RP Data CoreLogic has a highly accurate and timely suite of world-class property price indices that have transformed the way Australians measure and understand changes in the value of residential real estate.
The patented RP Data CoreLogic Daily Home Value Index can be produced for various geographic demarcations from suburb to state or nationally. It can also be produced across all properties or divided between property types, such as units and houses. While the RP Data CoreLogic Daily Home Value Index is the benchmark index produced by RP Data and defines a new global standard in index construction, there are three “classes” of CoreLogic RP Data Indices produced:
1. RP Data CoreLogic Hedonic Indices (RP Data CoreLogic Daily Home Value Index Construct)
The first class of index, known as the “hedonic” model, had not been commercially produced in Australia prior its introduction by RP Data. The RP Data CoreLogic Daily Home Value Index is a hedonic model. This index utilises comprehensive information on the attributes and characteristics of residential properties (such as location, land size, and bedrooms) to measure “quality-adjusted” changes in property value over time and to also impute the value of dwellings having a certain set of characteristics (but no current sales price) by observing the sales prices and characteristics of other dwellings which have recently been observed as selling.
The “hedonic” method has the advantage that it utilises attribute (or characteristic) information of each dwelling to mitigate biases that are otherwise prevalent in median and repeat sales property price indices. Another important advantage of a hedonic imputation index is that its calculation frequency can be daily and that it tracks the value of an entire portfolio of property, not just the prices of properties observed to sell in a given period.
2. RP Data CoreLogic Stratified Median Price Indices
The second class of indices RP Data have produced are based on a median and “stratified” median price series. Stratification is a process for creating subsets of houses which are qualitatively similar. Unique price series are created for these subsets which are then aggregated to estimate suburb-adjusted price movements in the overall market. The strata definitions used to classify properties into subsets are based on price, geography, land size, and interactions of these variables. The stratified median index that RP Data produces for units and apartments, groups suburbs by their long-term median transaction price. The stratified median index that RP Data produces for houses groups suburbs by their long-term price to land size ratio.
Median and stratified median indices are simple to calculate and provide the price of the middle ranking property observed to sell in a given measurement period. Further, rather than selecting the 50th percentile (ie, the median) from whatever properties are observed to sell over a particular measurement period, other percentiles such as the 25th or 75th percentile can equally be selected using the same method so as to create other percentile series.
The major shortcoming of median (or any other selected percentile) price series is that they do not represent changes in the value of the residential property market portfolio. Median prices do not track the same properties through time. In fact, it is most unlikely that any of the properties observed selling in one period will be the same as the properties selling in the next measurement period when examining quarterly or monthly median price series. By way of example, in one period the median priced house observed may happen to be a 3 bedroom home and in the next period it may be a 4 bedroom home. Further, in order to provide useful statistics, one needs to aggregate a sufficiently large numbers of property sales over what can be long periods of time. As such, the publication frequency of median price series is often limited to quarterly, although RP Data do offer a monthly median series.
The fact that median or other percentile based series cannot be used to track changes in value of a market portfolio does not make them wrong: it is simply that they have different applications than hedonic indices. For example, median price series are useful in answering economic policy questions relating to housing affordability.
3. RP Data CoreLogic Repeat Sales Indices
The third type of index estimates the performance of the market by analysing the returns on individual properties for which there are at least two observed sales prices, each at different points in time. RP Data publish a suite of five different “repeat-sales” indices, all replicating methods in the academic literature.
The first is called the “linear” weighted repeat-sales model (see Case and Shiller (1987)). This was the first repeat-sales model to identify that the dispersion of price appreciation of properties is likely to be related to the time between sales, making explicit adjustments that mitigate the biasing effect of this.
The second and third repeat-sales models, developed by Calhoun (1996) and Webb (1988), extend the Case and Shiller model to allow for “non-linearities” in the relationship between time and return dispersion.
The fourth repeat-sales model of Goetzmann and Spiegel (1995) is motivated by the fact that in many cases the features of properties are not constant through time. The Goetzmann and Spiegel model attempts to controls for elements of price appreciation that are not related to the time between sales (such as the addition of a bedroom) using statistical methods, but, without the use of attribute data to confirm whether there have, or have not, in fact been changes in property attributes. To the extent that a property appreciates faster than other properties, the Goetzmann and Spiegal model is likely to assume its appreciation is as a result of a non-temporal change such as a renovation.
The fifth repeat-sales model is based on the seminal paper of Bailey, Mourse and North (1967).
Repeat sales indices seek to control for changes in the composition of properties selling in different periods without requiring any property attribute data (other than address). They do this by only examining properties which have two separate sales records.
The disadvantage of repeat sales indices is that: (i) they only cover between 30% and 50% of the properties in a given city; (ii) new properties cannot be accounted for (and neither can old properties for which there is no prior sales information on record); (iii) improvements to properties cannot be explicitly accounted for; (iv) there is potential significant sample selectivity bias toward more frequently traded properties (eg, apartments or lower value homes may trade more or less frequently than higher value homes); and they substantially revise through time.
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