The Power of Three: Analyzing Comparables with Least Adjustment, Sales Recency, and Geographic Proximity (Part 3 of 3)
Analysts and appraisers often use comparable sales analysis to determine a property's value. However, there are various methods in comparable sales analysis, each with its own advantages and limitations. This blog post will explore three primary comparable-based valuation methodologies: Least Adjustment, Sales Recency, and Geographic Proximity. We aim to showcase the nuances of each approach by using the same statistically derived adjustment matrix and analyzing 20 comparable sales, providing valuable insights for analysts and appraisers navigating the world of comparable-sales-based property valuation.
1. Least Adjustment Method
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Dataset and Variables
A
regression analysis using home sales data from a specific town from January 2023 to June 2024 helped generate coefficients for an adjustment matrix to value a series of subject properties with a valuation date of July 1, 2024.
One
of the variables used in the analysis is "Months Since," which represents the number of months since the sale. For example, a sale in January
2023 had a value of 18 (July 2024 minus January 2023), while a sale in June 2024 had a value of 1.
The
table illustrates adjustments for 20 comparable sales that were used to value
the subject property with the following characteristics:
- Land SF=6,534
- Bldg Age=8
- Heated SF=1,914
- Baths=2.5
- Exterior Wall=Hardiboard.
The adjustment coefficients used are as follows:
- Months Since=217.11
- Land SF=2.08
- Bldg Age=-264
- Heated SF=145.40
- Baths=33,301
No adjustment was applied for the exterior wall, as
all comparables have the same Hardiboard exterior wall as the subject property.
While the "Months Since" variable provided the time adjustment for the comps,
no location adjustment was used, as all 20 comparables were chosen from the same
neighborhood as the subject property.
Since the "least" adjustment comparables methodology
was used in this solution, the adjustments were absolute, so an
adjustment of +20,000 was treated the same as one with -20,000. Of the 20
comparables, the average of the 10 comps requiring the least adjustments
contributed to the subject's value conclusion.
Analysis
This comparables analysis
involves adjusting the sale prices of 20 comparable properties to determine the
value of a subject property with specific characteristics. The adjustments are
made based on various property attributes, such as Land SF, Building Age, Heated SF, Baths, and Months Since (the sale took place), as well as an adjustment for
the Exterior Wall material.
1. Adjustment
Methodology:
- Using the "least adjustment"
methodology, the 10 comparables requiring the least adjustments
contributed to the subject property's value conclusion. This method aims
to identify the most similar properties to the subject by minimizing the required adjustments.
- The adjustments were absolute, meaning the direction of the adjustment (positive or negative) did not affect the calculation. This approach simplifies the analysis and
ensures that adjustments are treated uniformly regardless of the direction
of change.
2. Specific
Adjustments:
- The adjustments were determined based on
the coefficients derived from a regression analysis. The adjustments for
each variable (Land SF, Building Age, Heated SF, and Baths) were specific
values added or subtracted from the comparables' sale prices.
- The adjustment for "Months
Since" was calculated as 217.11 times the number of months since the
sale occurred. This adjustment factor accounts for the temporal aspect of the sales data and helps capture changes in property values over time.
3. Average
of 10 Least-adjusted Comps:
- The average sale price of the 10
comparables with the least adjustments was used to estimate the subject
property's value. This approach prioritizes properties most
similar to the subject in terms of their characteristics, leading to a
more accurate value estimate.
- The average of the 10 least-adjusted
comparables was calculated to be $439,664, which serves as the basis for
the subject property valuation.
In conclusion, this comparable analysis utilized a
systematic approach to adjust the sale prices of comparable properties based on
specific property characteristics and time-related factors. By applying the
least adjustment methodology and using absolute adjustments, the analysis aimed
to provide a reliable estimate of the subject property's value while minimizing
the impact of outliers and data errors.
Important to Know (for New Analysts)
The use of absolute adjustments is
appropriate within the "least adjustment" methodology of comparables
analysis.
When employing the "least
adjustment" approach, the main goal is to identify the most comparable
properties to the subject property by minimizing the adjustments required to align the comparables' characteristics with those of the subject. This methodology focuses on selecting properties that require the least adjustment to match the subject property's features, thereby reducing the
potential for introducing bias or error into the valuation process.
By using absolute adjustments, in which the direction of the adjustment (positive or negative) does not affect the calculation, the analysis ensures a consistent and standardized treatment of adjustments applied to comparable properties. This approach allows
for a more precise comparison between properties and simplifies the valuation
process by considering the magnitude of the adjustment rather than the
direction of change.
Therefore, in the specific
context of the "least adjustment" method for comparables analysis, the
use of absolute adjustments is appropriate and aligns with the goal of
selecting the most similar properties to the subject property while maintaining
consistency and minimizing potential biases.
2. Sales Recency Method
Again, from the same 20 comparables, the average of the 10 most recent comps contributed to the
subject's value conclusion.
Using signed adjustments (positive
or negative) in the "sales recency" methodology is significant as it allows for a
more precise valuation by considering the direction and magnitude of the
adjustments. For instance, positive adjustments indicate that a comparable
property was superior in certain respects to the subject property, while negative adjustments indicate inferiority.
In this case, the adjustments for
each comparable have been calculated based on the specific characteristics of
the subject property and the comparables, reflecting how each property differs in Land SF, Building Age, Heated SF, Baths, and the
recency of the sale. This approach enables a more nuanced valuation considering the similarities and differences between the subject property
and the comparables.
The prioritization of more recent
sales in the valuation process, as indicated by the emphasis on the
"Months Since" variable, aligns with the principle of sales recency
methodology. Focusing on recent sales, this methodology captures current market conditions and trends more accurately, providing a more relevant basis
for valuing the subject property.
Moreover, averaging the ten most recent adjusted comps that contribute to the subject's value conclusion ensures the valuation reflects the most up-to-date market data, given the
preference for recent sales in the analysis. This approach helps mitigate the
impact of potentially outdated or less relevant data from older sales, leading
to a more accurate valuation of the subject property.
Overall, the detailed analysis
and use of signed adjustments in this comparable solution demonstrate a
thorough and systematic approach to property valuation. This approach considers the
specific characteristics of each property and prioritizes recent sales data to
arrive at a reliable estimation of the subject property's value.
Important to Know (for New Analysts)
Using signed adjustments in
the "sales recency" comparable method is appropriate.
This method considers the recency
of sales, prioritizing more recent transactions over older ones in the
valuation process. By applying signed adjustments that reflect the direction
and magnitude of the differences between the subject property and the
comparables, the analysis more effectively accounts for the variations in property characteristics and market conditions.
In contrast, the least adjustment
method typically involves absolute adjustments that do not differentiate
between whether a property is superior or inferior to the subject property in a
particular aspect.
Therefore, by using signed
adjustments, the sales recency comparable solution can better reflect each comparable property's relative strengths and weaknesses compared to the
subject property. This leads to a more insightful and reliable valuation result
that accounts for the most recent market trends and conditions while also considering each property's specific characteristics.
3. Geographic
Proximity Method
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The "geographic
proximity" comparable method utilizes the comparables based on their
physical proximity to the subject property. This method assumes that properties near the subject are more likely to share similar characteristics, thereby providing a more accurate valuation.
This methodology used 10 comparables instead of 20 from the same dataset. Of the ten comparables, the average of the five comps geographically closest to the subject informed the value conclusion.
In this case, the signed
adjustments (positive or negative) are applied to each comparable sale to
reflect how its specific features differ from those of the subject property.
These adjustments are necessary to ensure that the comparable sales are aligned with the subject property, accounting for differences in variables such as Land SF, Building Age, Heated SF, and Baths.
Using signed adjustments allows
for a more nuanced comparison of the comparables to the subject property. By
applying adjustments to account for specific differences in characteristics,
the final adjusted sale prices better reflect the subject property's
market value. This approach is particularly beneficial when valuing properties
in a homogeneous neighborhood with similar characteristics.
Overall, the analysis confirms
that the use of signed adjustments in the geographic proximity methodology is
appropriate for valuing the subject property and ensures a more accurate
valuation based on the specific characteristics of the comparable properties.
Important to Know (for New Analysts)
Including a map showing the comparables selected under the geographic proximity
methodology is customary and highly beneficial in the valuation
process. By providing a visual representation of the locations of comparable sales relative to the subject property, the map offers crucial
context and transparency to the analysis.
The map helps clarify the physical proximity of the comparables to the subject property, reinforcing the rationale for selecting these specific properties for comparison. It
also allows for a quick and intuitive visualization of how the selected comps
are distributed geographically, which can aid in assessing the reliability of
the comparables and the validity of the geographic proximity methodology.
Furthermore, the map can be valuable during discussions or presentations, providing a clear
visual reference that complements the numerical data and adjustment grid. It
can help stakeholders, such as clients or appraisal reviewers, to grasp the
geographic context of the comparables and the subject property more effectively.
Pros and Cons of each Methodology
Each of the three primary comps
selection methodologies – Least Adjustment, Sales recency, and Geographic
Proximity – has its own set of advantages and disadvantages. Here are the pros
and cons of each methodology:
1. Least Adjustment Methodology:
·
Pros:
o
Easy to understand and apply: This method
involves selecting comparable properties that require the least amount of
adjustment to align with the subject property.
o Can
be useful in neighborhoods with diverse properties: In areas with a wide range
of property types, this method may help identify the most comparable sales.
· Cons:
o
Ignores property characteristics: This
approach focuses primarily on minimizing adjustments, which may lead to
overlooking key differences in property features and conditions.
o May
not account for market trends: Does not consider how recent sales or geographic
proximity may impact the subject property's market value.
2. Sales
Recency Methodology:
Pros:
o
Reflects current market conditions: It prefers more recent sales, which may better reflect current market
trends.
o Provides
insight into market changes: By focusing on recent sales, this method can offer
a glimpse into how property values have evolved over time.
Cons:
o Limited historical data: Prioritizing recency may result in fewer comparable sales to choose from, especially in slower market conditions.
o May not capture long-term trends: Relying solely on recent sales could overlook longer-term market trends that impact property values.
3. Geographic
Proximity Methodology:
Pros:
o
Considers localized trends: Selecting
comparables based on geographic proximity can provide insights into specific
neighborhood dynamics and market conditions.
o Aligns
with market segmentation: Recognizes that properties in close physical
proximity are more likely to have similar characteristics and values.
Cons:
o
Limited comparables selection: Depending on
the neighborhood size or property availability, the pool of comparable sales
may be restricted.
o Ignores
property uniqueness: Emphasizing geographic proximity may overlook unique
features contributing to a property's value.
In conclusion, each comp's selection methodology has its
own strengths and limitations. The choice of methodology should be guided by the
specific characteristics of the subject property, the available data, and the
local market conditions. Combining elements of multiple methodologies or
customizing the approach based on the property's unique attributes can often
lead to a more robust and accurate valuation.
Averaging
the Three Values
In comparable sales analysis, it is
generally not recommended to average results from different valuation methodologies, even when the values are not significantly different. Each
valuation method has its own assumptions, strengths, and limitations, and combining them in this way may not provide a comprehensive or accurate representation
of the subject property's value.
The three comparable sales-based
valuation methodologies involve distinct approaches and considerations in
determining property values. By averaging the values derived from these
methodologies, one risks diluting each method's specific insights and
adjustments, potentially leading to a less precise and reliable overall
valuation.
Instead, it is advisable to critically
evaluate the results of each valuation methodology based on its merits, the
underlying assumptions, and the specific characteristics of the subject
property, considering factors such as the quality and relevance of the
comparables selected, the appropriateness of the adjustment matrices used, and
the rationale behind the adjustments applied.
If the values produced by the different
methodologies are not significantly divergent, it may be more appropriate to
carefully review the methodology that best aligns with the subject property's
characteristics and market conditions. This approach ensures that the final
value conclusion is based on a solid foundation, supported by a thorough, methodologically
sound analysis.
In summary, while multiple valuation
methodologies and viewpoints must be considered in the valuation process, it is
generally recommended that the most appropriate and robust methodology be used
to determine the subject property's value, rather than averaging results from different
methods.
Series Conclusion
This
three-part series has explored a novel approach to comparable sales analysis
for valuing single-family homes. We began by leveraging a correlation matrix to
uncover potential biases and multicollinearity among key property features.
This data-driven foundation ensured a more robust regression model, ultimately
generating an adjustment matrix. This matrix provided a systematic and
objective way to account for property-specific differences within the
comparable data set.
We have unlocked a more informed valuation conclusion by integrating statistical analysis with traditional comparable sales analysis. Applying the
adjustment matrix alongside classic valuation methodologies like Least
Adjustment, Sales Recency, and Geographic Proximity has significantly reduced
the subjectivity inherent in traditional adjustments. This approach leads to greater consistency and reliability and enhances the accuracy of
property valuations, a significant benefit for real estate professionals.
This series
has presented a more robust and objective framework for comparable sales methodology. My upcoming book will delve even deeper, exploring the application
of this methodology to a broader range of property types and geographical
scales, including county-level valuations with town-specific adjustments and applications for valuing townhouses, condominiums, Planned Unit Developments (PUDs), Multi-Unit Developments (MPUDs), and more. By expanding
the scope of analysis, this book will aim to empower analysts and appraisers with a
powerful new tool for generating accurate and defensible property valuations
across a broader spectrum of the real estate market.
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