TAF and USPAP - great analysis

[RCA]

BTW, the agent said 90% of the homes purchased in Carmel are all cash down. 52% of the homes in other areas were all cash down. It is an EXPENSIVE area to live. Buyers from all corners of the world buy homes in Carmel and the surrounding area. I have appraised many homes in the area, mostly Monterey, Pacific Grove, Marina, Seaside, Carmel Valley - and Carmel.

 

[Zoe]

We are talking about SCA, so throw out the cost data except possibly for new construction. I was just in an AI conference yesterday that had a couple of high-volume real estate agents discussing real estate along the California Central Coast. In Carmel, new construction SFR costs $4,500/sf for many homes. The agent said he recently sold a 2,500 sf home for $10.5M in Carmel. Santa Cruz - Monterey/Carmel real estate has been booming since the pandemic. Many wealthy people from Silicon Valley started moving in that direction. He said people see a house, fall in love with it, and are often willing to pay whatever it takes to get it (more or less).

The set of good sales comparables is usually very limited, so you have to deal with large differences and a number of differences that all interrelate. It is a complex dynamic, and it is very, very difficult to deal with features in isolation. GLA interacts with room counts with lot size with location and so on. If you adjust one up or down - you need to adjust all the others. It is a situation where all the impact of all features on sale price have to be handled as a whole. Paired sales treats these features in isolation, under the assumption they do not interact with other features. That is a MAJOR failure of paired sales. Linear Regression assumes that a given features impact on price is constant throughout its range of value. That is absolutely not the case, especially if we have to expand outward from the subject beyond its original subdivision (if it were even part of some subdivision originally). Of course in older areas with many updates, you can have major differences from one house to the next neighboring house.

The only thing that works consistently and is NOT a black-box approach (such as Random Forest, Neural Networks, ...) is MARS (aka "earth"). Earth can be used to increase your understanding of a neighborhood or market area. It will tell you that, for example, The value of a bathroom, if a house has more than 3 bathrooms, is impacted by whether or not the house has an ADU. Yes, it can tell you that and give you an approximate value for the combination of ADU and bathroom count as an adjustment to the value contribution on top of the contribution from the total bathroom count. It is that precise. I can say, I can go into a completely new market area, run Earth and walk away with a better knowledge of market value in that area, than an appraiser who has been working there for 20 years.

In the SF Bay Area, the value contribution of GLA changes as you go up in value - and usually at specific points or "knots." So you may have $700/sf up to 1200sf, then $750/sf for each additional square foot up to 2500/sf, then something like $500/sf for each additional square foot over 2500 sf and up to 4000 square foot and then have it level off beyond 4000 sf or even drop off. You may get a nice R2 of 0.80 or higher from such a model. On the other hand, a linear regression model may do no better than 0.40 or 40%.

You see, in real estate, at least in a large mixed Metro area like the SF Bay Area, there are many different neighborhoods, which are either mostly custom-built or originate from older subdivisions with houses that have been updated over many years. There is certainly no single underlying parametric distribution like the normal distribution. No - the variances in property values change over all feature ranges. The most you can do is just fit a model to the whole mess - a model that is good at replicating previous sale prices and does good on tests for robustness on the value of unknown sales ( We do this 100 times: Randomly divide our 200 sales into 10 partitions of 20 sales, then randomly choose 9 out of the ten partitions to build a new Earth (aka MARS) model to test on the remaining 10th partition and see how close it comes to estimating the sale prices in that 10th partition. Then, choose the best model - or average the best models into one. )

Not true. Depreciated cost is always an indicator of value. Same with income cap approach. Sales Comparison is in the middle.

Your probably living in cookie cutterville with houses costing $4,500/sf to build. Why do you even bring up cost or depreciated cost?

 

[Zoe]

Are they renting these houses that cost $4,500/sf to construct?

 

[Dublin ohio]

The bedroom adjustment was higher than expected,

Out of curiosity. How much was the bedroom adjustment

 

[RCA]

It's a shame that some voices on the forum still hold strong beliefs in the effectiveness of paired sales despite their evident challenges in accurately reflecting the complexities of real estate markets. Your insights from the AI conference, especially regarding the booming real estate in Carmel and Santa Cruz due to Silicon Valley migration, shed light on how unique each transaction can be.

It's clear that traditional valuation methods struggle to capture all the nuances involved, such as the interaction between GLA, room counts, lot size, and location, which all influence property values in intricate ways.

I just completed an analysis where the bedrooms and living area showed a strong correlation of 0.74, both were on a curve. The bedroom adjustment was higher than expected, but everything resolved smoothly when I restricted the selection to sales with exactly two bedrooms, matching the subject property. The subject property was in the $1,200,000 range, and the model's error was only 1% off the actual price.

Well, the transition on knots with Earth/MARS is sudden, and one could argue it would be better to smooth the transition out, at least at times. There is generally little to be gained by that. Indeed, as you go from one subdivision to another, of course, you WILL see sudden sharp changes in pricing behavior. So, sudden changes tend to in fact be the rule in many market areas. Curvilinear is often very biased because those curves have to follow smooth equation-enforced changes that do NOT reflect reality.

After working with MARS and Earth for many years, I have learned to look at things THE WAY THEY ARE. Reality is usually ugly and far more complex than you would think. That is reality.

Real Estate is in the world of non-parametric statistics, - or data mining.

 

[Zoe]

I have h&b use issue there or some kind of bias on $4,500/sf to construct. Can you put a 10 story condo development there?

Maybe mixed use development?

Commercial on bottom floor? Idk.

 

[Zoe]

That land has to be very expensive at $4,500/sf to construct.

4,500 sf house is like $20 million.

Brand spanking new.

 

[Dublin ohio]

That land has to be very expensive at $4,500/sf to construct.

4,500 sf house is like $20 million.

It's California Zoe. The median price is about 50% more than the national median. Only Hawaii is higher. Hard to relate the California market to anywhere in non coastal America

 

[Zoe]

Is it sitting on ocean and an acre or two with 800 ft of waterfront rights? H&B use comes into play on MV opinion and property rights.

 

[RCA]

I don't understand why you insist that regression is best for complex high-value properties. That is one of the most ridiculous claims ever. Best application for regression is cookie cutter properties.

All three statements are actually correct, justifiable, or partially correct while simultaneously highly misleading.

First some clarification

1. You are responding to a post that did not specify any alternative method to "paired sales". You assumed "regression," but there are many types of regression that are pretty different from one another. This fact alone points to your lack of general intelligence and knowledge on the issue.

2. Your wording, as already alluded to, is highly ambiguous, lacking specifics and really mixed up. We have to make some assumptions about what you mean. Lets assume you mean MARS or Earth non-parametric regression and that you are comparing it to "Paired sales". At the very outset, that is like comparing Einstein to some retard, night to day.

I don't understand why you insist that regression is best for complex high-value properties.

If we look at your many posts on the subject, you clearly don't understand anything beyond paired sales, and possibly simple linear regression.

That is one of the most ridiculous claims ever.

MARS/Earth regression is just as good for simple and complex high-value properties, complex low-value and medium-value properties, and all. In fact, even when comps are lacking, you can duplicate sales to reach the minimum number of sales to allow MARS or Earth to operate. Then you are in even the worst case, no worse off than paired-sales, most likely better off.

Best application for regression is cookie cutter properties.

Certainly, MAR/Earth Regression is the best method for cookie cutter properties, since it is the best method for all types of of properties for the SCA.

It would be best if you went back to school, Joe. I couldn't tell you which school you could get into if any. But in Maryland, you should be able to find something.