Critique of Part 1

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.

A Wolfe Island couple, the Kenney’s, 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

Critique of Part 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.

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.

Critique of the Lansink hatchet job

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.

PROFESSIONAL QUALIFICATIONS

Completed the Ecogift Training Workshop, July 2001, for the valuation of Ecological Gifts as has
been established by Environment Canada.

Successfully completed the ‘Certificate Program’ in real estate as presented by The Ontario Real
Estate Association.

Licensed under the Ontario Real Estate and Business Brokers Act in 1968 and as a Broker, November
17, 1986, Registration No. 1914433.

Licensed under the Ontario Real Estate and Business Brokers Act in 1968 and as a Broker, November
17, 1986, Registration No. 1914433.

EXPERIENCE

Appraisal assignments have included:

• Airports; harbours; cemeteries; funeral homes;
• Institutional buildings; hospitals; police stations; schools; and churches;
• Office towers; industrial properties;
• Retail commercial properties; hotels; motels;
• Mobile home/trailer parks; marinas; golf courses;
• Farms and farm land; intensive farms (e.g. pork; dairy; chicken; mushroom)
• Land,
  • Ecologically sensitive; parks and conservation; aggregate resource;
  • Commercial/industrial/residential land;
  • Building lots – on water and not on water;
  • Ecologically sensitive; parks and conservation; aggregate resource;
  • Landfill sites;
  • First Nation reserve lands;
• rights-of-way; easements; market rent studies; rail corridors; pipelines; and
• other property types.

Assignments have been carried out in Ontario, Quebec, Manitoba, Saskatchewan, the Caribbean, the
State of Utah, and New Zealand.

MEMBERSHIP IN PROFESSIONAL ASSOCIATIONS

Appraisal Institute of Canada since 1972:

AACI, P.App:
Accredited Appraiser Canadian Institute, May 1982

Royal Institution of Chartered Surveyors since November 2009:

MRICS:
Member Royal Institution of Chartered Surveyors, November 2009

PARTICIPATION IN PROFESSIONAL ASSOCIATIONS

London and St. Thomas Association of Realtors®, Member, Education Committee 1987, 1988, and 1989
(Chairperson 1988); Board Director 1989 and 1990; Member, Ethics Committee 1989; Member, Ethics
Appeal Committee 1990.

Member of the Investigating Committee (1989 – 1992) and the Adjudicating Committee (1992 – 1995) of
the Appraisal Institute of Canada, Ontario Association. These committees addressed public complaints
against members of the Appraisal Institute of Canada, Ontario Association.

Local Architectural Conservation Advisory Committee (LACAC) 1990-1992, appointed by London City
Council to serve as a member at large.

Assessment Review Board of Ontario, a Land Tribunal: Member May 14, 1992 to May 14, 1998. Appointed
by an Order in Council, as recommended by the Attorney General, approved and ordered May 14, 1992
and again on May 14, 1995 by the Lieutenant Governor of Ontario.

PUBLICATIONS AND STUDIES by Ben Lansink

TEACHING

Taught the Assessment Program at Fanshawe College, London, 1980s.

Certified instructor, Courses 1101 and 1102, Appraisal Institute of Canada, 1980s until the end of
the 1990s.

Note: Appraisal Institute of Canada and the University of British
Columbia’s Sauder School of Business are now partners in offering a program designed for people
seeking to join the real property valuation profession–and for valuation practitioners already
employed in the industry who want to upgrade their education. The University of Guelph, Athabasca
University, L’Université du Québec à Montréal; Seneca College and Langara College also offer courses
recognized by the Appraisal Institute of Canada.

Certified instructor for the subject “The Principles of Mortgage Financing” for Ontario colleges as
administered by The Ontario Real Estate Association, 1975-1985.

Course instructor, Introduction to Appraisals, 1995-1996, Indian and Northern Affairs Canada.

CONTINUING EDUCATION, CONFERENCES

Ben Lansink is a member of the OEA and participates in its Seminars / Conferences.

Ben Lansink is a member of the OEA and participates in its Seminars / Conferences.

Ben Lansink attends most of the yearly conferences of the Appraisal Institute of Canada and its
Annual General Meetings.

EXPERT WITNESS EXPERIENCE

The Federal Court of Justice has jurisdiction over criminal, civil, and
family cases, and is the
largest superior trial court in Canada. The Divisional Court, Small Claims Court, and Family Court
are all branches of the Superior Court of Justice.The Superior Court of Justice has jurisdiction
over criminal, civil, and family cases, and is the largest superior trial court in Canada. The
Divisional Court, Small Claims Court, and Family Court are all branches of the Superior Court of
Justice.

Environment and Land Tribunals Ontario brings together five Ontario tribunals and boards which
adjudicate matters related to land use planning, environmental and heritage protection, property
assessment, land valuation and other matters. The land tribunals are the
Assessment Review Board,
the Board of Negotiation, and the Ontario Municipal Board.

The Federal Court. is Canada’s national trial court which hears and
decides legal disputes arising in
the federal domain, including claims against the Government of Canada, civil suits in
federally-regulated areas and challenges to the decisions of federal tribunals.

Over the years Mr. Lansink has been accepted as an expert witness to assist the
Superior Court of
Justice, the Land Tribunals, and the Federal Court.