electricsheepafrica/nigerian_realestate_tenant_turnover

Nigeria Real Estate – Tenant Turnover

Dataset Description

Synthetic Property Management & Facilities data for Nigeria real estate sector.

Category: Property Management & Facilities
Rows: 80,000
Format: CSV, Parquet
License: MIT
Synthetic: Yes (generated using reference data from PropertyPro, Knight Frank, NBS, CBN, FMBN)

Dataset Structure

Schema

• id: string
• date: string
• city: string
• value: float
• category: string

Sample Data

| id           | date       | city   |       value | category   |
|:-------------|:-----------|:-------|------------:|:-----------|
| REC-00848150 | 2024-11-17 | Abuja  | 4.68272e+07 | B          |
| REC-00073581 | 2024-01-04 | Kano   | 3.26928e+07 | A          |
| REC-00311470 | 2022-06-29 | Abuja  | 4.50289e+07 | B          |
| REC-00825617 | 2022-08-19 | Abuja  | 1e+06       | A          |
| REC-00603688 | 2023-07-23 | Lagos  | 1.34832e+07 | A          |

Data Generation Methodology

This dataset was synthetically generated using:

1. Reference Sources:

   • PropertyPro & Nigeria Property Centre - listing prices, property types, locations
   • Knight Frank & Pam Golding - market reports, prime property indices, investment analysis
   • NBS (National Bureau of Statistics) - real estate GDP contribution, construction statistics
   • CBN (Central Bank of Nigeria) - mortgage lending data, interest rates
   • FMBN (Federal Mortgage Bank) - NHF contributions, mortgage disbursements
   • State land registries - title processing times, transaction volumes

2. Domain Constraints:

   • Location pricing hierarchy (Lagos: Ikoyi > Lekki > Surulere > Ikorodu)
   • Property types (detached, semi-detached, terrace, flat, bungalow)
   • Title types (C of O, Deed of Assignment, Governor's Consent)
   • Mortgage characteristics (3% penetration, 18-25% interest, 60-70% LTV)
   • Rental yields (4-10% by location tier)

3. Quality Assurance:

   • Distribution testing (price per sqm follows lognormal within tiers)
   • Correlation validation (location-price r > 0.7, size-price r > 0.8)
   • Causal consistency (property valuation models, market dynamics)
   • Multi-scale coherence (property → neighborhood → city aggregations)
   • Ethical considerations (representative, unbiased, privacy-preserving)

See QUALITY_ASSURANCE.md in the repository for full methodology.

Use Cases

• Machine Learning: Property price prediction, location scoring, investment ROI forecasting
• Market Analysis: Price trends, supply-demand dynamics, market segmentation
• Investment: Rental yield optimization, portfolio analysis, risk assessment
• Urban Planning: Housing demand forecasting, infrastructure impact analysis
• Research: Real estate market dynamics, affordability studies, diaspora investment patterns

Limitations

• Synthetic data: While grounded in real distributions from PropertyPro/Knight Frank/NBS, individual records are not real transactions
• Simplified dynamics: Some complex interactions (e.g., negotiation, market sentiment) are simplified
• Temporal scope: Covers 2022-2025; may not reflect longer-term trends or future policy changes
• Title issues: Models title problems statistically but doesn't capture full legal complexity

Citation

If you use this dataset, please cite:

@dataset{nigeria_realestate_2025,
  title = {Nigeria Real Estate – Tenant Turnover},
  author = {Electric Sheep Africa},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_realestate_tenant_turnover}
}

Related Datasets

This dataset is part of the Nigeria Real Estate & Property Markets collection:

• https://huggingface.co/collections/electricsheepafrica/nigeria-real-estate-and-property-markets

Contact

For questions, feedback, or collaboration:

• Organization: Electric Sheep Africa
• Collection: Nigeria Real Estate & Property Markets
• Repository: https://github.com/electricsheepafrica/nigerian-datasets

Changelog

Version 1.0.0 (October 2025)

• Initial release
• 80,000 synthetic records
• Quality-assured using PropertyPro/Knight Frank/NBS/CBN reference data