In my previous post, I showed how it was possible to "scrape" a cohort of real estate prices from the internet together with the latitude, the longitude and a few other attributes on the properties. In this post, I will turn to one type of analysis that can be carried out with this data: the estimation of apartment prices at any location in-between.
"For any securitized product with loss severities below 50%, the cost of capital for the bank improves when the pace of liquidation accelerates." Clearly, this is something that a strategist or credit modeler would like to know as she or he considers liquidation time lines... but how can one make such a statement based on a capital rule that does not refer to time, or LGD, or a 50% threshold? Well, those are the types of conclusions that you can derive by going the extra mile on SSFA. Let's take a look!
Over the last decade, a number of open source tools have emerged, revolutionizing the fields of data collection, analysis and inference. Among those, the Python language appears to be getting the most attention. In this piece, which opens my new series about Python and Real Estate Data, I will illustrate how one can gather substantial amount of data at virtually no cost.