Israel Property Market Signals — Yad2 & Madlan Analytics
Under maintenancePricing
Pay per usage
Turn Israeli property listings into market intelligence: days on market, price-drop history, motivated-seller scores and per-neighbourhood absorption. Works with any listings scraper.
Pick the dataset produced by your listings scraper run — a Yad2, Madlan or Homeless Actor, or any source with a price and an ID per record. This Actor analyses listings; it does not fetch them, so nothing is charged twice.
Listing records as JSON, instead of a dataset ID. Handy for testing. Each record needs an identifier (id, listingId or adNumber) and a price (price, priceIls or askingPrice); rooms, squareMeters, floor, city and neighborhood are all used when present.
Keeps listing IDs from different sites separate, so the same number on Yad2 and Madlan is never treated as one property. Use a consistent value per tracker.
The memory this run belongs to. Use one per market you follow — for example tlv-sale or haifa-rent — and always the same value for the same market. Mixing markets into one tracker contaminates every median and cannot be undone afterwards.
How many consecutive runs a listing must be missing before it is reported as delisted. 2 is recommended for scheduled runs: it costs one day of latency and stops a single failed scrape being read as a wave of sales.
Only for loading old data. If you have archived scrapes, run them through oldest first with the date each was captured (YYYY-MM-DD) and the Actor builds real history in minutes instead of waiting weeks for it. Leave empty for normal daily runs.
Discards everything remembered for this Tracker ID and begins a new baseline. Days on market restarts at zero for every listing. There is no undo.
Computes and returns results without recording them. Use it to try different score weights against the same data without disturbing the history you have built up.
Only return listings scoring at or above this, from 0 to 100. Leave at 0 to get everything; set around 60 to see just the sellers who look ready to negotiate. You are billed on rows returned, so filtering here lowers your cost.
Adds one row per neighbourhood with median price per m², median days on market, share of listings that have cut price, absorption rate and the agent/private split.
Neighbourhoods with fewer active listings than this are still returned, but flagged lowConfidence — their medians are drawn from too small a sample to rely on.
