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 (62%)
• Sign in (22%)
• Errors (15%)

Live Outage Map

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

City	Problem Type	Report Time
Preston	Website Down	28 minutes ago
Manchester	Website Down	5 hours ago
Manchester	Website Down	8 hours ago
Manchester	Website Down	14 hours ago
Manchester	Website Down	1 day ago
Manchester	Website Down	1 day 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:

• 
  Corner Card Collector (@CCCollectorTCG) reported 5 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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  RedxDevilx44 (@RedxDevilx44) reported 8 minutes ago

  @Card_Shop_Guys ebay takes down one ad and all the cringe YouTubers run with it. wild

• 
  0xbonus (@coral_crypt) reported 13 minutes ago

  @anglio @eBay eBay’s broken, man. Your tracking should’ve been enough.

• 
  Sean (@firinzlol) reported 13 minutes ago

  @ThePokeMD It’s fine had a similar issue before. eBay rep basically said if it is a fake card then it will always get rejected so this “miscategorization” isn’t related to authenticity etc

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  collectorfix (@collectorfix) reported 16 minutes ago

  @CardPurchaser @CardPurchaser why have you all-of-a-sudden been focusing primarily on selling cards and how on eBay lately? Like micro breaking it down. Like obsessed with it.

• 
  Zamiel Cano (@ZamielJC) reported 16 minutes ago

  @PeakHobby @BosCardHunter I have had issues with eBay which is USPS. BGS10 Miss Fortune Showcase “Lost” Total bullshit

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  Macy 🛸 (@mace_face18) reported 18 minutes ago

  @EmilyDiorXO So stupid. I can get a new head off of ebay for ~$60 but I've literally had this vacuum less than a year. 2-3 times per week usage except the last few months just once per week. The piece itself is not broken I'm seeing but it's breaking the other removable parts (burning them)

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  Florida Sports Card Haven (@flasprtscardhvn) reported 18 minutes ago

  @Gbpoker0710 Thanks man.. I sure hope not. Hope just emptying the stomach and going home tomorrow. Shut my ebay down for 2 weeks just in case. 👊

• 
  ☆ (@LucilleFilm) reported 19 minutes ago

  @drivenbyfilms ppl are saying the screener was for an already released film but even then, this could still get him in trouble. there's a reason why ebay listings don't go up until months after release and awards szn.

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  slicko (@haribo4me) reported 19 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?

• 
  Genius Business (@GeniusBusiness_) reported 20 minutes ago

  PayPal's real product in its early years wasn't payments. It was survival. The company was building something that regulators, banks and its own biggest platform had no category for. Stefan Heck: "It was a true disruption of a very established, very regulated industry, right? Payments, banking… and they were doing things that didn't fit an existing category." Not fitting a category meant almost everyone with power over PayPal had a reason to switch it off. Reid Hoffman, who became Peter Thiel's firefighter chief, describes the job he was handed: "Make sure eBay doesn't drive us off the service. Make sure Visa doesn't shut us down. Persuade the federal government that it's okay that we're not a bank." Nancy Lublin on the person they sent to handle it: "He was the wolf. I mean, he was the fixer." John Lilly remembers how routine the existential threats became: "I remember we had breakfast one time. He said, 'I have to leave at 8:30 because I got to get on the plane to New York to go talk to Eliot Spitzer, the attorney general of New York, who's suing PayPal for basically anti-money laundering type things.' And he's like, 'I just got to go fix this and here's how I'm going to fix it because New York was convinced that PayPal is being used for gambling, all sorts of other things at the time.'" There was no playbook, because the category didn't exist yet. So the company argued from first principles: "It was fun because part of what ends up happening is you say, 'All right, well, what's a bank? Like, what defines a bank? How do I persuade them? I've never talked to a regulator before. All right, let me go figure out how this looks.'" While all this was happening, PayPal was outgrowing its own ability to operate. June Cohen: "Their customer numbers had grown so exponentially that they could not keep up with customer service and they actually just made the decision that that was a fire they weren't going to fight at that moment." That was a decision, not a failure. The company sorted its problems by one test: "There was a whole set of fires at PayPal where if you didn't solve them, value of the company zero, out of business." John Lilly on how that worked in practice: "Here's the one, two, three top priorities. Everything else I'm not going to worry about right now because if I get these three things right, everything else will be okay." The fire that passed the test was eBay: "If eBay had turned us off, if we were no longer to operate on eBay, no initial users, no traction, no network effect, no ability to grow." And eBay had every incentive to do exactly that. John Lilly: "eBay had its own payment system that was competing with PayPal. PayPal was winning. And so I think those got very complicated and eBay very much didn't want to have to buy PayPal and eventually they felt like they had to." That's the ending. PayPal survived on someone else's platform long enough to become the thing that platform had no choice but to acquire. A reporter at the time: "We know these guys very well obviously because so much of their business is in fact on eBay. The synergies of putting these two companies together were incredibly outstanding and it just seemed like the right time to get this deal done." PayPal didn't win by solving its problems. It won by correctly ranking them and letting the rest burn.

• 
  James Sager:Father of the Smart Phone,AI,Techaform (@JamesSager) reported 22 minutes ago

  @grok @iamelijahfloyd @Grok I bought several magnets like that on ebay in order to work on a SRM motor... How would they be mounted so they're engaged to slow a metal disc like a tire rim?

• 
  T-Bob Hebert (@TBob53) reported 27 minutes ago

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

• 
  The Really Bad Golfer (@therlybadglfer) reported 31 minutes ago

  @eBay why do you allow terrible people like this on your platform? He replies to feedback with threats and stalking (when I ran a background check), brings in users family's (you have a 10 year old), deflects any blame or responsibility, and it just horrible. You should kick him off the platform.

• 
  Olivia Kory (@oliviaakory) reported 37 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.

• 
  dxrawr (@dxrawrTV) reported 38 minutes ago

  @JDAlexOfficial @eBay They are bloated and it’s why many individuals online rally against using eBay, Walmart, Amazon, and big corporations that have pulled the wool over our eyes and kind of forced lots of local businesses to shut down

• 
  Number 5 (@NumberFiveAlive) reported 39 minutes ago

  @clarkknowscards @phatjer300 @OnlyCharizard If you select “buyer requested cancellation” it will not ding your eBay account. All the other options from the drop down menu do. I lost top rated seller once for cancelling just 2 orders within a couple months.

• 
  𝓙𝓞𝓝 𝓟𝓞𝓝 🇮🇹 (@JohnnyPonJR) reported 39 minutes ago

  @eBay You have a serious counterfeit golf club problem going on.

• 
  Darren *Ego Brianiac* Richardson (@S0UNDK1LLAH66) reported 42 minutes ago

  @The_Top_Loader Your only problem with Snes controllers is the plastic decaying or the seals in the D-pad & buttons vapourising. Even those, eBay will accommodate you.

• 
  Psygnosevo (@ytevo79) reported 42 minutes ago

  Ive had an £80 offer from ebay for the first game (pictured) down from £100, but the manual has a tear mark so that puts me off. But I might pop back into that shop and get the sequel first as its a good price if its good condition.. Tis a shame only now Im interested in these!

• 
  David Eyes (@EyesDavid88066) reported 47 minutes ago

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

• 
  WesleyTech (@WesleyTech) reported 48 minutes ago

  eBay is the king of terrible customer experiences. 1 example of many: They make you solve a captcha in order to LOG OUT of your account on the web app 🤬

• 
  Disgruntled_Maverick (@UncleVic13) reported 50 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 ****!!!

• 
  Jen-X (@JenX_Based) reported 52 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 🤬

• 
  Bird Dog Gaming (@BirdDogGaming) reported 54 minutes ago

  It’s so easy to open your phone, hop on eBay and buy a PS2 game Any time I want to add an NES game to the collection I have to sit down and figure out what inserts came with it and which variant I want to buy and THEN dig through listings to see if there’s even a CIB listed

• 
  Mr Hankey (@hivanchi) reported 58 minutes ago

  @paypal - leaving another message to be lost in wilderness of the internet. Had to do a chargeback for what seems to be a clear item not received issue. Never open these type of cases, but both @eBay and @paypal denied a simple case. Both their customer services are subpar.

• 
  Penni (@tulipgennaro) reported 59 minutes ago

  Selling on eBay is so slow

• 
  Mike Boysen (JTBD/acc) (@mikeboysen) reported 60 minutes ago

  Most people never see the inversion coming. They see the crash. The coup. The empty bank account. The factory that will not ship. Then they analogize: more staff, more robots, more of last year’s stack. Innovators do the opposite. They stay in the problem until the cost curve breaks. PayPal ousted Musk in 2000 for pushing a rebrand and a Unix-to-Windows NT migration. He did not wage a public war. He stayed a shareholder. eBay paid $1.5 billion in 2002. His ~11.7% stake — about $180 million — funded SpaceX and Tesla. By late 2008 both companies were days from death. Three Falcon 1 failures. Tesla about to miss payroll. One last rocket. Flight 4 worked on September 28. NASA signed $1.6 billion in December. He put his remaining cash into a $20 million Tesla bridge that closed Christmas Eve. Analogical thinking said “stop.” Physics said “one more launch.” Reusable first stages. Manual labor where the robots jammed. 5,000 Model 3s a week after he ripped out the over-automated line and slept on the floor. 75% of Twitter’s headcount gone so the cost of running the network inverted. That is not grit as a slogan. That is inverting labor, capital, and infrastructure until unit economics work. Follow people who have innovated at least once. Not the ones narrating the next analog. I built a platform to help average people think like that before you commit the capital. More on that soon. This is 100% a real interview LOL

• 
  𝕒𝕤𝕥𝕣𝕚𝕕 / 𝕝𝕚𝕝𝕒𝕔 💜♥️💚 purples lover (@purpofsecurity_) reported 1 hour ago

  @toppatmin @astrrrx i just looked at ebay in hopes of finding anything on there but no😭 why did they take the store down i need that ******* amongi rhm poster

• 
  J & C Collectibles (@JC_colleccoast) reported 1 hour ago

  🚨 SOS Card Peeps! Need some help ASAP please. 🚨 I received an offer on My Acuff 1/1 card on eBay. The guy offered me a fair price and is now negotiating with me…I’m totally fine with that, but in our chat (on eBay) the user name keeps changing…when I click on the first name at the top of the chat it seems like he is a legit seller/buyer. When the other name pops up, it says “error” Is someone trying to scam me or is this some kind of issue with eBay’s messaging?!? Thanks in advance!