Kefiw

Archived noindex page. Kefiw's public focus is Property decision help.

Archived page

This older Kefiw page is kept for reference, marked noindex, and removed from the primary sitemap. The current Kefiw experience is focused on property decisions: cost, quotes, damage, buying, selling, owning, and packets.

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Five Asset Liquidator Mistakes

Errors that keep gig drivers from seeing the real number.

These five mistakes all make gig work look more profitable than it is. Fix them before deciding to continue.

The tool is simple — gross minus miles times rate. The mistakes are all in the miles and the adjacent costs. These five make the real hourly look fatter than it is, and every one pushes drivers to keep driving longer than the math supports.

Quick answer

These five mistakes all make gig work look more profitable than it is. Fix them before deciding to continue.

What you are trying to do
Errors that keep gig drivers from seeing the real number.
Best next step
Asset Liquidator
Limit to remember
Treat this as a practical aid for the task, not a replacement for professional judgment.

Key points

  • Under-logging miles. Only the in-app "delivery miles" are shown on the driver dashboard. Your actual miles include pickup approach, deadhead returns, and mis-routing. Real miles are often 15–30% above app-reported.
  • Skipping deadhead. Deadhead miles = driving without a passenger/order. They still wear the car. If you drove 30 minutes to a gig zone and back, those miles count at $0.67 too.
  • Ignoring wear on non-gig driving. Gig work pushes your car past the maintenance thresholds faster, which increases costs on your personal driving too. The IRS rate captures this in aggregate but not for specific adjacent costs like more frequent detailing, higher insurance premiums after gig disclosure.
  • Forgetting quarterly tax. 1099 income triggers self-employment tax (15.3%) plus income tax. Real post-tax profit is ~70–80% of the pre-tax real profit the calculator shows.
  • Underrating the health tail. Long gig hours correlate with worse sleep, worse diet (drive-through while driving), more stress. The real hourly ignores these — but they become medical costs over time.

Examples

  • Deadhead invisible
    App shows 180 miles. Actual odometer: 240 miles. $0.67 × 60 additional miles = $40.20 unaccounted cost. Real profit drops by $40.
  • Post-tax reality
    Real profit $100/shift. Self-employment + federal + state at 25% effective → $75 actually pocketed. Real hourly drops 25% across the board.
  • Adjacent wear
    Insurance premium up $40/mo after gig endorsement. Tire replacement at 25k instead of 40k — $200/year earlier. Neither shows in the IRS rate as used; both are real.

When to use which tool

Related

Frequently asked questions

Is there a simple rule of thumb? Trust & accuracy

Cut the app-reported hourly in half — that's the real-hourly floor for most setups. If you're still above your target wage, fine. If not, the gig isn't working.

What about electric vehicles?

EVs lower fuel cost but depreciation is uncertain and tires wear faster due to torque and weight. Early data suggests the $0.67 rate is roughly right for EVs too — the mix shifts but the total is similar.

How should I use a decision framework in real life? How-to

Use a decision framework to expose the tradeoff, not to outsource the decision. Write down the inputs, compare the output with your constraints, then ask what would change the answer. The strongest use is scenario testing: base case, conservative case, and failure case.

Is this financial, legal, or tax advice? Trust & accuracy

No, this is not legal, financial, tax, medical, or professional advice unless the page explicitly says that use case is supported. It organizes assumptions so you can inspect them. Verify high-stakes choices with qualified people who can review facts, contracts, regulations, and downside risk.

What assumption matters most in a decision model? Edge case

The most important assumption is usually the one you are least certain about and most emotionally attached to. Change that input first. If the recommendation flips after a small change, the decision is fragile and needs more evidence before you treat the model as useful.