Introduction
The elections for the State Legislature are as important an issue for New Mexicans as the elections for the United States House and Senate. Significant changes have occurred as a result of the 2018 elections, where the state government passed a minimum wage increase, increased gun control, and reformed the tax system. In November, the Democrats are looking to increase their majorities in the House and Senate while the republicans are looking to take the majorities. Primary elections resulted in the defeat of some of the more conservative Senate Democrats by progressive insurgents, which may change both the partisan and ideological composition of the body. Contrary to the importance of these and many other state legislative elections, very few models have been made to predict the results of the upcoming elections. Here, a predictive model was developed based on past partisan performance, statewide polling, and fundraising to give New Mexicans more information on the upcoming elections.
In the 2018 elections, Democrats picked up a net of 8 seats in the house, greatly expanding their majority. These seats, seven of which are in the greater Albuquerque area, were won by Hillary Clinton in 2016 and are expected to stay with the Democrats. Almost all of the remaining Republican seats are in safely red areas. Two seats in Valencia county (7 and 8) are likely to stay red, but are steadily growing bluer. Republicans won districts 22, 57, and 60 by less than 3 points in 2018, and these seats are expected to be the most competitive. However, Democrats failed to nominate a candidate in district 60. Two other Albuquerque area districts (31 and 44) are still light red, but are slowly moving Democratic. On the other side, Democrats may have difficulty holding onto their closest flips, especially districts 20 and 27, where the incumbents are not seeking reelection. These districts are predicted to stay blue, but may shift as new polling is added to the model.
Results
The model is highly reactive to changes in statewide polling. Due to Joe Biden’s large lead in the presidential polling, Democrats are predicted to win the median district by approximately 15 points. Based on this, the model predicts that Democrats will win an average of 47.6 seats to the Republican average of 22.4 seats. This results in the Democrats gaining an average of 1.6 seats. If the Democrats are able to reach 47 seats, they will reach a 2/3 supermajority. The exact predicted voting margins are as follows:
Note: “Representative +100” means that the winner is uncontested by another major party.
Predicted Seat Margins
District |
Winner and Margin |
Percent chance of winning |
1 |
Rodney Montoya +100 |
100% Republican |
2 |
James Strickler +100 |
100% Republican |
3 |
T. Lane +100 |
100% Republican |
4 |
Anthony Allison +9.19 |
96% Democratic |
5 |
Doreen Johnson +100 |
100% Democratic |
6 |
Eliseo Alcon +36.81 |
100% Democratic |
7 |
Kelly Fajardo +3.16 |
70% Republican |
8 |
Alonzo Baldonaro +14.59 |
97% Republican |
9 |
Patricia Lundstrom +100 |
100% Democratic |
10 |
G Romero +31.40 |
100% Democratic |
11 |
Javier Martinez +63.41 |
100% Democratic |
12 |
Art De La Cruz +100 |
100% Democratic |
13 |
Patricia Roybal Caballero +43.38 |
100% Democratic |
14 |
Miguel Garcia +100 |
100% Democratic |
15 |
Dayan Hochman-Vigil +16.73 |
99% Democratic |
16 |
Antonio Maestas +32.70 |
100% Democratic |
17 |
Deborah Armstrong +38.52 |
100% Democratic |
18 |
Gail Chasey +100 |
100% Democratic |
19 |
Sheryl Williams Stapleton +59.68 |
100% Democratic |
20 |
Meredith Dixon +8.73 |
92% Democratic |
21 |
Debra Sarinara +100 |
100% Democratic |
22 |
Jessica Velasquez +2.72 |
69% Democratic (Flip) |
23 |
Damon Ely +21.52 |
100% Democratic |
24 |
Elizabeth Thomson +22.09 |
100% Democratic |
25 |
Christine Trujillo +40.96 |
100% Democratic |
26 |
Georgene Louis +100 |
100% Democratic |
27 |
Marian Matthews +6.06 |
89% Democratic |
28 |
Melanie Stansbury +10.86 |
97% Democratic |
29 |
Joy Garratt +11.48 |
97% Democratic |
30 |
Natalie Figueroa +20.93 |
100% Democratic |
31 |
William Rehm +3.42 |
77% Republican |
32 |
Candie Sweetser +10.88 |
88% Democratic |
33 |
Micaela Cadena +34.08 |
100% Democratic |
34 |
Raymundo Lara +37.59 |
100% Democratic |
35 |
Angelica Rubio +32.02 |
100% Democratic |
36 |
Nathan Small +21.18 |
100% Democratic |
37 |
Joanne Ferrary +21.69 |
100% Democratic |
38 |
Rebecca Dow +7.89 |
86% Republican |
39 |
Rodolpho Martinez +12.97 |
98% Democratic |
40 |
Roger Montoya +38.40 |
100% Democratic |
41 |
Susan Herrera +100 |
100% Democratic |
42 |
Kristina Ortez +61.90 |
100% Democratic |
43 |
Christine Chandler +26.89 |
100% Democratic |
44 |
Jane Powdrell-Culbert +9.43 |
94% Republican |
45 |
Linda Serrato +100 |
100% Democratic |
46 |
Andrea Romero +64.69 |
100% Democratic |
47 |
Brian Egolf +67.77 |
100% Democratic |
48 |
Tara Lujan +100 |
100% Democratic |
49 |
Gail Armstrong +100 |
100% Republican |
50 |
Matthew McQueen +19.56 |
100% Democratic |
51 |
Rachel Black +30.68 |
100% Republican |
52 |
Doreen Gallegos +34.94 |
100% Democratic |
53 |
Willie Madrid +9.64 |
98% Democratic |
54 |
James Townsend +100 |
100% Republican |
55 |
Cathrynn Brown +100 |
100% Republican |
56 |
Zach Cook +31.18 |
100% Republican |
57 |
Billie Helean +2.00 |
61% Democratic (Flip) |
58 |
Candy Ezzell +100 |
100% Republican |
59 |
Greg Nibert +45.88 |
100% Republican |
60 |
Joshua Hernandez +100 |
100% Republican |
61 |
Randall Pettigrew +100 |
100% Republican |
62 |
Larry Scott +100 |
100% Republican |
63 |
Martin Zamora +10.96 |
90% Republican |
64 |
Randal Crowder +100 |
100% Republican |
65 |
Derrick Lente +61.49 |
100% Democratic |
66 |
Phelps Anderson +100 |
100% Republican |
67 |
Jackey Chatfield +100 |
100% Republican |
68 |
Karen Bash +14.51 |
95% Democratic |
69 |
Harry Garcia +34.15 |
100% Democratic |
70 |
Ambrose Castellano +38.07 |
100% Democratic |
Methodology
The partisanship and elasticity of each district was determined from recent elections for New Mexico House of Representatives, President, and United States Senate. A polling factor was then applied uniformly to each district. Funding leads, based on publicly available data, were applied to each district. Each funding lead affected a district in accordance with national US House data. The computed leads and margins of error were used to generate confidence intervals for each district.