About

I’ve been buying, repairing, and reselling keyboards for about four years, since before high school, as Taipan Keys. I go over each board, clean and test the switches, check the case and PCB for damage, fix what I can, and write listings that say what condition it’s in.

For the last two of those years I kept proper records of all of it: what each board sold for, what condition it was in, whether it still had the box, how it was listed. That’s what this site is built on. It’s also why the data starts in mid-2024 and not four years ago. It goes back as far as my records are complete.

The Taipan Value Engine is what I did with those records: a resale price estimator built from the sales, and the Market Tracker, the dataset and trend chart behind it.

I built it because the questions that kept coming up were more interesting than any one sale. Why does the box move the price so much? How fast does collab hype wear off? Does a careful listing really sell for more? The data was already in a spreadsheet, so I tried to answer them properly.

I also sell in this market, which I’d rather say up front than bury. The estimator is free, has no ads or affiliate links, needs no account, and publishes its own error rate, including the cases where it does badly.

The sample is small, and it’s just me doing this. Every number comes with a confidence level and a list of what could be off, and where the data is thin the site says so instead of guessing. I stick to careful words like “observed” and “suggests,” and I’m most careful with hyped boards. I want it to hold up for someone who really knows keyboards and still make sense to someone new.

All data used here comes from two sources:

• My own sales: keyboards I sold through my own eBay listings. These are the most reliable, because I knew the condition, the packaging, the listing, and the sale price first-hand.
• Manual marketplace review: publicly visible listings on eBay, Reddit (r/mechmarket), StockX, Facebook Marketplace, Discord, and Reverb. I look at each one myself. Nothing is scraped. I write down the platform, date, price, condition, and how good the listing was at the time I saw it.

I don’t use any third-party databases, price aggregators, or bought datasets, and I don’t store buyer names, usernames, or addresses anywhere. During giveaways, people can contribute sales they made or bought, with a public link as proof; the ones I check and approve are added here marked as community-contributed, so you can always tell them apart from my own records. The site counts how many estimates are run, with no cookies and nothing that identifies you. During a giveaway, the Reddit username and the feedback you enter are kept only until the winner is confirmed.

Each observation records the following:

Condition is assessed by direct examination (my own sales) or from photos and descriptions (marketplace observations). The grading scale:

For marketplace observations, condition is inferred from photos and stated description. Observer assessment may differ from actual condition.

Listing quality and photo quality are each scored on a 1–10 scale based on the following rubric:

The engine picks one of three estimators depending on how much evidence the dataset holds for that keyboard, and it tells you which one it used.

1. Direct comparables. When the dataset holds at least three recorded sales of the exact model (and, for collabs, the same collab), those sales set the range. Each comparable is adjusted to the listing’s condition, packaging, and completeness, and the middle spread of the adjusted prices becomes the estimate. Sales from the last twelve months are preferred when enough exist. Repaired and restored boards use this estimator too, with their disclosure discount applied on top, since a repaired board is still the same board.

2. Rule-based multipliers. When the dataset has the keyboard’s model or collab but fewer than three sales of that exact model, a set of multipliers runs against the launch price: condition (mint 1.0 down to poor 0.35), box (+12%), accessories (+4–8%), collaboration tier (+15%, +40%, or +100%), platform (−13% to +4%), and stock status (repair/restore −3 to −5%, disclosed damage −25%). Keyboards with no collab behind them take an additional 0.85 depreciation factor, measured from the dataset’s own non-collab sales. Non-limited, non-custom boards are capped at their retail price, since something still purchasable new rarely resells above new. Recorded sales of the exact model override that cap.

3. Market floor anchoring. When nothing in the dataset matches the keyboard’s model or collab (a matching brand alone isn’t enough), the engine refuses to estimate until the user supplies the lowest current listing they can find, and then anchors on that figure alone. Across the nine model-and-collab groups where the dataset holds both asks and sales, the median sale landed at 0.967 of the lowest active ask, so that listing is a good predictor. An earlier version blended in a retail-derived depreciation ratio at 25% weight; validation against third-party listings showed it doing harm (a used GMMK Pro asks around $80 against a $350 launch price, so the retail component computed $270 and pulled the estimate up by 77%), and it was removed. Retail is now used only as a ceiling on this path.

A separate ordinary-least-squares regression, fitted offline on the same history (150+ observations; R² ≈ 0.56, typical error ±31%), is shown alongside the estimate as a third signal wherever the dataset can support it. It captures launch price, accessories completeness, collaboration tier, and brand, and includes only effects significant at p < 0.05.

Confidence depends on how much sales evidence there is. Sharp photos and a careful description can lower a confidence rating but never raise it: without at least three recorded sales of the exact model, confidence can’t exceed medium, and with none it can’t exceed low. When the user supplies a lowest active listing for a keyboard the dataset does cover, the attribute estimate is never altered by it; a separate market-adjusted range blends the two (70/30) and a cross-check note appears in the drivers and risks.

The engine doesn’t use live price feeds. Every formula and coefficient is fixed and explained on this page.

I tested the engine against a set of listings from public marketplaces, none of them mine, and double-checked each one before using it. Here’s how it did:

• The direct-comparables estimator performs well. For a model with nine recorded sales it returned $400–$483 against actual sales of $400–$500.
• The multiplier layer still runs high on plain, non-collab boards, by roughly 35% to 75% against third-party asking prices. The dataset holds eleven non-collab sales, of which only eight were usable for calibrating depreciation (one has no known launch price; two are a bundle and a launch-window sale). All of them are my own listings, which are better photographed and described than a typical private listing and sell accordingly. This is the engine’s weakest area, and I’ve only partly fixed it so far.
• Sharply hyped boards can run the other way. A Wooting 80HE was asking well above what the multiplier layer predicted, reflecting launch-window demand the engine doesn’t model.
• Across the sample, the engine’s range contained the observed asking price in 5 of 14 cases, with a mean absolute error of 42% against the midpoint. Asking prices aren’t sale prices, so this is a rough measure, and not a flattering one.

• The sample is small. The dataset holds 150+ observations (100+ completed sales and 50+ active listings), mostly Higround and collab keyboards. Roughly a quarter of them are custom builds, which are left out when pricing a stock keyboard, so a stock query draws on 89 sold observations. Only eleven of those are non-collab boards, and only eight of those eleven were usable for calibrating depreciation. Effects that didn’t reach statistical significance on this sample (condition, box, stock status) are left out of the regression on purpose and handled by the rule-based layer.
• It depends on the prices you enter. The engine anchors on a launch price, and some of the launch prices it fills in have drifted from what manufacturers charge now. If a board is still sold new, the cheapest current brand-new price is the better input, and the lowest active listing should count discounted new ones too. The estimate is only as good as those numbers.
• I’m not a neutral observer. I buy and sell in this market, so the data leans toward the models and setups I work with most, and my listings tend to be better presented than a typical private sale.
• Other people’s listings are judged from photos. For listings that aren’t mine, condition, accessories, and stock status come from the photos and description, not from having the board in hand, so they can be wrong.
• Platforms differ. Each one has its own fees, buyers, and pricing habits, so comparing prices across them only goes so far.
• Prices change over time. Observations run from July 2024 to September 2026and each one is dated, so the older ones may not match today’s market. The comparables estimator prefers sales from the last twelve months when there are enough of them, and lowers its confidence when the newest matching sale is more than six months old.
• It’s built for enthusiast boards. The engine is calibrated on enthusiast and collab keyboards (mechanical and hall-effect), which tend to hold value. Mass-market office keyboards lose value differently, and the dataset has none of them. When nothing matches a keyboard, the engine says so, hides the statistical estimate, drops its confidence to low, and requires the lowest current listing you can find, which then becomes the only anchor.
• The keyboard check can be fooled. Inputs are checked against a list of known keyboard brands rather than a list of banned words, because a banned-word list only catches what someone thought to ban. Boutique makers, group buys, and one-off builds won’t be on the list, so an unrecognized brand asks you to confirm it’s a keyboard instead of refusing. Nobody checks that confirmation.

The Taipan Value Engine isn’t affiliated with, sponsored by, or endorsed by any keyboard company or any marketplace. Brand names are here so you know which keyboard is which.

When a board I list has been repaired, restored, or modded, the listing says so. I never describe a custom build as an official product. And I don’t make claims about any company’s finances, decisions, or plans that I can’t back up.

• No scraping or automated data collection
• Doesn’t claim to cover the whole keyboard market
• Doesn’t predict future prices
• Doesn’t read big conclusions into small samples