Abstract
The residential real estate market is big and affords investors investment opportunities. The price changes are key in determining the overall return. Structural and atheoretical models are the two main approaches to modeling real estate prices. Structural models link prices to fundamental factors such as economic indicators and property supply, amongst others. Atheoretical models attempt to predict prices by leveraging on the statistical properties of time series data and may be extended to augment fundamental factors. This study focused on time series modeling using ARIMA. The objective of the paper was to identify a suitable ARIMA model that is efficient in predicting house prices in Nairobi. The training data was for the period 2010Q3 to 2019Q2. The out of sample test data was for six quarters: 2019Q3 to 2020Q4. The Box-Jenkins methodology was adopted. Seven ARIMA models and six AR models were identified, estimated, and used in predicting prices using out of sample data. The study found out that AR models outperformed ARIMA models. The paper contributes to knowledge being among the first to apply ARIMA in Nairobi house market using hedonic house prices. The paper may inform investment strategy and portfolio management by investors. It may inform policy since house price forecasts may have social and economic effects.
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