Search radius is the single most consequential parameter in a rental comp query. Get it wrong in either direction and your rent estimate falls apart: too tight and you have no comps at all, too wide and you are blending submarkets with rent levels that have nothing to do with your target property. This guide covers the mechanics of radius selection in depth - the three-zone model, urban vs suburban vs rural calibration, minimum sample size thresholds, and the auto-expanding algorithm that handles edge cases automatically.

If you want the broader context on accuracy factors, see our post on best practices for rent estimate accuracy. For foundational mechanics of pulling comps, start with how to find rental comps for any address. This post goes deep on the radius problem specifically.

Why Radius Matters More Than Most Parameters

Most practitioners focus on filtering by bedroom count and square footage, which makes sense - a 1BR comp is not comparable to a 3BR. But radius is where estimates go catastrophically wrong, not incrementally wrong. A 10% error in square footage tolerance costs you 2-3% accuracy on the estimate. A wrong radius choice can produce an estimate that is $400/month off because you captured a completely different submarket.

The mechanism works in both failure directions. A radius that is too tight - say 0.1 miles in a suburban area with low listing density - returns zero or one comp. With a single data point, there is no estimate, only a guess. The confidence score collapses. You cannot detect outliers. You cannot compute a range. One overpriced listing skews everything.

A radius that is too wide creates the blending problem. In Atlanta, a 2.0-mile radius centered on Midtown captures Buckhead (luxury high-rises), West Midtown (converted lofts), and older garden apartment complexes near Georgia Tech. These submarkets trade at $1,400, $2,100, and $1,650 respectively for comparable 1BR units. The median across all three is a number that is wrong for every submarket. Your estimate lands at $1,720 when the correct answer for your specific address is $2,100, and you have just left $380/month on the table - or made a losing investment decision based on bad data.

The Three-Zone Model

The framework that works in practice is a three-zone model. Think of it as concentric circles with different interpretations attached to each ring.

Primary Zone: 0.25 to 0.5 Miles

This is the same-neighborhood zone. Properties within this ring share the same block-level dynamics: proximity to transit stops, walkability to restaurants, noise profiles, school assignment zones, and parking availability. In dense urban markets, this is almost always your preferred comp pool. The comps here require the least adjustment. If you have 5+ comparable units within 0.25 miles, use them and ignore everything outside.

Secondary Zone: 0.5 to 1.0 Miles

The adjacent-neighborhood zone. Properties here are broadly similar but may have material differences - one side of a major street, slightly different transit access, a park that creates a rent premium on one edge. Comps from the secondary zone are usable but should be reviewed for any obvious market boundary crossings. Weight them slightly less than primary zone comps if you are doing a weighted average.

Tertiary Zone: 1.0 to 2.0 Miles

The broader submarket zone. At this distance in an urban market, you are almost certainly blending distinct neighborhood identities. Use tertiary zone comps only when the primary and secondary zones are too thin - typically fewer than 5 qualifying comps. When you fall back to this zone, your confidence score should reflect the uncertainty. An estimate built on tertiary-zone comps alone deserves a confidence score no higher than 60%.

Urban vs Suburban vs Rural Calibration

The same numeric radius means completely different things depending on market density. This is where a lot of off-the-shelf tools fail - they apply a fixed default radius and call it a day.

Dense Urban Markets

In a market like Midtown Manhattan or downtown Chicago, 0.5 miles crosses 4-6 distinct micromarkets. Hell's Kitchen, Midtown West, and Clinton Hill are all within 0.5 miles of each other but trade at materially different rent levels. Use your tightest radius - 0.2 to 0.3 miles - and accept that you may need to supplement with secondary-zone comps if density is low enough. In Manhattan specifically, even 0.25 miles can capture co-ops, condos, and market-rate rentals that should never be averaged together.

Recommended starting radius for dense urban: 0.25 miles. Expand to 0.5 miles only if primary zone returns fewer than 5 comps. Stop there.

Suburban Markets

In a suburban market like Naperville, IL or Plano, TX, the listing density is lower and neighborhoods are more homogeneous over larger distances. A 0.25-mile radius might return zero results in a subdivision of single-family rentals where individual houses are the comp pool. A 1.0-mile radius is a reasonable starting point. Expand to 1.5 miles before falling back to HUD FMR data.

Recommended starting radius for suburban: 0.75 to 1.0 miles. Cap at 2.0 miles before supplementing with FMR.

Rural Markets

Rural markets require completely different thinking. In a county with 8,000 people, there may be 12 rental units in a 10-mile radius. The concept of "same neighborhood" breaks down. You are comparing entire towns, not blocks. In rural Montana or rural Mississippi, you may need a 10+ mile radius to collect even 5 comps. At this point, HUD FMR data for the county becomes the floor and often the most defensible single estimate you have.

Recommended approach for rural: Start at 3.0 miles, expand in 2-mile increments to a cap of 15 miles, then fall back to HUD FMR as the primary estimate with any available comps as corroboration.

The Minimum Sample Size Rule

No matter what radius you use, the estimate is only as good as the sample. The practical minimums:

These thresholds assume your comps are genuinely comparable - same bedroom count, within 20% of the target square footage, and listed within the last 90 days. If you loosen any of those filters to hit the sample size minimum, document it and adjust the confidence score accordingly.

The outlier problem: With fewer than 5 comps, a single mispriced listing can swing your estimate by 15-20%. At 10+ comps, you can drop the top and bottom quintile (the standard trimmed mean approach) and still have a robust sample. Sample size is your best defense against bad data points.

The Similarity Override Rule

Radius is a geographic filter, but geography is not the only thing that separates submarkets. Some of the most important market boundaries are not distance-based. Never use a comp that crosses:

These override rules require local knowledge or structured geographic data (TIGER shapefiles for roads, NCES data for school districts, OpenStreetMap for parks). When building automated systems, encoding these boundaries as exclusion polygons is the right approach - it is more work upfront but it dramatically improves estimate quality for edge cases.

PostGIS Radius Query Mechanics

If you are running your own comp database on PostgreSQL with PostGIS, the radius query looks like this:

-- Find all listings within 0.5 miles of a target address
-- Assumes listings table has a geom column (GEOGRAPHY type)
-- and target coordinates are in WGS84 (SRID 4326)

SELECT
    l.id,
    l.address,
    l.bedrooms,
    l.bathrooms,
    l.sqft,
    l.monthly_rent,
    l.listed_date,
    ST_Distance(
        l.geom::geography,
        ST_MakePoint(-84.3880, 33.7838)::geography
    ) / 1609.34 AS distance_miles
FROM listings l
WHERE
    l.bedrooms = 2
    AND l.sqft BETWEEN 700 AND 1100
    AND l.listed_date >= NOW() - INTERVAL '90 days'
    AND ST_DWithin(
        l.geom::geography,
        ST_MakePoint(-84.3880, 33.7838)::geography,
        0.5 * 1609.34  -- convert miles to meters
    )
ORDER BY distance_miles ASC, l.listed_date DESC;

The key is using ST_DWithin on a GEOGRAPHY column, which handles the curvature of the earth correctly. At city scales the difference is small but at rural scales (5+ miles) it matters. Always index your geom column with CREATE INDEX ON listings USING GIST (geom) - without the index, a radius scan on 1M+ listings takes seconds instead of milliseconds.

The Auto-Expanding Radius Algorithm

In production, you do not want to hardcode a radius and hope. The right approach is an auto-expanding algorithm that starts tight, checks sample size, and expands if needed. Here is a Python implementation that mirrors how the RentComp API handles the radius_miles parameter internally:

import requests

def get_comps_with_auto_radius(address, bedrooms, sqft, api_key,
                                market_type="suburban"):
    """
    Auto-expanding radius comp fetch.
    market_type: "urban" | "suburban" | "rural"
    """
    radius_schedule = {
        "urban":    [0.25, 0.5, 0.75],
        "suburban": [0.75, 1.25, 2.0],
        "rural":    [3.0,  6.0, 10.0, 15.0],
    }
    min_comps = 5
    schedules = radius_schedule.get(market_type, radius_schedule["suburban"])

    for radius in schedules:
        resp = requests.post(
            "https://api.rentcompapi.com/v1/comps",
            headers={"Authorization": f"Bearer {api_key}"},
            json={
                "address": address,
                "bedrooms": bedrooms,
                "sqft": sqft,
                "radius_miles": radius,
                "max_age_days": 90,
            }
        )
        resp.raise_for_status()
        data = resp.json()

        comp_count = data.get("comp_count", 0)
        confidence = data.get("confidence_score", 0)

        if comp_count >= min_comps:
            return {
                "result": data,
                "radius_used": radius,
                "expanded": radius != schedules[0],
                "confidence_score": confidence,
                "comp_count": comp_count,
            }

    # Exhausted all radius options - return best available
    return {
        "result": data,
        "radius_used": schedules[-1],
        "expanded": True,
        "confidence_score": data.get("confidence_score", 0),
        "comp_count": data.get("comp_count", 0),
        "warning": "Insufficient comps - consider HUD FMR as floor estimate",
    }


# Example usage
result = get_comps_with_auto_radius(
    address="1234 Peachtree St NE, Atlanta, GA 30309",
    bedrooms=2,
    sqft=950,
    api_key="your_api_key",
    market_type="urban"
)

print(f"Radius used: {result['radius_used']} miles")
print(f"Comp count: {result['comp_count']}")
print(f"Confidence: {result['confidence_score']}")
if result.get("expanded"):
    print("Warning: radius was expanded from default")

How the RentComp API Handles Radius Internally

The radius_miles parameter in the RentComp API accepts values from 0.1 to 25.0. The default is 1.0 mile, which is intentionally conservative for suburban markets. If you pass a specific value, it is treated as a hard cap - the API will not expand beyond it even if the comp count is below the confidence threshold. Instead, it returns whatever comps it finds and sets the confidence score accordingly.

The relationship between comp count and confidence score is roughly:

Confidence is also penalized for recency - comps older than 60 days receive a staleness discount, and comps older than 90 days are excluded entirely by default. You can override this with the max_age_days parameter, but extending beyond 90 days in a rising or falling market introduces its own bias.

Three Case Studies

Dense Urban: Atlanta Midtown

Target: a 2BR/2BA unit at 12th and Peachtree in Midtown Atlanta, 1,050 sqft, asking $2,250/month. Starting radius: 0.3 miles. Result: 14 comps, median $2,190, confidence 84%. The tight radius correctly excluded Buckhead (higher) and Old Fourth Ward (lower) from the sample. Expanding to 0.5 miles adds 9 more comps but drops the median to $2,080 by pulling in older garden apartments south of 10th Street. The 0.3-mile estimate is the right one here.

Suburban: Naperville, IL

Target: a 3BR/2BA townhouse in a subdivision, 1,450 sqft, asking $2,400/month. Starting radius: 0.75 miles. Result: 3 comps - not enough. Expand to 1.25 miles: 8 comps, median $2,320, confidence 74%. The estimate is good. The 1.25-mile radius captures the three nearby townhouse subdivisions that are comparable. Going wider to 2.0 miles starts pulling in apartment complexes that are not comparable to townhouses, so 1.25 is the right stop.

Rural: Fergus County, Montana

Target: a 2BR house outside Lewistown, MT, 1,100 sqft. Starting radius: 3.0 miles. Result: 1 comp. Expand to 6.0 miles: 2 comps. Expand to 10.0 miles: 3 comps. Still below the minimum threshold. At this point, fall back to HUD FMR for Fergus County: $789 for a 2BR (FY2026). Use the 3 comps as a secondary reference: they range from $750 to $900, consistent with FMR. Return the HUD FMR as the primary estimate with low confidence (42%), flag for human review.

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