Automated Valuation Modeling (AVM) is a popular method that uses algorithms to estimate the market value of a property. AVMs draw from several data sources, including public records, MLS listings, and appraisal data, to create statistical models that can predict a property's worth based on its attributes and the sales prices of comparable properties.
These
models are often used by lenders during the mortgage lending process, as well
as real estate agents and homeowners who want to determine the value of a property.
However, the accuracy of AVMs can be affected by the quality of the data used
to create them and other factors that influence the value of a property, such
as its location, condition, and unique features.
Adjusting sale prices based on time before using them as the dependent variable in a regression model is crucial.
Property values can fluctuate over time due to inflation, economic conditions, and local real estate market changes.
There are various ways to adjust sale prices, such as using a time trend factor. This factor is determined by dividing the current median sales price for a specific property type by the median sales price of the same property type from a year ago. Adjusting sale prices for time ensures that your regression model is more accurate and dependable.
Sid: I am creating a regression model to forecast the sale prices of single-family homes in a specific county. To do this, I need to adjust the sale prices based on time and make them irrelevant to the sale date. Let's assume that the median sale price by the end of December 2023 is $260,000. Please provide me with the monthly adjustment factors - with $260,000 as the numerator - that I can use for this purpose. Additionally, can you explain the process you utilized to obtain these factors?
ChatGPT: To adjust the sale prices based on time and make them irrelevant to the sale date, you can calculate the monthly adjustment factors using the formula:
Adjustment Factor = Median Sale Price / Sale Price by the End of December 2023
Given that the median sale price by the end of December 2023 is $260,000, here are the monthly adjustment factors for the provided data:
2023-01: 260,000 / 255,000 = 1.0196
2023-02: 260,000 / 255,000 = 1.0196
2023-03: 260,000 / 255,000 = 1.0196
2023-04: 260,000 / 254,950 = 1.0214
2023-05: 260,000 / 255,000 = 1.0196
2023-06: 260,000 / 256,900 = 1.0125
2023-07: 260,000 / 250,000 = 1.0400
2023-08: 260,000 / 260,000 = 1.0000
2023-09: 260,000 / 250,000 = 1.0400
To obtain these adjustment factors, you divide the median sale price for each month by the sale price by the end of December 2023. This adjustment factor reflects the relative change in sale prices compared to the end of the year.
For example, in January 2023, the adjustment factor is 1.0196, indicating that the median sale price for that month is approximately 1.96% higher than it would be by the end of December 2023.
In July 2023, the adjustment factor is 1.0400, suggesting that the median sale price for that month is approximately 4.00% higher than it would be by the end of December 2023.
These adjustment factors allow you to standardize
the sale prices across different time periods and make them comparable,
regardless of the sale date.
Sid to Beginners:
Remember to:
1.
Roll up all parcel-level sales to monthly medians before
starting the process.
2.
Use quarterly medians if using a time series of two years or
more.
3.
Apply Moving Averages when using ARIMA.
Note: All conversations here are unedited. E&OE.
Note: ChatGPT is a registered trademark of OpenAI.
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