FASE 2: ANÁLISIS CAUSAS

Based on the dataset, the SOM produces16 _{a feature map of several character-} istic combinations of attributes. It is possible to interpret a ‘typical value’ for each node according to a given feature, as an indicator for the given combina- tion of attributes that may indicate a particular neighbourhood. To enable vis- ual examination of the feature maps, differences in this value estimate across the map are depicted with grey shading; lighter shades represent the higher values. For example, when examining the map layers for price and age, light colours indicate areas with high price per square metre and old buildings, whereas dark colours indicate areas with low price per square metre and new buildings. This makes it possible to see at a glance which areas have a high per-unit price level, which areas have an old building stock and the extent to which these two layers overlap. In other words, it shows whether there are any associations between these two factors. For example, the feature-map shows that old buildings contribute to the segmentation of the data set, and that these areas belong to the more expensive cases. A similar visual analysis can be made for all of the input variables. For a more quantitative analysis, the ‘typical values’ of the nodes can be post-processed, using another compu- tational technique.

The resulting feature map is shown by layers in Appendix A. The labels are based on sub-districts, and they indicate location within metropolitan Hel- sinki; they are used to calibration of the feature map. In this way, a particu- lar locational label becomes a symbol for a particular combination of varia- bles and submarket structure. The following results were interpreted from the map:

n single-family housing forms two separate homogeneous clusters: (1) a larg- er group comprised of areas of mixed nature from all three main munic- ipalities (the darker neurons on the lower right of the map), suggesting a physically homogeneous space across municipal boundaries and (2) anoth- er, much smaller group in southern Espoo (the darker neurons on the lower left side of the map);

n the most expensive (per square metre) areas are the most urbanised, with the least open space and the best commercial services (inner Helsinki neu- rons in the upper and lower left corners of the map);

16 The soft ware packages used in the study are SOM_ PAK and LVQ_PAK, in MS/DO S, produced by the Labora- tory of Computer and Information Science at the Helsinki University of Technology.

n the least expensive areas are positioned along the upper middle and right corner, most notably in the outlying neighbourhood of Jakomäki (914142), which is a symbol of poverty and social externalities;

n the newest building stock is positioned on the right and the oldest on the left, forming two clearly distinct submarkets;

n four or five clusters indicate submarkets with larger dwellings (i.e., three or more rooms);

n low-status areas overlap with areas that have many social externalities and vice versa; high status areas overlap with areas that have few externalities; n shoreline proximity brings a clear price premium for the neurons on the

left of the map; it also indicates the Espoo high-status areas in the lower corners of the map (the feature can be seen from the ‘open space’ indica- tor, because being surrounded by water automatically means the absence of undeveloped land)17_;

n the average, less interesting cases are situated in the centre of the map (but they may still indicate market segmentation).

The main idea is that the visual SOM analysis generates the same three or four submarkets that could be expected according to a priori knowledge of the Helsinki housing market18_:

(1) locations in the inner city and the nearest old suburbs (1a) absolute top location; high-price areas

(1b) older, low-status working-class areas; still relatively high prices (2) other locations (e.g., multi-storey housing, low status, low price) (3) detached and terraced housing; also low-density, multi-storey housing.

17 Note that the ‘urbanisation’ indicator and the ‘open space’ indicator overlap with regard to central Helsinki neurons on the left side of the map, but not with regard to the suburbs that are positioned in the lower right corner of the map. In the latter case, proximity to the seashore is reflected only in the open-space indicator. In the former case, it is reflected in both indicators, as the city centre of Helsinki is surrounded by water and urban by definition. To give an impression of the magnitude in price premiums: shoreline proximity brings a clear price premium for two segments defined by location and house type. It was thus possible to select two combinations of housing-attribute levels, approximated as two locations that are of same magnitude in all other characteristics that are used as input (i.e., house type, age, rooms, price level, status, social externalities, services and distance to CBD), but that differ with regard to water. One location on Tammisalo (an island) and another in Hakunin- maa-Maununneva (in northwestern Helsinki) are as far away as possible from any water. It was then possible to calculate the difference between these two areas in terms of price per square metre. The premium in favour of the island location was FIM 731/ FIM 6026 = 12%. (This is substantially lower than the result obtained by Laakso, which were based direct or immediate vicinity of the coast.

18 For a more detailed elaboration including a comparison with a k-means analysis on the same data, see Kauko (2002).

We cannot conclude, however, that these segments overlap with any definite spatial boundaries. For example, the western part of Helsinki alone comprises areas from all four segments. The neighbourhoods of Lauttasaari and Munkki- niemi belong to segment (1a), even though they are not situated on the penin- sula. Meilahti and Ruskeasuo are represented in both segments (1a) and (1b). Konala clearly belongs to segment (2), while Munkkivuori may be classified under segment (3). Nevertheless, the identification labels based on sub-dis- trict can serve as proxies for a dominant combination of observed-attribute levels, and can therefore help to structure the housing-market data, first visu- ally and then more formally with the LVQ.