Housing supply in Australia : the impact of the availability of development finance

Before the Global Financial Crisis many providers of finance had growth mandates and actively pursued development finance deals as a way of gaining higher returns on funds with regular capital turnover and re-investment possible. This was able to be achieved through high gearing and low presales in a strong market. As asset prices fell, loan covenants breached and memories of the 1990’s returned, banks rapidly adjusted their risk appetite via retraction of gearing and expansion of presale requirements. Early signs of loosening in bank credit policy are emerging, however parties seeking development finance are faced with a severely reduced number of institutions from which to source funding. The few institutions that are lending are filtering out only the best credit risks by way of constrictive credit conditions including: low loan to value ratios, the corresponding requirement to contribute high levels of equity, lack of support in non-prime locations and the requirement for only borrowers with well established track records. In this risk averse and capital constrained environment, the ability of developers to proceed with real estate developments is still being constrained by their inability to obtain project finance. This paper will examine the pre and post GFC development finance environment. It will identify the key lending criteria relevant to real estate development finance and will detail the related changes to credit policies over this period. The associated impact to real estate development projects will be presented, highlighting the significant constraint to supply that the inability to obtain finance poses.

A heterogeneous computing approach to simulation of the Heston Stochastic Volatility Model

Stochastic volatility models are of fundamental importance to the pricing of derivatives. One of the most commonly used models of stochastic volatility is the Heston Model in which the price and volatility of an asset evolve as a pair of coupled stochastic differential equations. The computation of asset prices and volatilities involves the simulation of many sample trajectories with conditioning. The problem is treated using the method of particle filtering. While the simulation of a shower of particles is computationally expensive, each particle behaves independently making such simulations ideal for massively parallel heterogeneous computing platforms. In this paper, we present our portable Opencl implementation of the Heston model and discuss its performance and efficiency characteristics on a range of architectures including Intel cpus, Nvidia gpus, and Intel Many-Integrated-Core (mic) accelerators.

Data mining on prices of highway project main material based on WEB

The management of main material prices of provincial highway project quota has problems of lag and blindness. Framework of provincial highway project quota data MIS and main material price data warehouse were established based on WEB firstly. Then concrete processes of provincial highway project main material prices were brought forward based on BP neural network algorithmic. After that standard BP algorithmic, additional momentum modify BP network algorithmic, self-adaptive study speed improved BP network algorithmic were compared in predicting highway project main prices. The result indicated that it is feasible to predict highway main material prices using BP NN, and using self-adaptive study speed improved BP network algorithmic is the relatively best one.