1000 resultados para Housing forecasting


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Forecasting is an integral part of all business planning, and forecasting the outlook for housing is of interest to many firms in the housing construction sector. This research measures the performance of a number of industry forecasting bodies; this is done to provide users with an indicator of the value of housing forecasting undertaken in Australia. The accuracy of housing commencement forecasts of three Australian organisations – the Housing Industry Association (HIA), the Indicative Planning Council for the Housing Industry (IPC) and BIS-Shrapnel – is examined through the empirical analysis of their published forecasts supplemented by qualitative data in the form of opinions elicited from several industry “experts” employed in these organisations. Forecasting performance was determined by comparing the housing commencement forecast with the actual data collected by the Australian Bureau of Statistics on an ex-post basis. Although the forecasts cover different time periods, the level of accuracy is similar, at around 11-13 per cent for four-quarter-ahead forecasts. In addition, national forecasts are more accurate than forecasts for individual states. This is the first research that has investigated the accuracy of both private and public sector forecasting of housing construction in Australia. This allows users of the information to better understand the performance of various forecasting organisations.

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"One in its [i.e. Office of Housing and Urban Programs] series of Occasional Papers"--Fore.

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Presentation provided to a PhD Colloquium between two Australian and one Malaysian University providing the opportunity to inform and critique progress of students concerning their selected topic. This presentation essentially involves "The conceptualisation, sensitivity and measurement of holding costs and other selected elements impacting housing affordability" as provided by Gary Owen Garner of QUT, with research objectives thus: 1. To establish the nature and composition of holding costs over time, as related to residential property in Australia, and internationally. 2. To examine the linkages that may exist between various planning instruments, the length of regulatory assessment periods, and housing affordability. 3. To develop a model that quantifies the impact of holding costs on housing affordability in Australia, with a particular focus on the consequences of extended assessment periods as a component of holding costs. Thus, provide clarification as to the impact of holding costs on overall housing affordability.

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It is widely held that strong relationships exist between housing, economic status, and well being. This is exemplified by widespread housing stock surpluses in many countries which threaten to destabilise numerous aspects related to individuals and community. However, the position of housing demand and supply is not consistent. The Australian position provides a distinct contrast whereby seemingly inexorable housing demand generally remains a critical issue affecting the socio-economic landscape. Underpinned by high levels of immigration, and further buoyed by sustained historically low interest rates, increasing income levels, and increased government assistance for first home buyers, this strong housing demand ensures elements related to housing affordability continue to gain prominence. A significant, but less visible factor impacting housing affordability – particularly new housing development – relates to holding costs. These costs are in many ways “hidden” and cannot always be easily identified. Although it is only one contributor, the nature and extent of its impact requires elucidation. In its simplest form, it commences with a calculation of the interest or opportunity cost of land holding. However, there is significantly more complexity for major new developments - particularly greenfield property development. Preliminary analysis conducted by the author suggests that even small shifts in primary factors impacting holding costs can appreciably affect housing affordability – and notably, to a greater extent than commonly held. Even so, their importance and perceived high level impact can be gauged from the unprecedented level of attention policy makers have given them over recent years. This may be evidenced by the embedding of specific strategies to address burgeoning holding costs (and particularly those cost savings associated with streamlining regulatory assessment) within statutory instruments such as the Queensland Housing Affordability Strategy, and the South East Queensland Regional Plan. However, several key issues require investigation. Firstly, the computation and methodology behind the calculation of holding costs varies widely. In fact, it is not only variable, but in some instances completely ignored. Secondly, some ambiguity exists in terms of the inclusion of various elements of holding costs, thereby affecting the assessment of their relative contribution. Perhaps this may in part be explained by their nature: such costs are not always immediately apparent. Some forms of holding costs are not as visible as the more tangible cost items associated with greenfield development such as regulatory fees, government taxes, acquisition costs, selling fees, commissions and others. Holding costs are also more difficult to evaluate since for the most part they must be ultimately assessed over time in an ever-changing environment, based on their strong relationship with opportunity cost which is in turn dependant, inter alia, upon prevailing inflation and / or interest rates. By extending research in the general area of housing affordability, this thesis seeks to provide a more detailed investigation of those elements related to holding costs, and in so doing determine the size of their impact specifically on the end user. This will involve the development of soundly based economic and econometric models which seek to clarify the componentry impacts of holding costs. Ultimately, there are significant policy implications in relation to the framework used in Australian jurisdictions that promote, retain, or otherwise maximise, the opportunities for affordable housing.

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We examine the time-series relationship between housing prices in eight Southern California metropolitan statistical areas (MSAs). First, we perform cointegration tests of the housing price indexes for the MSAs, finding seven cointegrating vectors. Thus, the evidence suggests that one common trend links the housing prices in these eight MSAs, a purchasing power parity finding for the housing prices in Southern California. Second, we perform temporal Granger causality tests revealing intertwined temporal relationships. The Santa Anna MSA leads the pack in temporally causing housing prices in six of the other seven MSAs, excluding only the San Luis Obispo MSA. The Oxnard MSA experienced the largest number of temporal effects from other MSAs, six of the seven, excluding only Los Angeles. The Santa Barbara MSA proved the most isolated in that it temporally caused housing prices in only two other MSAs (Los Angels and Oxnard) and housing prices in the Santa Anna MSA temporally caused prices in Santa Barbara. Third, we calculate out-of-sample forecasts in each MSA, using various vector autoregressive (VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different MSAs. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of turning points in housing prices that occurred in 2006:Q4. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of turning points.

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We examine the time-series relationship between housing prices in Los Angeles, Las Vegas, and Phoenix. First, temporal Granger causality tests reveal that Los Angeles housing prices cause housing prices in Las Vegas (directly) and Phoenix (indirectly). In addition, Las Vegas housing prices cause housing prices in Phoenix. Los Angeles housing prices prove exogenous in a temporal sense and Phoenix housing prices do not cause prices in the other two markets. Second, we calculate out-of-sample forecasts in each market, using various vector autoregessive (VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different cities. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of turning points in housing prices that occurred in 2006:Q4. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of turning points.