# Israel Used-Car Deal Finder - Underpriced Cars (`swerve/il-car-deals`) Actor Scores live Yad2 used-car listings against the official Levi Yitzhak market value and surfaces the underpriced ones. For car dealers, flippers, and buyers hunting a deal. Returns each listing with its market value, the gap, and a deal score. - **URL**: https://apify.com/swerve/il-car-deals.md - **Developed by:** [Swerve](https://apify.com/swerve) (community) - **Categories:** E-commerce, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Israel Used-Car Deal Finder Find used cars on Yad2 that are priced below what comparable cars are listed for right now. The actor pulls a pool of live Yad2 car listings, groups them into cohorts of similar cars (same manufacturer, model, and year, adjusted for mileage), computes a robust fair market price per cohort, and flags any listing priced meaningfully below it. The pitch is simple: priced below what similar cars are actually listed for right now. No external price guide, no guesswork, only the live Yad2 market. ### What it does 1. Pulls a comparison pool of Yad2 car listings (the market baseline). 2. Groups them into cohorts by manufacturer, model, and year, with Hebrew-aware name matching. 3. Computes a robust fair market price per cohort using a trimmed median, and adjusts for mileage and model year. 4. Compares each listing's asking price to its cohort's fair value. 5. Flags the listings priced below market, with a plain evidence line ("X% below the median of N comparable sales") and a data-strength indicator so you can judge each number. ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `manufacturer` | string | (none) | Limit the scan to one manufacturer, Hebrew or English (e.g. `Toyota` or `טויוטה`). A single manufacturer builds denser cohorts and finds more deals. Leave empty to scan all makes. | | `maxItems` | integer | 60 | How many listings to score and return rows for. Caps the output only. No upper cap. | | `corpusItems` | integer | 1500 | How many comparable Yad2 listings to pull as the market baseline. More comparables make valuations more stable. | | `minDealPct` | integer | 8 | Flag a listing when its price is at least this percent below the fair market price (the threshold is raised when less data backs the valuation). | | `maxPlausiblePct` | integer | 60 | Discounts larger than this percent are treated as data errors (typos, parts cars) and never flagged as deals. | | `onlyDeals` | boolean | false | On: return only the flagged deals. Off: return every scored listing with its market value and evidence. | | `proxyConfig` | object | Israeli residential | Proxy configuration. Israeli residential proxy recommended (Yad2 is reached from Israel). | For local testing you can also pass `listings` (an array of Yad2 car listings) to skip the live pull and value against the pool you provide. Pass a representative pool so the cohorts are meaningful. #### Example input ```json { "manufacturer": "טויוטה", "maxItems": 60, "corpusItems": 1500, "minDealPct": 8, "maxPlausiblePct": 60, "onlyDeals": false, "proxyConfig": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "IL" } } ```` ### Output Each row is one scored listing. | Field | Type | Description | |-------|------|-------------| | `listingId` | string | Yad2 listing id | | `url` | string | Link to the Yad2 listing | | `manufacturer` | string | Manufacturer (Hebrew where available) | | `model` | string | Model (Hebrew where available) | | `subModel` | string | Sub-model / trim from Yad2 | | `year` | integer | Year of production | | `km` | integer | Kilometers | | `hand` | integer | Number of previous owners | | `askingPrice` | integer | Asking price on Yad2 (ILS) | | `fairValue` | integer | Fair market price from comparable Yad2 listings, adjusted for mileage and year (ILS) | | `dealGap` | integer | Fair value minus asking price (ILS). Positive means priced below market. | | `dealPct` | integer | Percent below market (positive means underpriced) | | `isDeal` | boolean | True if priced below market by at least the threshold with enough comparables | | `matched` | boolean | Whether the listing was matched to a comparison cohort | | `evidence` | string | Plain-language proof behind the price, e.g. "15% below the median of 11 comparable sales" | | `dataStrength` | string | How much data backs the valuation: strong, moderate, limited, or unrated (describes the evidence base, not doubt about the deal) | | `cohortTier` | string | Comparison tier used (see How it works) | | `cohortSize` | integer | Number of comparable listings used for the valuation | | `cohortMedianKm` | integer | Median mileage of the comparison cohort (km) | | `priceSpreadPct` | integer | Price spread within the cohort (robust deviation as percent of median) | | `kmAdjustmentPct` | integer | Percent the mileage adjustment moved the fair value versus the cohort baseline (0 if not adjusted) | | `cohortDealerShare` | integer | Percent of the cohort listed by dealers | | `kmAdjusted` | boolean | Whether the fair value was adjusted for the listing's mileage | | `suspectReason` | string | Why a listing was not flagged (see below) | | `scrapedAt` | string | When scored (ISO date) | #### Example output row ```json { "listingId": "abcd1234", "url": "https://www.yad2.co.il/vehicles/private-cars/abcd1234", "manufacturer": "טויוטה", "model": "קורולה", "subModel": "1.8 היברידי", "year": 2019, "km": 78000, "hand": 1, "askingPrice": 92000, "fairValue": 108000, "dealGap": 16000, "dealPct": 15, "isDeal": true, "matched": true, "evidence": "15% below the median of 11 comparable sales", "dataStrength": "moderate", "cohortTier": "T1", "cohortSize": 11, "cohortMedianKm": 81000, "priceSpreadPct": 9, "kmAdjustmentPct": 1, "cohortDealerShare": 64, "kmAdjusted": true, "suspectReason": null, "scrapedAt": "2026-06-16T10:00:00.000Z" } ``` ### How it works The fair value is computed against the live Yad2 market, never an external price guide. 1. **Cohorts.** Listings are grouped by manufacturer and model (Hebrew names preferred, with edit-distance folding so spelling variants like `ספורטז` and `ספורטג'` land in the same cohort). Sub-model and trim are deliberately not part of the key, so cohorts stay dense; trim variance is absorbed by the robust median below. 2. **Comparison tiers.** For each listing the actor tries the tightest comparison first and falls back only when there are too few comparables: - `T1` (high): same model, same year, similar mileage. No mileage curve needed. This is the literal pitch. - `T2` (high): same model, same year, mileage adjusted. - `T3` (medium): within one year, year and mileage adjusted. - `T4` (low): any year, same model, year and mileage adjusted. 3. **Robust math.** Within a cohort the actor uses the median (not the average) and trims outliers with a median-absolute-deviation filter, so one mislabeled, salvage, or typo listing cannot drag the fair value. The mileage effect is estimated robustly from the cohort itself (a Theil-Sen slope), clamped to a sane depreciation band, and falls back to a conservative prior when the cohort is too thin to estimate it. 4. **Evidence, shown plainly.** Every row carries an `evidence` line ("X% below the median of N comparable sales") so you can judge each price on the proof, not a label. A `dataStrength` field (strong, moderate, limited, unrated) says how much data backs it: thin cohorts, missing mileage, fallback mileage estimates, and wide price spreads all lower it. The deal threshold is raised when the data is weaker, so the actor never flags a deal off a thin or noisy cohort. Used-car prices vary widely within the same make, model, and year (mileage, trim, condition), so most rows read "limited" or "moderate" by design, that is honesty about the data, and the evidence line lets the number speak for itself. When a listing is not flagged, `suspectReason` explains why: - `subject_data_invalid`: the listing's own price, year, or mileage is out of range. - `salvage_suspect`: the listing reads as a salvage or parts car (valued, but never flagged as a deal). - `insufficient_comps`: not enough comparable listings to value it. - `implausible_gap`: the discount is larger than `maxPlausiblePct`, treated as a data error. - `below_threshold`: priced below market, but not by enough given how much data backs it. ### Notes and limits - The fair value is based on Yad2 **asking** prices of comparable cars, not confirmed sold prices. It reflects what similar cars are listed for right now, which is the relevant benchmark for spotting an underpriced listing. - Deals only flag when there are enough comparable listings. A rare model with few comparables on the market will be valued at limited data strength or not at all, by design. - Scanning a single manufacturer produces denser cohorts and more deals than scanning all makes with the same pool size. - Salvage and parts cars are detected from the listing text and excluded from cohorts; a salvage listing is still valued but never flagged as a deal. - Prices and mileage are in the units Yad2 uses (ILS, kilometers). ### Use cases - Car dealers and flippers scanning for stock priced below the market. - Buyers hunting for a specific make or model below its going rate. - Building a price-tracking dataset of where the used-car market is actually pricing each model right now. # Actor input Schema ## `manufacturer` (type: `string`): Limit the scan to one manufacturer (Hebrew or English, e.g. 'Toyota' or 'טויוטה'). A single manufacturer builds denser comparison cohorts and finds more deals. Leave empty to scan all makes. ## `maxItems` (type: `integer`): How many Yad2 car listings to score and return rows for. No upper cap. This only caps the output; the comparison pool can be larger (see Comparison pool size). ## `corpusItems` (type: `integer`): How many comparable Yad2 listings to pull as the market baseline. More comparables make valuations more stable (thicker per-model/year cohorts = higher-confidence deals), but larger pools take longer to fetch and cost more. Default 800. ## `minDealPct` (type: `integer`): Flag a listing when its price is at least this percent below the fair market price computed from comparable Yad2 listings (after a confidence-based adjustment). ## `maxPlausiblePct` (type: `integer`): Discounts larger than this percent are treated as data errors (typos, parts cars) and never flagged as deals. ## `onlyDeals` (type: `boolean`): On: return only listings priced below market by at least the threshold. Off: return every scored listing with its market value and confidence. ## `proxyConfig` (type: `object`): Israeli residential proxy recommended (Yad2 is reached from Israel). ## Actor input object example ```json { "manufacturer": "טויוטה", "maxItems": 60, "corpusItems": 800, "minDealPct": 8, "maxPlausiblePct": 60, "onlyDeals": false, "proxyConfig": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "IL" } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "manufacturer": "טויוטה", "proxyConfig": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "IL" } }; // Run the Actor and wait for it to finish const run = await client.actor("swerve/il-car-deals").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "manufacturer": "טויוטה", "proxyConfig": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "IL", }, } # Run the Actor and wait for it to finish run = client.actor("swerve/il-car-deals").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "manufacturer": "טויוטה", "proxyConfig": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "IL" } }' | apify call swerve/il-car-deals --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=swerve/il-car-deals", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Israel Used-Car Deal Finder - Underpriced Cars", "description": "Scores live Yad2 used-car listings against the official Levi Yitzhak market value and surfaces the underpriced ones. For car dealers, flippers, and buyers hunting a deal. Returns each listing with its market value, the gap, and a deal score.", "version": "1.0", "x-build-id": "E4KE8D0kizV9h8nbr" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/swerve~il-car-deals/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-swerve-il-car-deals", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/swerve~il-car-deals/runs": { "post": { "operationId": "runs-sync-swerve-il-car-deals", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/swerve~il-car-deals/run-sync": { "post": { "operationId": "run-sync-swerve-il-car-deals", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "manufacturer": { "title": "Manufacturer (optional)", "type": "string", "description": "Limit the scan to one manufacturer (Hebrew or English, e.g. 'Toyota' or 'טויוטה'). A single manufacturer builds denser comparison cohorts and finds more deals. Leave empty to scan all makes." }, "maxItems": { "title": "Max listings to score", "minimum": 1, "type": "integer", "description": "How many Yad2 car listings to score and return rows for. No upper cap. This only caps the output; the comparison pool can be larger (see Comparison pool size).", "default": 60 }, "corpusItems": { "title": "Comparison pool size", "minimum": 0, "type": "integer", "description": "How many comparable Yad2 listings to pull as the market baseline. More comparables make valuations more stable (thicker per-model/year cohorts = higher-confidence deals), but larger pools take longer to fetch and cost more. Default 800.", "default": 800 }, "minDealPct": { "title": "Minimum deal % to flag", "minimum": 0, "type": "integer", "description": "Flag a listing when its price is at least this percent below the fair market price computed from comparable Yad2 listings (after a confidence-based adjustment).", "default": 8 }, "maxPlausiblePct": { "title": "Maximum plausible discount %", "minimum": 0, "type": "integer", "description": "Discounts larger than this percent are treated as data errors (typos, parts cars) and never flagged as deals.", "default": 60 }, "onlyDeals": { "title": "Return only the deals", "type": "boolean", "description": "On: return only listings priced below market by at least the threshold. Off: return every scored listing with its market value and confidence.", "default": false }, "proxyConfig": { "title": "Proxy configuration", "type": "object", "description": "Israeli residential proxy recommended (Yad2 is reached from Israel).", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "IL" } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```