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.

Kenya Real Estate Market, Kenya Real Estate Market Size, Kenya Real Estate Market Trends, Kenya Real Estate Market Forecast, Kenya Real Estate Market Risks, Kenya Real Estate Market Report, Kenya Real Estate Market Share

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 analysis of Kenya's real estate market including price trends, property types, investment opportunities, and regional comparisons.

Kenya Banking System: Credit Facilities: Private Sector: Real Estate data was reported at 369,430.000 KES mn in Jun 2018. This records an increase from the previous number of 368,913.000 KES mn for May 2018. Kenya Banking System: Credit Facilities: Private Sector: Real Estate data is updated monthly, averaging 29,110.500 KES mn from Jan 1999 (Median) to Jun 2018, with 234 observations. The data reached an all-time high of 369,430.000 KES mn in Jun 2018 and a record low of 19,135.190 KES mn in Jun 1999. Kenya Banking System: Credit Facilities: Private Sector: Real Estate data remains active status in CEIC and is reported by Central Bank of Kenya. The data is categorized under Global Database’s Kenya – Table KE.KB004: Banking System: Credit Facilities.

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 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.

Kenya Banking System: Net Domestic Credit: Private Sector: Real Estate data was reported at 361.100 KES bn in Aug 2018. This records a decrease from the previous number of 372.600 KES bn for Jul 2018. Kenya Banking System: Net Domestic Credit: Private Sector: Real Estate data is updated monthly, averaging 43.400 KES bn from May 2000 (Median) to Aug 2018, with 220 observations. The data reached an all-time high of 372.600 KES bn in Jul 2018 and a record low of 18.300 KES bn in Apr 2001. Kenya Banking System: Net Domestic Credit: Private Sector: Real Estate data remains active status in CEIC and is reported by Central Bank of Kenya. The data is categorized under Global Database’s Kenya – Table KE.KB003: Banking System: Net Domestic Credit.

In 2020, the real estate industry contributed 9.1 percent to Kenya's Gross Domestic Product (GDP). Real estate kept a constant trend in GDP contribution during the period observed, with very slight fluctuations. Nevertheless, the share of 2020 was the lowest since 2016.

Kenya GDP: Real Estate data was reported at 342,531.000 KES mn in Sep 2024. This records an increase from the previous number of 339,174.000 KES mn for Jun 2024. Kenya GDP: Real Estate data is updated quarterly, averaging 184,455.000 KES mn from Mar 2009 (Median) to Sep 2024, with 63 observations. The data reached an all-time high of 342,531.000 KES mn in Sep 2024 and a record low of 60,023.000 KES mn in Mar 2009. Kenya GDP: Real Estate 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.A009: SNA 2008: GDP: by Industry: Current Price.

The demand for apartments in Kenya dominated the country's housing market in the fourth quarter of 2020. This house type accounted for ** percent of the total number of units traded in the period. Maisonettes followed, with a share of ** percent, while bungalows participated with a **** percent share.

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.

Abstract

The 2023/24 Kenya Housing Survey (2023/24 KHS) provides a comprehensive analysis of housing conditions, affordability and tenure across the country. The survey was conducted by the Kenya National Bureau of Statistics (KNBS) in collaboration with the State Department for Housing and Urban Development, the State Department for Labour and Skills Development, the Kenya Space Agency, the Directorate of Resource Survey and Remote Sensing and the Central Bank of Kenya. The primary objective of the 2023/24 KHS was to provide up-to-date housing statistics to facilitate evidence-based planning and decision making in the housing sector. In addition, the survey addressed housing challenges in line with national development goals and international commitments such as the Sustainable Development Goals (SDGs). Data collection was carried out from 7th March to 10th May 2024 in all the 47 counties and targeted both households and institutions. The survey collected data on various aspects of housing, including the stock of dwellings, household spending on housing, land and dwelling ownership, access to utilities, housing affordability, overcrowding, durability of construction materials, and economic and financial statistics related to housing. Additionally, it collected information on the age, size, and characteristics of dwellings. Satellite imagery analysis was also used to assess changes in built-up areas and green spaces in Nairobi City, Mombasa, Kisumu, and Nakuru counties. Different players in the housing sector including tenants and home owners, Housing Financiers, Developers, Water Service Providers, Built Environment Professionals and Housing Regulators (County Government Physical Planning Department, Lands Department and National Environmental Management Authority) were interviewed.

SURVEY DESIGN The survey employed a cross-sectional study design to collect data for estimating housing indicators at national, rural, urban and county levels. To achieve this, a hybrid data collection system was incorporated, targeting both households and institutions. The household component of the survey was designed independently from that of the institutions. A sample survey was conducted for the households, while a census was carried out for all identified institutions key in the housing sector.

SCOPE AND TARGET POPULATION The survey covered all 47 counties to ensure that the coverage was comprehensive and representative of the entire country. The household component targeted residential housing units in both urban and rural areas while the institutional component targeted housing developers, real estate firms, Water Service Providers, County Governments-Physical Planning Departments, NEMA and Land Administration Department. The professional component targeted members from Engineers Board of Kenya (EBK), Kenya Institute of Planners (KIPs) and Board of Registration of Architects and Quantity Surveyors (BORAQS).

DATA QUALITY The quality of data for the Housing Survey was ensured through a multi-step approach. This began with defining the survey's content and scope, designing survey instruments, conducting a pre-test and pilot survey, training survey personnel, and incorporating technology for data collection and transmission. Additionally, data validation, analysis, creation of final report tables, and stakeholder engagement were all integral parts of the process. A thorough process was undertaken to review and refine the survey instruments aimed at eliminating redundancies and ensuring the questions were accurate and relevant to the current housing development programs and addressed user needs. The data collection tools were integrated into CAPI with in-built checks and controls to ensure consistency and flag out any outliers in the data. A multilevel supervision of the data collection exercise also ensured that the probability of any errors going unnoticed was minimized significantly. To further support the data quality assurance, a dashboard based at the headquarters was also used to monitor the data as fieldwork continued. Upon completion of the data collection, edit specifications were developed by subject matter specialists to provide a basis for cleaning and editing of the data. The specifications were subsequently coded into programs using statistical applications and subjected on the raw data to derive a cleaned dataset that developed the tables in the report.

THE KENYA HOUSING SURVEY DATA COLLECTION TOOLS

I. Household Questionnaire The Household Questionnaire for the 2023/24 Kenya Housing Survey is structured into multiple sections, covering different aspects of housing and household characteristics. The key sections included; Information for Household Members; Household composition, age, gender, relationship to the head and the Socio-economic characteristics such as education and employment status. Household Amenities; Access to essential services (water, electricity, sanitation, internet), Cooking fuel and lighting sources. Dwelling Unit Characteristics; Type of dwelling unit (permanent, semi-permanent, informal), Construction materials (walls, floors, roofing), Number of rooms and occupancy. Environmental and Location Aspects; Waste disposal methods, Drainage and pollution concerns in the neighborhood. Transport and Infrastructure; Accessibility to roads, public transport, and major services (schools, hospitals, markets). Disability; the Accessibility of housing and services for persons with disabilities. Land Ownership and Tenure; Land ownership status, size, tenure system (freehold, leasehold, informal). Household Individual Integrated Module; Employment and economic activities of household members, Income sources and levels. Tenants' information; Rent payment details, lease agreements, landlord-tenant relationships. Owners' information; Mortgage details, home-ownership financing sources and common Challenges in acquiring housing.

II. Kenya Housing Survey Institutional Questionnaire The 2023/24 Kenya Housing Survey Institutional Questionnaire related to real estate development is structured into multiple sections. This Questionnaire was administered to developers and real estate firms and the key sections included: Types of real estate projects undertaken, Number of completed and ongoing projects, Challenges faced in real estate development, Information on specific housing projects (location, type, cost), Financing sources and ownership structure, Construction materials and environmental considerations, Details on commercial, industrial, and institutional buildings, Occupancy rates and rental/sale prices. Questions about market trends, demand, and pricing, Factors affecting property transactions, Prices, unit sizes, and buyer demand trends, Rental prices, occupancy rates, and tenancy duration, Market conditions for office spaces, retail, and mixed-use developments, Information on warehouse developments, rental prices, and usage.

III. County Government questionnaire This Questionnaire captured about basic details about Counties and Questions related to building applications and approvals (e.g., number of residential building applications received and approved in different years). Factors considered in approval of construction permits, such as existing use, visual impact, and emerging technologies. There are also Questions about urban planning and land use, including Number of urban centers classified as towns, municipalities, and cities. Finally, the number of approved and pending physical and land use development plans.

IV. Financiers' Questionnaire The 2023/24 KHS collected information on housing development financing with a focus on respondents within the housing development sector. These included commercial banks, microfinance banks, SACCOS and other institutions that provide finance for housing development, including financial details, funding information, and related metrics.

V. Lands Department Questionnaire This Questionnaire aimed at collecting data related to land administration and management. specific data related to land management, policies, financial data, or other related metrics.

VI. State Department for Housing and Urban Development Questionnaire This questionnaire was used to collect information from the State Department for Housing and Urban Development targeting policy housing and urban development issues.

VII. Built Environment Professionals Questionnaire This questionnaire collected information from built environment professionals involved in the planning, design, and construction of housing in Kenya. The data collected was be used to assess the state of the housing sector, challenges faced, and trends in building and urban development from the perspective of Built Environment Professionals Questionnaire. The Built Environment Professionals interviewed are Valuers, Architects, Planners, Engineers (Civil/Structural/Mechanical/Electrical), Building Surveyors, Land Surveyors, and Quantity Surveyors involved in the planning, design, construction, and maintenance of the built environment.

VIII. National Environment Management Authority Questionnaire This survey data collection tool targeted all the National Environment Management Authority offices (NEMA) to gather insights into their licensing process for housing development projects and related environmental regulations.

IX. Water Sewerage & Service Providers Questionnaire The Water Sewerage & Service Providers (WSSP) section - this was a structured data collection tool in the delivery of water and sanitation services and within the context of housing and urban development. The survey tool or a research questionnaire targeting WSSPs to collect data on Water and sewer connection applications, Types of developments being connected (residential vs. mixed-use), Sewer coverage percentages, Costs, timelines, and challenges in providing services and Plans for future infrastructure

Kenya Consumer Price Index (CPI): Nairobi: Middle and Upper Income Group: Housing data was reported at 177.610 Oct1997=100 in Jun 2009. This stayed constant from the previous number of 177.610 Oct1997=100 for May 2009. Kenya Consumer Price Index (CPI): Nairobi: Middle and Upper Income Group: Housing data is updated monthly, averaging 130.480 Oct1997=100 from Jan 2000 (Median) to Jun 2009, with 114 observations. The data reached an all-time high of 177.610 Oct1997=100 in Jun 2009 and a record low of 116.070 Oct1997=100 in Jan 2000. Kenya Consumer Price Index (CPI): Nairobi: Middle and Upper Income Group: 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.

Kenya GDP: 2001p: Real Estate, Renting and Business Services data was reported at 87,209.000 KES mn in 2013. This records an increase from the previous number of 83,583.000 KES mn for 2012. Kenya GDP: 2001p: Real Estate, Renting and Business Services data is updated yearly, averaging 64,811.074 KES mn from Dec 1996 (Median) to 2013, with 18 observations. The data reached an all-time high of 87,209.000 KES mn in 2013 and a record low of 50,764.457 KES mn in 1996. Kenya GDP: 2001p: Real Estate, Renting and Business Services 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.A030: SNA 1993: GDP: by Industry: Chain Linked 2001 Price: Annual.

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=3,662, vars=15) contain data from sub-Module E5 regarding non-food items that may or may not have been purchased (e.g., construction items such as wood poles and thatching grass). Each household with data for these construction items over the past 12 months has multiple records (for the two construction items in sub-Module E5). (3,662 records divided by 2 construction items = 1,831 Module E households with sub-Module E5 construction data.)

Kenya Employment: WE: PR: Real Estate Activities data was reported at 4.200 Person th in 2017. This records an increase from the previous number of 4.100 Person th for 2016. Kenya Employment: WE: PR: Real Estate Activities data is updated yearly, averaging 3.750 Person th from Jun 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 4.200 Person th in 2017 and a record low of 3.500 Person th in 2010. Kenya Employment: WE: PR: Real Estate Activities 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.G005: Employment: by Sector and Industry: International Standard of Industrial Classification Rev 4.

This statistic shows the rental yields for prime industrial real estate in selected cities in Africa in 2020. In 2020, the rental yield in Nairobi, Kenya, was *** percent for prime industrial real estate.

The African prefabricated houses market is experiencing robust growth, driven by rapid urbanization, increasing infrastructure development, and a growing need for affordable and quickly deployable housing solutions. The market's Compound Annual Growth Rate (CAGR) exceeding 5.50% indicates significant expansion potential over the forecast period (2025-2033). Key market drivers include government initiatives promoting affordable housing, the rising middle class with increased disposable income, and the efficiency and cost-effectiveness of prefabricated construction compared to traditional methods. The demand for both single-family and multi-family prefabricated units is fueling market growth across various regions, with Nigeria, South Africa, Egypt, and Kenya emerging as leading consumers. While challenges such as fluctuating raw material prices and a need for improved regulatory frameworks exist, the overall market outlook remains positive. The presence of established players like Karmod Prefabricated Building Technologies and Kwikspace Modular Buildings Ltd., alongside emerging local companies, fosters competition and innovation within the industry. The market's segmentation by house type allows for targeted approaches catering to diverse customer needs and preferences, furthering market expansion and penetration. The historical period (2019-2024) likely saw a steady increase in market size laying the groundwork for the robust projected growth. The continued expansion of the African prefabricated houses market is expected to be fueled by several factors. Firstly, a sustained increase in construction activities across major African cities will create a heightened demand for housing. Secondly, the adoption of sustainable building practices and environmentally friendly materials is gaining traction, and prefabricated construction aligns well with these goals, leading to increased adoption. Thirdly, the potential for leveraging technology in design and manufacturing processes further boosts the efficiency and cost-effectiveness of prefabricated housing, making it a more attractive option. However, consistent policy support and investment in infrastructure are crucial to ensure the sustained growth of this sector. Addressing challenges like skilled labor shortages and logistical complexities will also be crucial for unlocking the full potential of the prefabricated housing market in Africa. Future market analysis should consider the impact of evolving building codes and regulations on the market growth and the increased adoption of innovative building materials and technologies. Recent developments include: May 2023: A new prefab housing structure is under development by Amsterdam-based architecture firm NLE. They installed a model in Africa's Cape Verde to understand its viability's various aspects as floating houses. The idea is to reduce the overall cost emanating from land prices., January 2022: Housing is one of the major challenges of a city resident in Addis Ababa, the capital city of Ethiopia. Hence, the Addis Ababa City Administration laid the foundation for 5,000 prefabricated houses in the Akaki Kaliti sub-city. Addis Ababa City Administration also stated that 2 million houses would be built during the ten-year development plan.. Notable trends are: Shift Towards Prefab Housing due to High Pricing in Egypt.

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.