Abstract:
Aim: This was an investigative study on affordable housing in the wake of global pandemics: A reality or a mirage the Kenyan perspective? 22 % of Kenyans stay in towns and the inhabitants in these cities continue to grow at the rate of 4.2 % annually. This growth rate has outstripped the supply of housing units built. For instance, Nairobi needs a minimum of 120,000 new houses per
annually to satisfy the demand but a paltry 35,000 units are constructed annually. The excess
demand is likely to continue pushing the housing prices beyond the reach of many Kenyans.
Studies conducted in Kenya on housing prices focused on non-macroeconomic determinants and
more importantly none of the studies globally envisaged how global pandemics can influence
housing prices. Therefore, the influence of global pandemics like Corona Virus Disease (COVID19) and macroeconomic factors on housing prices in Kenya remains unknown.
Study Design: Correlational research design. Original Research Article
Kiganda and Shavulimo; SAJSSE, 8(2): 14-23, 2020; Article no.SAJSSE.60816
15 Methodology: The study employed unrestricted Vector Autoregressive analysis involving quarterly time series from quarter 1 of 2014 to quarter 1 of 2020 with a dummy variable measuring the influence of COVID-19. Results: Results indicated that the total money supply had a positive influence on inflation that was highly influenced by extended broad money. Conclusion: From the results, it was concluded that some macroeconomic factors, time trends and global pandemics like COVID-19 influence housing prices in Kenya. Professional, administrative and support services, time trend, transport and storage, information and communication, real estate and housing prices at lag 1 increased housing prices in Kenya by 0.41%, 0.41%, 0.94%, 0.37% and 0.59% respectively given unrestricted VAR coefficients and tstatistics of 0.41(4.184), 1.27 (9.862), 0.19 (2.740), 0.94 (10.178) and 0.59 (6.055) for the variables. Housing prices at lag 1 and 4, COVID-19, other services and tax on products reduced housing prices in Kenya by 0.26%, 0.99%, 3.29%, 1.01% and 0.05% respectively given unrestricted VAR coefficients and t-statistics of -0.26(-2.366), -0.99 (-8.770), -3.29 (-4.550), -1.01 (- 6.568) and -0.05 (-2.807) for the variables respectively. Economic growth, financial and insurance activities and previous housing prices at lag 5 had no influence on housing prices in Kenya.
