AI for Property Management
AI for property management: every property's numbers, without the spreadsheet marathon
I build finance and operations systems for multi-location property operators. One live dashboard across every location, month-end packets that build themselves, and occupancy next to the numbers it drives.
Sound Familiar?
Where property operators lose the most time
Month-end close means exporting the general ledger and reconciling it in Excel, one property at a time
Nobody can say which property is behind budget this month, or why, without a day of spreadsheet work
Occupancy lives in one system and financials in another, so nobody sees them side by side
What Gets Built
What AI for property management looks like in practice
Most of the time a property team loses goes to the same few jobs: pulling reports, checking the ledger, building the close, and chasing occupancy. These four systems take those jobs over. They sit on top of the software you already run, so nothing gets replaced.
Live P&L dashboard across properties
Every location's P&L in one place: revenue and NOI against budget, month-over-month variance flags, a six-month trend, run-rate projections for partial months, and a plain-English AI summary for each location when new data lands.
GL exports that load themselves
The accounting system (Yardi, in the build below) already emails daily general ledger exports. A watcher picks them up and loads them into the dashboard. Nobody downloads, renames or forwards a file.
Month-end packets per property
A close packet for each location, built from the same data: income statement against budget, balance sheet, cash flow, AR and AP aging, and occupancy. Delivered as a PDF and an Excel workbook.
Occupancy next to the financials
Occupancy pulled daily for every location, backfilled for prior months and locked automatically at month end, so the close numbers don't drift.
A Real Build
25N Coworking: five locations, one dashboard
At the time, 25N ran five coworking locations across the Chicago and Dallas-Fort Worth metro areas. The finance team was reconciling general ledger exports in Excel location by location, with no single view of occupancy or how each location stood against budget.
I started with a competitive analysis for the leadership team, delivered 9 hours after kickoff. Then I built the finance side: a dashboard fed from the daily GL exports their accounting system already sent, a rules-based GL check that flagged problem entries before the close, a month-end financial packet for each location, and occupancy pulled daily. The finance team reviewed every round, and their feedback went straight back into the build.
What Was Delivered
- Financial dashboard rolling up all 5 locations, refreshed daily
- GL check that flags entries breaking per-account rules before the close
- Automatic ingestion of the daily GL exports the accounting system already emails
- Month-end financial packet per location, in PDF and Excel
- Daily occupancy tracking with historical backfill and month-end lock
5Locations in one dashboard
DailyGL and occupancy refresh, while the engagement ran
PDF + ExcelClose packet per location
9hrsFirst delivery after kickoff
How I Help
Start with a free audit, then a scoped build
Free AI Audit
Free · Delivered in minutes
For when you know the reporting eats your team's week but don't yet know what to automate first.
Finance Dashboard + Close Automation
Free for 30 days · Then $1,000 setup + $300/mo
For when the close eats days every month and you want every property's numbers in one place.
Want to talk it through first? See how I work as an AI consultant for small business, or read about AI for coworking spaces.
FAQ
Questions about AI for property management
What does AI for property management actually do?
Do we have to switch from Yardi or our current software?
Have you built this for a real estate operator?
Where should a property manager start with AI?
Is our financial data kept confidential?
About
Who you'll work with

Hey there,
I'm Max Wexley, the founder of Wex Advisory, based in New York City. I studied at the University of Michigan (class of 2024) and come from a background in finance.
I build AI automation, dashboards and reporting systems for small and mid-sized businesses. I scope, build and support every project personally, so you always talk to the person who built your system.
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