You just toured a 120-unit garden-style apartment complex. The broker wants your LOI by Friday. Before you can underwrite rent growth, you need to know what the market is actually charging: not just the subject’s posted rents, but what the five closest comps are asking, unit by unit, with concessions and square footage side by side.
That research usually takes a full afternoon. You open HelloData, pull comps, then bounce between property websites and listing aggregators to fill in the gaps. By the time the spreadsheet is formatted and the map is built, half your day is gone on work that isn’t analysis; it’s assembly.
That’s exactly what this task is built to fix.
What This Task Does
Give the Real Estate Analyst three inputs: the subject property address, the number of comps you want, and whether you’d like to confirm the comp set before rent research begins. If you have specific preferences (include a certain property, exclude anything beyond two miles, focus on studios and one-bedrooms), drop those in the optional notes field.
From there, the AI coworker identifies the subject via HelloData, pulls the closest comparable properties by similarity score and proximity, then kicks off a dedicated rent research workflow for each property. It captures asking rents, square footages, concessions, and bed/bath configurations across the subject and every comp. Once the research is complete, it builds a formatted Excel rent comp table and generates a property map pinning the subject and all comps with key details.
The whole process takes roughly 15 minutes of your time. The AI does the rest.
Who This Task Is For
Every multifamily deal starts with the same question: what are comparable properties charging? The data exists, but assembling it into a clean, side-by-side format is the part that eats the clock.
This task is built for:
- Acquisitions Analysts who need rent comps before building a proforma and can’t afford to spend half a day on data collection
- Underwriters who need to validate in-place rents against market asking rents with unit-level detail
- Asset Managers benchmarking their portfolio’s pricing against nearby competitors on a quarterly or monthly basis
- Brokers and Advisors preparing comp packages for listing presentations, BOVs, or client advisory work
In short: if you already have a subject address, this task gives you the rent comp package.
Why It Matters
Rent comps are the foundation of every multifamily underwriting. You can’t size rent growth, validate in-place rents, or build a credible proforma without knowing what comparable properties are charging at the unit level.
Most CRE professionals know this. The issue isn’t whether rent comps matter. It’s that pulling them properly, property by property, unit type by unit type, with square footage and concessions included, takes real time.
The bottleneck isn’t judgment. It’s the research and assembly. Logging into HelloData, identifying comps, visiting each property’s website, capturing rents by floorplan, formatting everything into a presentable spreadsheet, then building a map. That’s 30 minutes on a good day, longer if any of the comps have incomplete online data.
Without this task, what usually happens is a shortcut: fewer comps, rougher data, a table that gets the job done but doesn’t hold up under scrutiny. The deal moves forward, but the rent assumptions carry more risk than they should.
That’s the multiplier.
What the Output Looks Like
The rent comp package generated by this task includes:
- A formatted Excel rent comp table with the subject and all comps, organized by property and unit type
- Unit-level rows showing bed/bath, square footage, asking rent, rent per SF, concessions, and notes
- A header block with the subject property name, address, unit count, year built, and analysis date
- Visual separation between property groups so comps are easy to scan at a glance
- A property map pinning the subject and all comps with key property details
The output is not a rough sketch. It’s a deliverable you can drop into an IC memo, email to a capital partner, or hand to your underwriting team, the kind you’d expect from an analyst who spent an afternoon on it.
CRE Agents is a platform built for commercial real estate professionals who want to move faster without cutting corners. Task #4 is just the beginning.
Frequently Asked Questions About Multifamily Rent Comp Research With AI
Yes, and the task is designed with that expectation. The Excel output gives you structured, source-based data, but you should spot-check rents against property websites, especially for comps where online listings may be outdated or incomplete. Pay particular attention to concessions and square footage, as these vary the most across sources. Treat it the same way you’d treat a comp table from a junior analyst: trust the structure, verify the numbers that matter most to your underwriting thesis.
The task uses HelloData to identify the subject and pull comps, then runs a dedicated web research workflow for each property to capture current asking rents, unit sizes, and concessions from property websites and listing platforms. This means the data reflects what properties are actively advertising, which is the standard for asking rent comps. The AI flags data gaps in the Notes column when a property’s website doesn’t list square footage or concessions for a given unit type, so you always know where to dig deeper.
Absolutely. Each run produces a standalone rent comp package for one subject property, so you can run it as many times as needed. Asset managers benchmarking rents across a portfolio typically run one task per property and compile the results. The 15-minute turnaround per property means you can comp an entire 10-property portfolio in a single morning, something that would normally take a full analyst two to three days.