Albouy

Article

Albouy is a recurring person in the Astral Codex Ten archive, appearing 2 times across 2 issues between December 09, 2021 and December 11, 2021. The archive places it in contexts such as ""Between 2005-2010, the urban land value for all of New York City was worth about $2.5 trillion, according to Albouy, Ehrlich, and Shin (just the land).""; “worth about $2.5 trillion, according to Albouy, Ehrlich, and Shin”; “Albouy, Ehrlich, and Shin (2018)“. It most often appears alongside Alexandra Elbakyan, Astral Codex Ten, ATCOR.

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Category: People
Mention count: 2
Issue count: 2
First seen: December 09, 2021
Last seen: December 11, 2021

Appears In

- Alexandra Elbakyan (2 shared issues)
- Astral Codex Ten (2 shared issues)
- ATCOR (2 shared issues)
- Australia (2 shared issues)
- Count Bla (2 shared issues)
- Fortress Of Doors (2 shared issues)
- George (2 shared issues)
- Georgism (2 shared issues)
- Georgists (2 shared issues)
- Henry George (2 shared issues)
- Henry George Theorem (2 shared issues)
- International Association of Assessment Officers (2 shared issues)

External Links

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Does Georgism Work? Part 1: Is Land Really A Big Deal?

December 09, 2021 · Original source

Between 2005-2010, the urban land value for all of New York City was worth about $2.5 trillion, according to Albouy, Ehrlich, and Shin (just the land).

Inline links: Albouy, Ehrlich, and Shin

Here's a graph of America's total aggregate land value over time, according to twelve different estimation methods. My sources are The Lincoln Institute, Larson (2015), Albouy, Ehrlich, and Shin (2018), The American Enterprise Institute, PLACES Lab, the Federal Reserve via a method worked out by Matt Yglesias, Larson (2019/2020), and Jeffrey Johnson Smith's 2020 book Counting Bounty: The Quest to Know the Worth of the Earth.

Inline links: Lincoln Institute, Larson (2015), Albouy, Ehrlich, and Shin (2018), The American Enterprise Institute, PLACES Lab, Federal Reserve, Matt Yglesias, Larson (2019/2020), Counting Bounty: The Quest to Know the Worth of the Earth

The middle values in red include Larson (2015), who uses a "hedonic regression" model, and Albouy, who builds a model that only looks at vacant land sales.

Does Georgism Work, Part 3: Can Unimproved Land Value be Accurately Assessed Separately From Buildings?

December 11, 2021 · Original source

This method operates on the same basic logic that I used when I hand-estimated the land value of that San Francisco house in Part I based on the value of the empty lot next door. However, it makes the whole procedure systematic. It can easily and accurately estimate the land value of a property with a big fat building on it simply by smoothly interpolating the known values of the nearby parking lots. Of course, it has limitations. First and foremost, it's a highly local operation, so if you have properties you're trying to value that don't have nearby pure land sales data, you can't really do much with this. Also, most people assume that city centers have less market transactions for undeveloped land than the countryside, as did I until I read that paper by Albouy in Part I. But in any case, this is just one method in your toolbox and might not be sufficient by itself. Its key advantage is that it works directly from true market data for land and doesn't need or want any other subjective data. In the end, basic kernel estimation just fills in the land value of unmeasured locations with a local weighted average of known locations. Nonparametric adaptive regression Kolbe, et al. build on the kernel regression method with a technique called Adaptive Weights Smoothing (AWS), which runs in several iterations and adds additional weight to any observed data points that are sufficiently close to the point being estimated. I'm not 100% sure about what all the math means, but it seems like it's basically a "smarter" version of the basic kernel method. Left: Nonparametric kernel regression, Right: Adaptive Weights Smoothing. I think the authors goofed and printed the same figure twice with different headings because they're identical if you overlay them in Photoshop. Semiparametric regression Now, the above two methods assume you have plenty of "pure" land sale records to work with. But if you're trying to work out prices in the city center, you've probably mostly got land and buildings mixed together. To do this effectively, we need more data, and this is where the "parameter" in "semiparametric" comes in. The model described in Kolbe et al. seems like a flavor of multiple regression analysis that takes the price, the location, and various characteristics of the building and feeds it into a regressor. But we've got "semi" parametric here. What does that mean? Well, if you already know how certain relationships between the data work a priori, it's better to enforce those relationships yourself rather than leave it to the computer. Here, we enforce the assumption that if two properties are right next to each other, then the value due to location is going to be essentially identical. This algorithm starts by ordering things geographically and then working out the differences in observed price by regressing on the difference between remaining property characteristics. In this method, the power of "location, location, location" is not something we're leaving to the regressor to discover by itself. Results of the Semiparametric regression method, we can see some significant differences from the simple kernel-based model. As you can see above, this gives you more detailed and likely more accurate results, and you're better able to assess the values of properties with buildings on them, even in the absence of pure land sales. This technique is more complicated and bakes in assumptions about the power of location, but otherwise doesn't assign subjective human weights to the various property characteristics. The chief human bias comes in the form of deciding which property characteristics are measured and made legible to the model in the first place. Okay great, but how accurate are the above three methods? Their main point of comparison is this thing called the "Bodenrichtwerte," or BRW. I think that means "ground-level-values" in English, and it's an expert-assessed map of land values for Berlin done the traditional way. The nonparametric kernel regression method has a correlation of 0.704 with the traditional method and has the added disadvantage that it's not able to produce estimates for the city center, only the outlying areas. Furthermore, the BRW map does show sharp discontinuities, which is another knock against the kernel method, at least for the city center. What about the iterative method? Kolbe et al. find that "the agreement between [Adaptive Weights Smoothing] land value estimates and, both, land prices and BRW land values is fairly good for all values of λ." Doing some quick checks, their values seem to be within about 85% of the BRW values. A different Kolbe et al. paper called Identifying Berlin's land value map using adaptive weights smoothing goes into more detail and claims to give "similar" values to that of the BRW. For the semiparametric method, they "found a strong positive correlation of 0.845" between their numbers and a previously expert-assessed set done using the traditional method. That sounds pretty good. It seems their margin for error is about plus or minus 15% compared to the traditional expert method. I'd like to see more direct comparisons against market transactions themselves, though, because if the prior expert assessments are wrong, then the main achievement here is improved efficiency, not accuracy. However, this method doesn't seem to be dramatically less accurate than the old way of doing things. The last three models came from the Berlin case study, where you have excellent market transaction data in an extremely wealthy and high-trust society. But what if you're trying to assess land in a developing nation with poor market transaction records, weak institutions, and widespread poverty? Innovative Land Valuation Model (iLVM) This is the particular name of the method described in Development of an Innovative Land Valuation Model (iLVM) for Mass Appraisal Application in Sub-Urban Areas Using AHP: An Integration of Theoretical and Practical Approaches by Bencure, Tripathi, Miyazaki, Ninsawat, and Kim. They used BayBay City, Philippines as their case study. Whereas the previous models are very "hands-off" and let the computer work out the relationships between prices and property characteristics, here you get expert human opinion directly involved in building the model, baking in weights that directly embody judgments like "properties next to major roads are more valuable." These judgments are based on expert opinions that presumably come from observed experience but are a priori judgments nonetheless. Here, look at this big complicated flowchart. The "Analytic Hierarchy Process" in the box on the left is a particular kind of method for getting experts to set weights. The authors give this reason for using it: Despite criticism pinpointed by other scholars, the AHP remains the commonly used in many research fields and practical applications. This is because the AHP: (1) overcomes human difficulty in making simultaneous judgment among factors to be considered in the model; (2) is relatively simple as compared to other MCDA [multi-criteria decision analysis] methods; (3) is flexible to be integrated in various techniques such as programming, fuzzy logic, etc.; and (4) has the ability to check consistency in judgment After identifying a list of "factors" that can affect land value, they group them into taxonomical buckets: Note that certain factors like "Coastline" appear in multiple buckets; this captures the various influences a characteristic can have. For instance, land on the coast tends to be more economically valuable because of tourism, shipping, fishing, etc., so that goes under "economic." But land that's next to the coast is also more likely to flood, so it also goes under "environmental." And then there are various land use restrictions that apply specifically to coastal areas, so it goes under "legal" as well. In this way, a single factor like "the property is on the coastline" can have both positive and negative effects on land value (e.g., it's more economically valuable but it also might flood, and there are certain things you aren't allowed to do there). The next step is to set down some rules for how sensitive each factor is to location and distance. So here we can see that the economic benefit of being on the coast is most strongly felt if you're within half a kilometer of the ocean, but the environmental effect (e.g., risk of flooding) is most strongly felt when you're within 0.03 kilometers. And so on and so forth. Your experts help you work out all these rules. Note that for a few of these factors (such as land use and slope), you use metrics other than distance (e.g. land use classification and grade). Then you take all that stuff and assign everything a value between 0 and 5. Your team of experts then uses this table to come up with a set of weights for everything. What essentially comes out of this is a big linear equation with a bunch of coefficients for every one of your factors, which is then broadly fit to the observed market prices. When you're done, you can take any property on your list, multiply each of its characteristics by its respective weight, run that through your equation, and calculate the predicted price of the land. So how accurate is it? The authors compare it to standard Multiple Regression Analysis and claim it fares better. The Root Mean Square Error is quite a bit less than MRA. In addition, I think it's also saying that the MRA algorithm decided that only four of the factors were significant and basically ignored all the rest. By contrast, iLVM was able to maintain contributions from all the factors, because it doesn't leave that decision to the computer. I'm not 100% sure; it's not clear from the paper. The authors claim that about 67% of the variability is explained by their model, but they note that there are some areas where the model can be off by more than a factor of 1.0 in either the positive or negative direction. One thing that's kind of fun about this model is that you can make neat graphs like this that show the individual contribution of each factor: The main downside to this model is that it relies on a whole lot of subjective expert opinion and can be questioned on that basis. That said, it can be cheaply deployed in a transparent and consistent way across a large area. You can see why that's attractive for a developing nation with weak institutions and poor market transaction records; the argument is that this is a significant improvement over the former status quo. I wonder how well this model performs when you feed it better market transaction data, and how that would compare against all the others methods under identical conditions. More research is needed. Rather than drag you through a bunch more research papers, I'll just leave these others I found cited in the above studies: Killić et al. (2019) - Fuzzy expert system for land valuation in land consolidation processes

See if you can improve on the state of the art. How close to ground truth can you get? Once the first study is done, you'd want to test it in another area–maybe Australia, Denmark, Germany, or the Philippines. If Georgism is true, and the only thing standing in the way is being able to pull off accurate assessments, then let's just get better at doing that. We're the species that split the atom and travelled to the moon. Surely we can handle this. 6.2. Total Land Value of the United States It's really annoying that we don't confidently know this figure, and it has huge implications for LVT policy. Technically, this is an "assessment" problem, but in practice, when you're assessing the entire USA, you're often falling back on big black-box buckets of aggregated property values rather than building a database of direct ground-truth market transactions yourself. In Part I, we saw how big the difference was between Albouy, who used pure land sales directly from the market, and Larson, who applied the cost approach to official figures. If one of you readers has MLS access for all 50 states and/or a bunch of other records, it'd be interesting to see if we could settle this debate once and for all. 6.3. A Push for More Open Real Estate Market Transaction Data To my knowledge, there's no good, one-stop shop for solid, historical, ground truth real estate market transaction data that's uniform and detailed across, say, the entire United States. I'm well aware of how important access to solid data is for researchers. I run a site called www.gamedatacrunch.com that just quietly scrapes public metrics from the PC video game store Steam (they don't mind–I asked). I'm constantly getting requests from researchers to dump slices from my DB for them, which I'm always happy to do. If not for making this data available, those research papers might not be happening. So many questions that are answerable in principle go unanswered in practice simply for want of access to data, and then smart people make bad policy decisions because of that ignorance. In principle, I suppose nothing would legally stop someone from scraping listing prices on Zillow and Redfin all day, every day, but I have a feeling I'd probably get sued if I did that. (Just checked with my lawyer; he says it's a legal grey area but probably wouldn't end well for me.) If you're an eccentric billionaire who wants to do something for Georgism, instead of building a $400 billion super city in the desert, you could buy Redfin for about 1% of that and make their data available to researchers. In any case, whether improved access to consistent, country-wide data were to come from data mining or repeal of real estate non-disclosure laws, it would be an invaluable resource for researchers. 6.4. Empirical examination of ATCOR If ATCOR (All Taxes Come Out of Rent) holds up empirically, it would be a super big deal. Then, it wouldn't matter whose land value estimates you accept, because you'd always be able to shift taxes off of income and capital and onto land without losing revenue. Mason Gaffney cites a few cases where it's supposed to have been observed, but we could really dig into this further. A claim this tantalizing really needs to be nailed down and resolved once and for all. 6.5. Responses to Comments I've been absolutely drowning in comments since the first article posted and there's no way I'll be able to address everything. Doing full justice to some of these will require their own entire articles, but I can leave some brief notes here. Zoning Many people replied that Land Value Tax is useless until or unless you first fix zoning. First of all, Georgists are natural allies in fixing restrictive zoning policies. This is something they definitely want and will fight for. Second, one of the reasons for restrictive zoning policies is broken incentives. A city doesn't have a huge incentive to repeal restrictive zoning policies because it isn't hurting their tax base. According to Georgists, a city whose tax base is land value has well-aligned incentives. It is incentivized to maximize land value by making the city a more desirable place to live, which also raises their tax base. It is dis-incentivized to over-assess or over-tax the land, however, because that will cause people to leave, which will lower their land values and also their tax base. One of the principle things that depresses land values and the tax base in this scenario is restrictive zoning. I personally don't care whether you first pass LVT or first repeal restrictive zoning, you can and should do both. Either one helps the other along. Transitional Politics Honestly this needs its own entire article without me going out on a limb and accidentally saying something dumb. Suffice it to say, a lot of smart people have spent a lot of time thinking about this, and you'll have to wait for a future article to find out what they are. I will let the commentariat duke this one out in the meantime. Corruption Some people agreed to all of the points raised in theory, but pointed out that human beings are wicked sinners, and LVT will be bent towards the malevolent will of our overlords, just like the old policies. And they're not wrong! The problem with this argument is that it's a fully general argument against change. The overlords game every system to their benefit. Rely on standardized tests? They'll game the SAT's with phony disability accommodations and outright cheating. Abolish standardized tests? They'll make their kids take fifty extracurriculars and pay a ghost writer to pen their college entrance essay about their life-changing volunteer work in Ghana. The right question is not "can the rich game this system?" but rather, "can they game it less than the existing one?" This is why you should keep standardized tests, even though rich people can and do game them. The evidence shows that on balance standardized tests are one of the few ways a minority student from a poor background even has a chance to move upwards. So let's dig in. The chief way you can game Land Value Tax is to cozy up to your local assessor and get them to say your land is garbage and it's not valuable. However, you have to do this kind of corruption in the open. Your land value assessment is public record, and highly visible on a map, and will stick out like a sore thumb unless the entire area has been corrupted too. I grant that motivated people could plausibly pull this off to various degrees. You might be able to get the assessor to lie about your land value, but what's the status quo we're comparing against? We don't even know how much cash money value is being socked away in Switzerland and the Caymans, let alone by whom. And even if we did, good luck figuring out how to lure that back to a taxable jurisdiction. Land at the very least can't run or hide. My dream is for us to commoditize open source mass appraisal systems and push for public real estate transaction records everywhere, so that organizations and educated members of the public can do their own land value audits at scale. And again, this is something that just needs to be subjected to empirics. We can sling theory back and forth at each other all day, but the proof is in the pudding. There are places that have done Land Value Tax in the past, and there are places that do it today. A good candidate for a future article is looking at case studies of where LVT has been tried and explicitly look for this problem. Finally, defeatism is corruption's best friend. If you believe everything I'm saying here, and your only obstacle is fear of corruption, and you accept that LVT's vulnerability to corruption is not any worse than the status quo's...then why not just get out there and fight for the world you want to see? Nothing good ever came without a struggle. Finally, we come to the most important comment of all. By George Some people said I did the whole "By George" schtick too much. I'm sorry you feel that way, but... by George, the people have spoken: 6.6. Future Direction This won't be my last article on Georgism, but I haven't yet decided whether to post them on my own blog, Fortress Of Doors, or some standalone site. Nor have I decided what topic should come next. In the comments, feel free to weigh in with which direction you'd like to see me go, as well as any issues you felt were unresolved to your satisfaction. Also, please point out any places where my math looks weird, I was just plain wrong, or where I have misunderstood or misstated the research I'm citing. Thanks very much to this readership and to our host, Scott, for graciously letting me share these findings with you. Acknowledgements: I would like to thank the following people and organizations without whom this series would not have been possible: My wonderful wife Emily, for everything

Inline links: www.gamedatacrunch.com, $400 billion super city in the desert, Redfin, 1% of that, real estate non-disclosure laws, cites a few cases, evidence, shows, he people have spoken, https://substackcdn.com/image/fetch/$s_!5S5P!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fd56e9475-5ec5-4122-a9c3-38eaa388d7e7_594x504.png, Fortress Of Doors

Astral Codex Ten

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Albouy

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Astral Codex Ten

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Albouy

Albouy

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