The only rental data source built for macroeconomic models
Housing is roughly 44% of the CPI basket and rental housing drives 40% of consumer spend, which makes it one of the clearest windows into where inflation and interest rates are headed. Yet it stays one of the most opaque asset classes to model. Census-style aggregates only get you the headline number. Get direct-from-source data pulled from property management systems, the source of truth of property managers, through the largest free rental listing platform in the US.
First-party • No survey data • Unit-level data across SFH and multifamily since January 2020
Trusted by Leading Brands
Broad macro data can’t tell you where rates are headed
Aggregate isn’t accurate enough
Rent growth feeds directly into inflation readings, one of the clearest available inputs for anticipating the next governmental decisions. ACS-style aggregates can show that rents moved, but a modeled, lagging estimate isn’t built to carry a signal precise enough to act on ahead of consensus.
Not granular enough to act on
A national or state-level survey or census rent figure blends markets that may be moving in opposite directions into a single number. And it doesn’t reveal signals below the surface that signify risk or surface opportunity. Dwellsy IQ’s data gives you data at whatever level a model needs — State, County, MSA, Zip, and unit — so the signal isn’t averaged away before it reaches the model.
Granular, timely rent data exists
Dwellsy IQ delivers the same national, cross-market breadth funds already use for macro modeling, built up from unit-level records sourced directly from property management systems, not modeled or estimated.
Built for macroeconomic and inflation modeling
Historical depth for model training
Historical data from January 2020 to present, spanning real volatility and market cycles, including the post-pandemic period when rate hikes cooled rent growth and pushed up carrying costs across the market.
National and cross-market coverage
Time-series rent data across 800+ MSAs and 16,000+ ZIP codes, built for macro models that need broad geographic coverage across all classes of real estate.
Granular and accurate underneath the aggregate
Unit-level records across SFH and multifamily roll up into whatever geographic or time-series cut a model needs, with every unit carrying a Unique Rental Unit Identifier (URU) that never changes, for clean longitudinal tracking without duplication.
Backtesting infrastructure
Sandbox access to historical data so your team can backtest a data source against the last several years of market moves before committing to it, the same way you’d validate any other model input.
Flexible delivery for quant infrastructure
API access for direct integration into proprietary models, AWS S3 for full dataset drops into your own cloud environment, and CSV, JSON, TSV or Parquet delivery for analysts working outside a data warehouse.
Direct-Sourced Signal
25,000+ direct property manager relationships behind the sourcing, unreplicable by internal pipelines, the exact issue internal engineering teams run into when they try to build this instead of buying it.
How investors and hedge funds use Dwellsy IQ’s rent data
Macro Housing and Inflation Forecasting
Funds building macroeconomic models plug in time-series rent indices across 800+ MSAs, since housing costs are a major, closely watched component of inflation data. This is the primary way funds use this data today: informing a broad view of where inflation and consumer spending are headed.
Interest Rate Direction Research
Because rent growth feeds directly into inflation readings, funds and research teams use it as a leading indicator for where interest rates are likely headed next, informing everything from portfolio positioning to the timing of real estate investment decisions, since both high and low rate environments carry distinct risks for a fund’s broader model.
REIT Performance Modeling
Investment teams tracking REIT exposure use unit-level rent data to model performance ahead of earnings, rather than relying on lagging, self-reported comps.
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.
Find out if there’s signal — fast
The fastest way to know if Dwellsy IQ improves your model is to run your own backtest. Talk to our team to scope a sandbox dataset for your asset class, geography, and time range.
FAQ
Have you already backtested this data yourselves?
No, and that’s intentional. We provide the historical dataset so your team can run its own backtest against your own model, since a fund needs to validate signal internally before trusting it, not take our word for it.
What makes Dwellsy IQ data different from Census, ACS, or CoreLogic-style aggregates?
Those sources give you a broad, high-level view, which may result in lagging or inaccurate estimates. Dwellsy IQ delivers the same breadth, built from unit-level records sourced directly from property management systems instead of modeled data from a survey sample.
Why can’t I just build a rental data pipeline internally?
Building this internally means replicating direct relationships with more than 25,000 property managers. Depending on the source, it may also mean absorbing the data science costs of cleaning the data and taking on the associated legal exposure. That is a difficult moat for an internal engineering team to replicate.
How far back does the historical data go?
January 2020 to present, covering both the COVID-era rent shock and the post-pandemic normalization period — real market cycles for model training and backtesting.
Do you cover both single-family rentals and multifamily?
Yes, and both asset classes are covered within the same underlying dataset, which supports cross-asset modeling as well as asset-class-specific theses.
Can Dwellsy IQ data speak to interest rate direction, not just housing prices?
Yes. Rent growth is a major, closely watched input into inflation readings, which makes it a relevant leading indicator for funds and research teams forming a view on where rates are likely headed.
How is this different from scraped data other providers sell?
Scraped data is pulled periodically from listing sites, carries legal exposure, and is prone to duplication, false, and stale listings. Dwellsy IQ is sourced directly from property management systems and immediately made public on our marketplace, capturing final asking rents where they’re actually set and keeping the data legally compliant.
What delivery methods are available for enterprise feeds?
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 TSV, CSV, JSON, or Parquet.
Is any of this data restricted or subject to compliance issues?
No. The dataset is public, owner-disclosed listing data at the unit level, not scraped, not survey-based, and not PII. That sourcing model also reduces legal exposure relative to scraped alternatives.
Equal Housing Opportunity