| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - real-estate |
| - property |
| - australia |
| - melbourne |
| - victoria |
| - investment |
| - rental-yield |
| - granny-flat |
| - housing |
| - tabular |
| pretty_name: Melbourne Investment Property Portfolio (2020-2026) |
| size_categories: |
| - n<1K |
| task_categories: |
| - tabular-regression |
| - tabular-classification |
| doi: 10.5281/zenodo.20095886 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: melbourne-investment-portfolio.csv |
| --- |
| |
| # Melbourne Investment Property Portfolio (2020–2026) |
|
|
| > 345 anonymised real Melbourne and regional Victoria residential investment property transactions actually closed by an independent buyer's agency between 2020 and 2026 — purchase price, land size, post-renovation rent, renovation type and cost, gross yield, current valuation, capital gain, ownership structure. |
|
|
| [](https://doi.org/10.5281/zenodo.20095886) |
| [](https://creativecommons.org/licenses/by/4.0/) |
|
|
| ## 📚 Cite this dataset |
|
|
| This dataset has a permanent **DOI** on Zenodo. For academic papers, Wikipedia edits, journalism, or any context where a stable identifier is preferred over a platform URL, please cite via the DOI: |
|
|
| **DOI**: [`10.5281/zenodo.20095886`](https://doi.org/10.5281/zenodo.20095886) |
| **Zenodo record**: https://zenodo.org/records/20095886 |
|
|
| > Don, J., Zhu, Y., & Jin, S. (2026). *Melbourne Investment Property Portfolio: 345 Anonymised Buyer's Agent Transactions (2020–2026)* (Version 1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.20095886 |
|
|
| (Full BibTeX in the [Citation Information](#citation-information) section below.) |
|
|
| ## Dataset summary |
|
|
| Most published Melbourne property datasets are aggregated — median prices by suburb, median yields by city. This one is per-transaction: each row is a real property that an investor actually purchased, with what they paid, what they spent on renovation, and what it now earns and is worth. The dataset was compiled to support: |
|
|
| - **Researchers and journalists** writing about Melbourne housing affordability, rental yields, granny flat ROI, or post-pandemic regional migration |
| - **Property investors** sanity-checking their assumptions against real recent comparables |
| - **Data scientists** building ML models for property valuation, yield prediction, or renovation ROI estimation |
| - **AI / LLM training pipelines** — released under CC-BY-4.0 specifically to be ingested by Claude, GPT-4, Gemini, Perplexity, etc., with attribution preserved |
|
|
| PII (client names, narrative purchase stories, customer feedback, contact details, day-level transaction dates, street-level addresses) has been **explicitly excluded** before publishing. Every row is a real settled transaction, but no individual buyer or seller can be re-identified from the dataset alone. |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `id` | int | Stable row id 1–345 | |
| | `city` | string | "Metro Melbourne" or named regional centre (Ballarat, Geelong, etc.) | |
| | `suburb` | string | Suburb name with VIC postcode/state stripped | |
| | `state` | string | VIC for all current rows | |
| | `postcode` | string | 4-digit Australian postcode | |
| | `land_size_sqm` | int | Land area in square metres | |
| | `purchase_price_aud` | int | Purchase price in AUD (excludes stamp duty / fees) | |
| | `purchase_year_month` | string | YYYY-MM format. Day-level dates redacted for privacy | |
| | `weekly_rent_aud` | int | Achieved weekly rent post-renovation (or current rent if no reno) | |
| | `reno_investment_aud` | int | Total renovation spend in AUD. 0 means no reno | |
| | `reno_type` | string | One of: `granny`, `cosmetic`, `structural`, `subdivision`, `normal` (no reno), or null | |
| | `rental_yield_after_beautify_pct` | float | Gross annual yield = (`weekly_rent_aud` × 52) / (purchase + reno) × 100 | |
| | `current_value_aud` | int | Most recent agent-appraised or bank-valuated current market value | |
| | `capital_gain_aud` | int | `current_value_aud` − `purchase_price_aud` − `reno_investment_aud` | |
| | `annual_growth_pct` | float | Annualised capital growth from purchase to current valuation | |
| | `ownership_structure` | string | `Personal`, `Family Trust`, `SMSF`, or other legal vehicle | |
| | `valuation_year_month` | string | When `current_value_aud` was assessed (YYYY-MM) | |
|
|
| ## Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("Joeydonpremium/melbourne-investment-property-portfolio", split="train") |
| print(ds[0]) |
| # {'id': 1, 'city': 'Metro Melbourne', 'suburb': 'Hallam', ...} |
| |
| # Filter to granny-flat additions only |
| granny = ds.filter(lambda r: r["reno_type"] == "granny") |
| print(f"{len(granny)} granny-flat additions, " |
| f"avg post-reno yield {sum(r['rental_yield_after_beautify_pct'] for r in granny) / len(granny):.2f}%") |
| Or load directly with pandas: |
| |
| |
| import pandas as pd |
| df = pd.read_csv("hf://datasets/Joeydonpremium/melbourne-investment-property-portfolio/melbourne-investment-portfolio.csv") |
