Drivers of housing financing in Kenya
Authors: Kilonzo Hilary Mati , Muriithi Moses , Ongeri Benedicto Onkoba 
Published: January 2025

Purpose

Housing finance is frequently difficult to provide in developing nations due to unstable macroeconomic conditions and a lack of supportive legal, technological and regulatory frameworks (Lea and Bernstein, 1996). Governments in these countries have, therefore, created a range of organizations and initiatives to improve the flow of capital to the housing market on a footing that is affordable to their populations given the household income levels (Ram and Needham, 2016). Housing, however, is by its very nature a significant investment requiring a considerable capital outlay at the onset (Dasgupta et al., 2014). This makes acquiring it challenging, particularly in underdeveloped nations where saving tendencies are quite low partly because of low-income levels (Keller and Mukudi-Omwami, 2017). As a result, many developing nations struggle with severe housing issues that lead to slums, overcrowding and related health issues.

Design/methodology/approach

The theoretical model for analyzing housing finance in Kenya in this study incorporates both demand and supply aspects, drawing from Brueckner’s (1994) framework. This model divides factors influencing demand into certainty and uncertainty conditions faced by households. In terms of certainty, the model considers factors that households can predict reliably. First is income, households are assumed to have stable income, allowing accurate assessment of budget constraints and mortgage decisions. Second is interest rates. While interest rates fluctuate, the model assumes that households have information about current rates, enabling informed decision-making. Finally, existing housing costs, such as rent or mortgage payments, are treated as fixed and predictable, facilitating accurate budget planning. Conversely, uncertainty factors include future income, future interest rates and housing prices. Households face uncertainty regarding future income, which can impact their mortgage repayment ability due to job market changes or unforeseen events. The model does not predict future interest rate changes, which can affect the affordability of mortgages. Furthermore, future fluctuations in housing prices add uncertainty to the benefits of homeownership and mortgage debt. Due to these uncertainties, the model in this study assumes certainty conditions, focusing on households maximizing their utility. In Brueckner’s model, a utility function captures household preferences and well-being linked to consumption choices, specifically between housing (H) and nonhousing goods (N). The utility function helps determine optimal income allocation, influenced by income (M), prices (P) and return on savings (t). The utility maximization problem involves selecting optimal amounts of housing and nonhousing consumption while managing housing credit (C).

Findings

The study confirms a significant long-run relationship between house finance and several macroeconomic variables, including interest rates on credit, inflation, unemployment and gross domestic product (GDP). The negative and significant error correction term indicates the presence of an equilibrium relationship, suggesting that the housing finance market in Kenya self-corrects swiftly in response to economic shocks. This efficiency could be attributed to increasing competition among financial institutions or a growing public awareness of housing finance options, implying a relatively well-developed market. Such responsiveness suggests that government policies aimed at influencing housing finance might have a quicker impact. For instance, introducing subsidies to reduce credit rates could rapidly boost housing finance activity (World Bank, 2019). However, the flip side of a fast-adjusting market is potential volatility, where rapid swings in economic factors could lead to significant fluctuations in housing finance availability, posing risks for both lenders and borrowers (Braun et al., 2022). Moreover, a rapid adjustment might not necessarily reflect a perfectly healthy market; it could indicate underlying issues like speculation or easy access to credit, potentially leading to bubbles or financial instability (Agnello et al., 2020).

Originality/value

This study reveals key insights into the determinants of housing finance in Kenya, demonstrating a significant long-run relationship between housing finance and economic variables such as interest rates, inflation, unemployment and GDP. The efficient adjustment of the housing finance market to economic changes suggests that government policies can rapidly influence housing finance, although this responsiveness also implies potential volatility and risks, including financial instability. Policymakers should, therefore, focus on maintaining macroeconomic stability and monitoring the housing market for signs of overheating. Encouraging competition among lenders and diversifying housing finance products can help ensure sustainable market adjustments. Credit interest rates show a modest but positive relationship with housing finance, suggesting that a stable lending environment could stimulate activity. Policymakers should manage credit availability to prevent excessive expansion and instability, enhancing financial inclusion and fostering competition in the banking sector. Inflation positively impacts housing finance, with rising inflation driving demand for real assets like housing. However, significant interest rate hikes by the Central Bank to combat inflation could reduce mortgage affordability. A flexible interest rate policy, along with targeted interventions like subsidized rates for first-time buyers, is necessary to balance market stimulation with inflation control. Unemployment’s negative impact on housing finance underscores the need for robust unemployment benefits and job training initiatives to support financial stability during job losses. Targeted housing finance programs for low- and middle-income earners can also improve mortgage accessibility. The positive correlation between GDP growth and housing finance indicates that economic expansion drives housing demand. Policymakers should prioritize initiatives that promote long-term economic growth, such as infrastructure development and innovation. Finally, the insignificance of savings interest rates in influencing housing finance suggests that traditional monetary policy may have limited effects. Promoting financial literacy and developing tailored savings instruments could strengthen the connection between savings and housing finance over time.