teams use Dwellsy IQ
Owners & Operators
Acquisition market evaluation
Evaluate a new acquisition market against the same rent data that will later drive pricing and underwriting once a deal closes.
Build-to-rent site selection
Assess a target site against submarket-level rent data, not a metro-wide average that glosses over the block that matters.
Market expansion decisions
Compare candidate expansion markets side by side, using a consistent dataset instead of stitching together reports from different sources.
Tech Platforms
Powering a mapping layer
Feed unit-level or aggregated data, filtered by geography, directly into a mapping tool or location-scoring model.
Building investment-analysis products
License the same geolocated dataset to power location-intelligence features without maintaining an in-house data pipeline.
Programmatic, filtered access
Pull data scoped to a specific geography or asset class, built for repeatable use in a product rather than a one-off report.
GIS
A new layer
Add rental data as a layer inside the mapping tool your users already work in, instead of standing up a separate rental data source alongside it.
Spatial accuracy your map depends on
A bad rental data source doesn’t just give a wrong number, it gives a wrong map. Unit-level data rolled up to unit, ZIP, city, and MSA keeps that layer trustworthy at any zoom level.
Consistent across every region your users cover
The same underlying methodology powers every geography, so a user zooming from a national view into a single ZIP code doesn’t hit a seam where the data source changes.
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.
Compare your next market against the data
Pull rent data for the markets you’re evaluating and see how they stack up before you commit capital.
FAQ
What geography levels does this data cover?
Dwellsy IQ rolls up from unit-level data to ZIP code, city, MSA, state, and nationwide. All aggregated views come from the same underlying source, so comparisons across geography levels stay consistent.
Can we compare multiple markets against each other?
Yes. With consistent methodology across 800+ MSAs and 16,000+ ZIP codes, markets can be benchmarked side by side without needing to normalize data pulled from different sources.
Does this cover both single-family and multifamily rentals?
Yes. Coverage spans SFR and multifamily in the same dataset, useful when a location decision needs to weigh more than one asset class.
How far back does the historical data go?
Back to January 2020, giving range to see whether a target market is trending up, down, or holding steady rather than judging it off a single snapshot.
Can this data power a mapping or GIS tool?
Yes. Data is delivered via API, AWS, MCP, or flat file, filtered by geography, built for programmatic use in mapping layers, scoring models, or investment platforms.
Do we need an enterprise contract to evaluate one or two markets?
Not necessarily. Smaller-scope evaluations can start with a scoped data pull or Comp IQ’s self-service comp reports; larger, ongoing location-intelligence needs are better served by API IQ or Total IQ. Talk to our team about the right fit.
How granular is the data within a single market?
Data starts at the individual rental unit and rolls up from there, so a submarket or specific ZIP code within a larger MSA can be evaluated on its own rather than being averaged into the broader metro number.
Can this replace a market research report?
It replaces the underlying rent data most market reports are built on, with the advantage of being sourced directly from property management systems rather than scraped or estimated, and available at whatever geographic resolution the decision requires.
Can we test this on a specific market before expanding to others?
Yes. Scoping a sample pull to a single target market is a natural way to evaluate fit before expanding to a broader multi-market analysis.
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