Understanding REINZ data

REINZ data

REINZ collates property data from members and other sources – providing unconditional and settled sales data so you know you are getting the most up-to-date and accurate New Zealand residential property data.

Our data goes back to 1992, adding depth to our insights into market trends, sales information, location comparisons, market analyses and more.

Why do property analysts use the median over the average?

There are technical reasons why the median provides a more accurate picture of what is happening to the prices of houses rather than the average.

As an example, assume there are 11 houses sold in a month with a price range of $200,000 to $300,000 and an average price of $250,000. Now replace one of those houses with a house that sold for $1 million. The average is now $318,182, even though 10 of the 11 houses for the month sold for less than this value. The median would be the price of the middle house sold in the range (in this case the sixth house), which more accurately reflects what the majority of the houses sold for.

REINZ uses medians to provide a more accurate measure of the mid-point of house prices that reflects what most people are going to be buying and selling houses for.

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Why is real estate data seasonally adjusted?

Seasonal Adjustment is very common with economic and financial statistics. The reason REINZ seasonally adjusts is to account for well-known and persistent effects in the data that mask what is really going on. The purpose of seasonal adjustment is to remove these effects and allow analysis of what the underlying trends are.

For example, we know that there are fewer house sales in December and January, and there are more sales in November and March. This happens every year. There is always a lift in sales in November compared to October.

By seasonally adjusting the data we can see whether the lift in sales was just part of the normal lift we would expect, or whether something more important is going on.

For example, in November 2011 the number of sales increased by 20% compared to October 2011. But after adjusting for the seasonal effect (sales always lift in November) sales increased by only 4.3%. Thus 15.7% of the increase was due to the normal lift in sales we would expect in November and 4.3% was the increase after taking into account this normal lift.

The seasonally adjusted figure is an important indicator of the underlying market trends.

The timing of Easter can also affect house sales, for example if Easter is in March then sales may be lower compared to a March when Easter is in April. If there was no seasonal adjustment we might conclude that sales fell in the March with Easter for other reasons, rather than recognising that the holiday may have lowered sales.

Seasonal adjustment, as the name suggests, takes account of effects that happen on a regular annual cycle. It can’t adjust for one-off events such as the Canterbury earthquakes or the Rugby World Cup.

What is the HPI and why is it important?

Data on median and average house prices is open to being skewed by market composition changes. This means observed changes in these values could be almost entirely due to the changed nature in the underlying sample (e.g. an unusually large representation of high end housing sales) rather than changes in the true market value.

The REINZ HPI takes many aspects of market composition into account resulting in greater accuracy.

It does this by analysing how prices in a market are influenced by a range of attributes such as land area, floor area, number of bedrooms etc. to create a single, more accurate measure of housing market activity and trends over time.

Using the Reserve Bank’s preferred Sale Price to Appraisal Ratio (SPAR) methodology, the REINZ HPI uses unconditional sales data (when the sale is unconditional) rather than at settlement, which can often be weeks later. It is therefore more accurate and timely.

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