Do housing prices reflect their fundamentals in urban areas in Kenya?

Thesis(Master) --KDI School:Master of Public Policy,2018This research aims to empirically measure the effects of fundamental variables on housing prices in Kenya’s urban areas. To that end, specific analyses performed in this study include: (1) testing the effects of mortgage lending rates, housing stock, population changes, and GDP per capita on the housing prices. Using the data obtained from four different sources (Hass Consult Kenya, the Central Bank of Kenya, Kenya National Bureau of Statistics, and the World Bank), a hedonic housing price model is estimated, to test the hypothesized effects of the market fundamentals: that is, a negative sign for the housing stock (in the housing price equation); and a positive sign for the GDP per capita, population, and bank lending rate. The results show that the coefficients for bank lending rate, population and GDP per capita had positive signs as expected while housing stock coefficient had a negative sign as expected. Specifically, a unit increase in bank lending rate would lead to an increase in residential housing price by 0.003% in the following two quarters. On the other hand, a one percentage increase in GDP per capita would increase the residential housing prices by 0.53% in the next seven quarters. For population, a one percentage increase would lead to a 59.96% increase in residential housing prices. This is because as population increases, the demand for housing will increase with a larger proportion as compared to the supply which is inelastic in the short-run. For the housing stock, a one percentage increase in the housing stock will reduce the housing price by 0.02% in the next 5 quarters. This means that when new housing units are supplied, housing price will fall since demand will also fall. Overall, interest rate and population were not statistically significant at 95% significance level because their p values were greater than 0.05 while housing stock and GDP per capita were statistically significant at 95% significance level. The Price to Income Ratio for Kenya in 2017 was estimated at 16.62, which means that housing prices in Kenya are 16.62 times higher than average income, depicting unaffordability of housing in the country. The following recommendations were therefore proposed:- the government to partner with housing cooperatives and provide incentives to the private sector to increase the stock of affordable houses in the country; the government to enable GDP growth by enhancing investments, control population growth and also encourage consumer spending that can increase the country’s GDP per capita giving households more income to invest in housing; the CBK to control the bank lending rates to allow potential home owners and developers borrow for housing. This will in turn reduce the cost of construction and consequently the overall cost of housing; as well as increase the housing stock which will reduce housing prices.CHAPTER ONE: INTRODUCTION CHAPTER TWO: KEY FACTS/ TRENDS ABOUT THE HOUSING SECTOR IN KENYA CHAPTER THREE: LITERATURE REVIEW CHAPTER FOUR: MODEL AND HYPOTHESIS CHAPTER FIVE: RESULTS CHAPTER SIX: AFFORDABLE HOUSING IN KENYA CHAPTER SEVEN: CONCLUSION AND POLICY IMPLCATIONSOutstandingmasterpublishedRuth Nafuna WEKESAH