Nowcasting the Condominium Price Index Using Google Search Data
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Abstract
As land prices continue to escalate, the demand for condominiums has correspondingly increased. However, if this increasing demand is driven by speculation, condominium prices may not accurately reflect actual market demand. Analyzing trends in the Condominium Price Index is crucial for entrepreneurs, investors, and the public to make informed decisions. Additionally, understanding these trends helps clarify the relationship between the real estate cycle and the business cycle, both of which serve as indicators of economic downturns and recoveries.
This study investigates the potential of Google Trends as a leading indicator for the Condominium Price Index by employing a nowcasting model. Unlike previous research, this study adopts mixed-data sampling (MIDAS) techniques to incorporate data with varying frequencies. The empirical findings indicate that integrating macroeconomic variables and Google Trends data into autoregressive (AR) models enhances their explanatory power. Furthermore, the Augmented Distributed Lag MIDAS (ADL-MIDAS) model demonstrates superior forecasting performance, particularly in atypical market conditions.
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