When demand for property is high and supply is low, prices go up. This is a well known law of economics. In fact, nothing else affects prices – only supply and demand.

Most experts will tell you that in order to get capital growth you should search for areas close to schools and shops with good transport, preferably with water views and close to the CBD in a suburb with a cafe culture, emerging businesses, entertainment venues, hospitals, universities, parks, character housing… and the list goes on.

These types of locations should definitely be in demand from buyers. But there are two short-comings with this kind of advice:

1) No objectivity in determining the level of demand and

2) No consideration of the other side of the equation – supply.

If there is an oversupply of property, prices will fall. So investors should perform research to also determine the supply characteristics of an area. You should consider both sides of the equation. In other words, you should know the demand to supply ratio (DSR).

With regard to objectivity, it is no good saying, “Properties are in demand in suburb XYZ and there is not much supply”. How much are they in demand? How limited is the supply? Is suburb XYZ better than suburb ABC? You need to be calculated in your reckoning. Ideally, you’d want a number for the DSR for a location.

If you can come up with a value for the DSR for an area, then you know the level of pressure there is on property prices in that area. Ideally, to come up with a DSR figure you need figures for demand and you need figures for supply. Then you divide the demand figures by the supply figures and there’s your DSR. The higher the DSR, the bigger the future growth will be. And conversely, the lower the DSR, the bigger the fall will be.

There are two approaches to getting demand and supply information: guestimates and statistics. Both are notorious liars, so you’ll need to be careful in how you go about analysing the data.

Guestimates (a.k.a. fundamentals) is the method most commonly used by investors. It is a case of observing fundamental characteristics of a location. For example, you notice various demand characteristics such as: cafes opening on every corner, local businesses hiring more staff, a new school is being built, etc. Similarly, you may get an idea of supply characteristics by checking what development plans have been lodged with the local council recently or whether there are many vacant blocks available in the area, etc.

I call them “Guestimates” because they have no objective figure. They can also be called, “fundamentals” because they consider the fundamental drivers of price growth. The point is they’re not quantitative in nature. They’re dependent on the investor’s “feel” for demand and supply in the area.

You can get around this problem to some extent by giving a score between 1 and 10 for each fundamental supply and demand characteristic a location has. Then you tally up the total demand score and compare to the total supply score. You could also apply a scaling factor to each characteristic according to how important you believe it is. So important characteristics are multiplied by 1.25 and unimportant ones by 0.75 for example.

If you follow this procedure consistently for a number of locations, you’ll begin to know what a good fundamental DSR is and what a bad one is.

Although this method is still rather subjective, it provides insight into the long term growth prospects of a suburb, whereas statistics only show the current prospects for growth, which may change in 6 months time.

Statistics are objective but suffer from anomalies. The trick to using statistics is to gather as many as possible from as many varying sources as possible to hopefully filter out such anomalies.

There are supply and demand statistics of interest readily available. This makes it quick and easy to see if a suburb of interest warrants further in-depth research using the guestimate/fundamental method described earlier.

You can determine a DSR for the suburb of your choice right now in ten minutes if you have internet access. Use the following procedure to gather demand and supply statistics about your chosen suburb.

Days on market

This is the average number of days a property will be for sale before eventually selling. If this figure is low it means either there is high demand from buyers or low supply from sellers or both. The buyers act quickly on an opportunity before competing buyers snap it up.

This figure for a suburb can easily be found at the back of either the Australian Property Investor magazine (API) or Your Investment Property magazine (YIP). Be sure to check figures for both houses and units since they may differ significantly.

Each suburb has a different time on market profile. So what might be slow for one suburb could be fast for another. Expensive properties usually take longer to sell than cheaper ones.

In general, if a property spends 50 days or less on the market, I would consider that to be a hot market. 100 days or more, I’d consider as a cold market.

Discount

This is the average difference between the original listing price and the eventual selling price for properties in a suburb. A low discount figure shows strong demand from buyers to meet the price of the sellers. The buyers are more easily meeting the expectations of sellers. The sellers are in control and there is less opportunity for negotiation on price.

This figure can be found from the suburb profile on the Domain website. Click on “Property Reports” then enter a suburb for the “Suburb Profile”. Enter a suburb name and click search. Scroll down to the property prices for houses and units just under the map of the suburb. There’s a line labelled “Discounting”. Check the figures for both houses and units.

A discount of around 4% or lower is considered a market in high demand in my opinion. A discount of 8% or more is a buyer’s market – low demand.

Auction Clearance Rate

This is the percentage of auctions that result in a sale. A sale at auction is more likely to occur when there is high demand and low supply. This figure can be found from the Domain website for the last month of auctions in the same manner as you found the discount earlier.

The auction clearance rate can also be found for the last week of auctions from the http://www.realestate.com.au website. Click on “Auction Results” in the left menu.

As a rough guide a clearance rate of 80% or more is considered high, 70% is good and less than 60% is considered low.

Stock on market as percentage of dwellings

This is the number of properties currently for sale in a suburb as a percentage of the number of properties in that suburb.

Not all suburbs are the same size. 50 properties for sale in a suburb may mean a high supply if there are only 1000 properties in the area in total. But if there are 20,000 properties in that suburb in total, 50 would mean a low supply. So we need to calculate the number of properties for sale as a percentage of properties in total. A low figure represents an under supply of properties. Either there are few new dwellings being built or the existing ones are tightly held by owners.

You can visit Domain, Home Hound Real Estate for a count of properties currently for sale or stock on market figures from SQM Research. These figures are also published in the back of the API and YIP magazines.

Finding the total number of properties in a suburb is a little trickier than looking it up. The local council may have figures on their website or you could call them. Usually a council will govern multiple suburbs. So you’ll need to find figures specifically for the suburb of interest to you.

You can get an estimate of the number of properties in a suburb by getting population figures for a post code from Domain’s suburb profile. Divide that by the number of suburbs within the post code. Divide that again by 2.5 (an estimate for the number of people per household). Now you have a rough figure for the number of properties in the suburb in total.

Because of the complexity of calculating this figure, it is hard to put an accurate pass mark on it. As a rough guide I’d say that anything more than 3% is a fail and anything less than 1% is a pass.

Rental Yield

The average yield for a suburb is easy to find, turn to the pages in the back of either the API or YIP magazine. A high rental yield means there is high demand from renters but low supply. It represents a high “rental” DSR. But we want high “owner” DSR. It doesn’t immediately translate to demand from buyers, but probably will some time in the not too distant future since a high rental yield usually precedes strong price growth. Any yield above 5% is considered good. But each location has its own yield characteristics. Expensive suburbs have traditionally low yields. Familiarity with a suburb’s normal yield will highlight when it is unusually high.

Vacancy Rate

A low vacancy rate means that there is either high demand for rental accommodation or low supply or both. The vacancy rate is the average time a property spends vacant over the course of a year as a percentage. This data can be found for a post code from the back of the API magazine or from SQM Research. A 3% vacancy rate is considered a normal vacancy rate. 4% and above should trigger alarm bells. 2% is great and 1% or lower means there is a rent boom due soon.

Rental Availability

Ideally you want few properties for rent as a percentage of properties in the suburb. A low figure here represents a high proportion of owner-occupiers to investors or an under supply of rental properties. The proportion of renters to owners can be obtained from web pages like the Domain suburb profile. Approximately 30% of property owners are investors. So if there are less than 30% renters, then that is generally good.

Online Search Demand to Advertised Supply

At the time of writing this can only be found from the Real Estate dot com dot au website and it only shows the number of searches performed on their website. Click on the “Suburb Data” menu on the left and select a state and suburb. If you scroll down you’ll see a graph showing supply and demand curves.

The blue line shows the change in the number of advertised properties in the suburb of interest. The green line shows the number of people looking for properties in that suburb. Note that because of scaling on the axes, the lines will always be relatively close to each other. So ignore the lines and focus on the figures. In the above example, there were 300 searches for property and 30 properties listed. This is about a 10 to 1 ratio of searchers (demand) to advertised properties (supply). A 10 to 1 ratio is low demand. High demand would be a 30 to 1 ratio or higher.

Putting it all together

By now you should have 8 statistics. We now need to combine them in a way to calculate a single DSR estimate. You just need to be careful how you combine these to get a single figure. Because statistics can be big liars, some of the figures could be extreme in some suburbs some of the time. These anomalies need to be treated carefully so the DSR value isn’t skewed too much from one bad statistic. An easy way to do this is the checklist approach. Each statistic is given a tick if it passes the minimum value for that characteristic. If a suburb has 7 ticks out of 8, it merits further research.

Another option might be to have a range of values for each statistic. For example: very good, good, normal, bad or very bad. You might also like to apply a scaling factor giving more credit to statistics you consider to be more important. But now it’s getting complicated and we haven’t even dealt with circumstances where data is sometimes unavailable.

This method for calculating the DSR for a suburb is taken from my book, “How to find property hot spots”. You can find more information on how to find suburbs with good capital growth prospects both short and long term from the Mustard Solutions website.

Now wouldn’t it be great to know the DSR for every suburb within Australia? Send me an email if you’re interested ( jeremy@mustardsolutions.com.au ). If you’re only interested in a few suburbs, send me an email and mention the suburb, state and post code you’re curious about. I’ll do the calcs and get back to you.

Invest at your best,

Jeremy.

Source by Jeremy Sheppard