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. research 15 min Multifamily Rent Comp Analysis (HelloData + Research) Builds a unit-level rent comp analysis for an existing multifamily subject property. Identifies the subject via HelloData, pulls nearby comps, researches current unit mix and asking rents across the subject and comps via a dedicated research workflow, and delivers an Excel rent comp table plus a property map. Who It’s For Acquisitions analysts, underwriters, and asset managers who need unit-level rent comps before underwriting a multifamily deal. What You Get Back An Excel rent comp table with unit-level asking rents, square footages, concessions, and rent-per-SF calculations, plus a property map showing the subject and all comps. Why It Matters Replaces an afternoon of manual comp research with a 15-minute task, giving you a formatted, model-ready deliverable before your next pipeline meeting. Skills Used Excel Document Style Guide Tools Used Research Multifamily Rents Generate Property Map Computer 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