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Search Candidates by Location: From CV Data to Map Results

By Andre · · 10 min read

Search Candidates by Location: From CV Data to Map Results

Candidate data cards flow toward a map with markers

You can search candidates by location right now: run a radius search around an office address, filter for remote or relocation willingness, and view results on a map for quick visual triage. We built this guide, with RecrutFlo in mind, to show you how. Most systems let you set distance in miles or kilometers, though city-level granularity varies by platform and country.

TL;DR:

  • Confirm the reference address and distance unit before searching; many platforms cap radius searches between 50 and 100 units and rank by proximity, not qualifications.
  • Collect exact postal codes and commute or relocation preferences on applications; structured fields geocode more reliably, while candidates near the radius boundary need manual verification.
  • Use a 10 to 15 mile radius for urban roles, but consider 25 to 50 miles for rural or suburban searches to find comparable pools.
  • Keep remote eligibility and relocation willingness separate from radius filters, so qualified candidates outside the boundary are not excluded from consideration.
  • Map views reveal candidate clusters, but check whether distance means straight line or commute time; partial addresses and pin limits can reduce accuracy.
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Table of Contents

How location search works: radius, single-location, and multi-location filters

Location filters usually come in two shapes. A “within X” radius search pulls every candidate inside a set distance of a point you choose, which works well for office-based roles where commute time matters. A “more than X” filter does the opposite, useful when you want candidates far enough away to qualify for relocation packages or remote-only postings.

Multi-location searches add another layer. Some tools let you search multiple cities with OR logic (any of these locations), while others support AND logic for narrower combinations, such as candidates near a headquarters and open to a satellite office.

A few practical notes on how these filters behave:

  • Most platforms default to a specific unit (miles or kilometers) based on your account region, so check this before setting a radius.
  • Many tools cap the maximum searchable radius, often somewhere between 50 and 100 units, which matters for rural or regional searches.
  • Results typically rank by proximity to your chosen point, not by relevance, so you may need to re-sort by skills or experience afterward.

Always confirm which address you’re measuring from: your company headquarters, a specific job site, or a candidate-entered preference, since each gives different results.

Collecting and standardizing candidate location data

Clean location data starts before the search box, at the point candidates submit their information. A free-text line on a CV like “based in the greater metro area” tells you little compared to structured fields for country, city, and postal code. Structured fields let search tools match consistently, while free text forces manual review every time.

Loose location data arranged into structured fields

Pro Tip: Ask applicants for an exact postal code and commute preference on your application form, not just a city name, to cut down on manual follow-up later.

Geocoding is the process that makes radius and map searches possible: it converts an address into latitude and longitude coordinates that a system can measure distance against. The ArcGIS findAddressCandidates API accepts single-line or multi-field address input and supports a location parameter that biases results toward nearby candidates, which is the kind of logic most recruitment platforms rely on behind the scenes.

A few fixes reduce friction once you’re geocoding candidate records:

  • Normalize distance units across your team so radius filters don’t silently switch between miles and kilometers.
  • Manually verify candidates who land right at the edge of your radius, since geocoding can round to a city center rather than an exact street address.
  • Prompt candidates directly for commute tolerance or relocation willingness instead of inferring it from their listed address.

Geocoding APIs commonly let you bias search results by proximity, which is why accurate address input matters more than it might seem at first glance.

Step-by-step: run a reliable location-based search and shortlist candidates

A location search works best when you follow the same sequence every time, rather than adjusting filters on the fly.

  1. Pick your reference point and units. Decide whether you’re measuring from company headquarters, a specific job site, or a candidate’s stated preference, and confirm whether your search runs in miles or kilometers.
  2. Choose an initial radius based on the role. An office-only position might call for a tight 10 to 15 mile radius, while a hybrid role can justify 30 miles or more.
  3. Layer in remote, hybrid, and relocation filters. Apply these alongside skills and experience filters so you’re not just sourcing by geography alone.
  4. Review the map view and validate edge cases. Check candidates who sit right at your radius boundary, since geocoding imprecision can place someone just inside or outside the line. Save the search and turn on alerts so new matches surface automatically.

Pro Tip: Widen your radius before you narrow your skills filters. A bigger geographic pool gives you more room to find the right experience match without starting the search over.

This sequence keeps your search repeatable, which matters when you’re running the same role across multiple office locations or revisiting a search weeks later with fresh applicants.

Map view and visualization: when to map candidates and what to look for

Mapping earns its place when distance itself is part of the decision, not just a filter you set and forget. Commute-sensitive roles, regional hiring drives, and searches where you want to spot geographic clusters all benefit from a visual layout rather than a list.

When you switch to map view, look for a few specific things:

  • Pin clustering that groups nearby candidates, which helps you spot dense talent pools at a glance.
  • Radius overlays showing exactly which candidates fall inside versus outside your search boundary.
  • Whether the tool calculates straight-line distance or actual commute time, since the two can differ significantly in areas with limited transit or road access.

Watch for limits too: some platforms cap how many candidates they’ll pin at once, and partial addresses can throw off accuracy.

Best practices: distance thresholds, remote vs. relocation flags, and calibrating searches with data

Distance thresholds should shift with geography. Urban hiring often works fine with a tight 10 to 15 mile radius, since transit density means candidates further out still have reasonable commute options. Rural or suburban searches usually need 25 to 50 miles to surface a comparable pool, since candidates are spread more thinly.

Treat remote work and relocation willingness as separate filters rather than folding them into your radius search. A remote-eligible candidate living across the country shouldn’t be excluded just because they fall outside your geographic boundary, and a candidate willing to relocate shouldn’t be filtered out just because they currently live far away.

  • Start narrow for office-first roles and widen only when your initial radius returns too few qualified matches.
  • Flag remote candidates separately from your radius results so they don’t get lost in a location-only filter.
  • Use relocation willingness to expand your pool selectively, not as a default filter for every search.

Relocation willingness itself varies widely. Global survey data from 2025 shows that willingness to relocate differs significantly by country and industry, and a significant portion of employees surveyed said they’d consider relocating internationally for up to two years. That variation means a blanket relocation filter applied the same way across every market will likely miss qualified candidates in some regions and over-include in others.

How RecrutFlo helps: inbox-to-searchable profiles for location-based sourcing

When CVs arrive through a shared inbox, RecrutFlo detects the attachments automatically, parses the document, and extracts structured fields including candidate location, skills, and experience. That structured data is what makes a clean radius or map search possible in the first place, since free-text CV lines rarely geocode reliably on their own.

Because the parsing happens the moment a CV lands in Gmail, Microsoft 365, Outlook, or IMAP, there’s no manual re-entry step between receiving a resume and being able to search candidates by location alongside skills and experience filters.

Author perspective: balancing pool size and hire speed when using location filters

Tightening a radius speeds up hiring when a role genuinely requires daily office presence and your pipeline is already healthy. It backfires when you apply the same tight radius to a hybrid role out of habit rather than necessity, quietly shrinking a pool that didn’t need shrinking.

My practical fix: ask every applicant for an exact postal code and a one-line commute or relocation preference on the application form itself. That single field saves far more manual cleanup later than any filter setting does.

— Marco

Try RecrutFlo: next steps and links

If your team receives CVs through a shared inbox rather than a dedicated applicant tracking system, we built RecrutFlo to close that gap. We connect directly to shared mailboxes, detect CV attachments as they arrive, and turn them into searchable profiles with structured location, skills, and experience fields, no separate upload step required.

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A few ways to get started:

  • See how inbox parsing and structured candidate profiles work before you commit to anything.
  • Compare plans on our pricing page, including the Free tier and paid monthly plans at different price points.
  • Add processing capacity as needed with one-off runs available for additional CVs.

Check our pricing page to find the plan that fits your team’s volume.

FAQ

How to search for candidates for free?

Many platforms offer a free tier or trial that includes basic location and keyword search, though filter depth and candidate volume are usually capped. RecrutFlo’s Free plan, listed on our pricing page, lets you test inbox-based candidate search before upgrading.

What is the 70/30 rule in hiring?

The phrase “search for jobs by location” isn’t tied to a single recognized standard in recruitment research, and definitions vary depending on who’s using it.

How to search for jobs by location?

Jobseekers typically use location filters on job boards, entering a city, postal code, or radius to narrow listings to a commutable area. Local and regional job boards, such as LKN Jobs for the Lake Norman and Charlotte area, often let users set radius and category filters directly on the search page.

What is candidate location?

Candidate location refers to the geographic data tied to a candidate’s profile, typically their city, region, postal code, or coordinates derived through geocoding. LinkedIn’s Recruiter help documentation notes that location search granularity varies by country and that results can include candidates who are open to relocating even if they currently live elsewhere.

Sources

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Search Candidates by Location: From CV Data to Map Results | RecrutFlo