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Brookfield Corporation

A scoring tool that ranks assets for one fund

Oct 4, 2026Capability study
AI & Machine LearningReal Estate

Services Provided

AI & Machine LearningData & Analytics

Tech Stack

PythonNext.jsSupabaseTypeScriptAzure

Overview

Airodevs built a portfolio scoring tool for one real estate fund team that ranks every asset on a blended score of income stability, lease rollover risk and capital expenditure need. Analysts open a single ranked list instead of rebuilding it from five spreadsheets each quarter, and can drill into the drivers behind any asset's score.

Client

A diversified global asset manager with a large real estate arm, holding office, retail and logistics assets across multiple countries through several managed funds. One fund team covering a regional office and retail portfolio asked for a faster way to compare assets ahead of quarterly allocation calls.

Challenge

Before each allocation review, analysts rebuilt a ranked asset list from separate leasing, capex and debt schedules held in different spreadsheets. Rolling up net operating income, upcoming lease expiries and planned capital spend into one comparable score took two analysts most of a week, and the ranking logic varied slightly between cycles, making quarter-over-quarter comparisons unreliable.

Solution

We built a scoring model that pulls net operating income, lease expiry schedules and capex forecasts into Supabase, then calculates a weighted score per asset using fixed, versioned rules the team can see and adjust. A Next.js dashboard shows the ranked list, a breakdown of each asset's three sub-scores and a trend view against the prior three quarters, so analysts walk into allocation meetings with one agreed number per asset.

Areas of Impact

Quarterly allocation review preparation, which now starts from one ranked list instead of five spreadsheets, lease rollover tracking, where upcoming expiries are flagged automatically against the score, and capital planning discussions, where the capex sub-score gives a consistent basis for comparing requests across assets of different sizes.

Results

78%
less analyst prep time

allocation review prep down from 4.5 days -> 1 day per cycle

100%
of assets scored on one consistent method

scoring sources consolidated from 3 spreadsheets -> 1 model

19%
more assets flagged early for lease risk

expiries over 18 months out caught per cycle, up from 9 -> 16 assets

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Office

Toronto, Ontario
Canada

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