Fundrise launches RealAI: property-grade intelligence for anyone in residential
Fundrise is rolling out RealAI, an AI platform aimed at giving single- and multifamily pros instant, property-level market intelligence. Think neighborhood income and migration trends, comps, average rents, and asset-specific detail-pulled together in seconds.
Ben Miller, Fundrise's co-founder and CEO, says it goes further than generalized chat models because it's built on a purpose-built real estate dataset. The product starts with residential and is slated to expand into other commercial sectors within six months. Pricing: free for the first 12 uses, then $69 per month for the standard plan.
What RealAI actually does
- Surfaces neighborhood-level indicators: income, migration, household formation, and rent trends.
- Pulls comps and average rents down to the individual property.
- Runs simulations to score the "best property to buy" based on your inputs.
- Taps a reported 3.5 trillion datapoints covering essentially every property in the U.S.
Miller's pitch is straightforward: "It does the work of a real estate analyst, and it's for anyone." The goal is to give smaller operators and individual investors the same level of insight big shops pay machine learning teams to build.
Why this matters to operators and investors
Large firms already use AI for portfolio analysis and asset management, but many tools are locked behind enterprise relationships. Platforms at firms like JLL are typically only available to employees and clients.
RealAI is open to the broader market with a simple subscription, which could compress underwriting cycles, raise the bar on comps, and tighten rent and expense assumptions. For smaller teams, that levels the playing field on speed and depth of analysis.
Data sources and compliance you should consider
Fundrise says RealAI blends public records and private databases. It also includes information on people living and working in properties-such as education level, credit scores, and income-and gathers some of that from social media.
That opens real questions for practitioners. Before you fold outputs into your workflow, pressure-test how you use personal and credit-related data to avoid fair housing issues and improper screening. Review how any insights will be applied in underwriting, marketing, and tenant decisions.
- Confirm how data is collected, refreshed, and consented.
- Align use with Fair Credit Reporting Act requirements where applicable (FCRA overview).
- Document your policies for model-driven screening and pricing decisions.
- Keep a human in the loop for edge cases and final calls.
Pricing and access
- Free for the first 12 uses.
- $69 per month for the standard plan after that.
If the data quality checks out in your markets, the free tier is enough to benchmark it against your current stack before you commit.
The workforce impact is coming
Industry leaders are blunt about where this goes. Barry Sternlicht of Starwood Capital has said that tasks done by teams today will be handled by AI agents at a fraction of the cost. Miller is equally direct: AI will cut roles across commercial real estate. He hasn't let people go at Fundrise, but he paused hiring.
For operators, that points to a practical shift: fewer generalists, more tech-fluent specialists, and faster cycles from sourcing to disposition.
Who's behind RealAI
Fundrise started in 2012 with a mission to open access to private real estate. It moved from crowdfunding to a fund structure and now manages about $3 billion across real estate and technology strategies, with more than 2 million investors and a $10 minimum. The firm's tech fund holds private stakes in companies like OpenAI, Databricks, and Anthropic.
Miller's posture is clear: use technology to break incumbents' advantages and make institutional-grade tools broadly available.
Practical ways to put RealAI to work this quarter
- Site selection: Score submarkets by rent growth, migration, and income, then validate on the ground.
- Underwriting: Tighten rent comps and expense assumptions; stress-test value-add scenarios.
- Pipeline triage: Rank inbound deals by projected returns and risk flags before deeper diligence.
- Asset management: Benchmark your asset's KPIs against local peers monthly, not annually.
- Investor communication: Use data visualizations and comps to back up quarterly updates.
How to pilot it in a week
- Run 12 free queries across three target ZIPs. Compare outputs with your last two closed deals.
- Pick one live deal. Rebuild the underwriting with RealAI comps and rent trends. Note deltas.
- Red-team assumptions: where is the model overconfident or thin on data?
- Decide: replace, augment, or pass. If augmenting, define exactly which steps it owns.
- Train your team on prompts and review workflows. If you need structured upskilling, see AI courses by job role.
The bottom line
If you work in residential investment or operations, RealAI could compress the time from question to insight. Test it against your current process. If it holds up, codify where it saves time or improves accuracy-and put a human check where it doesn't.
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