Home        About       News       Library       Contact
1

Age-Reinvestment Matrix
Also referred to as the "funding-life cycle matrix".

A matrix that plots the relationship (correlation) between the age of a facility and its capital reinvestment requirements, presented on a scatter plot.




Purpose
The primary purpose of the age-reinvestment correlation is as follows:


Variables
The age-reinvestment matrix is comprised of two dimensions, as follows:
  • The horizontal axis - is represented by the age of a facility
  • The vertical axis - is represented by the reinvestment requirements for long-range stewardship.


Horizontal Axis
The horizontal (x axis) is comprised of the following age classes derived from the facility lifecycle model:


Vertical Axis
Two alternative measures can be used to quantify the funding requirements on the vertical axis:
Various reinvestment formulas have been applied to establish the quantum and reinvestment rates and funding trajectories for the vertical (y-axis):
The relative merits and limitations of each of these formulas are discussed on their respective pages of this interactive glossary.


Methodology
Listed below are the key steps in the methodology to derive and analyze the condition-age matrix:

  • Establish the age of each facility and organize this into the five lifecycle stages.
  • Normalize the data based on factors such as building class, climate zone, etc.
  • Establish the funding requirements through a reserve study (capital needs assessment) or other appropriate study.
  • Plot the correlations of age to reinvestment on the matrix
  • Establish the date of the matrix
  • Add a trend line to the scatter plot.
  • Determine which facilities are located above the trendline (poor performers) and below the trend line (high performers).
  • Review against the data in other matrices, such as condition-priority matrix.


Analysis
The scatter plot resulting from an age-reinvestment matrix can be analyzed in a variety of ways, including

  • Point(s) of Inflection
  • Delta (max.-min)
  • "High" performers, "average" performers and "poor" performers
  • R2 analysis
  • Trend line fitting and analysis
  • Copula pattern analysis
  • Outliers
  • Before and after analysis
Listed below are the five patterns that can be discerned in the scatter plot data:

Performance and Variances


Variances in the age-reinvestment matrix, could arise from:

Above the trend line:
  • The appraiser underestimated the building reproduction cost - Like us, they are also fallible.
  • Big rehab projects on existing buildings are tremendously cost-inefficient compared to new construction. The overhead of working on habited buildings is horrendous.
  • If we are simultaneously high on the GFA measure then perhaps we have overestimated the capital load. Unless, perhaps, our GFA calculation is too low.
Below the trend line:
  • There are perhaps huge sitework costs (roadways and in-ground infrastructure), which may not necessarily be factored into the building reproduction cost. We should look into how extensive the site improvement considerations are in the CRN.
  • Some buildings require more preservation reinvestment than others (think wood siding vs. vinyl siding). In these cases, the repetitive cycles of painting, urethane top coats and other such protective measures may have a compounding effect on the capital load. 


Evaluation
Listed below are some of the merits and advantages of the condition-age matrix:
  • An intuitive and helpful representation of data.
Listed below are some of the limitation of the condition-priority matrix:
  • The matrix does not reveal the proximity of the individual capital projects to the base year.
  • Statistical outliers.


Management Principles
  • How do we confirm that a building legitimately occupies an outlier position or do we have an error in our data?
  • How do we qualitatively and quantitatively establish different levels/degrees of building performance/distress?
  • When we identify a ‘poor’ performer what is the appropriate handover process?
  • What lessons can be learned from the ‘high’ performing building and articulated into best practices?

    Examples of some types of projects that are considered normal during each of the lifecycle stages.
    Fig. A five stage lifecycle model of facilities, which establishes the funding/reinvestment requirements as the facility ages.



    An example of an age-reinvestment matrix for a class of buildings.
    Fig. An example of an age-reinvestment matrix for a single class of buildings over a 60-year period (horizontal axis).


    The "high" performers identified on a scatter plotPoor performing buildings identified on a scatter plot
    Fig. The "high" performers identified on a scatter plot (left). and the "Poor" performers identified on a scatter plot (right). 


    The population of data on a scatter plot
    Fig. The population of data identified on a scatter plot.


    Trend line for a data set
    Fig. Trend line for a data set.


    Outliers relative to the trend line of a scatter plot
    Fig. Outliers relative to the trend line of a scatter plot.


    The "high" performers identified on a scatter plot
    Fig.  The "high" performers identified on a scatter plot.



    Some explanations where certain facilities occupy outlier positions and are classed as "poor" performers on the age-reinvestment matri
    Fig. Some explanations where certain facilities occupy outlier positions and are classed as "poor" performers on the age-reinvestment matrix (above the average trend line).


    Some explanations where certain facilities occupy outlier positions and are classed as "high" performers
    Fig.  Some explanations where certain facilities occupy outlier positions and are classed as "high" performers on the age-reinvestment matrix (below the average trend line).






    Fig. Comparison of the use of CRN and GFA as the vertical (y) axis on the matrix.




    Fig.  Comparison of the pre- and post-rehabilitation phases of a building on the matrix.



    Fig. Comparison of a building class Age-Reinvestment matrix compared to a copula family.



    See also:
    See other matrices:
    Compare with:


    (c) Copyright Asset Insights, 2000-2013, All Rights Reserved. "Insight, foresight and oversight of assets". Google