Category Archives: Property values

Vyn and Property Values

In January 2014 Dr. Richard Vyn, a professor at Guelph University in Ontario, published a study concluding that wind turbines have no significant effect on surrounding home prices.  The report was published formally in the Canadian Journal of Agricultural Economics in September 2014.  It didn’t attract much attention until fairly recently, when a spate of news articles appeared.  They pretty much all had the same headline, saying that a new peer-reviewed study showed wind turbines had no effect on home prices.

Vyn’s study applied a hedonic approach to studying the issue, much in the same way that previous academic studies have done.  I’ve studied many of them at some length and posted my critiques on this site.  It is important to note that none of these authors are real estate professionals.  Many of them are sponsored by or associated with an institution that has skin in the wind industry.  In every case they use statistical techniques built on questionable assumptions and come up with the results that please their sponsor/associates.

My quick look at Vyn when it first came out told me his report was more of the same, so I didn’t waste much time on it, never bothering with a posting.  But the recent media flurry got me to look at it again, and I can confirm it really is more of the same.

HIS STUDY

Vyn centered his study on the Melancthon project in Melancthon Township, Ontario.  He got sales data from the Ontario assessor, MPAC, for a total of 11 townships surrounding the project.  He looked at two basic effects of wind turbines on the neighbors – distance from the nearest turbine and visibility of the turbine(s).  He split the data up into farm properties and residential properties and analyzed them separately.  He ran a series of regressions that produced a series of sloped lines on graphs, i.e. sales prices vs. square footage.  The slopes of what the paper was primarily interested in, i.e. sales prices vs. distance to a turbine, were all close enough to zero that they “suggest that these wind turbines have not significantly impacted nearby property values“.

It isn’t until you read through the entire 26 pages that you discover that Vyn, to his credit, has what appear to be significant reservations about his study.  Examples:

  1. Page 11/375: These relatively low numbers [in close proximity to turbines] of post-turbine period observations, which may impede the ability to detect significant effects, represent a potential limitation of this study.
  2. Page 24/388: However, while the results indicate a general lack of significantly negative effects across the properties examined in this study, this does not preclude any negative effects from occurring on individual properties.  [Followed by a segue to Lansink]
  3. Page 25/389: While surveys have indicated that residents often perceive that the existence of wind turbines within their viewshed will reduce the value of their property, such perceptions have not often been corroborated by analyses of sales data, perhaps due, in part, to data limitations with respect to sales in close proximity to turbines.
  4. Page 25/389: The existence of limitations in the analysis undertaken in this paper should not be overlooked.

However, overlooking these self-acknowledged limitations is exactly what the media and proponents will invariably do.  One has to wonder how many reporters just read the abstract, not wanting to pay for the entire report.

LETS START FROM SCRATCH

If you were going to try to figure out if wind turbines actually lowered house prices, how would you go about it?  I think most of us would start by setting up a baseline of house prices at different distances from a project at a point in time well before the project was on anybody’s radar.  Then we’d take the current prices and see how they compared.  MLS and MPAC both have this type of historical data, albeit I don’t know how granular it might be.  Regardless, you’d think this would be a starting point.  And this is exactly what Lansink, a real estate professional, did.  But for reasons that we can all speculate upon this is not what academics (i.e. Vyn) and other government-supported researchers (i.e. Hoen) do.

It would have been nice, for example, if Vyn had published the mean sales prices of homes in each of the 11 townships he studied, unmodified by all his statistical manipulations.  While Melancthon Township and the Melancthon project don’t overlap entirely, I’d think they overlap enough for an effect to appear, if there was one.  In Vyn’s particular case, he used GIS to look at sales within different distances of the project itself, so he could have published those averages as well.  But he didn’t, and I have to wonder why.

The most charitable answer is he thought other factors were present that would distort these averages, things like different ages and sizes of houses close to the project when compared with those further away.  Too bad he doesn’t discuss what those distortions might be.  Or perhaps since other academics have been using hedonic techniques he felt his study wouldn’t be looked at by them if he didn’t also.  Or perhaps he did look at those averages and didn’t like what he saw.

The paper is full of detailed and barely understandable (for me, at least) talk about things like spatial correlation, continuous specification, multicollinearity and so on.  It was obviously written for an academic audience and is pretty much an academic exercise.  But what is appropriate in an academic setting isn’t necessarily appropriate for the general public, and Vyn had to know that his study would be used as a bludgeon by wind energy proponents against that public.

It seems that academics and policy wonks tend to think in grand terms.  This type of study seems to imply that property value decreases aren’t worthy of policy consideration unless they are widespread.  And even though both Hoen and Vyn are careful to note the possibility of individual property losses, their main thrust is to dwell on the larger picture and that is certainly what the media and industry care about.  Individual tragedies be damned, no matter how many there are.

PROBLEMS

So right off the bat I’m leery of this type of study.  But there are other problems with how this study was put together and some of the rather basic assumptions that went into it.  I mentioned upstream that Vyn looked at distance and visibility.  Academic property studies that look at visibility are common, generally claiming that “how the turbines look” is an important part of the opposition to them.  Certainly for the larger public visibility is an issue – after all they can be seen for miles.  But visibility as an issue for house prices pales in comparison with noise.

Vyn mentions noise in passing: “While earlier literature also examined the issue of noise, the reduced emphasis on the noise disamenity appears to reflect improvements in turbine technology (Moran and Sherrington 2007).”  Is he kidding?  Using a reference from 2007, when the sizes of turbines were a fraction of what they are now?  Is he so encapsulated on his campus he hasn’t seen, for example, CBC’s (hardly an opposition organization) Wind Rush?  And how did Moran know there were improvements?  He doesn’t say, it is simply an assertion, one that Vyn has carelessly adopted.

I’ll be charitable and say I think the reason so many academics focus on visibility goes back to their thinking on grand terms, where visibility has the potential of affecting large numbers of homes, while noise has a much smaller radius.  Also, it is difficult to write impressive-sounding studies when you’ve got a handful of properties that are rendered uninhabitable (and quite often unsellable).  You can’t do multiple hedonic regressions with just a few points; you’re forced into (gasp!) doing comps and repeat sales, just like Lansink did.  It sounds much better to have thousands of data points, regardless if they convey the reality of what the neighbors are faced with.

And Vyn certainly had lots of data points – 5414 residences.  And, true to form, very few within 5 km of a wind turbine – 123 (I think).  As an aside, another benefit of using visibility is making the radius so large that any house price effects are greatly diluted, often into insignificance.  Which typically makes the sponsors/associates very happy.

But even that wasn’t good enough for Vyn.  He used Melancthon plus the surrounding 10 townships.  And Ontario townships are very large.  Some of his sales are 50 km from the project.  He mentions using them as a control group, but I see no sign that he ever did so in his analysis.  A picture:

vyn-study-area

The yellow line defines his area.  The yellow push pins are the approximate extents of the Melancthon project, while the red line is the approximate 5 km boundary around the project.  That area covers about 300 sq km, or roughly 7% of the total area he studied.  The sales within that area, 123, represent just 2.3% of the total.  Even considering that Melancthon is the least-densely populated township of the 11, that still is quite a low rate.  Vyn was right to comment about how this might skew his conclusions.

Picture one of Vyns ‘sales prices vs. distance’ regression lines stretching from the center of the project to the edge of his study area.  To show an effect, that line would have to be sloped off of horizontal.  Imagine how difficult it is to slope an otherwise flat line when only 10% of it (maybe 5 km out of 50) is subject to the effect you are looking for.

SUMMARY

Sadly the media, government, proponents and industry will all use this study to justify the continuing assault on rural Ontario.  Almost all will do so without actually reading the study, not to mention taking the time to think about what it really says and the basis upon which it was written.  When you have an agenda truth isn’t important; truthiness is.  And this report supplies truthiness in spades.  Whatever truth it supplies is buried deeply enough to not bother wind’s supporters.

LINKS

Vyn studies, sorry I couldn’t find a free copy of the entire report.

For samples of media reports:

And the industry:

My previous postings:

 

 

MPAC and Wolfe Island, again

INTRO

Several months ago Stewart Fast, a new professor at Queens University in Kingston, Ontario, undertook a study of why southern Ontario was such a hotbed of anti wind energy sentiments.  His conclusions were interesting, and I’ll be having more to say about them in a future posting.  As part of his study he looked at property values and in particular he looked at MPAC (the Ontario real estate assessors), Wolfe Island and the property assessment reductions thereon.

As it happens, I had also looked at MPAC and Wolfe Island and posted on it about 18 months ago.   It seems that Fast and I used the same FOIA-obtained spreadsheet.  My main conclusion was that there seemed to be a large number of large reductions on Wolfe Island, but there wasn’t enough of a pattern to convincingly tie the reductions to the 86 wind turbines on Wolfe’s west end.

I’ve also posted on MPAC and property assessments in a 4-part series.  My main conclusion, contained in part 1’s section, was that MPAC seemed to be hiding the reductions by lowering the values in neighborhoods that just coincidentally happened to be around wind turbines, but not formally incorporating distance to a wind turbine into their regressions.

What Dr. Fast’s work added to mine was that (1) he was able to group MPAC’s reductions on Wolfe Island by their distance to the nearest wind turbine, and (2) he reminded me of how to use chi-square to test the differences between the bands for statistical significance.  The quick summary is that MPAC has been providing reductions to properties close to wind turbines significantly more often that those further away.  And I’m not using the word “significantly” in some fuzzy qualitative manner – I mean “significantly” in the hard statistical quantitative manner.  In other words, the odds of the getting a wind-turbine-centered pattern just randomly are vanishingly small.  Wolfe Island provides a good hard-to-refute example of how MPAC is finessing the numbers to deny the obvious.

THE DATA

The raw data (i.e. the spreadsheet) is quite detailed, so to save space here’s the summary of it.  There are 4 major areas in the municipality of Frontenac Islands:  Wolfe West (where the turbines are), Wolfe East, Howe Island and Simcoe Island.  The number of properties and total reductions are in the following table.

fi-data-base

As both Fast and I have written, these numbers aren’t really indicative of anything having to do with wind turbine proximity. About the only thing that stands out is that Simcoe Island had a far higher rate than the other areas, which was at least partially due to reasons other than wind turbines.

The next step was what Fast added: he was able to use GIS software to group the reductions into buffers based on the distance to the nearest wind turbine.  He had 5 buffers: < 1km, 1 – 2 km, 2 – 5 km, 5 – 10 km and > 10 km.  He used chi-square to see if there were significant differences between the buffers and found that the 1 – 2 km and 2 – 5 km buffers were significantly more likely to have reductions than the other buffers.  As he said, this may be suggestive but is not quite conclusive.

Dr. Fast graciously provided me the data that went into his buffers and I re-ran his chi-square calculations to make sure I could replicate his results.  Initially I thought the non-significance of the < 1 km buffer (you’d expect the buffer closest to the turbines to show the most significant effect) was due to the income-producing nature of any land close to a wind turbine, plus the setback that rendered about 25% of that buffer unoccupied.  While those could be important, I also noticed that by chance there were a lot of reductions just outside of the 1 km border.  As an example I show the following picture of the reductions around Wolfe’s main city, Marysville:

wi-reductions-wegmap-st-marys

The 1 km buffer ends about where Highway 95 T’s: to the left is inside that buffer while to the right is in the 1 – 2 km buffer.  Since all the buffer borders are fairly arbitrary anyway, I decided to proceed with 4 buffers, with my results below.

fi-data-buffers

As the buffers get closer to the wind turbines you can see that the ratios of reductions to properties generally gets higher.  The chi-square is a test to see if these ratios could simply be due to chance.  The “Chi-2 p” column provides the p (probability) that this buffer varies from the total Frontenac Islands ratio by random chance.  The two buffers closest to the wind turbines have less than a 1% chance of having values that high by chance, while the buffer farthest from the wind turbines has a much less than 1% chance of having a ratio that low by chance.  Note that BOTH close-in and far-away reductions are significantly different from the mean, and in directions that are BOTH consistent with the hypothesis that wind turbines are associated with property assessment reductions.

DISCUSSION

In my earlier posting on the MPAC 2012 study I predicted that they would lower assessments close to wind turbines while never explicitly recognizing wind turbines as the cause.  I offered up Wolfe Island as an example of how this might proceed.  Thanks to Dr. Fast and a fair amount of serendipity we now have a solid indication that MPAC is proceeding as predicted.  While some aberrations in assessments and reductions would be expected (some chi-squares show significance where none really exists), the pattern shown above is just too consistent to be cast aside as coincidental or anecdotal.  We proposed a hypothesis that there would be more reductions closer to the wind turbines and the data clearly support that hypothesis.

Dr. Fast does have a point that this is indirect evidence of lower values.  After all, we don’t have the municipality’s “bufferized” assessed values, not to mention bufferized sales data.  In the 2012 study, MPAC did provide bufferized assessments for the entire province and they show a 25% decrease within 5 km of the turbines, a result that somehow got lost in their summary.  As for actual sales, there have been so few, especially on Wolfe’s west end, that any sort of statistically-valid testing would be difficult.  In the meantime, reductions will have to serve.  That MPAC seems to be going out of its way to hide this trend indicates that MPAC is being used to implement a political agenda – a problem greater than just wind turbine assessments.

Overall, some 22% of the properties in the Frontenac Islands were granted reductions by MPAC.  I’d love to know if that is typical – it would be interesting to study the assessments and reductions in the municipalities with and without wind turbine projects.  Unfortunately, as even Dr. Fast commented, MPAC has made getting their data just about impossible.

In his report Fast says that the evidence of reductions due to wind turbine proximity is “suggestive but not conclusive”.  Given the numbers above you are of course free to come to your own conclusion, but to me they are more than “suggestive”.  Perhaps there are confounding effects from the 2008 melt-down and following economic malaise, but I’d think they would affect the area as a whole.  If it isn’t the wind turbines that produce this rather clear pattern, then what is?

MPAC’s 2012 Study

Last week the Ontario Municipal Property Assessment Corporation (MPAC) released the 2012 version of their continuing study (following one in 2008) of wind turbines and property values in Ontario, entitled Impact of Industrial Wind Turbines on Residential Property Assessment In Ontario.  To sum it up, they still find no evidence that wind turbines cause property value declines.

The study consists of a 31-page main section [backup link] along with 12 appendices.   MPAC seems to have their own language and it isn’t easily penetrated by a layman. I’ve read over it carefully several times and there are still aspects of it that escape me.  The appendices are generally beyond anyone who is not a professional.  On page 4 they state their goals for this version of the study:

Specifically, the study examined the following two statements:

1. Determine if residential properties in close proximity to IWTs are assessed equitably in relation to residential properties located at a greater distance. In this report, this is referred to as Study 1 – Equity of Residential Assessments in Proximity to Industrial Wind Turbines.

2. Determine if sale prices of residential properties are affected by the presence of an IWT in close proximity. In this report, this is referred to as Study 2 – Effect of Industrial Wind Turbines on Residential Sale Prices.

Their two main conclusions, on page 5, are:

Following MPAC’s review, it was concluded that 2012 CVAs of properties located within proximity of an IWT are assessed at their current value and are equitably assessed in relation to homes at greater distances. No adjustments are required for 2012 CVAs. This finding is consistent with MPAC’s 2008 CVA report.

MPAC’s findings also concluded that there is no statistically significant impact on sale prices of residential properties in these market areas resulting from proximity to an IWT, when analysing sale prices.

Actually, there are three parts to this study, with the third contained in Appendix G [backup link].  Early in 2013 one Ben Lansink published a pretty solid study that showed property value declines of anywhere from 22% to 59% and averaging about 37% on residential properties close (all within 1 km) to IWTs, which I posted on at the time.  Apparently Lansink’s work was solid enough that MPAC felt obliged to attack it.

For me to critique all three parts would make for a very long posting, so I’m going to divide it up.  Obviously the details will follow in my subsequent postings, but for the impatient let me summarize below.

Part 1, are MPAC’s evaluations close to IWTs as accurate (equitable, in their words) as those further away?  This section is only of tangential interest to me, as the central question isn’t MPAC’s accuracy, but rather the effect of IWTs on prices.  It seems that, given MPAC’s explanations, their appraisals are accurate.  Still, there are some items in this part that are of interest.  For example, it seems that MPAC has been playing games to get the appraisals to agree with the market while hiding the effect of wind turbines.  They studied turbines 1.5mw and larger, not older turbines and the areas in Ontario where the impact has already been felt.

Part 2, do IWTs have an effect on properties closer to them?  This section is of central interest.  Unfortunately there are only 5 pages in Part 2, leaving lots of details missing.  Things like the sales prices within the close-in areas. MPAC’s major tool for doing mass appraisals (4.7 million in Ontario) is multiple regression analysis and we’ve had lots of experience with how that can be manipulated to obtain the answer your sponsor wants.  Instead of providing us the prices and letting us judge for ourselves what any effects might be, they opaquely run those prices through their regressions and voila! claim there’s nothing to see here!

But whoever wrote Part 2 must not have been talking to whoever wrote Part 1.  On page 18, well within part 1, there’s Figure 2.  It’s purpose there is to show how close the appraisals are to the sales data (the paired blue and green bars) for the different distances from the IWTs.

gulden-mpac-raw-dataNote the blindingly obvious.  Prices (and appraisals) within 5 km of IWTs are substantially lower than those further away.  I’ve added the horizontal lines so we can better determine the values, which are noted to the side.   Michael McCann, among others, has done a number of studies on IWTs and prices, and his overall conclusion is a decline of 25-40%, with almost 100% in some cases.  Does anyone want to calculate the decline from 228,000 to 171,000?  Perhaps the disparity is due to something as simple as the spread between rural and urban properties, but don’t you think MPAC would at least mention something?  Nope.  Nada.

Part 3, what are the problems with Lansink’s study?  Appendix G is more or less readable and provides an excellent example of what David Michaels book, Doubt is Their Product, talks about.  MPAC throws up, by my count, 7 objections to Lansink’s methodology; of which exactly zero actually indicate that Lansink’s numbers are wrong.  Sewing confusion seems to be the most logical explanation.  As an example, objection #4 of the 7 is that for some of the pre-IWT prices Lansink used, gasp!, MPAC’s own appraisals.  Perhaps whoever wrote Appendix G didn’t bother reading the conclusions in Part 1.

There’s more details, of course, in the following postings.

Critique of Part 1

Critique of Part 2

Critique of the Lansink hatchet job

MPAC 2012, Study 1

If you haven’t already please read the summary posting as an introduction.  This is the second of four postings on the MPAC study and covers MPAC’s Study 1.  My third posting, covering Study 2, is here.  And my fourth posting, covering the Lansink critique, is here.

Part 1 of MPAC’s 2012 study asks if MPAC has as equitably assessed properties close to IWTs as properties further away.  This part, although of only tangential interest to wind opponents like myself, occupies the central part of the entire study.  We think the larger question is: do IWTs reduce property values, not whether MPAC is clever and honest enough to correctly recognize those reductions.

MPAC is in the business of mass assessments, nearly 5 million in Ontario.  Given this volume they have no choice but to use computers and computer-friendly techniques to do their assessments.  That translates to a significant reliance on multiple regression analysis.  They determine what sorts of characteristics influence the selling prices and then use the computers to find out how much influence each characteristic has.  In their experience, 85% of the selling price can be calculated using 5 characteristics, or variables: location, building area, construction quality, lot size and age of the home adjusted for renovations and additions.  Note that distance to a wind turbine is not one of their characteristics and MPAC seems determined to keep it so.  But also note that location could be used in lieu of distance – more on this later.

MPAC uses the ASR, Assessment-to-Sales Ratio, to determine if their assessments are accurate.  It is simply the assessment divided by selling price, with a ratio of 1.0 being a perfect match.  MPAC expects ratios between 0.95 and 1.05, and presents what seems to be an endless series of charts demonstrating this, primarily in the appendices.  While obviously MPAC (actually everyone) has an interest in accuracy their emphasis on it seems misplaced in a study entitled Impact of Industrial Wind Turbines on Residential Property Assessment In Ontario, which to me and most residents is quite a different question.

Just think of the ramifications if MPAC decided to include distance from an IWT in their regressions.  I have little doubt it would make Ontario’s lawyers very happy.  It would also put Ontario’s very-pro-IWT ruling party in a difficult political spot.  And don’t forget that the board of MPAC is appointed by the Minister of Finance, who is a member of the ruling party’s cabinet.

Upstream I mentioned that MPAC could use the location variables that already exist in their regressions to finesse their way out of this problem.  I point to Wolfe Island as an example of how this might work.  The western half of WI is now home to 86 IWTs, a project that had been in development since roughly 2000.  If this half constitutes a “neighborhood” then MPAC could reduce the values in that neighborhood in a uniform manner and never have to recognize the elephant in the room.  As it happens, I posted on MPAC’s actions on Wolfe Island about 18 months ago.  In the 7 years when the wind project went from being developed to operational, the roughly 700 properties on Wolfe received the following number and average reductions:

  • 2005/06: 130, 9.3%
  • 2006/07:  33, 15.2%
  • 2007/08:  12, 28.8%
  • 2008/09:  34, 12.4%
  • 2009/10:  44, 29.0%
  • 2010/11:   22, 30.0%
  • 2011/12:  27, 24.0%

That’s a total of 302 reductions, which seems like a rather large percentage of the properties there.

UPDATE – I revisit the Wolfe Island story here.  My suspicions are confirmed.

A Wolfe Island couple, the Kenneys, asked for a reduction which they say MPAC was willing to grant, although MPAC wouldn’t let IWTs be used as the reason.  It ended up in court, and a local paper had a reasonably good account of it.  Perhaps MPAC’s reluctance to admit the obvious is that once they admit it they must then include distance in their regressions and doing that (and the legal and political repercussions) is just too unpleasant.  So they limp along, using the location instead.

Their favored overall chain of logic seems to be: since the ratios in neighborhoods close to IWTs aren’t much different from those further away, and since those ratios indicate their assessments are accurate, and since MPAC doesn’t include distance to an IWT in their regressions, ergo distance from an IWT isn’t a factor in reducing values.  Part 1 of this study is a necessary part of this chain.   So the real main purpose of this part of the study (and the study as a whole) seems to be to publicize MPAC’s skills at keeping the assessments in line with reality, and at the same time deflect how MPAC is going about doing this. MPAC is, after all, in a tight spot.  The reality is that home prices take a dive when close to IWTs.  MPAC somehow has to lower the assessments around IWTs to keep the ASRs in line while keeping their bosses happy.

Unfortunately, the wind industry will be using this study for quite a different purpose – to bolster their argument that IWTs don’t impact home prices in the first place.

MPAC 2012, Study 2

If you haven’t already please read the summary posting as an introduction.  This is the third of four postings on the MPAC study and covers MPAC’s Study 2.  My second posting, covering Study 1, is here.  And my fourth posting, covering the Lansink critique, is here.

Details of Study #2

I fear that this part will be a difficult one for most people to follow, not to mention being lengthy.  Feel free to skip it.  But I think it is important to document what this Study contains, and MPAC made no effort to make understanding it easier.  I recommend you print out Study 2’s  5 pages (pdf pages 26 to 30) and have them at hand as you read this.

The purpose of Study 2 is to “study the effect of proximity to industrial wind turbines on residential sale prices.” In summary, Study 2 finds that “With the exceptions noted above, no distance variables entered any regression equations for any of the other market areas.”  Say what?

It seems that people who are in the business of estimating real estate prices tend to fall into one of two camps.  First are those who make their living providing services to the people who actually own the properties, with real estate brokers being the most obvious examples.  These people tend to focus on one property at a time and generally use comps or repeat sales to obtain their estimates.  Second are those who make their living providing services to people who don’t actually own the property.  Academics and mass appraisers (like MPAC) are the most obvious examples.  These people tend to focus on many properties at a time and generally use statistical techniques like multiple regression analysis to obtain their estimates.  The second class tends to think in terms of rejecting the null hypothesis – you assume there is no difference between two sets (in this case close-in prices and far-away prices) unless you have “statistical significance”.  As a snarky aside, getting to statistical significance in real estate can be quite a challenge, given the wide variance among prices, and can be even more difficult when your sponsor/boss doesn’t want you to do so.

So of course MPAC used their main tool, regression equations that run multiple regression analyses.  They created three new variables based on distance from an IWT  and entered these into regression equations to see if the new variables were statistically significant.  If they aren’t statistically significant they don’t “enter” into the regression equations.  As for the exceptions (which we’ll get to shortly), out of 30 possibly significant variables, only 4 were significant and 3 of them were positive!  Whew!

So right off the bat MPAC is using a tool that doesn’t provide the answers the actual owners of potentially affected properties  really care about.  A binary statistical significance indicator does not provide an answer to the “how much” and “how likely” questions a homeowner is going to have.  In this case, MPAC has skipped through the study so opaquely that I can’t even have much confidence in my critique.  There’s just too many omissions, too many unexplained leaps, too many dangling statements.

There are just 5 pages in Study 2.  The first of these (page 25 of the study) lists the three new distance variables and sets their criteria for statistical significance at either 5% or 10%.  For those unfamiliar with that concept, the significance is a measure of the odds two populations are in fact just randomly part of the same larger population.  In this case, a 5% significance means that there is only a 5% chance that the prices of the close-in homes are the same as the far-away home prices.  In other words, there’s a 95% chance that the close-in prices are different from the far-away prices.  What if there’s only an 80% chance your home value will drop?  Not significant, from MPAC’s perspective.

The second page (page 26) is dominated by Table 9.  For MPAC’s purposes Ontario is divided into 130 “market areas”.  These areas presumably have some common basis that allows them to be treated as a unit for their regression equations.  Unfortunately I couldn’t find where the areas were or how many homes were in each.  Of the 130 MPAC found 15 that had large enough turbines in them to be of interest.  These 15 are listed in Table 9, along with the numbers of sales within each of the 3 distance variables for both pre-construction and post-construction.  MPAC didn’t bother adding them up either horizontally or in total, but I did.  The numbers inside the grid add up to 3136, which would be the total sales within 5 km in all the areas.  But if you add up their numbers along the bottom you come up with 3143.  It turns out that their 142 should be 139 and their 1584 should be 1580.  Now this isn’t much of an error, except that any pre-teen with a spreadsheet and 10 minutes wouldn’t have made it.

At the bottom of page 26 they introduce pre-construction and post-construction periods, and that only two of the 15 have enough sales to test both distances and periods.  Most of the remaining 13 have “sufficient sales within 1 KM to test the value impact within that distance”.  Also that the “sales period to develop valuation ranges from December 2008 to December 2011”. And that Table 10 provides a summary.

The third page (page 27) is dominated by Table 10.  It lists the remaining 10 market areas that presumably have “sufficient sales within 1 KM to test the value impact within that distance”.  2 of these have enough sales to test both distance and periods while the other 8 have enough sales to test just the distance.  For each of the 10 areas MPAC list square footage etc and median adjusted prices.  Are these the prices for the entire area or just within 1 km?  MPAC doesn’t say.  What is the criterion for “sufficient”?  MPAC doesn’t say.  Nor does MPAC include what should obviously be included – both tables.  I suspect they are for the entire area, in which case they are useless for our purposes, at least without the close-in comparison.

Presuming the criteria for inclusion into Table 10 is the 1 km test mentioned on page 26, one has to wonder how 26RR010 and 31RR010 got into it, as Table 9 shows they had zero sales within 1 km.  Snark alert – maybe the missing 7 sales from Table 9 took place in these areas?  And if 1 km isn’t the criterion, what is?  MPAC never says.

At the bottom of page 27 they mention that some sales at the 5 km distance were in urban as opposed to rural market areas and thus were eliminated.  They don’t say how many, nor what their effects on the regressions might be.  They also reiterate their statistical significance levels.

On the fourth page (page 28) they present two more tables, 11 and 12.  Table 11 lists the 8 market areas that had sufficient sales (within 1 km?) to test the distance variables while Table 12 lists the 2 market areas that had sufficient sales to test both distance and periods.  These tables made absolutely no sense to me until I noticed Appendix F.

For all 10 areas they entered the 3 distances and ran their regressions.  In Appendix F they list all the “excluded” variables, in this case all the distance-related variables that didn’t get to statistical significance.  They apparently are called “excluded” since, being “insignificant” they don’t enter into MPAC’s final pricing calculations.  If you look at the “sig” column you will not see any value less than .100, or the 10% significance level MPAC mentioned on pages 25 and 27.  I assume by omission (and that’s all I can do here) that any of the 3 distance variables that are NOT listed in Appendix F are in fact significant.

On my first pass through Appendix F I came up with 6 omitted, and thus assumed significant, variables.  Two of the omissions were for zero sales, for areas that shouldn’t even be there by the <1 km criterion.  But, maybe the < 1 km variable was never even entered on the exclusion listing in Appendix F, so maybe I had erroneously assumed it was not excluded when in fact it didn’t exist in the first place.  So maybe the criterion for inclusion in Table 10 wasn’t significant sales less than 1 km, but rather significant sales less than 5 km out.  Just a typo, right? At least Table 11 now is consistent with Tables 9 and 10.

Finally! Out of the 30 tests (10 areas times 3 tests) I count 4 that are significant.  Those 4 make up the “non-DNE” entries in Tables 11.  MPAC provided absolutely no guidance or explanation about any of this, apparently writing for a very small audience.

Table 12 shows the 2 areas that had enough sales to test both distance and periods.  You’d think that they’d be creating 6 variables for each of them instead of the 3 variables the other 8 areas received.  Looking at Appendix F all you see is the same 3 as everyone else got.  And all of those variables were excluded.  But Table 12 shows 2 of the variables being significant for 26RR010.  Perhaps Appendix F was based on a 5% significance level and Table 12 was based on 10%.  Who knows?

I can only guess that the dollar amounts in Tables 11 and 12 are the effects of being in those areas upon the prices.  So, in the Kingston area (05RR030), if you live within 1 km of an IWT, you can expect the value of your home to increase by $36,435! Very impressive – 5 digit accuracy, especially with a sample size of 7.

Finally, thank goodness, we come to the fifth page (page 29).  It is the Summary of Findings and contains more words than the rest of the Study put together.  This section mostly lists the significant variables and adds some fairly cryptic commentary.

Some Commentary

As I read through and dissected this Study I couldn’t escape the sense that MPAC didn’t want to put much effort into it.  Any narrative or explanations or even public-friendly conclusions are absent.  The tables that are included are ok, once you take the time to figure them out, but what about all the stuff they should have included but didn’t?  Things like the median prices in the areas represented by the 30 variables.  Or an Appendix F1 that shows the included variables, allowing us to see the t-scores etc for ourselves.  Etc., etc.

These missing items cause this Study to be terribly opaque.  I hope my explanation above is accurate, but I can’t be sure due to all the missing items.  Maybe the Study reaches valid conclusions, but I sure can’t verify that.  Perhaps MPAC thinks we should just trust them to be an honest pursuer of the truth.  Sorry, that no longer flies, if it ever did.  You have to wonder, is there some reason other than laziness or stinginess that this Study seems so empty?  In addition to the opacity the Study includes several cryptic items that MPAC never explains.  For example, from the summary, what do these sentences actually mean?

“Upon review of the sales database, it was determined that the IWT variables created for this study were highly correlated with the neighbourhood locational identifier. This strong correlation resulted in coefficients that did not make appraisal sense, and thus have been negated for the purposes of this study.”

If you look at the excluded variables in Appendix F you notice that most of them are named “NBxxxx”.  Probably those are neighborhood identifiers the somehow overlay the market areas.  MPAC never mentions how many there are or what the criteria are for forming one.  But pretty obviously the areas around an IWT could easily coincide with their neighborhoods.  So what gets negated?  Some of the coefficients? All of them?   MPAC provides no further information.

As an aside, I found it interesting to scan over the other excluded variables to see what sorts of things MPAC puts into their regressions.  Many of them make no sense and they seem to vary greatly from market to market.  I can’t help but think of a bunch of regression-heads sitting at their desks hurriedly making up variables and desperately running regressions in an effort to get the ASRs closer to one (ASRs are covered in Study 1).

I’ll leave (thankfully, believe me) this Study behind with the final thought that it seems so slapped together, so opaque, so disjointed that perhaps even MPAC themselves weren’t sure what significance it holds.  Unfortunately, the wind industry won’t care about any of that, and will use this study to continue harming Ontario residents.

 

 

MPAC 2012 and Lansink

If you haven’t already please read the summary posting as an introduction.  This is the fourth of four postings on the MPAC study and covers MPAC’s Lansink critique.  My second posting, covering Part 1 of the study, is here.  And my third posting, covering Part 2, is here.

Ben Lansink is a professional real estate appraiser based in Ontario.  In February 2013 he published a study of two areas (Melancthon and Clear Creek, Ontario) where 12 homes all within 1 km of an IWT were sold on the open market.  He used previous sales and MPAC assessments to establish what the prices were before the IWTs arrived and then compared that with the open market prices after they went into operation.  The declines were enormous, averaging above 30%.  The following (thankfully clickable) spreadsheet snapshot gives a good summary of his results.

lansink-spreadsheet

In quite a departure from MPAC’s style, Lansink lists every sale, every price, every time-related area price increase rate and every source.  Lansink establishes an initial price at some time before the IWTs were installed, applies a local-area inflation rate over the period between the sales, and compares the “should-have-been” price with what the actual sales prices was after the IWTs were installed.  In all 12 cases the final price was lower than the initial price, leading to an actual loss on the property.  When the surrounding real estate price increases were factored in, the resulting adjusted losses are even greater.  The compulsive reader might notice that the numbers above vary slightly from Lansink’s.  In order to check his numbers I reran all his calculations in the above chart and there are some rounding errors – like on the order of < $10.  I posted on Lansink’s study when it came out, along with a second posting on a previous version of his study.

These numbers are pretty easy to understand, and for most actual property owners are a hard-to-refute indication of what awaits us should we be unfortunate enough to own property within 1 km of an IWT.  It is powerful enough and inconvenient enough that MPAC felt the need to single it out for a hatchet job, which is contained in the 7 pages of Appendix G.   The first couple of pages are introductory stuff.  Starting in the middle of page 2 they start their critique with, by my count, 7 issues with Lansink’s methodology.  The 7 are:

  1. Lansink uses the local area MLS price index in calculating the inflation rate.  MPAC points out, correctly I guess, that within the MLS local area there could be neighborhood variances that could differ from MLS’s area average.  MPAC has lots of neighborhoods defined (see Appendix F for a sampling) and it would be more accurate to use them.  While more discrete data is generally a good thing, I think most people are quite willing to accept the local area MLS price index as a reasonable proxy.  Besides – how would Lansink obtain MPAC’s neighborhood data?  He used the best that he had, and that best is no doubt good enough for everyone besides MPAC.   As you increase the number of neighborhoods you necessarily decrease the number of homes in each, increasing the chances of distortion by a single transaction.  Issue #5 below will mention this as a problem from the opposite direction.  No doubt if Lansink would have used neighborhoods MPAC would be criticizing him for not using the more reliable area average.  Additionally – how far apart could a neighborhood be from the local area average?  Does MPAC provide any indication that this caused an error in Lansink’s conclusions?  Of course not.
  2. Lansink used just two points to “develop a trend”.  I have no idea what they are talking about.  Lansink is not developing any trends.  As with neighborhoods, MPAC has more discrete timing adjustments than what Lansink used.  In theory, more discrete data might be more accurate.  In practice, maybe not, due to outliers.  A monthly MLS area average is good enough for, again, everybody but MPAC.  Additionally – how far apart could a their timeline be from the local area average? Does MPAC provide any indication that this caused an error in Lansink’s conclusions?  Of course not.
  3. Two homes in Clear Creek have their initial and final sales 8 and 15 years apart and there was likely something changed in the interim, affecting the price.  People are always doing things to change the value of their homes – does MPAC have any indication that something substantial changed in one of these properties?  If not, this is simply idle speculation, designed to instill confusion.  Does MPAC provide any indication that this caused an error in Lansink’s conclusions?  Of course not.
  4. For the other 5 home in Clear Creek Lansink used MPAC’s 2008 evaluations as the initial price, and MPAC is complaining about that.   MPAC is apparently unaware of how ironic this sounds.  They just finished, in this very study, bragging about how close their ASR’s were to one.  Does MPAC provide any indication that this caused an error in Lansink’s conclusions?  Of course not.
  5. For the properties in Melancthon Lansink used the buyout prices from CHD (the wind project developer) as the initial prices.  To confirm these prices were at least in the ballpark of local market prices he obtained a local per square foot average price and it compared favorably with the prices paid per square foot by CHD.  Since there was only 4 samples in this part of his study, even one outlier becomes a possible source of distortion and this is one of MPAC’s  “major concerns”.  This seems an odd criticism, coming from someone who relied upon the data in Table 9, with its fair share of single-digit samples.  Does MPAC provide any indication that this caused an error in Lansink’s conclusions?  Of course not.
  6. MPAC found one house with a basement and since footage in basements is treated differently from footage above ground, this would have changed the square footage price used by Lansink in his comparison with the local average.  Since there are only 4 houses in this sample, it would have moved the average up. MPAC spends the bottom of page 2, all of page 3 and part of page 4 discussing basements and whether they are finished or not.  Does MPAC provide any indication that this caused an error in Lansink’s conclusions?  Of course not.
  7. I’ll quote issue #7 in its entirety so you can fully appreciate it.  “One final issue with the sales used in the Lansink study was that the second sale price was consistently lower than the first sale price despite the fact the time frame being analyzed was one of inflation. The absence of variability in the study make them suspect.”  Suspect?  THESE ARE PUBLIC RECORDS.  There’s nothing suspect about them.  These are facts.  They won’t change.  If they don’t fit your narrative perhaps your narrative needs to change, eh?  Does MPAC provide any indication that this caused an error in Lansink’s conclusions?  Of course not.

These 7 issues are an excellent example of spreading confusion,  hoping that some of it will stick, saying whatever you can come up with to discredit an opponent.  When you’re reduced to spending over a page discussing basements it provides an idea of just how desperate you are.

The second part of MPAC’s critique involves them running their own study of resales to see how it compares with Lansink’s.  They find 2051 re-sales that were part of this same study’s ASR calculations (in Study 1).  They use their more discrete time variables in place of Lansink’s MLS local area averages.  They use multiple regression analysis because “Paired sales methods and re-sale analysis methods are generally limited to fee appraisal and often too tedious for mass appraisal work.” Their conclusion: “Using 2,051 properties and generally accepted time adjustment techniques, MPAC cannot conclude any loss in price due to the proximity of an IWT.”

In spite of the voluminous tables and examples, MPAC leaves some very basic questions unanswered.  Like where were these 2,051 properties located and how were they selected?  There’s no mention of them in the body of the 2012 study.  Over what period were the resales captured?  What were the prices of the close-in re-sales vs the far-away re-sales? Lansink has documented 7 losing resales within 1 km – why does your summary say zero?

MPAC has this habit of expecting us to be impressed with large amounts of data, without divulging where it came from and what filters might have been employed.  Same with throwing all these numbers into a computer and expecting us to uncritically accept the output.  In short, MPAC expects us to trust them to be fully honest, fully competent and fully independent.  I hate to be the bearer of bad news to the fine folks at MPAC, but that trust is no longer automatic for increasing segments of Ontario’s population.  Lansink’s numbers are out in the open and are processed in a way that anyone can verify.  Your numbers suddenly appear and rely upon computers with undocumented processes that always support the agendas of your bosses.  Your methods may be satisfactory to some media, some politicians, some courts and all trough-feeders, but please don’t be surprised that they are not satisfactory to those of us living in the trenches.

 

Hoen, again

Ben Hoen has just published his latest property value study [backup link].  It was issued under the auspices of Berkeley Labs, sponsored by the Office of Energy Efficiency and Renewable Energy (Wind and Water Power Technologies Office) of the U.S. Department of Energy.  This study contains 51,276 sales, more than any previous study, spread over 9 states, 27 counties and 67 different wind projects.  The study goes for 38 pages, concluding:  “Across all model specifications, we find no statistical evidence that home prices near wind turbines were affected in either the post-construction or post-announcement/preconstruction periods.”

No doubt the industry and its supporters like AWEA will be loudly broadcasting this result.  No doubt sympathetic reporters and politicians will pick up on the buzz and accept its conclusions with no further reflection, not to mention actually taking the time to read the study.  That’s a shame, as this latest Hoen study is just one more example of how statistics can be used to prove just about anything you want proved.  And the DOE really wants to prove that wind turbines don’t affect home prices.

Take the AWEA blog entry linked to above, titled “Wind Power Has No Effect On Property Values”.  Except that isn’t what the study actually said.  What Hoen said was they didn’t find any statistical evidence of any effect.  There is quite a bit of evidence in this study that home prices near turbines take a beating, but when the statisticians finish with it all that somehow disappears.

If we look at the pre-statistical-manipulation numbers we can get a pretty fair idea of the effects of wind turbines on prices.  Below is a snapshot of the first set of figures in Hoen’s Table 7 (page 25).hoen-2013-table-7aThe PA, PAPC and PC columns show the average prices as a nearby project moves from Pre-Announcement to Post-Construction.  As always, I’m interested in comparing the close-in properties (in this case, less than 1 mile) with the far-away properties (in this case, more than 3 miles).  Even before the project is formally announced (and I can tell you first hand that a project is known about well before any formal announcement) nearby home prices are 15% lower than their lucky neighbors.  Even worse, as time marches on and the project goes into operation the price difference widens to 28%.  These numbers just happen to be in the same ballpark as those quoted by McCann, Lansink and Sunak.

Hoen admits these price differences exist at the top of page 24, but goes on to say: “Both conclusions of adverse turbine effects, however, disregard other important differences between the homes, which vary over the periods and distances.”  And what would those important differences be?  His raw data includes just house square feet, acres and age.  The close-in houses have, on average, about 100 more square feet than the far-aways (roughly 1550 vs 1650).  They average 2 acres vs. somewhat less than 1.  They are older, averaging about 60 years vs 50.  Would you be willing to trade off an extra 10 years of age for a much larger lot and some extra space?  I think most people would, so there’s nothing in the raw data to explain the difference – except, of course, the elephant in the room, the turbines.  You could try to argue that maybe there’s some other problem with the close-ins.  Did I mention these data came from 9 states, 27 counties and 67 projects?  It is quite a stretch to propose something else, as even a landfill at every one of the close-in neighborhoods wouldn’t be enough (Hoen, page 4).  It seems that wind turbines are in a class of their own when it comes to decreasing house prices.

So how does Hoen ever get to the conclusion that he does?  I have to confess, the study is written in such a way that I can’t figure it out.  I’m reasonably literate and numerate for a layperson, and if I can’t figure it out it means one of two things.  Either it was written for a specific audience (in this case, professional statisticians) or it was meant to obfuscate.

Hoen tries to make it look like he tried to do an unbiased study, and maybe he did.  But to get from these raw data to his conclusions requires a lot of manipulations and there’s no way we can ferret out all of them.  As just one example, he pools the sales data from all 9 states, as disparate as Oklahoma and New Jersey.  Are properties in Oklahoma and New Jersey from the same population?  Of course not, but pooling them increases the Standard Deviation, making statistical significance more elusive – and NOT finding statistical significance is what his sponsor really wants.  When the raw data shows one thing and the conclusions show another I get suspicious, especially when the flip is in the sponsor’s interests.

Partly as a result of the pooling, Hoen’s average house price was (from Table 3 on page 21) $122,475 with a standard deviation of $80,367.  To get to “statistical significance” you generally have to get two populations (in this case, the close-ins and the far-aways) about 2 SD’s apart.  Clearly that won’t happen here, no matter how much the wind turbines affect the prices.  Finding a group of smaller apples on size alone is pretty hard work when you’ve pooled apples and peaches together.

I have no doubt that someone could take this same data and more convincingly make the opposite case, that wind turbines do affect house prices.  Unfortunately, we all know the payer calls the tune, and in this case Mr. Hoen appears to be making a living by faithfully playing the right tune.

Seven Threats

MSN recently had an article by Brian O’Connell, who mostly writes for MainStreet.  In it he listed 7 things that can drive the price of your home (or rental rates) down.  While the first thing he mentioned wasn’t wind turbines, it was pretty close – power plants.  He related that a study out of UC Berkeley found that living within 2 miles of a power plant decreased its value by 4 to 7 %.  And sometimes power plants aren’t nearly as bad as wind turbines.

O’Connell, 7 Neighborhood Threats to Your Home’s Value

O’Connell, backup link

Davis, UC Berkeley, The Effect of Power Plants on Local Housing Values and Rents

Davis, backup link

 

Lansink and Clear Creek

Ben Lansink is on a roll.  Earlier this month (October 2012) he published the first-ever case study on the effects of wind turbines on property values, based on 5 sales and resales in the Melancthon, Ontario area.  Not content with that, he has just published the second-ever case study on those effects, this time based on 7 sales/appraisals and resales  in the Clear Creek, Ontario area.  The results are depressingly similar, as related in the following (thankfully clickable) chart:

His study is 58 pages long and includes the supporting data from both areas.  For Clear Creek he eliminated (as he did in Melancthon) farm properties and properties with turbines on them.  Of these 7, 6 were homes and 1 was a vacant “bush” lot.  Two of the homes were sold well before the project went into operation and resold well afterwards.  The other five were appraised by MPAC, Ontario’s tax assessor, before the project and then resold on the open market after the project went into operation.

In the Melancthon study Lansink verified that the original sales to the developer were at reasonable market values; in Clear Creek no developer was involved so this step was unnecessary.  In desperation, the wind industry might try to argue that the MPAC assessments weren’t accurate but I wouldn’t hold my breath waiting for them to present any evidence to that effect.  These numbers are hard to refute.

When discussing property values, the wind industry seems fond of statistical significance.  In that spirit, I offer a quick recap of the 12 properties Lansink has studied.  The average decline was 36.99%, with a standard deviation of 12.26.  That calculates to 3.02 SD’s from zero – zero being what the wind industry is claiming.  That, in turn, translates to a 99.87% chance that the wind industry is WRONG.  I’m guessing that those are about the same odds that the wind industry will try to ignore this second very powerful study, and continue quoting the flawed and weak but more agreeable Hoen study.

Lansink on Property Values

There are 3 major techniques used to establish property values, in decreasing order of accuracy:  case studies, paired analysis (aka comps) and regression.  A case study looks at the same properties selling multiple times, a paired analysis uses a similar properties selling at the same time, and a regression study gathers data on all the sales in an area and attempts to figure out how different factors affect the price.

Ben Lansink is a professional Real Estate appraiser based in Ontario.  One of his areas of expertise is property value reductions.  Recently he published a case study [backup copy] containing 5 sales/resales of property in the Melancthon area. These 5 properties were purchased by the developer when it became clear that the previous owners seriously complained and subsequently resold to third parties with industry-protecting covenants on the titles.

The wind energy industry consistently claims that they have studies (i.e. Hoen) that show wind projects do not reduce property values.  Every study they quote uses the weakest technique, regression, to arrive at that conclusion, along with statistical significance.  I’ve written a number of critiques of these studies, all of which have significant problems.  Regression and statistical analysis, aside from being the least accurate, are also relatively easy to game to suit the sponsor, and gaming has been rampant.  Lansink is the first case study I’m aware of.  If the wind industry were serious in discovering the truth about the effect of their projects on real estate values they would adopt the findings of this study.  But when a man’s salary depends on him not understanding something…

As it turns out, I had posted on most of the same properties some time ago, so these reductions are not new news.  What Lansink’s report supplies is a more formal and complete analysis of the sales by a professional.

The study runs 76 pages, the majority of which is the documentation of the sales and resales of the 5 properties.  The summary chart is on page 62, and it tells you pretty much all you need to know (click to enlarge):

In my earlier posting I just had assumed that the sales and resales prices reflected the current market values at the times of the sales – mainly, that the developer made a fair market offer when buying the previous owners out.  Lansink takes the time to compare these sales prices to surrounding prices and finds that, indeed, the developers made what appear to be honest pre-project market value offers to the previous owners.  Since the resales were made on the open market there can be no doubt about their accuracy.  Additionally Lansink factors in the area’s general real estate price increases during the several-year interval between the sales and resales. As large as my numbers were, his are larger.

The result is a very robust study that in any sane world would be adopted by everybody who had an interest in an honest reckoning.  The consequences of having this fine study actually adopted (i.e. by the courts) are pretty painful for the industry.  I’m guessing that the industry will try to ignore it, and if/when forced to confront it, they will mumble something about Lansink being an anti-wind agitator who produces biased and anecdotal evidence.  Never mind that it is far stronger than everything they have been quoting for years now.

Will the Ontario government and legal systems care about this?  I’m guessing not.  Accepting inconvenient facts is not this government’s strong suit.  As I mentioned earlier, this isn’t exactly new news.  No sentient being should be surprised by this.  Unfortunately this government is determined to push these projects in no matter the harm to the neighbors.

Wolfe Island, Property Values and MPAC

Wolfe Island is located at the far eastern end of Lake Ontario and is traditionally considered to be the start of the St. Lawrence River and the Thousand Islands.  There is no doubt it is part of one of the loveliest areas in the world as well as an important area for birds.  No matter; there are now 86 wind turbines on the island’s western half.  For many of the residents on the island this project has been a disaster, and part of their response has been to ask for reductions in their tax assessments from MPAC, the folks who do the assessing for the province.

It is a one-sided contest.  The Kenney’s appeal is instructive.  It was the two of them against a small army of lawyers from MPAC as well as the government.  I understand that MPAC was willing to give them a reduction, but the sticking point was that the Kenneys wanted the wind turbines listed as a cause.   The wind industry (for obvious reasons) really wants to maintain the fiction that wind projects do not affect home prices and even a small breach in that fiction might cause the entire edifice to come tumbling down like the house of cards that it is. Continue reading Wolfe Island, Property Values and MPAC

A Nail for Hoen’s Coffin

Ben Hoen was the lead author of the LBNL studyThe Impact of Wind Power Projects on Residential Property Values in the United States: A Multi-Site Hedonic Analysis“.  This Hoen 2009 study remains the largest ever undertaken, with nearly 7500 properties.  It found:  “Specifically, neither the view of the wind facilities nor the distance of the home to those facilities is found to have any consistent, measurable, and statistically significant effect on home sales prices“.  Hoen is the primary study quoted by wind energy proponents to rebut the common-sense notion that the nuisance created by nearby wind turbines will lower home prices.  This study is not without its problems;  I’ve previously posted on some of them.

Recently I read over another property value study, this one from Germany, “The Impact of Wind Farms on Property Values: A Geographically Weighted Hedonic Pricing Model“, by Yasin Sunak, 2012.  The main purpose behind it was to study the effect of pooling together properties from different locations and running regressions against the entire pool, compared with analyzing different neighborhoods separately.  Many of the studies routinely quoted by proponents pooled their data.  As an example, Hoen’s 7500 properties came from within 10 miles of 24 projects in 9 states and were all pooled together before he did any analyses.  I’ll cover the pooling issues first, but this study contained additional results that are equally important.

Pooling Example

To give you an example of how this might be a problem I created the following simplified example.  Below are shown close-in (<2km) and more distant prices in two separate neighborhoods.  Neighborhood #1’s price averaged $100,000 and #2 averaged $300,000.  After the project goes in the close-in neighborhoods suffer a 25% decline in prices for homes within 2km of the project.

Given that the means in both neighborhoods are over 2 standard deviations apart,  no statistician would suggest that the turbines had no effect.  A t-test shows that the chance of either of these distributions occurring by chance is 5 in a million.  But if we simply combine (“pool”) the neighborhoods and then run the analysis we get quite a different answer:

This is the same data as before, with the same declines as before, but now the standard deviation is large enough that most statisticians (especially those who are serving the interests of their sponsors) would declare there is no statistically significant effect of wind turbines on home prices.

Although my sample just considered the effect of pooling on standard deviations, the same principle applies to the effects on regression analysis: similar computational techniques are used to calculate both.  Sunak provides an analogous regression example at the bottom of page 7, figure 2.

Sunak

Sunak’s study involved several neighborhoods around just one project in Germany.  Although it was just one project he was still working with a surprisingly large number of sales, slightly over 1400.  Even more surprising, he used just vacant residential lots, so any complications from the number of bathrooms etc are avoided.  This large a sample in this small an area is unprecedented, and provides what is arguably the most accurate assessment of the effects of wind turbines on property values to date.

Sunak first pooled all the sales and ran traditional regressions against them.  For Sunak’s purposes this was just a prelude, but his results are extraordinary.  Lot prices decreased 0.209% for every 1% closer a property was to the project, a result that was statistically significant.  Put another way, prices of lots within 2km of the project decreased an average of 25.2%, again statistically significant.  Another surprising result was that being subject to shadow flicker also reduced the price enough that it was almost statistically significant as well.

Sunak then tested these results for something called spatial autocorrelation and finds some amount of it, which indicates that prices are influenced not just by the distance to the project, but also by where they are.  He then runs the regression using Geographically Weighted Regression and finds that the spatial autocorrelation has decreased markedly.  The single result that was previously applied across the entire area was not indicative of what happened within each neighborhood.  Sunak writes: “The application of the GWR revealed a more complex picture of the influencing effects through the weighting of spatial relationships and local variations in the data. Based on local estimates, the GWR revealed negative, wind-farm-related effects that are attributable to strong local influences of the wind farm site. Therefore, predominantly biased by local clustering, global estimations are not appropriate in capturing the impacts of wind farm proximity on property prices.

 Conclusion

There were two major results of this study.  First, the practice of pooling sales data from disparate areas is inappropriate and would tend to downplay the effect of the wind turbines on prices.  In the case of Hoen, one has to wonder if he was aware of this effect and went ahead with the pooling anyway, knowing that doing so served the interests of his sponsor.  Second, when enough data is collected from one area the effect of wind turbines is unmistakeable and irrefutable.

My thanks to Michael McCann, who alerted me to this study, and whose own studies indicate a loss of, guess what, 25% (or more) for properties within 2 miles of a project.

CanWEA on Property Values

Last month the Canadian Wind Energy Association, CanWEA, had a press release concerning property values.  Their headline stated:

09/14/2011    Ontario farm property values hit record levels contrary to claims by opponents to wind energy

The press release goes on for about a page, extolling the virtues of wind energy (of course), but the essence of the release is contained in the second paragraph:

“It is promising to hear that the value of agricultural property has increased in most regions of Ontario, especially in areas like Chatham-Kent and Windsor-Essex where wind energy has also enjoyed significant growth over the past few years,” said Chris Forrest, vice-president of communications with CanWEA. “This would seem to run contrary to claims made by opponents that wind energy has decreased property values. Chatham-Kent, for example, has enjoyed a significant increase in farm land value in 2011 while also seeing several new wind farms come online.”

Read the headline and the quoted paragraph carefully.  Note the reference to farm land.  Nobody to my knowledge has ever made any statement one way or another on wind turbines effect on farm land prices.  So when CanWEA talks about “contrary to claims by opponents” they are lying.  There have been no claims by opponents to be contrary to.

What we opponents have vigorously claimed is that recreational and residential home prices are affected, and sometimes drastically so for the closer properties.  CanWEA’s press release is of course silent on these issues.  Actually, the ReMax study that CanWEA quoted  doesn’t provide enough information one way or the other to come to any conclusions about the effect of wind turbines on farm land prices either.

This kind of press release is absolutely typical for the wind energy industry – totally deceitful.  They take a snippet of information that is meaningless and wordsmith it around, making it sound like it is important, hoping to fool the foolish.  Unfortunately, there seem to be a lot of foolish around, and I’d bet this press release ends up being used as “proof” that the opponents are just nimby’s.

Links

CanWEA Press Release.

Re/Max Market Trends Farm Land 2011.

Actual buyout and resale figures.

My property values postings.

Derry Gardiner on Best and Highest Use, starting on slide 12.

 

Buyouts and Resales

I think it a moral imperative that if your industrial project causes significant problems for the neighbors you ought to buy them out. Unfortunately, morals are in short supply in the wind energy industry and there is a history of their fighting the neighbors tooth and nail before doing so. And even in doing so the old owners are always (as far as I can tell) contractually restricted from talking about the problems and the new neighbors (if the property is resold) are restricted from complaining about any aspect of the wind project’s operation.

In spite of the industry’s attempts to keep these buyouts and resales secret, land purchases are generally public records and usually include the price.  So far I have come across a total of six buyouts where presumably the original owner was paid something approximating a pre-project fair market value and the property was subsequently sold to a third party for the presumably new fair market value.

The industry has consistently maintained that their projects do not affect home prices.  If that were so, we’d expect there to be little change in the old and new fair market values.  When you stop laughing, below is a chart of the six resales.  The first two are from Somerset, Pennsylvania in 2002 and the last four are from Melancthon, Ontario in 2009 (click to enlarge).

Now, a sample size of 6 is pretty small, but what are the odds on all 6 moving in the same direction, and by such rather large amounts?  If you think wind turbines have no effect on house prices how do you explain these numbers?

The most common attempt is to say the recession (or something, anything else) caused it.  If that were true we’d expect the effect to be noticeable on house prices in general.  So what about Pennsylvania in 2002?  Here’s the trend chart from Trulia, with 2002 marked.

And what about Ontario in 2009?  Here’s some numbers from Scotia.

In neither case did the overall housing market decline during the periods where these transactions took place.  So much for the overall market decline theory.

These kind of unsupported and ultimately false assertions are typical of wind energy proponents’ claims, and not just with regard to property values.  They will say whatever they can think of to blame whatever the problem is on something other than their treasured wind turbines, with no regard to it being true.

Wolfe Island 2000 to 2010

For both property values and birds, islands make for good research locales.  There’s not much of any way for an agenda-driven “researcher” to fudge the area under study.  I’ve previously written about Wolfe Island (Ontario) and property sales over the last 2 years.  I’ve recently compiled the MLS residential sales from 2000 to 2010 and placed them on a series of Google Earth maps of the Island, one per year plus some accumulations.

In 2000 wind turbines on Wolfe were not on the horizon.  By 2002 some prospecting was likely going on, but there was still no widespread public knowledge of the potential.  By 2003 the projects were being mentioned in public meetings, maps were being drawn and studies initiated.  In 2004 through 2006 the size of the project moved up and down, from a start of 24 turbines to proposals of 150, placed on both the eastern and western halves.  By 2007 the final 86 turbine number was set and the eastern half was spared.  Construction started in 2008 and they went operational in 2009.

Each of the pictures below is clickable if you want the full size.  You can also get my kml file if you want – just ask.  I’ve driven the western half of Wolfe so the locations of those properties should be accurate.  I didn’t drive the eastern half and so many of those locations are approximate, and a handful are guesses.  If anyone out there notices a wrong location please let me know.  There was a lot of typing and pasting in prepping these pictures and mistakes do slip in.

At this time I’ll just be posting the pictures and I’ll let you make your own conclusions.  I’ve got some more research to do and then I’ll be posting my conclusions.  One thing you cannot say is that they had no effect.

2000

Wolfe Island 2000

2001

Wolfe Island 2001

2002

Wolfe Island 2002

2003

Wolfe Island 2003

2004

Wolfe Island 2004

2005

Wolfe Island 2005

2006

Wolfe Island 2006

2007

Wolfe Island 2007

2008

Wolfe Island 2008

2009

Wolfe Island 2009

2010

Wolfe Island 2010

2000-2002 (before turbines known about)

Wolfe Island 2000-2002

2003-2010 (after turbines known about)

Wolfe Island 2003-2010

Wolfe Island 2000-2010.  The entire picture, including the turbines.

The entire picture for Wolf Island

Wolfe Island Property Sales

As I’ve mentioned in other places, the issue of property values around wind projects has been a contentious one.  The wind industry trots out its favorable reports that show no loss of value, and I (among others) demonstrate how flawed and biased these reports are.  Since I live part-time on Amherst Island, which is right next to Wolfe, I’ve been following Wolfe very closely, and not just property values.

One theme I’ve heard over the last few years is that the tax appraisers (MPAC) have not reduced the evaluations on Wolfe because there have been no sales.  This seems to be incorrect, as a search of the MLS sales data base reveals 23 properties that have been sold on WI in 2009 and 2010.  From everything I’ve heard MPAC is pretty dysfunctional, so what they end up doing with WI evaluations is anybody’s guess.  But I thought it would be worthwhile to take a look to see what properties ended up selling.

There are two parts to this posting.  First is showing a picture of Wolfe with the turbines and sold properties displayed.  Thanks to Earth Google that can be done with a high degree of accuracy. By the way, if anyone wants my kml file I can send it – let me know.   Second is comparing the sold properties with the original WI noise study that was done before the project was built.

The Pictures

Here’s the picture of Wolfe with the locations of the 86 turbines displayed, which can be enlarged by clicking on it.  Note that they are all on the western half of the island.

To that picture we now add the sales for 2009 and 2010.  The identifiers are the year sold and the last 4 digits of the MLS number.

Noise Study

It is pretty obvious that there have been very few sales among the turbines.  I went back and compared the sales with the noise study prepared by the developer’s consultants (28mb, be careful downloading!). There are approximately 600 residential properties in the western half of WI.  Of these, 280 were located within 1000m of a turbine and were thus included in the noise study.  Of the 600 properties a total of 11 were sold during these two years.  Of those 11 just 3 were within the 1000m boundary.  In other words, out of 280 properties within 1000m only 3 have sold in the last two years.  Out of the remaining 320 properties 8 have sold.  I don’t know how many properties there are on the eastern half of the island – I’d guess less than 600 – and of those 12 have sold.  The following table shows the figures.  Note that I didn’t bother driving all the way to the end of the eastern half to verify their locations, so I didn’t present any hard distances.

The distances are in meters, and the first 11 rows are all in the western half.

I did run some ratios on distance vs. price vs. tax rates, but clearly this sample is too small to prove anything one way or another.  There’s no trends either way.  Certainly there’s not enough data to even entertain doing any regression analysis.

I plan on continuing to explore WI’s sales.  If I can get data from 2000 on it might be instructive, as I could show pictures with the sales year-by-year as the project developed.  Stay tuned.

Iberdrola Knows Property Values

The wind energy industry has long maintained that there are no losses in property values around wind energy projects, and have produced several studies that claim to support that.  Unfortunately even a cursory look at these studies reveals fatal (and in my opinion, intentional) flaws.

If the wind industry actually believed their propaganda on this issue, you’d think they’d be willing to guarantee either compensation or buyouts for the neighbors.  After all, it shouldn’t cost them very much and would go a long way to eliminate almost all the opposition.  But as we all know, actions speak louder than words.  Iberdrola, the large Spanish wind energy company, wanted to build a project in Hammond, upstate NY.  The council there, as part of the approval, proposed such a guarantee to protect their residents.  No doubt you can guess Iberdrola’s reaction.  Read the story below.

Original Link.

Backup Link.

Or Live by a Nuclear Plant?

When discussing the effects of living near a wind project, proponents often ask “Would you rather live next to a nuclear plant?”  Of course, that’s not the real choice.  To replace a typical nuclear plant you’d have to have something like 7,000 turbines that would stretch 20 miles in all directions plus maybe 5 typical gas plants for when the wind didn’t blow.

The same question is often asked with a coal plant replacing the nuclear plant.  In an earlier post, Rather Live by a Coal Plant?, I presented a case where a family that found itself in that situation would far rather live by the coal plant than next to wind turbines.

Now the residents of Hinkley Point in the UK have shown where their preference lies.  They’ve lived next to the nuclear plant there for 20 years, but they want no part of a proposed wind project built next to the nuke.  Proponents apparently cannot accept just how disruptive their wind turbines are when compared to coal and nukes and just about anything else.

Backup Link.

Rather Live by a Coal Plant?

In many public discussions about wind turbines and the nuisance they create there seems to be a variant of Godwin’s law: the longer the discussion goes on, the closer to 1.0 the odds on someone mentioning living next to a coal plant. From Nova Scotia there was this item [backup link] about the Frasers, who are having tremendous problems with seven wind turbines built too close to their house. No new news about that, but what interested me was that they also lived not just next to the turbines, but also next to the existing coal plant. They’d been happily living next to the coal plant for some years, even built their house there after that plant was there. But the wind turbines proved too much.

I know this is just one data point, but here’s some actual evidence that one family would rather live by a coal plant than by wind turbines.

McCann Testimony

Michael McCann is a professional real estate appraiser who works out of Illinois.  He’s been in the business for many years and during that time has given expert testimony in hundreds of cases.  Recently he gave testimony to the Adams County (Illinois) Board, which was considering zoning rules for wind projects. As the construction of new wind energy projects has continued, the evidence of all the problems – noise, property value declines, health, wildlife – has continued to build up as well.  For those of us who have been watching this for the last several years it seems as though no matter what facts are brought to the table, the building continues.  Mr. McCann, like many of us, has sharpened his writings to reflect his growing disbelief that so many people seem to have lost their senses.  This testimony, while still professional, is pretty sharply written, and is well worth a read.  His basic message is that prices drop anywhere from 25% to 40% within 2 miles of a project, and within the project they can drop to zero.

Continue reading McCann Testimony