Datasets:

Joeydonpremium
/

melbourne-investment-property-portfolio

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.

📚 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 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 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

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")