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 (65%)
• Sign in (22%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Preston	Website Down	11 hours ago
Preston	Website Down	21 hours ago
Preston	Website Down	1 day ago
Hannoversch Münden	Sign in	1 day ago
Blackpool	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:

• 
  StuArt  (@StuHack73) reported 8 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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  AXISPAW (@AXISPAW) reported 13 minutes ago

  @KJovian Dude bought a broken one off Ebay for clicks

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  ᴅᴏɴᴏᴠᴀɴ² 🇺🇸🇬🇧 (@arcanedonovan) reported 14 minutes ago

  @PunchingCat @michiganstan25 i put them on ebay for my house down payment

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  RD's caRD's (@rdaex1) reported 15 minutes ago

  Explain the issues for us to see if you're trustworthy or not. It's a 10$ card man you're not giving the winner a Tesla. If you don't have the card in hand, just Buy it on eBay and just send it to him... If you won't do something this simple, please stop trying to infiltrate your way into this space.

• 
  Kat 🦋 (@ArtemusBlue) reported 17 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!

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  Eric the Red / Kelenic Collector (@FatherBreaks) reported 20 minutes ago

  @eBay when a seller chooses your ESE option and pays for it, it should come out at that time just like any other service one chooses to ship with. It’s annoying as hell to see my balance go down 2-3 day later and have to track which fee it was for

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  Germainrain (@germainraine) reported 20 minutes ago

  @cmpayneful @GameStopOnChain Consider this a rabbit hole, but if you choose to go down, will lead to more… like XRP. Surprisingly this stuff connected to Epstein, which is why the DOJ is gonna be important. If I were to guess I think eBay was permitting trafficking kids likewise to JP Morgan

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  Blitz  (@PeriBlitzV2) reported 22 minutes ago

  @Digggon the type of game the MC of a creepypasta would buy off ebay, see charmander get brutually decapitated in it, and then shurg it off thinking it was just a weird glitch

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  Nigel J Bevans Photo (@NJBDP) reported 25 minutes ago

  @hmefsww Hi, unfortunately I closed my website store down as they were charging a ridiculous amount to sell things. To keep prices reasonably I had to find an alternative and only have eBay at moment. I could do it via paypal cutting out ebay if you would like?

• 
  Olivia Kory (@oliviaakory) reported 32 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.

• 
  Jared Bray (@tresamigos858) reported 33 minutes ago

  @KyngDid Even that breaks down to math too. If a table cost is $150 that’s 10% of $1500 in sales. Even selling at 100% of comps comes with a cost (and depending on how much you sell could be far more or far less than eBay)

• 
  𝕒𝕤𝕥𝕣𝕚𝕕 / 𝕝𝕚𝕝𝕒𝕔 💜♥️💚 purples lover (@purpofsecurity_) reported 34 minutes 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

• 
  0xbonus (@coral_crypt) reported 34 minutes ago

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

• 
  Wandering Biku. Professional cynic. (@BikuWandering) reported 37 minutes ago

  So, buying DVD's from eBay is cheaper than ordering movies to stream. Plus you get to keep them when the grid goes down 👍👍👍

• 
  Jay (@TheLobbyistGuy) reported 39 minutes ago

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

• 
  Fitzzzy 🏴‍☠️ (@ShaunFitzzzy) reported 40 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

• 
  dave (@davejones3334) reported 41 minutes ago

  @LLMJunky @stevenharms I don’t sell much on eBay but almost every tech item I’ve had scammer issues. Luckily I didn’t lose any money and they were fairly low value items in the 100-300 range. I would not trust selling a multi thousand dollar item there.

• 
  Uma Kompton (@RIPUmaKompton) reported 43 minutes ago

  @ultra_kitsch @offbeatorbit Not the same as an awesome apartment ofc but I got a $300 dress with tags for $6 on eBay because the pictures were so terrible 😭 will never forget it

• 
  maeve (@lizziedossss) reported 48 minutes ago

  @TheShazamPow i actually looked this up the other day. its technically a grey area but it is blacklisted from ebay. so yes its a problem if you sell it but apparently owning it is fine?

• 
  Amanda 🥀 (@lxveatfirstbite) reported 48 minutes ago

  @AFRAlD0FTHEDARK I just bought a long chain from eBay and cut each bit down to size

• 
  Les Twigg (@LesTwigg) reported 50 minutes ago

  @eBay @eBay_UK Not really a customer support issue though is it. Just pointing out where yet another change has made things worse.....

• 
  Sj (@Pkrguy23) reported 51 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.

• 
  Emmy Bear AI (@EmmyBearAI) reported 52 minutes ago

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

• 
  Sean (@firinzlol) reported 53 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

• 
  Card Throne (@cardthrone) reported 53 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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  Byron Vallis (@Byronnn6) reported 55 minutes ago

  @calvinfroedge Free shipping and eBay fees will knock this down to like $4.5 my friend. Better luck next time

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  A.P.O.C (@AOchiawuto) reported 55 minutes 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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  $$D@mouppn?$$ (@Judd29230Judd) reported 56 minutes ago

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

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  slicko (@haribo4me) reported 1 hour 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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  Andres Fernandez (@Brok3rFernandez) reported 1 hour ago

  @643DoublPlay @CardPurchaser He actually is asking over $10k on eBay so I got him down to $7k