Calculator inputs
Example data table
Sample inquiry history to demonstrate how recency and grouping can change effective impact.
| Date range | Hard inquiries | Context | Decay | Effective |
|---|---|---|---|---|
| 0–30 days | 3 | Two card checks, one personal loan | 1.00 | 3.00 |
| 31–90 days | 2 | Auto shopping (grouped to one event) | 0.65 | 1.30 |
| 91–180 days | 1 | Utility financing check | 0.40 | 0.40 |
| 181–365 days | 4 | Older activity with limited current weight | 0.20 | 0.80 |
Use the calculator to compare "apply now" vs "wait 60 days" scenarios by shifting counts into older buckets.
Formula used
This estimator converts inquiry counts into effective inquiries using time decay:
Rate-shopping groups multiple auto/mortgage checks inside a chosen window, reducing effective inquiries:
The score penalty uses diminishing returns plus sensitivity multipliers:
- Per, Cap, Exp depend on strictness (lenient/typical/conservative).
- Sensitivity adjusts for file thickness, utilization, payment rate, new accounts, and bureaus hit.
- Adjusted score = Baseline score - Penalty (clamped 300–850).
How to use this calculator
- Enter your current score and core profile context (utilization, payment rate).
- Fill hard inquiry counts by age bucket; include only hard checks.
- If you are shopping auto or mortgage rates, enable grouping and add window counts.
- Select strictness to match your lender environment, then calculate.
- Use the breakdown to see which bucket drives impact and how fast it fades.
- Download CSV or PDF to compare scenarios across applications.
Inquiry recency drives most short term movement
This calculator weights inquiries by age because newer pulls matter more. The decay schedule uses 1.00 for 0–30 days, 0.65 for 31–90, 0.40 for 91–180, and 0.20 for 181–365. For example, 3 recent inquiries plus 2 in the 31–90 bucket produce 3.00 + 1.30 = 4.30 effective inquiries.
Rate shopping can compress multiple checks into one event
When comparing auto or mortgage offers, many scoring approaches treat clustered pulls as a single shopping event. The tool estimates this by counting inquiries inside a 14–90 day window, then reducing duplicate pulls with a factor. With 3 auto pulls inside 45 days, the model can discount roughly two of them, lowering the adjusted effective inquiry load.
File thickness and bureau coverage change sensitivity
A thin credit file can react more to new checks, so sensitivity increases to 1.35 versus 1.00 for average and 0.80 for thick files. If inquiries appear on multiple bureaus, visibility rises; the estimator adds 12% sensitivity per additional bureau. Two bureaus implies a 1.12 multiplier, while three bureaus implies about 1.24, before other factors.
Utilization and payment stability amplify inquiry risk signals
Inquiry impact is not isolated from the rest of the profile. Utilization above 30% increases sensitivity up to 1.25, reflecting higher revolving balance pressure. On time payment rate below 99% adds up to about 1.20 sensitivity. These multipliers do not change inquiry counts, but they scale the penalty to reflect underwriting caution when several risk indicators stack.
Strictness settings map to different underwriting climates
The strictness control adjusts Per, Exp, and Cap to model lenient, typical, or conservative environments. Typical uses Per 4.2, Exp 0.85, and a 45 point cap. Conservative increases Per to 5.2 and the cap to 60 points. This keeps penalties realistic under heavy inquiry volume while still applying diminishing returns as effective inquiries rise.
FAQs
Do soft inquiries affect the estimate?
Soft inquiries are shown for context only. They usually do not reduce scores and are not included in effective inquiry totals or the penalty calculation in this estimator.
How do I use the rate-shopping window?
Enable grouping, set a window between 14 and 90 days, then enter auto and mortgage pulls within that period. The calculator discounts duplicates so the impact reflects a single shopping event.
Why does file thickness change the penalty?
Short histories have fewer accounts to offset new activity. The tool applies a higher sensitivity multiplier for thin files and a lower one for thick files, which can change the same inquiry count into different point impacts.
What does effective inquiries mean?
It is a time weighted inquiry total. Recent checks count more than older checks using decay factors, so the model reflects fading influence as inquiries age.
Can this predict an exact score drop?
No. It provides a planning estimate based on common scoring behaviors and underwriting signals. Real outcomes depend on the scoring model, bureau data, lender policy, and the rest of your credit profile.
How can I reduce inquiry impact before applying?
Limit new applications, finish rate shopping within a tight window, and let time pass so inquiries move into older buckets. Keeping utilization lower and making all payments on time can also reduce sensitivity.