Hoon Han, Prem Chhetri, and Jonathan Corcoran A study of housing fragmentation and price variations in the Brisbane housing market Hoon Han, Prem Chhetri, and Jonathan Corcoran AHURI QLD Research Centre The UQ Social Research Centre University of Queensland

Objectives To quantify the changing spatial pattern and association in the distribution of median house values across suburbs during 1991-2004; To identify a set of predictors that are both robust and parsimonious; To develop a discriminant function model to differentiate suburb characteristics and examine the creation of sub-markets within the urban residential space.

Data sets Digital data Department of Natural Resources and Mines MapInfo StreetPro dataset 8.0.1 Australian Bureau of Statistics- Statistical Local Areas Department of Local Government and Planning- zoning data Derived data Distance calculation- close to work Proportion of open space Convenience to public transport (Railway stations, City Cat Ferry Route, etc.) Dwelling density Diversity indices for lifecycle and occupation using simpson’s index of diversity Elevation diversity for measuring attractiveness of landscape using neighbourhood operation

Modelling geographic differentiation of house price Step 1: Quantification of spatial patterns and association in house price using measures of spatial autocorrelation Step 2: Derivation of neighbourhood characteristics using GIS Step 3: Extraction of the underlying dimensions of neighbourhood characteristics Step 4: Discriminating suburbs on the basis of the embedded factorial ecologies of urban social space

Hidden Stories The suburbs that are close together have similar house values than those that are further apart. Expensive suburbs are more likely to be surrounded by expensive suburbs and vice versa. The patterns evidenced the ‘spill over effect’. There was a clustering tendency among high price suburbs till 2001, recent trends however detect a reversal towards dispersion.

LISA - Spatial Clusters and Significance A high value of local Moran means a clustering of similar values; while a low value indicates a clustering of dissimilar values. Hot spots and cold spots Creation of spatial enclaves of high socio-economic status or areas of residual poverty. A local moran for an area i, is basically the mean deviation of I multiplied by the sum of the products of the mean deviations for all j values and the spatial weights defining the spatial relationship between i and j. Red: High- high: hot spots Blue, low-low, cold spots

Drivers of geographical differentiation of Brisbane housing market – a factorial ecology of urban social space AGE AND FAMILY IS CONCENTRIC ETHNICITY IS PATCHY SOCIO-ECONOMIC IS SECTORAL

The Residential Mosaic Product of varying supply and demand Different groups to different types of housing to different parts of the city Developed for the US cities but is also applicable to Australian cities AGE AND FAMILY IS CONCENTRIC ETHNICITY IS PATCHY SOCIO-ECONOMIC IS SECTORAL

Factorial ecologies of urban space spaces ETHNIC CONCENTRATIONS NEAR CITY CENTRE RATHER THAN IN ANY ZONE OR SECTOR THE SO CALLED GHETTOES SOCIO-ECONOMIC STATUS IS MAINLY SECTORAL FAMILY LIFE CYCLE OCCURS ZONALLY – OLDER CLOSER TO THE CITY CENTRE SO EVIDENCE OF BOTH SECTORAL AND ZONAL PATTERNS IN MODERN CHICAGO

Dimensions, Variables and Measures AGE AND FAMILY IS CONCENTRIC ETHNICITY IS PATCHY SOCIO-ECONOMIC IS SECTORAL

Underlying Components of Neighbourhood Characteristics Principal Component Analysis with varimax rotation: four factor solution, explains about 75 % of the variability in the dataset. Component 1: High access high mobility (25 % of variance) Close to work, high to medium density housing, higher residential mobility, high dwelling density Component 2: High socio-economic status (21 % of variance) Greater concentration of rich, symbolic analysts, low unemployment, absence of public housing and industrial areas Component 3: Cultural diversity (13 % of variance) Greater proportion of people born overseas, High percentage of people who speak language other than English Component 4: Aesthetic (13 % of variance) Attractive landscape, greater proportion of open space

High access & high mobility Socio-economic status Component Loadings Variables High access & high mobility Socio-economic status Cultural Diversity Aesthetics % of people living in flats/units .931 .120 .015 .002 % of rental dwellings .894 -.350 .072 -.064 Close to work -.818 .013 .062 .060 Dwelling density .772 .067 -.017 -.160 Residential mobility -.690 -.210 -.201 .014 %of rich people -.071 .019 .058 % of symbolic analysts .368 .872 -.020 .049 Unemployment rate .279 -.701 .394 .221 % of people in public housing .249 -.663 -.022 -.014 Occupational diversity .392 .600 .054 .055 % of industrial areas -.076 -.417 .039 -.069 % of people born overseas .042 .052 .982 .027 % of people speaking languages other than English .032 -.150 .954 -.067 % of open space -.077 .023 .970 Attractiveness index -142 .117 -.004 .966

Neighbourhood operation One or more foci (parameter 1); The neighbourhood membership, for example a set of locations around each focal cell that are within a specified distance or direction (parameter 2); and finally a function to be performed on the cells within the defined neighbourhood (parameter 3). 4.2 4.0

Accessibility Measures Using a Composite calculation a more appropriate measure of proximity of a cell can be estimated. Euclidean (0.94km) Network (1.08km) Composite (Euclidean and Network – 2.03km) Accessibility is defined as the measure of the capacity of a location to be reached by, or to reach different locations. Therefore, the capacity and the arrangement of transport infrastructure are key elements in the determination of accessibility.

Accessibility to Schools and shopping centres Proximity to transport Proximity to coast and open spaces Close to work

High access

Socio-economic status

Cultural diversity factor

Aesthetic factor

Predicted Median House Price 2004 Actual Median House Price 2004

Key Findings Median house price at the suburb level was found to be spatially dependent; further use of spatial autoregression will be conducted in the future. Expensive suburbs were more likely to be surrounded by expensive suburbs; whilst poorer or less expensive suburbs were surrounded by poorer suburbs. This tendency has showing a slight decline since 2001, though a positive clustering still embedded on the map. Geographic variations were detected in house price; Western outer suburbs (Pullenvale, Kenmore, Chapel Hill, Brookfield, Fig Tree Pocket etc) were clearly differentiated by the discriminant model from suburbs in southern outer Brisbane (Inala, Rocklea, Wacol, Acacia Ridge, Darra-Summer etc.).

Conclusions A parsimonious approach – a set of limited but robust variables The ‘socio-economic status’ and ‘accessibility’ factors are found to be good predictors to discriminate Brisbane housing market. The ‘aesthetics’ and ‘cultural diversity’ factors are not significant, indicating that the urban social areas are relatively homogenous in terms of those two factors as compared to to Sydney and Melbourne where patterns of ethnic segregations are relatively more pronounced. Suburbs that are either at the higher or the lower ends of the price spectrum are more clearly discriminated; whilst suburbs in the middle are less clearly differentiated through the identified functions.