Rental data behind published housing scholarship
Affordability studies, macroeconomic forecasting, and longitudinal academic work all need rental data that holds up under peer review, and most of what’s available doesn’t: scraped, self-reported, or averaged too coarsely to be rigorous. Get historical rental datasets sourced directly from property management systems, scoped to a study’s region and time range.
Never scraped • No survey data • Citable, direct-sourced data back to January 2020
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Most rental data doesn’t hold up to scholarly scrutiny
Coarse data, averaged away
Most rental data available for academic use is scraped from listing sites or self-reported through infrequent surveys, averaged at a level too coarse to support rigorous, citable analysis: hard to defend once colleagues start asking where the numbers actually came from.
The question a reviewer always asks
Sooner or later, a co-author or reviewer asks where the numbers actually came from. Scraped data raises questions about duplication and legal footing. Survey data raises questions about how recent and how granular it really is. Either one can stall a paper at the exact moment it’s supposed to hold up.
A source you can name in your methods section
Dwellsy IQ is sourced directly from property management systems, the systems of record where final asking rents are actually set, not scraped from listing sites or aggregated from a survey. That’s a source that can be described plainly and specifically in a paper’s methodology.
Built for how universities actually work with data
Scoped and priced per geography
Most research doesn’t need a national dataset, it needs a specific ZIP code, city, MSA, county, or state, over a specific time range. Dwellsy IQ data can be scoped to exactly that footprint, with packages starting at $5,000 per geography, so a research budget covers exactly what a project needs, not a license sized for a much bigger organization.
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 studies examining a specific asset class, or comparing across them, don’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.
Cross-Institution Consistency
The same underlying source and methodology apply everywhere it’s used, so a study replicated across two different regions, or built on by a second institution later, isn’t reconciling two different vendors’ definitions of “rent.”
Documented Update Cadence
Data is refreshed continuously at the source, so a snapshot pulled for a study reflects when it was drawn rather than an unclear or undocumented collection date, useful when a paper needs to state exactly when its data was current.
How universities use Dwellsy IQ’s rent data
Housing Affordability Studies
Unit-level, direct-sourced rent data gives affordability researchers a documented foundation that can be described plainly in a methodology section, instead of a community-level average that doesn’t hold up to scrutiny.
Rent data for policy & affordability analysis →Disaster Recovery and Housing Market Research
University researchers studying how a rental market recovers after a disaster use unit-level data to analyze tenant demographics, market conditions, and affordability in the affected area, work that speaks to who gets left behind in a rebuilding effort.
Rental Market Transparency Research
Faculty examining how and where rental units get advertised combine property-level listing data with demographic data to study transparency and behavior across the rental market.
Poverty and Economic Well-Being Research
University centers building a national picture of poverty and economic well-being draw on rent-price research as part of their evidence base, alongside other economic indicators.
Public Policy and Housing Affordability Guides
University programs producing public-facing guides on the housing crisis cite rent-price data to ground the picture they present for a general audience, not just an academic one.
Rent data for policy & affordability analysis →Rent Index and Methodology Research
Academic work on how rent itself should be measured uses Dwellsy IQ as a possible data source for building quality-adjusted rent indices, the kind of groundwork that shapes how future studies define the numbers they report.
Longitudinal and Panel Research
Every unit carries a persistent identifier that never changes, so researchers tracking the same units or markets across years get apples-to-apples comparisons without reconciling duplicated or re-listed units.
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 the data your research needs, scoped to your study
Whether you need a one-time historical snapshot for a single geography or a multi-year phased dataset for a longitudinal study, Dwellsy IQ scopes rental data to your research design starting at $5,000 per geography.
FAQ
Has Dwellsy IQ data actually been used in published academic work?
Yes. It’s been used directly in published and working research, including University of Wisconsin–Madison, University of Michigan, UC Santa Barbara, Arizona Law Review, and more.
Can we cite Dwellsy IQ as a data source in our methodology?
Yes. Data is sourced directly from property management systems through the Dwellsy marketplace, not scraped or self-reported, which gives you documented, describable provenance for a methods section.
Can we purchase data for a single county or metro area instead of a national dataset?
Yes. Dwellsy IQ data can be scoped to a specific county, MSA, or state, and to a specific time range, rather than a full national dataset.
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.
Will Dwellsy IQ data hold up to peer review?
The data is sourced directly from property management systems through the Dwellsy marketplace, not scraped or self-reported. That direct sourcing and the documented pipeline behind it can be described plainly in a paper’s methodology section.
We don’t have grant funding secured yet — can we still access the data?
Yes. For individual researchers or departments without confirmed funding, Dwellsy IQ’s academic reseller partner, Dewey, provides access on a rev-share basis with no upfront cost. Talk to our team to be routed appropriately.
What’s the difference between Dwellsy IQ and scraped or survey-based housing data?
Scraped data is pulled from listing sites, is prone to duplication, and carries legal risk. Survey data is self-reported, collected at the community-average level, and often months old. Dwellsy IQ is sourced directly from property management systems at the unit level, continuously.
Can we get data for both single-family and multifamily housing?
Yes. Dwellsy IQ covers SFR and multifamily in the same dataset, so a single purchase can support cross-asset-class research.
Is any of this data personally identifiable or private?
No. The dataset is public, owner-disclosed listing data at the unit level. There is no PII and no private data involved.
Equal Housing Opportunity