Case studies · Zeroth

Real estate · Property management

Finding the revenue that walks out with lost managements

Every property management agency loses managements each quarter: a landlord sells, an owner moves back in, or a competitor quietly takes the file. Most agencies count the losses and move on. Working out where each property actually went, and what it cost, means looking up every address by hand, so it never happens.

We built a quarterly automation that does the looking up.

Discuss a build like this

The system

Ingests the agency's list of ended managements, in whatever layout it exports
Resolves each property on realestate.com.au and classifies the outcome: sold, re-leased by another agency, or returned to the owner
Attributes each loss to the competitor now holding the file, and applies the agency's own fee and commission rates
Delivers a branded quarterly report: losses by manager, financial impact, competitor attribution, quarter-on-quarter trend

Results

A pilot run classified 167 ended managements with no manual lookups
Surfaced over $1.1M in sales commission that had followed lost managements to competing agencies
Every dollar figure derives from the agency's own rates. The automation estimates nothing

Real estate · Sales & rentals

A competitive landscape report, on demand

Principals know who their competitors are. What they don't know is the numbers: how much stock each one holds right now, what they've sold in the last three, six, twelve months, and who is gaining share in each suburb. Assembling that from realestate.com.au by hand takes the better part of a day, so it rarely gets done.

We built an automation that assembles it while you make a coffee.

Discuss a build like this

The system

Give it one agency; it maps every agency competing in that agency's operating suburbs
Current for-sale and for-rent stock, sold volumes in 3, 6, 9 and 12-month windows, leased volumes, medians and market share per suburb
Ranks the subject agency against the field, and generates a branded report: interactive HTML plus a print-ready A4 PDF
Runs on demand or on a schedule, and lands in the principal's inbox

Results

A five-suburb landscape covering dozens of competing agencies compiles in 10 to 20 minutes
Data cost of a few dollars per run, against the better part of a day of manual pulling
The methodology and its data limitations are printed in the report, so every number can be defended

Real estate · Lead generation

Outreach drafted by the system, approved by a human

Prospecting properly means reading each profile, judging fit against your criteria, and writing an email that sounds like you, repeated hundreds of times. Generic mail-merge gets ignored, and doing it properly doesn't scale. The usual answer is to automate the sending and hope. We think that's how firms end up with a reputation problem.

So we built the drafting, and kept a human on the send button.

Discuss a build like this

The system

Reads the target agent profiles from realestate.com.au and evaluates each against the user's own criteria, written in plain English
Drafts an individual email per qualified agent, in the user's voice and style
Then it pauses: the drafts arrive by email, and nothing sends until the user replies with a yes
Reply "approve, but make it shorter" and it redrafts before sending. Every step lands in an audit trail

Results

The human's job reduces to one approval reply per batch, instead of hours of reading and writing
Zero emails sent without explicit human approval: by design, not by policy
While it waits for approval, it costs nothing to run

Systems we've built, and what they actually did.

Where the manual work went away.

Finding the revenue that walks out with lost managements

The system

Results

A competitive landscape report, on demand

The system

Results

Outreach drafted by the system, approved by a human

The system

Results

Smaller systems, same standard.

A consultancy on the OS

Zeroth runs on Zeroth

Regional agency database

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