Kenya Consumer Price Index (CPI): Nairobi: Housing data was reported at 176.470 Oct1997=100 in Jun 2009. This stayed constant from the previous number of 176.470 Oct1997=100 for May 2009. Kenya Consumer Price Index (CPI): Nairobi: Housing data is updated monthly, averaging 137.655 Oct1997=100 from Jan 2000 (Median) to Jun 2009, with 114 observations. The data reached an all-time high of 176.470 Oct1997=100 in Jun 2009 and a record low of 120.730 Oct1997=100 in Jan 2000. Kenya Consumer Price Index (CPI): Nairobi: Housing data remains active status in CEIC and is reported by Kenya National Bureau of Statistics. The data is categorized under Global Database’s Kenya – Table KE.I004: Consumer Price Index: Oct1997=100.

🏡 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? 🚀

Comprehensive Airbnb dataset for Nairobi, Kenya providing detailed vacation rental analytics including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

Abstract

The Kenya National Housing Survey (KNHS) was carried out in 2012 to 2013 in 44 counties of the Republic of Kenya. It was undertaken through the NASSEP (V) sampling frame. The objectives of the 2012/2013 KNHS were to: improve the base of housing statistics and information knowledge, provide a basis for future periodic monitoring of the housing sector, facilitate periodic housing policy review and implementation, assess housing needs and track progress of the National Housing. Production goals as stipulated in the Kenya Vision 2030 and its first and second Medium Term Plan, provide a basis for specific programmatic interventions in the housing sector particularly the basis for subsequent Medium Term frameworks for the Kenya Vision2030; and facilitate reporting on the attainment of the Millennium Development Goals (MDG) goals particularly goal 7, target 11.

The 2012/2013 KNHS targeted different players in the housing sector including renters and owner occupiers, housing financiers, home builders/developers, housing regulators and housing professionals. Whereas a census was conducted among regulators and financiers, a sample survey was conducted on renters and owner occupiers, home builders/developers and housing professionals. To cover renters and owner occupiers, the survey was implemented on a representative sample of households - National Sample Survey and Evaluation Program V (NASSEP V) frame which is a household-based sampling frame developed and maintained by KNBS - drawn from 44 counties in the country, in both rural and urban areas. Three counties namely Wajir, Garissa and Mandera were not covered because the household-based sampling frame had not been created in the region by the time of the survey due to insecurity.

Considering that the last Housing Survey was carried out in 1983, it is expected that this report will be a useful source of information to policy makers, academicians and other stakeholders. It is also important to note that this is a basic report and therefore there is room for further research and analysis of various chapters in the report. This, coupled with regularly carrying out surveys, will enrich the data available in the sector which in turn will facilitate planning within the government and the business community.

One of the main challenges faced during the survey process was insufficient information during data collection. This could serve as a wake-up call to all county governments on the need to keep proper records on such issues like the number of housing plans they approve, housing finance institutions within their counties, the number of houses that are built within the county each year and so on since they have the machinery all the way to sub-location level.

Geographic coverage

The survey covered all the districts in Kenya. The data representativeness are at the following levels -National -Urban/Rural -Provincial -District

Analysis unit

• Households
• Indviduals

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame utilized in the renters and owner occupiers and home builders/ developers was the current National Sample Survey and Evaluation Program V (NASSEP V) frame which is a household based sampling frame developed and maintained by KNBS. During the 2009 population and housing census, each sub-location was subdivided into approximately 96,000 census Enumeration Areas (EAs).

In cognizance of the devolved system of government and the need to have a static system of administrative boundaries, NASSEP V utilizes the county boundaries. The frame was implemented using a multi-tiered structure, in which a set of 4 sub-samples were developed. It is based on the list of EAs from the 2009 Kenya Population and Housing Census. The frame is stratified according to county and further into rural and urban areas. Each of the sub-samples is representative at county and at national (i.e. urban/rural) level and contains 1,340 clusters. NASSEP V was developed using a two-stage stratified cluster sampling format with the first stage involving selection of Primary Sampling Units (PSUs) which were the EAs using Probability Proportional to Size (PPS) method. The second stage involved the selection of households for various surveys.

2012/2013 KNHS utilized all the clusters in C2 sub-sample of the NASSEP V frame excluding Wajir, Garissa and Mandera counties. The target for the household component of the survey was to obtain approximately 19,140 completed household interviews.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The survey implemented a Paper and Pencil Interviewer (PAPI) technology administered by trained enumerators while data entry was decentralised to collection teams with a supervisor. Data was keyed from twelve (12) questionnaires namely household based questionnaire for renters, owner occupier and home builders, building financiers such as banks and SACCOs, building professionals such as architects, valuers etc., institutional questionnaires covering Local Authorities, Lands department, Ministry of Housing, National Environmental Management Authority, Physical Planning department and, Water and Sewerage Service providers and housing developers. Each of these questionnaires was keyed individually.

The data processing of the 2012/13 Kenya National Housing Survey results started by developing data capture application for the various questionnaires using CSPro software. Quality of the developed screens was informed by the results derived from 2012/2013 KNHS pilot survey. Every county data collection team had a trained data entry operator and two data analysts were responsible for ensuring data was submitted daily by the trained data entry operators. They also cross-checked the accuracy of submitted data by doing predetermined frequencies of key questions. The data entry operators were informed of detected errors for them to re-enter or ask the data collection team to verify the information.

Data entry was done concurrently with data collection therefore guaranteeing fast detection and correction of errors/inconsistencies. Data capture screens incorporated inbuilt quality control checks triggered in case of invalid entry. Such checks were necessary to guarantee minimal data errors that would be removed during the validation stage (data cleaning).

In data cleaning, a team comprising subject-matter specialists developed editing specifications which were programmed to cross-check raw data for errors and inconsistencies. The printed log file was evaluated with a view to fixing errors and inconsistencies found. Further on, they also developed data tabulation plans to be used on the final datasets and cross checked tabulated outputs were used in writing the survey basic report.

Abstract

The 2015/16 Kenya Integrated Household Budget Survey (KIHBS) was conducted over a 12-month period to obtain up-to-date data on a range of socioeconomic indicators used to monitor the implementation of development initiatives. The Survey collected data on household characteristics, housing conditions, education, general health characteristics, nutrition, household income and credit, household transfers, information communication technology, domestic tourism, shocks to household welfare and access to justice. The findings are presented at national, county, rural and urban domains.

Household Characteristics The findings of the 2015/16 KIHBS basic characteristics of the population show that the sex ratio is 97.5. About 70 per cent of households were headed by males and the reported average household size was 4 members. The age dependency ratio declined to 81.6 per cent in 2015/16 KIHBS as compared to 84.0 per cent recorded in 2005/06 KIHBS. Majority (54.4%) of the population aged 18 years and above are in monogamous unions. At the national level, 8.4 per cent of children were orphans.

Housing Conditions and amenities Information regarding housing conditions and ownership, access to water, energy, sanitation and waste disposal was collected in the 2015/16 KIHBS. Bungalow was the most common dwelling type of housing occupied by 55.4 per cent of the households. About 60 per cent of households reported that they owned the dwellings that they resided in. The findings show that 72.6 per cent of households use improved drinking water sources. The statistics show that six out ten households had access to improved human waste disposal methods. Overall, 41.4 per cent of households were connected to electricity from the main grid.

Education Findings on education are presented for; pre-primary, primary, secondary, middle level college and university levels; and informal education, Madrassa/Duksi. Nationally, 89.4 per cent of the population aged three years and above had ever attended school. The overall Gross Attendance (GAR) for pre-primary, primary and secondary levels was 94.4 per cent, 107.2 per cent and 66.2 per cent, respectively. The population aged 3 years and above that did not have any educational qualification was 49.7 per cent. Most of the population aged 3 years and above that had not attended school cited not being allowed to attend by parent(s) as the reason for non-attendance. The proportion of the population aged 15-24 years that was literate, based on respondents' self -assessment, was 88.3 per cent.

General Health Characteristics General health characteristics discussed in the report comprise: morbidity by sex, health seeking behaviour, utilization of health care services and facilities, disability and engagement in economic activities and health insurance coverage. Information on child survival such as place of delivery, assistance during delivery, immunization and incidences of diarrhoea is also presented. The results show that two out of ten individuals reported a sickness or injury over the four weeks preceding the survey. Majority of the individuals (55.5 %) with a sickness or injury visited a health worker at a health facility for diagnosis. Disabilities were reported by 2.8 per cent of the population. Slightly more than a third of persons with disabilities reported having difficulty in engaging in economic activities. moderately stunted. A higher proportion (32.4%) of children in the rural areas were moderately stunted compared to those in urban areas (24.5%). Overall, 13.0 per cent of children were moderately wasted while 6.7 per cent were moderately underweight. The statistics further indicate that 98.8 per cent of children aged 0-59 months were ever breast fed. The mean length of breastfeeding nationally stood at 16.8 months. Porridge was the most common type of first supplement given to majority (35.9%) of children aged 0-23 months. The survey findings show that eight out of ten children participated in community-based nutritional programmes.

Household Income and Credit Household income is the aggregate earnings of all household members. It includes all forms of income arising from employment, household enterprises, agricultural produce, rent, pension and financial investment. The discussion in this report focuses on income from rent, pension, financial investment and other related incomes. Information is also provided on access and sources of credit. At national level, 7.2 per cent of households reported having received income from rent, pension, financial investment and other related incomes within the 12 months preceding the survey. A third of the households sought credit and over 90 per cent successfully acquired credit.

Household Transfers Transfers constitute income, in cash or in kind, that the household receives without working for it and it augments household income by improving its welfare. Three out of ten households reported having received cash transfers within the 12 months preceding the survey period. The average amount received per household from cash transfers was KSh. 27,097. Majority of households received cash transfers through a family member. Money transfer agents were the preferred mode of transmitting money for most beneficiaries of transfers received from outside Kenya. Over half of the households gave out transfers in kind.

Information and Communication Technology The 2015/16 KIHBS collected information on ICT equipment use and ownership. Findings show that three in every four individuals aged 18 years and above owned a mobile phone with an average number of 1.3 SIM cards per person. The most commonly used ICT equipment is the radio and mobile phone, reported by 79.3 per cent and 68.5 per cent of individuals aged 3 years and above, respectively. The highest proportion (50.3%) of those that did not own a mobile phone cited its high cost as the reason. Urban areas had the highest proportion of population with ownership of a mobile phone. Nairobi City County had the highest proportion of population with a mobile phone while Turkana County had the lowest. The population aged 3 years and above that reported using internet over the last three months preceding the survey was 16.6 per cent. Three in every ten households had internet connectivity and use of internet in mobility was reported as the most common place of use of internet. The internet was used mainly for social networking. No need to use the internet was the most predominant reason for not using the internet reported by 30.1 per cent of those who did not use it.

Domestic Tourism Domestic tourism comprises activities of residents travelling to and staying at least over a night in places outside their usual environment within the country, for not more than 12 months, for leisure, business or other purposes. At national level, 13.4 per cent of individuals reported that they travelled within Kenya in the 3 months preceding the survey. Visiting friends and relatives was reported by the highest proportion (71.1%) of individuals taking trips. Majority of those who took a trip (66.4%) reported that they sponsored themselves. Transport costs accounted for the largest share (38.4%) of expenditure on domestic tourism. Majority of those who did not take a trip reported high cost as a reason.

Shocks to Household Welfare A shock is an event that may trigger a decline in the well-being of an individual, a community, a region, or even a nation. The report presents information on shocks which occurred during the five-year period preceding the survey and had a negative impact on households' economic status or welfare. Three in every five households reported having experienced at least one shock within the five years preceding the survey. A large rise infood prices was reported by the highest proportion (30.1 per cent) of households as a first severe shock. Most households reported that they spent their savings to cope with the shock(s).

Justice The survey sought information from household members on their experiences regarding grievances/disputes, resolution mechanisms, status of grievance/dispute resolution and costs incurred. Majority of households (26.2%) experienced grievances related to succession and inheritance. Approximately seven out of ten households that experienced grievances reported that they were resolved by parties from whom they sought interventions. Lawyers on average received the highest amount of money (KSh 59,849) paid to a primary organization for grievance resolution through a formal channel. Courts accounted for the highest informal costs averaging KSh 6,260 in grievance resolution.

Geographic coverage

The survey covers all the Counties in Kenya based on the following levels National, Urban, Rural and County

Analysis unit

Households Indviduals within Households and Community

Kind of data

Sample survey data [ssd]

Sampling procedure

Design and Sample Selection The second Kenya Integrated Household Budget Survey 2015/16 will be the eighth household budget survey to be conducted in Kenya following those conducted in 1981/82, 1983/84, 1992, 1994, 1997 and 2005/06. The KIHBS 2015/16 is a multi-indicator survey in nature with the main objective of updating the household consumption patterns in all the Counties.

KIHBS 2015/16 is designed to provide estimates for various indicators at the County-level. A total of 50 study domains are envisaged. These are; all the forty-seven (47) counties (Each as a separate domain), urban and rural (each as a separate domain at National level), and lastly the National-level aggregate.

Sampling frame The sampling frame used for KIHBS 2015/16 is the fifth National Sample Survey and Evaluation Program (NASSEP V) master frame developed from the Population and Housing Census (KPHC) conducted in