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In fast-developing regions, like Southeast-Asia, monitoring urban areas presents a challenge given the lack of publicly available data. This is an issue that precludes the nuances of a citys growth and undermines the way land-use is considered with respect to planning. The issue of data availability is very much present in the small nation of Brunei. Little is still known about the spatiotemporal evolution of its urban realm; in particular, with regard to its national development planning. The country presents an interesting case-study given the pursuit of two highly opposing master-plans within the last 40 years. Its first master-plan advocated polycentric growth away from the capital city, Bandar Seri Begawan. Its latest plan, however, currently promotes compact growth within a predefined urban footprint as a development control mechanism. This paper looks at remotely-sensed satellite-data to examine the way in which the two implemented master-plan strategies have translated into actual land-use thus far. It employs a supervised classification procedure; and characterises urban land into three typologies: urban infill, edge growth, and leapfrog growth. The papers findings suggest that residential developments play a stronger role in driving urban growth in Brunei. This is particularly true for public housing, which has typically persisted in isolated pockets of low-density at the city-fringe. This study has produced a novel data-set of urban land-use and land-cover in Brunei; and, its quantification may prove useful in understanding the impacts of master-planning and the formulation of development policies both within Brunei, and the wider Southeast-Asian region.

Over the last decade, the emergence and significant growth of home-sharing platforms, such as Airbnb, has coincided with rising housing unaffordability in many global cities. It is in this context that we look to empirically assess the impact of Airbnb on housing prices in Sydney—one of the least affordable cities in the world. Employing a hedonic property valuation model, our results indicate that Airbnb’s overall effect is positive. A 1% increase in Airbnb density is associated with approximately a 2% increase in property sales price. However, recognizing that Airbnb’s effect is geographically uneven and given the fragmented nature of Sydney’s housing market, we also employ a GWR to account for the spatial variation in Airbnb activity. The findings confirm that Airbnb’s influence on housing prices is varied across the city. Sydney’s northern beaches and parts of western Sydney experience a statistically significant value uplift attributable to Airbnb activity. However, traditional tourist locations focused around Sydney’s CBD and the eastern suburbs experience insignificant or negative property price impacts. The results highlight the need for policymakers to consider local Airbnb and housing market contexts when deciding the appropriate level and design of Airbnb regulation.

Over the last decade, the emergence and significant growth of home-sharing platforms, such as Airbnb, has coincided with rising housing unaffordability in many global cities. It is in this context that we look to empirically assess the impact of Airbnb on housing prices in Sydney—one of the least affordable cities in the world. Employing a hedonic property valuation model, our results indicate that Airbnb’s overall effect is positive. A 1% increase in Airbnb density is associated with approximately a 2% increase in property sales price. However, recognizing that Airbnb’s effect is geographically uneven and given the fragmented nature of Sydney’s housing market, we also employ a GWR to account for the spatial variation in Airbnb activity. The findings confirm that Airbnb’s influence on housing prices is varied across the city. Sydney’s northern beaches and parts of western Sydney experience a statistically significant value uplift attributable to Airbnb activity. However, traditional tourist locations focused around Sydney’s CBD and the eastern suburbs experience insignificant or negative property price impacts. The results highlight the need for policymakers to consider local Airbnb and housing market contexts when deciding the appropriate level and design of Airbnb regulation.

Australia is currently undergoing sweeping changes in transforming and digitizing its planning and development sectors. However, numerous challenges still exist in consolidating and making accessible essential data in the country to effect evidence-based development policy-making. This has been argued to have tangible consequences in formulating solutions to urban problems, such as housing delivery, and driving new urban innovations that are data-focused. In this chapter, we discuss a new urban data governance model in the context of the development of a novel single housing data and analytics platform, which has been formulated based on Australia’s current issues on data disparity, ownership, and interoperability. This platform, the Australian Housing Data Analytics Platform, seeks provide researchers with an integrated data repository and transparent analytical capabilities that hopes to drive collaboration, public participation, and data democratization across the country. In line with PlanTech principles developed through the Australian Planning Institute, this chapter describes how data in Australia can be made as a public good and integral commodity for policy-makers for the better planning for our cities.

Cities often show residential income segregation, and the price of housing is generally related to employment accessibility, but how do these factors intersect? We analyse Greater Sydney, Australia, a metropolitan area of 5 million people. Sydney is found to have reasonably even employment accessibility by car, reflecting the increasingly polycentric nature of the modern city; however, it also shows considerable income segregation and variance in property prices between different parts of the city. Entropy is used to examine diversity and mixing of different income groups. Finally, hedonic price models using ordinary-least squares and geographically-weighted regression techniques show the differing effects of employment accessibility on house prices in different parts of the city. The results show that accessibility has small to negative effects on prices in the most valuable areas, suggesting that other effects such as recreational access and employment type/quality may be more important determinants of house prices in these areas.