Back of the Envelope Series on NFL Stadium Deals


With three NFL teams vying to relocate to Los Angeles, much of the press coverage has been devoid of financial analysis.  This series will use ten years worth of NFL valuation data from Forbes magazine to try and understand the financial implications for both the teams and the communities.    In the past 10 years, the following NFL teams have built new stadiums:

  • Arizona Cardinals
  • Indianapolis Colts
  • Dallas Cowboys
  • Minnesota Vikings
  • New York Giants
  • New York Jets
  • Kansas City Chiefs
  • New Orleans Saints
  • San Francisco 49ers

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A Google Map of Michigan’s 2014 School Score Cards

The Michigan Department of Education (MDE) has released their annual school score cards.  The web interface for viewing the data isn’t particularly good, so I’ve created a custom Google map that displays each school, the score card status, free lunch information, 11th grade test scores, 5th grade test scores,  and demographic information.  The MDE has an official guide for understanding the score card information.

The map adopts the MDE Score Card’s practice of using colors to denote success levels.

green-dotGreen – attain 85% or greater of possible points


lime-dotLime – attain at least 70% but less than 85% of possible points


yellow-dotYellow – attain at least 60% but less than 70% of possible points


orange-dotOrange – attain at least 50% but less than 60% of possible points



Under Performing SchoolsRed – attain less than 50% of possible points


purple-dotPurple — meet all applicable Participation and Compliance Factor requirements;  have no full academic year students

black-dotBlack –Schools that participate in the free lunch program but do not have MDE Score Cards.

Here is the 2014 map of Michigan schools with integrated performance information.  Be sure to click on the icon to display the data!



Identifying the Over and Under Achieving Michigan Schools

The Michigan Department of Education (MDE) ranks schools past on test scores and than reports the percentile a schools is in.  This tells us the absolute performance, but leaves out information.  For example, the percentage of students getting free lunches greatly impacts performance.  A school that has a lot of free lunch students that beats expectations, could be adding more value than a school with high absolute performance.  I’ve created a map that shows under and over performing schools based on location, demographics, and socio-economic status.    The model captures the top 10% and the bottom 10%.

OverperformingIndicates a school that over performed on one or more of math, reading, science


Under Performing SchoolsIndicates a school that under performed on one or more of math, reading, science.


Be sure to click on the icon to get the details about the school.

The icky math details are below.

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Comparing NHA Charter School Performance with Nearest Neighbor

Responding to a recent Detroit Free Press series, the CEO of National Heritage Academy (NHA) had this to say:

Apples-to-apples comparisons to neighboring district schools help explain the appeal. More than 90% of NHA’s Michigan schools had a higher overall proficiency rate than neighboring district schools on the Michigan Educational Assessment Program.

Which raises interesting questions that no one else has documented because life is short and math is hard.  In this post I’m going to compare NHA schools performance with the nearest neighbor for 5th grade MEAP results in Math, Reading, and Science.

I’ve created a custom Google map to document the locations (be sure to click on the icons to see the schools results, demography, and socio-economic indicators).

Below is the analysis, but watch out, there will be MATH!

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Evaluating Michigan Schools MME Results with Regression Analysis

In my last post, I looked at factors impacting Michigan primary school test performance.   In this post I look at high school performance using the Michigan Merit Examination (MME)  11th grade  results to determine what is the  role of race, location, poverty, and school type (public, not for profit, and for profit charter).   In all of the exam subjects (Math, Reading, Science, Social Studies, and Writing) for profit charters out perform not for profit charters and public schools at a 95%, or higher, confidence level.  Not for profit charter schools typically outperform public schools at lower confidence level than their for profit comparisons because their are so few in the data (only 16 as opposed to 79).  The higher the ratio of Asian and female students, the better the school performs on the exams.

One of the advantages of a regression analysis is that it can be used to forecast what the result should be based on the factors involved.   Some schools will vary more (above and below) the forecast.  I hope to illuminate that distinction using an online map in a future post.

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Using Regression Analysis to Identify Factors in MI Primary School Test Performance

The Detroit Free Press recently ran an interesting series of stories about charter schools in Michigan which made me wonder if I could use regression analysis to partition the variation in test results on the basis of race, poverty, location, and type of school.  This analysis uses MEAP scores for the 5th grade.

tl;dr — For profit charter schools generally produce worse results than public or not for profit charters while not for profit charters produce better results.

If you’re thinking “eeeeeeeeeehhhhhhhwwwwwwwww math”, I hope to soon incorporate the results into an on line map to make it easy to look at the results for particular schools .

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More Statistics on Sacramento Housing

In a previous post, I showed a pretty simple regression analysis of housing prices and house size for my Zip code. The zip code was used as an easy way to include location in the output. Using PostGIS and geographic data from the City of Sacramento, this post will show a regression analysis ( using the R statistical programming project) using the city’s designated neighborhoods. The raw data real estate data comes from the Sacramento Bee. After describing the model, I’ll apply it the last few months of home sales (not used in developing the model), and see how well it does at predicting results.    Continue reading