Rent data built for the deal
Close to $1 trillion flows into U.S. rental housing every year. Diligence should start with the actual units, not the market they sit in. Get unit-level, first-party rent data to evaluate a deal against real rents.
Unit-level • Historical since 2020 • Never scraped
Trusted by Leading Brands
A thesis is only as good as the diligence under it
The questions a deal actually depends on
Evaluating a rental asset or portfolio means answering specific questions: what are these units actually renting for, how has that changed over time, and does the deal’s revenue assumption hold up against real market data.
Most diligence data doesn’t answer them
Scraped listings introduce duplication and legal risk into diligence. Survey data is aggregated and lagged. Neither gives you an auditable answer, just a directional read on “the market.”
Unit-level diligence, not a proxy
Final asking rents, 50+ standardized attributes and 250+ amenities at the unit level mean a thesis can be tested against the actual units in a deal, not an average standing in for them.
What makes Dwellsy IQ’s data different for investment analysis
Alpha starts with sourcing
Our data comes straight from PMS systems through the Dwellsy marketplace. It can’t be scraped or rebuilt internally. No competing fund can replicate it.
Built to be trusted
Lead-driven marketplaces have no reason to fix a stale or duplicated listing — they still get paid for them. Our marketplace, by contrast, is free for renters and property managers, so the business runs on reliability, not volume.
Signal you can act on
Every listing is disclosed directly by the property manager and reflects real rent movement as it happens — no scraped duplicates, no survey delay, no stale data standing between you and a real position.
Track a unit across time
The URU (Unique Rental Unit Identifier) follows a specific unit across listings and time, enabling longitudinal, apples-to-apples analysis for a property or portfolio without reconciliation work.
Scoped or comprehensive, either way
Pull a targeted historical snapshot to validate a specific deal thesis, or license a comprehensive, ongoing feed across SFR and multifamily for full-scale investment infrastructure.
Built to stress-test against a full cycle
Historical depth back to January 2020 covers the COVID-era disruption and the post-pandemic normalization, giving investment teams a full market cycle to validate a thesis against before capital moves.
How investment analysis teams use Dwellsy IQ’s rent data
Institutional Investors
Acquisition diligence
Verify a target property or portfolio’s actual rent performance against the seller’s pro forma before capital moves.
Portfolio-level investment decisions
Evaluate a multi-property portfolio using consistent, unit-level data across every asset, no blending different sources for different properties.
Thesis validation
Pull a scoped historical dataset to stress-test a specific investment thesis, a single asset class, a single geography, a defined time range, before committing to a larger data relationship.
Hedge Funds
Longitudinal market studies
Study how specific submarkets, asset classes, or unit types have performed since January 2020 to inform where to deploy capital next.
Consistent data across every asset
No reconciling different sources for different properties in a portfolio, every asset is measured against the same unit-level source.
Scoped access before scaling up
Start with a targeted historical pull tied to a specific deal or market before committing to a comprehensive, ongoing feed.
Researchers & Universities
Longitudinal housing studies
Use historical, unit-level data spanning a full market cycle to study how specific submarkets or asset classes have actually performed.
Defensible data lineage
Every figure traces back to an owner-disclosed listing sourced directly from a property management system, not a scraped or self-reported estimate.
Depth since 2020
Historical depth back to January 2020 covers the COVID-era disruption and post-pandemic normalization, a full cycle to study.
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.
Evaluate the asset in front of you
Talk to our team about a data pull scoped to the specific asset, portfolio, or geography behind your next investment decision.
FAQ
Can we get data scoped to just the properties in a specific deal?
Yes. Historical and current data can be scoped to a specific geography, asset class, or time range to support diligence on a specific acquisition or portfolio.
How far back does historical data go?
January 2020 to present, which covers the COVID-era disruption and the post-pandemic normalization, useful for understanding how a specific asset or market performed through a full cycle.
What is the URU and why does it matter for portfolio analysis?
The URU is a permanent, unit-level identifier that stays fixed to a specific rental unit even as it appears across different listings or platforms over time. For portfolio-level analysis, that means tracking the same units across years without reconciling different records.
Do you cover both single-family and multifamily?
Yes, both asset classes are in the same underlying dataset and can be licensed together or scoped separately depending on the portfolio.
Is this data reliable to validate an acquisition thesis?
It’s sourced directly from property management systems, not scraped or surveyed, so it reflects what units actually rented for, at the unit level. That’s a more auditable basis for diligence than scraped listings or lagged survey averages.
Can we start with a small, one-time historical pull before a bigger commitment?
Yes. Scoped, one-time historical pulls are available for validating a specific thesis before committing to an ongoing feed.
What’s the difference between Total IQ and Trends IQ for investment analysis?
Total IQ delivers the raw unit-level dataset, the right choice for property or portfolio-level diligence. Trends IQ delivers smoothed market-level indices, better suited to broader trend context alongside the unit-level work. Many investment teams use both.
Do you provide data on specific submarkets or ZIP codes?
Yes. Data rolls up to ZIP code, city, MSA, state, and nationwide, all built from the same unit-level source.
Is scraped data a reasonable shortcut for deal diligence?
No. Scraped listings carry duplication, staleness, and legal exposure, all of which undermine the diligence process rather than support it.
Can researchers access this data for longitudinal housing studies?
Yes. Researchers and academic institutions use historical, unit-level data for affordability and market studies. See the Investment Analysis and Policy and Affordability Analysis pages for details relevant to your specific research question.
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