Introduction
As continued from the House of Representatives prediction, the New Mexico State Senate is set to undergo significant changes as a result of this year’s primaries. Progressive challengers defeated incumbent Democratic leaders in districts 5, 28, 30, 35, and 38. Of these, districts 28, 30, and 35 are possible targets for Senate republicans. District 35 is especially notable, as Donald Trump won the district by 10 points in 2016 and Republican Gubernatorial candidate Steve Pearce won by 7 points in 2018. On the other hand, Democrats appear well-suited to pick up several seats in central New Mexico. In the 2018 House of Representatives election, Democrats flipped seven seats in the greater Albuquerque area (districts 15, 20, 27, 28, 29, and 30) and this momentum seems to be carrying over to the upcoming Senate elections. Flips in districts 10, 20, 21, 23, and 29 are within reach this year. The Senate’s composition is currently 26 Democrats to 16 Republicans, and due to the greater number of flip opportunities for Democrats, they are expected to pick up several seats.
Interestingly, the Republicans nominated candidates in every district except 6, which is heavily Democratic. The Democrats did not nominate a candidate in districts 1, 2, 7, 27, 32, 40, 41, and 42. District 40 has a partisanship of R+5.95 and may be winnable by Democrats given the national climate. Several Libertarian candidates are running in heavily red and blue seats and are not expected to affect the results.
Results
The average result of the prediction is that Democrats win an average of 28.4 seats to the 13.6 Republican seats. This is an average net of 2.4 seats for the Democrats. If the Democrats are able to pick up 3 seats, they will hold a supermajority in the Senate.
New Mexico Senate predictions
District |
Winner and Margin |
Percent Chance of Winning |
1 |
William Sharer +100 |
100% Republican |
2 |
Steven Neville +100 |
100% Republican |
3 |
Shannon Pinto +33.08 |
100% Democratic |
4 |
George Munoz +37.23 |
100% Democratic |
5 |
Leo Jaramillo +42.08 |
100% Democratic |
6 |
Roberto Gonzales +100 |
100% Democratic |
7 |
Pat Woods +100 |
100% Republican |
8 |
Pete Campos +31.22 |
100% Democratic |
9 |
Brenda McKenna +11.78 |
94% Democratic |
10 |
Katy Duhigg +7.42 |
82% Democratic (Flip) |
11 |
Linda Lopez +46.21 |
100% Democratic |
12 |
Gerald Ortiz Y Pino +42.26 |
100% Democratic |
13 |
Bill O’Neill +42.26 |
100% Democratic |
14 |
Michael Padilla +35.19 |
100% Democratic |
15 |
Daniel Ivey-Soto +25.37 |
100% Democratic |
16 |
Antoinette Sedillo-Lopez +61.05 |
100% Democratic |
17 |
Mimi Stewart +36.13 |
100% Democratic |
18 |
Bill Tallman +11.53 |
95% Democratic |
19 |
Gregg Schmedes +9.92 |
88% Republican |
20 |
Martin Hickey +10.77 |
98% Democratic (Flip) |
21 |
Mark Moores +7.57 |
86% Republican |
22 |
Benny Shendo +43.83 |
100% Democratic |
23 |
Harold Pope +3.20 |
64% Democratic (Flip) |
24 |
Nancy Rodriguez +68.55 |
100% Democratic |
25 |
Peter Wirth +71.17 |
100% Democratic |
26 |
Jacob Candelaria +39.32 |
100% Democratic |
27 |
Stuart Ingle +100 |
100% Republican |
28 |
Siah Correa Hemphill +11.47 |
92% Democratic |
29 |
Gregory Baca +0.71 |
54% Republican |
30 |
Pamela Cordova +5.95 |
88% Democratic |
31 |
Joseph Cervantes +38.17 |
100% Democratic |
32 |
Cliff Pirtle +100 |
100% Republican |
33 |
William Burt +33.63 |
100% Republican |
34 |
Ron Griggs +34.19 |
100% Republican |
35 |
Neomi Martinez-Parra +0.78 |
55% Democratic |
36 |
Jeff Steinborn +19.24 |
100% Democratic |
37 |
William Soules +19.73 |
100% Democratic |
38 |
Carrie Hamblen +35.91 |
100% Democratic |
39 |
Elizabeth Stefanics +16.94 |
97% Democratic |
40 |
Craig Brandt +100 |
100% Republican |
41 |
David Gallegos +100 |
100% Republican |
42 |
Gay Kernan +100 |
100% Republican |
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.