Is there an API for Zillow price history?

Not from Zillow directly — its public API was retired in 2021. Third-party APIs like APIllow return the price_history array for any US property by address, ZPID, or Zillow URL: every listing, price change, pending, and sale event with date, price, and source.

How far back does Zillow price history go?

Often decades. The timeline merges MLS listing events with public-record sales, so a property's 1998 sale can appear alongside its 2026 listing, each with an event type and source.

Can I get tax history through the same API?

Yes. The same property response includes a tax_history array with assessed value and tax paid by year — commonly used for assessment appeals and expense modeling.

Why is price history missing when I scrape a Zillow page?

On off-market homes, Zillow omits much of the sales record from the standard page payload, so page-level scrapers come back empty. APIllow runs a dedicated enrichment step for off-market properties and recovers a full price history for roughly 8 in 10 of them.

Zillow Price History API: The Full Event Timeline in JSON

A property's price history is its negotiation file. Three price cuts in ninety days says more about the seller than any listing description. A purchase two years ago at 30% below today's ask tells you exactly where the anchor is. A de-list-and-relist pattern tells you the days-on-market counter has been reset — and by how much.

Zillow displays this timeline on every property page, but there is no official API for it. This guide shows how to pull the full price and tax history for any US property as JSON — what the events look like, what they mean, and how to build price-cut detection on top of them.

What a price history payload looks like

Request any property by address, ZPID, or Zillow URL:

curl -X POST https://api.apillow.co/v1/properties \
  -H "x-api-key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"addresses": ["1874 Bromton Dr, Lyndhurst, OH 44124"]}'

The price_history array comes back newest-first, one entry per event:

"price_history": [
  {"date": "2024-06-14", "event": "Sold",            "price": 289000, "source": "Public Record"},
  {"date": "2024-05-02", "event": "Pending sale",    "price": 285000, "source": "MLS"},
  {"date": "2024-04-29", "event": "Price change",    "price": 285000, "source": "MLS"},
  {"date": "2024-04-18", "event": "Listed for sale", "price": 299000, "source": "MLS"},
  {"date": "2009-08-21", "event": "Sold",            "price": 176500, "source": "Public Record"}
],
"tax_history": [
  {"year": 2025, "tax_paid": 5840.12, "value": 254300},
  {"year": 2024, "tax_paid": 5512.40, "value": 241800}
]

Two things to notice. The timeline reaches back decades — MLS events merged with public-record sales. And every event carries its source, so you can weight an MLS-sourced sale differently from a county-record one.

Reading the events

Event	What it means	What it signals
`Listed for sale`	Property hit the market at this ask	Start of the days-on-market clock
`Price change`	Ask moved while listed	Cuts = seller motivation; the gap and cadence matter
`Pending sale`	Offer accepted	Ask-at-pending vs final sold price = negotiation room
`Listing removed`	De-listed without a sale	Followed by a new listing = DOM reset; real history persists here
`Sold`	Sale closed	The comp: pair with `last_sold_date` for recency filters

Example: detect motivated sellers with a price-cut scan

The classic use case: scan a batch of properties and rank by how aggressively the ask has dropped.

def price_cut_signal(prop):
    # Events since the most recent listing, oldest first
    hist = sorted(prop.get("price_history", []), key=lambda e: e["date"] or "")
    listing = [e for e in hist if e["event"] == "Listed for sale"]
    if not listing:
        return 0.0
    since = [e for e in hist if e["date"] >= listing[-1]["date"] and e["price"]]
    first, last = since[0]["price"], since[-1]["price"]
    return (first - last) / first  # 0.05 = 5% total cut since listing

Run it over a ZIP's active inventory (a ZIP-code query returns exactly that — the for-sale listings) and sort descending: the top of the list is your call sheet. The Python tutorial covers the batch-and-poll pattern this plugs into.

Tax history rides along

The tax_history array — assessed value and tax paid, by year — is in the same response, no extra call. The two workhorse uses: assessment appeals (assessed value climbing faster than the price events justify) and expense modeling for rental underwriting, where last year's actual tax paid beats any estimate. Divide last_sold_price by the assessment in the sale year and you get the county's assessment ratio — useful for sanity-checking any valuation in the area.

The off-market catch (and why scrapers come back empty)

If you have tried to scrape price history yourself, you have probably seen it work on active listings and return nothing on off-market homes. That is Zillow's doing, not yours: on off-market properties, most of the sales record is omitted from the standard page payload. It lives in a separate internal data path. APIllow enriches every off-market property against that path and recovers a full price_history for roughly 8 in 10 off-market homes — the mechanics are covered in the sold data API guide, this article's sibling.

Pull a price history now

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Final takeaway

Price history is the highest-signal-per-byte field in property data, and it is one API call away: every listing, cut, pending, and sale — plus tax history — in a single JSON response. Just know the one asymmetry: active listings carry their history in plain sight, while off-market homes need the enrichment step we run for you.