# RECORD 7409c1d2d6de0d63dfd17da30463090cc23beeee # TITLE: Property Supply, Economic Factors and House Prices in Nairobi Kenya # YEAR: 2024.0 VENUE: Journal of African Real Estate Research DOI: 10.15641/jarer.v9i2.1457 # AUTHORS: D. Chirchir; Mirie Mwangi; Cyrus Iraya # PAGES: 32 CANDIDATE TABLE PAGES (1-based): [18, 23] # ABSTRACT: Residential real estate is an important asset class for both institutional and individual investors. The housing bubble in the US led to a global financial crisis in 2007 – 2008. As such, understanding the factors that influence property prices is central to the investment process and portfolio management. The objective of the paper was to first construct a Nairobi house price index and then determine the relationship among economic factors, property supply, and house prices. The study period was ten years (2011Q1 to 2020Q4). Autoregressive Distributed Lag (ARDL) model was adopted to estimate long run and short run relationships. The Nairobi real estate price index was developed using hedonic model based on the data collected on actual selling price and characteristics of a sample of houses in Nairobi spanning ten years. The paper finds that GDP and inflation have significant positive long run effect on house prices. Property supply has a significant negative long run effect on real estate prices. However, interest rates do not have significant effect on house prices. In the short run however, GDP had a significant negative effect on prices. The speed of adjustments towards equilibrium relationship was 71.9%. The real estate market seems to be efficient despite its illiquid nature. The paper contributes to knowledge through its construction of house price index in Nairobi. Also, it included property supply a departure from past studies. The findings may have implications for investors, banks, regulators of securities and policy makers. The government of Kenya may be motivated to commission an official house price index. ===== PAGE 1 (score 5, numeric-paren 0, table-words 0, var-words 5) ===== Journal of African Real Estate Research Volume 9(2) 2024 55 Journal of African Real Estate Research Volume 9, Issue 2 www.journals.uct.ac.za/index.php/JARER/ Property Supply, Economic Factors and House Prices in Nairobi Kenya Dan Chirchir1* (https://orcid.org/0000-0003-0049-7566), Mirie Mwangi2 (https://orcid.org/ 0000-0002-5947-4468) and Cyrus Iraya3 (https://orcid.org/ 0009-0007- 2883-3580) 1 2 3 University of Nairobi To cite this article: Chirchir, Mwangi and Iraya (2024) Property Supply, Economic Factors and House Prices in Nairobi Kenya. Journal of African Real Estate Research, 9(2), pp.55-86. DOI: https://doi.org/10.15641/jarer.v9i2.1457 Abstract Residential real estate is an important asset class for both institutional and individual investors. The housing bubble in the US led to a global financial crisis in 2007 – 2008. As such, understanding the factors that influence property prices is central to the investment process and portfolio management. The objective of the paper was to first construct a Nairobi house price index and then determine the relationship among economic factors, property supply, and house prices. The study period was ten years (2011 Q1 to 2020 Q4). Autoregressive Distributed Lag (ARDL) model was adopted to estimate long-run and short-run relationships. The Nairobi real estate price index was developed using a hedonic model based on the data collected on actual selling prices and characteristics of a sample of houses in Nairobi spanning ten years. The paper finds that GDP and inflation have significant positive long-run effects on house prices. Property supply has a significant negative long-run effect on real estate prices. However, interest rates do not have a significant effect on house prices. In the short run, however, GDP had a significant negative effect on prices. The speed of adjustments towards the equilibrium relationship was 71.9%. The real estate market seems to be efficient despite its illiquid nature. The paper contributes to knowledge by including property supply, a departure from past studies. The findings may have implications for investors, banks, regulators of securities and policymakers. The government of Kenya may be motivated to commission an official house price index. Keywords: ARDL, ECM, Cointegration, Hedonic *Corresponding author’s email address: dchirchir@uonbi.ac.ke ©2024 The Author(s). Published by UCT Library. This is an open-access article under the CC BY 4.0 license. https://creativecommons.org/licenses/by/4 ===== PAGE 18 (score 14, numeric-paren 0, table-words 5, var-words 2) ===== Journal of African Real Estate Research Volume 9(2) 2024 72 Table 4: Diagnostic test results Test Method Statistic Prob Autocorrelation Breusch Godfrey LM test 2.62 0.6232 Heteroscedasticity Breusch-Pagan test 22.80 0.2985 Normality Jarque-Bera 0.24 0.8854 Source: Author (2023) Table 5: Multicollinearity test Variable VIF Inflation Index 2.592 Interest 2.448 GDP 1.833 LN Value of Approved Plans 1.516 Mean VIF 2.097 Source: Author (2023) The Augmented Dickey-Fuller and Phillips-Perron unit root tests were used to test for stationarity with outcomes depicted in Table 6 and Table 7 respectively. Table 6: Stationarity test (Augmented Dickey-Fuller) Variable Test statistic P- value I (0) Test statistic P- value I (1) Price Index -2.162 0.220 -5.517 0.000  Interest -2.014 0.281 -4.019 0.001  GDP -3.077 0.028  Value of Approved Plans -3.451 0.009  Inflation Index 0.157 0.969 -6.56 0.000  Source: Author (2023) ===== PAGE 19 (score 12, numeric-paren 0, table-words 4, var-words 3) ===== Journal of African Real Estate Research Volume 9(2) 2024 73 Table 7: Stationarity test (Philips-Perron) Variable Test statistic P- value I (0) Test statistic P- value I (1) Price Index -2.564 0.101 -9.012 0.000  Interest -1.207 0.671 -3.577 0.006  GDP -3.276 0.016  Value of Approved Plans -4.286 0.001  Inflation Index -0.326 0.922 -5.910 0.000  Source: Author (2023) GDP and log of the value of approved plans were stationary at levels [(I (0)]. On the other hand, the price index, interest, and inflation index were not stationary at levels. However, the series became stationary at first difference [1 (1)]. The stationarity test resulted in mixed stationarity. The study therefore adopted the ARDL model. The ARDL model was implemented in five steps and the results are presented as follows. The ARDL optimal lags were determined as (1 1 0 0 0) using BIC as shown in Table 8. Table 8: Optimal lags selection Model N ll (null) ll (model) Df BIC . 36 -145.1738 -127.8625 7 280.8096 Price Index 1 GDP 1 Inflation Index 0 Interest 0 LN of Value of Approved Plans 0 Source: Author (2023) The ARDL (1,1,0,0,0) was specified as follows: 𝑃𝑟𝑖𝑐𝑒𝐼𝑛𝑑𝑒𝑥𝑡 = 𝐶0 + ∅𝑃𝑟𝑖𝑐𝑒𝐼𝑛𝑑𝑒𝑥𝑡−1 + 𝛽1𝐺𝐷𝑃𝑡+ 𝛽2𝐺𝐷𝑃𝑡−1 + 𝛽3𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛𝐼𝑛𝑑𝑒𝑥𝑡+ 𝛽4𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡+ 𝛽5𝐿𝑛 𝑉𝑎𝑙𝑢𝑒𝑜𝑓𝐴𝑝𝑝𝑟𝑜𝑣𝑒𝑑𝑃𝑙𝑎𝑛𝑠𝑡+ 𝑢𝑡…………………………………..… [4] The ARDL was run to test for the model’s goodness of fit. Table 9 summarises the result. ===== PAGE 23 (score 19, numeric-paren 0, table-words 7, var-words 2) ===== Journal of African Real Estate Research Volume 9(2) 2024 77 Table 12: ECM Regression results ARDL (1,1,0,0,0) regression Sample: 2011q2 thru 2020q4 Number of obs = 39 R-squared = 0.5599 Adj R-squared = 0.4774 Log likelihood = -138.18548 Root MSE = 9.2368 D. Price Index Coefficient Std. err. T P>t ADJ Price Index L1. -0.719 0.140 -5.150 0.000 LR GDP 484.340 176.530 2.740 0.010 Inflation Index 0.706 0.145 4.880 0.000 Interest 242.373 163.963 1.480 0.149 LN Value of Approved Plans -18.722 8.042 -2.330 0.026 SR GDP D1. -221.910 92.540 -2.400 0.022 _cons 88.785 66.075 1.340 0.188 Source: Author (2023) The results indicate that economic factors and property supply explain 55.99% of the variation in house prices. Inflation and GDP have a positive significant effect on house prices while the value of approved plans has a negative effect. However, interest rates had no significant long- run effect (p>0.05) on house prices. The adjustment factor of negative 0.719 is significant (t= -5.15; p<0.05). This indicates that 71.9% of the deviations from the long-run equilibrium in a particular quarter will be corrected in the subsequent period. The high speed of adjustment may point to the efficiency of the housing market as it experiences correction from shocks. This tends to agree with the stock- flow model and EMH. ===== PAGE 24 (score 5, numeric-paren 0, table-words 2, var-words 1) ===== Journal of African Real Estate Research Volume 9(2) 2024 78 In the short run, changes in GDP (t = -2.40; p<0.05) have a significant effect on prices. A 1% increase in GDP will lead to a 2.21% decline in real estate prices in the short run. This seems out of step with the theoretically expected relationship. The validity of the outcome of the hypothesis test was subject to post-estimation tests. The model passed the four tests as shown in Table 13, Figure 6 and Figure 7. Specifically, there was no autocorrelation and heteroscedasticity on the fitted model. The errors were also normally distributed. Table 13: Summary of post-estimation tests Test Method Statistic Prob Autocorrelation Breusch Godfrey LM test 4.021 0.4032 Heteroscedasticity Breusch-Pagan test 30.33 0.2994 Normality Jarque-Bera 0.395 0.8206 Source: Author (2023) The fitted model assumes that the estimated parameters will remain stable over time. Cumulative sum (Cusum) and Cumulative sum of squares (CUSUMSQ) tests for parameter stability were used (Brooks, 2019). The fitted model was found to be stable as shown in Figure 6 and Figure 7.