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The valuation of difficult properties

Canadian Property Valuation Magazine

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2020 – Volume 64 – Book 4
The valuation of difficult properties
George Canning, AACI, P.APP

The valuation of difficult properties

By George Canning, AACI, P.APP

This article deals with valuing properties that are unique, not because of their specific use, but because of their history and architectural appeal.

The former Carnegie Library is located in Ingersoll, Ontario, which had a population of 13,000 at the date of valuation. It was one of 125 Carnegie libraries built in Canada with funds from the Andrew Carnegie Foundation in the US. The building is a testimony to the awareness of the role of libraries and educational institutions in combatting illiteracy. As well as being a renowned library, it is interesting to note that the building housed Canada’s first adult art class. 

The building is 119 years old and is historically designated. It is a raised single floor of approximately 4,200 square feet. Almost perfectly square in design, it has a thrust front entrance with a decorative central arch. The corners are jetted to form a column with an iconic capital at the top. The middle support buttresses have a similar capital. The upper part of the building has a band course with some decorative bracketing. The front and side windows have a brick arch with a top medallion and termination dripstones. The renovations and changes to the building have carefully preserved its architecture and increased the overall utility by opening up the lower level. 

The building was purchased in February 2018 and subsequently renovated to provide upper-end quality space for a wide variety of venues. There are no other facilities in Ingersoll similar to this building and the only comparable structure is located 5 km to the south. 

The zoning of the property shows a wide variety of potential uses. The properties surrounding the building consist of commercially orientated space, some of which have trade names, while others are commonly found in the marketplace.

Since the opening of the facility, it has been well received by the general public, with several weddings and social events booked. Unfortunately, there are no historical income and expense statements that a valuer could use for an income approach.

A cost approach is out of the question for two reasons. First, these properties are not bought and sold based on a depreciated building value or the land component. Second, there are no land sales in the core area of Ingersoll to support any type of land value. Because of this, the only possible solution to the value of the property is the direct comparison approach.

Regardless of the type of commercial property to be appraised, it is important to determine the general trends in the local community. This can be accomplished by doing four things:

(1) Since the subject property is located in the core area, walk the downtown core and observe the amount of vacant space. In our case, it was 10%, which is not out of line with communities the size of Ingersoll. 

(2) Analyze the dollar value of building permits issued by the town over the last five to 10 years. Ingersoll is in a positive growth cycle, particularly in the development of residential housing. Commercial development has fluctuated up and down, which is typical of smaller communities like Ingersoll. 

(3) Complete a time plot of all one-floor commercial properties that sold in the community over the last 10 to 20 years. This provides a good view of market activity and, more importantly, its direction. 

(4) Determine the economic strength of the community by viewing the types of local industries as well as other factors such as highway access. For example, Ingersoll is relatively close to Highway 401 and the 2,500-employee CAMI automotive plant is located in the community. 

The purpose of gathering this information is to determine the strength of the commercial market in which the valuer is working rather than focusing on the unique commercial property being appraised. The goal is to establish a baseline for the subject property relative to the general composition of its marketplace as a commercial building.

Since marketability is a factor that needs to be considered in valuing unique properties, an appraiser needs to consider the possible market arrangements of the property. (See Table 1)

                               Table 1

Perfect competitionmanymany
Bilateral monopolymanyone
Bilateral oligopolyfewfew

It also helps to examine the possible uses of the property relative to the zoning bylaw. Another aspect is to lay out the search parameters for similar comparable sales for the direct comparison approach. Table 2 reviews the possible uses of the property under Ingersoll’s current zoning bylaw.

          Table 2

Apartment buildingNOMultiple unit dwellingNO
Boarding or lodging houseNOSpecial needs homeNO
Dwelling unit converted to 4 unitsNOAmusement arcadeNO
Dwelling unit in the upper portion of a non-residential buildingNOAssembly hallNO
Group homeNOAutomobile service stationNO
Home occupationNOBank or financial institutionNO
Long-term care facilityNOBar or tavernYES
Billiard or pool hallNOConvenience storeNO
Bowling alleyNODaycare centreNO
Bus stationNODrycleaning establishmentNO
Business or professional officeYESEating establishmentYES
Building supply storeNOFitness clubNO
Business service establishmentYESFuneral homeNO
Commercial schoolNOGovernment administrative officeNO
Hotel or motelNOMicrobreweryNO
LaundromatNOMotor vehicle dealershipNO
Medical centreYESParking lotNO
Personal service establishmentYESPlace of worshipYES
Printing companyNORecreation buildingNO
Public garageNORetail storeYES
Public libraryNOService shopNO
Public useNOTheatre or cinemaNO
Veterinary clinicNOWholesale outletNO

Conducting this exercise showed that, relative to the many uses within the zoning bylaw, only a few potential uses were going to emerge. This means that the marketing is going to be ‘thin’ unless a potential purchaser is able to secure rezoning.

Attention then needed to be turned to identifying sales for the direct comparison approach. This was a daunting task that required reviewing over 2,000 sales of commercial properties throughout Southwestern Ontario. This was accomplished by using MPAC property line records and methodically going through each sale picture on the computer. Table 3 shows the counties searched.

Table 3


MPAC categorizes sales activity by a given classification. The problem is that not all sales are placed in the right classification. To find the appropriate sales, a variety of real estate product classifications were searched. (See Table 4)

Table 4

400Small office building under 7,500 square feet
401Small medical/dental under 7,500
409Retail one story generally over 10,000 square feet
410Retail one story generally under 10,000 square feet
411Restaurant conventional
432Banks and similar institutions generally less than 7,500 square feet
441Tavern/public house
445Limited service hotel
462Country Inns, small inns
470Multiplex consisting of retail/offices and other uses greater than 10,000 square feet
471Same as above less 10,000 square feet
473Retail with more than 1 non-retail use
477Retail with offices less than 10,000 square feet. Offices could be on the second floor
499Unspecified commercial property
600All institutional properties not specifically defined
610Other educational institutions
731Library and/or literary institutions
734Banquet hall
735Assembly hall/community hall
736Clubs, private, fraternity
800Special purpose
832Government historical building or monument

Results of the search

After searching through the sales data, there were about 35 observations of properties that were improved with a unique or different building. They were chosen because of architectural interest, original building construction dates, and differences in overall building design and use. The possible number of sales selected represented 1.5% of the entire possible dataset. Of this latter group, there were only about six indexes that could be considered as comparable sales. 

The search tells us that properties with unique architectural features that were used as some type of institutional property in the past do not trade very frequently. Certainly, an arguable point is that there are not many of these types of properties in the marketplace. The other statement that could be made is that these properties are present in the marketplace, but they have not traded. This means that they are viable entities and have market acceptance. However, at the end of the day, we are seeing a limited number of sales.

The reason why we do not see many observations of these sales in the marketplace is that they require a large capital input to bring the buildings up to a modern and unique standard. Therefore, there is only a limited number of buyers that wish to preserve and modernize these types of buildings. As well, there is a limited number of buyers who want to purchase these buildings after they have been renovated and modernized. We did not condition on any specific sale price range when we searched for comparable sales.

The following schedules indicate the indexes chosen to provide evidence as to the subject property’s value by direct comparison. These indexes represent the entire population of sales data, even though they are drawn out of a common pool. 

Analysis of the data

The differences between the low and high selling prices per square foot of building, inclusive of the land, was 80%. This was unexpected, since we anticipated a much larger spread going into the analysis given the differences in the sales in terms of building size, location, etc. There were no other possible sales to use in the analysis from the database.

The key to the direct comparison puzzle was the selection of the predictor variables that would aid the valuer in both reducing and explaining the 80% difference in the spread of the comparable sale prices. Fortunately, we elected to use quality point as an analytical tool in the direct comparison approach. 

The predictor variables found to be the most effective were location, building size, architecture, condition, and lot size. The predictor variables that were not effective were historical designation, zoning, parking, and age of building. It was noted that time was not a factor, since we tried various annual time adjustments and there was no response to the coefficient of variance (%) which is centered on the mean adjusted selling price per square foot of building (main and basement).

For the sake of brevity, we found that the quality point analysis reduced the variation in the overall selling price per square foot of building from 80% to 6%. This was only accomplished by selecting the correct predictor variables, scoring the variables using an ordinal scale of 1-4-9-16-25-36-49, and using a built-in solver found in excel. All of which was the quality point platform for analysis. This solver was essential in establishing the weights for each predictor variable. It was interesting to note that the weights allocated to the predictor variables told an important story regarding the processes of reducing variation in the selling prices of the comparables. (See Table 5)

Table 5

Building size11%
Lot Size8%

The dominating predictor variables in our case were the architecture of the buildings on the sale properties and their condition. Building size also returned a fairly high weight in explaining the differences in price. The analysis of the data may indicate that, in future valuations of unique properties, these could be potential predictor variables that might help to explain and reduce the selling price of the unique property comparables.


Being presented with valuing unique properties, particularly ones with an interesting history and improved with buildings possessing good architectural detail, is a formidable task. It is easy to get overwhelmed with the assignment. To alleviate this burden, it is important to break the appraisal challenge into manageable parts. Doing so will lead the real estate practitioner to the right valuation conclusion. 

Many real estate practitioners do not look beyond the borders of the actual geographical location of the unique property at hand. Location (5%) was not a significant factor in the analysis of our dataset. Therefore, search parameters should include as many similar counties or regions that are located in relatively close proximity. Do not go with the first set of sales that you see. Create a selection of the potential sales and sort them into three categories: very comparable, somewhat comparable, and least comparable.

Finally, isolate the market arrangement of the unique property at hand. This will aid the real estate practitioner in the justification of the end value only after completing ‘good on-the-ground research.’ The use of quality point analysis was a significant tool in testing for the correct predictor variables and to prove one’s adjustments or decisions regarding comparable sales. It does this two ways. First, by observing the COV% (coefficient of variance) around the mean of the adjusted unit of comparison. The lower the number from the ‘going in’ differences of the sales (80%) indicates that the valuer is on the right track. Second, quality point can use the allocated scores of the predictor variables to predict the actual selling price of each sale. This predicted price is compared to the actual price to measure the residuals. In this analysis, the residuals were between 2.0% and 9.16%, with an average of 5.08%. In other words, this valuer was not far offin the selection of predictor variances and their allocated scores. 

As a footnote, the owners of the building have hired a management company to run the facility so that they can attend to other real estate matters.