Net savings going remote.

Running an office costs a company 8,000-15,000 per employee per year and 4,000-8,000 elsewhere once rent, utilities, furniture, cleaning, and snacks are totalled. Remote work trades this for a home-office stipend (typically 500-2,000 one-off plus 50-150/month) and potentially some productivity impact. The savings calculation compares the three numbers against each other.

50 employees at 10,000/year office cost = 500,000. Remote stipend 2,000/year per person = 100,000. Productivity loss of 5% on 60k average salary = 150,000 in lost output. Net saving: 500k - 100k - 150k = 250,000/year going remote, or 5,000 per employee.

Productivity research is mixed and depends heavily on role type. Software engineers and writers often show productivity gains remote; sales and collaborative roles often show losses. Factor in your actual mix rather than using a blanket assumption. Also include hidden costs: lower office utilisation means landlord negotiating power is weaker, and you may still owe lease through contract term.

Quick example

With number of employees of 50 and office cost per employee of 10,000 (plus remote stipend per year of 2,000 and productivity impact of 5%), the result is 250,000.00. Change any figure and watch the output shift — it's often more useful to see the pattern than to memorise the formula.

Which inputs matter most

You enter Number of Employees, Office Cost per Employee, Remote Stipend per Year, Productivity Impact %, and Avg Employee Salary. Two inputs usually tip the answer one way or the other. Identify which ones matter most by flipping each value past a round threshold and watching whether the winning option changes.

What's happening under the hood

Office savings = employees × (office cost - remote stipend). Productivity cost = employees × salary × loss %. Net = savings - productivity cost. The formula is listed in full below. If the number looks off, you can retrace the calculation by hand — that's the point of showing the working.

What to do with a low result

A disappointing result is information, not a judgement. Pick the single input that dragged the figure down most and focus the next quarter on that one factor. Breadth-first improvement rarely works; depth-first on the worst input usually does.

What this doesn't capture

The score is a composite of the inputs you provide. Life context — job security, family obligations, health, housing — doesn't appear in the math but shapes the real picture. Use the number as a prompt, not a verdict.