Growth of housing prices in Kenya and its dynamic relationship with selected macroeconomic variables

Abstract

In Kenya housing prices are considered high and have still continued to rise. This has made housing affordability and access a preserve of the top income earners. Consequently, large population live in houses with reduced access to clean water, sanitation, unreliable and unhealthy energy sources, increased exposure to diseases and low levels of financial security. Arable land is also being converted to residential centers which is constraining on public goods provision and agricultural output. The government of Kenya has struggled to make housing affordable and also to encourage housing access and homeownership. This effort has not been quite successful since the housing prices continue to rise and also declining access to descent housing. The variables the housing prices respond towards and could inform the policies to manage the housing prices are not clear. Housing prices behavior have also been known to influence business cycle dynamics by affecting aggregate expenditure and also the performance of the financial system through their effect on the profitability and stability. The purpose of this study was therefore to analyze the variables the housing prices respond to both in short-run and in long-run. The study also examined the dynamic relationship between housing prices and selected macroeconomic variables. In doing this, the study used time series data for the period 1960 to 2015 and adopted an ARDL and VAR models. The ARDL and VAR models were selected since the housing prices behaves differently from other goods’ prices, and as such, previous values of housing prices and other variables used were required to explain current behaviors. The ARDL model is best suited for small sample size and has a capacity to estimate shortrun and long-run dynamics. The ARDL model also has no burden of establishing the order of integration and it distinguishes dependent and explanatory variables. With ARDL, it is possible to use differing optimal number of lags among the variables. For VAR, the study used Toda and Yamamoto (1995) methodology. This is a modified version of granger causality test based on augmented VAR modeling. The study findings show that the sources of housing prices growth include household consumption expenditure, construction cost and property taxes both in the short-run and long-run. Private capital inflows and households’ indebtedness have a positive transitory effect to the housing prices. Against the popular view, supply of housing have no effect on housing prices. For the dynamic relationship between housing prices and the selected macroeconomic variables, the results indicate that the housing prices dynamically relate with the selected macroeconomic variables. The study therefore concludes that housing prices have a positive contemporaneous impact on the selected macroeconomic variables indicating the existence of mutually reinforcing cycles between the housing prices and the selected macroeconomic variables. Therefore, there is need to observe the housing prices to avoid the cost that could result in case of instability in the housing market.