Consistent Upward Momentum in House Prices in Third Quarter of 2016
Nairobi, Tuesday, November 8, 2016 - The average house prices in Kenya recorded a 2.2 per cent growth during the third quarter of 2016, signifying sustenance of the upward trajectory. According to the Kenya Bankers Association - Housing Price Index (KBA-HPI), the quarter three house prices change represent a faster movement compared to the previous five quarters where they were consistently below 2 per cent and therefore portraying broad market stability in the housing sector.
The supply of housing units has been in response to the broad demand characteristics in the market as reflected by the new units being put up in the market and the support of the financial sector generally and the banking industry in particular towards home acquisition that has been enabled by a generally stable macroeconomic environment. Consistent with the previous two quarters, there has been a sense of taste consistency amongst buyers, with the main drivers being: The size of the house (as measured by plinth area, number of bedroom, bathroom, and presence of domestic staff quarter); Whether or not the house is located in a gated community (which comes with superior ambience environment, controlled development, security, privacy and scenic value); and Proximity to social amenities such as shopping malls, tarmacked roads, schools, hospitals and presence of parking lot. A breakdown of the index by house type indicates that apartments accounted for the largest share of the total sale transaction at 58.56 per cent with maisonettes and bungalows accounting for 24.31 per cent and 17.13 per cent respectively. The market, however, seems skewed in terms of the regions and the type of the house, with apartments' transactions being more on the lower and middle market end. Maisonettes and bungalows on the other hand seem to be concentrated in the upper end market. According to KBA Director of Research and Policy, Jared Osoro, this speaks to the developers' appetite on the type of the houses they put up in specific regions as being influenced by affordability as well as the type of the clientele in the region. The rising middle class in Kenya, can be deemed to have more preference for apartment which are likely to be more affordable as compared to maisonettes and bungalows. "The new units being put up in the market are mainly targeting the middle end of the market, with the lower end experiencing supply constraints arising mainly from the tendency of developers inclining more towards renting than selling. This has been evident even in the opening up of new geographical areas for housing development in response to physical infrastructure expansion, especially transport", said Mr. Osoro. Evidently, there was no influence of the recent amendment of the Banking Act on the quarter three KBA-HPI movement since the effective rate of the capping of interest rates came into effect two weeks into the end of the quarter. The law is however likely to influence the demand and supply dynamics on the housing market based on how it will shape the flow of funds towards supporting both home ownership and housing development. KBA CEO reiterated the importance of tracking house price movements. "It is important for all stakeholders in the real estate market to keep track of developments in the market, more so price patterns. This is because, prices underpin the demand and supply patterns and therefore is a key information that would influence market positioning", said KBA CEO, Habil Olaka.
|Housing Price Index Issue 8 - 2016|
Note to Editors:
To better guide policy makers and investors on the trends in the housing sector, the banking industry's umbrella body, the KBA, launched the KBA-HPI in February 2015. The Index has quickly been recognised as a credible analytical tool that is useful for tracking housing sector dynamics and price movements. The Kenya Bankers Association Housing Price Index follows the Laspeyers Index method where the index is computed by getting the ratio the estimated current quarter price from the hedonic method multiplied the weights of the preceding quarter to the price of the preceding quarter multiplied by the respective weights of that quarter. The weights of the quantitative variables are obtained by getting their respective mean values. For the dummy variables however, their weights are computed as the proportions of the number of houses possessing a certain attribute to the total number of houses.
The KBA-HPI regions are based on clustered price ranges across several counties as follows: