Cross-match with permits, utilities, and land records

Use existing lists from planning, utilities, land administration, or licensing offices to find properties or contacts missing from the tax roll. This often produces faster results than starting with citywide fieldwork.

• Other offices already hold better data than the tax register.

• The city needs fast coverage gains inside one billing cycle.

• A small team can clean lists, match records, and follow up on likely misses.

• Basic data-sharing permission or workable MOUs with partner agencies.

• Clear matching rules and a verification step before new records are activated.

• It will disappoint if partner datasets are weak, outdated, or legally inaccessible.

• Address standards may need cleaning before matching becomes reliable.

The political risk is usually low to moderate, but the city still needs to show that new records will be handled fairly and that residents are not being drawn into a process they do not understand.

The full card would help the city plan which partner datasets to use first, how to agree simple data-sharing arrangements, how to match records in practice, and how to verify and absorb likely matches without creating confusion or duplicates.

• Link the roll to permits, sales, and new service connections

• Give each property one ID in one register.

This is often one of the fastest ways to widen the register because it uses information that already exists. It also tends to be cheaper than starting with fieldwork everywhere. In many cities, the real challenge is not the absence of data but the fact that information is scattered across agencies and never brought back into the tax register in a disciplined way.

Use existing data that other agencies already hold to identify properties or contacts that the tax roll is missing.

A city where utilities, planning, land administration, or licensing offices hold better and more current information than the tax register.

A first list of likely unregistered or mislinked properties that can be verified and loaded within one billing cycle.

Choose the first partner datasets, authorise a simple data-sharing arrangement, and assign one small team to run matching and verification.

Revenue administration, supported by data staff or a small analyst team, with named liaisons in partner offices.

• Planning, utility, land, or licensing offices hold lists that are newer or more complete than the tax roll.

• The city needs a quick gain in coverage and does not want to wait for a citywide survey.

• At least a few staff can clean lists, run basic matching logic, and follow up on uncertain cases.

• Start with one or two partner datasets that are operationally useful, rather than negotiating an ambitious all-agency system from the start.

• Decide the practical matching keys first: address, meter number, permit number, GPS point, or another workable reference.

• If address data are weak, prepare for a cleaning step or pair this work with a basic address and location-reference initiative.

• Choose whether the city will begin with periodic spreadsheet exchanges or a more automated feed later on.

• Set a confidence rule for likely matches so staff know which cases can move quickly and which require human review.

• Decide whether new matches create provisional records first or go directly to the operational register after validation.

First 90 days

• Map the most promising partner datasets and select the first pilot partner or partners.

• Agree a simple file format, transfer method, and point of contact in each office.

• Run a first manual match to understand how messy the data are before trying to automate anything.

6 to 12 months

• Verify the most promising unmatched records through desk checks, phone checks, or short field visits.

• Add or update records and keep a source log so the city can learn which partner dataset is yielding the best results.

• Refine address conventions, matching rules, and workflows based on the first rounds.

12 to 24 months and beyond

• Move to a more routine cycle so new partner data are not just used once.

• Automate the easiest transfers only after the city has stabilised the basic logic and quality checks.

• Use experience from the first feeds to support wider integration or trigger-based maintenance later.

• The city usually needs permission to receive and use core non-sensitive data fields for tax administration, often through a simple MOU or administrative order.

• Privacy rules need to be handled seriously even when the exchange is operational rather than technologically advanced.

• A small analyst function matters more than a large IT project at the start.

• Partner offices need named contacts; otherwise the process depends on personal relationships and quickly fades.

• Verification capacity is essential so false matches do not flow directly into billing.

• Bad matching rules can create duplicates or attach the wrong person to the wrong property.

• Weak partner data can waste staff time unless the city starts with the highest-value datasets.

• An overcomplicated technical design can delay progress where a simple recurring spreadsheet exchange would already work.

• Number of likely matches identified.

• Share of likely matches verified.

• Number of new records added or existing records corrected through partner data.

• Time between receipt of partner data and update of the tax register.

• This card often works best alongside PT-COV-13 on address and location referencing, PT-COV-07 on one register and one ID, and PT-COV-08 on creating permanent trigger-based data flows.

• Which partner office has the cleanest and most useful data to start with?

• What is the strongest matching field available today?

• Who will review uncertain matches before records are activated?

• How often can the city realistically run the matching cycle in the first year?