A good article, thank you for writing it up. I kinda feel like it leaves out most of the important bits though; in real life, nearby houses can be quite heterogeneous, and teasing out what impact this should have on their market value seems like the primary challenge of making fair and accurate valuations.
Hey Isaac! So this is only the first of many articles to come, and if there's interest believe me we will dive deep into all the rest of these questions. This in particular is a good question, and of course in the real world nearby homes are in fact not perfectly identical. Part of the open source library I'm working on is a clustering algorithm that accounts for this -- fractally dividing a neighborhood up into clusters based on tiers of the most important physical characteristics (building type, building size, building age, building quality, building condition, etc).
We'll be more than happy to get into all the nitty gritty details when we release that, and I look forward to your feedback.
I was just already at 8,000+ words for the opener so I figure I had to end somewhere 😅
I look forward to reading the rest! I was getting curious about this recently actually; I bought a house for ~$300k and just a couple years later with no apparent change in the area it was being appraised at ~$450k, which was confusing.
I would certainly like to see the market comps they used. There *is* a pretty common phenomenon where buyers anchor on the price they paid and are shocked at how fast the market moves after they move in and check out of the housing market - but bottom line, either there’s market evidence to support your valuation or there isn’t. If you’re being compared to homes too far away or of the wrong class, you could likely mount a successful protest.
> If a property assessor overvalues a home in a wealthy neighborhood, they will be sure to hear of it come protest season. In this way, any valuation errors on the higher end tend to get swiftly corrected, while there’s less pressure coming from the low end.
I'm confused by this part; you say they only protest overvaluation, which makes sense, but then conclude that *any* error will get corrected by this mechanism. Why would this apply to undervaluations too?
If that's your reading, I might have worded it awkwardly. The property tax protest mechanism mostly only catches overvaluations. To the extent it catches undervaluations, it's when someone notes that their neighbor got a lower valuation than they did. But that's marginal.
The pressure to catch undervaluations usually comes from state oversight boards. For instance, the Texas state comptroller's property value study is there to make sure that local appraisal districts are not valuing too low. (They also check if valuations are too high, but there's an actual incentive not to undervalue, because the state is on the hook to supplement funds for poor school districts, and it frowns on local governments that freeload on state funds through undervaluation)
Can you elaborate on how regression to the mean applies here? Regression to the mean is I think primarily an issue when some random-over-time process is measured as being far away from the mean; we expect it to move towards it in the future. By what mechanism would an assessment be affected?
I mean it just as a general sort of gravitational pull I tend to see, caused by any number of small factors. The most significant explicit cause is typically data bias. Forgive me if I'm misusing the term.
Sales data for average-priced properties is the most numerous. If you are missing even a few sales in high end neighborhoods, that tends to pull their valuations down. And if you are missing even a few sales in low end neighborhoods, that tends to pull their valuations up.
But then there's missing data in characteristics, because (as we'll talk about in future articles) valuations are built as models where characteristics get assigned dollar values in a complex way. When you are missing characteristic data, you fill in with assumptions -- typically whatever is average. And this has a tendency to pull the valuation for whatever you filled in towards the average value as well.
Thank you for this effort to inform your readers. That is a whole ton and a half of work.
…39 minute read(!)
But I will read it.
A good article, thank you for writing it up. I kinda feel like it leaves out most of the important bits though; in real life, nearby houses can be quite heterogeneous, and teasing out what impact this should have on their market value seems like the primary challenge of making fair and accurate valuations.
Hey Isaac! So this is only the first of many articles to come, and if there's interest believe me we will dive deep into all the rest of these questions. This in particular is a good question, and of course in the real world nearby homes are in fact not perfectly identical. Part of the open source library I'm working on is a clustering algorithm that accounts for this -- fractally dividing a neighborhood up into clusters based on tiers of the most important physical characteristics (building type, building size, building age, building quality, building condition, etc).
We'll be more than happy to get into all the nitty gritty details when we release that, and I look forward to your feedback.
I was just already at 8,000+ words for the opener so I figure I had to end somewhere 😅
I look forward to reading the rest! I was getting curious about this recently actually; I bought a house for ~$300k and just a couple years later with no apparent change in the area it was being appraised at ~$450k, which was confusing.
I would certainly like to see the market comps they used. There *is* a pretty common phenomenon where buyers anchor on the price they paid and are shocked at how fast the market moves after they move in and check out of the housing market - but bottom line, either there’s market evidence to support your valuation or there isn’t. If you’re being compared to homes too far away or of the wrong class, you could likely mount a successful protest.
> If a property assessor overvalues a home in a wealthy neighborhood, they will be sure to hear of it come protest season. In this way, any valuation errors on the higher end tend to get swiftly corrected, while there’s less pressure coming from the low end.
I'm confused by this part; you say they only protest overvaluation, which makes sense, but then conclude that *any* error will get corrected by this mechanism. Why would this apply to undervaluations too?
If that's your reading, I might have worded it awkwardly. The property tax protest mechanism mostly only catches overvaluations. To the extent it catches undervaluations, it's when someone notes that their neighbor got a lower valuation than they did. But that's marginal.
The pressure to catch undervaluations usually comes from state oversight boards. For instance, the Texas state comptroller's property value study is there to make sure that local appraisal districts are not valuing too low. (They also check if valuations are too high, but there's an actual incentive not to undervalue, because the state is on the hook to supplement funds for poor school districts, and it frowns on local governments that freeload on state funds through undervaluation)
Can you elaborate on how regression to the mean applies here? Regression to the mean is I think primarily an issue when some random-over-time process is measured as being far away from the mean; we expect it to move towards it in the future. By what mechanism would an assessment be affected?
I mean it just as a general sort of gravitational pull I tend to see, caused by any number of small factors. The most significant explicit cause is typically data bias. Forgive me if I'm misusing the term.
Sales data for average-priced properties is the most numerous. If you are missing even a few sales in high end neighborhoods, that tends to pull their valuations down. And if you are missing even a few sales in low end neighborhoods, that tends to pull their valuations up.
But then there's missing data in characteristics, because (as we'll talk about in future articles) valuations are built as models where characteristics get assigned dollar values in a complex way. When you are missing characteristic data, you fill in with assumptions -- typically whatever is average. And this has a tendency to pull the valuation for whatever you filled in towards the average value as well.
Ignoring outliers for a calculation on how far away the outliers are from the mean is pretty wild.
This is why I always pay close attention to untrimmed ratio studies.