i485 EB Field Offices efficiency - proper analysis (data FY 2023-2025)
Hey everyone,
A while back, I posted an analysis on Employment-Based (EB) efficiency across Field Offices. While that data was a helpful starting point, the mathematical framework was not proper. It treated USCIS queues as static blocks and aggregated simple processing errors.
Today, I’m releasing a drastically updated framework based on continuous-flow queueing theory. This new model finally bridges the gap between what USCIS officially reports (massive, terrifying backlogs) and what we actually see on crowd-sourced trackers (cases getting approved in weeks).
Here is the evolution of the methodology and the new metrics:
1. The "Paper Transfer" Illusion & Stagnant Baselines
Have you ever wondered why a field office mathematically looks like it takes 150 months to clear its backlog, but people on community trackers report approvals in 2 weeks?
This is the Macroscopic vs. Microscopic illusion. USCIS officially assigns thousands of cases to FOs on paper that physically remain in boxes at the NBC, or are permanently stalled (background checks, RFEs).
The Fix: I now calculate a "Stagnant Baseline" (the historical average pending cases) for every single FO. By subtracting this baseline from their official Pending count, we isolate the Active Backlog—the cases actually moving across an officer's desk.
2. The 1.5x Multiplier (Uniform Arrival)
In my old post, I multiplied quarterly clearance rates by 3 to get the "months to clear." That assumed every single case arrived on Day 1 of the quarter and waited the whole time. In reality, cases flow in continuously. Assuming a uniform distribution, the median arrival time is exactly halfway through the quarter (1.5 months). So, if an FO clears exactly what it receives (a Clearance Rate of 1.0), the true average wait time for that batch is 1.5 months, not 3.
3. Fixing the "Average of Ratios" Bug
Previously, calculating a clearance timeline per quarter and then averaging those quarters together caused wild anomalies. If an FO like Santa Ana had a single quarter where their clearance temporarily dropped to 1 case, the ratio would explode to hundreds of months, poisoning their 3-year average. The Fix: The timeline is now calculated as a Ratio of Averages across the entire 3-year aggregate (Total Active Load ÷ Total Cleared). This completely neutralizes single-quarter statistical anomalies and accurately ranks consistent performers.
4. "Negative" Processing = Hidden NBC Transfers
In old models, processing numbers often inexplicably turned negative. A negative processing rate just means an FO's backlog dropped massively without corresponding approvals or denials. This indicates cases were quietly re-routed back to the NBC or elsewhere. This is now explicitly tracked as Transfers TO NBC, which officially counts toward an FO clearing its queue.
The New Metrics
Here is how to read the new data (all volumetric numbers are now presented as clean integers per quarter):
- Assigned per Quarter: Raw number of new cases officially added to the FO's docket.
- Transfers FROM NBC per Quarter: The "hidden" influx. Calculated when an FO processes way more cases than it was officially assigned.
- Cleared per Quarter: (Accepted + Denied + Transfers TO NBC). The total macroscopic volume of cases that successfully left the FO.
- Average Clearance Rate: A ratio of output to input (
Cleared ÷ Incoming).1.0means the FO is clearing cases exactly as fast as they arrive for this quarter. - Months to Clear (The Static Backlog): A macroscopic projection. If the FO received zero new cases, this is how many months it would take to burn through their entire official static Pending backlog.
- Months to Clear Non-Stagnant (The Active Pipeline): The most accurate timeline for a new applicant. It measures how many months a newly transferred, active case expects to wait, assuming it bypasses the "dead" stagnant baseline and strictly waits on the active pipeline volume
(Active Load + Active Backlog) ÷ Cleared * 1.5.
The Data
| Field Office | Cleared per Quarter | Average Pending | Average Clearance Rate | Months to Clear | Months to Clear Non-Stagnant |
|---|---|---|---|---|---|
| 🚀 St. Albans, VT | 543 | 163 | 0.98 | 0.9 | 1.63 |
| 🚀 Potomac, Service Center | 9 | 3 | 1.02 | 1 | 1.71 |
| 🚀 Charlotte Amalie, VI | 43 | 45 | 1.53 | 3.14 | 1.47 |
| 🚀 San Juan, PR | 112 | 119 | 1.13 | 3.19 | 1.55 |
| 🚀 Imperial, CA | 179 | 209 | 0.99 | 3.5 | 1.96 |
| 🚀 Boise, ID | 359 | 433 | 0.97 | 3.62 | 1.93 |
| 🚀 Spokane, WA | 124 | 155 | 1.05 | 3.75 | 1.7 |
| 🚀 Nashville, TN | 871 | 1116 | 1.02 | 3.84 | 1.5 |
| 🚀 Yakima, WA | 138 | 184 | 1.03 | 4 | 1.68 |
| 🟢 Anchorage, AK | 100 | 135 | 0.97 | 4.05 | 1.85 |
| 🟢 Tucson, AZ | 244 | 342 | 0.85 | 4.2 | 2.23 |
| 🟢 Harlingen, TX | 204 | 296 | 0.95 | 4.35 | 2.15 |
| 🟢 Los Angeles County, CA | 1628 | 2377 | 0.91 | 4.38 | 1.94 |
| 🟢 San Fernando Valley, CA | 1460 | 2319 | 0.91 | 4.77 | 1.93 |
| 🟢 Kendall, FL | 310 | 534 | 1.15 | 5.17 | 1.62 |
| 🟢 Fresno, CA | 369 | 689 | 1.03 | 5.6 | 1.78 |
| 🟢 Helena, MT | 32 | 60 | 0.96 | 5.62 | 2.02 |
| 🟢 Fort Myers, FL | 154 | 289 | 1.13 | 5.63 | 1.55 |
| 🟢 Honolulu, HI | 121 | 234 | 1.04 | 5.8 | 1.96 |
| 🟢 El Paso, TX | 246 | 513 | 0.94 | 6.26 | 2.22 |
| 🟢 Providence, RI | 175 | 369 | 1.08 | 6.33 | 1.6 |
| 🟢 Agana, GU | 87 | 199 | 0.71 | 6.86 | 2.94 |
| 🟢 Wichita, KS | 62 | 151 | 0.98 | 7.31 | 1.8 |
| 🟢 Louisville, KY | 402 | 987 | 0.94 | 7.37 | 1.83 |
| 🟢 Buffalo, NY | 306 | 793 | 1.02 | 7.77 | 1.72 |
| 🟢 San Bernardino, CA | 535 | 1423 | 0.93 | 7.98 | 2.19 |
| 🟡 Hialeah, FL | 362 | 974 | 0.99 | 8.07 | 1.78 |
| 🟡 Manchester, NH | 158 | 439 | 0.96 | 8.34 | 1.95 |
| 🟡 Los Angeles, CA | 1190 | 3510 | 0.84 | 8.85 | 2.17 |
| 🟡 Cincinnati, OH | 420 | 1273 | 0.88 | 9.09 | 2.19 |
| 🟡 Portland, ME | 85 | 260 | 0.9 | 9.18 | 2.17 |
| 🟡 Miami, FL | 441 | 1354 | 1.17 | 9.21 | 1.47 |
| 🟡 Reno, NV | 44 | 138 | 1.01 | 9.41 | 1.7 |
| 🟡 Las Vegas, NV | 118 | 386 | 0.99 | 9.81 | 1.72 |
| 🟡 New Orleans, LA | 274 | 919 | 1.16 | 10.06 | 1.4 |
| 🟡 Queens, NY | 413 | 1411 | 0.9 | 10.25 | 1.95 |
| 🟡 Nebraska, Service Center | 3253 | 11109 | 1.56 | 10.25 | 1.65 |
| 🟡 Columbus, OH | 420 | 1458 | 0.96 | 10.41 | 1.94 |
| 🟡 Albany, NY | 188 | 659 | 0.91 | 10.52 | 1.9 |
| 🟡 Montgomery, AL | 264 | 937 | 1.28 | 10.65 | 1.34 |
| 🟡 Brooklyn, NY | 420 | 1511 | 0.82 | 10.79 | 2.25 |
| 🟡 St. Paul, MN | 544 | 1981 | 0.97 | 10.92 | 2.02 |
| 🟡 Albuquerque, NM | 110 | 402 | 1.45 | 10.96 | 1.94 |
| 🟡 Long Island, NY | 418 | 1602 | 0.98 | 11.5 | 1.82 |
| 🟡 Cleveland, OH | 346 | 1330 | 0.86 | 11.53 | 2.2 |
| 🟡 West Palm Beach, FL | 256 | 986 | 1.32 | 11.55 | 1.32 |
| 🟡 San Francisco, CA | 1187 | 4739 | 1.3 | 11.98 | 2.29 |
| 🔴 Boston, MA | 826 | 3341 | 1.02 | 12.13 | 1.67 |
| 🔴 New York, NY | 705 | 2856 | 0.98 | 12.15 | 1.76 |
| 🔴 Newark, NJ | 860 | 3504 | 1.31 | 12.22 | 1.49 |
| 🔴 Texas, Service Center | 3378 | 13892 | 2.09 | 12.34 | 1.53 |
| 🔴 Omaha, NE | 128 | 548 | 0.95 | 12.84 | 1.85 |
| 🔴 Baltimore, MD | 734 | 3141 | 1.06 | 12.84 | 1.48 |
| 🔴 Santa Ana, CA | 587 | 2518 | 0.86 | 12.87 | 2.24 |
| 🔴 Fort Smith, AR | 95 | 409 | 1.01 | 12.92 | 1.66 |
| 🔴 Memphis, TN | 235 | 1015 | 1.32 | 12.96 | 2.04 |
| 🔴 Oakland Park, FL | 314 | 1360 | 1.05 | 12.99 | 1.78 |
| 🔴 Charleston, SC | 153 | 665 | 1.1 | 13.04 | 1.51 |
| 🔴 Pittsburgh, PA | 263 | 1149 | 0.93 | 13.11 | 2.03 |
| 🔴 Des Moines, IA | 125 | 554 | 1.05 | 13.3 | 1.67 |
| 🔴 Indianapolis, IN | 326 | 1462 | 1.04 | 13.45 | 1.83 |
| 🔴 San Diego, CA | 496 | 2310 | 0.87 | 13.97 | 2.27 |
| 🔴 Oklahoma City, OK | 108 | 516 | 0.94 | 14.33 | 2.14 |
| 🔴 Denver, CO | 285 | 1398 | 0.99 | 14.72 | 1.79 |
| 🔴 Orlando, FL | 604 | 3008 | 1.09 | 14.94 | 1.56 |
| 🔴 Salt Lake City, UT | 154 | 799 | 0.91 | 15.56 | 1.9 |
| 🔴 Detroit, MI | 513 | 2826 | 0.94 | 16.53 | 1.99 |
| 🔴 Greer, SC | 115 | 634 | 1.09 | 16.54 | 1.54 |
| 🔴 Tampa, FL | 339 | 1879 | 1.15 | 16.63 | 1.78 |
| 🔴 Houston, TX | 866 | 4930 | 0.87 | 17.08 | 2.3 |
| 🔴 Hartford, CT | 260 | 1602 | 0.93 | 18.48 | 1.96 |
| 🔴 Portland, OR | 260 | 1625 | 0.89 | 18.75 | 2.38 |
| 🔴 Washington, DC | 522 | 3320 | 1.08 | 19.08 | 1.58 |
| 🔴 Sacramento, CA | 345 | 2218 | 0.85 | 19.29 | 2.15 |
| 🔴 Seattle, WA | 1056 | 6793 | 0.84 | 19.3 | 2.39 |
| 🔴 Norfolk, VA | 170 | 1122 | 1.08 | 19.8 | 1.68 |
| 🔴 Jacksonville, FL | 157 | 1040 | 0.99 | 19.87 | 1.74 |
| 🔴 St. Louis, MO | 211 | 1431 | 0.95 | 20.35 | 1.89 |
| 🔴 San Antonio, TX | 606 | 4129 | 0.97 | 20.44 | 1.85 |
| 🔴 Mount Laurel, NJ | 221 | 1514 | 0.98 | 20.55 | 1.84 |
| 🔴 Kansas City, MO | 142 | 988 | 1.02 | 20.87 | 2.46 |
| 🔴 Philadelphia, PA | 501 | 3487 | 0.87 | 20.88 | 2.21 |
| 🔴 Lawrence, MA | 245 | 1753 | 0.92 | 21.47 | 2.01 |
| 🔴 Milwaukee, WI | 123 | 902 | 0.86 | 22 | 2.27 |
| 🔴 Phoenix, AZ | 248 | 1826 | 1.08 | 22.09 | 1.94 |
| 🔴 Atlanta, GA | 641 | 4756 | 0.94 | 22.26 | 1.96 |
| 🔴 Raleigh, NC | 314 | 2397 | 0.86 | 22.9 | 2.2 |
| 🔴 Charlotte, NC | 269 | 2147 | 0.85 | 23.94 | 2.45 |
| 🔴 Chicago, IL | 568 | 4617 | 0.82 | 24.39 | 2.23 |
| 🔴 California, Service Center | 858 | 7795 | 0.46 | 27.26 | 6.61 |
| 🔴 San Jose, CA | 1236 | 11300 | 0.77 | 27.43 | 2.96 |
| 🔴 Dallas, TX | 879 | 8227 | 0.88 | 28.08 | 2.17 |
| 🔴 New Jersey Central, NJ | 219 | 3752 | 0.47 | 51.4 | 4.64 |
| 🔴 Vermont, Service Center | 2 | 418 | 0.62 | 627 | 4.85 |
| ❓ Charleston, WV | 0 | 0 | nan | nan | nan |
| ❓ Christiansted, VI | 0 | 0 | nan | nan | nan |
| ❓ Chula Vista, CA | 0 | 0 | nan | nan | nan |
| ❓ Dover, DE | 0 | 0 | nan | nan | nan |
If you want the full data you can find it here with all the intermediate computations go here.
To be honest I could do a better analysis given the average overall processing time for i485s posted by USCIS per fiscal year (in July 2026 it sits at 5.7 months for the median acceptance for i-485 EB) and embed this prior to the predictive model but it might become too chaotic and I am also super bored to do that as well.