Appraisal Statistics: Regression - Do Your Really Know How Much Work It Is?

[Retired-70]

MARS is unnecessarily expensive. Many regression programs can be better adapted to typical appraiser work.
http://www.realstat.com/

https://download.cnet.com/windows/business-spreadsheets/3260-20_4-101662-1.html

I can only laugh at this sort of thing. Try running your software on the Boston Housing Dataset and see how far you get. MLR does not work very well in the SF Bay area. As far as the data in the example you provided, it is fake, and has just a few attributes. The homes were all built around the same time, with some variation, so I'm thinking that they are homogenous. But then again it is fake data, so I can't comment much more than that. On the classic "Boston Housing Data" from 1970, MARS will provide and R2 that is over 90%.

I've attached the data set for you to try (remove the .txt extension). Here is a link that describes the fields: https://www.kaggle.com/c/boston-housing

"medv" is like a Sale Price, although it is the median home price for a census tract. The other fields are attributes - which I assume you can feed in to real state (maybe you have to rename them as something more kosher.)

But MLR won't do very good on this, the values for most attributes are non-linear. In reality almost nothing is linear, even GLA/Price. Increase the GLA enough and the price starts to level off and even drop - too large a house in some areas is a negative feature as it cost more to maintain and heat and there just isn't a demand for the extra living area. Market conditions are also non-linear. It's total BS, - add to that you are using statistics that assume an underling normal distribution. Oh boy .....

 

[Retired-70]

First let me say I appreciate Bert's thread Topic. Its very good and needed in our community. I am learning a lot about what I don't know.

Well I don't find Corelogic Report of any use at our level. Actually if you think about it , they are not saying anything that isn't already known or maybe better said already predictable. Why? History repeats itself. If the analytics were so correct than who would lose any money in the Stock or Housing Market. There conclusion is at one point in time and its based on historical data. There going out on a limb by saying a decline is in the future in certain markets. That's under the extraordinary assumption that a trend in an area remains the same in spite of other factors that are unknown or at best possibly predictable. How is that valuable to me? Well I suppose it means i should adopt or lean towards a MV less than the highest , maybe mid range or the lowest indicator on a particular assignment using corelogic prediction? That is not the answer. I think the answer is the Lender should use this in the lending decisions/policy ...Not ME. Having said that maybe lenders should steer away from alternative valuation products that are not as comprehensive in those areas.

Trump has a long ways to go to reduce all those negative trade balances. IF the other countries like Germany don't retaliate by increasing tariffs on their side too much, we should get through this. What that means, is that there is plenty of potential room for the economy to expand. There are going be bumps and some industries are going to have mostly temporary problems. But, I'm optimistic that we are entering a new era that should be very nice. More pay - what does that mean? If you work in San Francisco and live in Sacramento, it is not any fun at all commuting 2-3 hours each way. You want to move closer. The closer you get, the fewer houses for sale. You've had it with the commute and are desperate to find something for sale that you can afford, and you and your wife are earning $300K/year and can sell your existing home to make a $200K downpayment. So, you can afford $1M for a home. It may look like a shack, but you've had it with the commute, so you're happy to pay the price for it. Anyway, you are going to be closer to the beach, San Francisco and all kinds of nicer and better things.

- Maybe another topic for discussion: But that is probably the main reason Trump was elected - he has been for an improved trade balance going back to 1980 or so. Increase tariffs on imports until what we import equals what we export. That will reduce imports on many items and increase their price making them more affordable to produce in the US, that leads to more production and employment. The increase in employment can however can be offset by losses in industries that depend heavily on imports. But at the same time, the increase in production gradually leads to lower prices, which in turn makes exports more attractive to foreigners. - This sort of thing has to be done carefully and not too fast.

 

[Retired-70]

Bert, just to get some context how many of these are you doing and how many traditional assignments do you do in a month?

When I was doing residential appraisals, after the first year, I always did ANSI or BOM standard measurements to the inch, floor plans with Chief Architect Professional/Premier, including wall thickness, interior walls and main fixtures, and used MARS in most cases, and wrote the reports per AI classes on report writing. Since I could encounter "dead" areas between rooms, sometimes my interior measurements plus wall thickness didn't add up to the exterior measurements, so I sometimes had to compensate by adding a few inches here or there to room sizes. But I could say the interior room dimensions were +-6 inches. I did the cost approach using the California Franchise Board cost tables, because they more realistic and Marshall and Swift and often came fairly close to market value (but were more work to calculate) --- So, two days for an appraisal was standard, maybe going out to five days for some property in a new area that I needed to get more information on or was a problem in some way. Many would consider this overkill. But you go and do this for a stripmall, where the owner has really never known the rentable space. You do it all exact and give exact area calculations - you might imagine that becomes useful over time, as lease contracts come up for renewal, assuming your measurements are greater than what he had on the books - and more times than not, that was the case.

 

[Terrel L. Shields]

R2 is not the end all and what works on mega scale is too oft worthless in small markets. Only an idiot would pay 10k for a program that cannot possibly make them 5k in a year.

 

[glenn walker]

My experience with regression or similar analyses is that it can be as wacky as a Mad Scientist on LSD and as Bert stated it should work over a large number of similar properties in the Subject neighborhood or area but my experience is it simply a hit and miss. I guess my problem is when doing residential appraisals is that I simply want an-easy and accurate tool to use and so far regression has proven to be not very reliable.

I kind of feel for Bert because engineers love statistics but there are not enough appraisers willing to spend money on a program to make it profitable SO if I was Bert I wold keep my day job as a Commercial Appraiser because at $800 to $1,500 a pop that's a lot more than trying to sell a program to appraisers and as far as getting a job as a Engineer last time I checked Silicon Valley had no shortage of young highly educated software and IT engineers

 

[Meandering]

it can't "work" over large data sets because the decisions that settled on a final price are too random.

Did a property sell for slightly more $$$ because Mom lives around the corner?
Did a property sell for slightly more $$$ because the wife grew up in the neighborhood, or even in that exact house?
i know several people that went back and bought grandma or mom's old house after it was listed for sale in the MLS.

How about, did that house sell for less because several rooms not pictured in the MLS were painted floor to ceiling black or bright red? Or someone was murdered there?

Did a house sell for more because the next door neighbor just got elected to the state congress/senate/chief of police/mayor/township supervisor?

Did the house sell for more because it was once part of a larger parcel owned by someone famous from history?

Did a house sell for less because it was in the news a kidnapper lived there/grew up there/dated someone there?

Did a house sell for more or less because there is a local ghost story about it?

There are far more local knowledge reasons that need to be researched, considered and applied to the valuation question of ALL the comps selected or not, in an appraisal.

But just throw enough excrement in the fan and make everyone believe that what's left on the wall is the "Real" deal.


.

 

[glenn walker]

it can't "work" over large data sets because the decisions that settled on a final price are too random.

Did a property sell for slightly more $$$ because Mom lives around the corner?
Did a property sell for slightly less $$$ because the wife grew up in the neighborhood, or even in that exact house?
i know several people that went back and bought grandma or mom's old house after it was listed for sale in the MLS.

How about, did that house sell for less because several rooms not pictured in the MLS were painted floor to ceiling black or bright red? Or someone was murdered there?

Did a house sell for more because the next door neighbor just got elected to the state congress/senate/chief of police/mayor/township supervisor?

Did the house sell for more because it was once part of a larger parcel owned by someone famous from history?

Did a house sell for less because it was in the news a kidnapper lived there/grew up there/dated someone there?

Did a house sell for more or less because there is a local ghost story about it?

There are far more local knowledge reasons that need to be researched, considered and applied to the valuation question of ALL the comps selected or not, in an appraisal.

But just throw enough excrement in the fan and make everyone believe that what's left on the wall is the "Real" deal.


.

Marion don't muck up the gears in Fannie Mae's Big Green Regression Machine !

 

[Artemis Fowl]

I can only laugh at this sort of thing. Try running your software on the Boston Housing Dataset and see how far you get. MLR does not work very well in the SF Bay area. As far as the data in the example you provided, it is fake, and has just a few attributes. The homes were all built around the same time, with some variation, so I'm thinking that they are homogenous. But then again it is fake data, so I can't comment much more than that. On the classic "Boston Housing Data" from 1970, MARS will provide and R2 that is over 90%.

I've attached the data set for you to try (remove the .txt extension). Here is a link that describes the fields: https://www.kaggle.com/c/boston-housing

"medv" is like a Sale Price, although it is the median home price for a census tract. The other fields are attributes - which I assume you can feed in to real state (maybe you have to rename them as something more kosher.)

But MLR won't do very good on this, the values for most attributes are non-linear. In reality almost nothing is linear, even GLA/Price. Increase the GLA enough and the price starts to level off and even drop - too large a house in some areas is a negative feature as it cost more to maintain and heat and there just isn't a demand for the extra living area. Market conditions are also non-linear. It's total BS, - add to that you are using statistics that assume an underling normal distribution. Oh boy .....

I don't see how such a sample would ever be applicable for most singular property appraisal problems. Even in mass appraisal the population is refined into subsets prior to analyses and dispersion is controlled. I thought we were talking about singular property adjustment support with regression. That is actually a useful application to most of us. If you are talking about appraising properties with regression...I think this is the wrong audience. (I do understand that both solutions result from the same process). As a demonstration of the capabilities of MARS...yes it is convincing. Not $15,000 convincing but very cool.

 

[glenn walker]

I don't see how such a sample would ever be applicable for most singular property appraisal problems. Even in mass appraisal the population is refined into subsets prior to analyses and dispersion is controlled. I thought we were talking about singular property adjustment support with regression. That is actually a useful application to most of us. If you are talking about appraising properties with regression...I think this is the wrong audience. (I do understand that both solutions result from the same process). As a demonstration of the capabilities of MARS...yes it is convincing. Not $15,000 convincing but very cool.

MARS is worth $200 to $250 because appraisers are cheap and Corelogic can give it for a lot less LOL : )

 

[Retired-70]

I don't see how such a sample would ever be applicable for most singular property appraisal problems. Even in mass appraisal the population is refined into subsets prior to analyses and dispersion is controlled. I thought we were talking about singular property adjustment support with regression. That is actually a useful application to most of us. If you are talking about appraising properties with regression...I think this is the wrong audience. (I do understand that both solutions result from the same process). As a demonstration of the capabilities of MARS...yes it is convincing. Not $15,000 convincing but very cool.

Regression doesn't know anything about what is behind the data. You have a target variable like SalesPrice and predictor variables like GLA sf, stories, roof type and so on. Whatever method you have for extracting adjustments should work on and such dataset. Like I said, I can't give you real data because of licensing constraints. But this is real data, has been around a long time and is very well understood. The free online videos on salford-systems.com use this dataset to show how MARS works.