Today, critical decisions, like where and how rental assistance is targeted and how to intervene in neighborhoods experiencing housing instability, are made with data that is years old, imprecise, and often incomplete.
We’re building a free, open platform to solve this.
Made possible by:
Beyond the gaps in ACS and Zillow, other sources are paywalled (e.g., CoStar), scattered across listing sites (e.g., Craigslist), or siloed in rent registries and other government databases.
More than 50% of rental stock in the Bay Area is invisible.
Share of rental stock by property type, 2024
The Open Rent Initiative is building the nation's first open, public rent data infrastructure, connecting and standardizing building-level rent information across regions so governments and nonprofits can act quickly to help families find homes and stay housed.
A comprehensive open-data platform with engagement tools and products that make rental data and insight readily accessible to policymakers, researchers, and the public.
Integrating granular data from diverse channels at the property level, or at the finest geographic resolution available within each market.
Partner locally with those who know the community and its data well, leaning on creative sourcing and model estimation where direct data is unavailable.
Open Rent closes the information gap for organizations supporting low- and moderate-income families
Case workers at tenant-counseling clinics, legal-aid offices, and homelessness-prevention programs triage limited resources without knowing which parcels and blocks face the greatest housing-stability risk.
Property-level signals from rent spikes, ownership changes, and eviction-filing patterns, so frontline teams can deploy outreach, emergency rental assistance, and right-to-counsel services to the specific parcels where tenants are at risk.
Cities lack visibility into the full rental stock, making it difficult to develop sound policy, enforce existing regulations, and provide the public with credible transparency.
A live, property-level view of rental stock and rental data, so cities can move from reactive enforcement to proactive, property-level compliance monitoring and targeted housing-stability policy.
CRA investment decisions rely on HUD assessment-area data that is both too coarse to locate specific LMI investment opportunities and too stale to reflect current market conditions. By the time a CRA team has current FMRs, the tight-market window has closed and LMI households have already been priced out.
Real-time LMI rental stock and rent data at the property level within each assessment area, so CRA teams can target capital to specific parcels and neighborhoods where it qualifies, and document impact their examiners and boards can defend.
NOAH (naturally occurring affordable housing) is invisible in tract-level data; preservation funds cannot prioritize individual parcels they cannot see, and the tenants in unsubsidized affordable units are often at the highest risk of displacement.
A property-level inventory of NOAH and at-risk affordable stock, parcel by parcel, with risk signals from ownership changes, permit activity, and rent trajectories.
Build the core data infrastructure. Integrate Bay Area foundational datasets. Launch the Mountain View pilot in June 2026.
Extend data pipelines to cover the entire Bay Area, including building estimation models where relevant (rent prices, vacancy, displacement risk). Launch Los Angeles Metro and deepen and expand existing collaboration with HousingLink in Minnesota.
Expand data sets and user features. Expand across California and Minnesota. Begin expansion to other states.
We're raising multi-year support to expand from the Mountain View pilot to the full Bay Area through 2026, launch a Los Angeles Metro pilot in fall 2026, and stand up the first Twin Cities deployment with local partners.
Lead and co-funder roles are open. We can also scope a partnership around a single jurisdiction or a single question, such as displacement risk estimation, tools for city housing staff, or NOAH preservation analytics.
If any of this fits your portfolio, we'd like to talk.