Housing Industry Analytics: Short-Sale versus Foreclosure

There's been an explosion of 'short sales' of housing properties by banks in the US market. In the east coast markets, there's been a 60-100% spike in short sales, why? If the banks wait too long and then foreclose, the properties loses too much value and the final returns may be lower. On the other hand, a short sale is immediate, and long term risk is eliminated. As the article in the NECN link above says:

"In a "short sale," banks agree to let someone who owes more on their mortgage than their home is worth to sell it to a new buyer, with the bank typically writing off tens of thousands of dollars in the process. But what the bank gains is avoiding the cost, protracted process and uncertainty around taking the home or condo by foreclosure and then trying to resell it as a bank-owned property."

A first look indicates that this is a decision optimization problem under uncertainty that is somewhat similar to (but not the same as) that faced by fashion retailers who are trying to clear their end-of-season perishable inventory. Do they markdown apparel right now or should they wait for some more time? If they wait, then over time, the 'fashion statement' value deteriorates and the retailer may have to more aggressively markdown to attract customers and clear inventory. On the other hand, if they markdown right now, that may turn out to be a hasty and expensive decision, with a certain probability. So really there are two decisions to be made: when to markdown and by how much?

A bank may own a majority stake in several properties whose values are depreciating over time in an over-capacitated market (property owners are unlikely to have the cash to maintain or make improvements), which they need to get off their books without losing much. Using stochastic optimization methods available in the field of Operations Research ("the science of better"), they may be able to do a much better job of profitably managing their inventory (it won't be surprising if they are already doing this). Stochastic optimization methods are specifically designed to work with probabilities of scenarios, as opposed to a deterministic approach that assumes everything is perfectly known in advance, although the latter often turns out to be a reasonable and quick first approximation. An alternative that may be especially appealing to pessimistic banks looking to avoid worst-case meltdowns is 'Robust Optimization' that can operate without formal probability distributions and can among other things, help minimize a bank's maximum regret. Another advantage of OR methods is that they are typically not capital intensive, and the ROI on successful projects can be remarkably high. In short, OR can be very, very useful here.


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