eBay status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of eBay reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by eBay users through our website.

• Website Down (63%)
• Sign in (22%)
• Errors (14%)

Live Outage Map

The most recent eBay outage reports came from the following cities:

City	Problem Type	Report Time
Preston	Website Down	1 hour ago
Munich	Errors	2 hours ago
Preston	Website Down	7 hours ago
Melbourne	Errors	11 hours ago
Preston	Website Down	13 hours ago
Preston	Website Down	19 hours ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

eBay Issues Reports

Latest outage, problems and issue reports in social media:

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  Olivia Kory (@oliviaakory) reported 5 minutes ago

  "All models are wrong, but some are useful." My friend @josephfwyer wrote the best summation I've seen of how we view the world at Haus. I'm saving you a click and posting his full substack article below. Please tell us where we are right, but more importantly, tell us where we are wrong! The Hitchhiker’s Guide to Marketing Measurement and Decisions A map of the Haus Analytics Platform The first two posts in Decisions and Data have been about what not to do: don’t trust statistical significance to make budget calls for you. Fair enough, but now we’re going to go from “stop doing that” to “do this instead.” So this post is the map. It’s the whole path from the data everyone starts with in marketing measurement to a system that recommends decisions and checks its own work. I’m really proud to say this is what Haus Analytics has built. We’ve walked down this intellectual map the last few years and now I want to walk you down it. Each stop on the path deserves its own deep dive, and those are coming. Today is just the strategic view of the problems and solutions. Last-click attribution has big shortcomings Speed-running last-click attribution real quickly: You click on a digital ad on a advertising platform. Later you buy something on the advertiser’s site, where a snippet of code reports the conversion back to the platform. Roll that up and you get the reporting most marketers watch daily. Suppose for a campaign there was a thousand clicks and a hundred tracked purchases on just a hundred dollars of spend. You’ve acquired those conversions for one dollar each. Streams of this kind of attribution data are flowing constantly and reporting ultra granular data. The problem is when you ask if this advertising spend “caused” those conversions. Around 2011, the economist Steve Tadelis was consulting at eBay and looked hard at their paid search spend. The ad in question sat at the top of search results page when you searched “eBay,” directly above the free organic link to eBay. His hypothesis was that people typing “eBay” into a search engine were largely already on their way to buying something at eBay. Radical, I know. He got his chance to look deeper when Ebay shut down brand keyboard bidding while negotiating with a major search engine. After 3 months of data had populated, he found that organic clicks rose to absorb almost all of the traffic the ads had been getting credit for. eBay was paying approximately $20M/yr to the search engine for customers it was already getting. He eventually published the results in a peer-reviewed journal. The word for what attribution measures is correlation. The word for how much customer activity advertising causes is “incrementality”: purchases that happen because of the ad and would not have happened otherwise. Modern targeting makes the gap between attribution and incrementality on digital channels worse because platforms can model each user’s propensity to buy your product and show ads to the people most likely to purchase anyway. Hold on to this: attribution is biased, but it is also fast and absurdly detailed. I’ll come back to it later. Experiments are where incrementality comes from If you want to know what customer activity an ad caused, you need a comparison: people who saw it versus equivalent people who didn’t. That’s called an experiment. Keeping track of individuals in experiments can be messy (although some ad platforms can pull it off) so a workhorse in this industry is the geographic experiment. You can randomly assign market areas in a country into receiving advertising (treatment) or not (control). Then you simply look at the difference in conversions between the two. Randomization removes that attribution correlational bias that tricked Ebay into lighting money on fire (and many, many other companies even today). On average, the differences between the treatment and control regions wash out, so the estimate is centered on the true incrementality. That sounds really simple, so why does Haus have all these experimentation scientists? It’s a lot of work to improve precision without messing up accuracy. Precision is the measure of how far off the estimate can be when it is off. Think of it like darts. A tight cluster in the upper right of the dart board is precise but inaccurate. A loose scatter centered on the bullseye is accurate but imprecise. Randomization gets your cluster centered on the bullseye. What the science team does all day is making the cluster tighter without dragging it off the bullseye. If you have a great experiment design and analytical model then you will get a precise estimate on the real number. At eBay, the real number for brand search keywords was close to zero. But that’s one company at one moment. I’ve seen brand search come back near zero for one advertiser and strongly incremental for another. You don’t know until you test. So everyone needs to test to find how much money they are leaking. As much as I love experiments, they have limitations. An experiment tells you the causal effect at the spend level you tested. That’s only one data point! You can’t trace a curve through one data point and you need a curve to allocate budgets because every channel eventually hits diminishing marginal returns. Causal MMM, debiasing the model Marketers have been fitting models to trace diminishing return curves for a long time. Media mix models (MMMs) trace your sales on your spend across multiple ad channels and let statistics tell you how much each is driving. In theory this gives you the full diminishing curve for every channel at once. In practice, MMM has two mortal flaws. Multicollinearity. Businesses tend to turn all their channels up and down together, so when sales change, the model struggles to tell which channel did it. Seasonality. Businesses spend the most going into Black Friday and Christmas season, which is exactly when people’s propensity to buy surges without needing ads. So were the gains in sales caused by the ads or the season? All models are wrong, but some are useful. MMM is very wrong, but very useful because it can go all the way to a budget recommendation when an experiment can’t. So we want to fix MMMs. We do that by stopping treating experiments and MMM as rival methodologies and instead merge them together. You ran a geo experiment and learned that at last quarter’s spend, a particular ad channel truly drove some specific number of purchases. We require the model’s curve for that channel to pass through the experiment data point. We call this experiment calibration. Pinning one channel’s curve to ground truth also disciplines the others, because the remaining sales have to be explained by the remaining channels plus organic demand. The seasonal bias gets squeezed out the same way. That’s causal MMM (cMMM): the curve-tracing power of an MMM, anchored to the causal truth of the experiments. And Haus refreshes it weekly instead of the traditional once-a-quarter-two-quarters-later read. Causal attribution, debiasing the daily feed A weekly model of whole channels is still too slow and too coarse for the person making changes every day. Remember what attribution had going for it: daily, granular, ad-level. Attribution is wrong, but what if how wrong is predictable? So apply a similar calibration move we did with cMMM but to Attribution. If your experiments show that only five percent of the purchases the channel claims are truly incremental, then discount its daily feed by 95%. Do that per channel. You can’t have experiments everywhere so let a model reason about how the causal correction shifts over time and across spend levels. Now the daily numbers marketers already watch become numbers they can trust. We also show the raw platform-reported figures next to the corrected ones, so you can always see what the feed said and what we did to it. Architect, where measurement becomes decisions Everything up to this point is measurement, and measurement adds no value unless it impacts decisions. We’re now going to get into the most exciting part of the Haus stack. We call it Architect. It takes the experiments, the causal MMM, and the causal attribution feed, and turns them into specific recommendations: move this much budget from here to there. Then, it measures what happened after you made the move and reports back within a couple of weeks. Did revenue improve? By how much? Continually updating its expectations and recommending again. Across the first companies acting on these recommendations so far, the average adopted Architect recommendation has improved conversions by 10%. Some companies have stacked changes and watched the gains compound. Architect earned the trust to make those changes by tracing the logic through the experiment, cMMM, and cAttribution data. That is why we’ve been working so hard to mak every layer underneath an automated and scalable system. An experiment alone is an isolated data point. A calibrated model alone is a forecast. The loop of recommend, act, verify, and update is the thing that turns marketing measurement from a reporting function into the situation room where strategic decisions are made. One more note, since the obvious question in 2026 is “why not just have an AI do all this?” Some companies will sell you exactly that right now. Personally, an AI reading raw attribution data inherits every bias in it and without each layer built, tested, debiased, and made explainable, you can’t trust if it’s going to work or know why it told you to move the money. The entire stack is what makes an automated recommendation trustworthy. Wrapping it up That’s the map! Attribution gives you speed and detail but with debilitating bias. Experiments remove the bias but are solitary data points. cMMM extends the causal truth to actionable curves. cAttribution pushes causal truth back into the daily feed. Architect reads the outputs of the whole stack and closes the loop by recommending and verifying decisions. In the coming weeks I’ll talk through some stops on the map with some illustrative math and examples of where things can go wrong. If you only take one thing from the altitude view, take this: never trust a marketing number that hasn’t been anchored to an experiment somewhere.

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  Jen-X (@JenX_Based) reported 8 minutes ago

  Pull tooth out 🦷solves problem. Simple. I'm not about keeping bad teeth in my fkn head. Nope. I'll get another one later. Pull it out & move on. Im looking for a dentist to do that now.💰😒 😒I'd have done this already but 😡 ebay STILL has all my funds held. @eBay 🤬

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  Mr8000 (@mr8mil) reported 8 minutes ago

  A few weeks back I bought this card from a tip on @enzo_tcg discrod. This card comes from the Blue Trial Deck of FW from 2023, basically one of the first leader cards of all FW. Hard to find in EN, this is the only version I could get my hands on EBay. Even if this card does not meet expectations down the road, I love the “vintagy” look of it. Source and user name: dissociated, thanks for bringing this into attention. Appreciate it!

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  Just Looking By The Way (@JustLooking_BTW) reported 9 minutes ago

  @ChevanceLo1 @TrevorAllenMD @YugiMuto91 Literally just takes a eBay listing & dropping it off at an ups or etc. to sell this thing, like you can’t be this slow to think winning the tournament was what he was referring too

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  Richard Fitch (@Sweetfitchie) reported 12 minutes ago

  @CardPurchaser How many cards are being sent now at $80? I doubt everyone will stop. Plus ebay showed me a 49.99 grade with psa offer on a card i was looking at so some people are probably using that. This could be the new model. Prices may never come back down.

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  Cori (DemShenaniganss) 🦝 | #TDST ⚡️ (@DemShenanigans) reported 12 minutes ago

  @Artemishowl_ Former SH staff circa early days (2010s) here: Can confirm, ebay kept the **** parts and got rid of the people like us who made sure that the same or better upgrades were offered. Afaik even shut down the main corporate office thay used to be in CT

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  Card Throne (@cardthrone) reported 16 minutes ago

  I run Card Throne with a handful of Grok Bots. Each one owns a job. They don't all sit in one chat answering random questions. Scout does sourcing. Japanese inventory, carts, what to buy next. Grading plays and margins off my actual fees and costs. It never checks out. I still have to say purchase. Build is engineering. It only works on Card Throne Room, our seller tool. Scan a stack of cards, list to eBay, inventory management. When it's real code, it summons a cloud agent with pstack /poteto-mode. Throne runs the storefronts. Brings me the issues that actually have to move that day. Ledger does the books. Payouts, fees, receipts, the monthly P&L. Sentry monitors for any Card Throne Room bugs and pings Build if something needs fixed. Chief sits on top so I'm not pinging six bots to find out whose job a thing is. I still make the final calls.

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  Disgruntled_Maverick (@UncleVic13) reported 16 minutes ago

  What ******** @eBay !!! Why ******** do you make it imposible to talk to a live representative???? I pay a **** load of fees to be given the run around!!! Fix your ****!!!

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  ZaxCardZone (@ZaxCardZone) reported 16 minutes ago

  Made my first mistake on eBay tn. Listed a card (over $20) with my PWE shipping cost (I set a flat rate of 1.25 for PWE). Shipping is $6, anyway to change/fix this or am I just out the $5. No big deal if so, this is what happens when I list cards extremely tired. @CardPurchaser

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  Carolina24 (@c_collector24) reported 16 minutes ago

  @pasch_8759_eBay @CardPurchaser @tm1515152005 Had the same issue yesterday (TWICE) with the same Kirby Puckett card that I even talked to an eBay representative about and he suggested I just relist it? No idea what they’re doing with their new AI filters

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  EverydayReseller (@EverydayResell) reported 17 minutes ago

  Day 208 Total sales: $737.96 eBay earnings: $504.99 Buy cost (COG): $82.27 Net profit: $422.72 ROI: 514% Items sold: Electrical equipment, printer ink, printer parts, automotive transmission parts, marine electronics, & a toilet repair component. Another strong day. $737 in sales & over $422 in net profit from just 7 completed sales. What stands out to me is the variety. Electrical, automotive, marine, printing, plumbing, you don't necessarily need to specialize in one category to build a profitable eBay business. You need to get good at finding the right products at the right price. That's still the biggest lesson I've learned through this journey. A $100 sale isn't nearly as exciting if you paid $90 for it. But when you can consistently find inventory for a fraction of its potential selling price, the numbers start working in your favor. Today's $82.27 in COG turned into $422.72 in profit. That's the game. Buy right. List consistently. Be patient. Keep improving. 208 days down. On to Day 209. 📦💪

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  Dorian Muthig (@dorianmuthig) reported 19 minutes ago

  @RDKLInc Yeah… they don’t want to give you the working ones anymore, either. It’s starting to get so bad, even the repairable ones are sorted out first, too. Even on eBay, most “defective, for parts, not working”, are listed after repair attempts only, and they try to scam not disclosing

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  Pokémon Deals, Restock and Alerts (@PokemonRestockr) reported 20 minutes ago

  @J_Vetter14 @Stassibaby12 @Pacho9_3 I assume it was ebay to took the seller down, keep me updated on what happens. Wild times

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  StuArt  (@StuHack73) reported 24 minutes ago

  @TheRealBonJavi @johnshanks1 They’re worth about as much as a Jim'll Fix It badge on eBay, bin them.

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  GamerTex (@GamerTex) reported 26 minutes ago

  @card_history @UpperDeckSports In 1999 Upper Deck told me about a few of the first Patch cards ever made. The best and worst as they put it. The WORST was a yet unreleased employee card with a distinct error i how the patch was inserted. A few months later the first Jordan employee card was released. After years of looking I never found the Jordan card that was described. The 'best' patch they described is attached. I found it after years of searching the newsgroups, sportsnet and ebay.

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  Fitzzzy 🏴‍☠️ (@ShaunFitzzzy) reported 28 minutes ago

  @Psycho0111 It all comes down to if there is any material info out there. With everything around eBay it might be hard to get around the safe harbor laws Also, I’m not sure if the pre earnings release counts, we get the full thing next week

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  Mojo 🇺🇸 (@QMojo8) reported 29 minutes ago

  @OffDaHook35 Sold some slabs at LCS today and narrowed my pc down to a small box. I have a 2000 card lot on eBay to sell the rest. A lot are cards from packs I ripped this year. LCS wouldn’t even look at them

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  Sj (@Pkrguy23) reported 31 minutes ago

  @clarkknowscards DM me and I will tell you how I got out of eBay jail from their error which plagued me for almost 6 months. Should work the same for you.

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  Tyler (@SeeifIhaveit) reported 32 minutes ago

  Beware of this eBay seller. Purchased a J Baez lot when he was hot and I had to cancel and refund him because it sold on Mercari probably 15 minutes prior. He purchased it literally as I was taking down the eBay listing. I messaged him and apologized and refunded him immediately. Well… he left negative feedback and said he never got the cards.

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  Penni (@tulipgennaro) reported 34 minutes ago

  Selling on eBay is so slow

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  T-Bob Hebert (@TBob53) reported 37 minutes ago

  I remember getting this issue in a giant haul from eBay when I was younger

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  Ask About My Dining Room Tables replyguy/acc, PhD (@agamemnus_dev) reported 39 minutes ago

  @aupdegraff24 @CatNihon Same with eBay. I keep calling them about a listing that they suspended showing them that it should be back online. They said they will fix it within 2-3 business days and they never do.

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  HuntXr🫩 (@hunter_xzn) reported 39 minutes ago

  @anglio @eBay Woah woah Why didn’t you use some other platform or you didn’t know about this error ?

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  Artamos (@Artamos_) reported 40 minutes ago

  @MommaOcco eBay and other retailers that have 3rd party sellers need to crack down on presale/preorder listings. Should be only on hand product

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  slicko (@haribo4me) reported 43 minutes ago

  @GoatBeardzDD @MaiDuat31103 When company A acquires company B, doesn’t the stock price of A typically go down while B goes up? So in this case shouldn’t we expect GME to dip if they are successful at acquiring eBay?

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  Zack Morris (@Palladin0) reported 49 minutes ago

  @packaddictsnw Never had an issue w eBay standard shipping once. Most likely buyer being shady

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  Corner Card Collector (@CCCollectorTCG) reported 50 minutes ago

  When selling on eBay if you see a third-party shipping company do you have issues or concern shipping the card? I always seem to have problems with ship my cards.

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  Sweethoneyxd (@Gods_sunset) reported 51 minutes ago

  @JRho_11 Starting the video w/ that musty *** helmet he prob got off eBay is insane. Literally hit record while putting the helmet in frame andddd putting his top down. Mean time he prob circling an empty parking lot

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  David Eyes (@EyesDavid88066) reported 56 minutes ago

  @benonwine Ready for some enterprising youngsters to go and take down the tents and eBay them. Used once only.

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  🦊 (@whatthefox___) reported 57 minutes ago

  @kaetoceros Only people on eBay are paying that much and the sales are slowing down. People at my locals were selling them for 150