# Quality Description Definitions: Rents of Dwellings

__1. Relevance____2. Methodological description____3. Correctness and accuracy of the data____4. Timeliness and promptness of the published data____5. Accessibility and transparency of the data____6. Comparability of the statistics____7. Coherence and consistency__

## 1. Relevance

The annual statistics on rents describe the annual levels and changes of rents for the rental dwelling stock. The statistics contain data on rents for the reference year classified by area, type of financing, number of rooms and year of construction. The statistics also include information about the development of rents over a longer time span.

The purpose of the statistics on rents is to provide information on trends on the rental markets for all those interested in them.

*The data and the data suppliers*

The data of the annual statistics on rents are compiled from the interview data collected in connection with the Labour Force Survey for the quarterly statistics on rents and from the data in the Social Insurance Institution’s Housing Allowance Register. Statistics Finland’s data on the dwelling stock, obtained from the Building and Dwelling Register of the Population Register Centre, and data on migration and population structure are also exploited in the compilation of these annual statistics.

*Concepts:*

*Rented dwelling:* A rented dwelling refers to a dwelling
the tenant occupies on the basis of a tenancy agreement, whereby
the tenant pays rent for the right of use of the dwelling and the
related amenities. The data published in the statistics concern
those tenancies only where the tenant has exclusive tenure over the
entire dwelling. The statistics do not include such rented
dwellings whose rent for some reason, such as family relationship,
is well below the market level, nor do they extend to student
dwellings, sheltered accommodation or old people’s homes.

*Rent:* In these statistics the concept of rent includes
separately payable water and heating charges, but not compensations
paid for the use of amenities such as sauna or laundry room.
Telephone and electricity charges are also excluded. The published
average rents have been calculated per square metre of dwelling per
month (€/m2/month). The given average rents per square metre
are weighted geometric averages per square metre.

*Number of rooms* : The concept of number of rooms
excludes kitchen. The room number category of 3h+ refers to
dwellings with at least three rooms.

*Type of financing:* A government-subsidised dwelling
refers to a dwelling built with a government-subsidised housing
loan, whose rent is determined on the cost coverage principle. Most
government-subsidised dwellings are owned by local government.
Non-subsidised dwellings are other than government-subsidised
dwellings.

*New tenancy:* In the annual statistics a new tenancy
refers to a tenancy that has started during the statistical
reference year. Thus, in the 2008 annual rent statistics new
tenancies refer to tenancies started during 2008.

*Distribution parameters:*

Q1 (lower quartile) = 25% of the rents per square metre are lower than or equal to the lower quartile.

Median = The middle rent of all rents per square metre arranged in size order.

Q3 (upper quartile) = 75% of the rents per square metre are lower than or equal to the upper quartile.

*Classifications* **:**

Regional classification: The statistics use diverse combinations of geographic areas, such as Greater Helsinki Area, satellite municipalities surrounding the Greater Helsinki Area, major regions and urban sub-areas. The Greater Helsinki Area comprises Helsinki, Espoo, Vantaa and Kauniainen, which in statistics is included in Espoo. The satellite municipalities are Hyvinkää, Järvenpää, Kerava, Kirkkonummi, Nurmijärvi, Rii-himäki, Sipoo, Tuusula and Vihti. Regions have been combined into the major regions of Southern Finland, Western Finland, Eastern Finland and Northern Finland complying with the NUTS2 regional division of the European Union. The urban sub-areas are formed of postal code areas using price level and location as the criteria. Details of the used regional classifications can be found on the homepage of the statistics under classifications.

## 2. Methodological description

In practice, the calculation of indices for rent levels, i.e. indices for average rents per square metre and changes in them, can be distinguished in the calculation of an-nual rent statistics. Rent level indices are calculated from weighted average rents. Quality adjustment is applied in calculating changes in rents.

*Calculation of average rents per square metre*

Geometric mean rents per square metre

H = number of strata in category s nh = number of dwellings of stratum h included in the statistics

Nh = number of dwellings of stratum h included in the frame

Nh/nh = stratum weight

neliövuokrahj = rent of dwelling j of stratum h

alahj = area of observation j of stratum h in square me-tres

Average rents

*Index calculation*

The intention of the rent index is to describe how much more or less people have to pay, on average, for a rented dwelling of the same quality during the reference period in comparison to the base period. The measure-ment of trends in housing rents would be easy if dwellings of exactly the same quality were rented during each period, because average rents could then be used direct to measure price development.

However, as the dwellings rented at different points of time are dissimilar, changes in average rents per square metre do not depict pure price trends but are also influenced by the characteristics of rented dwellings. It is not appropriate to compare the rents of dissimilar dwellings direct. The index calculation employs meth-ods aiming to ensure that the reported price changes do not reflect structural changes in the stock of rented dwellings.

In the index calculation, price indices are first calculated for relatively small strata of the rental dwelling stock. After this, the value of the index and the percentage changes can be calculated at the desired aggregate level, e.g. the whole country. There are 46 geographical areas and the dwellings within each area are divided into five sections (A-E) according to the data source and rental market segment:

A. Government-subsidised

B. Old non-subsidised, data source interview (= tenants receive housing allowance)

C. Old non-subsidised, data source interview (= tenants do not receive housing allowance)

D. New non-subsidised, data source Housing Allow-ance Register (= tenants receive housing allowance)

E. New non-subsidised, data source interview (= ten-ants do not receive housing allowance)

This produces 230 strata, for which price changes are calculated:

– from the same dwellings for government-subsidised (A) and old non-subsidised tenancies receiving housing allowance (B)

– for new non-subsidised tenancies receiving housing allowance (D), changes are calculated by category in detailed classes (postal code area, number of rooms, area, year of construction) and change at the area level is obtained as Laspeyres’ chain index

– changes for the interview data (C and E) are calcu-lated with regression models.

Logarithmic rent per square metre is used as the de-pendent variable in the regional regression models. Besides the micro-strata, the influence of postal code area is controlled, and the explanatory variables used in the models are area of dwelling and its square root, age of dwelling and its square root, type of building and number of rooms, as well as a dummy variable indicat-ing the time the data relate to.

Price indices for the whole country

wh = aggregation weight of stratum h, which is the area of dwellings of the rental dwelling stock in stratum h in square metres.

The frame of the rental dwelling stock (from which the aforementioned aggregation weights are obtained) that is used for calculating the annual statistics is formed from the Population Register Centre’s Building and Dwelling Register by drawing from it all dwellings that are indicated in it as being permanently occupied, are not in institutional use (student dwellings, sheltered accommodation or old people’s home), and are occu-pied by the tenant on the basis of a tenancy agreement. New tenancies in the frame were identified with the Population Register Centre’s data on the population structure. Dwellings receiving housing allowance could be identified through the Social Insurance Institution’s Housing Allowance Register.

The frame of the 2009 rent statistics contains a total of 718,469 dwellings, of which 357,266 are government-subsidised and 361,203 non-subsidised.

## 3. Correctness and accuracy of the data

The data for the statistics derive from the interviews for the quarterly rent statistics and from the Social Insurance Institution’s Housing Allowance Register. The interview data are obtained in connection with the monthly Labour Force Survey and are based on random sampling.

Use of the Housing Allowance Register improves the reliability of the statistics because the register covers exhaustively all dwellings where the tenant receives housing allowance. However, in assessing the reliability of the statistics it should be noted that there are certain problems in linking the Housing Allowance Register to (the Building and Dwelling Register of ) the Population Information System, especially in respect of pensioners and students. Moreover, although the Population Infor-mation System is exhaustive, all the data in it are not necessarily always up-to-date.

The Housing Allowance Register only contains information about the rents of the dwellings that receive housing allowance, not the entire stock of rented dwellings. The data on housing allowances covers all dwellings that receive housing allowance. The rent level for other dwellings (490,000) is estimated from 16,000 items of data obtained with interviews.

With view to the reliability of the statistics, it has been decided that average rents per square metre and dispersion figures will not be published for categories with fewer than 20 observations.

The statistics are compiled using such parameters of rents per square metre with which cases clearly deviat-ing from the market level, such as tenancies based on employment/family relationships, and erroneous data can be eliminated. Parameters for rents per square meter have been set for government-subsidised and non-subsidised dwellings as follows:

In the statistics for year 2009 the parameters are: rents exceeding EUR 18 per square metre in the Greater Helsinki Area and EUR 13 per square metre elsewhere in the country have been removed from the data on government-subsidised dwellings. For government-subsidised dwellings, the lowest inclusion limits for rents per square metre were set at EUR 5.5 in the Greater Helsinki Area, EUR 5 in the satellite munici-palities, major towns and in the rest of the country.

For non-subsidised dwellings, the upper inclusion limits of rents per square meter are EUR 37 in the Greater Helsinki Area, EUR 25 in the satellite municipalities and major towns, EUR 20 in other municipalities with a population of over 20,000 and EUR 17 in other municipalities with a population of under 20,000. The lower inclusion limit for the rents of non-subsidised dwellings is first set at either EUR 4 or EUR 5 per square metre (depends on the geographical area), and the lowest 8 per cent of the remaining observations are then removed by category. A further final checking elimination is then done with the rent limits set at EUR 8.5 for the Greater, Helsinki Area, EUR 6.5 for the satellite municipalities and major towns and EUR 5 for the rest of the country.

## 4. Timeliness and promptness of the published data

The annual rent statistics are published yearly in the end of February and the published data are final.

## 5. Accessibility and transparency of the data

The data in this publication describe rent levels and changes in rents in 2009. Detailed statistics have been updated into the database tables of the Rents of dwellings -statistics. The database tables and additional information are available at the homepage of the statistics: www.stat.fi > Topics > Housing > Rents of dwellings.

## 6. Comparability of the statistics

Besides annually, Statistics Finland also publishes sta-tistics on rents quarterly. The compilation of the quar-terly statistics deviates in certain respects from that of the annual statistics.

The clearest difference between the two sets of statistics is that in addition to interview data, the annual statistics also utilise data from the Housing Allowance Register, which are not used in the quarterly statistics. Thus, the basis of the data for the annual statistics is considerably broader than the one for the quarterly statistics.

Another factor influencing the comparability of the statistics is the used definition of new tenancy. In the annual statistics a new tenancy refers to a tenancy that has started during the year the statistics examine, i.e. in the 2008 statistics new tenancies mean those entered into during the year 2008. In the quarterly statistics, a new tenancy refers to one that has stated within less than 12 months from the reference month of the Labour Force Survey. In other words, data obtained from the 1st quarter of 2008 on a tenancy entered into in, e.g. October 2007, is interpreted as relating to a new tenancy in the quarterly statistics, but is regarded as concerning an old tenancy in the annual statistics, i.e. one not entered into during 2008.

In consequence of the above, the figures in the quarterly and annual statistics may deviate slightly. The figures in the annual statistics can be regarded as more accurate. Due to the larger number of observations, the annual statistics provide more exhaustive data than the quarterly statistics. The annual statistics include certain towns on which data cannot be published quarterly due to low number of observations. In addition, the annual statistics give data classified by year of construction, and more accurate data on new tenancies.

Along with the revision of the rent statistics, which took effect from the beginning of 2004, the inquiry and the processing of the statistics were reviewed as follows:

*Reference point of time:* The old rent statistics
described the situation in April of the reference year. The revised
rent statistics describe the whole reference year.

*Inquiry data:* The interview data obtained in connection
with the Labour Force Survey replace the previously used
questionnaire inquiry data. The interview data improves the
coverage of non-subsidised dwellings. However, due to reasons
connected with the Labour Force Survey the sample now concentrates
more on larger dwellings and the dispersion of the rents of smaller
dwellings is now greater than in the old statistics.

*Control parameters:* The control parameters used in the
validation of data on deviant observations have been altered to
some extent.

*Average rents per square metre:* In connection with the
revision, geometric mean was introduced in place of arithmetic mean
in both annual and quarterly rent statistics in the measuring of
average rents per square meter. This change is defensible because
the geometric mean reduces the influence of extreme observations
and it can, therefore, be regarded as a better measure in
calculating average rents. According to test calculations with the
rent statistics data, depending on the area/category the geometric
mean produces approximately two to five per cent lower average
rents per square metre than the arithmetic mean. It should be borne
in mind that when comparing rent statistics over a longer time
period, the statistics prior to 2004 were produced using arithmetic
means rather than geometric means.

New tenancies: In the revised annual rent statistics, a new tenancy refers to a tenancy started in the year the statistics examine. In the old rent statistics (because of the time when the inquiry data were collected) a new tenancy referred to a tenancy started within less than 12 months before April of the reference year, e.g. in the 2009 statistics during the period from 1 April 2008 to 31 March 2009.

It should be noted that the year-on-year change in the 2004 rent statistics is not comparable to the old 2003 rent statistics. Tables from the 2003 rent statistics produced with the revised method are obtainable from Statistics Finland.

## 7. Coherence and consistency

Apart from the statistics compiled by Statistics Finland, no other regular data are produced on rents of dwellings in Finland. Statistics Finland publishes annual and quarterly statistics on rents. More on differences be-tween them under Section 6.1.

Source: Rents of dwellings, Statistics Finland

Inquiries: Martti Korhonen 09-1734 3451, Paula Paavilainen (09) 1734 3397

Director in charge: Kari Molnar

Updated 22.2.2009

Official Statistics of Finland (OSF):
Rents of dwellings [e-publication].

ISSN=1798-1018. 2009,
Quality Description Definitions: Rents of Dwellings
. Helsinki: Statistics Finland [referred: 20.4.2019].

Access method: http://www.stat.fi/til/asvu/2009/asvu_2009_2010-02-22_laa_001_en.html