House Prices Soften Further on the Back of Demand and Supply Slowdown
Nairobi, July 28th 2017 - The residential property prices continued to increase at a modest pace during the second quarter of 2017. According to the Kenya Bankers Association - Housing Price Index (KBA-HPI), the average house prices in Kenya recorded a minimal increase of 0.98 per cent compared to the 1.10 per cent rise in quarter one 2017. This is the slowest price increase since the third quarter of 2016.
While the Index depicts a sense of overall price stability, the trend over the past four quarters points to the underlying demand and supply characteristics. The slowdown in the rate of house price growth illustrates a demand slow-down on the back of the broader economic environment characterised by a general sense of slow growth. According to KBA Director of Research and Policy, Jared Osoro, the economy's real growth during the second quarter of 2017 was the slowest performance compared to the corresponding quarters since 2013 and is a pointer to the plausibility of the downward revisions of the economy's growth outlook for the year. The overall economic performance therefore has an obvious influence on demand in the housing market as it has on other markets.
"The demand attributes of the housing market have largely been influenced by the sustained decline in the pace of credit growth in the private sector," said Mr. Osoro.
Private sector credit expanded at a measly 2.10 percent in May 2017 compared to 4.70 percent in September 2016. This decline was against the background of the amended banking act that introduced the capping of interest rates. Consistent with the previous quarters, the house price drivers in quarter two of 2017 included the size of the house, number of bedrooms, bathrooms, presence of a backyard, parking lot, master-ensuite, balcony and separate dining area. However, unlike the other quarters, the presence of Domestic Staff Quarters (DSQ) seems to have been insignificant in determining the price implying that potential home owners are inclined to utilising the area set for DSQ as an additional bedroom rather than space for letting out. Similarly attributes such as presence of swimming pool also seem to have had an insignificant effect on the prices during the quarter.
Presence of elevator in apartments buildings was seen to be a drag on prices given that it is associated with additional service charges; similar drag on prices, linked to the price sensitivity of buyers in the back of the tough demand conditions, is seen in gated areas that tend to come with higher service charges. The inter-play between the demand and supply side of the market has had to contend with the environment of political sentiments around the forthcoming general elections that has engendered a wait-and-see attitude on both the developers and potential home owners.
Aggregation of the Index by house type indicates that apartments accounted for 82.66 per cent of the total number of units sold in quarter two of 2017 with maisonettes and bungalows accounting for 10.70 per cent and 6.64 per cent respectively.
KBA CEO Habil Olaka observed that the demand and supply of housing units is likely to remain subdued for as long as the credit conditions that affect the demand side are binding on the supply side. "The market demand attributes have continued to influence the supply side of the housing market. They point to the fact that a number of developed units remain unsold for a longer period," Mr. Olaka said.
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: