Rent data that holds up in an underwriting model
A wrong DSCR number either kills a good loan or funds a bad one, and a ZIP-code average can’t tell you what a specific building will actually collect in rent. Dwellsy IQ delivers property-level data sourced directly from property management systems, built to plug into loan origination systems and underwriting models without needing to justify the source.
Property-level, not ZIP-code averages • Never scraped • Built for LOS integration
Trusted by Leading Brands
A wrong number costs you the loan either way
ZIP averages can’t underwrite a specific asset
A DSCR calculation on a specific asset needs a rent number tied to that asset, not a neighborhood blend — a ZIP-code average can’t tell you what a specific 12-unit building or single-family rental will actually collect.
Get the number wrong, and it costs you the loan either way
A rent number that’s too low kills a loan that should have closed. Too high, and it funds a loan that shouldn’t have. Both outcomes trace back to the same gap: a rent figure that isn’t tied to the actual property.
Underwritten on a number you don’t have to hedge
Dwellsy IQ delivers property-level rental data sourced directly from property management systems, built to plug into loan origination systems and underwriting models without forcing your team to explain away the source.
Built for origination teams
Property-level roll-up, not ZIP averages
DSCR needs a rent number tied to the actual asset, not a neighborhood blend. Unit-level data that rolls up to the property being financed, sourced directly from the property management systems where rents are set and updated — never inferred from a listing site or survey.
Fast, structured, built for your LOS
Data is delivered through API IQ or AWS S3 as a structured feed designed to sit inside a Loan Origination System — queryable by geography, property type, or attribute. Continuous, first-party updates mean your team isn’t waiting on a stale survey cycle to move a loan forward.
Test before you commit
Paying upfront for data that might not roll up the right way is a risk no lender needs to take. Scoped, low-commitment access instead, to confirm property-level granularity against your own loan book first.
Defensible under regulatory review
Underwriting models operating in a regulated environment need a data source that can withstand scrutiny. Legally compliant, owner-disclosed listing data — no scraping, no PII — which matters when a new vendor has to clear internal compliance review.
Historical depth for risk modeling
Data back to January 2020 supports longitudinal underwriting models that need to account for rent volatility through the COVID-era cycle and its aftermath.
SFR and multifamily in one feed
Whether you’re underwriting a single-family rental or a multifamily asset, both live in the same dataset with the same sourcing standard — no switching vendors or reconciling two data models across asset classes.
How lenders use Dwellsy IQ rent data
DSCR & Loan Underwriting
Underwriting analysts calculate DSCR on a specific asset using unit-level data that rolls up to the property being financed, not a neighborhood blend.
Loan Origination System Integration
Origination teams pull a structured feed via API IQ or AWS S3 directly into their LOS, queryable by geography, property type, or attribute.
Loan Book Validation
Underwriting teams test property-level granularity against their own loan book on scoped, low-commitment access before a larger agreement.
Regulatory & Compliance Review
Compliance teams clear internal review using legally compliant, owner-disclosed data with no scraping and no PII involved.
Longitudinal Risk Modeling
Risk modeling teams account for rent volatility through the COVID-era cycle and its aftermath using historical depth back to January 2020.
Cross-Asset Class Underwriting
Lenders with a book spanning multiple asset types underwrite SFR and multifamily loans from the same dataset, with no switching vendors or reconciling separate data models.
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.
Underwrite with a number you can defend
Pull property-level rent data for the assets in your pipeline and see how it compares to what you’re using now. Scoped access is available before any larger commitment.
FAQ
Does Dwellsy IQ provide property-level data, or just ZIP-code averages?
Property-level. Although we can also provide ZIP-code averages, every record in Dwellsy IQ is unit-level, with final asking rent and 50+ attributes and 250+ amenities tied to a specific rental unit. Aggregated views like ZIP or MSA are built from that same unit-level source, not sampled separately.
How does this integrate with our Loan Origination System?
API IQ delivers data programmatically via REST API, structured to be queried by geography, property type, or attribute and ingested directly into underwriting workflows and LOS platforms.
Can we test the data before committing to a contract?
Yes. Scoped, low-commitment access is available so your team can validate property-level granularity against your existing loan book before a larger agreement.
Is this scraped or self-reported data?
Neither. Dwellsy IQ is sourced directly from property management systems through the Dwellsy marketplace. Rents are captured where they’re set and updated — not scraped from listing sites, not self-reported on a survey.
How current is the data?
Data is updated continuously. Update frequency to your specific delivery method (API, S3, CSV, JSON, TSV or Parquet) may vary, but the underlying dataset does not run on a periodic survey or scrape cycle.
How far back does historical data go?
January 2020 to present, supporting longitudinal underwriting and risk models that need to account for multi-year rent trends.
We’re a loan origination platform — is this data useful for us?
It depends on what your team does with rent data internally. Dwellsy IQ is built for teams running their own underwriting models, AVMs, or DSCR calculations — data science or underwriting analyst teams that need to ingest property-level rent data programmatically. If your platform handles the origination workflow but doesn’t run internal risk models on rental income, raw data access likely isn’t the right fit, and you may be better served by a partner who already builds that layer for you.
What asset classes are covered?
SFR (single-family rental), BTR (build-to-rent), and multifamily — all in the same dataset, which matters if your book spans multiple asset types.
Is the data legally compliant for use in a regulated underwriting process?
Yes. Data is public, owner-disclosed listing information at the unit level, with no PII and no scraped sourcing — built to hold up under compliance review.
What geographic coverage does Dwellsy IQ have?
800+ MSAs and 16,000+ ZIP codes, covering roughly 75% of professionally managed U.S. rental housing.
Do we have to buy national coverage, or can we scope to our lending footprint?
Coverage can be scoped to the specific markets in your origination footprint rather than requiring a national commitment upfront.
How is Dwellsy IQ different from data aggregators that also serve lenders?
Dwellsy IQ is sourced directly from property management systems — not scraped or aggregated from third-party listing sites. That sourcing model is what allows the data to roll up cleanly to a single property with legal compliance intact.
What’s the fastest way to see if this fits our underwriting workflow?
Request API access and pull a scoped sample for markets in your current pipeline — the fastest way to see how it performs against assets you’re actively underwriting.
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