Pulling Better Property Comps with Spatial Analysis

# Pulling Better Property Comps with Spatial Analysis

Every real estate decision — flip, rental, refinance, sale — eventually reduces to one question: *what is this property worth right now?* And every answer starts with comps.

Most investors learned comp-pulling from a YouTube video: open Zillow, find three to five recent sales within a mile of the subject, average their $/sqft, multiply by your subject's square footage. That works in a pinch. It also leaves a lot of value on the table — and quietly produces bad underwrites when the local geography doesn't cooperate.

This guide walks through how to upgrade your comp pulling from "naive radius" to actual spatial analysis. The tools are free or cheap, the workflow takes 15 extra minutes per deal, and the result is a comp set you can defend to a partner, a lender, or a buyer.

## Why the naive 1-mile radius fails

The 1-mile radius is a circle drawn around the subject property. It treats every direction as equivalent. In real urban geography, that's almost never true.

Consider three scenarios:

**Scenario A: The river.** Your subject is two blocks east of a river. The 1-mile radius pulls in 30 comps on the east side and 20 on the west. But the west bank is a completely different submarket — different schools, different commute pattern, different price tier. Your "average" is a meaningless blend.

**Scenario B: The freeway.** The freeway slices your radius in half. North-of-freeway sells for $250/sqft; south-of-freeway sells for $180/sqft. The "average" of the two tells you nothing about your specific block.

**Scenario C: The school boundary.** A school district boundary cuts diagonally through your radius. Half your comps are in the "good" district, half in the other. If your subject is in the good district, you under-comp it. If it's in the other district, you over-comp.

In each case the naive radius produces a number that's plausible-looking and wrong. Spatial analysis fixes this by replacing the circle with a *meaningful boundary*.

## Replacing the radius with a polygon

Step one is to draw the comp set boundary yourself. On a mapping platform you can sketch any polygon — a neighborhood, a school zone, a side of the freeway, a walking radius from a transit stop — and use it as the comp filter instead of a circle.

In GeoDataMapper:

1. Open the [Real Estate suite](/real-estate) with your sales-history CSV loaded.
2. Use the polygon drawing tool to sketch the relevant boundary. Snap to the river, the freeway, the school edge — whatever defines your submarket.
3. The platform filters the comp set to sales inside the polygon.
4. Compute median $/sqft on the filtered set.

This single change typically tightens your comp set from 30+ noisy comps to 10–15 *relevant* comps, and the median often shifts by 5–15%. That shift is usually the difference between a deal that pencils and one that doesn't.

## Using walkability and drive-time boundaries

For urban and suburban properties, walkability and drive-time are often better boundaries than a fixed radius. A 10-minute walking radius captures the actual neighborhood as residents experience it; a 5-minute drive captures the catchment area for a retail-adjacent property.

You can generate these isochrones from OSRM, Mapbox Isochrone, or Google Distance Matrix. The output is a polygon you can use exactly like a hand-drawn one. The advantage is that the polygon adapts to real street topology — a property at the end of a cul-de-sac has a smaller walking radius than one on a connected grid, and the comp set reflects that.

## Statistical adjustments

Even within a tight, well-bounded comp set, comps differ from the subject on factors that affect price: square footage, bed count, year built, condition, lot size. Professional appraisers apply *adjustments* to each comp to normalize for these differences. Investors usually skip this step. They shouldn't.

The two most impactful adjustments:

**Square-footage adjustment.** If your subject is 1,500 sqft and a comp is 1,800 sqft, you can't just compare sale prices. Compute the comp's sale price minus the marginal value of the 300 extra square feet (typically 30–50% of the local $/sqft, not 100%). Adjusted sale price is what you compare.

**Condition adjustment.** A comp that sold for $250k in turnkey condition isn't a clean comp for your value-add subject. Apply a haircut (10–25%) for the condition gap, or filter the comp set to only sales in similar condition.

Both adjustments are easy to do in a spreadsheet column next to your map-filtered comp set. The combination — spatial filter plus statistical adjustment — is what professional BPOs and appraisers do. Investors who skip the second step are leaving a layer of precision on the table.

## Adding temporal filters

The fourth dimension is time. A sale from 9 months ago in a fast-moving market is not a clean comp for a sale today. Most investors use a 6-month window; in volatile markets we recommend 3 months.

If you're pulling a tight 3-month window and end up with too few comps, broaden the window and apply a *time adjustment*: estimate monthly appreciation (or depreciation) from a broader market index, and adjust each older comp forward to today's date. A comp from 9 months ago at $250k in a market appreciating 0.5% per month becomes a $261k comp for today's analysis.

## Validating your comp set

Before you trust the median, run two sanity checks:

**Distribution check.** Plot the $/sqft of your comps as a histogram. If they're tightly clustered, you have a clean comp set. If they're bimodal (two peaks), you're probably blending two submarkets and need to redraw your polygon.

**Outlier check.** Any comp more than 1.5 standard deviations from the median deserves a second look. Often it's a flip you're comping against a wholesale deal, or vice versa. Drop it from the set if it's genuinely different.

Both checks take 60 seconds in any spreadsheet. The discipline catches the comps that would otherwise quietly poison your number.

## Defending your comps to partners and lenders

The output of all of this is a comp set you can defend. When a partner asks "why did you use these comps and not those?" you have an answer: *the polygon includes only the submarket east of the freeway, within the same school district, within the last 90 days, adjusted for square footage and condition*. That answer beats "I pulled the closest five" every time.

Save the map view as a shareable URL and attach it to your underwriting memo. The partner clicks the link, sees the polygon, sees the comps highlighted, sees the subject. There's no ambiguity about what you actually used.

## A repeatable comp-pulling workflow

Here's the workflow we recommend, end to end:

1. Drop the subject property on the map.
2. Identify the relevant submarket boundary (river, freeway, school edge, isochrone).
3. Draw the polygon.
4. Filter sales to inside-polygon, last 6 months, within bed/bath tolerance.
5. Apply square-footage and condition adjustments in a sidecar spreadsheet.
6. Compute median adjusted $/sqft.
7. Multiply by subject square footage.
8. Histogram the comp set as a sanity check.
9. Save the map URL and attach to your underwriting memo.

Total time: 15–20 minutes per deal. Output: a comp number you can defend, attached to a visual that proves your work.

## Conclusion

Comps are the foundation of every real estate decision. The naive 1-mile radius is the most common shortcut and quietly produces the most bad underwrites. Replacing it with polygon-based spatial analysis, drive-time isochrones, statistical adjustments, and a visual record of your work moves your comp set from "guess" to "evidence."

The tooling for this used to live in expensive GIS software. It now lives in [GeoDataMapper's Real Estate suite](/real-estate), free to start. The 15 extra minutes per deal pays for itself the first time it changes your decision.