Measurement Model

The comparable sales method used by Orava Residential REIT is typically used for appraising apartments when they are being sold as individual apartments. The fair value of the Residential REIT’s portfolio is determined through a mass appraisal system using multi-variable regression based on asking price and purchase price material.

Ovaro Kiinteistösijoitus will no longer use this model after 2018 but will allow an external party to evaluate the value of the property portfolio.




The material consists of details of actual sales, supplied by estate agents, mainly concerning properties in the vicinity of properties owned by the REIT, of the sales prices of apartments sold by the REIT, as well as of the sales advertisements of apartments in the service part of the Sanoma Group. is the largest portal in Finland advertising apartments for sale, and the service contains, besides the advertisements of estate agents, also those submitted by private individuals. The sales advertisement material is received continuously from Oikotie in electronic format. According to the International Valuation Standards, when a market appraisal is made, the information used should be freely available and generally used in decision making. The benefit from using asking price material is that it is up to date and that all market parties can easily utilise it.


Inspection and enrichment of the material

When the measurement model is prepared, the material is checked and any data found to be clearly erroneous is corrected to or omitted. If the actual transaction price is available, the asking price is replaced with the transaction price, which is increased by the bargaining range estimated for the time of the transaction. The asking prices for the company’s own apartments that currently are, or previously have been, for sale are not used in the appraisal.


Bargaining range

Apartment asking prices typically include a bargaining range; in other words, sellers set their asking prices at a level that is higher than the lowest price at which the seller would be ready to conclude the transaction. The bargaining range must be taken into account as a reducing factor when determining the fair value – i.e. the expected transaction price.

The bargaining ranges used for determining the fair values are calculated quarterly, using 24-month data, as a function of the logarithm of population. The bargaining range function regarding population is calculated from the averages of factors of the four last quarterly estimations. The observations from the service are transferred in time for approximately two months, corresponding to the apartment type -specific marketing time of the (“old”) market analysis tool of the service. The same square meter price outlier cutoffs as used by Statistics Finland are also applied to the asking price material.

The postcode-specific price statistics of Statistics Finland and the material from the service are used to calculate, weighted with the numbers of observations, the average square meter prices in cities with over 15,000 inhabitants. The thus obtained average square meter prices are formed into price ratios, as the quotient of asking prices obtained from the service and transaction prices obtained from Statistics Finland. The bargaining range function is determined by explaining the bargaining range derived from the price ratio with the logarithm of the city’s population and a constant term. The quotients considered outlier observations are omitted when estimating the bargaining range.

It is case in apartments specifically built for rental use that the quality of the materials and the level of equipment is somewhat lower than in apartments built for owner-occupied housing. The property effect is estimated statistically each month by explaining the property dummies estimated for rental buildings as a function of their age in connection with the 12 latest monthly appraisals. Extrapolation is not allowed for the oldest properties, and the property dummy estimate corresponding to the oldest observation used in the estimation is used for estimating the properties older than that. Extrapolation is allowed for the latest observations. The property dummy used in the estimation is defined as an outlier, if its absolute value exceeds 0.35. The reduction factor is calculated separately for newer and older properties, and it is used in the appraisal of properties built as tenement buildings of which there are no price observations and where the share of ownership is over 50%.


Econometric model

The econometric model explaining the asking prices for apartments is estimated with the least squares method using the software application Gretl; the version currently in use is 2017c, build date 2017-07-18. The measurement model is continuously developed.

P = debt-free sales price

SIZE = floor area of the apartment

AGE = age of the building (= date of estimate – the time of completion of the building with one-month accuracy); if the month of completion is not known, the end of June is used

FL = the floor the apartment is on. If the model has less than 750 observed values, a model specification without the floor is used. If the apartment to be appraised is located higher than the apartment on the highest floor in the material, the effect is limited to the highest floor in the material.
D = a dummy variable which receives a value of 1 when the information indicated in the subscript is true and otherwise a value of 0
Condition: A variable with a category scale (four different values), divided into dummy variables. The reference category is “condition = good”. If condition = excellent –the factor has a negative sign; it is replaced by zero in the measurement. If condition = poor –the factor is given a higher value than the factor for condition = satisfactory; the factor for satisfactory condition is used for the measurement of apartments in less than satisfactory condition. Basically, the condition of an individual apartment in the measurement is based on the housing manager’s and the occupant’s opinions on the condition of the apartment. If the information is missing, the average for the property is used, and if that is not available, the age function of condition, derived from the asking price material of the service (3rd degree polynomial) is used by increasing its gradient by taking into account the assumption that wear and tear progresses faster in rental apartments than in the material used in the estimation consisting predominantly of owner-occupied apartments.
Sauna: A dummy variable that receives the value 1 when there is a sauna in the apartment
• Leased plot = a dummy variable that receives the value 1 when the building is on a leased plot The variable is left out of the model, if there are fewer than 15 leased plot observations or if its coefficient is positive. If the leased plot variable is missing from the model due to the above reasons, the model is expanded to cover all types of real estate properties (the building is an apartment block), or alternatively the area being analysed is expanded to cover the largest neighbouring municipality which has a population closest to that of the municipality where the building is located.

TD = time dummy that receives the value 1 when the observation is in one of the eight 3-month periods (data 24 months = 8*3 months). The latest 3-month period is the reference group. The dummy variable of the reference group is left out of the model.

ZIP = postal code area dummy that receives the value 1 when the property to be measured is in the postal code area indicated by the dummy

SQKM = dummy variable that receives the value 1 when the observation is inside the one square kilometre area (4 square kilometers if there are less than 15 properties in the one square kilometre area) around the property – the variable is used for assessing the impacts the micro-location inside the postal code area

LAT; LON = =the latitude and longitude coordinates of the property which are multiplied by the 1km2/4km2 dummy; these variables multiplied by the NKM dummy tilt the level set up by the NKM dummy so that the effect of properties in the nearby areas can be better taken into account

PROPERTY = a dummy variable that receives the value of 1 when the observation is located at the same address with the property to be appraised. If the property includes different housing types (apartment blocks vs. terraced and detached houses), a separate dummy can be set for each type. The same applies to situations where there are buildings of clearly different ages on the property. Several dummies can also be used for them. Furthermore, there are properties where the housing companies cannot be accurately distinguished from each other on the basis of the street address (for example in Vaasa, Myllykatu 11 A, B and C are different housing companies, but in the material, all have the address Myllykatu 11): In that case, the same dummy can be set for different housing companies

APARTMENT = a dummy variable which receives a value of 1 when the observation concerns the apartment being appraised, or if the observation is a transaction observation regarding an apartment in the same property

BUILDING TYPE: a dummy variable that receives the value 1 when the observation concerns a certain type of building (such as terraced house, semi-detached house, detached house, balcony-access house…). The reference group is “apartment block” When the apartment block model is used, the building type variable is of no significance, because properties other than apartment blocks have been eliminated from the observation material. The prices of single-family houses do not follow precisely the same factors as the prices of housing company apartments. The house observations irrelevant for the properties to be estimated [detached houses, semi-detached houses, …] are eliminated from the material. The properties to be eliminated are detached houses built before 1990 and detached houses with a plot bigger than 1,500 square meters.