High-credit-score districts swung toward Democratic control
Before the 2018 midterm elections, Credit Karma found that swing districts had higher-than-average VantageScore 3.0 credit scores, meaning control of the House of Representatives would be largely contested in districts with high-credit-scoring populations.
Now that election results are in, we see that Republican congressional districts with high median credit scores swung heavily toward Democratic control. To illustrate, let’s take a look at the 10 Republican districts that had the highest median VantageScore 3.0 credit scores before the midterms.
Nine of these Republican districts were rated as “swing” districts by the The Cook Political Report Partisan Voter Index (or PVI). And postelection, we see all nine of these swing Republican districts ended up electing Democratic representatives, who will be sworn in early next year.
Table 1. 10 Republican districts with the highest median VantageScore 3.0 credit score
|District name||Major cities and areas represented||Current rep||New rep||Median Vantage 3.0 credit score||Preelection status||Cook PVI|
|NJ-11||Dover, Morristown, Hanover, East Hanover, Madison, Hopatcong, Somerville||Rodney Frelinghuysen (R)||Mikie Sherrill (D)||691||Swing||R+3|
|VA-10||Winchester, Leesburg, Ashburn, Chantilly||Barbara Comstock (R)||Jennifer Wexton (D)||685||Swing||D+1|
|CA-49||Oceanside, Vista, Carlsbad, Encinitas||Darrell Issa (R)||Mike Levin (D)||681||Swing||R+1|
|NJ-07||North Plainfield, Cranford, Westfield, Scotch Plains||Leonard Lance (R)||Tom Malinowski (D)||678||Swing||R+3|
|CA-45||Irvine, Tustin, North Tustin, Villa Park, Anaheim Hills, Laguna Hills, Lake Forest||Mimi Walters (R)||Katie Porter (D)||675||Swing||R+3|
|NY-11||Staten Island, Southern Brooklyn||Dan Donovan (R)||Max Rose (D)||674||Swing||R+3|
|IL-06||West Chicago, Inverness, Wheaton||Peter Roskam (R)||Sean Casten (D)||674||Swing||R+2|
|CA-48||Costa Mesa, Huntington Beach, Laguna Beach, Aliso Viejo||Dana Rohrabacher (R)||Harley Rouda (D)||672||Swing||R+4|
|UT-03||Provo, Orem||John Curtis (R)||John Curtis (R)||671||Safe||R+25|
|MN-03||Brooklyn Park, Bloomington, Eden Prairie||Erik Paulsen (R)||Dean Phillips (D)||670||Swing||D+1|
Although Democrats did well this cycle in the above districts, it’s by no means true that Democrats represent only areas with higher-than-average credit. In fact, according to our postelection analysis, Democrats will continue to represent a disproportionate number of districts with lower-than-average credit. (All 15 of the lowest scoring districts are held by Democratic representatives.)
As for consumers, they can take heart. Postelection, both parties will serve districts that display a diversity of economic and credit situations. Each U.S. representative will continue to have a duty to devise policies that serve a large coalition of voters with wide ranging economic needs. You can make your voice heard by writing your representative or by attending town halls and other community events. And you can always work on improving your credit health, regardless of geography or political affiliation, by educating yourself about factors that influence your scores and following steps to build healthy credit habits.
To conduct this analysis, Credit Karma took a look at the most recent TransUnion credit report for users who had logged onto Credit Karma in the five months leading up to October 19, 2018. We then used aggregated ZIP code data from these credit reports to match groups of users to specific congressional districts across the country. Once we had sorted these groups into their respective congressional districts, we used the remaining information in their TransUnion credit reports to calculate the average and median values for all the data points listed in the article, including their VantageScore 3.0 credit scores.
The “swing” status was determined by the district-by-district ratings assigned by The Cook Political Report’s 2018 House Race Ratings. Districts considered “toss-ups” or “lean” districts were classified as “swing” in Credit Karma’s analysis; districts considered “likely” to vote for one party or the other were grouped with the “safe” districts. All percentages and figures have been rounded to the nearest whole.