housing-planning

Urban housing location and locational amenities play an important role in median house price distribution and growth among the suburbs of many metropolitan cities in developed countries, such as Australia. In particular, distance from the central business district (CBD) and access to the transport network plays a vital role in house price distribution and growth over various suburbs in a city. However, Australian metropolitan cities have experienced increases in housing prices by up to 120% over the last 20 years, and the growth pattern was different across all suburbs in a city, such as in Melbourne. Therefore, this study examines the impacts of locational amenities on house price changes across various suburbs in Melbourne over the three census periods of 2006, 2011, and 2016, and suggests some strategic guidelines to improve the availability and accessibility of locational amenities in the suburbs with less concentrated amenities. This study chose three Local Government Areas (LGAs) of Maribyrnong, Brimbank and Wyndham in Melbourne. Each LGA has been selected as a case study because many low-income people live in these LGAs’ areas. Further, some suburbs of these LGAs have maintained similar housing prices for an extended time, while some have not.The study applied a quantitative spatial methodology to examine the housing price distribution and growth patterns by evaluating the concentration and accessibility of locational urban amenities using GIS-based techniques and a spatial data set. The spatial data analyses were performed by spatial statistics methods to measure central tendency, Local Moran’s I of LISA clustering, Kernel Density Estimation (KDE), Kernel Density Smoothing (KDS). These tests were used to find the patterns of house price distribution and growth. The study also identified the accessibility of amenities in relation to median house price distribution and growth. Spatial Autoregressive Regression (SAR), Spatial Lag, and Spatial Errors models were used to identify the spatial dependencies to test the statistical significance between the median house price and the concentration and access of local urban amenities over the three census years.This study found three median house price distribution and growth patterns among the suburbs in the three selected LGAs. There are growth differences in the median house price for different census years between 2006 and 2011, 2011 and 2016, and 2006 and 2016. The Low-High (LH) median house price distribution clusters between 2006 and 2011 became High-High (HH) clusters between the census years 2011 and 2016, and 2006 and 2016. The median house price growth rate increased significantly in the census years between 2006 and 2011. Most of the HH median house price distribution and growth clusters’ tendencies were closer to the Melbourne CBD. On the other hand, the Low-Low (LL) distribution and growth clusters were closer to Melbourne’s periphery. The suburbs located further away had low access to amenities. The HH median house price clusters are located closer to stations and educational institutes. Better access to locational amenities led to more significant HH median house price clusters, as the median house price increased at an increasing rate between 2011 and 2016. The HH median house price clusters recorded more growth between 2006 and 2016. The suburbs with train stations had better access to most other locational amenities. Almost all HH median house price clusters had train stations with higher access to amenities.There was a consistent relationship between median house price distribution, growth patterns, and locational urban amenities. The spatial lag and spatial error model tests showed that between 2006 and 2011, and 2006 and 2016, there were differences in the amenities. Still, these did not affect the outcomes in observations, and were related only to immeasurable factors for some reason. Therefore, the higher house price in the neighbouring suburb could increase the price in that suburb. The research also found from the regression analysis that highly significant amenities confirming travel time to the CBD by bus, and distance to the CBD, were negatively related in all three previous census years. This negative relationship estimates that the house price growth is lower when the distance is longer. Due to this travel to the CBD by bus is not a popular option for households. The train stations are essential for high house price growth. The house price growth is low when homes are further away from train stations and workplaces.This thesis has three contributions. Firstly, it uses the Rational Choice Theory (RCT), providing a theoretical basis for analysing households’ mutually interdependent preferences of urban amenities that are found to regulate house price growth clusters. Secondly, the methodological contribution uses the GIS-defined cluster mapping and spatial statistics in queries and reasoning, measurements, transformations, descriptive summaries, optimisation, and hypothesis testing models between house price distribution and growth, and access to urban locational amenities. Thirdly, this research contributes to designing practical guidelines to identify local urban amenities for planning local area development.Overall, this thesis demonstrates that the median house price distribution and growth patterns are highly correlated with the concentration and accessibility of locational urban amenities among the suburbs in three selected LGAs in Melbourne over the three census years (i.e., 2006, 2011, and 2016). The findings bring to the fore the need for research at the local and state levels to identify specific amenities relevant to the middle-class house distribution strategy, which can be helpful for investors, estate agents, town planners, and builders as partners for effective local development. The future study might use social, psychological, and macroeconomic variables not considered or used in this research.

都市住宅区位及其配套设施在城市众多发达国家和地区（如澳大利亚）的郊区，对房屋中价位的分布与增长起着至关重要的作用。特别是，距离市中心商务区（CBD）的远近以及交通网络的可达性，对于城市各郊区房屋价格分布与增长的影响尤为显著。然而，澳大利亚的各大城市在过去二十年间，房价上涨幅度高达120%，且各郊区的增长模式各异，如墨尔本。因此，本研究旨在探究区位配套设施对墨尔本不同郊区在2006年、2011年和2016年三次人口普查期间房价变动的影响，并提出一些策略性指导方针，以提升郊区区位配套设施的可用性和可及性。本研究选取了墨尔本三个地方政府区域（LGAs）——马里伯恩、布里姆班克和温德姆作为案例研究，因为这些区域居住着大量低收入人群。此外，这些LGAs的一些郊区在较长时间内保持了相似的房价，而另一些则不然。研究采用定量空间方法，通过GIS技术及空间数据集评估区位城市配套设施的集中度和可及性，以探讨房价分布与增长的模式。空间数据分析通过空间统计方法进行，包括测量中心趋势、局部莫兰指数（LISA）聚类、核密度估计（KDE）和核密度平滑（KDS）等，旨在发现房价分布与增长的规律。研究还确定了与中价位房价分布和增长相关的配套设施的可及性。空间自回归回归（SAR）、空间滞后和空间误差模型被用于识别空间依赖性，以检验中价位房价与地方城市配套设施集中度和可及性在三次人口普查年间的统计显著性。本研究在三个选定的LGAs的郊区中发现了三种中价位房价分布与增长的模式。在2006年至2011年、2011年至2016年以及2006年至2016年之间，中价位房价在不同普查年的增长存在差异。在2006年至2011年期间的低-高（LH）中价位房价分布聚类，在2011年至2016年以及2006年至2016年的人口普查年间演变为高-高（HH）聚类。中价位房价增长率在2006年至2011年的人口普查年间显著上升。大多数HH中价位房价分布和增长聚类的趋势靠近墨尔本CBD。另一方面，低-低（LL）分布和增长聚类靠近墨尔本外围。距离较远的郊区对配套设施的可达性较低。HH中价位房价聚类位于车站和学府附近。更好的区位配套设施的可及性导致了HH中价位房价聚类的增加，因为中价位房价在2011年至2016年期间以递增的速度增长。HH中价位房价聚类在2006年至2016年间记录了更多的增长。拥有火车站的郊区对大多数其他区位配套设施的可达性较好。几乎所有HH中价位房价聚类都有火车站，且配套设施的可达性较高。中价位房价分布、增长模式与区位城市配套设施之间存在一致的关联。空间滞后和空间误差模型测试表明，在2006年至2011年和2006年至2016年之间，配套设施存在差异，但这些差异并未影响观察结果，且与某些不可测因素有关。因此，邻近郊区的房价上涨可能会带动该地区的房价上涨。研究还发现，通过回归分析，与公共汽车往返CBD的旅行时间和距离CBD的距离高度相关的配套设施具有显著的负相关性，在三个之前的人口普查年中均如此。这种负相关性估计，距离越远，房价增长率越低。因此，乘坐公共汽车往返CBD并不是家庭的首选交通方式。火车站对于高房价增长至关重要。当房屋距离火车站和工作地点越远时，房价增长率就越低。本论文有三个贡献。首先，它运用了理性选择理论（RCT），为分析家庭对城市配套设施相互依赖的偏好提供了理论依据，这些偏好被发现可以调节房价增长聚类。其次，方法论上的贡献在于运用GIS定义的聚类映射和空间统计在查询和推理、测量、转换、描述性总结、优化和假设检验模型之间，探讨房价分布与增长以及城市区位配套设施的可及性。第三，本研究为设计识别地方城市配套设施的实用指南做出了贡献，以规划地方区域发展。总体而言，本论文证明了在墨尔本三个选定的LGAs的郊区中，中价位房价分布与增长模式与区位城市配套设施的集中度和可及性高度相关，尤其是在2006年、2011年和2016年这三次人口普查年间。这些发现突显了在地方和州层面上进行研究的必要性，以确定与中产阶级房屋分布策略相关的特定配套设施，这对于投资者、房产经纪人、城镇规划者和建筑商作为有效地方发展的合作伙伴将大有裨益。未来的研究可能会考虑使用一些在此研究中未考虑或使用的社交、心理和宏观经济变量。