eBay Price History: How to See What an Item Sold For Over Time

"I know this item has been sold a bunch before then, and it seems this 3-month limit is the case with all items." That's one seller hitting the wall everyone eventually hits: eBay forgets its sales after about 90 days.

Here's what each option actually gives you, and the one that only gets better with time.

#Option 1: eBay sold search (~90 days)

Sign in, search, tick Sold items. You see individual sales with prices and dates, going back roughly 90 days. It's free and precise, but short. Step by step.

#Option 2: Product Research / Terapeak (~3 years)

eBay's Product Research, still widely called Terapeak, lives in Seller Hub and goes back about three years. It's free with a seller account. The catches:

• Averages, not sales. You see average sold price and totals, not each sale's price, date and condition.
• No export or API. It's a screen, not data you can keep. More on that.

It's the best free way to eyeball a long trend for one item.

#Option 3: Start your own price history today

Nobody gives away long sold history in a usable form. But nothing stops you from keeping it yourself. Log the median sold price for the items you care about once a day or once a week. In three months you have more history than eBay shows. In a year, you have a price chart nobody else has.

The full script:

import csv, datetime, os, requests

ITEMS = ["nintendo 64 console", "game boy color", "super nintendo console"]
KEY = os.environ["SALEHISTORY_KEY"]

with open("price_history.csv", "a", newline="") as f:
    w = csv.writer(f)
    for q in ITEMS:
        r = requests.get(
            "https://api.soldcomps.io/v1/sold",
            params={"q": q, "days": 7},
            headers={"Authorization": f"Bearer {KEY}"},
            timeout=30,
        )
        d = r.json()
        w.writerow([datetime.date.today(), q, d["stats"]["median"], d["count"]])

Schedule it with cron (or a GitHub Action), then open the CSV in Google Sheets and insert a line chart. Using days: 7 means each point is the median of that week's sales, so the chart reflects what's happening now rather than a 90-day blur.

Prefer no code? The Google Sheets formula does the same thing. Add a dated row per week.

#Why short windows matter too

Long history tells you the trend. Short windows tell you the current price. Card investors say it bluntly: "it needs to be more like a 2- or 3-week average in the current market rather than 3 months." When a market is moving, compare the last 7–14 days against the 90-day median:

• 7-day median well above the 90-day: prices are rising. Don't underprice.
• 7-day median well below: prices are falling. Sell sooner, buy more cautiously.