Target Audience: New Graduates/Analysts
Part 1 of 2
In
Automated Valuation Models (AVMs), time adjustment is crucial for accurately
assessing property values. This process involves applying quantitative
adjustments to the sale prices of comparable properties to reflect their
estimated value on a specific date, known as the valuation date. Time
adjustments are integral to AVMs as they ensure that the estimated value of a
property aligns closely with its market value at the specified valuation date.
By considering the impact of time on property values, AVMs can provide more
reliable and current valuations for real estate properties.
Imagine
you're valuing a house in May 2024. You have data on houses with similar
characteristics that sold in the previous year (2023). Without a time
adjustment, the AVM would directly compare these sale prices from 2023 to the
subject property in 2024, which wouldn't be accurate because the market might
have changed between those periods.
This
adjustment accounts for potential changes in the market and ensures that an AVM
built with the underlying sale prices from 2023 yields accurate results when
valuing unsold properties in 2024. This meticulous approach enhances the
precision and relevance of property valuations, reflecting the dynamic nature
of real estate markets and providing valuable insights for industry
professionals.
Example 1
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Suppose you are developing an Automated Valuation Model (AVM) for
a specific county using fifteen months of single-family home sales data – from
January 2023 to March 2024. The valuation date is April 1, 2024. You are
conducting a regression analysis with the sale price as the dependent variable
and three essential characteristics and months as independent variables. In
this regression output, the "MONTHS" variable you used represents the
number of months since the sale. Therefore, a sale in January 2023 will receive
a value of 15 (April 2024 minus January 2023), while March 2024 will be
assigned a value of 1. Now, applying the coefficient value to months, sales
for January 2023 will be upwardly adjusted by $18,615 ($1,240.97 multiplied by
15), and sales for March 2024 will be adjusted by $1,240.97 (multiplied by
1).
Note: No
other location variable is needed since the time adjustment must be at the
county level. You aim to use this regression analysis to derive a time
coefficient, which will help adjust all sales to the valuation date, leading to
a time-adjusted sale price. The time-adjusted sale price will then be used as
the dependent variable in the modeling dataset. The time-adjusted sale price will help
standardize the sales data to the same valuation date, enabling a more accurate
comparison and prediction of property values.
The regression
analysis you conducted has helped you achieve two key goals for your AVM:
1. Derive
a time coefficient:
The coefficient for the MONTHS variable ($1,240.97) represents the average
monthly change in sale price. You can adjust sale prices to your
valuation date (April 1, 2024). For example, a sale that closed in January 2023
can be adjusted by adding 15 months * $1240.97 to the sale price.
2. Identify
other essential factors:
The coefficients for the other variables (LAND AREA, LIVING AREA, BLDG AGE)
indicate the impact of these characteristics on sale prices. This information
can be used in the next stage of your AVM modeling process.
Overall, the regression analysis provides a statistically sound foundation for building your AVM. By incorporating the time coefficient and the identified relationships between other characteristics and sale prices, you can create a model that estimates sale prices for single-family homes in your county.
Example 2
In
this example, you use 27-month single-family home sales data from January
2022 to March 2024. The valuation date is April 1, 2024. Again, the
"MONTHS" variable represents the number of months since the sale. For
instance, a sale in January 2022 will receive a value of 27 (April 2024 minus
January 2022), while March 2024 will be assigned a value of 1. For example,
sales for January 2022 will be adjusted up by $12,869 ($476.62 multiplied by 27),
and sales for March 2024 will be adjusted by $476.62 (multiplied by 1).
Based on this
regression analysis, you have determined the coefficients for adjusting sale
prices based on the number of months since the sale. This time adjustment
allows you to standardize all sales to the April 1, 2024 valuation date.
To extend this
analysis to the modeling regression for your AVM, you will incorporate the time-adjusted
sale prices (dependent variable) into a new dataset alongside other relevant
features of the single-family homes in the county. By using this adjusted sale
price as the dependent variable and including other important variables (such
as property characteristics, location factors, market trends, etc.) as
independent variables, you can build a predictive model that estimates home
values accurately for the valuation date across the county. This modeling
regression will help you generate automated valuations for single-family
homes based on their unique attributes and the time adjustment derived from
this regression analysis.
Important
to Note
In
this case, where the intercept was forced to zero to primarily generate the
time coefficient for adjusting sale prices to a standard valuation date, it can
be considered statistically valid given the specific goal of deriving the
time-adjusted sale prices rather than estimating the actual property values.
When
moving to the modeling regression, it is recommended to include the intercept
in the equation for a more comprehensive and accurate valuation model. By
capturing the overall baseline level of property values, this practice can improve your AVM's predictive ability and reliability.
Conclusion
Considering
the volatility in monthly time statistics, a multiple regression-based analysis
is recommended to develop smoother and more accurate time adjustment factors in
automated valuation modeling. This analysis should include a combination of the
time, i.e., the "Months since Sale" variable, and essential
property characteristics such as Land SF, Living SF, and Year
Built. By incorporating these essential property characteristics variables
(alongside the time variable), analysts can thus create a comprehensive
framework that considers various factors influencing property values. This
regression analysis yields a time coefficient, which can be used to adjust the
raw sale price and generate a time-adjusted sale price, which will then serve
as the dependent variable in the modeling spectrum. This two-step regression
process will generate more accurate AVM values, targeting the valuation date.
Note: In part 2 of 2, we will
discuss the method of handling extended time series data.
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