Scope and methodology
These benchmarks come from public bid-tabulation and letting-result records that publish the full bidder list, aggregated by ConstructionBids.ai from six public sources: the Texas DOT bid-tabulation dataset, Florida DOT official letting results, Cook County (IL) bid tabulations, and the Virginia, Washington State, and New Jersey DOT letting reports. Read these limits before citing:
- Sample: 1,038 projects and 3,789 individual bidder rows, bid dates spanning 2016–2026 (most DOT records are 2024–2026; the Cook County records reach back to 2016). 966 projects had a complete bidder roster (every bidder's amount present).
- Sector skew — important: about 88% of the projects are state-DOT / highway lettings. Only ~12% (Cook County) is other public construction. This data describes transportation lettings; it does not represent vertical/building construction or public construction broadly.
- Geographic concentration: Texas is 58% of the projects. Per-metric jurisdiction counts are stated in each section below so you can judge breadth.
- "Winner" = apparent low bidder. Public lettings award to the lowest responsive bid, so we treat the lowest bid on each project as the winner. This is the apparent low bid, not a confirmed contract award.
- Spread uses the median, not the mean. A few projects with a near-zero low bid produce extreme ratios that make the mean meaningless.
- Win-rate and spread figures use complete-roster projects only, so a partial bidder list can't bias them.
- Non-bid rows are excluded. Some source datasets list the engineer's estimate (and similar markers) as a pseudo-bidder row; these are removed at ingestion and an analyzer guard rejects any that slip through, so estimates are never counted as bidders or winners.
- Trade/sector breakdown is intentionally omitted: the sample is overwhelmingly one sector (transportation), so a per-trade table would not be meaningful.
This page is regenerated by a committed, reproducible analysis script and updated as coverage broadens. If you reference this data, please attribute it to ConstructionBids.ai with a link (see "Cite this research").
Average bidders per project
How crowded is the typical public transportation letting? Across the sample, the average project drew 4.1 bidders (n = 1,030 projects with a bidder count, 6 states). Competition is real but not crowded — most highway lettings draw a handful of qualified firms rather than dozens.
Competition by region (limited to the states sampled)
This is not a national regional picture: each "region" below reflects only the one to three states we sampled there (named in the table) — the Midwest is just Cook County, IL and the West is just Washington State. Read these as directional, state-level signals, not definitive regional averages.
| Region | Avg. bidders per project | Primary source in sample |
|---|---|---|
| Midwest | 5.1 | Cook County, IL |
| South | 4.2 | TX, VA, FL DOT lettings |
| West | 2.8 | Washington State DOT |
| Northeast | 3.2 | New Jersey DOT |
The spread — from about 2.8 bidders in the West to 5.1 in the Midwest sample — is a useful reminder that "how many firms you're up against" depends heavily on where you bid.
Win rate and bid-hit ratio
The bid-hit ratio — how many bids it takes to win one job — is the number every contractor wants and few can benchmark. Treating the apparent low bidder as the winner, across 966 complete-roster projects in five states (TX, IL, NJ, VA, WA):
- Win rate: about 26% of bids submitted were the low bid.
- Bid-hit ratio: roughly 3.9 bids per win.
In practice, that means a contractor competing on these lettings should expect to lose far more often than they win — so pursuing better-fit work, rather than more work, is what protects estimating hours. This is also the real cost of monitoring free sources by hand.
Low-vs-high bid spread
How much daylight is there between the lowest and highest bid on the same project? Across 832 complete-roster projects in four states (IL, TX, VA, WA), the high bid sat a median of about 32% above the low bid. That gap is the pricing room competitors leave on the table — and a reminder that knowing the field before you price matters. A clean technical submission matters just as much — see avoiding the common disqualifiers.
Award-vs-estimate spread
How close do winning bids land to the agency's own engineer's estimate? Not yet measured. Of our current sources, only the Texas DOT dataset publishes the engineer's estimate, so a defensible multi-state figure isn't available yet. We've chosen not to publish a single-state number here; this section will be filled when more sources that expose the estimate are added.
Repeat-bidder concentration
On a given agency's work, how often do the same firms keep winning? Across 971 awards in five states, about 37% went to a contractor that had already won that agency's work within the sample — meaningful concentration, consistent with a transportation-letting market where a set of qualified prime contractors recurs. New entrants should expect familiar incumbents on much of an agency's work. These signals feed directly into choosing a bid platform that filters for your trades.
Cite this research
Please attribute these statistics to ConstructionBids.ai with a link to this page, and please carry the scope ("public transportation/highway lettings, six states, 2016–2026") so the numbers aren't misread as all public construction.
ConstructionBids.ai. "Public Transportation Bid Statistics: Bidders, Win Rates & Competition (2026)." https://constructionbids.ai/resources/transportation-bid-statistics
Media and researchers: contact support@constructionbids.ai for the underlying methodology, sample, or additional breakdowns.