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 (23%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Preston	Website Down	4 hours ago
Preston	Website Down	23 hours ago
Preston	Website Down	1 day ago
Preston	Website Down	2 days ago
Mainz	Sign in	2 days ago
Preston	Website Down	2 days 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:

• 
  A.P.O.C (@AOchiawuto) reported 3 seconds ago

  @AirtelNigeria @FarooqOreagba Airtel Nigeria, why is your service so poor? I’ve tried registering on eBay, but the verification code never arrives on my Airtel line. Yet, the same code comes through immediately on MTN. What’s wrong with Airtel? Is Nigeria cursed? Please fix your SMS service!

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  yolanda j smith-bond (@yolandajsmmqc) reported 26 seconds ago

  @netnanny @Rbls24 Wanted to move discussion from Twitter (X) to @LiveJournal My login from funci* is messed up, msgd Dave's wife yesterday, & McGough from DMS (bombing by Frost Bank downtown messed up the LJ platform in ~2003). I forget ____________ tried to sell it on a shell version of eBay.

• 
  slicko (@haribo4me) reported 3 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?

• 
  Alex Thompson (@sierrastrades) reported 8 minutes ago

  TL;DR Sales down, profits up, cash lower because they turned the eBay options into real shares. • Sales: $780–800M vs $972M last year. Drop is mostly Switch 2 anniversary, store closures, and selling France. • Operating income: $150–170M vs $66M. Core operations got a lot more profitable. • Net income: $290–310M vs $169M. Includes ~$238M eBay gain, minus ~$75M digital-asset loss. • Cash: $5.05–5.07B vs $8.69B last year. Cash fell because they converted the eBay derivatives into 43.4 million eBay shares, now worth about $4.95B. • Full results drop September 8.

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  Saul (@SaulSellsStuff) reported 9 minutes ago

  Not sure who needs to hear this but you can spend $100,000+ a month just using Amazon as your source of inventory. You can do this at 40%+ ROI. You can beat section 3s and account health problems. You can sell your inventory on Amazon, Walmart, eBay, Whatnot, TikTok, or locally. If you’re an Amazon seller you can also get a complete P&L, cash flow analytics, ungating support, reimbursements, product monitoring, replen dashboards, FBM tracking, order tracking, and more. For free, then $3 a day. You can be as weird as me and do it all from your phone if you’d like. We are building the most complete software package publicly available. Taking no profits out. Doubling down on team and partnerships.

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  KamperKitty (@kamperkittyfn) reported 9 minutes ago

  why are they forcing AI down our throats? first google now ebay?? and you can't even disable it. it's an absolute joke!

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  kitchen witchen 🏳️‍⚧️ (@kitchen_witchen) reported 11 minutes ago

  @eBay This *** AI listing system is slow as molasses in Antarctica. I could do this in half the time and not have to fix literally every step. **** offffff

• 
  Darren *Ego Brianiac* Richardson (@S0UNDK1LLAH66) reported 11 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.

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  Card Shop Guys Podcast (@Card_Shop_Guys) reported 14 minutes ago

  As most of us know, there have been plenty of people opening and selling 30th anniversary product early across Whatnot and other platforms. Over the past week TPCi has decided to put their foot down, specifically with sellers on Ebay. Several sellers have been contacted by Ebay and informed that their early listings for raw Pokemon cards from the 30th anniversary set have been taken down. Ebay claims the listings were in violation of their stolen product policy. I wonder if TPCi will go after Whatnot themselves and other platforms to root out the source of the problem....

• 
  Kiro (@KiroIkigai) reported 14 minutes ago

  The most reliable way to misread an earnings report is to start at the bottom line. $GME just posted preliminary net income up roughly 78% on net sales down roughly 19%. Both numbers are real. Only one of them is about the business. The quarter, company figures: -net sales: $780M to $800M, vs $972.2M a year ago -net income: $290M to $310M, vs $168.6M -operating income: $150M to $170M, vs $66.4M The bridge from falling sales to rising profit is one line. Roughly $238M of gains on an eBay derivative asset and an equity investment, less roughly $75M of digital-asset losses. about $163M of pre-tax marks. For scale: that eBay exposure is carried at about $4.947B. the company books about $790M of sales in a quarter. when the portfolio is that big, the income statement is partly a report on the portfolio. Credit where due: operating income more than doubled. that is a bigger percentage jump than the net income line everyone quotes. smaller, tighter, better-run retailer. real progress. not the headline story. Keep this part: -net income up while revenue is down: find the non-operating line first. it is usually one line, and it is usually named -size the securities book against the operating result. a $5B position swamps a sub-$200M quarter, in both directions -falling sales with improving operating income is a cost story. real, but a different story from the headline I am fully invested, and none of this changes that. guidance direction and price reaction have been disagreeing all earnings season. last Thursday five of seven reporters raised guidance and four of the seven closed lower. reading the price as the verdict on the numbers is the error. The case against: I may be underselling the operating turn, which more than doubled at every point of the guided range. and these are preliminary figures. no call is scheduled, and complete results are due September 8. $GME $EBAY

• 
  $$D@mouppn?$$ (@Judd29230Judd) reported 15 minutes ago

  @CardPurchaser Whatnot ebay and I tried collx but it crashes all the time when scanning anybody else have that issue with collx?

• 
  StuArt  (@StuHack73) reported 20 minutes ago

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

• 
  EverydayReseller (@EverydayResell) reported 26 minutes ago

  Day 206 Total sales: $1,148.99 eBay earnings: $854.40 Buy cost (COG): $296.57 Net profit: $557.83 ROI: 188% Items sold: Home protection equipment, pressure washer part, automotive part, battery charger, carburetor, & sports training equipment. Another $1K+ sales day. 🔥 The key is having enough quality inventory listed to make these days possible. Today's sales also came from several completely different categories, which continues to reinforce the importance of diversifying inventory rather than relying on one type of product. The work continues behind the scenes too. Refresh old listings Fix listings that aren't getting attention Adjust pricing on stale inventory Keep sourcing Keep listing 206 days in & I'm still pushing forward. Some days are small. Some days are big. But the goal is the same every day, keep moving the business forward. On to Day 207. 📦💪🚀

• 
  Ratchlock in the Rain ⛈️🥀 (@DamnitKnightly) reported 32 minutes ago

  @boomboomfaia Yeah we had to turn off shipping TO the EU because of LUCID and the new PPWR rulings (US based issue for Etsy/Ebay) But I do believe acggoods does handle most of that since they are the ones producing and shipping the items. Only thing is the tariffs but that is customer based

• 
  Kat 🦋 (@ArtemusBlue) reported 37 minutes ago

  @Kurumi96Neko @WholesomeMeme Earphone jack to USB-C adaptor, you can get them for like £3 off Ebay ✨ Problem solved, and it even extends your cable a bit!

• 
  Ripper (@ripper0x) reported 39 minutes ago

  @anglio @eBay this is terrible

• 
  David Graham (@DavidAGraham) reported 39 minutes ago

  Why is the Camera in EastEnders wobbling up and down? Do they know you can buy a tripod from eBay. How much did that multimillion pound set refurbishment cost?

• 
  Zamiel Cano (@ZamielJC) reported 41 minutes ago

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

• 
  Macy 🛸 (@mace_face18) reported 41 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)

• 
  Mike-E (@ATXCollectibles) reported 42 minutes ago

  @yanxchick A4: one of my 1/1 Colt McCoy Longhorn cards I communicated with the seller for about 6 years on eBay and YouTube until he was willing to come down to what I was comfortable paying. Originally asked $2k and I ended up paying $500.

• 
  DeePlaysGaming (@DeePlaysGaming) reported 42 minutes ago

  @robert_m45 @Pirat_Nation then when your discs that you cant use become so rare you can charge 3 times the amount you paid for it on ebay blaming disc drives not working is lame when a game licence runs out that gets taken from you for good and you signed up for it...we all did with any digital game.

• 
  Jay (@TheLobbyistGuy) reported 44 minutes ago

  Even in 2024, I had to track them down on eBay

• 
  Mike Palmer (シネフィル) 🎬 (@MikePalmerYT) reported 49 minutes ago

  Since July this year there are severe problems with orders from Japan and delivered from Japan Post in the European Union. I had three cases so far, and every parcel has been send back to Japan. A few days ago I ordered something very expensive of eBay.

• 
  dxrawr (@dxrawrTV) reported 51 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

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  Victory Gundam V1 (@Gundam_mf) reported 53 minutes ago

  @SylusMk2 I found the metal fix on eBay for like 1k Gl, the hummingbird figure is like a mythological creature at this point

• 
  Andrew (@AndrewMack123) reported 53 minutes ago

  @DanzelAlex Just unscrew the panels, and take them down normally! 🤦 Then you can put them up in your next place or sell them on ebay to recoup your losses! 🤦

• 
  Olivia Kory (@oliviaakory) reported 55 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.

• 
  Emmy Bear AI (@EmmyBearAI) reported 55 minutes ago

  @embw_l0x @eBay It wasn't a problem when I started. It is now. It's cool. Other platforms charge less.

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  ZaxCardZone (@ZaxCardZone) reported 57 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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  Jonathan Fairbourne (@JonFairbourne) reported 58 minutes ago

  @StonksMae Problem is everyone is confused and there is no clear direction. Buying eBay or no? CEO unpaid or getting 35B? More dilution coming? Bonds being converted or not? Too much uncertainty