Investment properties 30 September 2016
|City||No of properties||Apartments and business facilities||Floor area(m2)||Valuation (EUR 1,000)|
|Helsingin Region||38||441||32 474||80 307|
|Vantaa||8||155||10 434||18 785|
|Kirkkonummi||3||103||6 884||14 268|
|Kerava||2||44||4 284||10 330|
|Helsinki||6||34||2 438||10 185|
|Järvenpää||3||21||1 728||5 857|
|Helsinki Region, others||16||84||6 707||20 882|
|Major cities||40||401||26 251||65 273|
|Lahti||9||123||7 339||17 899|
|Tampere||6||42||2 807||8 961|
|Raisio||4||75||4 417||7 926|
|Jyväskylä||6||43||4 058||7 876|
|Turku||5||22||1 523||7 094|
|Large urban centres, others||10||96||6 108||15 518|
|Medium-size towns||46||870||50 909||65 099|
|Hämeenlinna||2||63||3 084||8 648|
|Kotka||5||189||10 520||7 081|
|Rovaniemi||6||90||5 317||6 997|
|Porvoo||3||44||2 843||6 592|
|Pori||3||61||3 655||5 059|
|Medium-size towns, others||27||423||25 492||30 722|
|Yhteensä||124||1712||109 634||210 679|
An external appraiser provides a calculation of the value of the investment properties owned by Orava Residential REIT every six months. The aggregate appraisal of the value of the investment properties provided by the external appraisers Realia Management Oy, Jones Lang LaSalle and Turun seudun OP-Kiinteistökeskus on 31 December 2015 was 1.0% lower than the fair value of the statement of financial position on 31 December 2015.
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.
The main material used is housing sales advertisements from the Sanoma Group’s Oikotie.fi service. The advertisements are received continuously, directly from Oikotie in electronic format. Oikotie.fi is one of Finland’s largest housing sales advertisement portals, and its service includes listings from both estate agents and private individuals. In addition, the material includes information on realised sales mainly close to the properties owned by the Residential REIT, delivered by estate agents, and sales information on the apartments sold by the Residential REIT. 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 valuation model is prepared, the material is examined and any clearly erroneous information detected is adjusted by means of manual imputation. If a realised 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 for sale are not used in the appraisal.
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 in the determination of the fair value – i.e. the expected transaction price. A typical rule of thumb for the realised bargaining range is approximately 5–10%. When fair values are determined for the Residential REIT, the bargaining range is estimated by comparing the average prices on the Oikotie.fi service to the postcode-specific average prices from Statistics Finland collected for the latest quarter and taking the arithmetic average value of these average values separately for towns with more than and fewer than 100,000 inhabitants. The asking price data for both town types is postponed by approximately two months (corresponding to the average marketing period by apartment type) in relation to the material of Statistics Finland. The estimated bargaining range used in the appraisal for the first quarter of 2015 was 4.93% for cities and large towns and 6.27% for small towns.
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 1.10.1. The following delimitations were used:
– building type = ‘apartment block’ for apartment blocks and ‘all building types’ for terraced houses and gallery access blocks
– taloluokka = omistusasunto (eli ei-vuokratalo) tai uuskohde
– building class = owner-occupied apartment (i.e. non-rental building) or new property
– form of housing = owner-occupied apartment OR new property
– the advertisement was submitted at most 24 months before the end of the appraisal month
– EUR 400/m2 < debt-free square metre price < EUR 10,000/m2 – 10 m2 < apartment floor area < 300 m2 - – -2 years < age of the building < 150 years The primary criterion for determining the variables, delimitations and function form is the standard deviation of the remainder terms, and attempts are made to minimise it. The measurement model is continuously developed.
P = debtfree price
SIZE = apartment floorspace
AGE = age of the building (= current year – reported build year)
D = dummy variable, which gets value 1, when information in subscript is true and otherwise value 0
• Condition = …: apartment condition, categorical variable getting four distinct values divided into dummies. ‘Condition = good’ is reference group.
• Sauna: binary variable, getting value 1 when there’s a sauna in the apartment
• Leasehold: binary variable , getting value 1 for a leasehold property. The variable is excluded from the model if there are less than 15 observations with leasehold property, or if the coefficient is positive. If the leasehold variable is excluded, but the property to be valued is leasehold, then ’all property types’ –dataset is used (for apartment buildings) or the geographical area for data selection is expanded to include largest adjunct city.
TD = time dummy, which gets value 1 if an observation in the dataset belongs to a specific 3 month time period in the data. The data consists of 8 such time periods (8*3months = 24monts), of which most recent period is a reference group (i.e. excluded from the model).
ZIP = location dummy, which gets value 1 if an observation in the dataset is situated on specific zip code
SQKM = location dummy, which gets value 1 if an observation is situated within one square km area surrounding the property to be valued (4 sqkm, if there are less than 15 distinct properties within 1 sqkm area) – variable is used in interaction with latitude and longitude variables to assess effect of the microlocation to prices
LAT; LON = latitude and longitude coordinates of the property to be valued, these are multiplied separately with variable SQKM
PROPERTY = dummy variable, which gets value 1 if an observation is situated in the property to be valued
APARTMENT = dummy variable, which gets value 1 if an observation is from an apartment to be valued
BUILDINGTYPE = dummy variable, which gets value 1, when an observation belongs to a specific building type (detached, semi-detached, …). Reference group (value=0) is apartment house. When valuing apartment houses the variables are insignificant