Location, location, location—it's not just a
real estate cliché; it's the cornerstone of fair property valuation. In this
blog post, we dive into the Proximity ("Nearest 5") method, a
powerful non-technical approach that establishes a locational baseline for your
appeal. By focusing on the closest comparable sales (comps), you minimize
variables such as neighborhood trends, traffic patterns, and proximity to
amenities that could skew values. This method is especially effective in
showing inequality: If the nearest similar properties are assessed (or sold) at
lower values per square foot, it erodes the assessor's "presumption of
correctness"—the legal starting point where the board assumes the official
assessment is right unless you prove otherwise.
Even if all comps are from the same Planned
Unit Development (PUD) as your subject property—making broader location
irrelevant—proximity still matters. Closer homes often share hyper-local
factors, such as the same street or block, which strengthens your equity
argument. We'll use a real-world example dataset from a county assessor's site
(anonymized for this post) to walk you through the process: filtering outliers,
selecting the top five closest comps, and deriving a value conclusion. This
mirrors what I did in my own successful appeal, where I started with nearby
comps and built a credible foundation before layering in technical analysis.
By the end, you'll see why proximity is your
primary filter—it anchors your case in undeniable geography—but similarity
remains the ultimate filter to ensure an apples-to-apples comparison.
Why Proximity
Matters: Establishing the Locational Baseline
Appeal boards prioritize comps that are
geographically close because they best reflect your property's
micro-environment. A home 0.1 miles away is far more relevant than one 2 miles
out, even if the distant one matches perfectly on paper. The "Nearest
5" method leverages this by:
· Sorting recent sales by distance (using
tools like Google Maps for straight-line miles).
· Filtering for basic similarity to avoid
distortions.
· Comparing the nearest five to your subject,
highlighting any over-assessment.
This approach directly challenges uniformity:
If nearby homes sold for less (adjusted for differences), why is yours valued
higher? Many states' laws (e.g., uniformity clauses in Texas, California, and
New York) require equitable assessments, which make this a strong hook.
Practical Tip: Use Google Maps or the assessor's GIS tools to measure distances. Aim for under 0.5 miles in suburban areas
like this example; expand slightly in rural spots, but explain why.
Step-by-Step: Applying the
"Nearest 5" Method with Real Data
Let's apply this to our example
dataset, pulled from a public county assessor site. The subject property is a
single-family home in a PUD: 1,647 sq ft of living area, 7,200 sq ft lot, built
in 2006, total area of 2,283 sq ft (including garage/porch), and no pool. We
started with 16 recent 2025 sales (all from the same PUD) for a January 1,
2026, valuation and measured distances via Google Maps.
Practical Tip: If you live in a non-HOA
environment, it is beneficial to extract comps from the same subdivision or
from similar contiguous neighborhoods to ensure comparability in local market
conditions, amenities, and property characteristics, thereby providing a more
accurate basis for property valuation comparisons within their specific
residential area.
Raw Dataset – Subject and 16 Comps
Step 1: Remove
Outliers
First, to ensure similarity, the comps that
differ significantly from the subject should be eliminated. This prevents
skewed results—e.g., a pool adds premium value, or oversized living space
implies a different market segment.
Rationale for Removals:
Living SF > 2,000 sq ft: These are larger homes (e.g., COMP3
at 2,306 sq ft, COMP7 at 2,093 sq ft, COMP11 at 2,090 sq ft, and COMP14 at
2,093 sq ft). They attract different buyers and command higher prices/SF,
distorting the baseline. The subject is 1,647 sq ft, so we focus on the
1,500–1,800 sq ft range.
Has Pool (YES): Pools add $20,000–$50,000 in value
(per market data and quantifiable via regression). COMP7, COMP12, and COMP14
are pool homes.
Lot Size > 9,000 sq ft or < 6,000 sq ft: Extreme lots affect usability and
value. Subject is 7,200 sq ft. COMP3, COMP4, COMP7, COMP13, and COMP16 have
larger lots, while COMP9 comprises a smaller lot.
Year Built outside 2005–2007: Newer builds (e.g., COMP8 2010 and
COMP16 2011) may have modern features, which can inflate value. Subject is 2006; this
keeps age/effective age similar.
Total Area > 3,000 sq ft: Indicates additions like large
garages or patios. COMP3 (3,189), COMP4 (3,211), COMP7 (3,937), COMP12 (3,337),
and COMP14 (4,044) comprise a larger total area. Although COMP9 (2,879) is
borderline, it should be removed as it also combines with a smaller lot.
Remaining non-outliers: COMP1, COMP2, COMP5, COMP6, COMP10,
and COMP15 are the non-outliers. These best match the subject's "Big
Three" (living SF, effective age via year built, and quality via total
area and no pool).
Step 2: Select
the Five Best Closest Comps
From the non-outliers, sort by distance
(ascending) and pick the top five. This prioritizes geography while ensuring
similarity (already filtered).
Rationale for Selection:
Proximity as Primary: Closest comps reduce locational
noise—even in the same PUD, a 0.07-mile neighbor shares more (e.g., views,
noise) than one 0.33 miles away.
Similarity is Ultimate: We only select from non-outliers, so
all are comparable. In ties (e.g., COMP1 and COMP2 at 0.33), we could choose
based on a better match (e.g., COMP1 has exact SF), but here the sort yielded a
clear top five without ties in the cutoff.
Final Five: COMP10 (0.07 mi, exact SF/year
built/total—ideal match), COMP6 (0.09 mi, exact SF/total), COMP5 (0.10 mi,
close SF/lot), COMP15 (0.11 mi, slightly higher SF but same year built/lot),
COMP1 (0.33 mi, exact SF but farther—still included as fifth for balance).
COMP2 (0.33 mi) was excluded as the sixth; if
needed, swap it if it better fits (e.g., lower price/SF variability), but the
top five by distance are objective.
Top Five Closest Comps
Including a Map
Including a map showing the locations of
the final five comps that contributed to the subject property's value can be a
valuable addition to your presentation. Here are a few reasons why adding a map
could enhance the overall presentation:
1. Visual Context: A map can provide
readers with a visual representation of the geographic proximity of the comps
to the subject property, helping them to better understand the location and
neighborhood characteristics of the properties in comparison.
2. Enhanced Clarity: Seeing the
spatial relationship between the subject property and the selected comps can
enhance the clarity of the analysis and reinforce the rationale behind choosing
these specific properties for comparison.
3. Persuasive Visual Aid: A map can
serve as a persuasive visual aid to support your argument regarding the
selection of comps and the impact of location on property valuation, further
strengthening your case for appealing over-assessments.
4. Engagement: Visual content such as
maps can increase engagement and interest, making your presentation more
appealing and interactive.
Overall, including a map showing the
final five comps can complement your write-up and spreadsheet analysis,
providing additional context and depth to your discussion on property valuation
using this method. It can help reinforce key points, enhance understanding, and
create a more compelling, visually appealing presentation.
Deriving a Value
Conclusion: The "Nearest 5" in Action
With our top five, we can calculate a simple
indicator, such as average price/SF, and then multiply it by the subject's
living SF to estimate market value.
Average Price/SF: (143 + 145 + 129 + 166 + 146) / 5 =
145.8
Estimated Subject Value: 145.8 × 1,647 ≈ $240,133
Rationale for Value Conclusion: This suggests the subject may be
over-assessed if its official value exceeds ~$240,000 (compare to the TRIM
notice). The range (129–166/SF) shows variability, but averaging smooths
it—COMP5's low (possible condition issue?) and COMP15's high (better finishes?)
balance out. For appeals, you can argue: "These nearest comps indicate a
fair value of $240,000, eroding the presumption of correctness." If
needed, you may adjust the value further (e.g., -$5,000 for COMP1's larger
lot).
Homeowner's Decision Tree: The "Nearest 5" Method
This simple, step-by-step decision tree helps
you systematically filter comparable sales (comps) to build a strong, credible
locational baseline for your property tax appeal. Start with all recent sales
from your county assessor's site (ideally 15–25+ in your area or PUD), measure
distances using Google Maps (straight-line preferred), and apply these filters
in order. The goal: End up with 3–5 highly similar comps that are as close as
possible, proving equity and undermining the assessor's presumption of
correctness.
1. Is the comp within 0.5 miles of your
subject property?
Yes → Proceed to next step.
No → Delete (or move to a secondary list). Rationale:
Proximity is the primary geographic filter. Comps farther away introduce
location variables (e.g., different streets, school zones, or traffic) that
weaken your argument. In suburban/PUD settings like our example, 0.5 miles is a
common practical threshold; in denser urban areas, tighten to 0.25–0.3 miles;
in rural areas, expand to 1 mile but explain why.
2. Does the comp have a pool (or major
feature like a pool) when your subject does not?
Yes → Delete.
No → Proceed. Rationale: Pools add
significant value ($20,000–$50,000+, depending on market), skewing price/SF and
making direct comparisons unfair. (If your home has a pool and the comp
doesn't, delete or adjust heavily later.)
3. Is the comp's living square footage
more than 25% larger or 25% smaller than your subject's?
Yes → Delete.
No → Proceed. Rationale: Size is one of
the "Big Three" drivers of value. A 25% threshold (for our 1,647 sq
ft subject: keep roughly 1,235–2,059 sq ft) keeps comps in the same market
segment. Larger/smaller homes often appeal to different buyers, with non-linear
price changes (e.g., diminishing returns on extra SF). This is a standard
guideline in appraisal practice and many appeal guides—tighter (e.g., 20%) for
precision, looser (30%) in sparse markets.
4. Does the comp have significant
differences in other key characteristics (e.g., year built/effective age more
than ~5–10 years off, extreme lot size differences, major additions like
oversized garages)?
Yes → Delete (or flag for heavy adjustment
later).
No → Keep. Rationale: These are common
causes of outliers. For example, a 2010+ build may have modern features that
inflate value; a lot that's 50% larger/smaller affects usability. In our
dataset, we removed comps with YEAR BUILT outliers (e.g., 2010/2011) and
extreme total area/lot sizes.
5. Are you left with at least 3–5
strong comps after filtering?
Yes → Success! You have your Geographic Anchor.
Sort these remaining comps by distance (closest first) to create your
"Nearest 5." Use them to calculate average price/SF, estimate your
fair value, and argue inequality (e.g., "These closest similar homes sold
at an average $145/SF vs. my assessed value, implying higher").
No → Relax filters slightly (e.g., expand
distance to 0.75 miles or size to 30%) and explain your reasoning in your
appeal (transparency builds credibility).
Quick Tips for Using the Tree:
Document every
step: Include a table
of all initial comps, note deletions with reasons, and show the final 3–5.
Visualize: Add a Google Maps screenshot with
pins for the subject and your top comps.
Nationwide note: Thresholds vary (e.g., some boards, like those in California or Texas, emphasize the same subdivision/neighborhood over
strict miles), but 0.5 miles + 20–30% sizes is widely accepted as reasonable.
This decision tree turns raw data into
defensible evidence—simple, repeatable, and board-friendly. Apply it to your
own dataset, and you'll have a rock-solid starting point.
Visual Aid Suggestion: Include a Google Maps screenshot
with a pin for the subject and the top five, color-coded by distance.
Conclusion
The beauty of the "Nearest 5"
proximity method is its simplicity and power: Start with geography to anchor
your case, filter rigorously for similarity, and you often have enough to
challenge an over-assessment right away. Property taxes don't have to be a
mystery or an unfair burden. With public data from your county assessor site
and a systematic approach like this, every day homeowners can make a strong,
evidence-based case.
By focusing on proximate comps that share key
characteristics with the subject property, such as living area, lot size, year
built, and the presence of certain features, homeowners can strengthen their
appeal and potentially secure a more favorable valuation. Whether analyzing the
average sale price or the price per square foot of these comps, you can use the
insights gained from this method to assert your case with confidence. Armed
with a solid understanding of how to use comparable sales data effectively, you
can navigate the intricacies of property assessments with greater clarity and
precision.
The "Nearest 5" method anchors your
appeal in geography, proving equity with hard-to-dispute proximity while
filtering for similarity to keep it credible. In our example, removing outliers
focused the analysis on truly comparable homes, leading to a defensible
$240,133 value estimate. Proximity is primary because it isolates location as a
constant; similarity is ultimate to avoid board rejections.
Disclaimer: The information provided in this post is
intended for educational and informational purposes only. It is not meant to
serve as professional advice or guidance on specific real estate or property
valuation matters. The methodologies and recommendations outlined in this guide
are general in nature and may not be applicable to all individual situations or
properties.
Readers are advised to consult with qualified
real estate professionals, such as real estate agents, appraisers, or tax
assessors, for personalized advice tailored to their specific circumstances.
Property valuation can be complex and nuanced, and decisions regarding
challenging property assessments should be made after thorough consideration of
all relevant factors and with the assistance of professionals in the field.
The author and the platform do not assume any
liability for any actions taken based on the information provided in this post.
Readers should exercise caution and conduct their own due diligence before
relying solely on the recommendations contained herein.
