Differences between owner and rental housing markets: an exploratory analysis applied to Portuguese urban

Transcription

1 Differences between owner and rental housing markets: an exploratory analysis applied to Portuguese urban context 1 Work submitted for ENHR 2013 Conference Paulo Batista 1, Monique Borges 2, João Marques 3, Eduardo Castro 4 1 pauloricardolb@ua.pt, 2 monique@ua.pt, 3 jjmarques@ua.pt, 4 ecastro@ua.pt, Group of Studies in Territory and Innovation. Department of Social, Political and Territory Sciences. University of Aveiro, Portugal Abstract The option for rental or own property in housing market is strongly associated with the particular socioeconomic context of the buyer. Home ownership can be seen as a synonym of safety and investment to the future or, on the other hand, it also means long-term risks and charges. For many socioeconomic and cultural reasons Portuguese housing market was defined by home-ownership. However, current economic and financial situation of families encourage the search for alternative solutions. The rental market comes as an important option, whose features and characteristics are important to understand and explore. To make this possible, it is necessary to study their specificities, pointing out some of its main features and identifying differences for both (sub) markets types. Many research questions arise, but this paper focuses on understanding what determines the demand and supply of these dwellings, and what distinguishes both markets, including their spatial characteristics. The exploratory approach was based on the use of hedonic pricing models and spatial econometric models in order to understand how to characterize both market segments, in a comparative framework. This analysis was developed for one case study: a medium sized urban housing market (composed by Aveiro and Ílhavo municipalities). The data used was provided by a house advertisement database (from 2005 to 2010), available at the Casa Sapo webportal. Keywords: Housing market; Owner; Rent; Submarkets; Housing market dynamics in Portugal In Portugal, during the past 20 years the access to housing was done mainly through homeownership, however this paradigm has been changed. 1 The authors thank The Portuguese Science Foundation who funded the project Drivers of housing demand in Portuguese urban system (DONUT). 1

2 Over the past decades, Portugal followed the European evolution trend of stabilization or reduction of the rental housing option, nevertheless the rental market here is particularly low (ECB, 2003). Portugal, in the late 70s, was characterized by serious shortages in housing access (Guerra, 2011), a generally reduced public funds for social policies and high levels of poverty. To face this constrains, the government opted for impose several controls in rental markets and enabling home-ownership. These policy options were characterized by: i) an extended rent control and legal rental requirements rigidity (especially until the 90's), ii) policies encouraging mortgage loans devalued rental housing choices iii) a judicial system ineffective in reducing the risks associated with the rental option (for example, the protection of landlords). Over recent years, the economic and financial crisis in Portugal was responsible for new housing tenure. This change is justified by two main reasons: on one hand, the decreasing of household income, followed by an increase in financing costs (credit terms), and on the other hand, the need for greater flexibility and labour mobility (Guerra, 2011). The data collected from the last census (INE, 2011), in Portugal, illustrate this new perspective of choosing a new house to live. The data indicates a growth of 6.7% of rented dwellings, where the rental regime assumes great importance in more urbanized areas or in ancient or historical urban areas. However, it should be noted that, despite this growth, there still are considerable differences between home-ownership and rental housing, 73% and 20%, respectively. The financial and economic situation of the past five years has justified the need to turn the rental housing choice more attractive. Significant efforts have been made and the public policy played a significant role: i) the end of most incentives (system of taxation) for home-ownership, ii) a thorough reform of the legal rental requirements (in transition phase) and iii) direct public investment, increasingly selective, fostering the rental option (e.g. through urban renewal programs and public-private partnerships with investment funds). This change of paradigm is a reactive response to a critical situation in the economy and the housing market in particular. The swiftness of reality and political options, in last five years do not allow a deep reflection on the different dimensions of the current situation of housing rental market in Portugal and of the spatial nature of the phenomenon. With this study, we want to contribute to the study of this reality. On the other hand, empirical studies that focus on the spatial dimension of the housing transaction phenomenon face operational challenges, including: a) difficult access to information in Portuguese housing markets and b) the lack of methodological consolidation, especially in studies that focus on the spatial dimension of the housing transaction phenomenon. Within the research project DONUT some of these issues were overcome for the Portuguese reality. Thus, an exploratory empirical analysis applied to a Portuguese urban medium sized city is presented. Objectives and methodology This paper aims at studying the differences between the rental housing market and the housing market buying in Aveiro-Ílhavo urban area, in which concerns: the housing market spatial 2

3 structure, housing characteristics and its housing prices. To achieve this objective the results were analysed according two main moments: i) A descriptive analysis: statistical measures were used to characterize the two urban market types, answering to the following questions: What is the sort of houses that has been sold? How many houses and where have been sold? What were the prices? This analysis is done disaggregated by zone characterized by being homogeneous territories, smaller than parishes, which in some cases are identified as residential neighbourhoods. ii) A hedonic model combined with a cluster analysis: these statistical procedures were used to identify spatial segmentation of housing markets, for both rental and buying, i.e., to analyse spatial heterogeneity. Housing market spatial structure The relevance of spatial segmentation to analyse housing market is broadly recognised in the spatial econometric literature. Spatial heterogeneity, in the context of housing analysis, if not analysed conveniently can be a potential source of specification errors. It occurs when a territorial segmentation exists in the housing market and, therefore, hedonic prices associated with different attributes are not constant over space. In other word, submarkets are distinctive because houses within them are viewed as (more or less) perfect substitutes by the households. In this way, two dwellings are elements of the same submarket ( ) if the degree of substitutability between them is sufficiently great to produce palpable and observable crossrelationships in respect to occupancy, sales, prices and rents, or in other words, whether the units compete with one another as alternatives for the demanders of housing space (Grigsby, 1963 p.34). Housing markets are usually characterized by being segmented and structured by a complex pattern and inter-related elements, rather than being determined by a single, homogeneous process of spatial organization. This is the reason why the correct consideration of spatial issues should be a major concerning. Many approaches can be used to delimit housing submarkets, typically defined in terms of: physical housing characteristics of the dwellings, geographical aspects, or socioeconomic characteristics of the neighbourhood. Using these main housing dimensions several approaches can be used to delimit homogeneous territorial units, namely, pre-existing geographic or administrative boundaries; expert knowledge (subjectively defined); statistical techniques (cluster analysis; hedonic models etc.) (Marques et al. 2012). In this explanatory work an initial hypothesis was considered: owner and rent housing options correspond to distinct housing markets and a specific housing hedonic pricing model was used for each type of market. The methodological approach adopted here uses as reference the methodology developed in Marques et al., (2012), which is a combination of two multivariate techniques, the multiple regression models and cluster analysis. Hedonic models are the standard tool and one of the most consolidated techniques, with great simplicity and versatility to analyse housing markets (Malpezzi, 2008). Usually, these models take a log-log form, to deal with the linear assumption between price and house characteristics: 3

4 with a linear assumption it is possible to apply an ordinary least square estimation method to define, easily, the parameters of the model market values for each house attribute. As pointed above, this paper intends to study the spatial pattern of housing prices for two types of markets. For this purpose, the hedonic models were used for two different purposes: first, to define the general picture for each type of market (own and rental), second, to analyses spatial segmentation of both types of markets. These approaches allow the identification of the implicit value of the three main housing dimensions: i) intrinsic or physical attributes, ii) neighbourhood characteristics and iii) temporal aspects. Our first model is defined as: (1) ( ) Where: P is the house price; A is the area; TOM is time on market; I are dummy variables of intrinsic characteristics (Flat, Preservation New, Used with less than 10 years and Used with 10 to 25 years ) and Z are dummy characteristics of locations (51 1 zones); T are year of transaction dummy; ε is the vector that represents the stochastic error or noise. As mentioned before, sophisticated methods can be developed and used to identify spatial heterogeneity, but to apply some them a very detail data are required, especially regarding the localization of each dwelling. In the case of this work the precise location of houses is not known. Property georeferentiation is done using the field micro zone. Because of confidentiality problems, the exact position of observations within each zone in unknown, which means that all observations located within a specific zone are attributed a unique coordinate. However, as argued by Palm (1978) or Maclennan and Tu (1996) spatial elementary structure can be correctly effected, based on subjectively expert knowledge, that is, delineated by real estate agents or appraisers; in this case, the zones (a small spatial delimitation than the smallest administrative boundaries) were defined by real estate agents and used as the elementary location information for each dwelling (Figure 1). In order to analyse spatial heterogeneity, the equation described above is subject of some transformations. The standard model (1) was extended adding slope dummies variables for each zone, capturing spatial valuing effects (for a more detail explanation see Marques, 2012). (2) ( ) ( ) Where: P is the house price; A is the area; TOM is time on market; I are dummies of intrinsic characteristics (Flat, Preservation New, Used with less than 10 yrs and Used with 10 to 25 yrs) and Z dummy variables for locations (51 1 zones); Z*I are slope dummies of each intrinsic attribute in the zone j; T are year of transaction dummy; ε is the vector that represents the stochastic error or noise Assuming the existence of spatial heterogeneity, the problem is how to identify and to delimit the various submarkets in each housing market type. The option of not considering the individual zones directly as submarkets is of interest due to the reduced number of observations in each unit of analysis. A simple analytical method was implemented, based on cluster analysis, using as the main criteria of measuring the distances the following dimensions: house prices, hedonic characteristics, hedonic prices of characteristics and hedonic price of each zone (the last two dimensions resulted from the second hedonic model (2), which were standardized). 4

5 General picture of Aveiro and Ílhavo municipalities The study area includes two municipalities, Aveiro and Ílhavo. The municipality of Aveiro has a total area of 200 km² and a total population of 78454; the municipality of Ílhavo has an area of 75km 2 and inhabitants (INE, 2011). If the area of the extensive lagoon is removed, the population density is 600 inhabitants per km 2, a typical value for a Portuguese urban agglomeration. It is located 50km from Porto, the second major Portuguese city, in the limit of that suburban transport system. This territory is crossed by important transport links: two of the most important highways, the national major railroad and the fourth major Seaport. This study area is characterised by the dominance of individual dwellings with one/two floors (~86% of buildings in Aveiro - Ilhavo), occupied for usual residence and whose residents are mostly owner-occupiers (~72%). A more detailed analysis, regarding the housing reveals: the existence of a greater number of dwellings per family and second-home residence in the coastal zone surrounding area (~30-40% of houses). In general, Aveiro - Ílhavo is characterized by the presence of ground floor dwellings, although some urban agglomerations (such as Aveiro and Ílhavo city centers) evidence expressive values for high-rise buildings (but aligned with national mean, for medium sized cities centers). Throughout the last decades (based on the census data) house construction has also increased in Aveiro-Ílhavo s region (~15% of buildings have less than 10 years), where sub-urban areas outline a territory with urban occupation density and construction dynamics above national and case study area average. In this territory, 20% of the usual houses are occupied with rent contracts. This value is specially high (40%) only in ancient / historical urban area the Aveiro city Center parish: Vera Cruz). ( see Monique et al., 2012 for a more detailed analysis of the figures). Figure 1 Case study area: Aveiro Ílhavo Municipalities 5

6 Spatial, social and economic urban structure The spatial configuration of housing market was firstly performed based on a combination of expertise knowledge and measures statistical measures. Six major zones (Figure 2) can be defined in the municipalities of Aveiro and Ílhavo. The criteria used for this spatial disaggregation is described in more detail in Marques (2012) but can be summarized as following: spatial urban structure and demographic characteristics: defining different levels of concentration, population density, population growth rate, education level, etc. settlement historical growth: defining different periods of urban development. special features: identifying zones with particular functions, such as, beach, harbour industrial and services areas. These three major criteria lead to the following spatial disaggregation: CBD Aveiro: The inner city of Aveiro is the core of the urban area; Sub-Urban Type A and B: A suburban area, which spreads around the city of Aveiro, where blocks of flats and clusters of detached houses are mixed with old rural settlements; Sub-Urban Type C: A semi-rural is dedicated to agriculture but where almost all population work in the manufacturing: pattern equivalent to that of the previous area but with a lower weight of new urban settlements. CBD Ílhavo: The city of Ílhavo is the second urban centre of the agglomeration. Gafanhas: Gafanha da Nazaré is where the port of Aveiro is located characterized by a mix of industrial and residential areas. Beaches: The seaside resorts Barra and Costa Nova, where secondary residences are predominant Figure 2 Spatial structure of Aveiro Ilhavo, based on a multidimensional analysis and expertise knowledge 6

7 Housing market database The database used for this empirical work is provided by the firm Janela Digital S.A., which owns and manages the real estate advertise portal database CASA SAPO. This portal is the largest site in Portugal of real estate diffusion. Since 2000 they stored information about 5 million of properties, available for transaction in Portugal, covering all the national territory. It is obvious that this kind of data source has a lot of scarce and potential erroneous information. It is important to clear that: a) Prices, are all asked prices (we use the last price inserted in database and we assume it as the transaction prices); b) The date of exit (used as transaction date) does not mean that the house was sold, but that the property was removed from database; c) There are many duplicate records which should be caused by two distinct facts: i) traded houses with very similar attributes e.g.: two flats, in the same house building; ii) because there is no requirement for exclusivity in most houses traded, sellers give the deal to various realtors who duplicate records in ads database. d) Non exclusivity between brokers in the selling process leads to significant levels of missing information about houses (e.g.: the exact location of each house); e) It is very difficult to assess the use (representativeness) of advertisement web services (such as Casa Sapo) by brokers. This is relevant because the level of digitalization of real estate firms and brokers are relatively recent / low and the internet doesn t cover the whole population. Table 1 Descriptive statistics of housing characteristics (after data cleaning) Type of market: Owner Rent Price ( /m2) Time on Market (TOM) Area (m2) Flat (1 = is a flat) Dwelling (1 = is a dwelling) Preservation - New (1 = is new) Preservation - Used with less than 10 yrs (1 = is used less than 10 yrs) Preservation - Used with 10 to 25 yrs (1 = is used with 10 to 25 yrs) Preservation - Used with more than 25 yrs (1 = is used with more than 25 yrs) Year of transaction (1 = year of transaction is 2005) Year of transaction (1 = year of transaction is 2006) Year of transaction (1 = year of transaction is 2007) Year of transaction (1 = year of transaction is 2008) Year of transaction (1 = year of transaction is 2009) Year of transaction (1 = year of transaction is 2010) Type Mean Mode Std. Dev. Min Max N (for dummy Mean variables, coded as 1) Mode Std. Dev. Min Max N (for dummy variables, coded as 1) Scalar Scalar Scalar Dummy Dummy Dummy Dummy Dummy Dummy Dummy Dummy Dummy Dummy Dummy Dummy For the specific case of Aveiro and Ílhavo properties were identified in the database between 2000 and The models used in this empirical work consider a database with

8 observations (15% of them related with the rental housing market), for recent years (2005 to 2010). This number of observations was obtained after data cleaning: for a more detailed description of the main challenges for cleaning the database and general procedures used for it see Batista (2010), Marques (2012) and Castro (2013) 2. The systematization of this information is presented in Table 1 and it is used as a starting point for this exploratory analysis. Spatial descriptive analysis of own and rental housing market Dimension of the markets Figure 3 shows a heterogeneous spatial pattern of transactions in both type of markets. The major quantity of transactions was held in the surrounds CBD of Aveiro and Ílhavo and closest to industrial zones (employment hubs). Characteristics of houses Figure 3 Spatial distribution of the number of transactions. The first message emerged from the analysis of the several housing characteristics, in both type of housing markets (owner and rental), is that there is a substantial differences in the spatial patterns. In fact, starting by analysing the distribution of the dimension Area (Figure 4) the proximity to the city centre implies a decreasing of housing area. Space had a higher demand in more central place, resulting in housing small average areas because potential high prices; Furthermore, this is the older settlement, where traditional houses are smaller (Aveiro was a fishing town were people had low income). In rental market some idiosyncrasies are visible: certain zones, far away of city centre, have an average area In fact, these patterns probably are caused by specifies of rental market: rental houses in more rural areas can potentially be linked with more older / used houses, traditionally from farmers: Aveiro rural areas had important agricultural activities, but this was a reality that was been changed rapidly in last two decades. 2 The database cleaning procedures had some adaptations from the followed original methodologies: the most important difference is that only no duplicated records were selected for this work. 8

9 Figure 4: Spatial distribution of housing area (represented by standard deviation) Note: the standard deviation for owner market is 50 m 2 and for rent 55 m 2 and the average is for owner market 154 m 2 and for rent 117 m 2. Regarding the type of dwelling (Figure 5) no significant differences were identified in the two markets. The markets are characterized by the concentration of flats traded in the CBD of Aveiro and Ílhavo and in territorial units near these urban centres. We can identify that this concentrate pattern was particularly stronger in the suburban type B and Beaches which is totally equal in both markets. As we move away from the city centre the single family dwellings become more frequent (see for instance Suburban type C). The rental market, in turn, is defined by a greater preponderance of flats, with the exception of Suburban type C area. Figure 5 Spatial distribution of flats (represented by the mode). Finally, concerning the level of preservation both markets are clearly linked to different patterns. In general, the owner market is characterized by the dominance of new dwellings while used dwellings are mainly represented in the rental market. A more detailed analysis allows to infer that the new housing provision is located special in the surrounding areas of the CBD of Aveiro and Ílhavo (city expansion areas) and in some rural areas (strong related spatially with accessibility nodes in these cases, related with motorway access); in contrast, in the rental market, new dwellings are located specially in closest areas of CBD of Aveiro. Analyzing the dwellings used with years it is visible a territorial continuum on the rental market in the Suburban type C (what reinforce our argument, used for area characteristic). On the other hand, the owner market denotes a small presence of these dwellings (appearing only in some areas of the Suburban type C). Finally, dwellings used with more than 25 years are mainly located in the 9

10 CBD of Aveiro, with higher expression in the rental market. This pattern for rental market can be justified with position of the rental option for investors: rental market, for oldest houses, can be a good alternative as the value for new houses in rental market isn t enough to justify rehabilitation. Figure 6 Spatial distribution of New dwellings (represented by the mode). Figure 7 Spatial distribution of Used dwelling with less than 10 years (represented by the mode). Figure 8 Spatial distribution of Used dwellings with an age between 10 and 25 years (represented by the mode). 10

11 Table 2 Traded houses by preservation category in Rental and Owner markets All Rent Owner N % of all N % of rent % of preserv. catg. N % of owner % of preserv. catg. New % 77 8% 3% % 97% Preservation - Used with less than 10 yrs % % 21% % 79% Preservation - Used with 10 to 25 yrs % % 21% % 79% Preservation - Used with more than 25 yrs 277 4% 59 6% 21% 218 4% 79% Hedonic prices of traded houses The hedonic price models contributed to obtain the hedonic prices of each zone (1) and the house characteristics in each zone (2) and with these models we can study the pattern of housing attribute values. As a consequence of the multicollinearity phenomena, all measures of house dimensions are related with some variable. It was used, as comparator element: the zone coded as 1000 (located in Aveiro city centre), the preservation category Used with age greater than 25 years, the single family house and the 2010 as the base year of trade. Starting with the spatial distribution of the house average prices in each zone (Figure 9) it is possible to identify two main differences: in the owner market exists a differentiation in prices above the average price, on the other hand, in the rent market this differentiation occurs especially below the average price. These differences also have a specific spatial structure: in the owner market these variations are above the average in central areas (CBD of Aveiro and Ílhavo) or in areas with special features (such as Beaches), by contrast, in the rental market these differences occur mainly in the Suburban areas with more rural characteristics. Figure 9 Spatial distribution of housing Prices (represented by standard deviation) Note: the standard deviation for owner market is 243 /m 2 and for rent 0,90 /m 2 ; and the average is for owner market 1082 /m 2 and for rent 4,35 /m 2 Regarding the spatial distribution of the implicit price of the area, it is similar in both cases. There are no significant differences in each zone s area valuation, and the hedonic price of this characteristic presents residual mean values and standard deviation in both markets. 11

12 The valuation of the type of dwelling (Figure 10), particularly in relation to single family houses, presents, as a specific feature, the existence of values below average in more areas where transactions occur than in the rental market, which presents a more homogenous behaviour. Figure 10 Spatial distribution of hedonic price of Flat (represented by standard deviation) Note: the standard deviation for owner market is 0,26 /m 2 and for rent 0,37 /m 2 ; and the average is for owner market 0,24 /m 2 and for rent -0,12 /m 2 Regarding preservation, Figure 11 shows that the value of new housing is characterized by the evidence of a more defined spatial structure valuation on the owner market. However, there is a clear division of the owner market patterns: we identify values above the average around the urban center of Aveiro and Ílhavo, and values below the average in the CBD of Ílhavo. The rental market analysis shows patterns in suburban with New-housing rent values stronger below and above the average, who suggests a clearly spatial location pattern, but limited in territory who suggests that this can be related with specificities of houses traded in this locations. Figure 11 Spatial distribution of hedonic price of New dwellings (represented by standard deviation) Note: the standard deviation for owner market is 0,45 /m 2 and for rent 0,37 /m 2 ; and the average is for owner market -0,22 /m 2 and for rent -0,04 /m 2 In which concerns to the used dwellings (Figure 12 e Figure 13) there is a spatial pattern difference in both markets: the rental market presents a great homogeneity in the spatial variation of the hedonic value of this characteristic; on the other hand, the owner market has distinct spatial differentiations, although difficult to associate with the spatial structures previously defined. It is also worth noting that there are no significant differences regarding the territorial patterns of the different values of used dwelling categories. 12

13 Figure 12 Spatial distribution of hedonic price of Use dwellings with age to 10 years (represented by standard deviation). Note: the standard deviation for owner market is 0,42 /m 2 and for rent 0,67 /m 2 ; and the average is for owner market -0,32 /m 2 and for rent -0,06 /m 2 Figure 13 Spatial distribution of hedonic price of Use dwellings with age to 10 years (represented by standard deviation) Note: the standard deviation for owner market is 0,42 and for rent 0,65; and the average is for owner market -0,34 and for rent -0,13 In relation to the value of each zone (Figure 14), the spatial patterns are quite similar in both markets. It is noteworthy, as expected, that the CBD of Aveiro and the Beaches as the areas that aggregate intrinsic zone values (location, amenities, other idiosyncrasies related to the neighbourhood) above averaged. This analysis also denotes a decreasing price gradient as we move away from these most valuable areas, with a more abrupt pattern in the owner market (although more homogeneous) and more gradual in the rental market. 13

14 Figure 14 Spatial distribution of hedonic price of Zones (represented by standard deviation) Note: the standard deviation for owner market is 0,17 /m 2 and for rent 0,12 /m 2 ; and the average is for owner market -0,17 /m 2 and for rent -0,11 /m 2 Considerations about temporal evolution pattern of traded houses Long time series on the housing market, in a micro scale level, are difficult to obtain for Portuguese housing market. Thus, it is not possible to develop this type of analysis in a comparative framework as we made for spatial patterns. For this purpose, are presented a descriptive analysis, based on some indicators, namely: variables illustrative of the evolution of average prices (Figure 15) and an approximation measure of the market inflation index (Figure 16), determined by the hedonic price associated to the dummies of each year in which the transaction occurred. The main conclusions are: i) The evolution of the annual prices average in both markets has distinct behaviours, namely: a slight upward trend in the rental market and a stagnant trend on the owner market. ii) The inflation index is different in both markets: the rental market presents a great growth trend, while the owner market reveals (for the period considered) a slight decrease, with values stagnation during the last three years. It should also be noted that the number of recorded transactions (Table 3) shows a successive increase during the period of analysis, aspect mainly related to the type of data source service, rather than with a more or less market transaction dynamic. In fact, with the 2008 financial crisis and the downturn in private consumption, it may have been a contraction in the number of transactions - this aspect was not explored in this work. Another interesting element in these figures is that the proportion of houses placed on both markets keeps stable, which may indicate a (natural and expected) rigidity supply, at least according to the short temporal period response reported here. 14

15 Figure 15 Average trading prices: left graph is for owner market and right graph is for rental market Figure 16 Value of each year dummy: left graph is for owner market and right graph is for rental market Table 3 Number of registered transactions by year All Rent Owner N % of all N % of rent % of yr. catg. N % of owner % of yr. catg % 3 0% 12% 23 0% 88% % 22 2% 9% 235 4% 91% % % 20% % 80% % % 13% % 87% % % 18% % 82% % % 12% % 88% Spatial submarkets of owner and rental trades: differences and similarities. From the variables previously described it can be deduced that both the owner and rental market have different territorial patterns. Therefore, a cluster analysis was used in order to determine their spatial limits and how they are grouped in space in order to provide a synthesis of the entire housing attributes. Based on the data used in the development of the hedonic model (2) and their results (slope dummies for each zone), the Ward s hierarchical clustering method allows to, separately, identify four major agglomerations. Each cluster characteristics (Figure 17) are summarized in Table 4 and Table 5, and can be described as follows. 15

16 Owner market CLUSTER 1: Includes the CBD of Aveiro and Ílhavo, most of the suburban area (type A and B) and the Beaches. It groups areas where a major number of transactions occur, which is also the areas where the average areas are relatively homogenous. This cluster is distinguished by a localization valuation values (Area values) above the average. The normal competition for space is reflected in both the above-average housing prices and values below the average on the Area attribute. CLUSTER 2: Has small expression of territorial aggregation corresponding to the CBD of Aveiro surrounding. Is where new construction are very valued, because this territory represents expansion urban areas with new centralities (shopping, employment hubs in services sector, ). In addition, this cluster combines the localization and house prices valuation, in general, below the average, with a significant number of older housing. CLUSTER 3: Is defined by willingness-to-pay for single family houses or flats with a favourable Area-Price relation, corresponding to transition areas between Suburban (type A and B) and Rural areas (Suburban type C). This cluster does not have a significant number of transactions, what can explain the above-average valuation for used dwellings, reassembling to a type of house with reduced supply when compared to the potential demand. CLUSTER 4: Refers mainly to the rural area (Suburban type C) and is defined by the below-average values of the neighbourhood attribute (housing), above-average values for the attribute Area, average values of housing transactions and by the predominance of single family houses in relation to flats (in fact, it is the only territorial unit where this phenomenon occurs). Rental market CLUSTER 1: Occupies an extensive area covering the central urban areas, suburban areas (type A and B), Gafanhas and Beaches, with no characteristic values, indicating a homogenous supply. This is even more relevant when they match those areas where there are a higher number of transactions, which indicates a very low level of differentiation (spatial) of the characteristics of dwellings traded in this market. CLUSTER 2: As happened in the owner market (cluster 3), this market presents an area of limited size (formed by one single territorial unit basis) where dwellings with a favourable Area-Price relation (large areas, low prices) are preferred. There are a reduced number of dwellings in the market, what may explain the valuation above the average of used dwellings. CLUSTER 3: Despite some similarities with cluster 2 of the owner market, this cluster records a particular valuation of flats and Area, contrasting with the depreciation of used dwellings. This aspect is 16

17 also associated with neighbourhood values above the average, establishing different demand characteristics on the rental market in these territories. This cluster also presents a significant number of older dwellings (which can enhance the few new-dwellings traded.) In terms of location in space, this category arises in two isolated territorial units, although both concentrated within the suburban area. CLUSTER 4: This cluster, as occurred in the owner market, is related to territories where single family houses available for trading prevail. In this case, there is no strong association between the territorial units location of this cluster and rural areas, also related to suburban areas. This cluster shows values below the average for both attributes neighbourhood and average transaction price. Table 4 Descriptive statistics for each cluster in owner market Ward's Method Prices Intrinsic Atributes Owner Used to Used 10 Used to Used 10 Zones Area Flat New House Area Flat New Clusters 10 yrs to 25 yrs 10 yrs to 25 yrs C C C C *Mean, by cluster, of standardized values of each zone Table 5 Descriptive statistics for each cluster in rental market Ward's Method Prices Intrinsic Atributes Rent Used to Used 10 Used to Used 10 Zones Area Flat New House Area Flat New Clusters 10 yrs to 25 yrs 10 yrs to 25 yrs C C C C *Mean, by cluster, of standardized values of each zone In global terms, the territorial patterns of both markets have significant differences. The owner market is significantly more territorially segmented, which can be explained by the vast majority of transactions occurring in this market. In comparative terms the general spatial structure approaches to the patterns of urban spatial and socio economic structure (defined a priori ). It can be distinguished a clear dichotomy between rural (suburban type C and Gafanhas) and urban (CBD's, Suburban type A and B, Beaches) due to the small number of clusters selected. Simultaneously, the existence of distinct small areas of the market seem to correspond to a phenomena related with the CBD of Aveiro adjacent areas, as expanded urban areas, with significant demand and reduced number of trades, which results in above values for the new houses because of its scarcity. On the other hand, there is a cluster where the most valued type of houses correspond to single family houses (values below the average due to the fact of being flats) where all the preservation levels are considered all valuable, matching a clear preferences profile: the single family households who like to live in quiet territories. Regarding the rental market, there is a reduced spatial segmentation, which leads the market to be dominated in all its territorial extension by low variability of characteristics and hedonic prices (cluster 1). At the same time, in the owner market, occurs a spatial and small-scale phenomenon related to the valuation of single-family houses (cluster 2) and devaluation of older buildings (cluster 3). In this case, this dynamic arises within suburban areas, with specific territorial units where there will be a smaller presence of new dwellings in the rental market. 17

18 Figure 17 Proposed submarkets for owner and rental markets Conclusions The territorial patterns described in this work confirm empirically that the form of acquisition of housing has distinct elements in the dynamics of the Aveiro-Ílhavo housing market. Indeed, the owner market has become much more segmented geographically compared to the rental market. This assumption is related to its size (number of transactions proportionately higher) and consequent maturity: greater number of transactions will be related to a greater number of agents involved, so a wider range of preferences - which present territorial patterns that fit with the territorial structures of physical occupation, social and economic space. The rental market is defined as slightly segmented territory. It is constituted either by the number of transactions or their territorial characteristics standards, describing a homogeneous market. However, it is possible to identify some similarities between both markets. This analysis highlights the valuation of new houses in areas where the housing market is especially marked by older dwelling as well as territorial areas distinguished by the preference for single-family houses small patches (spots) constituted by territorial units where the proportion of dwellings in the housing park is especially important. This factor occurs in the territorial units located in the city center (historic area of the city) and in small areas located in suburban, adjacent to urban centers, also marked by relevant concentrations of dwellings in the housing stock. As mentioned in the introduction, the rental market has been subject to very significant policy measures in recent years. The Census data show a significant increase in the number of rented dwellings as habitual residence. However, as this paper demonstrates, the market dynamics are still not significant, despite some signs of development. The territorial patterns show that the house ownership is an alternative solution to the market players, because: in terms of trading volume it is constituted mainly by characteristics consistently less valued; in terms of valuation of the attributes of housing, it presents a great homogeneity, characterized by the nonexistence of differentiation in houses according to preferences of different social groups. 18

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