Rent data built for researchers
Serious research and analytics work needs data that holds up to scrutiny, whether the output is a published trend report, a market index, or an economic forecast. Get direct-sourced rental data, scoped to the region and question your work covers.
Granular data • Timely • Direct-sourced data back to January 2020
Trusted by Leading Brands
The Census only gets you the headline number
The Census is still a survey
The American Community Survey is the default source for most research on rental housing, but it’s still a survey. It’s published on a lag of months and reports at the unit-type level (one-bedroom, two-bedroom, etc) rather than the individual unit. Depending on the geography, it gets worse from there: smaller counties, rural markets, and low-population MSAs get published far less often and far less reliably than large states.
The gap shows up right where it matters
A lagging, unit-type-level number is easy to overlook until a finding depends on the county or ZIP code the ACS barely covers. That’s usually the exact geography a policy question turns on, and it’s where a modeled estimate is weakest.
First-party timely data
Dwellsy IQ is sourced directly from property management systems, not surveyed, so it arrives at the individual unit level instead of rolled up into a studio, one-bedroom, or two-bedroom average. It’s continuously updated rather than published on a lag, and that holds true even in the small geographies the ACS covers least often.
Built for how research organizations actually work with data
Regional and county-level scoping
Most research doesn’t need a national dataset, it needs a specific county, MSA, or state, over a specific time range. Dwellsy IQ data can be scoped from national to exactly that footprint.
Longitudinal and panel-ready data
A Unique Rental Unit Identifier (URU) on every unit stays fixed across time, so tracking the same units or markets across years is apples-to-apples, without reconciling duplicated or re-listed units.
Asset class flexibility
Dwellsy IQ covers SFR and multifamily within the same underlying dataset, so a study examining a specific asset class, or comparing across them, doesn’t need to reconcile separate sources with different methodologies.
Historical depth back to January 2020
Data reaching back to January 2020 covers the COVID-era rent shock and the post-pandemic period, real market cycles for research that needs more than a recent-only window.
Consistent methodology
The same underlying source and methodology apply everywhere it’s used, so a study replicated across two different regions, or picked up by a second organization later, isn’t reconciling two different vendors’ definitions.
Flexible delivery for any workflow
Data can be delivered in bulk via AWS S3, SFTP, Azure, or Google Cloud Drive, through a REST API for direct integration, or via MCP IQ for agent-based workflows. Files are supported in CSV, TSV, JSON, or Parquet, so a research team can use whatever format fits without building around ours.
Attribute-level detail for specialized questions
Each unit carries structured attributes beyond rent and location: 50+ attributes and 250+ amenities per listing, covering photos, unit type, and other characteristics, supporting research questions that go beyond a simple price trend.
How research organizations use Dwellsy IQ’s rent data
Market Trend and Index Research
Research and analytics organizations publish recurring reports and indices on rental housing trends for a broad readership, including investors, lenders, and other analysts. Dwellsy IQ’s unit-level, first-party data supports that work with numbers traceable back to the property management systems where they were actually set, not modeled or estimated.
Macroeconomic and Rate-Sensitive Research
Because rent is a major, closely watched component of inflation data, research organizations use rent data to inform broader economic modeling and forecasting, not just housing-specific questions.
Rent data for economic forecasting →Portfolio and Sector Research
Some research organizations produce analyses that supports investment decision-making without making the investment themselves, comparing markets, asset classes, or portfolios at scale. Dwellsy IQ’s consistent methodology across SFR and multifamily supports that kind of side-by-side comparison.
Rent data for investment analysis →Regional and Market Research
Research covering a specific metro area, county, or region uses jurisdiction-bounded rent data at the ZIP-code through MSA level, without paying for or wading through coverage outside the study’s footprint.
Rent Index and Methodology Research
Research organizations working on how rent itself should be measured use Dwellsy IQ as a data source for building quality-adjusted rent indices, methodological groundwork that shapes how future reports and studies define the numbers they publish.
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 a study built on our data
Get an example of a completed research study using Dwellsy IQ data, so your team can see how it’s structured, scoped, and cited before committing to your own.
Get rental data scoped to your research question
Scope a dataset to a specific ZIP code, city, MSA, county, or state, for a fixed time range. Tell us your geography and time range, and we’ll get the data to you.
FAQ
How is Dwellsy IQ data different from Census or ACS data we’d otherwise use?
The ACS is survey-based, which means it’s collected periodically rather than continuously, published at the unit-type level (studio, one-bedroom, two-bedroom) rather than the individual unit, and covers smaller counties and low-population geographies far less often and less reliably than larger ones. Dwellsy IQ, on the other hand, is sourced directly from property management systems, which means real-time updates instead of a periodic survey cycle, data available at both the unit-type and individual unit level, and the same consistent coverage in low-population geographies as everywhere else.
Can Dwellsy IQ data support economic or investment-adjacent research, not just housing-cost research?
Yes. Because the dataset is unit-level and first-party, it supports work well beyond a single housing-cost figure, including inflation-adjacent economic modeling, rent index construction, and market or portfolio-level trend analysis.
Can we purchase data bounded to a specific state, county, or metro area?
Yes. Dwellsy IQ data can be scoped to a specific jurisdiction, national, state, metro, or county, and to a specific time range.
How far back does the historical data go?
January 2020 to present, covering the COVID-era rent shock and post-pandemic normalization, real market cycles relevant to most economic and market research.
Can delivery be structured for recurring, ongoing publication cycles?
Yes. For research organizations publishing on a recurring schedule, such as a monthly or quarterly index or trend report, delivery can be structured to match that cadence rather than requiring a single one-time pull.
Do you cover both single-family and multifamily rental housing?
Yes. Dwellsy IQ covers SFR and multifamily within the same dataset, giving research teams a full view of a region’s rental housing stock.
Is any of this data private or personally identifiable?
No. The dataset is public, owner-disclosed listing data at the unit level. There is no PII and no private data involved.
What format does the data come in?
Data can be delivered in bulk via AWS S3, SFTP, Azure, or Google Cloud Drive, through a REST API for direct integration, or via MCP IQ for agent-based workflows. Files are supported in CSV, TSV, JSON, or Parquet.
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