---
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.

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.20095886.svg)](https://doi.org/10.5281/zenodo.20095886)
[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](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")