89 5 Analysing the Housing Markets of Helsinki and Finland with the SOM
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5 Analysing the housing markets of Helsinki and Finland with the SOM The discussion on locational value formation and value modelling methodology has highlighted the role of housing submarkets and the institutional and behavioural aspects of value formation. Segmentation is determined at the aggregate level by institutional and physical constraints and at the micro level by individual perceptions and behaviour. It was therefore asserted that nonlinear, discontinuous and qualitative aspects need to be recognised. It was shown that a variety of established empirical modelling tools failed to recognise residual determinants of locational value. The main aspects were: the tradeoff between model bias and exactness, capturing the fuzzy nature of the relationship between price and its various determinants, spatial and other dependence, and the context specificity of a given location in terms of its institutional and physical structure. In general, paying attention to these aspects adds depth to the analysis over and above straightforward estimation. In Chapter 4 one alternative, the SOM, was described as a flexible technique of analysing locational value formation. One of the particular strengths of the SOM in comparison with multiple regression analysis or MLP networks is that it allows for market segmentation. This method becomes more powerful when it is combined with the LVQ, another neural network technique. The LVQ enables the evaluation and improvement of - the feature maps obtained by the SOM analysis. The model generated by the neural network method is highly inductive and independent of any formal hypothesis building, letting the data determine the outcome, instead of depending on distributions, confidence intervals and so forth. Next, the empirical analyses are performed to evaluate the following two claims: Firstly, the SOM-based method has added value in detecting market segmentation. Apart from a variable having an effect on the overall organisation of the map, the method should also show that the influence of a certain variable depends on a specific context and is thus only influential in the organisation of certain neurons or certain map layers (i.e. input variables, see Section 4.3). Secondly, the SOM-based approach has applicability as an assessment method. Given the connection to variable selections in earlier value modelling studies, the house price levels should be associated with indicators of attractiveness regarding the quality of the vicinity (in the Helsinki case: availability of services, the social and physical environment) and profile of the whole municipality (in the Finland case: demography, employment, housing production and the local public economy). Two empirical applications are included in this chapter. The first, reported in Section 5.1, is based on a sample from a nationwide price data set for Finland, including transactions from the year 1994. The results pertain to a straightforward application of the SOM and are intended to illustrate its capability of identifying submarkets across and within the 89 municipalities of the country. The second, reported in sections 5.2 to 5.4, is based on various samples from a set of prices for the Helsinki metropolitan area, including transactions from the year 1993. This application not only demonstrates the identification of submarkets, but also tests the applicability of the SOM as an assessment method. Furthermore, a number of sensitivity analyses as well as an evaluation of the added value of the LVQ classification approach are reported. Finally, some concluding remarks with ideas for follow up are presented in Section 5.5. 5.1 Analysis of the Finnish housing market with the SOM Figure 5.1. displays maps of the whole country and the southern part of Finland, Etelä- Suomi, in more detail. The population of Finland is about 5.2 million (in the year 2000). In the whole country there are 2.5 million dwellings (1999), approximately two-thirds of which are owner-occupied. The Helsinki metropolitan area is the central part of the greater Helsinki region, which is by far the largest agglomeration in the country, with approximately one fifth of the Finnish population. The Helsinki metropolitan area consists of four municipalities: Helsinki, Espoo, Vantaa and Kauniainen. The population of the whole area is about 950,000 (2000), 60% of whom live in the City of Helsinki. The number of dwellings in metropolitan Helsinki is 400,000 (1999), about 60% of which is owner-occupied. In the years in which the data was collected (1993-1994) the Finnish housing markets were recovering from a recession. (For more figures on the Finnish population, housing, and the economy, see www.stat.fi) 90 Figure 5.1 Map of Finland. (Source: modified illustration based on http://www.nls.fi/ptk/wwwurl/localmap.html.) Helsinki, with adjacent municipalities, is the area with by far the highest house prices. The other major cities (Tampere, Turku, Oulu) follow; house prices are far below their levels in the rest of the country. Another feature is that the western part of the country is for the most part financially and socially better off than the eastern part of the country. Comprehensive studies at this level are more rare than for metropolitan Helsinki and (to my knowledge) only one such study has been undertaken in recent years. Siikanen (1992) grouped the local housing markets in Finland on the basis of municipal demand and supply side data. 91 In the data for Finland, three variables describe the structural attributes of a dwelling, and there are thirteen municipal variables. It is widely known that some socioeconomic indicators, such as the level of income as well as the educational and professional distributions, influence the property prices of a given locality through an increase in potential demand (e.g. Adair et al. 1996; McGreal et al. 1998; Jenkins et al. 1999). The locality and location specific variables have been selected with respect to the attractiveness (i.e. the relative popularity as approximated by various indicators). In addition to a straightforward association between various variables and price (the hedonic price theory), the socioeconomic and other municipality-specific factors describe the unique nature of a given locality. They tell something about the municipal economy in question, which may lead to an observable spatial dispersal of price levels, or even to the emergence of separate market segments. Furthermore, most of the variables were related to the population in order to obtain significant findings other than just a rough division between cities and other municipalities on the basis of the size of the municipality. Table 5.1 describes the variables for the nationwide dataset. Each dataset comprises a full cross-section of condominium (i.e. securitised dwelling) transactions in the given year. However, the data does not contain transactions for real property (i.e. landed property, including the majority of detached houses), which are fewer in number and maintained in a separate system (by the National Land Survey). This dualism between securitised housing and residential property is a curiosity of the Finnish cadastral system. As these data are based on the former source, detached houses are relatively rare in comparison with semi-detached houses. The descriptive statistics of the dataset are given in Table 5.2. Before running the SOM algorithm, the variables were normalised in the field range 0-1 (see subsection 5.4.1 below). Table 5.1 Description of the variables in the data on Finland (1994) Variables: Unit of measurement Micro-level variables: (1) Price of the dwelling per sq.m. 1000 FIM (2) Age of the building 10 yrs (3) Dwelling format Semi-detached 1, terraced 2, multi-storey apartment 3, else 5 Municipality-level variables: (4) Net migration: in migration minus out migration, 0.1% in a year, as a share of the population previous year (5) Number of households 1000 (6) Average size of household Number of people (7) Proportion of one person households of all % households (8) Average income of the population ) 1000 FIM (9) Number of jobs in manufacturing Per 1000 dwellers (10) Number of jobs in services Per 1000 dwellers (11) Proportion of the population with (at least) % middle-level educational degree (12) Proportion of 0-14 years old children % (13) Average size of dwellings Sq.m. of floor-space per dwelling (14) The inverse housing density Sq.m. of floor-space per dweller (15) Housing construction relative to the housing 0.1% stock (16) Solidity of the municipality 1000 FIM/year 92 Table 5.2 Descriptive statistics: data on Finland. A random sample (1/50) of dwelling transactions in Finland during 1994 (N=1167) Variables Min Mean Max Continuous (1) Price/sq.m. 1.50 5.07 14.13 (2) Age 0.00 2.31 12.90 (4) Migration -30.57 4.36 17.09 (5) Hholds 0.46 72.19 251.39 (6) Hhold size 2.05 2.32 3.72 (7) 1-p hholds 14.00 36.20 46.00 (8) Income 55.90 87.26 141.30 (9) Manufact. 7.30 76.37 246.10 (10) Servicesa 12.40 54.51 87.30 (11) Education 30.80 46.20 60.50 (12) Children 15.33 18.31 32.54 (13) Size 60.05 70.64 104.59 (14) Density 28.05 33.54 41.08 (15) Construct 0.49 13.06 28.07 (16) Solidity -31.34 -1.16 6.58 Discrete N (3) Format 189 2 267 3 793 59 X9 a When comparing the mean values of variables (9) and (10) in the sample from Finland one may be surprised about the larger proportion of jobs in manufacturing than in services. The reason lies in the categorisations of Statistics Finland. In 1994 the proportions for jobs in manufacturing (incl. electricity, gas and water, but not building) and in all services were 21% and 62% respectively.