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Recursion AnalyzersA set of GUI based tools used for querying meticulously cleansed and standardized big mortgage data tables.
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BLOG
FHA Buyouts and Loan Counts1/08/2021
So once again we look at the theme of 2021 as a transition year. A lot of the year will be spent peering at the data to tease out emerging trends as we head towards the new normal. The discernment of new trends requires the use of new data, and new tools. We have recently brought into the Recursion data set FHA Neighborhood Watch data, which was discussed previously in the context of partial claims[1]. Read More 
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Recursion Data Featured in Debtwire article on Ginnie Mae Early Buyouts1/07/2021
Recursion data was quoted in the January 5th, 2021 Debtwire article on GNM Early Buyouts (EBOs). The author used Recursion data to support his assertion that GNM EBO supply is going up. The total CDR, (Constant Default Rate) the involuntary prepayment speed, jumped to 5.4% from 4.9% for nonbank servicers in December 2020 compared to November 2020. Read More |
Looking at Liabilities1/04/2021
The role of the Federal Reserve in the mortgage market is an ongoing theme in this blog, dating back to the early days of the Covid-19 crisis[1]. Besides the level of short-term rates, when we think about the Fed, we consider its direct intervention through the outright purchases of MBS. A common view of this “QE” policy can be seen in charts such as: Read More 
The Great Handoff12/30/2020
Updates of the Federal Reserve Financial Accounts (Z.1) data[1] always provide food for big-picture thoughts and the recent release of Q32020 data is no exception. Here is a look at the recent trend in holdings of MBS by sector: Read More 
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Recursion Co’s Chief Executive Officer Gives the New Year Opening Talk to PyData Chicago Group12/30/2020
Recursion Co’s Chief Executive Officer Li Chang will give a talk sponsored by PyData Chicago Group, the educational arm of the NumFOCUS non-profit organization on January 21st. Li will introduce how Recursion applies big data technology on Amazon Cloud to drastically cut down data processing time, reduce users’ costs, and bring transparency to the financial markets. More details can be found at https://www.meetup.com/PyDataChi/events/275326825/ Read More |
