Rent data built to find signal in a macro model
Rent is a leading input in many models and predictions, but only if the underlying data reflects real market movement. Get first-party, PMS-sourced rent indices built from real listing data to backtest, forecast, and model housing markets and CPI.
Direct-sourced • Historical since 2020 • SFR and multifamily
Trusted by Leading Brands
A model is only as good as the signal underneath it
If it doesn’t sharpen the model, it’s out
Quant teams backtest new data against existing models to see if it improves predictive power. If it doesn’t, they walk immediately.
Most rent data doesn’t survive that test
Scraped rent data is noisy and duplicated. Survey data lags the market by months and is published at the unit type level. Neither holds up as an input to a model forecasting inflation, regional housing stress, or REIT performance ahead of the rest of the market.
A signal source built to hold up
Direct-to-PMS sourcing through the Dwellsy marketplace means this data isn’t replicable by scraping or internal synthesis. It reflects actual market movement, not an average or an estimate.
What makes Dwellsy IQ’s data different for economic forecasting
A signal other funds can’t just copy
Dwellsy IQ’s data comes straight from property management systems through our marketplace, not scraped, not synthesized. Competitors can’t rebuild it by crawling listing sites.
No incentive to keep bad data live
Lead-driven marketplaces benefit from more listings, even when some are stale or duplicated. Dwellsy does not. Our marketplace is free for renters and property managers, so the value comes from providing reliable data.
A signal that holds up
Every listing is disclosed directly by the property manager and reflects real rent movement as it happens — without scraped duplicates, delayed surveys, or stale data.
Raw data or a ready-built index
Total IQ delivers the unit-level dataset directly, for teams building their own indices in-house. Trends IQ hands you the smoothed, ML-powered normalized time series — medians and LOESS applied across 800+ MSAs.
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 quant teams a full market cycle to stress-test a model before it goes live.
The same unit, tracked over time
Every unit carries a Unique Rental Unit (URU), our permanent identifier that remains consistent across listings and over time. Your backtest tracks the same units period over period, not a shifting sample that quietly changes what it measures.
How economic forecasting teams use Dwellsy IQ’s rent data
Quantitative Hedge Funds
Inflation modeling
Feed direct-sourced, unit-level rent trends into shelter inflation estimates instead of relying on lagging survey-based indices.
Signal generation and backtesting
Run a sandbox backtest against historical data since 2020 to evaluate whether Dwellsy IQ improves a model’s predictive power before committing to an enterprise feed.
Proprietary signal
Direct PMS sourcing means the underlying data isn’t replicable by scraping or building internally, a signal other funds can’t just copy.
Institutional Investment Firms
Housing trend forecasting
Model regional and national housing trends using time-series rent indices built from real listing data, smoothed for comparability across geographies.
REIT performance prediction
Forecast REIT performance using rent trend data underneath the properties those REITs hold, rather than relying solely on public disclosures and analyst estimates.
Consistent aggregation across geographies
Rent indices roll up to ZIP code, city, MSA, state, and nationwide, all from the same unit-level source, so regional comparisons hold up.
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 before you commit
Get a sandbox pull of historical data and backtest it against your model. Talk to our team about scoped or comprehensive access, depending on what your infrastructure needs.
FAQ
What makes Dwellsy IQ’s data useful for macro forecasting specifically?
It’s sourced directly from property management systems, not scraped or surveyed, and reflects real rent movement at the unit level before it’s aggregated into indices. That’s a different signal than a survey-based inflation proxy or a scraped listing snapshot.
Can we backtest Dwellsy IQ’s data against our existing model before committing?
Yes. A scoped historical data pull can be evaluated in a sandbox before any enterprise commitment.
How far back does the historical data go?
January 2020 to present, covering the COVID-era disruption and post-pandemic normalization, a full cycle for stress-testing forecasting models.
Does Dwellsy IQ cover both single-family and multifamily?
Yes. Both SFR and multifamily are covered and can be licensed together or separately.
Is survey-based shelter inflation data a reasonable substitute?
No. Survey data is collected at the community-average level, often only once or twice a year, and can lag the market by months, a meaningful gap for a model trying to forecast inflation in near-real time.
Can we get a scoped, one-time historical pull instead of an ongoing feed?
Yes. Scoped historical pulls are available for teams validating a specific investment or research thesis rather than building full quant infrastructure.
Is this data available elsewhere or is it exclusive to Dwellsy IQ?
Dwellsy IQ licenses data to multiple institutional buyers, and the sourcing model itself, direct PMS relationships through the Dwellsy marketplace, is not something competitors can replicate by scraping or building internally.
How is this different from data we could build ourselves by scraping listing sites?
An internal scrape carries legal exposure, coverage gaps, and duplication that direct PMS sourcing avoids entirely. Building it internally also costs data science time without guaranteeing the coverage or accuracy of a direct property manager relationship.
Do you provide raw data or only pre-built indices?
Both. Total IQ provides the raw unit-level dataset; Trends IQ provides the pre-built, smoothed index. Many quant teams use both together.
Equal Housing Opportunity