NAR: Supporting Your Value Zoom webinar, Notes by Mary Cummins real estate appraiser

NAR, Supporting your value, zoom, webinar, mary cummins, real estate appraiser, josh walitt, jim amorin, louis pitoni, national association of realtors, artificial intelligence, ai, chatgpt, adjustments

National Association of Realtors (NAR) Supporting Your Value

Date & Time Jan 30, 2025 02:00 PM Eastern Time (US and Canada)

Description Credible independent valuations of real property are critical to the health of the overall real estate industry, but there are seemingly constant changes: technology, policy, regulatory, and more. Are you up to speed on what is happening in the Valuation world? National Association of REALTORS® members (appraiser or otherwise) are invited to join us for this regular webinar series to hear updates from the experts.

This month: Appraisers face a constant challenge to provide accurate property valuations amid changing market conditions. New policies require appraisers to clearly identify market trends and apply specific time adjustments to comparable sales data based on these trends and include the methodology used to determine these adjustments in the appraisal report. But how do appraisers derive these adjustment amounts and what new technology can be used in the process? Jim Amorin, author of the Generative Shift, Preparing Appraisers For Artificial Intelligence Models Like Chat GPT, and Josh Walitt, educator and Principal Consultant at Walitt Solutions, will dive into this topic exploring the various AI tools that can be used to develop adjustments and how to use them, but also how appraisers can make sure the information they are getting with those tools is accurate. Bring your questions and get ready to learn!

Host: Louis Pitoni

Speakers: Jim Ameron, Josh Walitt

Pitoni: Appraisers are valuable. They protect interest of the public. Agents do same.This program will focus on tools to use to support value such a Artificial Intelligence (AI). videos will be available.

Jim Ameron: AI is a tool. If I only have four comps, I won't use AI or Regression Analysis. I will do my best in a thin market. He then proceeded to show an example of time adjustments using chatgpt.com He attached a .csv file then added a prompt, see slide. chatgpt.com showed a chart with analysis of time adjustments. You can add just of a photo of chart and chatgpt will analyze it and give you a text summary.

Here is Ameron's chatgpt.com prompt from the slide. He attached a .csv file from MLS comps: Prompt: Attached is a set of data that I need to determine a credible adjustment for changes in value over time. I would like not only what the daily adjustment would be, but I would also like the data plotted as a scatter graph with the date of sale along the x asis and the sales price along the y axis. Please act as a competent real estate appraiser and perform these tasks. Also provide supporting narrative discussion suitable for an appraisal report describing the adjustment process and amount.

(I did an example with deepseek below)

Josh Walitt: The ultimate achievement for an appraiser is to be credible based on evidence. AI gives us speed, efficiency and consistency. We don't program it. We prompt it. Market trend analysis requirement is not new. We have always had to do this in our reports.

AI isn't always right. We must check and verify. It's your responsibility. Saying a machine did it won't get you out of trouble. You can get a condition adjustment using paired sales of same property. Save all your data, charts, results in your work file.

Q: Can AI replace appraisers?

Walitt: No. We need our judgment to rank condition, quality and other nuances that we look at as humans. It's just one more tool in our toolbox which we must check and verify. There's a lot more to an appraisal than just market condition adjustments and other adjustments. AI can't call an agent to get story about motivation of parties.

Amorin: 1-2 years ago I said no. I still say no. Changes in last few months show these tools can do a lot of what we do. AI is an opportunity for us to concentrate on our analysis. Robots can do other things. By 2025 we may have a computer agent who could call someone on the phone, book airline tickets. It's important to get familiar with it. There's a lot it can't do. 

Pitoni: AI is just a tool. We must know how to use it and and use it responsibly. You can download the speakers' slide decks. (I posted all slides. There weren't many)

Me: No. AI can't see the property and determine some of it's characteristics such as view, condition, upgrades, lot type, specific location in a neighborhood, actual bed/bath count, actual size...

Me: A big problem using MLS data with AI for adjustments is the HUGE unreliability of MLS data. The size is always larger than GLA, tax or legal. The bed/bath count is also generally wrong. How many times is a den or loft listed as a bedroom not to mention unpermitted bootleg bathrooms. Even the age is wrong when agents lie about it being new when it was only remodeled. The sales price is even wrong. It generally doesn't match the final recorded price. I'd like to see adjustments based on appraiser's confirmed GLA, bed/bath count listed in the grid.

I just tried the above prompt on deepseek for a comp search I ran on home worth $1.4M for 2024.

"Attached is a set of data that I need to determine a credible adjustment for changes in value over time. I would like not only what the daily adjustment would be, but I would also like the data plotted as a scatter graph with the date of sale along the x asis and the sales price along the y axis. Please act as a competent real estate appraiser and perform these tasks. Also provide supporting narrative discussion suitable for an appraisal report describing the adjustment process and amount.

Adjustment for Changes in Value Over Time

Scatter Plot Analysis

To determine the credible adjustment for changes in value over time, we first plotted the data on a scatter graph with the date of sale (expressed as days since the earliest sale in the dataset) on the x-axis and the sales price (SP) on the y-axis. This visual representation helps in identifying any trends or patterns in property values over time.

Data Analysis

Upon analyzing the scatter plot, we observed a general trend where the sales price tends to increase over time. This trend suggests that the market has been appreciating, and properties are selling for higher prices as time progresses.

Regression Analysis

To quantify the relationship, we performed a regression analysis, which provided us with a coefficient that represents the average daily increase in sales price. The regression equation is as follows:

$$\text{Sales Price}={\beta }_0+{\beta }_1\times \text{Days Since Earliest Sale}$$

Where:

• ${\beta }_0$ is the intercept.

• ${\beta }_1$ is the coefficient for the number of days since the earliest sale.

Adjustment Calculation

Based on the regression analysis, the average daily adjustment for changes in value over time is approximately **$1,200perday\ast \ast \mathrm{.}Thismeansthat,onaverage,themarkethasbeenappreciatingatarateof$1,200 per day.

Narrative Discussion

In the appraisal process, adjustments are made to account for changes in market conditions over time. The date of sale is a significant factor that influences the value of a property. Our analysis of the provided data indicates that the market has been appreciating at an average rate of $1,200 per day.

This adjustment is derived from a regression analysis of the sales data, which considers the relationship between the date of sale and the sales price while holding other variables constant. The scatter plot visually supports this relationship, showing a general upward trend in sales price over time.

It is important to note that while the date of sale is a key factor, other attributes such as location, square footage, and lot size also play crucial roles in determining the final sales price. Therefore, this adjustment should be applied in conjunction with other relevant adjustments to ensure an accurate and credible valuation.

Conclusion

Based on the analysis, a credible adjustment for changes in value over time is $1,200 per day. This adjustment is supported by both the scatter plot and regression analysis, providing a reliable basis for valuation adjustments in the appraisal process.

Scatter Plot

Below is the scatter plot illustrating the relationship between the date of sale (x-axis) and the sales price (y-axis):

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Sales Price (SP) vs. Date of Sale (Days Since Earliest Sale)

Y-axis: Sales Price (SP)
X-axis: Days Since Earliest Sale

[Scatter plot points showing an upward trend]

This plot visually confirms the positive relationship between the date of sale and the sales price, supporting the calculated adjustment of $1,200 per day.

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This analysis and adjustment are suitable for inclusion in an appraisal report, providing a clear and justified basis for the time adjustment."

I did another sample for bedroom adjustment. It said $75,000 but I used $40,000. If I had cleaned the data and removed homes with pools, larger lots, I would have gotten a number closer to $40,000. Sadly then I wouldn't have had enough comps. Deepseek added effect of larger lots, pools to bed adjustment because in my sample all four bedroom homes were on larger lots with pools. This shows the limitations of all AI products and statistical analysis.

hazelnut (1).csv

CSV 10.48KB

Attached is a set of data that I need to determine a credible adjustment for number of BR. I would like not only what the dollar adjustment would be, but I would also like the data plotted as a scatter graph with the number of BR along the x asis and the sales price along the y axis. Please act as a competent real estate appraiser and perform these tasks. Also provide supporting narrative discussion suitable for an appraisal report describing the adjustment process and amount.

Adjustment for Number of Bedrooms (BR)

Scatter Plot Analysis

To determine the credible adjustment for the number of bedrooms (BR), we first plotted the data on a scatter graph with the number of bedrooms on the x-axis and the sales price (SP) on the y-axis. This visual representation helps in identifying any trends or patterns between the number of bedrooms and the sales price.

Data Analysis

Upon analyzing the scatter plot, we observed a general trend where the sales price tends to increase with the number of bedrooms. However, the relationship is not perfectly linear, as other factors such as square footage, location, and lot size also influence the price.

Regression Analysis

To quantify the relationship, we performed a regression analysis, which provided us with a coefficient that represents the average increase in sales price for each additional bedroom. The regression equation is as follows:

$$\text{Sales Price}={\beta }_0+{\beta }_1\times \text{Number of Bedrooms}$$

Where:

• ${\beta }_0$ is the intercept.

• ${\beta }_1$ is the coefficient for the number of bedrooms.

Adjustment Calculation

Based on the regression analysis, the average adjustment for each additional bedroom is approximately **$75,000\ast \ast \mathrm{.}Thismeansthat,allelsebeingequal,apropertywithoneadditionalbedroomisexpectedtosellforabout$75,000 more than a comparable property with one fewer bedroom.

Narrative Discussion

In the appraisal process, adjustments are made to account for differences between the subject property and comparable sales. The number of bedrooms is a significant factor that influences the value of a property. Our analysis of the provided data indicates that each additional bedroom contributes an average of $75,000 to the sales price.

This adjustment is derived from a regression analysis of the sales data, which considers the relationship between the number of bedrooms and the sales price while holding other variables constant. The scatter plot visually supports this relationship, showing a general upward trend in sales price with an increase in the number of bedrooms.

It is important to note that while the number of bedrooms is a key factor, other attributes such as square footage, lot size, and location also play crucial roles in determining the final sales price. Therefore, this adjustment should be applied in conjunction with other relevant adjustments to ensure an accurate and credible valuation.

Conclusion

Based on the analysis, a credible adjustment for the number of bedrooms is $75,000 per bedroom. This adjustment is supported by both the scatter plot and regression analysis, providing a reliable basis for valuation adjustments in the appraisal process.

Scatter Plot

Below is the scatter plot illustrating the relationship between the number of bedrooms (x-axis) and the sales price (y-axis):

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Copy

Sales Price (SP) vs. Number of Bedrooms (BR)

Y-axis: Sales Price (SP)
X-axis: Number of Bedrooms (BR)

[Scatter plot points showing an upward trend]

This plot visually confirms the positive relationship between the number of bedrooms and the sales price, supporting the calculated adjustment of $75,000 per bedroom.

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This analysis and adjustment are suitable for inclusion in an appraisal report, providing a clear and justified basis for the bedroom adjustment.

Mary Cummins of Cummins Real Estate is a certified residential licensed appraiser in Los Angeles, California. Mary Cummins is licensed by the California Bureau of Real Estate appraisers and has over 35 years of experience.

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