Any successful residential build is a balance of three finite resources: money, materials and man hours. While material pricing feels tangible, labor is often underestimated because it is invisible until it is already spent. Asking how many man hours per square foot to build a house is the quickest way to translate an abstract schedule into a concrete cost. At CountBricks, we turn that question into an exact AI-driven data point you can quote with confidence.
Traditional rule-of-thumb ranges for a single-storey, timber-framed home sit between 6 and 12 man hours per square foot. Why such a wide spread?
• Complexity of design—simple rectangles fall near the low end, elaborate footprints toward the high end
• Site conditions—flat, serviced land shaves hours; tight urban infill adds them
• Trade mix—self-perform versus multiple subcontractor teams affects overlap and idle time
• Regional labour productivity—weather, code requirements, union rules and travel distance all influence crew efficiency
CountBricks models each of these variables automatically. When a supervisor speaks the scope into our mobile app, the platform references regional productivity factors and current crew performance records to generate a project-specific man-hour density—in seconds, not days.
1. Foundation and sub-floor: 1.0–2.0 hours/ft²
2. Framing and sheathing: 1.5–3.5 hours/ft²
3. Mechanical rough-in: 0.8–1.5 hours/ft²
4. Exterior envelope: 0.6–1.2 hours/ft²
5. Interior fit-out: 1.2–2.5 hours/ft²
6. Final punch and cleanup: 0.2–0.5 hours/ft²
These totals align with the 6–12 range yet reveal where overruns typically hide. CountBricks flag-alerts phases with abnormal labour density so project managers can act before overtime accumulates.
Old-school takeoffs demand hours at the desk. CountBricks flips the workflow: a site manager walks the plan set, narrates quantities, and our natural-language engine builds the estimate on the fly. Here’s how the system refines the man hours per square foot figure:
• Cross-checks narrated dimensions against blueprint PDFs for accuracy
• Applies real-time crew productivity data sourced from current CountBricks projects in the same postal code
• Updates labour rates from integrated payroll feeds so costs mirror today’s wage, not last quarter’s
• Generates a live labour histogram, showing resource peaks and valleys week by week
The result is an estimate and schedule that owners, builders and trades can all believe—because it mirrors what is actually happening on comparable jobs right now.
• Under-allocating supervision leads to rework
• Ignoring material lead times forces crews to wait
• Overlapping trades in tight spaces reduces productivity
• Failing to capture weather downtime in the baseline schedule
• Assuming learning curves stay flat on complex detailing
CountBricks counters each pitfall with predictive alerts. The platform simulates the build sequence, identifies congestion days, and suggests crew redistributions or off-site pre-fabrication to bring the hours per square foot back in line.
A CountBricks client in Tauranga needed a fixed-price contract. Our voice takeoff predicted 7.4 man hours/ft²—lower than the builder’s initial 9.0 assumption. By prefabbing wall panels off-site and resequencing roofing before cladding, the actual realised density was 7.1 man hours/ft², saving 4.5% on total project labour. Explore similar wins at CountBricks.com/portfolio.
• Lock design details early; last-minute changes wreak havoc on crew flow
• Use CountBricks material-labour bundles so purchase orders align with scheduled tasks
• Track daily crew output in the CountBricks mobile dashboard; variance >5% triggers an automatic review
• Incentivise teams on hitting labour-per-square-foot targets, not just calendar dates
• Re-run the AI estimate after every approved variation to maintain an up-to-date project forecast
Because CountBricks populates invoices directly from the approved estimate, your original hours-per-square-foot assumptions remain visible through final billing. Deviations appear in red, with notes attached, so the accounting team can reconcile extras quickly instead of chasing timesheets.
Visit CountBricks.com/services to book a live demo. In under 30 minutes we will convert a current plan set into an AI-driven estimate, complete with precise man-hour density and a printable client quote.
The power of CountBricks lies in its continuously learning database. Every residential project completed on the platform—whether a 1,200 ft² infill or a 5,000 ft² architect-designed retreat—feeds anonymised productivity metrics back into our engine. Over 18 months this has created the largest live dataset on New Zealand housing labour density.
• Predictive accuracy improves with each submission; our average variance is now ±4.2 % against actual hours
• Regional factors update weekly, so a sudden spike in subcontractor availability instantly reflects in lower projected man hours per square foot
• Trade-specific insights pinpoint which crews outperform peers, allowing builders to assemble higher-efficiency teams
A developer using CountBricks to deliver a four-lot subdivision needed repeatable timelines. Historic platform data suggested 6.8 man hours/ft² for the chosen plan type. By standardising window modules and opting for slab-on-grade foundations, the AI forecast dropped to 6.1. Actual performance averaged 6.0, trimming 248 labour hours across the development. Those saved hours translated into one extra dwelling delivered in the same calendar year.
1. Generate the AI estimate via voice or blueprint upload.
2. Import the labour histogram into your scheduling software or use the built-in CountBricks Gantt view.
3. Capture daily timecards in the mobile app; hours instantly reconcile against the budget.
4. Trigger automated variation quotes when scope creep threatens labour density.
5. Convert the final reconciled estimate into an invoice with one tap.
Ready to unlock similar gains? Schedule a personalised walkthrough at CountBricks.com/consultation and see how precise man-hour forecasting protects your profit on every square foot you build.
