WHEN POLICY MEETS PERCEPTION: A MULTI-DIMENSIONAL ANALYSIS OF HOUSING LOAN DEMAND USING LTV RATIOS AND GOOGLE TRENDS

This study investigates whether online search behavior can serve as a leading indicator of mortgage loan distribution and housing price dynamics in Indonesia. Building on prior studies that highlight the predictive potential of internet search data, this research employs a complementary approach that integrates cross-correlation analysis and time-series econometric modeling to evaluate the relationship between public interest in housing-related keywords, particularly mortgage, and subsequent changes in housing market indicators. Unlike traditional models that rely solely on macroeconomic fundamentals, this study incorporates digital behavioral signals to enhance predictive capacity. The results confirm that a statistically significant relationship with both mortgage distribution and house price movements, suggesting that Google search data can provide forward-looking insights into housing demand trends. These findings contribute to the emerging body of literature on digital economics and offer practical implications for policymakers and financial institutions seeking to monitor and anticipate shifts in the real estate sector, especially in a post-pandemic context marked by changing consumer behavior. Keywords: Google Trends, housing loan, macroeconomics