The dataset comprises property listings scraped from Property24, a leading real estate platform in Kenya. It includes details such as property price, location, type, number of bedrooms, bathrooms, size, description, and status. This dataset can be utilized for various purposes, including price prediction modeling, market trend analysis, and investment decision-making. By analyzing this data, valuable insights can be gained into the dynamics of the Nairobi real estate market.

The housing price index in Kenya increased to ****** points in the fourth quarter of 2020. This was the first increase in two years. In the fourth quarter of 2018, the index reached a peak at *** points and, since then, it has declined persistently. According to the source, the recovery registered at the end of 2020 was related to an increase in homeowners' preference for newer buildings. Also, a decline in the supply of new units led to a growth in prices.

Comprehensive real estate market data and investment metrics for Kenya

The Gross Domestic Product (GDP) growth rate of Kenya's real estate sector grew by *** percent in the third quarter of 2023. This represented a slight increase in the growth rate compared to the corresponding quarter in 2022, which grew by * percent.

📌 Context This dataset provides a cleaned and structured snapshot of real estate listings in Nairobi, Kenya. It includes essential features like location, neighborhood, city reion and more. Nairobi is one of Africa’s fastest-growing cities, and its real estate market has attracted interest from both local and international investors. Understanding property trends in Nairobi can help investors, researchers, and urban planners make informed decisions.

🔍 Sources Original Source: The dataset was compiled from publicly available listings on popular Kenyan property website, Property24 Kenya.

Collection Method: Data was scraped using Python-based tools (e.g., BeautifulSoup, Selenium) and then cleaned by removing duplicates, handling missing values, and normalizing inconsistent formats.

💡 Inspiration The inspiration behind this dataset was to:

• Provide accessible and high-quality property data for data science and machine learning experiments.

• Explore pricing trends across different neighborhoods and property types in Nairobi.

• Support research in urban planning, investment analysis, and socioeconomic modeling.

📊 Potential Use Cases - Predicting property prices using regression models.

• Clustering neighborhoods based on property attributes.

• Real estate market trend analysis and visualizations.

• Building recommendation systems for property seekers.

Real estate added some 1.15 trillion Kenyan shillings (KSh), approximately 8.67 billion U.S. dollars, to Kenya's Gross Domestic Product in 2022. The annual value increased compared to 2021, reaching the highest during the period observed.

The prices for the cheapest newly built housing in two African countries, Sudan and South Sudan, exceeded ****** U.S. dollars in 2024. In the Seychelles, the price of the most affordable housing was about ****** U.S. dollars. Nigeria, Kenya, and Egypt all had house prices under 10,000 U.S. dollars.

🏡 Cost of Living in Nairobi, Kenya

📌 Overview

This dataset provides a detailed time-series estimate of the monthly cost of living across 20 different areas in Nairobi, Kenya from 2019 to 2024. It covers essential expenses such as rent, food, transport, utilities, and miscellaneous costs, allowing for comprehensive cost-of-living analysis.

This dataset is useful for:
✅ Individuals planning to move to Nairobi
✅ Researchers analyzing long-term cost trends
✅ Businesses assessing salary benchmarks based on inflation
✅ Data scientists developing predictive models for cost forecasting

📊 Data Summary

• Total Records: 60,000 (5 years of monthly data)
• Columns:
  • 🏠 Area: The residential area in Nairobi
  • 💰 Rent: Estimated monthly rent (KES)
  • 🍽️ Food: Grocery and dining expenses (KES)
  • 🚕 Transport: Public and private transport costs (KES)
  • ⚡ Utilities: Water, electricity, and internet bills (KES)
  • 🎭 Misc: Entertainment, personal care, and leisure expenses (KES)
  • 🏷️ Total: Sum of all expenses
  • 📆 Date: Monthly timestamp from January 2019 to December 2024

📍 Areas Covered

This dataset provides cost estimates for 20+ residential areas, including:
- High-End Areas 🏡: Kileleshwa, Westlands, Karen
- Mid-Range Areas 🏙️: South B, Langata, Ruaka
- Affordable Areas 🏠: Embakasi, Kasarani, Githurai, Ruiru, Umoja
- Satellite Towns 🌿: Ngong, Rongai, Thika, Kitengela, Kikuyu

🛠️ How the Data Was Generated

This dataset was synthetically generated using Python, incorporating realistic market variations. The process includes:

✔ Inflation Modeling 📈 – A 2% annual increase in costs over time.
✔ Seasonal Effects 📅 – Higher food and transport costs in December & January (holiday season), rent spikes in June & July.
✔ Economic Shocks ⚠️ – A 5% chance per record of external economic effects (e.g., fuel price hikes, supply chain issues).
✔ Random Fluctuations 🔄 – Expenses vary slightly month-to-month to simulate real-world spending behavior.

🔍 Potential Use Cases

• 📊 Cost of Living Analysis – Compare affordability across different Nairobi areas.
• 💵 Salary & Real Estate Benchmarking – Businesses can analyze salary expectations by location.
• 📉 Time-Series Forecasting – Train predictive models (ARIMA, Prophet, LSTM) to estimate future living costs.
• 📈 Inflation Impact Studies – Measure how economic conditions influence cost variations over time.

⚠️ Limitations

• Synthetic Data – The dataset is not based on real survey data but follows market trends.
• No Lifestyle Adjustments – Differences in household size or spending habits are not factored in.
• Inflation Approximation – While inflation is simulated at 2% annually, actual inflation rates may differ.

📁 File Format & Access

• nairobi_cost_of_living_time_series.csv – 60,000 records in CSV format (time-series structured).

📢 Acknowledgments

This dataset was generated for research and educational purposes. If you find it useful, consider citing it in your work. 🚀

📥 Download and Explore the Data Now!

This updated version makes your documentation more detailed and actionable for users interested in forecasting and economic analysis. Would you like help building a cost prediction model? 🚀