Custom multifamily underwriting softwareUnderwriting software that turns raw listings into
cap rates you can defend.
Custom software for real-estate investors who need to screen multifamily deals at scale: it collects listing data on a schedule, fills in rents and public records, computes NOI and cap rate from a regional expense model, and holds back anything incomplete.
Built for investors whose deal flow has outgrown a spreadsheet.
When investors call me about underwriting, it is usually one of these.
What a custom underwriting platform includes
Scoped to your markets and your criteria — you get the modules you need, not a bloated suite.
Scheduled data collection
Listings gathered on a schedule, with retries and monitoring so a failed collection run is visible.
Resilient collection
Built to cope with anti-bot defenses and with pages whose structure changes.
Rent and record enrichment
Market rents per unit and public records added to each property automatically.
AI where rules cannot reach
Property type read from photos, and unit counts and square footage pulled from listing text.
Cap-rate engine
NOI and cap rate computed from a detailed expense model that differs by region.
Expense model as data
Regional assumptions kept in a table your team can review and update, not buried in code.
Completeness checks
A listing with a missing price, unit mix or rent gets no cap rate and a stated reason, so nobody relies on a guessed number.
Review dashboard
Filter, sort and open any property to see the full calculation behind its cap rate.
Spreadsheets and manual underwriting vs. a custom build
A spreadsheet is a sensible tool for a handful of deals. Custom software earns its cost when volume and consistency are the problem.
| Factor | Spreadsheets and manual underwriting | Custom build with The Forge |
|---|---|---|
| Best for | A few deals, one analyst, quick experiments | Many properties screened on the same rules |
| Getting the data | Copied from listings and records by hand | Collected on a schedule and enriched automatically |
| Expense assumptions | Whatever the analyst remembers or pastes in | A regional model kept as reviewable data |
| Incomplete listings | Easy to miss; a blank rent can read as zero | Held back, with the missing input named |
| Explaining a number | Dig through the workbook | Inputs stored next to every result |
| Ownership | Lives in someone’s file | You own the code and IP |
Raw listings became cap rates automatically, with incomplete deals held back and explained.
An investment firm needed to evaluate multifamily properties at scale without slow, error-prone manual underwriting.
Read the multifamily underwriting & cap-rate platform case study →From first call to live — with a fixed quote
Call
Tell me which markets you screen, where the data comes from today and how your analysts underwrite a deal.
Build Plan
Data sources, the math, the quality checks and a fixed quote, before any code is written.
Build
Collection, enrichment and the cap-rate engine, checked against deals your analysts have already underwritten.
Launch and review
The dashboard goes live and your team starts screening deals from it.
Multifamily Underwriting Software: common questions
It depends on your markets and data sources. The first step is a Build Plan, from $500 and ready in 2–3 days, which maps the data, the math and the checks. The fee is credited in full toward the build, and you get a fixed quote before any code is written.
The platform I built for a multifamily investment firm collects listings on a schedule and adds market rents and public records. The collection is built to survive anti-bot systems and changing page structures, with retries and monitoring that surface failures so a broken run does not go unnoticed.
Money and rates are stored as exact decimals and rounded once, at display. Expense assumptions live in a table by region, and every input is saved next to the result. I also turn deals your analysts have underwritten by hand into automated tests, so a code change that moves a number fails the build.
They get no cap rate. A missing rent is marked incomplete rather than treated as zero, and the platform shows how many listings are missing which input, so your analysts know what to chase instead of re-checking everything.
Operating expenses differ from market to market. A single national assumption gives cap rates that look right but can lead to a bad investment decision.
You do. The client owns all code and IP on final payment.