Although the Merton option model estimates the default risk premium for risky loans (Merton 1974, Saunders 2008, Hull 2008), little has been done in literature and practice to price put options for home loans. We propose a new quantitative model to price put options on home loans by engaging default probabilities with the Black-Scholes model (Black and Scholes 1973, Hull 2008). By purchasing a put option, a homeowner obtains the right to sell the home back to a lender at a specified price and in a long-term future date. Hence, the market uncertainty due to short-term housing price fluctuations may be contained with the long-term confidence and guarantee that the put options have promised. We expect this to be an effective measure to stabilize the housing market and an attempt to fix the default issue related to mortgage-backed securities that is believed to be the root cause of the epic financial crisis around 2008. This would be especially helpful for homeowners in non-course states. As a result, this line of work is important and relevant to the nation's economy and individual Americans as the mortgage market is valued about 10.6 trillion U.S. dollars and about 30% household spending is on mortgages. The quantitative model observes the default probability of a single loan and then derives the volatility from the default probability. Using the Black-Scholes equation, put option prices are computed for different strike prices at different future dates. Our model has been the foundation of an Android GPS-enabled cloud service that is ready for practical implementations as financial services by banks and other institutions (see slides above). With the Android-based system, we may automatically compare put option prices to default risk premiums estimated with the Merton model by collecting empirical home data from Android users. The users may download the app (the user interface of the system) from Android Market. Note that the KMV model extends the Merton model for default risk premiums by estimating asset value and volatility that were claimed as not able to be directly observed for the Merton model (Saunders 2008). Yet our quantitative model service collects the value and volatility data (directly observed) for homes from Android devices. Hence, as an alternative to the KMV model, our model has solved a major issue of the Merton model.

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