One simple question - why people buy houses - is perhaps one of the most recurring and intriguing themes on research surface in housing. In the US, nearly two-thirds of the population owns houses (Tracy and Schneider (2001)). Optimal decision-making entails efficient risk management or hedging for those risks that can not be diversified away. Undiversified portfolio risk can be hedged by options (systematic risk) or insurance (idiosyncratic risk). The risk that can not be hedged or insured has to be borne by the agent. Another strategy is self-hedging documented by Han (2010, 2011) that focuses on ëself-hedgingí In the absence of optimal hedging arrangements, self-hedging may be a good alternative for households. Recently, Han (2010) investigates the role of hedging incentives and price risk on housing demand. Han (2010) distinguishes price rick and hedging risk on housing demand. The crucial factor for such a hedging effect is the degree of correlation between the prices. Sinai and Souleles (2005) present a rent risk versus asset price risk model. Households that do not own must rent, purchasing their housing services on a spot market, and thus subjecting themselves to annual fluctuations in rent. Owners, by contrast, avoid this rent uncertainty by buying a long-lived asset that delivers a guaranteed stream of housing services for a known up-front price. Unlike standard assets, houses effectively pay out annual dividends equal to the ex post spot rent, and so provide a hedge against rent risk. This study is about extending the hedging analysis by Han (2011) and Sinai and Souleles (2005). The basic question is what factors cause this hedging to be effective. And, does the effectiveness of hedging confined to a few geographical locations or MSAs. It is the second question that begs for disaggregated information for the households. This study takes care of issues such as endogeneity using tightest empirical strategies and a very novel technique to estimate hedging propensity.