Datasets:
metadata
license: mit
task_categories:
- tabular-regression
tags:
- nigeria
- real-estate
- property
- housing
- synthetic
- property-management-and-facilities
size_categories:
- 100K<n<1M
Nigeria Real Estate – Service Charges
Dataset Description
Synthetic Property Management & Facilities data for Nigeria real estate sector.
Category: Property Management & Facilities
Rows: 120,000
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-00077170 | 2024-07-18 | Ibadan | 3.3019e+07 | A |
| REC-00042994 | 2022-09-13 | Lagos | 1.77674e+07 | B |
| REC-00593516 | 2023-02-12 | Lagos | 2.85013e+06 | A |
| REC-00444500 | 2025-01-25 | Lagos | 2.39852e+07 | A |
| REC-00758440 | 2024-09-21 | Lagos | 4.15234e+07 | A |
Data Generation Methodology
This dataset was synthetically generated using:
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
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)
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 – Service Charges},
author = {Electric Sheep Africa},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_realestate_service_charges}
}
Related Datasets
This dataset is part of the Nigeria Real Estate & Property Markets collection:
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
- 120,000 synthetic records
- Quality-assured using PropertyPro/Knight Frank/NBS/CBN reference data