Rental data built to sit on a map
Rental data is one of the hardest layers to source cleanly at scale, and spatial tools inherit that noise. Dwellsy IQ data is unit-level, sourced directly from property management systems, rolling up cleanly to ZIP code, city, MSA, state, and national views — a real geographic hierarchy, not a flat export.
Unit-level and geolocated • Never scraped • Rolls up from ZIP to MSA
Trusted by Leading Brands
A messy layer underneath makes every map unreliable
Rental data is the hardest layer to source cleanly
Spatial tools inherit whatever noise sits in their data layers, and rental data is one of the hardest to source cleanly at scale — duplicated listings, fake, and stale prices, and inconsistent geography that breaks a clean rollup from ZIP to MSA.
A flat export doesn’t hold up across zoom levels
Area-level views that are sampled independently at each zoom level — rather than built from one unit-level source — drift out of sync with each other, so a map that looks right at the MSA level can misrepresent what’s happening at the ZIP or point level.
Unit-level data, built as one real hierarchy
Dwellsy IQ is sourced directly from property management systems, rolling up cleanly from unit-level records to ZIP code, city, MSA, state, and national views.
Built for spatial analysis workflows
A real geographic hierarchy
Unit-level data rolls up consistently to ZIP code, city, MSA, state, and nationwide — all built from the same underlying source rather than independently sampled at each level, across 16,000+ ZIP codes and 800+ MSAs spanning roughly 75% of professionally managed U.S. rental housing.
Unit-level precision for point-level mapping
Where a spatial tool needs to plot individual rental units rather than area averages, Dwellsy IQ’s unit-level records — with 50+ attributes and 250+ amenities per listing — support point-level mapping, not just choropleth-style area shading.
Historical depth for time-series maps
Data reaching back to January 2020 supports tools that visualize rent change over time — animated maps, trend layers, before/after comparisons — rather than a single static snapshot.
SFR and multifamily in one layer
Both major rental asset classes live in the same dataset, which matters for spatial tools that need to filter or blend by property type without stitching together separate sources.
Built to overlay against any spatial variable
Because rent data rolls up to the same geographic units most spatial datasets already use — ZIP, city, MSA — it joins cleanly against transportation networks, environmental datasets, topographic layers, or any other variable a platform is already mapping, without a separate normalization step.
Delivery for large-scale ingestion
Full data drops via AWS S3 for platforms ingesting an entire dataset into their own spatial database, a structured, filterable REST feed through API IQ for engineering teams pulling records programmatically, or SFTP and cloud storage options for teams standardized on a different pipeline.
How GIS and mapping platforms use Dwellsy IQ rent data
Transportation and Corridor Planning
Rental concentration and density data layers onto transportation networks to inform where a corridor, transit line, or road change would reach the most people, or the people it’s meant to serve, without needing a separate housing dataset stitched in.
Environmental and Ecological Correlation
Rental data joins against environmental and ecological datasets — habitat ranges, conservation zones, species population data — for platforms studying how rental development pressure intersects with land already accounted for elsewhere.
Topography and Terrain Analysis
Rental data layers against elevation, slope, and terrain datasets for platforms modeling how geography constrains where rental housing sits, or how terrain-driven factors like flood risk correlate with rental market patterns.
Site Selection and Market Potential
Rental data layers against demographic and consumer datasets to score candidate locations, the same workflow platforms already run for retail and commercial site selection, extended to residential and rental market contexts.
Accessibility and Specialized Housing Mapping
Unit-level amenity data includes accessibility-relevant attributes — wide doorways, wide hallways, accessible bathrooms — supporting platforms that map accessible or specialized housing inventory rather than relying on self-reported directories.
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.
Give your map a rental data layer it can trust
Pull a sample of geolocated, unit-level rental data for the regions your platform covers and see how it fits into your existing spatial pipeline.
FAQ
Does Dwellsy IQ provide data at the unit level, or only aggregated by area?
Both. The underlying source is unit-level — final asking rent, availability, 250+ amenities, and 50+ attributes per listing — and it rolls up consistently to ZIP code, city, MSA, state, and national views for tools that need area-level layers.
What geographic coverage does Dwellsy IQ offer?
16,000+ ZIP codes and 800+ MSAs nationwide, covering roughly 75% of professionally managed U.S. rental housing.
Can rent data be layered against non-housing datasets, like environmental or transportation data?
Yes. Because the data rolls up to standard geographies — ZIP, city, MSA — it joins cleanly against transportation networks, environmental datasets, topographic layers, or any other spatial variable a platform is already using, without a separate normalization step.
How do we access the data for integration into our mapping platform?
Full data drops via AWS S3 for platforms ingesting an entire dataset into their own spatial database, a structured, filterable REST feed through API IQ for engineering teams pulling records programmatically, or SFTP and cloud storage options for teams standardized on a different pipeline. Files are supported in CSV, TSV, JSON, or Parquet.
Can we test this against the data source we’re currently using before switching?
Yes. A scoped sample lets your engineering team benchmark Dwellsy IQ directly against whatever you’re running now — coverage, accuracy, and update consistency — before committing to a recurring feed. Most GIS and mapping teams treat this comparison as the real first step, not a formality.
Is this scraped data pulled from listing sites?
No. Dwellsy IQ is sourced directly from property management systems through the Dwellsy marketplace. It is never scraped and never survey-based.
How current is the underlying data?
Data is updated continuously. Actual refresh frequency for your specific use case depends on delivery method.
How far back does historical data go?
January 2020 to present, which supports time-series and trend-based map layers, not just a current-state snapshot.
What asset classes are included?
SFR (single-family rental) and multifamily — all within the same dataset, filterable by attribute.
Can we filter or query by specific attributes, not just location?
Yes. Each listing carries 50+ standardized attributes and 250+ amenities.
Does the data include any personally identifiable information?
No. All data is public, owner-disclosed listing information at the unit level. No PII, no private data.
Is the aggregated (ZIP/MSA) data independently sampled, or built from the same source as the unit-level data?
All aggregated views are built from the same underlying unit-level source — not independently sampled — which keeps the data consistent across zoom levels on a map.
Can we scope access to specific regions rather than buying full national coverage?
Yes, coverage and delivery can be scoped to the specific geographic footprint your platform needs.
Is Dwellsy IQ legally compliant for use in a product we expose to end users?
Yes. Data is public, owner-disclosed listing information with no PII and no scraped sourcing, which supports downstream exposure without inheriting scraping-related legal exposure.
Equal Housing Opportunity