With the triple whammy of low entry points and the promise of high yields and short term rapid capital growth, it’s easy to see why many property investors are lured into buying property in Australian mining towns. But what are some of the hidden dangers?

The resources boom in recent years has attracted a wealth of workers to once sleepy rural towns, resulting in large increases in population sizes and ultimately the demand for housing. With the transient nature of workers causing significant demand for rental accommodation, these towns offered a great opportunity for confident investors to cash in, which many of them did successfully.

The resources boom has been one of the biggest single influences on Australian real estate prices in recent years, seeing property in mining towns skyrocket and investors scrambling to grab a piece of the action. While the potential to make huge capital gains in a short period of time is extremely enticing, the risks prove just as great.

Why it’s inherently risky

I would consider investing in a mining town to be a risky or speculative investment for two main reasons.

Firstly, the life of the town has been built on the success of the resources industry which is dependent on commodity prices that are notoriously fickle. These prices are reliant on demand from overseas markets and if these markets falter, prices collapse and this affects the industry.

Secondly, as is often the case in mining towns, a large proportion of the population in the town is not permanent. You need to keep in mind that much of the population is employed by the industry and is transient. Should employment prospects dry up, so will the size of the population and the consequent demand for property. There is also a risk in that, as prices for property and rentals escalate, mining companies will find it more economical to fly workers in and out rather than pay for their accommodation.

Some of the main dangers

1. Getting the timing wrong

The resources industry is cyclical. Success is largely determined by your ability to predict the best time to enter and exit the market. Take for example the Pilbara in the north of Western Australia. In 2003 the median house price was $195,000, but by 2011some towns had reached a median price of over $1 million. If you invest at the peak of the market, you might find it takes a decade or longer too see any growth.

2. The population decreases after the construction phase

You need to consider the difference between the construction and operational phases of a mine. While the facility is under construction there could be the need for an abundance of workers for the first few years to get it up and running, but when complete the actual employees required may be far less resulting in a sharp drop in housing demand.

Stage one of the Gladstone Pacific Nickel project is a prime example of this, with 1200 jobs needed during construction, but permanent operational employment is a third of that level.

3. The mine closes or is mined out

One of the biggest dangers to your investment is that the mine which supports the town closes. The effects of this are catastrophic with a dramatic decrease in population and thus demand for your property. We saw this in recent times with the closure of the Ravensthorpe mine, but it’s certainly not the first time.

Mt Morgan, south-west of Rockhampton, is a classic example of a town that has not yet recovered since its gold mine closed in 1981. During itshey day, it was rated as one of the nation’s most gold rich locations, had a population over 10,000, and was bustling with wealth and prosperity. Today its population is just over 2,400people, its median house price in 2008 was just $77,000, with an unemployment rate of 21.4%.

What you should look for if you do invest

Making a solid investment decision in a mining town is not straightforward. If you are looking to invest in these areas, it’s important to undertake extensive research, investigating the location thoroughly before purchasing. It’s best to concentrate on towns that have a diverse industry and employment base, perhaps one that is strong not only in mining but also tourism or other industries so your investment is somewhat protected should one industry suffer or disappear altogether.

Another way to benefit in a less risky manner, is to invest in the nearest capital city or regional centre to a predicted boom area. Wealth from the mining will flow back to the major city areas. Keeping in mind real estate as a long-term investment, this strategy will assist you to survive beyond just the boom as these areas possess other desirable and stable factors to keep your investment sound.

Introduction:

Most marketers understand the value of collecting financial data, but also realize the challenges of leveraging this knowledge to create intelligent, proactive pathways back to the customer. Data mining – technologies and techniques for recognizing and tracking patterns within data – helps businesses sift through layers of seemingly unrelated data for meaningful relationships, where they can anticipate, rather than simply react to, customer needs as well as financial need. In this accessible introduction, we provides a business and technological overview of data mining and outlines how, along with sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis.

Objective:

1. The main objective of mining techniques is to discuss how customized data mining tools should be developed for financial data analysis.

2. Usage pattern, in terms of the purpose can be categories as per the need for financial analysis.

3. Develop a tool for financial analysis through data mining techniques.

Data mining:

Data mining is the procedure for extracting or mining knowledge for the large quantity of data or we can say data mining is “knowledge mining for data” or also we can say Knowledge Discovery in Database (KDD). Means data mining is : data collection , database creation, data management, data analysis and understanding.

There are some steps in the process of knowledge discovery in database, such as

1. Data cleaning. (To remove nose and inconsistent data)

2. Data integration. (Where multiple data source may be combined.)

3. Data selection. (Where data relevant to the analysis task are retrieved from the database.)

4. Data transformation. (Where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance)

5. Data mining. (An essential process where intelligent methods are applied in order to extract data patterns.)

6. Pattern evaluation. (To identify the truly interesting patterns representing knowledge based on some interesting measures.)

7. Knowledge presentation.(Where visualization and knowledge representation techniques are used to present the mined knowledge to the user.)

Data Warehouse:

A data warehouse is a repository of information collected from multiple sources, stored under a unified schema and which usually resides at a single site.

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Most of the banks and financial institutions offer a wide verity of banking services such as checking, savings, business and individual customer transactions, credit and investment services like mutual funds etc. Some also offer insurance services and stock investment services.

There are different types of analysis available, but in this case we want to give one analysis known as “Evolution Analysis”.

Data evolution analysis is used for the object whose behavior changes over time. Although this may include characterization, discrimination, association, classification, or clustering of time related data, means we can say this evolution analysis is done through the time series data analysis, sequence or periodicity pattern matching and similarity based data analysis.

Data collect from banking and financial sectors are often relatively complete, reliable and high quality, which gives the facility for analysis and data mining. Here we discuss few cases such as,

Eg, 1. Suppose we have stock market data of the last few years available. And we would like to invest in shares of best companies. A data mining study of stock exchange data may identify stock evolution regularities for overall stocks and for the stocks of particular companies. Such regularities may help predict future trends in stock market prices, contributing our decision making regarding stock investments.

Eg, 2. One may like to view the debt and revenue change by month, by region and by other factors along with minimum, maximum, total, average, and other statistical information. Data ware houses, give the facility for comparative analysis and outlier analysis all are play important roles in financial data analysis and mining.

Eg, 3. Loan payment prediction and customer credit analysis are critical to the business of the bank. There are many factors can strongly influence loan payment performance and customer credit rating. Data mining may help identify important factors and eliminate irrelevant one.

Factors related to the risk of loan payments like term of the loan, debt ratio, payment to income ratio, credit history and many more. The banks than decide whose profile shows relatively low risks according to the critical factor analysis.

We can perform the task faster and create a more sophisticated presentation with financial analysis software. These products condense complex data analyses into easy-to-understand graphic presentations. And there’s a bonus: Such software can vault our practice to a more advanced business consulting level and help we attract new clients.

To help us find a program that best fits our needs-and our budget-we examined some of the leading packages that represent, by vendors’ estimates, more than 90% of the market. Although all the packages are marketed as financial analysis software, they don’t all perform every function needed for full-spectrum analyses. It should allow us to provide a unique service to clients.

The Products:

ACCPAC CFO (Comprehensive Financial Optimizer) is designed for small and medium-size enterprises and can help make business-planning decisions by modeling the impact of various options. This is accomplished by demonstrating the what-if outcomes of small changes. A roll forward feature prepares budgets or forecast reports in minutes. The program also generates a financial scorecard of key financial information and indicators.

Customized Financial Analysis by BizBench provides financial benchmarking to determine how a company compares to others in its industry by using the Risk Management Association (RMA) database. It also highlights key ratios that need improvement and year-to-year trend analysis. A unique function, Back Calculation, calculates the profit targets or the appropriate asset base to support existing sales and profitability. Its DuPont Model Analysis demonstrates how each ratio affects return on equity.

Financial Analysis CS reviews and compares a client’s financial position with business peers or industry standards. It also can compare multiple locations of a single business to determine which are most profitable. Users who subscribe to the RMA option can integrate with Financial Analysis CS, which then lets them provide aggregated financial indicators of peers or industry standards, showing clients how their businesses compare.

iLumen regularly collects a client’s financial information to provide ongoing analysis. It also provides benchmarking information, comparing the client’s financial performance with industry peers. The system is Web-based and can monitor a client’s performance on a monthly, quarterly and annual basis. The network can upload a trial balance file directly from any accounting software program and provide charts, graphs and ratios that demonstrate a company’s performance for the period. Analysis tools are viewed through customized dashboards.

PlanGuru by New Horizon Technologies can generate client-ready integrated balance sheets, income statements and cash-flow statements. The program includes tools for analyzing data, making projections, forecasting and budgeting. It also supports multiple resulting scenarios. The system can calculate up to 21 financial ratios as well as the breakeven point. PlanGuru uses a spreadsheet-style interface and wizards that guide users through data entry. It can import from Excel, QuickBooks, Peachtree and plain text files. It comes in professional and consultant editions. An add-on, called the Business Analyzer, calculates benchmarks.

ProfitCents by Sageworks is Web-based, so it requires no software or updates. It integrates with QuickBooks, CCH, Caseware, Creative Solutions and Best Software applications. It also provides a wide variety of businesses analyses for nonprofits and sole proprietorships. The company offers free consulting, training and customer support. It’s also available in Spanish.

Australia mining dump trucks are impressively large machinery. For example, the CAT 797 dumper model has a capacity of 345 tonnes and is roughly 7 metres high. Although the dump trucks are massive pieces of machinery, they are relatively simple to operate. Therefore people from all different backgrounds are able to gain a position as a dump truck driver. In this capacity there are no age or gender restrictions. In fact, it is possible to gain employment in a driving position if you have had no previous experience working in a driving career.

In Australia mining dump truck jobs, you are not required to perform hard physical labour as with a number of other positions in the industry. This is another reason why these positions are popular to gain. More and more frequently, women are being hired by mining companies to fill dump truck driving positions. Mining companies realise the benefits of hiring women as they tend to have good safety records and minimise repairs costs. Women are certainly encouraged to apply for a dump truck position. The mining industry in Australia suffered only a minor downturn during the recent economic events, and it is now again growing at a frenetic pace.

While working as a mining dumper driver, you can expect to work 12 hour shifts usually working both day and night shifts. In most cases, you will required to work on a rostered basis. The roster that you work on does depend on the company that you gain employment with. Some rosters include 3 weeks on and 1 week off or 9 days on and 5 days off.

Although this is one of the most popular positions to gain in the industry, you are required to meet certain requirements. There is no age limit to gaining Australia mining driving jobs, in fact, workers close to retirement age are eligible for employment as long as they can meet the requirements of the mining companies. In order to gain employment in this capacity, mining companies may require that you meet their prerequisites.

Due to the popularity of this position, you may find it difficult to obtain this position without obtaining expert advice. A large number of people who apply for these types of positions, often find their application for a dump truck driving role is continuously rejected. To gain employment as a driver, you will need to ensure that you meet of all the requirements set out by the mining companies. Most of the employment criteria remains essentially the same fromt state to state, although there can be regional criteria to be aware of (for instance the entry level criteria in Western Australia differs from the Victoria, South Australia, New South Wales and Queensland).

There is certainly assistance available for those who are interested gaining Australia mining dump truck driving jobs – and lets face it, who wouldn’t be interested! If you are serious about wanting to working in the mining industry as a driver, there is a lot that you need to know before applying for jobs.