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Impact of Inaccurate Engineer’s Estimated Quantities on Unit Price Contracts

Authors:
  • Gransberg & Associates Inc.

Abstract and Figures

This paper discusses the issue of unbalanced bid prices in unit price highway contracts. It analyzes the reasons contractors unbalance their bids and looks for ways that allow public transportation agencies to discourage this practice. It reviews the results of a study of quantity estimating accuracy of 462 transportation projects in Oklahoma, and finds that one method to reduce unbalanced bid prices is for the agency to ensure that the bid quantities used in the engineer's estimate are as accurate as possible. Doing so reduces the need for contractors to unbalance to protect fixed costs and target profit on bid items that will underrun the quantity used in the engineer's estimate.
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Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
Impact of Inaccurate Engineer’s Estimated Quantities on Unit Price Contracts
By Douglas D. Gransberg, PE, MASCE,
1
and Caleb Riemer, EI
2
Abstract: This paper discusses the issue of unbalanced bid prices in unit price highway
contracts. It analyzes the reasons contractors unbalance their bids and looks for ways that allow
public transportation agencies to discourage this practice. It reviews the results of a study of
quantity estimating accuracy of 462 transportation projects in Oklahoma, and finds that one
method to reduce unbalanced bid prices is for the agency to ensure that the bid quantities used in
the engineer’s estimate are as accurate as possible. Doing so reduces the need for contractors to
unbalance to protect fixed costs and target profit on bid items that will underrun the quantity
used in the engineer’s estimate.
Key Words: Estimating; unit price contract; highway construction
Background
The development of accurate pre-bid cost estimates is founded on the development of an
accurate quantity estimate (UDOT 2007). This is particularly true on unit price contracts where
the competing construction contractors must bid the engineer’s estimated quantities even if they
are incorrect (Shexnayder and Mayo 2004). There are two administrative factors that impact the
accuracy of public engineer’s estimates. First, most public agencies have constraints upon their
1
Sam K. Viersen, Jr. Professor of Construction Science, University of Oklahoma, Norman, Oklahoma 73019-6141.
E-mail: dgransberg@ou.edu
2
Assistant Division Maintenance Engineer, Oklahoma Department of Transportation, Ada, Oklahoma,
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
ability to award construction projects based on the difference between the engineer’s estimate
and the apparent low bid. For instance, the Utah Department of Transportation (DOT) requires
the engineer’s estimate to be within 10% of the low bid (UDOT 2007), the Oklahoma DOT is
historically refrains from awarding to bids that are more than 7% over the engineer’s estimate
(ODOT 2008), and the US Army Corps of Engineers has more latitude but is nevertheless
constrained by the low bid being no more than 15% and 25% over the independent government
estimate for military and civil works projects respectively (USACE 1997). Secondly, the issue is
exacerbated by mandated maximum project contingency percentages, which limit the maximum
amount of contingency that can be included in the agency’s engineer estimates. For instance,
USACE is limited to 5% contingency on new construction projects (USACE 1997) and
Riverside County California cannot exceed 10% (Riverside 1999). The Utah DOT has more
flexibility in setting its contingency on a project-by-project basis but must justify contingencies
greater than 10% on projects with completed designs. The Washington DOT reported that the
cost of highway construction was up 12.2% in April 2008 (WSDOT 2008a). So projects awarded
in Riverside County with its 10% mandated contingency cap that month would be
underestimated by roughly 2.2%. These two factors combine to create a tendency to inflate
engineer’s estimates to ensure that needed infrastructure projects are awardable in periods of
construction price volatility, and when the maximum contingency is reached, to inflate the
estimated quantities as the only available avenue to increase the engineer’s estimate. This fact
was confirmed in informal surveys conducted by the primary author during ASCE continuing
education classes at 10 different DOTs. When asked if quantities were ever inflated to increase
project contingencies, at least one engineer in each class indicated that was a common practice.
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
This practice is especially problematic in periods of extreme volatility such seen for asphalt
prices in the first two quarters of 2008 (WSDOT 2008a).
The very structure of the unit price contract’s pricing mechanism demands engineer’s estimated
quantities be as accurate as possible to ensure the profitability of a balanced bid. When actual
quantities are less than the bid quantities, the contractor does not recover the fixed costs,
overhead and profit that were allocated to the quantities of work that were not installed and
hence for which it cannot be paid. Therefore, as most contractors have limited ability to pick and
choose which projects they bid and remain in business, inaccurate bid quantities lead to
unbalancing unit prices to recover all the costs associated with the project and to protect the
contractor’s target profit on the bid. The other side of that coin is the use of unbalancing to
increase the contractor’s profit margin. The subject of unbalanced bidding is certainly
controversial, and the purpose of this paper is not to advocate or apologize for unbalanced
bidding. It is to advocate that the owner’s engineer resist the temptation to arbitrarily increase a
project’s bid quantities to create a hidden project contingency and demonstrate the potential
impact of doing that in one typical public agency by an analysis of the accuracy of its estimating
practices.
Unit Price Contracting
“Unit price contracts are used for work where it is not possible to calculate the exact quantity of
materials that will be required. Unit price contracts are commonly used for heavy/highway work
(Shexnayder and Mayo 2004). When an owner selects unit price contracting, it is doing so to
share the risk of the final quantities of work with the contractor to reduce the price. This happens
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
because the contractor does not have to bid the worst possible case if the quantities of work are
not finite as it would be driven to do in a lump sum contract where it bore the entire quantity
risk. Unfortunately, many owners do not understand the dynamics of unit price bidding and
erroneously believe that by paying the contractor for every installed unit of work that they have
covered the contractor’s total costs. That only happens in a cost-plus contact where the owner
bears the entire quantity risk. Figure 1 shows the concept of when different types of contracting
methods should be used based on the owner’s ability to accurately estimate the quantities of
work. It shows that unit price contracts fall in the middle where neither the level of certainty nor
the cost of an erroneous estimate is high. Thus, the risk is shared, and owners must recognize that
the contractor’s major unit pricing contract risk is not getting to install the total amount of work
upon which they must bid.
[Insert Fig. 1]
Before getting into the mechanics of the unbalanced bid, one must understand the fundamentals
of developing a unit price for a given work item. Essentially, a unit price is the sum of all direct
costs, allocated indirect costs and the contractor’s profit for a given item of work divided by the
total number of units of work. This can be expressed as shown in equation 1:
UP = (DC + IC + P)/N (1)
Where: UP = unit price; DC = direct cost; IC = allocated indirect cost; P= allocated profit; and
N= number of units. This equation represents the method that is required by many public agency
estimating manuals (UDOT 2007; Riverside 1999; USACE 1997) and can be taken as the
owner’s perspective on construction cost estimating. Carr (1989) defines direct and indirect costs
as follows:
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
“A direct cost of an activity is physically traceable to the activity in an economic manner.
A direct cost is one not counted [accrued] if the activity is not performed. Indirect costs
are business costs other than direct costs of construction activities; they are not physically
traceable and are counted [accrued] even if the activity is not performed. Indirect costs
are also known as overhead.”
However, construction contractors tend to use a less theoretical approach, pragmatically
separating costs for unit price contracts into fixed and variable costs rather than direct and
indirect costs (Carr 1989). This results in a different way to calculate the same number as shown
in equation 2:
UP = (FC + VC + P)/N (2)
Where FC = allocated fixed cost, and VC = variable cost. Once again Carr furnishes a simple set
of definitions that are applicable to this approach:
“If a cost changes in proportion to a change in volume or quantity, it is variable. If a cost
remains unchanged in total, despite wide fluctuations in volume or quantity, it is
fixed.”(Carr 1989)
The reason for the changed perspective has to do with the dynamics of the unit price contract
itself. As the total variable cost changes proportional to the number of units installed, the
contractor will always recover its variable costs and therefore it is not at risk for the variable
costs. However, the contractor is at risk of not recovering the amount of fixed cost and target
profit that it has allocated for a given work item if it does not install the engineer’s estimated
quantity of units upon which it was required to bid. Conversely, if the contractor installs more
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
than the bid quantity for this item, it will recover the complete fixed cost for that item once it has
installed the bid quantity and the allocated fixed cost will become a windfall profit for the
number units installed above the bid quantity. Therefore, it can be seen that it is logical to
account for costs in a manner that reflects the risk inherent to contract’s payment mechanism. It
also allows for a more precise accounting for costs by directly associating different types of fixed
costs with specific work items rather than accumulating all indirect costs and then arbitrarily
allocating them to work items on a percentage basis.
Many public owners recognize the contractor’s risk of quantity underrun as well as their own
risk of quantity overrun and provide for renegotiation of unit prices for work items whose actual
quantities vary more than a given percentage of the bid quantity (Shexnayder and Mayo 2004).
Below is a list of typical quantity variation ranges above and below which the agency will allow
the bid unit price to be renegotiated:
New Mexico DOT: + 20% (NMDOT 2000)
Oklahoma DOT: + 25% (ODOT 1999)
Texas DOT: + 25% (TxDOT 2004)
Utah DOT: + 25% (UDOT 2007)
West Virginia DOT: + 25% (WVDOT 2000)
US Army Corps of Engineers: + 15% (USACE 1997)
Other agencies, such as the Alaska DOT, (2004) do not contain such clauses in their unit price
contracts. This creates a situation where the risk of underrun quantities is unbounded and
therefore increased. By including a unit price adjustment clause, the owner is limiting both its
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
own and the contractor’s quantity variation risk. This issue is best illustrated by example. Table 1
shows the contractor’s estimate for a pavement work item on a state project where the unit price
adjustment clause allows a variation + 20% before the unit price can be renegotiated. One can
see that the contractor chose to associate the cost of mobilizing and demobilizing the asphalt
batch plant and associated equipment with this particular work item. Additionally, a proportional
amount of the overhead and other general fixed costs and target profit was also allocated to this
item. Thus, the unit price of $90.00 per ton is a balanced unit price.
[Insert Table 1]
From Table 1, one can develop an algebraic formula to calculate the contractor’s cost for the
actual number of units installed:
Actual cost = Total FC + VC(N) (3)
Actual cost = $550K + ($77/ton x (actual tons HMAC))
Table 2 shows the results if the contractor in this example bids its balanced unit price and
experiences the allowable variation for which neither it nor the DOT can request renegotiation of
the bid unit price. It shows that if the actual quantity of work underruns by 20% that the
contractor will lose $30,000 because it is unable to recover all of its fixed costs for this item.
However, if the variation runs to a 20% overrun, the contractor more than doubles the profit that
was targeted for this item. In this example the item’s target profit was 2.3% and the actual profit
in the event of an overrun is now 5.2%. Thus, from this analysis, the risk for the contractor is
quantified at a $30,000 loss if the engineer’s estimated quantity is 20% too high, and the risk for
the owner is pegged at having to pay an additional $130,000 if the engineer’s estimated quantity
is 20% too low.
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
[Insert Table 2]
“The goal of unit price contracts is fairness to both parties” (Shexnayder and Mayo 2004), and
the spirit of unit price contracting is to share the risk of actual quantities of work. Therefore, the
contractor should not be required to complete work at a loss, and the owner should not have to
pay more than a reasonable amount of profit on any given item. The contractor has a mechanism
to manage its risk, and that is to submit unbalanced unit prices for work items where the
engineer’s estimated quantities are found to be high by the contractor’s own quantity estimate. In
the Table 2 case, the contractor will not recover $110,000 in fixed costs ($11/ton x 10,000 tons
underrun) and will lose $20,000 of if its target profit ($2/ton x 10,000 tons underrun). Therefore,
it will move $130,000 of fixed cost and profit from this item to another item whose bid quantity
is correctly estimated, increasing the unit price of that item. The owner’s only mechanism to
manage this risk is to ensure that the project’s bid quantities are accurate.
Unbalanced Bidding
Separating construction costs into fixed and variable categories also facilitates unbalancing unit
prices to protect against failing to recover the entire amount of fixed costs plus the target profit
due to errors in the engineer’s estimated quantities. There are two types of unbalanced bids
commonly recognized in the highway industry. They are as follows:
Mathematically unbalanced: “each bid item…fails to carry its proportionate share of the
overhead and profit in addition to the necessary costs for the item. The results are
understated prices for some items and enhanced or overstated prices for others.” (Manzo
1997)
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
Materially unbalanced: “… not only a disproportionate amount of overhead and profit
[has been shifted from a bid item], but also some portion of the actual cost of elements of
work. In this situation, the price… for some work can be understated and significantly
less than the actual cost of that work, with an overstatement of prices for other aspects of
the work.” (Manzo 1997)
The Federal Highway Administration (FHWA 1988) citing decisions by the US Comptroller
General uses the following definitions, which mirror the ones by Manzo:
"A bid is mathematically unbalanced if the bid is structured on the basis of nominal
prices for some work and inflated prices for other work; that is, each element of the bid
must carry its proportionate share of the total cost of the work plus profits" (FHWA 1988
emphasis added).
"A bid is materially unbalanced if there is a reasonable doubt that award to the bidder
submitting the mathematically unbalanced bid will result in the lowest ultimate cost to
the Government. Consequently, a materially unbalanced bid may not be accepted"
(FHWA 1988).
FHWA goes to state that “there is no prohibition per se against a contractor submitting a
mathematically unbalanced bid unless an SHA [state highway agency] has adopted a specific
contract requirement precluding such submittal (FHWA 1988).”
So from the above definitions, the significant difference between mathematically and materially
unbalancing is whether the bid price is less than the actual cost of the work. The bid unit price
for an item would be found to be mathematically unbalanced if only the fixed cost (called
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
overhead by Manzo) and the profit had been shifted to another item and the item was bid at its
variable cost. This interpretation is confirmed by the Oklahoma DOT (1999) who state: “The
State may reject a proposal as nonresponsive if the prices proposed are materially unbalanced
between line items or sub line items. A proposal is materially unbalanced when it is based on
prices significantly less than the cost for some work and prices which are significantly overstated
in relation to cost for other work.” The Ohio DOT (2005) takes this a step farther by quantifying
a mathematically unbalanced bid by requiring: “Bidders must bid at least the cost of the
materials for every item bid.” The Texas DOT regulations (TxDOT 2004) agree with both
Oklahoma and Ohio’s interpretations. TxDOT’s specifications in concert with federal regulation
23CFR 635.114 (2008) go on to state that a contract may be awarded to a mathematically
unbalanced bid but must reject a materially unbalanced bid. TxDOT points out two situations in
which material unbalancing are found:
There is an error in [the engineer’s estimated bid] quantities (too low) and the contractor
bids a high price on these items. In this case, the apparent low bidder might not be the
actual low bidder once the quantity error is corrected.
The contractor's bid prices are high on items of work occurring early in the project. In
this case, the apparent low bidder might not be the actual low bidder when the State's
financial loss of potential interest income is calculated” (TxDOT 2004).
The second TxDOT definition refers to a situation that is commonly called “front-loading”
(Shexnayder and Mayo 2004). Front-loading can become a form of mathematical or material
unbalancing. Front-loading is done to generate additional cash flow in the early phases of the
project (Mayer and Diekmann 1982). As this issue is not created by the engineer who develops
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
the bid quantities on a unit price contract, it is beyond the scope of this study. Therefore, the
paper will restrict itself to evaluating unbalancing only and make no effort to identify potential
front-loading in the sample population.
All three state DOTs cited above are allowed to award contracts to mathematically unbalanced
bids and must reject materially unbalanced bids. Thus, it can be concluded that mathematical
unbalancing is an expected practice due to the possibilities of errors in the engineer’s estimated
bid quantities, and that at least these three agencies see mathematical unbalancing as a necessary
part of unit price contracting. However, materially unbalancing a bid is deemed detrimental
when it appears that it will ultimately result in final cost that causes the agency to pay more that
it would have paid if the bid had been balanced. The issue of unbalancing is often associated
with unethical business practices (Doran 2006). This is a serious issue. The above discussion
leads to the inference that the ethical line has been crossed when a unit price is materially
unbalanced, i.e. bid at a price that is lower than its variable cost. Therefore, it is important that
engineers and construction contractors both understand and differentiate between mathematical
and material unbalancing.
To summarize this section and move on to the research about which this paper is focused, an
owner that opts to use unit price contracting is laying the foundation for mathematically
unbalanced bids. “Unit price contracts tend to draw unbalanced bidding” (Zack 2002). To
minimize the owner’s risk, it must strive to furnish an accurate set of bid quantities. Roberts
(1994) put it this way: “If the owner desires a reasonably balanced bid, then all effort must be
made to provide the following: an equitable mobilization pay item in the bid, realistic bid
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
quantities; and a changed quantity clause in the specifications.” The “equitable mobilization pay
item” speaks to the need for early cash flow to minimize carrying costs. “Realistic bid quantities
means diligently completing an accurate quantity estimate upon which to base the bid quantities
of work, and the “changed quantity clause is a mechanism to bound the risk of quantity
variations by permitting a renegotiation of unit prices as actual quantities of work significantly
vary with the bid quantities. Most public agency unit price contracts contain these three items.
So the point where the system can break down is when an engineer decides to alter the bid
quantities from those determined in the accurate quantity estimate to build an additional
contingency in the project to guard against construction price volatility. When this occurs, the
engineer is deliberately creating a quantity underrun, which in turn forces the construction
community to unbalance their unit prices to protect their fixed costs and target profit.
Objective and Methodology
The objective of the research was to evaluate the bid quantities generated for Oklahoma DOT
(ODOT) engineers’ estimates, determining if the agency is unintentionally triggering unbalanced
bidding due to inaccurate bid quantities. The research methodology was simple. First, a large
sample of 462 construction projects completed over a 4 year period from all eight of ODOT’s
divisions was collected from the agency’s AASHTO SiteManager database. (AASHTO 2005)
ODOT has over 5,500 separate pay items. The estimating errors of interest to this research are
the ones that occur in pay items where it is possible to induce inflated final quantities through
rounding, estimating assumptions, and double-counting. Other issues, such as items that are
schedule dependent, were dropped from the analysis. Thus, the pay items that had units of “lump
sum,” “each,” “day,” “sign day,” etc. were removed. Next, pay items that were administrative in
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
nature like “railroad flagging,” “asphalt binder adjustment,” and “mowing” were also removed.
Finally, ancillary pay items like “watering” and “temporary silt dike” were also dropped. The
objective was to filter the possible pay items down to those that are directly related to the
project’s technical design and drive the engineer’s estimated quantities of work.
The percentage of error for each pay item was computed using the following formula:
E
i
= [FQ
i
- BQ
i
)/BQ
i
] x 100 (4)
Where E
i
= percent error in pay item “i”; FQ
i
= final quantity pay item “i”; BQ
i
= bid quantity
pay item “i”. The output from this effort was then analyzed to eliminate projects with
characteristics that made them inappropriate for this study. Three types of projects were dropped
from the study sample. First, if project contained a pay item with a bid quantity whose value was
>0 and the final quantity = 0, it was removed because this indicated that either ODOT had
deleted this pay item or that it was an alternate material that had not been selected by the
contractor for installation. Second, if a pay item’s bid quantity = 0 and its final quantity > 0, it
was discarded because it was obviously the result of a change order pay item. Finally, if a pay
item’s error exceeded 100%, it was removed because this indicated that there was either a
significant error in the estimation process or a change in project scope, and there was no way of
differentiating between the two possible situations from the available data. This left the
population at 462 projects whose quantity estimate errors could be analyzed.
Next, pay items were grouped into six commodity-based categories to identify systemic errors in
estimating the quantities of specific types of work. For example, Portland cement concrete
measured by the square yard has 12 different pay items varying by thickness. The estimating task
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
here is to determine the total number of square yards of this material. Therefore, a possible
systemic error would relate to assumptions made by the engineer to calculate the area of irregular
shapes and a project with a large number of irregular shapes (i.e. driveways, intersection radii,
gores, etc.) would have a greater potential for erroneous quantities than one that was primarily
main lane paving. The average error for each commodity group was calculated, and then the
output was ranked from greatest to least error. Finally, the focus the analysis to a manageable
level, the six pay items shown in Table 3 that had the greatest error in its category were selected
for in-depth analysis by ODOT Division. Note that an ODOT division is the organizational level
at which plans are prepared and construction contracts are awarded and administered.
[Insert Table 3]
The in-depth analysis of quantity estimating error was performed at two levels. First, the error in
the six pay items of interest was assessed by ODOT Division. The purpose of this analysis was to
determine if individual division design groups had issues estimating the quantities of a given
item. The division-by-division analysis also reflects design and construction preferences that
may not be a reflection of the state-wide approach. The second level was to roll-up the division
errors and calculate a state-level error for each of the six pay items. This was done to assess
quantity estimating issues across the state and reflect ODOT design and construction policy.
Finally, the pay item errors were evaluated on an average error and absolute error basis. The
average error is merely the mathematical average of all the errors in the sample. The absolute
error accumulated the sample error without regard to sign. For example, the average error for an
item that had one project with a 50% overrun and another project with a 50% underrun would be
zero, indicating that the engineer’s estimated quantities were perfect when in fact there is a
serious problem. The absolute error for the same two-project sample is 50% and accurately
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
reflects the actual estimating issue. The absolute error also more closely reflects the unbalancing
issue because contractors will unbalance pay item unit prices to account for both overruns and
underruns. Therefore, the inference can be made that a high absolute error in a pay item
correlates to a high probability of triggering unbalanced unit pricing. So, an agency like ODOT
could have a low level of cost growth due to quantity overruns and still have an estimating
problem because the low cost growth is arrived at via compensating errors.
Results of the Research
Table 3 shows that quantity estimating errors are greatest in the tack coat pay item with 3 of 8
divisions experiencing an average error of >25% which is the point where ODOT specifications
permit a renegotiation of pay item unit price. This infers that these three divisions will need to
renegotiate the unit price of tack coat more often than the rest of the division in the state.
Additionally, in all divisions where the average error is negative indicating an underrun (i.e. the
engineer over-estimated the quantity so the contractor install fewer units than the number the bid
quantity). From a standpoint of triggering contractor unbalancing, underrunning the bid quantity
is the more dangerous of the two errors. While the study did not have access to contractor bid
data, the fact that all ODOT divisions are over estimating the actual quantity of this pay item
would logically lead to mathematically unbalancing away from this pay item to protect fixed
costs and profit. Tack coat also has the highest average and absolute error on a state-wide basis,
which makes it the pay item most often misestimated.
As previously stated, this study did not have access to actual contractor bid data to identify if
unbalancing had occurred as a result of bid quantity estimating errors. However, the FHWA
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
requires that bid be analyzed for unbalancing using a percentage system that is established by
each state (FHWA 2004). Both the Washington State and Wisconsin DOTs use the same
percentage range to determine whether a pay item has been “significantly” unbalanced and hence
may choose to reject the bid using the following definition:
An individual bid item will be significantly unbalanced if the difference between the
low bidder’s unit price and the estimate, expressed as a percent of the [engineer’s]
estimate, is greater than +50% or -75%” (WSDOT 2008b).
Therefore possible unbalancing in ODOT bid items can be analyzed using this accepted
definition.
[Insert Table 4]
Table 4 shows the results of the analysis of the tack coat pay item for all 16 projects in ODOT’s
April and June lettings. Before getting into the details, it is important to cite that this particular
period recorded severe asphalt price volatility (WSDOT 2008a). Thus, the impact of inaccurate
quantities would be proportionally higher than it had previously been. Additionally the
temptation to artificially increase bid quantities as a second contingency against unpredictable
pricing would also be high. Table 4 details the unit price used in the engineer’s estimate, the low
bidder’s tack cost price and the highest tack coat price bid by the other bidders. The numbers
shown in bold italic font indicate those unit prices that would have been found to be unbalanced
using the WSDOT definition. In the table, 6 of 16 winning bidders would have been found to
have unbalanced their tack coat prices and 9 of 16 of the high prices would have been found to
be unbalanced. One must put this into perspective. Tack coat pricing is not something that is
going to skew the costs of a paving project to a great degree. It was selected for this analysis
because it was found to be the item that had the greatest amount of observed error in ODOT
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
estimates. Nevertheless, the analysis does show that unbalancing is occurring in that pay item.
The issue becomes critical when a large error is made in a pay item that makes up a significant
percentage of the contract total bid price.
[Insert Table 5]
WSDOT (2008b) defines “significant” unit price difference when “a bidder has an item included
in the bid proposal where the difference between the total cost of the bid item and the estimate,
expressed as a percent of the estimated contract total, is greater than 0.50% for contracts less
than $2,000,000 and greater than 0.25% for contracts $2,000,001 and larger.” This is the case for
the items listed in Table 2 for “Asphalts” and in some cases “Portland Cement Concrete Paving.”
Table 5 shows the results for all asphalt pay items in the ODOT June 2008 letting. These are 33
different pay items on 14 projects and range from asphalt patching to various grades of asphalt
concrete pavement. Eight of the low bids would have been found to be unbalanced as would 18
of the high bid prices submitted. Once again unbalancing can be found in a pay item that has
been identified as one where a higher than normal quantity estimating error is found.
The relatively large percentage (44%) of potentially unbalanced bids on the asphalt pay items
that generally make up a significant percentage of total project shown in Table 5 should serve as
a warning to the engineers-of-record that estimating errors are present and lead them to cross-
check the bid quantities of the big ticket pay items to insure that they are accurate. It also
demonstrates the value of this type of post-award cost analysis. Utilizing the unbalancing
standards used in this paper as a benchmark can potentially enhance cost estimate accuracy as
well as discourage the deliberate overstatement of bid quantities that triggers unbalancing in the
first place.
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
Conclusions
The above analysis is not a criticism of ODOT project performance. In fact, ODOT has enjoyed
an average construction cost growth rate of just 1.34% over the past 19 years (Raymond 2008),
which is considerably better than the national rate of 4.8% (Gransberg and Villarreal 2002).
What it shows is that even in a well-managed program there will be errors and those errors can
result in unintended reactions by the contracting community. The paper’s primary object is to
argue for accurate establishment of bid quantities in unit price highway contracts, and it has
shown how quantity estimating errors can lead to unwanted unbalancing. Virtually every DOT in
the nation depends upon bid tabulations to form the foundation of its own internal cost
estimating system. Therefore, it is extremely important that the bid prices recorded in that
database reflect real costs and reasonable profits in order for DOT engineers to accurately
estimate the costs of needed transportation projects.
One conclusion that was developed in this analysis is that engineers and contractors must clearly
understand the difference between mathematical and material unbalancing. The document
reviewed in this paper furnish a clear definition for when a bid crosses the line from being
mathematically unbalanced to becoming materially unbalanced: if the pay item is bid at a rate
that is less than its variable cost, then it has been materially unbalanced. In most cases, this
demands that the bid be rejected; whereas, bids mathematically unbalanced pay items can usually
be awarded. Material unbalancing of unit prices is clearly unethical and is a practice that cannot
be condoned. On the other hand, mathematical unbalancing is a reaction to an error made in the
engineer’s estimated quantities and is permissible in most public projects.
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
Next, the issue of engineers artificially inflating the bid quantities as a means of providing a
contingency that would exceed the statutory cap on contingency percentages leads to the
conclusion that those rules are not beneficial to an agency’s capital improvement cost estimating
and management system. Imposing an arbitrary number on all projects large and small is short-
sighted and fails to recognize the fact that contingencies are used to manage risk. WSDOT
utilizes a system (called the Cost Estimate Validation Program) to set a unique contingency for
each project based on identified risks, and it has performed quite well for over 5 years (Molenaar
2005). Thus, the analysis leads to the conclusion that agencies with statutory constraints on
contingency percentages should change to project specific contingencies to eliminate the practice
of inflating bid quantities when the engineer believes the statutory percentage is inadequate to
reflect a given project’s cost risk.
Two more conclusions are reached from the analysis. First, when evaluating the performance of
a unit price estimating system, the analyst should use absolute rather than average error rates.
The average error rate includes compensating errors and as a result does not give an accurate
picture of the quality of quantity estimating practices. Secondly, the best means available to a
DOT to reduce mathematical unbalancing of unit prices is to reflect accurate quantities in its
engineer’s estimates and bid forms. Artificially inflating quantities as a means to increase the
project’s contingency makes those quantities inaccurate and that leads to mathematical
unbalancing. Unbalanced unit prices in bid tabulations then lead to further inaccuracies in future
DOT cost estimates. Therefore, it is imperative that engineers and quantity estimators who
prepare unit price bidding documents understand the importance of publishing an accurate set of
quantities upon which construction contractors can then bid.
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
Acknowledgements
The authors would like to acknowledge the support of the Oklahoma Department of
Transportation and especially the kind assistance of George Raymond, who allowed access to the
data, Brian Schmitt, who edited the report of outcomes, and Joel Hysmith, who assisted in
establishing the study database from AASHTO SiteManager.
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Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
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Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
List of Figure and Table Captions
Fig. 1. Contract Type Risk Concept – Quantity Certainty versus Cost of Error
Table 1. Balanced Bid Example.
Table 2. Balanced Bid Example with allowable variation of + 20%.
Table 3. Analysis of Error in Six ODOT Pay Items
Table 4. Analysis of Unbalancing in Tack Coat Unit Prices in the April and June 2008 ODOT
Lettings.
Table 5. Analysis of Unbalancing in Asphalt Unit Prices in the June 2008 ODOT Lettings.
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
Table 1. Balanced Bid Example.
Hot Mix Asphaltic Concrete (HMAC) Pay Item
Unit Price
Element
Total
Cost
Bid Quantity
Unit
Cost
Variable
Cost:
$3.85M
50K tons
$77/ton
Fixed Costs:
$250K
50K tons
$5/ton
$300K
50K tons
$6/ton
Target
Profit
$100K
50K tons
$2/ton
$4.50M
50K tons
$90/ton
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
Table 2. Balanced Bid Example with allowable variation of + 20%.
Item
Unit
Price/Cost
Actual
Quantity
Actual
Pay
HMAC Bid
$90.00/ton
50,000 tons
$4,500,000
Actual Cost
$550,000 +
$77(50,000 tons)
$4,400,000
Actual profit
$100,000
20% Underrun
HMAC Bid
$90.00/ton
40,000 tons
$3,600,000
Actual Cost
$550,000 +
$77(40,000 tons)
$3,630,000
Actual profit
<$ 30,000>
20% Overrun
HMAC Bid
$90.00/ton
60,000 tons
$5,400,000
Actual Cost
$550,000 +
$77(60,000 tons)
$5,170,000
Actual profit
$230,000
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
Table 3. Analysis of Error in Six ODOT Pay Items
Division
Solid Slab Sod Error
Traffic Bound
Surface Course
Error
Cold Milling Error
Average
%
Absolute
%
Average
%
Absolute
%
Average
%
Absolute
%
1
10.74
19.03
-3.24
38.72
-9.44
12.82
2
17.00
24.18
-9.52
24.28
-7.80
25.07
3
4.54
26.27
-4.72
24.85
-2.48
29.78
4
-17.86
33.79
9.12
14.87
3.57
17.83
5
19.29
23.90
-0.30
0.30
-13.45
15.19
6
2.19
17.80
1.07
2.97
-11.25
11.25
7
-11.58
24.38
-14.07
17.32
3.10
14.30
8
2.27
25.34
-2.43
21.87
-33.90
33.98
ODOT
3.32
24.34
-3.01
18.15
-8.95
20.03
Division
Asphalts Error
Tack Coat Error
Portland Cement
Concrete Paving
Error
Average
%
Absolute
%
Average
%
Absolute
%
Average
%
Absolute
%
1
-4.05
13.50
-13.31
23.28
28.87
39.93
2
-2.12
10.08
-6.32
18.88
-6.39
17.34
3
-0.10
10.58
-13.22
23.10
15.72
16.68
4
1.86
12.47
-16.48
23.10
5.17
14.16
5
4.56
6.86
-33.96
33.96
none
none
6
-0.20
1.26
-32.58
32.58
-0.94
2.14
7
-2.88
10.38
-55.83
55.83
8.39
9.74
8
3.14
12.08
-16.23
16.23
4.97
15.15
ODOT
0.03
9.65
-23.49
28.37
7.97
16.45
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
Table 4. Analysis of Unbalancing in Tack Coat Unit Prices in the April and June 2008 ODOT
Lettings.
Engineer’s
Estimated Tack
Coat Unit Price
($/gallon)
Low
Bidder’s
Unit price
($/gallon)
Highest
Unit Price
Bid
($/gallon)
Low Bidder’s Unit
Price
% of Engineer’s
Unit Price
Highest Unit
Price
% of Engineer’s
Unit Price
$0.60
$0.53
$1.05
-11.7%
75.0%
$1.50
$4.40
$6.00
193.3%
300.0%
$1.50
$1.68
$2.87
12.0%
91.3%
$1.56
$2.00
$2.00
28.2%
28.2%
$1.75
$3.00
$5.00
71.4%
185.7%
$2.00
$1.65
$2.55
-17.5%
27.5%
$2.00
$2.90
$2.90
45.0%
45.0%
$2.00
$3.36
$4.00
68.0%
100.0%
$2.50
$1.50
$5.80
-40.0%
132.0%
$2.50
$6.00
$7.00
140.0%
180.0%
$3.00
$7.00
$7.00
133.3%
133.3%
$3.00
$7.00
$7.00
133.3%
133.3%
$3.50
$2.60
$3.00
-25.7%
-14.3%
$3.50
$2.00
$4.00
-42.9%
14.3%
$4.00
$2.50
$3.25
-37.5%
-18.8%
$4.00
$2.00
$2.50
-50.0%
-37.5%
Gransberg, D.D. and C. Riemer, “Impact of Inaccurate Engineer's Estimated Quantities on Unit Price Contracts,”
Journal of Construction Engineering and Management, ASCE, Vol. 135 (11), November 2009, pp. 1138-1145.
Table 5. Analysis of Unbalancing in Asphalt Unit Prices in the June 2008 ODOT Lettings.
Engineer’s
Estimated Asphalt
Unit Price
($/ton)
Low
Bidder’s
Unit price
($/ton)
Highest
Unit Price
Bid
($/ton)
Low Bidder’s
Unit Price
% of Engineer’s
Unit Price
Highest
Unit Price
% of Engineer’s
Unit Price
$59.00
$100.00
$150.00
69.5%
154.2%
$60.00
$109.31
$128.60
82.2%
114.3%
$60.00
$75.38
$75.38
25.6%
25.6%
$62.00
$71.80
$106.39
15.8%
71.6%
$62.00
$68.78
$103.72
10.9%
67.3%
$62.00
$68.80
$103.32
11.0%
66.6%
$62.00
$68.28
$103.39
10.1%
66.8%
$62.00
$72.80
$105.91
17.4%
70.8%
$62.00
$90.00
$109.02
45.2%
75.8%
$64.00
$75.00
$95.50
17.2%
49.2%
$65.00
$87.00
$102.85
33.8%
58.2%
$65.00
$143.76
$169.13
121.2%
160.2%
$65.00
$76.15
$76.15
17.2%
17.2%
$66.50
$77.00
$92.75
15.8%
39.5%
$66.50
$77.00
$92.75
15.8%
39.5%
$67.00
$80.76
$91.00
20.5%
35.8%
$67.00
$86.81
$92.00
29.6%
37.3%
$68.00
$91.53
$101.00
34.6%
48.5%
$68.00
$104.00
$105.39
52.9%
55.0%
$70.00
$106.00
$108.00
51.4%
54.3%
$70.00
$100.00
$108.00
42.9%
54.3%
$70.00
$108.19
$121.00
54.6%
72.9%
$70.00
$108.00
$127.60
54.3%
82.3%
$70.00
$77.25
$115.00
10.4%
64.3%
$80.00
$85.00
$88.48
6.3%
10.6%
$85.00
$105.00
$107.53
23.5%
26.5%
$85.00
$77.80
$82.50
-8.5%
-2.9%
$85.25
$100.00
$149.25
17.3%
75.1%
$92.00
$88.50
$93.40
-3.8%
1.5%
$100.00
$101.95
$135.00
2.0%
35.0%
$150.00
$240.00
$250.00
60.0%
66.7%
$150.00
$120.00
$120.00
-20.0%
-20.0%
$200.00
$225.00
$320.00
12.5%
60.0%
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Project labor agreements (PLAs) are commonly found on larger California public works in urban areas. Nonetheless, they remain a controversial public procurement practice. One issue not examined in the literature is whether as some critics suggest, PLAs reduce the number of bidders on public projects. Analyzing 263 bid openings for community college construction in California over the period 2007 to 2016, this first-ever study of PLA effects on the number of bids finds that controlling for the location where the project occurred, the size of projects, the business cycle and the season when the project was let, the number of bidders on a project was not altered by the presence or absence of project of PLAs. This study also finds that relative to engineer’s estimates available on 99 of these projects, the lowest bids on prevailing wage projects were not higher than the lowest bids on projects without PLA agreements.
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This article presents results from the first statistically significant study of cost escalation in transportation infrastructure projects. Based on a sample of 258 transportation infrastructure projects worth US$90 billion and representing different project types, geographical regions, and historical periods, it is found with overwhelming statistical significance that the cost estimates used to decide whether such projects should be built are highly and systematically misleading. Underestimation cannot be explained by error and is best explained by strategic misrepresentation, that is, lying. The policy implications are clear: legislators, administrators, investors, media representatives, and members of the public who value honest numbers should not trust cost estimates and cost-benefit analyses produced by project promoters and their analysts.
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Highway megaprojects (construction projects over $100 million) are fraught with uncertainty. These projects have historically experienced increases in project costs from the time that a project is first proposed or programmed until the time that they are completed. Persistent cost underestimation reflects poorly on the industry in general but more specifically on engineers. Traditional methods take a deterministic, conservative approach to project cost estimating and then add a contingency factor that varies depending on the stage of project definition, experience, and other factors. This approach falls short, and no industry standard stochastic estimating practice is currently available. This paper presents a methodology developed by the Washington State Department of Transportation (WSDOT) for its Cost Estimating Validation Process. Nine case studies, with a mean cumulative value of over $22 billion, are presented and analyzed. Programmatic risks are summarized as economic, environmental, third party, right-of-way, program management, geotechnical, design process, construction, and other minor risks. WSDOT is successfully using the range cost output from this procedure to convey project costs to management and the public.
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This paper deals with a central and recurrent concern of managers responsible for highway tendering: formulating unit bids for Unit Price Proposals. Unit Price Proposals are prepared by the client and indicate contract items and (estimated) quantities deemed necessary to accomplish the proposal objective. The bidder is required to indicate unit prices or bids. These unit bids are multiplied by the indicated quantities and summed to arrive at the bid total. Virtually every tender (bid) is unbalanced in order to improve cash flow. Often the unbalancing is neither competent nor conscious. A linear programming model was devised to determine unit bids that maximize the present worth of future profit. The model characterizes the managerial environment with uncommon competence. Examples illustrate features of implementation. The model is especially useful for large and complicated contracts of long duration. These linear models have the remarkable feature that, for given project information, no other means of unbalancing will yield a greater present worth.
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Estimates of materials, time, and costs provide information to some construction decisions in a similar way that financial accounting information provides to others. Financial statements are required to comply with generally accepted accounting principles, described in accounting literature to ensure information is accurate and useful to decisions. This paper suggests general estimating principles that similarly guide good estimating practice. An estimate must be an accurate reflection of reality. An estimate should show only the level of detail that is relevant to decisions. Completeness requires that it include all items yet add nothing extra. Documentation must be in a form that can be understood, checked, verified, and corrected. Attention must be given to the distinction between direct and indirect costs and between variable and fixed costs. Contingency covers possible or unforeseen occurrences. Both the expected value of possible identified events and the expectation that events will occur that cannot be identified in advance.
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