Depreciation Methods for Property, Plant, and Equipment (PPE)

Straight-Line Method

The straight-line method associates the long-lived asset’s usefulness with its age.

Straight-Line Expense = (Cost – Salvage Value)/n

where n = number of years in asset’s useful life

Accelerated Methods of Depreciation

Accelerated methods of depreciation include:

  • Sum-of-the-Years Digits (SYD) expensing.
  • Double Declining Balance (DDB) expensing.

SYD Method

SYD method treats an asset as more useful in its early life by raising the depreciation expense for the early years.

SYD Example: If a company’s factory has a new conveyor belt with a useful life of 5 years, then SYD = 1+2+3+4+5 = 15.  This conveyor belt cost $100,000 and has a salvage value = $0.  The year two depreciation expense under the SYD method for the company will be calculated as follows:

($100,000 - $0) * (5 – 2 +1)/15 = $100,000*(4/15) = $26,667

SYD Depreciation Expense for Year “i” = (Cost – Salvage Value) * ((n – “# of the ith year” +1))/SYD

DDB Methods

DDB method accelerates the depreciation rate of the straight line method.

DDB Expense = (Cost – Accumulated Depreciation) * (2/n)

Unlike the time based methods of straight line and accelerated depreciation, the Units-of-Production (U-O-P) depreciation method is activity based.  A year’s depreciation expense on an annual income statement will include that year’s production as a fraction of total estimated lifetime production from the asset.

U-O-P Expense = ((Cost – Salvage Value)/# of Total Lifetime Units Estimated)* # of Units Produced in the Accounting Period.

Once a company has invested in a long-lived asset, it must:

  • Choose a depreciation method;
  • Estimate the useful life of the asset over which the depreciation will take place; and
  • Determine if the asset will have a salvage value at the end of its depreciable life.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.