There is a major effort on the part of the Federal government to try and increase neighborhood integration by race. It is an initiative doomed to fail both on political and practical grounds. We know from computer simulations that you only have to have a very weak race affiliation or aversion to have, within a few iterations, substantially segregated neighborhoods even when starting with completely randomly distributed populations. We also know that there are innumerable factors beyond race affiliation that go into home purchasing decisions.
As Big Data, internet, and computing skills keep working their magic, these trends of self-segregation (on factors beyond simple race affiliation or aversion) will continue to subvert the best efforts of central planners.
Stein's article is dealing with a simple and very practical problem and using big data to solve that problem. There is nothing antithetical or malign going on. If I have a low paying job but I highly value education for my child, where can I look for a home in Washington, D.C.?
The District’s oft-talked about millennial boom has led to a baby boom. Between 2010 and 2013, the number of children younger than 5 has increased by almost 20 percent in the District, from 33,000 to 39,000, according to Census figures.Stein goes on to show that you can use this data, a mash-up of real estate costs and school testing results to pinpoint where in the district you can find both cheap housing and good schools.
Strong public schools are crucial in helping to retain these millennials so they don’t decamp for the suburbs as soon as their children hit school age. And if these families don’t want to fork up college-like tuition for a private elementary school or rely on the increasingly competitive charter school lottery system — where about 44 percent of the city’s students are enrolled — they’re going to have to rely on their neighborhood public school.
So how much does it cost to purchase a house within the boundaries of what is considered to be a high-performing school? A lot. The median price for a typical three-bedroom home, for instance, zoned for a D.C. Public School elementary school where 80 percent or more students are proficient or advanced in reading costs more than $800,000.
The always-interesting District, Measured — a blog from the city’s Office of the Chief Financial Officer — sifted through this data to determine how much it would cost to purchase a house in a neighborhood zoned for a top public elementary school. The main, and expected, takeaway: The best schools are not equally distributed throughout the city. The most expensive homes and best schools are in upper Northwest neighborhoods, and the cheapest homes are east of the river, along with a high concentration of low-performing schools.
The median sales price of a house in a school zone where 60 to 80 percent of students are proficient or advanced in reading will run between the high $600,000s to more than $1 million.
The interactive graph below plots public elementary schools based on their test scores and the median sales price for a three-bedroom home in that school’s boundaries. Typically, the higher the test scores, the higher it costs to live there. There are neighborhoods, like Logan Circle and Petworth, that have experienced rapidly rising housing costs in recent years, though their schools, when measured by test scores, are not high-performing.
There is nothing wrong with this. In fact, this is an excellent use of big data. And you don't have to restrict yourself to good schools. You could do a similar mash-up for crime, for commute times, for restaurant density, etc.
From an economist's point of view, this is near ideal because you are getting a very close match between buyers and sellers with complete visibility of purchase specifications and availability. If, after an evening crunching the numbers, you can determine objectively that neighborhood X meets all your specifications in terms of what you are willing to pay, what quality of school you are willing to have, what kind of crime level you are willing to endure, how long you are willing to commute, etc. then your search becomes very efficient. I scan what is currently available in neighborhood X and buy what best meets my aesthetic interests or I wait until something closer to my interests turns up in that area. That is incredible efficiency and to be highly desired. It takes much of the uncertainty, risk and operational effort out of the house buying process.
But that is not all it does. Look at the maps in the article. Postmodernist ideologues steeped in critical theory look at these maps and see people self-segregating themselves based on race aversion or affiliation. But what this data illustrates clearly is that neighborhoods are self-segregating based on all sorts of choices, that may never be based on race and yet still have race segregation outcomes if there are differences in expressed desires that vary by race (or more correctly by culture, race being just a proxy for culture.) If one group values safety or education or reduced commute times or access to restaurants or whatever, more than another group, then big data and universal internet access will accelerate that assortative affiliation where like finds like.
This big data enables people to make better decisions to match their critical life choices. What this data crunching by Stein reveals is that there is an opportunity for low income people who highly value good education to find homes in Southwest Washington. I am willing to bet that SW Washington and the area around Nalle Elementary is likely to see a surge in gentrification. That is not bad in itself unless you are committed to trying to integrate neighborhoods. People's free choices tend to trump the narrow biases or preferences of the ideologically committed.