ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Finance



First Advisor

Sanjiv Sabherwal


In this dissertation, I use Google search frequency to construct a new measure of housing market-level sentiment and analyze its relation with housing prices. I term this measure as the Home Price Fear Index, or Fear Index or Fear for short. The Fear Index is based on Google Search volume for certain real estate and economic terms, such as foreclosure, recession, and market value. In the first essay, I examine the relation between the Fear Index at the national level and the Case/Shiller National Home Price Index. I find this relation to be inverse, with an increase in Fear predicting a decrease in home prices. The relation is robust to controlling for a number of relevant economic variables. I also find that housing prices respond differently to increases versus decreases in Fear. Increases in Fear result in a significant negative response in housing prices, while decreases in Fear evoke little response. This asymmetric response can be attributed to the “negativity effect,” which is widely discussed in the psychology literature. I also find that home prices are more sensitive to Fear during recessionary periods. In the second essay, I examine the relation between the Fear Index at the metropolitan statistical area (MSA) level and local home price changes. I construct 20 local Fear Indexes based on MSAs covered by Case/Shiller 20-City Composite Home Price Index. I find that forecasting ability of local Fear is comparable to those of other well-known predictors of housing price changes. Further, Fear in “cold” housing markets (cities with slow price appreciation) has a stronger effect than in “hot” markets (cities with rapid price appreciation). I also find that cities with high bankruptcy rates are more responsive to changes in Fear than low bankruptcy rate cities. Moreover, “cold” cities with high bankruptcy rates are the most responsive to negative sentiment. In the third essay, I examine the impact of volatility on the relation between the Fear Index and home price changes. Using standard deviation and idiosyncratic volatility as alternative measures of volatility, I find that response to Fear across MSAs is stronger as volatility increases. Further, cities with low volatility exhibit a similar response to increases versus decreases in Fear, while high volatility cities display an asymmetric response, with a significant and negative reaction to an increase in Fear but little reaction to a decrease in Fear. I also differentiate between downside volatility and upside or “good” volatility, and find that Fear has a stronger impact on housing price changes as downside risk goes up relative to the upside volatility. Finally, I find that it is the downside and not the upside volatility that affects Fear.


Real estate, Housing market, Sentiment index, Fear Index, Google trends, Volatility, Negativity effect


Business | Finance and Financial Management | Real Estate


Degree granted by The University of Texas at Arlington