# Australian Home Loan Rates (RBA) (`mrjerry/home-loan-rates`) Actor Curated time series of Australian housing-lending rates from the RBA F5 (Indicator) and F6 (Lenders Interest Rates) tables. Filter by loan purpose, rate type, segment, and institution scope. - **URL**: https://apify.com/mrjerry/home-loan-rates.md - **Developed by:** [Huxley](https://apify.com/mrjerry) (community) - **Categories:** Other, Open source - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Australian Home Loan Rates (RBA) [![Apify Store](https://img.shields.io/badge/Apify-Store-blueviolet?logo=apify)](https://apify.com/mrjerry/home-loan-rates) [![Python 3.11+](https://img.shields.io/badge/python-3.11%2B-blue.svg)](https://www.python.org) [![License: MIT](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE) Curated, semantically-named time series of Australian **housing-lending rates** from the Reserve Bank of Australia. Two source tables, one clean output shape: - **F6** — Lenders' Interest Rates (monthly, ~44 series): the rates actually on the books, broken down by loan purpose, rate type, LVR, loan size, and institution scope. - **F5** — Indicator Lending Rates (monthly, ~30 series): the *advertised* rates the banks publish. The RBA publishes these as wide CSVs with long, deeply-nested column names like `"Lending rates; Housing credit; New loans funded in the month; Owner-occupied; Fixed-rate, by residual fixed term; Less than or equal to 3 years"`. The Actor maps a hand-picked subset to short semantic keys an LLM can reason about directly. --- ### Output record shape ```json { "date": "2026-05-31", "source_table": "F6", "loan_purpose": "owner_occupier", "rate_type": "variable", "segment": "outstanding_all", "institution_scope": "all", "rate_pct": 6.2, "raw_column": "Lending rates; Housing credit; Outstanding; Owner-occupied; Variable-rate; All institutions", "fetched_at": "2026-07-09T14:30:12Z" } ```` | Field | Possible values | |---|---| | `source_table` | `F5`, `F6` | | `loan_purpose` | `owner_occupier`, `investor` | | `rate_type` | `all`, `variable`, `variable_discounted`, `fixed_3y`, `fixed_over_3y` | | `segment` | `outstanding_all`, `new_loans_all`, `lvr_low`, `lvr_high`, `size_small`, `size_mid`, `size_large`, `io`, `pi`, `indicator` | | `institution_scope` | `all`, `large`, `banks`, `mortgage_managers`, `securitised` | *** ### Quick start ```bash ## Most recent month, all curated series (12 records) apify call mrjerry/home-loan-rates --input '{"mode":"latest"}' ## All owner-occupier variable outstanding rates, 2024 onwards apify call mrjerry/home-loan-rates --input '{ "loan_purpose":"owner_occupier", "rate_type":"variable", "segment":"outstanding_all", "institution_scope":"all", "start_date":"2024-01-01" }' ## New loans only, broken down by LVR apify call mrjerry/home-loan-rates --input '{ "segment":"lvr_low", "institution_scope":"all" }' ## Investor rates, all-in, last 24 months apify call mrjerry/home-loan-rates --input '{ "loan_purpose":"investor", "institution_scope":"all", "start_date":"2024-07-01" }' ## F5 only (advertised rates, no F6) apify call mrjerry/home-loan-rates --input '{ "include_f5":true, "loan_purpose":"owner_occupier", "segment":"indicator" }' ``` From Python: ```python from apify_client import ApifyClient client = ApifyClient(token="YOUR_API_TOKEN") run = client.actor("mrjerry/home-loan-rates").call( run_input={"mode": "latest", "loan_purpose": "owner_occupier"}, ) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(f"{item['date']} {item['segment']:18s} {item['rate_pct']}%") ``` *** ### Inputs | Field | Type | Default | Description | |---|---|---|---| | `mode` | string | `rates` | `rates` (default) for the time series, `latest` for the most recent month only. | | `loan_purpose` | string | `all` | `owner_occupier`, `investor`, or `all`. | | `rate_type` | string | `all` | `variable`, `variable_discounted`, `fixed_3y`, `fixed_over_3y`, or `all`. | | `segment` | string | `all` | See the table above. `outstanding_all` = the average rate on all loans currently on the books. `new_loans_all` = the rate on loans funded in the most recent month. `lvr_low`/`lvr_high` = new loans at LVR <80% / ≥80%. `size_small`/`size_mid`/`size_large` = new loans under $600k / $600k–$1m / over $1m. `io`/`pi` = interest-only / principal-and-interest. `indicator` = the banks' advertised rates (F5). | | `institution_scope` | string | `all` | `all` = all institutions, `large` = the major banks, `banks` = the bank sector (F5), `mortgage_managers` = non-bank lenders, `securitised` = securitised loan book. | | `start_date` | string | — | ISO `YYYY-MM-DD`; include only records on or after this date. | | `end_date` | string | — | ISO `YYYY-MM-DD`; include only records on or before this date. | | `limit` | integer | — | Cap the number of records pushed. | | `include_f5` | boolean | `true` | Whether to include the F5 (advertised) rates. Set to `false` to only get F6 (actual rates on the books). | *** ### What's in the data **F6 (Lenders' Interest Rates)** is the authoritative source for "what are people actually paying?" The data is reported by APRA from the banks. As of mid-2026: - Owner-occupier variable outstanding: **~6.2%** - Owner-occupier fixed ≤3y outstanding: **~5.5%** (banks discount fixed to attract new lending) - Investor variable outstanding: **~6.4%** (slightly higher than owner-occupier, by ~20bp) - New loans funded in May 2026 (the most recent month) had a slightly lower variable rate than the outstanding average — banks are offering discounts to attract new business **F5 (Indicator Lending Rates)** captures the rates the banks *advertise* (the headline figures in their marketing). These are typically: - Owner-occupier standard variable: **~6.5–7.0%** (banks vary) - Owner-occupier 3-year fixed: **~6.5–7.0%** (the "specials") - Investor standard variable: **~6.7–7.2%** (a small premium over owner-occupier) F5 includes mortgage managers and the securitised-loan book in addition to the bank sector. *** ### How it works The Actor fetches two RBA CSV endpoints and curates the result: | Endpoint | Records | Cadence | |---|---|---| | `https://www.rba.gov.au/statistics/tables/csv/f6-data.csv` | 83 date rows × 42 columns | Monthly | | `https://www.rba.gov.au/statistics/tables/csv/f5-data.csv` | 810 date rows × 30 columns | Monthly | Each CSV is parsed (handling RBA's mixed `DD-Mon-YYYY` and `DD/MM/YYYY` date formats), and a hand-curated list of ~40 useful series is matched by substring against the column names. The match maps the RBA's nested English column names to short semantic keys, then filters and pushes to the Apify Dataset. *** ### Use as an LLM tool Three natural agent tasks this Actor enables: 1. **"What are owner-occupier variable rates right now?"** — `mode: "latest", loan_purpose: "owner_occupier", rate_type: "variable", segment: "outstanding_all"`. 2. **"Show me new-loan rates broken down by LVR"** — `segment: "lvr_low"` then `segment: "lvr_high"` and compare. 3. **"How have rates moved over the last 12 months?"** — `mode: "rates", start_date: "<12 months ago>", loan_purpose: "owner_occupier", rate_type: "variable"`. *** ### Pairs well with - **`rba-statistics`** — the parent Actor that exposes all 213 RBA tables including F5 and F6 by their raw column names. Use this Actor when you want a curated, semantically-named view; use `rba-statistics` when you want every column including the niche series we don't curate. - **`cdr-register`** — lists which banks are on CDR. Combine with home-loan-rates to ask "of the 117 banking brands on CDR, what are their published variable rates?" *** ### Local development ```bash ## Install pip install -r requirements.txt ## Run with a test input echo '{"mode":"latest"}' > storage/key_value_stores/default/INPUT.json apify run ## Inspect ls storage/datasets/default/ cat storage/datasets/default/000000001.json ``` *** ### Limits & gotchas - **Curated subset.** The Actor maps ~40 of the ~74 series across F5 and F6. The full RBA data is in the `rba-statistics` Actor. - **Monthly cadence.** Both F5 and F6 are published monthly; if you need weekly or daily data, see RBA's other tables (`f11.1` for daily exchange rates, etc.). - **No retry on transient errors.** Add `tenacity` if the RBA is unreachable and you need resilience. - **Series-breaks tables** (`f6-series-breaks`, `f5-series-breaks`) aren't included. Pull them separately via `rba-statistics` if you need to reconstruct rate histories across methodology changes. *** ### License MIT. # Actor input Schema ## `mode` (type: `string`): What the Actor should do. 'rates' (default) returns the curated time series of housing-lending rates. 'latest' returns only the most recent month's records (one per curated series). ## `loan_purpose` (type: `string`): Filter by who the loan is for. 'owner\_occupier' = the borrower lives in the property. 'investor' = the borrower is renting it out. 'all' = both. ## `rate_type` (type: `string`): Filter by how the rate is set. 'variable' = moves with the cash rate. 'fixed\_3y' = fixed for ≤3 years (residual). 'fixed\_over\_3y' = fixed for >3 years. 'all' = no filter. ## `segment` (type: `string`): Filter by what the rate applies to. 'outstanding\_all' = the average rate on all loans currently on the books. 'new\_loans\_all' = the rate on loans funded in the most recent month. 'lvr\_low' = new loans with LVR <80%. 'lvr\_high' = new loans with LVR ≥80%. 'size\_small'/'size\_mid'/'size\_large' = new loans under $600k / $600k–$1m / over $1m. 'io'/'pi' = interest-only / principal-and-interest. 'indicator' = the banks' advertised rates (from F5). 'all' = no filter. ## `institution_scope` (type: `string`): Filter by who is reporting. 'all' = all institutions (banks + non-banks). 'large' = the major banks only. 'banks' = the bank sector (F5 indicator rates). 'mortgage\_managers' = non-bank lenders (F5). 'securitised' = securitised loan book (F5). ## `start_date` (type: `string`): Optional. Include only records on or after this date. ## `end_date` (type: `string`): Optional. Include only records on or before this date. ## `limit` (type: `integer`): Optional. Cap the number of records pushed. ## `include_f5` (type: `boolean`): Whether to include the F5 (advertised / indicator) rates in the output. Default true. Set false to only get F6 (outstanding / new loans) data. ## Actor input object example ```json { "mode": "rates", "loan_purpose": "all", "rate_type": "all", "segment": "all", "institution_scope": "all", "include_f5": true } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("mrjerry/home-loan-rates").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("mrjerry/home-loan-rates").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 '{}' | apify call mrjerry/home-loan-rates --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=mrjerry/home-loan-rates", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Australian Home Loan Rates (RBA)", "description": "Curated time series of Australian housing-lending rates from the RBA F5 (Indicator) and F6 (Lenders Interest Rates) tables. Filter by loan purpose, rate type, segment, and institution scope.", "version": "0.1", "x-build-id": "zU3bnPh90yoBVdXUH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/mrjerry~home-loan-rates/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-mrjerry-home-loan-rates", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/mrjerry~home-loan-rates/runs": { "post": { "operationId": "runs-sync-mrjerry-home-loan-rates", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/mrjerry~home-loan-rates/run-sync": { "post": { "operationId": "run-sync-mrjerry-home-loan-rates", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "rates", "latest" ], "type": "string", "description": "What the Actor should do. 'rates' (default) returns the curated time series of housing-lending rates. 'latest' returns only the most recent month's records (one per curated series).", "default": "rates" }, "loan_purpose": { "title": "Loan purpose", "enum": [ "all", "owner_occupier", "investor" ], "type": "string", "description": "Filter by who the loan is for. 'owner_occupier' = the borrower lives in the property. 'investor' = the borrower is renting it out. 'all' = both.", "default": "all" }, "rate_type": { "title": "Rate type", "enum": [ "all", "variable", "variable_discounted", "fixed_3y", "fixed_over_3y" ], "type": "string", "description": "Filter by how the rate is set. 'variable' = moves with the cash rate. 'fixed_3y' = fixed for ≤3 years (residual). 'fixed_over_3y' = fixed for >3 years. 'all' = no filter.", "default": "all" }, "segment": { "title": "Segment", "enum": [ "all", "outstanding_all", "new_loans_all", "lvr_low", "lvr_high", "size_small", "size_mid", "size_large", "io", "pi", "indicator" ], "type": "string", "description": "Filter by what the rate applies to. 'outstanding_all' = the average rate on all loans currently on the books. 'new_loans_all' = the rate on loans funded in the most recent month. 'lvr_low' = new loans with LVR <80%. 'lvr_high' = new loans with LVR ≥80%. 'size_small'/'size_mid'/'size_large' = new loans under $600k / $600k–$1m / over $1m. 'io'/'pi' = interest-only / principal-and-interest. 'indicator' = the banks' advertised rates (from F5). 'all' = no filter.", "default": "all" }, "institution_scope": { "title": "Institution scope", "enum": [ "all", "large", "banks", "mortgage_managers", "securitised" ], "type": "string", "description": "Filter by who is reporting. 'all' = all institutions (banks + non-banks). 'large' = the major banks only. 'banks' = the bank sector (F5 indicator rates). 'mortgage_managers' = non-bank lenders (F5). 'securitised' = securitised loan book (F5).", "default": "all" }, "start_date": { "title": "Start date (YYYY-MM-DD)", "type": "string", "description": "Optional. Include only records on or after this date." }, "end_date": { "title": "End date (YYYY-MM-DD)", "type": "string", "description": "Optional. Include only records on or before this date." }, "limit": { "title": "Record limit", "minimum": 1, "type": "integer", "description": "Optional. Cap the number of records pushed." }, "include_f5": { "title": "Include F5 indicator rates", "type": "boolean", "description": "Whether to include the F5 (advertised / indicator) rates in the output. Default true. Set false to only get F6 (outstanding / new loans) data.", "default": true } } }, "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 } } } } } } } } } } ```